Podcasts about Human Connectome Project

David Van Essen is an Alumni Endowed Professor of Neuroscience at Washington University School of Medicine in St. Louis. In this conversation, we talk about David's path to becoming a neuroscientist, the Human Connectome project, hierarhical processing in the cerebral cortex, and much more.BJKS Podcast is a podcast about neuroscience, psychology, and anything vaguely related, hosted by Benjamin James Kuper-Smith.Support the show: https://geni.us/bjks-patreonTimestamps0:00:00: David's childhood: ravens, rockets, and radios0:05:00: From physics to neuroscience (via chemistry)0:13:55: Quantitative and qualitative approaches to science0:19:17: Model species in neuroscience0:31:35: Hierarchical processing in the cortex0:46:54: The Human Connectome Project0:55:00: A book or paper more people should read0:58:01: Something David wishes he'd learnt sooner1:00:31: Advice for PhD students/postdocsPodcast linksWebsite: https://geni.us/bjks-podTwitter: https://geni.us/bjks-pod-twtDavid's linksWebsite: https://geni.us/VanEssen-webGoogle Scholar: https://geni.us/VanEssen-scholarBen's linksWebsite: https://geni.us/bjks-webGoogle Scholar: https://geni.us/bjks-scholarTwitter: https://geni.us/bjks-twtReferences & linksDavid's autobiographical sketch for the Society for Neuroscience (in Volume 9): https://www.sfn.org/about/history-of-neuroscience/autobiographical-chaptersFelleman & Van Essen (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex.Glasser, Coalson, Robinson, Hacker, Harwell, Yacoub, ... & Van Essen (2016). A multi-modal parcellation of human cerebral cortex. Nature.Hubel & Wiesel (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. The Journal of physiology.Maunsell & Van Essen (1983). The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey. Journal of Neuroscience.Sheldrake (2021). Entangled life: How fungi make our worlds, change our minds & shape our futures.Van Essen & Kelly (1973). Morphological identification of simple, complex and hypercomplex cells in the visual cortex of the cat. In Intracellular Staining in Neurobiology (pp. 189-198).Van Essen & Maunsell (1980). Two‐dimensional maps of the cerebral cortex. Journal of Comparative Neurology.Van Essen (2012). Cortical cartography and Caret software. Neuroimage.Van Essen, Smith, Barch, Behrens, Yacoub, Ugurbil & WU-Minn HCP Consortium. (2013). The WU-Minn human connectome project: an overview. Neuroimage.Wooldridge (1963). The machinery of the brain.

Estudios: - Aumento de Testosterona en Hombres al beber poco alcohol https://pubmed.ncbi.nlm.nih.gov/12711931/ - [Associations between alcohol consumption and gray and white matter volumes in the UK Biobank](https://www.nature.com/articles/s41467-022-28735-5) (*Nature Communications*) - [Gut Microbiota at the Intersection of Alcohol, Brain, and the Liver](https://www.mdpi.com/2077-0383/10/3/541) (*Journal of Clinical Medicine*) - [Tolerance to alcohol: A critical yet understudied factor in alcohol addiction](https://www.sciencedirect.com/science/article/abs/pii/S009130572100054X?via%3Dihub) (*Pharmacology Biochemistry and Behavior*) - [Associations Between Drinking and Cortical Thickness in Younger Adult Drinkers: Findings From the Human Connectome Project](https://onlinelibrary.wiley.com/doi/10.1111/acer.14147) (*Alcoholism: Clinical and Experimental Research*) - [Moderate Alcohol Consumption and the Risk of Breast Cancer](https://www.nejm.org/doi/10.1056/NEJM198705073161902?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed) (*The New England Journal of Medicine*) - [Can alcohol promote aromatization of androgens to estrogens? A review](https://www.sciencedirect.com/science/article/abs/pii/S0741832900001245?via%3Dihub) (*Alcohol*) Sé parte de nuestra comunidad de Creadores y obtén recursos, herramientas y estrategias gratuitas para mejorar tu vida y alcanzar tu máximo potencial. Encuéntranos en: - Este Canal: https://bit.ly/3et1LTB - Spotify: https://spoti.fi/2XN3zSe - iTunes: https://apple.co/2ZlON5O 2. CONECTEMOS EN… - Facebook: https://www.facebook.com/MarceloZegar... - Instagram: https://www.instagram.com/creadorespodcast/ - TikTok: https://www.tiktok.com/@chelozegarra 3. ÚNETE A NUESTRO EXCLUSIVO GRUPO KIERO EMPRENDER EN FACEBOOK: - https://bit.ly/32RfDEl --- Send in a voice message: https://podcasters.spotify.com/pod/show/creadorespodcast/message

Dr. Deanna Barch is a psychologist and Vice Dean of Research for the College of Arts and Sciences at Washington University. She is the George B. Couch Professor of Psychiatry and former Chair of the Department of Psychological and Brain Sciences as well as Professor of Radiology. Dr. Barch is known for her work using neuroimaging to characterize cognitive deficits in patients with mental illness such as schizophrenia. She is one of the principal investigators for the Human Connectome Project, a multi-institutional effort to map connectivity in the healthy human brain to improve our understanding of how it is altered by disease and development. In this interview, we talk about schizophrenia and its treatment and risk factors, how brain connectivity develops, and tools to improve mental health, among other fascinating topics. Dr. Barch is an expert on the brain and was generous to share much of the results of her very productive career, so we hope you enjoy this episode. Title music: World Is Holding Hands by WinnieTheMoog https://creativecommons.org/licenses/by/4.0/legalcode

In this episode of the Combat Fitness Podcast, we dive into the topic of overtraining. We discuss whether it's a real phenomenon or just a myth. We explore the factors that contribute to overtraining, including nutrition, recovery, and training intensity. We also highlight the importance of proper nutrition and how it can impact your performance and recovery. No action is requested from the viewers, but it's an informative episode that will provide valuable insights into optimizing your training routine. Some Documents: Tolerance to alcohol: A critical yet understudied factor in alcohol addiction: https://bit.ly/3CmfCYo Associations Between Drinking and Cortical Thickness in Younger Adult Drinkers: Findings From the Human Connectome Project: https://bit.ly/3AeUosJ Can alcohol promote aromatization of androgens to estrogens? A review: https://bit.ly/3dJjGHZ Examine - Alcohol & Hangover: https://bit.ly/3QHYpx4

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.22.550168v1?rss=1 Authors: Howell, A. M., Warrington, S., Fonteneau, C., Cho, Y., Sotiropoulos, S., Murray, J. D., Anticevic, A. Abstract: The thalamus is composed of functionally and structurally distinct nuclei. Previous studies have indicated that certain cortical areas may project across multiple thalamic nuclei, potentially allowing them to modulate distributed information flow. However, there is a lack of quantitative investigations into anatomical connectivity patterns within the thalamus. Consequently, it remains unknown if cortical areas exhibit differences in the spread of their thalamic connectivity patterns. To address this knowledge gap, we used diffusion magnetic resonance imaging (dMRI) to compute brain-wide probabilistic tractography using data from 828 healthy adults collected by the Human Connectome Project. Additionally, we examined post-mortem data from six macaque monkeys to assess cross-species generalizability. To measure the spatial spread of anatomical connectivity patterns within the thalamus, we developed an innovative framework that quantifies the spatial properties of each cortical area's within-thalamus connectivity patterns. We then leveraged resting-state, myelin, and human neural gene expression data to test if the spread of within-thalamus connectivity patterns varied along the cortical hierarchy. These results revealed two broad cortico-thalamic tractography motifs: 1) a sensorimotor cortical motif characterized by focal thalamic connections targeting posterolateral thalamus, which potentially supports fast, feed-forward information flow; and 2) an associative cortical motif characterized by diffuse thalamic connections targeting anteromedial thalamus, which potentially supports slower, feed-back information flow. These results were consistent among human subjects and were also observed in macaques, indicating generalizability. In summary, these findings demonstrate that sensorimotor and association cortical areas exhibit distinct spatial connectivity patterns within the thalamus, which may support functionally-distinct cortico-thalamic information transmission. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.20.549806v1?rss=1 Authors: Ng, J., Yu, J.-C., Feusner, J. D., Hawco, C. Abstract: The capacity of the brain to adaptively and flexibly reconfigure into different network connectivity patterns may underlie general cognitive ability, or g. Network reconfiguration can be captured via assessments of dynamic connectivity (dFC), which quantifies dominant temporally recurrent connectivity patterns, or "states", common across the population. While standard dynamic measures focus on quantifying relative time spent within states, or the probability transitioning between states, these metrics fail to capture variability between individuals present in connectivity patterns across states. Here, we provide individualized assessments of connectivity flexibility and stability over time by considering within-state pattern stability, the difference in patterns across state transitions, and how well a given state represents the "typical" state in a particular individual. We leveraged resting-state fMRI data from the large-scale Human Connectome Project and data-driven multivariate Partial Least Squares Correlation to examine emergent relationships between dynamic network properties and cognition. We found that higher g was associated with maintaining distinct states over other states, efficient reconfiguration (i.e., less pattern change during common small-magnitude state transitions such as a state to itself, and greater pattern change among rare transitions between very different states), and less reconfiguration away from population-typical patterns. These results demonstrate that higher cognitive abilities are associated with greater state-specific stability, greater connectivity differences when transitioning between distinct states, and reconfiguration into more typical, potentially optimal, connectivity patterns within states. This suggests a link between general cognition and the efficiency of reconfiguration connectivity patterns into stable, well-defined, and typical network states. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.13.548883v1?rss=1 Authors: Torabi, M., Mitsis, G. D., Poline, J.-B. Abstract: Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly-used dFC methods. We implemented seven dFC assessment methods in Python and used them to analyze fMRI data of 395 subjects from the Human Connectome Project. We measured the pairwise similarity of dFC results using several similarity metrics in terms of overall, temporal, spatial, and inter-subject similarity. Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Surprisingly, the observed variability in dFC estimates was comparable to the expected natural variation over time, emphasizing the impact of methodological choices on the results. Our findings revealed three distinct groups of methods with significant inter-group variability, each exhibiting distinct assumptions and advantages. These findings highlight the need for multi-analysis approaches to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds, and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox that enables multi-analysis dFC assessment. This study sheds light on the impact of dFC assessment analytical flexibility, emphasizing the need for careful method selection and validation, and promoting the use of multi-analysis approaches to enhance reliability and interpretability of dFC studies. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.06.27.546709v1?rss=1 Authors: Maddaluno, O., Della Penna, S., Pizzuti, A., Spezialetti, M., Corbetta, M., de Pasquale, F., Betti, V. Abstract: Using hands proficiently implies consolidated motor skills, yet malleable to task demands. How the brain realizes this balance between stability and flexibility is unknown. At rest, in absence of overt input or behavior, the communication within the brain may represent a neural prior of stored memories. This magnetoencephalography study addresses how the modulation of such stable connectivity, induced by motor tasks, relates to proficient behavior. To this aim, we estimated functional connectivity from 51 participants of the Human Connectome Project during rest and finger tapping in alpha and beta bands. We identified two groups of participants characterized by opposite patterns of connectivity strength and topology. High and low performers showed distributed decreases and increases of connectivity, respectively. However, while dexterous individuals also show modulations of the motor network, low performers exhibited a stability of such connections. Furthermore, in dexterous individuals, an increased segregation was observed through an increment of network modularity and decrease of nodal centrality. Instead, low performers show a dysfunctional increased integration. Our findings reveal that the balance between stability and flexibility is not fixed; rather it constrains proficient behavior. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Dr. Emma Robinson is a Senior Lecturer (Assoc. Professor) at King's College London. Her development of the Multimodal Surface Matching (MSM) software for cortical surface registration has been instrumental to the development of the Human Connectome Project's multimodal parcellation of the human cortex. She is currently developing interpretable machine learning models to aid in the personalized prediction of disease progression. In this interview, Dr.Robinson describes the advantages of interpretable machine learning models, and the methodological challenges she faced during the development of this framework. Her approach to identifying disease-related changes in individual brain scans attempts to circumvent two of the limitations of traditional approaches: (1) the over-reliance on population averages, and (2) the opacity of “black-box” machine learning algorithms such as deep neural networks. In addition, Dr. Robinson shared that, following her extensive experience working on the Human Connectome Project, she realized that traditional image registration methods may not be sufficient for individualized predictions. Finally, Dr. Robinson shared how her relationship with her mentors shaped the trajectory of her current career. Her mentors not only guided her on the application of computational methods to neuroscience, but also encouraged her to develop her own methods. At OHBM 2023, Dr. Robinson will present how her work contributes to improved personalized predictions of cortical features in patient populations and how interpretable machine learning approaches can enhance precision.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.20.537642v1?rss=1 Authors: Park, Y. J., Lee, M. J., Yoo, S., Kim, C. Y., Namgung, J. Y., Park, Y., Park, H., Lee, E.-C., Yun, Y. D., Paquola, C., Bernhardt, B., Park, B.-y. Abstract: Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community, and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.19.537270v1?rss=1 Authors: Pan, R., Dickie, E. W., Hawco, C., Reid, N., Voineskos, A. N., Park, J. Y. Abstract: Clusterwise inference is a popular approach in neuroimaging to increase sensitivity, but {most existing methods are} currently restricted to the General Linear Model (GLM) for testing mean parameters. Statistical methods for variance components testing, which are critical in neuroimaging studies that involve estimation of narrow-sense heritability or test-retest reliability, are seriously underdeveloped due to methodological and computational challenges, which would potentially lead to low power. We propose a fast and powerful test for variance components called CLEAN-V ('CLEAN' for testing 'V'ariance components). CLEAN-V models the global spatial dependence structure of imaging data and computes a locally powerful variance component test statistic by data-adaptively pooling neighborhood information. Correction for multiple comparisons is achieved by permutations to control family-wise error rate (FWER). Through analysis of task-fMRI data from the Human Connectome Project across five tasks {and comprehensive data-driven simulations}, we show that CLEAN-V outperforms existing methods in detecting test-retest reliability and narrow-sense heritability with significantly improved power, with the detected areas aligning with activation maps. The computational efficiency of CLEAN-V also speaks of its practical utility, and it is available as an R package. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.04.535586v1?rss=1 Authors: Abramian, D., Eklund, A., Ozarslan, E. Abstract: Diffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motion of water molecules within the tissue. Numerous diseases and processes affecting the central nervous system can be detected and monitored via diffusion MRI thanks to its sensitivity to microstructural alterations in tissue. The latter has prompted interest in quantitative mapping of the microstructural parameters, such as the fiber orientation distribution function (fODF), which is instrumental for noninvasively mapping the underlying axonal fiber tracts in white matter through a procedure known as tractography. However, such applications demand repeated acquisitions of MRI volumes with varied experimental parameters demanding long acquisition times and/or limited spatial resolution. In this work, we present a deep-learning-based approach for increasing the spatial resolution of diffusion MRI data in the form of fODFs obtained through constrained spherical deconvolution. The proposed approach is evaluated on high quality data from the Human Connectome Project, and is shown to generate upsampled results with a greater correspondence to ground truth high-resolution data than can be achieved with ordinary spline interpolation methods. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.18.533265v1?rss=1 Authors: Tripathi, V., Somers, D. C. Abstract: The cerebellum is gaining scientific attention as a key neural substrate of cognitive function; however, individual differences in the cerebellar organization have not yet been well studied. Individual differences in functional brain organization can be closely tied to individual differences in brain connectivity. 'Connectome Fingerprinting' is a modeling approach that predicts an individual's brain activity from their connectome. Here, we extend 'Connectome Fingerprinting' (CF) to the cerebellum. We examined functional MRI data from 160 subjects (98 females) of the Human Connectome Project young adult dataset. For each of seven cognitive task paradigms, we constructed CF models from task activation maps and resting-state cortico-cerebellar functional connectomes, using a set of training subjects. For each model, we then predicted task activation in novel individual subjects, using their resting-state functional connectomes. In each cognitive paradigm, the CF models predicted individual subject cerebellar activity patterns with significantly greater precision than did predictions from the group average task activation. Examination of the CF models revealed that the cortico-cerebellar connections that carried the most information were those made with the non-motor portions of the cerebral cortex. These results demonstrate that the fine-scale functional connectivity between the cerebral cortex and cerebellum carries important information about individual differences in cerebellar functional organization. Additionally, CF modeling may be useful in the examination of patients with cerebellar dysfunction, since model predictions require only resting-state fMRI data which is more easily obtained than task fMRI. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.14.532658v1?rss=1 Authors: Robinson, T. D., Sun, Y. L., Chang, P., Chen, J. J. Abstract: Progressive age-related changes in white matter morphometry and microstructure have been found throughout the brain. Both declines in white matter (WM) volume and deterioration of microstructural integrity have been observed. Predicting these changes across WM tracts and building an integrated model of age-related WM trajectories has proven challenging. While tractwise differences in volume and microstructural declines are common targets of investigation, there has been relatively little exploration into other attributes of tract morphology or its relation to microstructural measures in vivo. This study seeks to examine ten WM tracts for tract-wise differences in WM volume, length, the ratio of volume to length (VLR), and microstructural integrity as measured by fractional anisotropy (FA) and mean diffusivity (MD) using diffusion MRI data from the Human Connectome Project in Aging (HCP-A). From these measures, we analyzed relationships between morphometry and microstructure in the aging brain with the goal of laying the foundation for a unified model of age-related changes that relates WM microstructure/morphometry and developmental trajectories. Results indicated wide variation in rates and patterns of decline between tracts, as well as tract-specific interactions between tract VLR and microstructure. Robust sex differences were also identified. Our findings demonstrate the need for further exploration of the mechanisms behind both macro- and microstructural differences across the aging brain. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.14.532686v1?rss=1 Authors: Kim, Y., Joshi, A. A., Choi, S., Joshi, S. H., Bhushan, C., Varadarajan, D., Haldar, J. P., Leahy, R. M., Shattuck, D. W. Abstract: There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The BrainSuite Anatomical Pipeline (BAP) extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The BrainSuite Diffusion Pipeline (BDP) processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for geometric image distortion, and fitting diffusion models to the DWI data. The BrainSuite Functional Pipeline (BFP) performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. BFP coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. Each of these outputs can then be processed during group-level analysis. The outputs of BAP and BDP are analyzed using the BrainSuite Statistics in R (bssr) toolbox, which provides functionality for hypothesis testing and statistical modeling. The outputs of BFP can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.10.528083v1?rss=1 Authors: Santos, F. A. N., Tewarie, P. K. B., Baudot, P., Luchicchi, A., Barros de Souza, D. A. N., Girier, G., Milan, A. P., Broeders, T., Centeno, E. G. Z., Cofre, R., Rosas, F. E., Carone, D., Kennedy, J., Stam, C. J., Hillebrand, A., Desroches, M., Rodrigues, S., Schoonheim, M., Douw, L., Quax, R. Abstract: Network theory is often based on pairwise relationships between nodes, which is not necessarily realistic for modeling complex systems. Importantly, it does not accurately capture non-pairwise interactions in the human brain, often considered one of the most complex systems. In this work, we develop a multivariate signal processing pipeline to build high-order networks from time series and apply it to resting-state functional magnetic resonance imaging (fMRI) signals to characterize high-order communication between brain regions. We also propose connectivity and signal processing rules for building uniform hypergraphs and argue that each multivariate interdependence metric could define weights in a hypergraph. As a proof of concept, we investigate the most relevant three-point interactions in the human brain by searching for high-order "hubs" in a cohort of 100 individuals from the Human Connectome Project. We find that, for each choice of multivariate interdependence, the high-order hubs are compatible with distinct systems in the brain. Additionally, the high-order functional brain networks exhibit simultaneous integration and segregation patterns qualitatively observable from their high-order hubs. Our work hereby introduces a promising heuristic route for hypergraph representation of brain activity and opens up exciting avenues for further research in high-order network neuroscience and complex systems. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.10.528061v1?rss=1 Authors: Willbrand, E. H., Jackson, S., Chen, S., Hathaway, C. B., Voorhies, W. I., Bunge, S. A., Weiner, K. S. Abstract: Identifying structural-functional correspondences is a major goal among biologists. In neurobiology, recent findings identify relationships between performance on cognitive tasks and the presence or absence of small, shallow indentations, or sulci, of the human brain. Here, we tested if the presence or absence of one such sulcus, the paraintermediate frontal sulcus (pimfs-v) in lateral prefrontal cortex, was related to relational reasoning in young adults from the Human Connectome Project (ages 22-36). After manually identifying 2,877 sulci across 144 hemispheres, our results indicate that the presence of the pimfs-v in the left hemisphere was associated with a 21-34% higher performance on a relational reasoning task. These findings have direct developmental and evolutionary relevance as recent work shows that the presence or absence of the pimfs-v is also related to reasoning performance in a pediatric cohort, and that the pimfs-v is exceedingly rare in chimpanzees. Thus, the pimfs-v is a novel developmental, cognitive, and evolutionarily relevant feature that should be considered in future studies examining how the complex relationships among multiscale anatomical and functional features of the brain give rise to abstract thought. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.09.527639v1?rss=1 Authors: Popp, J. L., Thiele, J. A., Faskowitz, J., Seguin, C., Sporns, O., Hilger, K. Abstract: Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = .25, p less than .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = .19, p less than .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.01.526659v1?rss=1 Authors: Hannum, A., Lopez, M. A., Blanco, S. A., Betzel, R. Abstract: The human brain is a complex network comprised of functionally and anatomically interconnected brain regions. A growing number of studies have suggested that empirical estimates of brain networks may be useful for discovery of biomarkers of disease and cognitive state. A prerequisite for realizing this aim, however, is that brain networks also serve as reliable markers of an individual. Here, using Human Connectome Project data, we build upon recent studies examining brain-based fingerprints of individual subjects and cognitive states based on cognitively-demanding tasks that assess, for example, working memory, theory of mind, and motor function. Our approach achieves accuracy of up to 99% for both identification of the subject of an fMRI scan, and for classification of the cognitive state of a previously-unseen subject in a scan. More broadly, we explore the accuracy and reliability of five different machine learning techniques on subject fingerprinting and cognitive state decoding objectives, using functional connectivity data from fMRI scans of a high number of subjects (865) across a number of cognitive states (8). These results represent an advance on existing techniques for functional connectivity-based brain fingerprinting and state decoding. Additionally, 16 different pre-processing pipelines are compared in order to characterize the effects of different aspects of the production of functional connectomes (FCs) on the accuracy of subject and task classification, and to identify possible confounds. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.11.523655v1?rss=1 Authors: Rowe, E., Zhang, Y., Garrido, M. Abstract: Conscious visual motion information follows a cortical pathway from the retina to the lateral geniculate nucleus (LGN) and on to the primary visual cortex (V1) before arriving at the middle temporal visual area (MT/V5). Alternative subcortical pathways that bypass V1 are thought to convey unconscious visual information. One flows from the retina to the pulvinar (PUL) and on to MT; while the other directly connects the LGN to MT. Evidence for these pathways comes from non-human primates and modest-sized studies in humans with brain lesions. Thus, the aim of the current study was to reconstruct these pathways in a large sample of neurotypical individuals and to determine the degree to which these pathways are myelinated, suggesting information flow is rapid. We used the publicly available 7T (N = 98; 'discovery') and 3T (N = 381; 'validation') diffusion MRI datasets from the Human Connectome Project to reconstruct the PUL-MT and LGN-MT pathways. We found more fibre tracts with greater density in the left hemisphere. Although the left PUL-MT path was denser, the bilateral LGN-MT tracts were more heavily myelinated, suggesting faster signal transduction. We suggest that this apparent discrepancy may be due to 'adaptive myelination' caused by more frequent use of the LGN-MT pathway that leads to greater myelination and faster overall signal transmission. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.08.523152v1?rss=1 Authors: Mastrovito, D., Hanson, S., Hanson, C. Abstract: The default mode network (DMN) is a collection of brain regions including midline frontal and parietal structures, medial and lateral temporal lobes, and lateral parietal cortex. Although there is evidence that the network can be subdivided into at least two subcomponents, the network reliably exhibits highly correlated activity both at rest and during task performance. Current understanding regarding the function of the DMN rests on a large body of research indicating that activity in the network decreases during task epochs of experimental paradigms relative to inter-trial intervals. A seeming contradiction arises when the experimental paradigm includes tasks involving autobiographical memory, thinking about one's self, planning for the future, or social cognition. In such cases, the DMN's activity increases and is correlated with attentional networks. Some have therefore concluded that the DMN supports advanced human cognitive abilities such as interoceptive processing and theory of mind. This conclusion may be called into question by evidence of correlated activity in homologous brain regions in other, even non-primate, species. Thus, there are contradictory findings related to the function of the DMN that have been difficult to integrate into a coherent theory regarding its function. Using data from the Human Connectome Project, we explore the temporal dynamics of activity in different regions of the DMN in relation to stimulus presentation. We show that generally the dorsal portion of the network exhibits only a transient initial decrease in activity at the start of trials that increases over trial duration. The ventral component often has more similarity in its time course to that of task-activated areas. We propose that task-associated ramping dynamics in the network are incompatible with a task-negative view of the DMN and propose the dorsal and ventral sub-components of network may rather work together to support bottom-up salience detection and subsequent top-down voluntary action. In this context, we re-interpret the body of anatomical and neurophysiological experimental evidence, arguing that this interpretation can accommodate the seeming contradictions regarding DMN function in the extant literature. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.31.522374v1?rss=1 Authors: Tetereva, A., Pat, N. Abstract: For decades, neuroscientists have been on a quest to search for a biomarker that can help capture age-related cognitive decline. One well-known candidate is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI data. Here we aim to formally evaluate the utility of Brain Age as a biomarker for capturing cognitive decline. Using 504 aging participants (36-100 years old) from the Human Connectome Project in Aging, we created 26 age-prediction models for Brain Age based on different combinations of MRI modalities. We first tested how much Brain Age from these age-prediction models added to what we had already known from a chronological age in capturing cognitive decline. Based on the commonality analyses, we found a large degree of overlap between Brain Age and chronological age, so much so that, at best, Brain Age could uniquely add only around 1.6% in explaining the variation in cognitive decline. Next, the age-prediction models that performed better at predicting chronological age did not necessarily create better Brain Age for capturing cognitive decline beyond chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining cognitive decline. Lastly, unlike Brain Age, Brain Cognition, or a predicted value based on machine-learning models built to predict cognitive abilities from brain MRI data, provided much higher unique effects. Brain Cognition added over 11% to explain the variation in cognitive decline beyond chronological age, leading to around a 1/3-time improvement of the total variation explained. Accordingly, while demonstrating the limited utility of Brain Age, we provided a solution to improve our ability to use brain MRI data as a biomarker for cognitive decline. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.19.519033v1?rss=1 Authors: Tanner, J., Faskowitz, J., Teixeira, A. S., Seguin, C., Coletta, L., Gozzi, A., Misic, B., Betzel, R. Abstract: The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are assigned based on diffusion parameters or inferred using a statistical model, although the model generally scales poorly limiting its applications to relatively small networks. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighted schemes. Here, we explore a simple regression-based, explanatory model that endows reconstructed fiber tracts with directed and signed weights. Benchmarking this method on Human Connectome Project data, we find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples. Next, we analyze the resulting network using graph-theoretic tools from network neuroscience, revealing bilaterally symmetric communities that span cerebral hemispheres. These communities exhibit a clear mapping onto known functional systems. We also study the shortest paths structure of this network, discovering that almost every edge participants in at least one shortest path. We also find evidence of asymmetric edge weights, that the network reconfigures in response to naturalistic stimuli, and that estimated edge weights differ with age. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.14.520417v1?rss=1 Authors: Labache, L., Ge, T., Yeo, B. T. T., Holmes, A. J. Abstract: Hemispheric specialization is a fundamental feature of human brain organization. However, it is not yet clear to what extent the lateralization of specific cognitive processes may be evident throughout the broad functional architecture of cortex. While the majority of people exhibit left-hemispheric language dominance, a substantial minority of the population shows reverse lateralization. Using twin and family data from the Human Connectome Project, we provide evidence that atypical language dominance is associated with global shifts in cortical organization. Individuals with atypical language organization exhibited corresponding hemispheric differences in the macroscale functional gradients that situate discrete large-scale networks along a continuous spectrum, extending from unimodal through association territories. Analyses revealed that both language lateralization and gradient asymmetries are, in part, driven by genetic factors. These findings pave the way for a deeper understanding of the origins and relationships linking population-level variability in hemispheric specialization and global properties of cortical organization. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.01.518720v1?rss=1 Authors: Assem, M., Shashidhara, S., Glasser, M. F., Duncan, J. Abstract: Theoretical models suggest executive functions are supported by both domain-general and domain-specific processes. While some brain imaging studies claim executive tasks recruit a domain-general multiple-demand (MD) brain system, many studies argue the spatially coarse results of traditional imaging methods have blurred fine-grained functionally fractionated domain-specific systems into one. To address this challenge, we scanned participants using the high spatial resolution multimodal MRI approach of the Human Connectome Project while they performed tasks targeting executive demands of updating, shifting and inhibition. The results show that different executive activations overlap, at the single subject level, within MD regions. Critically, each task's topography shifts within MD regions to form a unique intersection with adjacent fine-grained resting-state networks. In this intersection, the strongest activations arise at neurobiologically defined network borders. Outside cerebral cortex, matching results are seen in circumscribed regions of the caudate nucleus, thalamus and cerebellum. Using precise imaging methods, these results suggest a novel framework whereby partially-specialised networks recruit neighbouring MD areas to generate distinct executive functions. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.16.516610v1?rss=1 Authors: Franca, L. G. S., Ciarrusta, J., Gale-Grant, O., Fenn-Moltu, S., Fitzgibbon, S. P., Chew, A., Falconer, S., Dimitrova, R., Cordero-Grande, L., Price, A. N., Hughes, E., O'Muircheartaigh, J., Duff, E., Tuulari, J. J., Deco, G., Counsell, S. J., Nosarti, C., Arichi, T., Edwards, D., McAlonan, G., Batalle, D. Abstract: Brain functional dynamics have been linked to emotion and cognition in mature individuals, where alterations are associated with mental ill-health and neurodevelopmental conditions (such as autism spectrum disorder). Although reliable resting-state networks have been consistently identified in neonates, little is known about the early development of dynamic brain functional connectivity and whether it is linked to later neurodevelopmental outcomes in childhood. In this study we characterised dynamic functional connectivity in the first few weeks of postnatal life and evaluated whether early dynamic functional connectivity: i) changes with age in the neonatal period ii) is altered by preterm birth and iii) is associated with neurodevelopmental and behavioural outcomes at 18 months. We used the Kuramoto Order Parameter as a metric of global brain synchrony and defined transient brain states (modules) using Leading Eigenvector Analysis (LEiDA) in a cohort of term-born (n=324) and preterm-born babies (n=66) scanned at term equivalent age from the developing Human Connectome Project. We assessed whether neonatal brain state features (mean synchrony, metastability, entropy, fractional occupancy, dwelling times) and state transition probabilities were associated with postmenstrual age at scan , postnatal days at scan and preterm-birth; and correlate with neurodevelopmental outcomes at 18 months measured using the Bayley Scales of Infant and Toddler Development, and atypical social, sensory and repetitive behaviours measured by the Quantitative Checklist for Autism in Toddlers (Q-CHAT). On a global scale, preterm-born infants had lower mean synchronisation and metastability, with reduced mean synchronisation associated with higher Q-CHAT scores at 18 months of age. On a modular scale, we identified six transient states of neonatal dynamic functional connectivity: three whole-brain synchronisation states and three regional synchrony states occupying occipital, sensory-motor, and frontal regions. Mean synchrony, metastability, fractional occupancy and dwelling times of these brain states were correlated with postmenstrual age and postnatal days at scan. Preterm-born infants had increased fractional occupancy of frontal and occipital states. Higher neonatal sensory-motor synchronisation was associated with lower motor and language outcome scores at 18 months. Lower frequency of occurrence of whole-brain synchronisation states and higher frequency of occurrence of the sensory-motor state were associated with higher Q-CHAT scores at 18 months. Thus, we have shown for the first time that a dynamic landscape of brain connectivity is already established by the time of birth in the human brain. This landscape is altered by preterm birth and its profile is linked to neurodevelopmental outcomes in toddlerhood. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.10.511329v1?rss=1 Authors: Nath, T., Caffo, B., Wager, T., Lindquist, M. Abstract: Mediation analysis is used to investigate the role of intermediate variables (mediators) that lie in the path between an exposure and an outcome variable. While significant research has focused on developing methods for assessing the influence of mediators on the exposure-outcome relationship, current approaches do not easily extend to settings where the mediator is high-dimensional. These situations are becoming increasingly common with the rapid increase of new applications measuring massive numbers of variables, including brain imaging, genomics, and metabolomics. In this work, we introduce a novel machine learning based method for identifying high dimensional mediators. The proposed algorithm iterates between using a machine learning model to map the high-dimensional mediators onto a lower-dimensional space, and using the predicted values as input in a standard three-variable mediation model. Hence, the machine learning model is trained to maximize the likelihood of the mediation model. Importantly, the proposed algorithm is agnostic to the machine learning model that is used, providing significant flexibility in the types of situations where it can be used. We illustrate the proposed methodology using data from two functional Magnetic Resonance Imaging (fMRI) studies. First, using data from a task-based fMRI study of thermal pain, we combine the proposed algorithm with a deep learning model to detect distributed, network-level brain patterns mediating the relationship between stimulus intensity (temperature) and reported pain at the single trial level. Second, using resting-state fMRI data from the Human Connectome Project, we combine the proposed algorithm with a connectome-based predictive modeling approach to determine brain functional connectivity measures that mediate the relationship between fluid intelligence and working memory accuracy. In both cases, our multivariate mediation model links exposure variables (thermal pain or fluid intelligence), high dimensional brain measures (single-trial brain activation maps or resting-state brain connectivity) and behavioral outcomes (pain report or working memory accuracy) into a single unified model. Using the proposed approach, we are able to identify brain-based measures that simultaneously encode the exposure variable and correlate with the behavioral outcome. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.29.510097v1?rss=1 Authors: Behjat, H., Tarun, A., Abramian, D., Larsson, M., Van De Ville, D. Abstract: Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brain mask as nodes of high-resolution, subject-specific graphs. We define the strength of local voxel-to-voxel connections using diffusion tensors and orientation distribution functions derived from diffusion-weighted MRI data. We study the graphs' Laplacian spectral properties on data from the Human Connectome Project. We then assess the extent of inter-subject variability of the Laplacian eigenmodes via a procrustes validation scheme. Finally, we demonstrate the extent to which functional MRI data are shaped by the underlying anatomical structure via graph signal processing. The graph Laplacian eigenmodes manifest highly resolved spatial profiles, reflecting distributed patterns that correspond to major white matter pathways. We show that the intrinsic dimensionality of the eigenspace of such high-resolution graphs is only a mere fraction of the graph dimensions. By projecting task and resting-state data on low-frequency graph Laplacian eigenmodes, we show, firstly, that brain activity can be well approximated by a small subset of low-frequency components, and secondly, that spectral graph energy profiles differ under different functional loads. The proposed graphs open new avenues in studying the brain, be it, by exploring their organisational properties via graph or spectral graph theory, or by treating them as the scaffold on which brain function is observed at the individual level. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

Welcome back to the Season 2 premiere of Neurotech Pub!In this episode, host and Paradromics CEO Matt Angle sits down with fellow Founder/CEOs Carolina Aguilar, Brian Pepin, and Kunal Ghosh to talk shop about building cutting edge neurotech companies from the ground up. We dive deep into business strategies, the neurotech fundraising landscape, emerging therapeutics, and more. We also provide an insider's view of the intersections of data, pharma, and med devices that are shaping the future of healthcare. Pour yourself a cold one and settle in! Check out full video with transcript here: Check out video and a full episode transcript here. 00:00 | Updates & News >> INBRAIN Neuroelectronics raised a $17M Series A >> Rune Labs raised a $22.8 Million Series A >> Inscopix Launched Cloud-Based Platform for Data Management and Analysis2:15 | Meet the panel and pick up a book1:54 | Jester King Brewery  2:25 | Rune Labs  2:50 | Neurostimulator for deep brain stimulation therapy  3:23 | INBRAIN Neuroelectronics  4:11 | Inscopix  5:24 | Ursula K. Le Guin's 'The Dispossessed'  6:19 | Yuval Noah Harari's 'Sapiens: A Brief History of Humankind'  6:32 | Daniel G. Miller's 'The Tree of Knowledge'  6:40 | Jiddu Krishnamurti's 'The Book of Life'  7:34 | Barack Obama's 'A Promised Land,' ‘Dreams from my Father,' & ‘The Audacity of Hope'  7:56 | Karl Popper's 'The Open Society and Its Enemies'9:25 | Venture Capital in Neurotech34:44 | Business Strategy in Neurotech40:32 | Tom Oxley, CEO, Synchron  43:58 | Dr. Thomas Insel  44:06 | Mindstrong Mental Health Care  44:35 | Aduhelm controversy  52:25 | Galvani Bio  59:39 | Percept Neurostimulator  1:00:32 | Neuromodulation and the future of treating brain disease  1:07:21 | Software as a Medical Device FDA Guidance1:09:12 | State of Animal Model Systems1:14:28 | α-Synuclein in Parkinson's Disease  1:18:01 | Alto Neuroscience  1:18:36 | Flatiron Foundation  1:18:45 | Gaurdent Health  1:19:03 | Melanoma Trends & Rates1:21:41 | The Pharma-Data-Device Ecosystem 1:21:42 | Frank Fischer, Chairman of Neuropace  1:22:28 | Neurotech Pub Season 1, Episode 9  1:26:35 | Roche acquisition of Flatiron Health & merger with Foundation Medicine   1:27:12 | Companion Diagnostics  1:28:29 | Adhulem and PET imaging  1:29:09 | Resignations at the FDA over Alzheimer's Drug  1:29:32 | Derek Lowe's take on the Aducanumab Approval, FDA Committee Votes, Halting the Aducanumab Trials, & The FDA Advisory Committee Briefing Document on Aducanumab  1:31:39 | Donanemab receives breakthrough therapy designation in 2021  1:36:58 | Mapping the Frontal-Vagal Pathway  1:37:09 | The Human Connectome Project  1:40:07 | Teal Organizations and Holacracy  1:41:18 | Society for Neuroscience  1:44:37 | Affymetrix (Thermo Fisher Scientific)  1:44:39 | IlluminaWant more?Follow Paradromics & Neurotech Pub on Twitter  Follow Matt, Brian, Carolina, & Kunal on Twitter

Have you ever wondered how our brain really works? This episode is about one of the most ambitious projects in the history of neuroscience, The Human Connectome Project. The purpose of the project is to help show us what's happening under our skull by building a detailed activity map of the human brain. This would also enable research into brain disorders such as dyslexia, autism, Alzheimer's, and schizophrenia. Neura Pod: - Twitter: https://twitter.com/NeuraPod - Patreon: https://www.patreon.com/neurapod - Medium: https://neurapod.medium.com/ - Spotify: https://open.spotify.com/show/2hqdVrReOGD6SZQ4uKuz7c - Instagram: https://www.instagram.com/NeuraPodcast - Facebook: https://www.facebook.com/NeuraPod - Tiktok: https://www.tiktok.com/@neurapod Opinions are my own. Neura Pod receives no compensation from Neuralink and has no formal affiliations with the company. I own Tesla stock and/or derivatives. --- Support this podcast: https://anchor.fm/neura-pod/support

Deanna Barch is a professor of psychology, radiology, and psychiatry at Washington University. Deanna is a key contributor to the Human connectome project, which aims to build a network map of the anatomical and functional connectivity within the healthy human brain, as well as to produce a body of data that will facilitate research into brain disorders such as dyslexia, autism, Alzheimer's disease, and schizophrenia. We discuss what is known about the way that our brain are connected, and the impact of stress and trauma on brain development. More information about Deanna's work can be found here: https://psych.wustl.edu/people/deanna... https://sites.wustl.edu/ccplab/people... Information about the Human Connectome Project can be found here: https://www.humanbrainproject.eu/en/

Claude Cruz is a retired electrical and biomedical engineer, with diverse interests and experience that span neural systems, human cognition, community-building and human sexuality. His current focus is on simulating large neural systems, and on developing a coaching practice to help people create meaningful “deep connections” with others.In this episode, Claude and Maxwell discuss natural philosophy, IBM, Intel, electrical and biomedical engineering, neural sciences, understanding human behavior, human cognition, growing up with a direction in life, artificial intelligence (AI), “soul” is a squishy word, Elon Musk, autonomous vehicles, the “Singularity”, feedback loops, Neuralink, synapses, the Human Connectome Project, silicon transistors, the Brain and Cognitive Sciences Meetup, Moore's Law, heat is always an issue with computer chips, building notebook computers, working at IBM near Stanford, creating software tools, Steve Jobs, Apple, Xerox Park - invention of the computer mouse, survival of the fittest, community is the reason we have survived, dinosaurs lives were simple, human world is more complex and difficult, focus on the good stuff - don't keep staring in the rearview mirror, bad experiences changing your future for better or worse, why is marriage a thing, marriage is socially useful, stable family structure, the diamond industry, romantic love, monogamy, infidelity, and “Sex at Dawn” - by Christopher Ryan. All production by Cody Maxwell. Artwork by Cody Maxwell. Opening graphic assets by UlyanaStudio. Opening music by Cody Maxwell. sharkfyn.com/maxwells-kitchen-podcast

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.03.366419v1?rss=1 Authors: Vainik, U., Paquola, C., Wang, X., Zheng, Y.-Q., Bernhardt, B., Misic, B., Dagher, A. Abstract: Human brain plastically adapts to environmental demands. Here, we propose that naturally occuring plasticity in certain brain areas should be reflected by higher environmental influence and therefore lower heritability of the structure of those brain areas. Mesulam's (1998) seminal overview proposed a hierarchy of plasticity, where higher-order multimodal areas should be more plastic than lower-order sensory areas. Using microstructural and functional gradients as proxies for Mesulam's hierarchy, we seek to test whether these gradients predict heritability of brain structure. We test this model simultaneously across multiple measures of cortical structure and microstructure derived from structural magnet resonance imaging. We also account for multiple other explanations of heritability differences, such as signal-to-noise ratio and spatial autocorrelation. We estimated heritability of brain areas using 984 participants from the Human Connectome Project. Multi-level modelling of heritability differences demonstrated that heritability is explained by both signal quality, as well as by the primary microstructural gradient. Namely, sensory areas had higher heritability and limbic/heteromodal areas had lower heritability. Given the increasing availability of genetically informed imaging data, heritability could be a quick method assess brain plasticity. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.28.359711v1?rss=1 Authors: Rutherford, S., Angstadt, M., Sripada, C., Chang, S.-E. Abstract: Large datasets, consisting of hundreds or thousands of subjects, are becoming the new data standard within the neuroimaging community. While big data creates numerous benefits, such as detecting smaller effects, many of these big datasets have focused on non-clinical populations. The heterogeneity of clinical populations makes creating datasets of equal size and quality more challenging. There is a need for methods to connect these robust large datasets with the carefully curated clinical datasets collected over the past decades. In this study, we use resting-state fMRI data from the Adolescent Brain Cognitive Development study and the Human Connectome Project to discover generalizable brain features for use in an out-of-sample predictive model to classify young (3-10yrs) children who stutter from fluent peers. We achieve up to 72% classification accuracy using 10-fold cross validation. This study suggests that big data has the potential to yield generalizable biomarkers that are clinically meaningful. Specifically, this is the first study to demonstrate that big data-derived brain features can help differentiate children who stutter from their fluent peers and provide novel information on brain networks relevant to stuttering pathophysiology. The results provide a significant expansion to previous understanding of the neural bases of stuttering. In addition to auditory, somatomotor, and subcortical networks, the big data-based models highlight the importance of considering large scale brain networks supporting error sensitivity, attention, cognitive control, and emotion regulation/self-inspection in the neural bases of stuttering. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.25.353920v1?rss=1 Authors: Abramian, D., Larsson, M., Eklund, A., Aganj, I., Westin, C.-F., Behjat, H. Abstract: Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detachability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing WM fMRI data that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the WM BOLD signal is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the WM BOLD signal as they are incapable of adapting to the underlying domain of the BOLD signal in white matter. The fundamental element in the proposed method is a graph-based description of the WM that encodes the underlying anisotropy observed across WM, derived from diffusion MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject's unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth WM fMRI data, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project's 100-unrelated subject dataset, across seven functional tasks, showing the capacity of the proposed method for detecting streamline-like activations within axonal bundles. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.20.347518v1?rss=1 Authors: Pietsch, M., Christiaens, D., Hajnal, J. V., Tournier, J.-D. Abstract: MRI scanner and sequence imperfections and advances in reconstruction and imaging techniques to increase motion robustness can lead to inter-slice intensity variations in Echo Planar Imaging. Leveraging deep convolutional neural networks as universal image filters, we present a data-driven method for the correction of acquisition artefacts that manifest as inter-slice inconsistencies, regardless of their origin. This technique can be applied to motion- and dropout-artefacted data by embedding it in a reconstruction pipeline. The network is trained in the absence of ground-truth data on, and finally applied to, the reconstructed multi-shell high angular resolution diffusion imaging signal to produce a corrective slice intensity modulation field. This correction can be performed in either motion-corrected or scattered source-space. We focus on gaining control over the learned filter and the image data consistency via built-in spatial frequency and intensity constraints. The end product is a corrected image reconstructed from the original raw data, modulated by a multiplicative field that can be inspected and verified to match the expected features of the artefact. In-plane, the correction approximately preserves the contrast of the diffusion signal and throughout the image series, it reduces inter-slice inconsistencies within and across subjects without biasing the data. We apply our pipeline to enhance the super-resolution reconstruction of neonatal multi-shell high angular resolution data as acquired in the developing Human Connectome Project. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.28.317180v1?rss=1 Authors: Taoudi-Benchekroun, Y., Christiaens, D., Grigorescu, I., Schuh, A., Pietsch, M., Chew, A., Harper, N., Falconer, S., Poppe, T., Hughes, E., Hutter, J., Price, A. N., Tournier, J.-D., Cordero-Grande, L., Counsell, S. J., Rueckert, D., Arichi, T., Hajnal, J. V., Edwards, A. D., Deprez, M., Batalle, D. Abstract: The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. With the rise of advanced imaging methods such as diffusion MRI, the study of brain connectivity has emerged as an important tool to understand subtle alterations associated with neurodevelopmental conditions. Brain connectivity derived from diffusion MRI is complex, multi-dimensional and noisy, and hence it can be challenging to interpret on an individual basis. Machine learning methods have proven to be a powerful tool to uncover hidden patterns in such data, thus opening an opportunity for early identification of atypical development and potentially more efficient treatment. In this work, we used Deep Neural Networks and Random Forests to predict neurodevelopmental characteristics from neonatal structural connectomes, in a large sample of neonates (N = 524) derived from the developing Human Connectome Project. We achieved a highly accurate prediction of post menstrual age (PMA) at scan on term-born infants (Mean absolute error (MAE) = 0.72 weeks, r = 0.83, p

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.18.304402v1?rss=1 Authors: Braver, T. S., Kizhner, A., Tang, R., Freund, M. C., Etzel, J. A. Abstract: The Dual Mechanisms of Cognitive Control (DMCC) project provides an ambitious and rigorous empirical test of a theoretical framework that posits two key cognitive control modes: proactive and reactive. The framework central tenets are that proactive and reactive control reflect domain-general dimensions of individual variation, with distinctive neural signatures, involving lateral prefrontal cortex (PFC) in interactions with other brain networks and circuits (e.g., frontoparietal, cingulo-opercular). In the DMCC project, each participant is scanned while performing theoretically-targeted variants of multiple well-established cognitive control tasks (Stroop, Cued Task-Switching, AX-CPT, Sternberg Working Memory) in three separate imaging sessions, that each encourage utilization of different control modes, plus also completes an extensive out-of-scanner individual differences battery. Additional key features of the project include a high spatio-temporal resolution (multiband) acquisition protocol, and a sample that includes a substantial subset of monozygotic twin pairs and participants recruited from the Human Connectome Project. Although data collection is still continuing (target N=200), we provide an overview of the study design and protocol, planned analytic approaches and methodological development, along with initial results (N=80) revealing novel evidence of a domain-general neural signature of reactive control. In the interests of scientific community building, the dataset will be made public at project completion, so it can serve as a valuable resource. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.19.304758v1?rss=1 Authors: He, J., Zhang, F., Xie, G., Yao, S., Feng, Y., Bastos, D. C. A., Rathi, Y., Makris, N., Kikinis, R., Golby, A. J., O'Donnell, L. J. Abstract: The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. Anatomically, the RGVP can be separated into four subdivisions, including two decussating and two non-decussating fiber pathways, which cannot be identified by conventional magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the anatomy of the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for tractographic reconstruction of the RGVP. In this study, four different tractography algorithms, including constrained spherical deconvolution (CSD) model based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) tractography methods, are compared for reconstruction of the RGVP. Experiments are performed using diffusion MRI data of 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Anatomical measurements are used to assess the advantages and limitations of the four tracking strategies, including the reconstruction rate of the four RGVP subdivisions, the percentage of decussating fibers, the correlation between volumes of the traced RGVPs and a T1w-based RGVP segmentation, and an expert judgment to rank the anatomical appearance of the reconstructed RGVPs. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.04.283820v1?rss=1 Authors: Guell, X., Schmahmann, J. D., Gabrieli, J. D., Ghosh, S. S., Geddes, M. R. Abstract: A central principle in our understanding of cerebral cortical organization is that homotopic left and right areas are functionally linked to each other, and also connected with structures that share similar functions within each cerebral cortical hemisphere. Here we refer to this concept as interhemispheric functional symmetry (IHFS). While multiple studies have described the distribution and variations of IHFS in the cerebral cortex, descriptions of IHFS in the subcortex are largely absent in the neuroscientific literature. Further, the proposed anatomical basis of IHFS is centered on callosal and other commissural tracts. These commissural fibers are present in virtually all cerebral cortical areas, but almost absent in the subcortex. There is thus an important knowledge gap in our understanding of subcortical IHFS. What is the distribution and variations of subcortical IHFS, and what are the anatomical correlates and physiological implications of this important property in the subcortex? Using fMRI functional gradient analyses in a large dataset (Human Connectome Project, n=1003), here we explored IHFS in human thalamus, lenticular nucleus, cerebellar cortex, and caudate nucleus. Our detailed descriptions provide an empirical foundation upon which to build hypotheses for the anatomical and physiological basis of subcortical IHFS. Our results indicate that direct or driver cerebral cortical afferent connectivity, as opposed to indirect or modulatory cerebral cortical afferent connectivity, is associated with stronger subcortical IHFS in thalamus and lenticular nucleus. In cerebellar cortex and caudate, where there is no variability in terms of either direct vs. indirect or driver vs. modulatory cerebral cortical afferent connections, connectivity to cerebral cortical areas with stronger cerebral cortical IHFS is associated with stronger IHFS in the subcortex. These two observations support a close relationship between subcortical IHFS and connectivity between subcortex and cortex, and generate new testable hypotheses that advance our understanding of subcortical organization. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.25.265546v1?rss=1 Authors: Helmer, M., Warrington, S. D., Mohammadi-Nejad, A.-R., Ji, J. L., Howell, A., Rosand, B., Anticevic, A., Sotiropoulos, S. N., Murray, J. D. Abstract: Associations between high-dimensional datasets, each comprising many features, can be discovered through multivariate statistical methods, like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). CCA and PLS are widely used methods which reveal which features carry the association. Despite the longevity and popularity of CCA/PLS approaches, their application to high-dimensional datasets raises critical questions about the reliability of CCA/PLS solutions. In particular, overfitting can produce solutions that are not stable across datasets, which severely hinders their interpretability and generalizability. To study these issues, we developed a generative model to simulate synthetic datasets with multivariate associations, parameterized by feature dimensionality, data variance structure, and assumed latent association strength. We found that resulting CCA/PLS associations could be highly inaccurate when the number of samples per feature is relatively small. For PLS, the profiles of feature weights exhibit detrimental bias toward leading principal component axes. We confirmed these model trends in state-of-the-art datasets containing neuroimaging and behavioral measurements in large numbers of subjects, namely the Human Connectome Project (n {approx} 1000) and UK Biobank (n = 20000), where we found that only the latter comprised enough samples to obtain stable estimates. Analysis of the neuroimaging literature using CCA to map brain-behavior relationships revealed that the commonly employed sample sizes yield unstable CCA solutions. Our generative modeling framework provides a calculator of dataset properties required for stable estimates. Collectively, our study characterizes dataset properties needed to limit the potentially detrimental effects of overfitting on stability of CCA/PLS solutions, and provides practical recommendations for future studies. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.05.236679v1?rss=1 Authors: Ferreira, F., Ashburner, J., Akram, H., Zrinzo, L., Zhang, H., Lambert, C. Abstract: The ventralis intermedius nucleus (Vim) is centrally placed in the dentato-thalamo-cortical pathway (DTCp) and is a key surgical target in the treatment of severe medically refractory tremor. It is not visible on conventional MRI sequences; consequently, stereotactic targeting currently relies on atlas-based coordinates. This fails to capture individual anatomical variability, which may lead to poor long-term clinical efficacy. Probabilistic tractography, combined with known anatomical connectivity, enables localisation of thalamic nuclei at an individual subject level. There are, however, a number of confounds associated with this technique that may influence results. Here we focused on an established method, using probabilistic tractography to reconstruct the DTCT, to identify the connectivity-defined Vim (cd-Vim) in vivo. Using 100 healthy individuals from the Human Connectome Project, our aim was to quantify cd-Vim variability across this population, measure the discrepancy with atlas-defined Vim (ad-Vim), and assess the influence of potential methodological confounds. We found no significant effect of any of the confounds. The mean cd-Vim coordinate was located within 1.9 mm (left) and 2.1 mm (right) of the average midpoint and 4.9 mm (left) and 5.4 mm (right) from the ad-Vim coordinates. cd-Vim location was more variable on the right, which reflects hemispheric asymmetries in the probabilistic DTCT reconstructed. The superior cerebellar peduncle was identified as a potential source of artificial variance. This work demonstrates significant individual anatomical variability of the cd-Vim that atlas-based approaches fail to capture. This variability was not related to any methodological confound tested. Lateralisation of cerebellar functions, such as speech, may contribute to the observed asymmetry. Tractography-based methods seem sensitive to individual anatomical variability that is missed by conventional neurosurgical targeting; These findings may form the basis for translational tools to improve efficacy and reduce side-effects of thalamic surgery for tremor. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.30.228361v1?rss=1 Authors: Silson, E. H., Zeidman, P., Knapen, T. H., Baker, C. I. Abstract: The initial encoding of visual information primarily from the contralateral visual field is a fundamental organizing principle of the primate visual system. Recently, the presence of such retinotopic sensitivity has been shown to extend well beyond early visual cortex to regions not historically considered retinotopically sensitive. In particular, human scene-selective regions in parahippocampal and medial parietal cortex exhibit prominent biases for the contralateral visual field. Here we used fMRI to test the hypothesis that the human hippocampus, which is thought to be anatomically connected with these scene-selective regions, would also exhibit a biased representation of contralateral visual space. First, population receptive field mapping with scene stimuli revealed strong biases for the contralateral visual field in bilateral hippocampus. Second, the distribution of retinotopic sensitivity suggested a more prominent representation in anterior medial portions of the hippocampus. Finally, the contralateral bias was confirmed in independent data taken from the Human Connectome Project initiative. The presence of contralateral biases in the hippocampus, a structure considered by many as the apex of the visual hierarchy, highlights the truly pervasive influence of retinotopy. Moreover, this finding has important implications for understanding how this information relates to the allocentric global spatial representations known to be encoded therein. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.30.229427v1?rss=1 Authors: Anderson, K. M., Ge, T., Kong, R., Patrick, L. M., Spreng, R. N., Sabuncu, M. R., Yeo, B. T. T., Holmes, A. Abstract: Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and predictive of behavior, it is not yet clear to what extent genetic factors underlie inter-individual differences in network topography. Here, leveraging a novel non-linear multi-dimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n=1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M=0.33, SD=0.071), relative to unimodal sensory/motor cortex (h2: M=0.44, SD=0.051). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multi-dimensional estimation of heritability (h2-multi; M=0.14, SD=0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions, and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.27.222778v1?rss=1 Authors: Cottaar, M., Bastiani, M., Boddu, N., Glasser, M. F., Haber, S., Van Essen, D. C., Sotiropoulos, S. N., Jbabdi, S. Abstract: Many brain imaging studies aim to measure structural connectivity with diffusion tractography. However, biases in tractography data, particularly near the boundary between white matter and cortical grey matter can limit the accuracy of such studies. When seeding from the white matter, streamlines tend to travel parallel to the convoluted cortical surface, largely avoiding sulcal fundi and terminating preferentially on gyral crowns. When seeding from the cortical grey matter, streamlines generally run near the cortical surface until reaching deep white matter. These so-called "gyral biases" limit the accuracy and effective resolution of cortical structural connectivity profiles estimated by tractography algorithms, and they do not reflect the expected distributions of axonal densities seen in invasive tracer studies or stains of myelinated fibres. We propose an algorithm that concurrently models fibre density and orientation using a divergence-free vector field within gyral blades to encourage an anatomically-justified streamline density distribution along the cortical white/grey-matter boundary while maintaining alignment with the diffusion MRI estimated fibre orientations. Using in vivo data from the Human Connectome Project, we show that this algorithm reduces tractography biases. We compare the structural connectomes to functional connectomes from resting-state fMRI, showing that our model improves cross-modal agreement. Finally, we find that after parcellation the changes in the structural connectome are very minor with slightly improved interhemispheric connections (i.e, more homotopic connectivity) and slightly worse intrahemispheric connections when compared to tracers. Copy rights belong to original authors. Visit the link for more info

Cognitive and Neurobiological Imaging, Human Connectome Project for disordered emotional states, Imaging systems for oral cancer screening, and Better camera designs for convolutional neural networks using synthetic images Prof Brian A. Wandell is a professor at the Psychology Department of Stanford University. He is also a member of Electrical Engineering, Ophthalmology, and the Graduate School of Education. He is the founding Director of Stanford’s Center for Cognitive and Neurobiological Imaging and he founded the Stanford Center for Image Systems Engineering Program. Prof. Wandell’s research centers on imaging science and technology, spanning neuroscience measurements of the visual cortex and reading development to simulation and design of imaging systems. --- Send in a voice message: https://anchor.fm/scientificsense/message Support this podcast: https://anchor.fm/scientificsense/support

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.01.183038v1?rss=1 Authors: Cole, M., Murray, K. D., St-Onge, E., Risk, B., Zhong, J., Schifitto, G., Descoteaux, M., Zhang, Z. Abstract: There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. However, several fundamental problems are still not well considered when conducting such connectome integration analyses, e.g., "Which structure (e.g., gray matter, white matter, white surface or pial surface) should be used for defining SC and FC and exploring their relationships", "Which brain parcellation should be used", and "How do the SC and FC correlate with each other and how do such correlations vary in different locations of the brain?". In this work, we develop a new framework called surface-based connectivity integration (SBCI) to facilitate the integrative analysis of SC and FC with a re-thinking of these problems. We propose to use the white surface (the interface of white matter and gray matter) to build both SC and FC since diffusion signals are in the white matter while functional signals are more present in the gray matter. SBCI also represents both SC and FC in a continuous manner at very high spatial resolution on the white surface, avoiding the need of pre-specified atlases which may bias the comparison of SC and FC. Using data from the Human Connectome Project, we show that SBCI can create reproducible, high quality SC and FC, in addition to three novel imaging biomarkers reflective of the similarity between SC and FC throughout the brain, called global, local, and discrete SC-FC coupling. Further, we demonstrate the usefulness of these biomarkers in finding group effects due to biological sex throughout the brain. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.25.172387v1?rss=1 Authors: Dhamala, E., Jamison, K., Jaywant, A., Dennis, S., Kuceyeski, A. Abstract: How white matter pathway integrity and neural co-activation patterns in the brain relate to complex cognitive functions remains a mystery in neuroscience. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how multimodal brain connectivity relates to cognition. Specifically, we evaluate whether integrating functional and structural connectivity improves prediction of individual crystallised and fluid abilities in 415 unrelated healthy young adults from the Human Connectome Project. Our primary results are two-fold. First, we demonstrate that integrating functional and structural information - at both a model input or output level - significantly outperforms functional or structural connectivity alone to predict individual verbal/language skills and fluid reasoning/executive function. Second, we show that distinct pairwise functional and structural connections are important for these predictions. In a secondary analysis, we find that structural connectivity derived from deterministic tractography is significantly superior than structural connectivity derived from probabilistic tractography to predict individual cognitive abilities. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.21.157412v1?rss=1 Authors: Quinn, A. J., Green, G. G., Hymers, M. Abstract: The spatial and spectral structure of oscillatory networks in the brain provide a readout of the underlying neuronal function. Within and between subject variability in these networks can be highly informative but also poses a considerable analytic challenge. Here, we describe a method that simultaneously estimate spectral and spatial network structure without assumptions about either feature distorting estimation of the other. This enables analyses exploring how variability in the frequency and spatial structure of oscillatory networks might vary both across the brain and across individuals. The method performs a modal decomposition of an autoregressive model to describe the oscillatory signals present within a time-series based on their peak frequency and damping time. Moreover, an alternate mathematical formulation for the system transfer function can be written in terms of these oscillatory modes; describing the spatial topography and network structure of each component. We define a set of Spatio-Spectral Eigenmodes (SSEs) from these parameters to provide a parsimonious description of oscillatory networks. Crucially, the SSEs preserve the rich between-subject variability and are constructed without pre-averaging within specified frequency bands or limiting analyses to single channels or regions. After validating the method on simulated data, we explore the structure of whole brain oscillatory networks in eyes-open resting state MEG data from the Human Connectome Project. We are able to show a wide variability in peak frequency and network structure of alpha oscillations and reveal a distinction between occipital high-frequency alpha and parietal low-frequency alpha. The frequency difference between occipital and parietal alpha components is present within individual participants but is partially masked by larger between subject variability; a 10Hz oscillation may represent the high-frequency occipital component in one participant and the low-frequency parietal component in another. This rich characterisation of individual neural phenotypes has the potential to enhance analyses into the relationship between neural dynamics and a person's behavioural, cognitive or clinical state Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.16.155937v1?rss=1 Authors: Kim, J.-H., Zhang, Y., Han, K., Choi, M., Liu, Z. Abstract: Resting state functional magnetic resonance imaging (rs-fMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rs-fMRI data. Here we establish a variational auto-encoder, as a generative model trainable with unsupervised learning, to disentangle the unknown sources of rs-fMRI activity. After being trained with large data from the Human Connectome Project, the model has learned to represent and generate patterns of cortical activity and connectivity using latent variables. Of the latent representation, its distribution reveals overlapping functional networks, and its geometry is unique to each individual. Our results support the functional opposition between the default mode network and the task-positive network, while such opposition is asymmetric and non-stationary. Correlations between latent variables, rather than cortical connectivity, can be used as a more reliable feature to accurately identify subjects from a large group, even if only a short period of data is available per subject. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.01.128306v1?rss=1 Authors: Kassinopoulos, M., Mitsis, G. D. Abstract: The blood oxygenation level-dependent (BOLD) contrast mechanism allows someone to non-invasively probe changes in deoxyhemoglobin content. As such, it is commonly used in fMRI to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed through the years to correct for the associated confounds. This study sought to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. RETROICOR, a technique commonly used to model fMRI fluctuations induced by cardiac pulsatility was compared with a new technique proposed here, named cardiac pulsatility model (CPM), that is based on convolution filtering. Further, this study investigated whether the variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations as well as with systemic low frequency oscillations (SLFOs) present in the global signal (i.e. mean fMRI timeseries averaged across all voxels in gray matter). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain significantly more variance in fMRI compared to RETROICOR, particularly for subjects that presented high variance in heart rate during the scan. The amplitude of the fMRI pulse-related fluctuations did not seem to covary with PPG-Amp. That said, PPG-Amp explained significant variance in the GS that did not seem to be attributed to variations in heart rate or breathing patterns. In conclusion, our results suggest that the techniques proposed here can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately than model-based techniques commonly employed in fMRI studies. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.05.28.121137v1?rss=1 Authors: Faber, M., Przezdzik, I., Fernandez, G., Haak, K. V., Beckmann, C. F. Abstract: Convergent evidence from neuroimaging, computational, and clinical research has shown that the anterior temporal lobe (ATL) is critically involved in two key aspects of semantic cognition: the representation of semantic knowledge, and the executive regulation of this knowledge. Both are necessary for integrating features to understand concepts, and to integrate concepts to understand discourse. Here, we tested the hypothesis that these differential aspects of integration map onto different patterns of ATL connectivity. Specifically, we hypothesized that there are two overlapping modes of functional connectivity of the ATL that each predict distinct aspects of semantic cognition on an individual level. We used a novel analytical approach (connectopic mapping) to identify the first two dominant modes connection topographies (i.e. maps of spatially varying connectivity) in the ATL in 766 participants (Human Connectome Project), and summarized these into 16 parameters that reflect inter-individual differences in their functional organization. If these connection topographies reflect the ATL's functional multiplicity, then we would expect to find a dissociation where one mode (but not the other) correlates with cross-modal matching of verbal and visual information (picture vocabulary naming), and the other (but not the former) correlates with how quickly and accurately relevant semantic information is retrieved (story comprehension). Our analysis revealed a gradient of spatially varying connectivity along the inferior-superior axis, and secondly, an anterior to posterior gradient. Multiple regression analyses revealed a double dissociation such that individual differences in the inferior-superior gradient are predictive of differences in story comprehension, whereas the anterior-posterior gradient maps onto differences in picture vocabulary naming, but not vice versa. These findings indicate that overlapping gradients of functional connectivity in the ATL are related to differential behaviors, which is important for understanding how its functional organization underlies its multiple functions. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.05.26.116152v1?rss=1 Authors: Bodin, C., Pron, A., Le Mao, M., Regis, J., Belin, P., Coulon, O. Abstract: While there is a profusion of functional investigations involving the superior temporal sulcus (STS), our knowledge of the anatomy of this sulcus is still limited by a large variability across individuals. Several "plis de passage" (PPs), annectant gyri buried inside the fold, can separate the STS into distinct segments and could explain part of the observed variability. However, an accurate characterization is lacking to properly extract and fully understand the nature of PPs. The aim of the present study is twofold: i. to characterize the STS PPs by directly identifying them within individual STS, using the geometry of the surrounding surface and considering both deep and superficial PPs. ii. to test the hypothesis that PPs constitute local increases of the short-range structural connectivity. Performed on 90 subjects from the Human Connectome Project database, our study revealed that PPs constitute surface landmarks that can be identified from the geometry of the STS walls and that they constitute critical pathways of the U-shaped white-matter connecting the two banks of the STS. Specifically, a larger amount of fibers was extracted at the location of PPs compared to other locations in the STS. This quantity was also larger for superficial PPs than for deep buried ones. These findings raise new hypotheses regarding the relation between the cortical surface geometry and structural connectivity, as well as the possible role of PPs in the functional organization of the STS. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.05.07.082271v1?rss=1 Authors: Dimitriadis, S. I., Messaritaki, E. I., Jones, D. K. Abstract: The human brain is a complex network of volumes of tissue (nodes) that are interconnected by white matter tracts (edges). It can be represented as a graph to allow us to use graph theory to gain insight into normal human development and brain disorders. Most graph theoretical metrics measure either whole-network (global) or node-specific (local) properties of the network. However, a critical question in network neuroscience is how nodes cluster together to form communities, each of which possibly plays a specific functional role. Community partition analysis allows us to investigate the mesoscale organization of the brain. Various algorithms have been proposed in the literature, that allow the identification of such communities, with each algorithm resulting in different communities for the network. Those communities also depend on the method used to weigh the edges of the graphs representing the brain networks. In this work, we use the test-retest data from the Human Connectome Project to compare 32 such community detection algorithms, each paired with 7 graph construction schemes, and assess the reproducibility of the resulting community partitions. The reproducibility of community partition depended heavily on both the graph construction scheme and the community detection algorithm. Hard community detection algorithms, via which each node is assigned to only one community, outperformed soft ones, via which each node can be a part of more than one community. The best reproducibility was observed for the graph construction scheme that combines 9 white matter tract metrics paired with the greedy stability optimization algorithm, with either discrete or continuous Markovian chain. This graph-construction scheme / community detection algorithm pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs were better reproduced than provincial hubs. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.05.04.072868v1?rss=1 Authors: Mahadevan, A. S., Tooley, U. A., Bertolero, M. A., Mackey, A. P., Bassett, D. S. Abstract: Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternate methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of six different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that correlation-based measures (Pearson and Spearman correlation) have a relatively high residual distance-dependent relationship with motion compared to coherence and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of correlation-based measures, however, may be offset by their higher test-retest reliability and system identifiability. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.05.05.076273v1?rss=1 Authors: Koike, S., Tanaka, S., Okada, T., Aso, T., Asano, M., Maikusa, N., Morita, K., Okada, N., Fukunaga, M., Uematsu, A., Togo, H., Miyazaki, A., Murata, K., Urushibata, Y., Autio, J. A., Ose, T., Yoshiomoto, J., Araki, T., Glasser, M. F., Van Essen, D. C., Murayama, M., Sadato, N., Kawato, M., Kasai, K., Okamoto, Y., Hanakawa, T., Hayashi, T., Brain/MINDS Beyond Human Brain MRI Group Abstract: Psychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based diagnostic and therapeutic biomarkers are not yet well established. The Brain/MINDS Beyond human brain MRI project (FY2018 ~ FY2023) is a multi-site harmonization study aiming to establish clinically-relevant imaging biomarkers using multiple high-performance scanners, standardized multi-modal imaging, and a study design that includes traveling subjects. This project began with 13 clinical research sites that collect MRI data on psychiatric and neurological disorders across the lifespan and three research sites that design and develop measurement procedures, neuroimaging protocols, data storage and sharing, and analysis tools. Brain images obtained with the Harmonization protocol (HARP) are preprocessed and analyzed using approaches developed by the Human Connectome Project, generating preliminary cortical structure, function, and connectivity measures that are comparable across scanners. The use of 'travelling subjects', in which study participants travel to multiple sites to undergo scanning with standardized neuroimaging techniques, enable us to minimize the measurement bias between scanners and protocols and to increase the sensitivity and specificity of case-control studies. All the imaging and demographic and clinical data are shared between the participating sites and will be made publicly available in 2024. To the best of our knowledge, this is the first multi-site human brain MRI project to explore multiple psychiatric and neurological disorders across the lifespan. The Brain/MINDS Beyond human brain MRI project will help to identify the common and disease-specific pathophysiology features of brain diseases and develop imaging biomarkers for clinical practice. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.04.24.060657v1?rss=1 Authors: Zhang, Y., Tetrel, L., Thirion, B., Bellec, P. Abstract: A key goal in neuroscience is to understand the brain mechanisms of cognitive functions. An emerging approach is brain decoding, which consists of inferring a set of experimental conditions performed by a participant, using pattern classification of brain activity. Few works so far have attempted to train a brain decoding model that would generalize across many different cognitive tasks drawn from multiple cognitive domains. To tackle this problem, we proposed a domain-general brain decoder that automatically learns the spatiotemporal dynamics of brain response within a short time window using a deep learning approach. By leveraging our prior knowledge on network organization of human brain cognition, we constructed deep graph convolutional neural networks to annotate cognitive states by first mapping the task-evoked fMRI response onto a brain graph, propagating brain dynamics among interconnected brain regions and functional networks, and generating state-specific representations of recorded brain activity. We evaluated the decoding model on a large population of 1200 participants, under 21 different experimental conditions spanning 6 different cognitive domains, acquired from the Human Connectome Project task-fMRI database. Using a 10s window of fMRI response, the 21 cognitive states were identified with a test accuracy of 89% (chance level 4.8%). Performance remained good when using a 6s window (82%). It was even feasible to decode cognitive states from a single fMRI volume (720ms), with the performance following the shape of the hemodynamic response. Moreover, a saliency map analysis demonstrated that the high decoding performance was driven by the response of biologically meaningful brain regions. Together, we provide an automated tool to annotate human brain activity with fine temporal resolution and fine cognitive granularity. Our model shows potential applications as a reference model for domain adaptation, possibly making contributions in a variety of domains, including neurological and psychiatric disorders. Copy rights belong to original authors. Visit the link for more info

Linking human behavior to resting-state brain function is a central question in systems neuroscience. In particular, the functional timescales at which different types of behavioral factors are encoded remain largely unexplored. The behavioral counterparts of static functional connectivity (FC), at the resolution of several minutes, have been studied but behavioral correlates of dynamic measures of FC at the resolution of a few seconds remain unclear. Here, using resting-state fMRI and 58 phenotypic measures from the Human Connectome Project, we find that dynamic FC captures task-based phenotypes (e.g., processing speed or fluid intelligence scores), whereas self-reported measures (e.g., loneliness or life satisfaction) are equally well explained by static and dynamic FC. Furthermore, behaviorally relevant dynamic FC emerges from the interconnections across all resting-state networks, rather than within or between pairs of networks. Our findings shed new light on the timescales of cognitive processes involved in distinct facets of behavior. Liégeois R, Li J, Kong R, et al. Resting brain dynamics at different timescales capture distinct aspects of human behavior. Nat Commun. 2019;10(1):2317. Published 2019 May 24. doi:10.1038/s41467-019-10317-7. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Sections of the Abstract, Introduction, and Discussion are presented in the Podcast. Link to the full-text article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6534566/

In this week’s episode of "Marketing Today," Alan talks with Michael Platt, who is the James S. Riepe University Professor at the University of Pennsylvania. He also holds three professorships there: in marketing at the Wharton School; in neuroscience at the Perelman School of Medicine; and in psychology at the university’s School of Arts and Sciences. In addition, he is the director of the Wharton Neuroscience Initiative. In the course of their discussion, Pratt talks at length about an article he co-authored with Leslie Zane, "Cracking the Code on Brand Growth," as well as a yet-to-be-published study regarding people’s feelings about and affinities for their smartphones and how that relates to brand choice and loyalty. He also touches on the risks big brands face in not innovating, the even greater impact neuroscience will have in the future on marketing, advertising and design, and, last but not least, how his polymathic ways fuel his passion.  "I’m very passionate about what I do; I’m very passionate about connecting all these disciplines," says Platt. "One of the things that drew me to Wharton and Penn, however, which is new in terms of opportunity, is really making the science applicable, making it useful for people — whether they’re in business or in society in general. How can we take all of what we’re doing here in the academy and in the sciences and translate it and make it accessible, so people understand it, so they’re interested in it? And actually give them tools to reach their own kind of peak performance and ultimately enhance their own well-being." Highlights from this week’s “Marketing Today” podcast include:  From anthropology to neuroscience, Platt discusses his background and how he is “trying to understand how our brains decide.” (1:31) Marketing, neuroscience, and psychology: Platt on his multidisciplinary balancing act. (7:14) “Cracking the Code on Brand Growth” — Platt talks about the article (and podcast) he collaborated on with Leslie Zane. (9:15) Platt elaborates on a relational hypothesis of branding. (12:51) Platt defines and explains just what a “connectome” is, and he discusses the Human Connectome Project and its implications for marketers. (20:38) “In some cases, neuroscience will provide better return on investment than you get if you’re just using survey and self-report techniques.” (31:22) Don’t be a dopamine: Platt explains how Dollar Shave Club’s innovative approach gave consumers’ brains a jolt. (33:46) Platt’s ability to move among different disciplines dates back to his high school days. (40:47) The future of marketing and its connection to neuroscience. (46:28) Support the show.

Production Early 80s VFX. So much rotoscoping. The dawn of computer generated graphics. Disney and art assets. Computational limitations creating art styles. Late-70s computing Hard drives! Huge ones.Huge mainframes. Pre-internet networks. Anthropomorphised computing Sentient computer antagonists. Legal protections for artificial life. Chess as the soon-to-be-obliterated pinnacle of artificial intelligence. AI Takeovers Master Control program.Skynets. Conflicting interests between man and machine. AI villainy is different from human villainy. Good AI to compete with the bad ones. Maybe the only way to stop a bad A.I. with a nuclear arsenal is a good A.I. with a nuclear arsenal Death-frisbees Chakram! Old-school sharpened frisbee weapons of south Asia. Accidental godliness Accidentally creating cargo cults. Self-aware life in servitude. Responsibilities when creating intelligences. Digitization Digitizing three-dimensional objects. Destructive and non-destructive methods. How we scan books. Brain uploading. Human Connectome Project. mcp:~ $ cat /dev/jeff_bridges > /dev/null Mainframes and the Unix Revolution by Computerphile: YouTube Support the show!

In our September Cerebrum article, “The Human Connectome Project: Progress and Prospects,” David Van Essen, Ph.D., and Matthew Glasser, Ph.D., write about an ambitious six-year collaboration between neuroscientists at various institutions to map the brain with the help of 1,200 volunteers and ever evolving magnetic resonance imaging (MRI) technology. In this podcast, the pair discuss their role, some of the unexpected surprises, and what they hope to discover in the project’s next phase.

Through the groundbreaking Human Connectome Project, researchers like Deanna Barch have spent years mapping the complex wiring of the human brain. Barch, who chairs the Department of Psychological and Brain Sciences at Washington University in St. Louis, provides a behind-the-scenes look into the project and helps us understand the links between brain connectivity and human behavior.

The Human Connectome Project has collected data of hundreds of individuals ranging from brain imaging to genetic and lifestyle information. Now researchers from the University of Oxford have used this information to see how much our lifestyle choices and personality traits are reflected in our brains. Karla Miller explained their findings to Connie Orbach. Like this podcast? Please help us by supporting the Naked Scientists

The Human Connectome Project has collected data of hundreds of individuals ranging from brain imaging to genetic and lifestyle information. Now researchers from the University of Oxford have used this information to see how much our lifestyle choices and personality traits are reflected in our brains. Karla Miller explained their findings to Connie Orbach. Like this podcast? Please help us by supporting the Naked Scientists

Antoine Jerusalem, Associate Professor at the Department of Engineering Science, talks about the importance of Open Data for his work on the computational modeling of materials and his involvement in the Human Connectome Project.

Building on the work of the Human Connectome Project, which is identifying the neural pathways that underlie brain function and behavior, a new study at Washington University School of Medicine in St. Louis is aiming to identify how those pathways are different in people with psychiatric illnesses. Researchers are using high-resolution imaging tools to identify structural and functional connectivity patterns in the brains of patients with psychiatric disorders and then comparing those scans to others taken of the brains of people who don’t have the disorders. The idea is to see whether, and how, connectivity patterns change in the brains of people with those illnesses.

[intro music]   Host – Dan Keller Hello, and welcome to Episode Twenty-one of Multiple Sclerosis Discovery, the podcast of the MS Discovery Forum. I’m your host, Dan Keller.   This week’s podcast features an interview with Dr. Paul Matthews about imaging in multiple sclerosis. But to begin, here’s a brief summary of some of the latest developments on the MS Discovery Forum at msdiscovery.org.   We recently reported on a draft of a review released by the Agency for Healthcare and Research Quality about discontinuing disease-modifying therapies in patients with MS. Though the report’s main conclusion was that little evidence is available to assess the risks and benefits of discontinuing therapies, several MS groups came together to criticize the report during the open comment period. Groups like the National MS Society and Medical Partners 4 MS raised concerns that the review was not conducted properly and that insurance providers may use it as justification to reduce coverage of DMTs for MS. The AHRQ told Multiple Sclerosis Discovery Forum that they would consider the comments carefully and make any necessary revisions.   MSDF’s parent organization, the Accelerated Cure Project, is launching a new research resource called iConquer MS. Hollie Schmidt, Vice President of Scientific Operations at the Accelerated Cure Project, recently wrote a blog post explaining that the new initiative aims to take data and biosamples from 20,000 people with MS and make them open-access to researchers. We want your input about what you may want to do with such a resource. If you’re interested, go to the blogs section of MS Discovery Forum under the “News and Future Directions” tab and click on the blog post titled, “Invitation to Share Your Thoughts on a New MS Research Resource.”   Our list of meetings and events is ever-growing. We’ve posted multiple meetings of all shapes and sizes sprinkled throughout 2015 and even into 2016. And if you know of a meeting that’s not yet listed, please do submit what information you have. We’ll take care of the rest. Just go to “Meetings and Events” under the “Professional Resources” tab on our website and click on the “submit new item” button to tell us about your event. We’re even willing to list local departmental seminars and journal clubs.   [transition music]   Now to the interview. Professor Paul Matthews is at Imperial College London in brain sciences. He met with MSDF to talk shop about imaging in MS.   Interviewer – Dan Keller Welcome, Professor Matthews. What do you see now as new modalities or new ways of doing imaging, and what’s coming along?   Interviewee - Paul Matthews Thanks, Dan. Imaging continues to reinvent itself in areas particularly like MS. Magnetic resonance is becoming more and more powerful with use of particularly multiband techniques, allowing multiple coils to be used to accelerate the imaging process, and because of that being able to collect much more data to enhance particularly diffusion images. So, for example, within the Human Connectome Project, development of new multiband techniques has accelerated imaging to the point where very high resolution diffusion tensor images can be acquired in spaces of 15-20 minutes. The implications of this for MS are that we can begin to develop powerful approaches to expression of the diffusion tensor information in terms of diffusion parallel to the fibers, perpendicular to the fibers, and free diffusion that is anisotropic. This means that potentially we’re going to be able to separate out free-water contributions from those contributions arising from myelin and axonal loss, providing a very powerful complement to magnetization transfer images.   A second area of major development in magnetic resonance is the increased use of ultra-high field systems at 7T, and potentially higher, for applications in MS. The first advantage this has brought is for increased spatial resolution that can be used to begin to image cortical lesions with a really impressively enhanced sensitivity. The second area has been new kinds of contrast. The high magnetic fields allow new susceptibility-weighted contrast to be generated which provides a powerful way of visualizing vessels. It’s very clearly defining the vessels at the center of most of the inflammatory lesions, helping a little bit with differential diagnosis, but even more importantly helping us understand what the microvascular architecture is in and around lesions.   A second potential advantage of the ultra-high field is simply increasing the sensitivity of MR for applications in magnetic resonance spectroscopy. We’ve known for a long time that signals from myo-inositol can help us understand glial components of inflammatory lesions, but there’s increasing interest in applying this kind of tool to measurements of glutathione, to provide indices related to reactive oxygen species generation, and potentially also to measuring excitotoxic neurotransmitters such as glutamate.   In a completely different space, positron emission tomography (PET) has begun to play a renewed kind of role in MS. I’ve always been a little bit disappointed that more wasn’t done with it over the last decade or so since pioneering studies that demonstrated that assessments of energy metabolism based on simply the fluorodeoxyglucose signal not only discriminated people with MS from healthy volunteers, but, more importantly, began to show discrimination between different stages of the disease and a relationship to cognitive impairment, with potentially reversible components with treatment. Now, that still is an area of potential work.   But more recently focus has shifted particularly to use of ligands that bind to the 18 kilodalton translocator protein which provides a marker of microglial inflammation in the brain. While it’s not entirely specific and with the caveat that we have little understanding of the relationship between the TSPO expression and the microglial phenotype, it clearly is highlighting some very interesting things. First, we found that the TSPO binding by ligands is increased multifocally in brains of people with MS; it’s increased multifocally in the white matter and in the grey matter. Moreover, increases in binding in both regions are related to degrees of disability; patients with higher disability show increased binding particularly in the cortex.   There’s emerging evidence, driven first by elegant preclinical studies done by the Finnish group and some human studies yet to be fully reported, that there are also strong treatment effects with powerful amino modulators. So because this provided us a window that is clearly giving us information distinct from that provided by T2 hyperintense lesions on MRI or by gadolinium enhancement on MRI, it promises a powerful adjunct.   And, finally, just to kind of round that idea out, it’s clear that it will be the combination of MR and PET that’s powerful rather than PET replacing MR in some way in our diagnostic or monitoring armamentarium for treatment. One manufacturer has already started supplying commercially integrated MRI-PET systems. Another manufacturer is expected to do so very soon, and potentially a third. This may become a platform for brain imaging that is very powerful for disorders like MS that have multifocal manifestations where the registration – the precise registration – between the MRI and the PET becomes important. Moreover, the potential to use dynamic MRI acquisitions where we’re just imaging very, very rapidly throughout the entire PET scanning period to follow the position of the head within the PET scanner may allow a new kind of precision of special resolution in the PET scan that allows MS studies where we rely on this very much to be done with far greater precision than it’s been possible in the past. So with these developments in MR, with the new radioligands in PET, and with this new technology for integrated MRI-PET, I think the brain imaging is off in incredibly new spaces.   Now I can’t close the discussion of imaging without at least making a mention of the revolution in applications of optical coherence tomography that have been conducted over the last five years in particular for MS. This is really exciting, too. It’s an inexpensive examination that can be performed very rapidly in any clinic that provides very high-resolution measures of optic nerve fiber layers, of multifocal edematous regions within the nerve fiber layer, all of which can provide measures to stage MS and its associated neurodegeneration, and potentially to usefully monitor it in assessing the progress of patients on treatments. It’s an exciting time for imaging.   Interviewer - MSDF Now just to clarify, this is optical coherence tomography of the retina and its surrounding structures.   Interviewee - Dr. Matthews Yeah, Dan, thanks for clarifying that. Absolutely. So it’s an eye examination, but it’s an adjunct because the retina is just an extension of what we study in the brain.   Interviewer - MSDF Either using metabolic markers or following metabolism with PET or something else, or using other ligands and markers, can you discern or image where remyelination is occurring?   Interviewee - Dr. Matthews So, of course, the world of PET is a big one because what we can observe changes with the type of radiotracer that we use. Recently, Yanming Wang, who I had the privilege of collaborating with at Case Western, published, I think, a really groundbreaking paper. Although it was a preclinical study, I think it shows the way we could be moving in this space. Using a novel radiotracer that he developed called MeDAS – MeDAS for short – this carbon positron-emitting isotope-incorporated tracer allows specific myelin proteins to be imaged, and thus provides a marker of myelin integrity in life. Yanming has shown how it can selectively image myelin, it can image both established myelin and new myelin being formed, and he demonstrated in a proof of concept study in rodents that the dynamics of demyelination and remyelination following therapeutic intervention can be followed, and moreover, that the therapeutic effect can be quantified relative to an untreated control group.   Really exciting and a potentially important adjunct to MTR or diffusion measurements in human studies. The trick of moving a tracer from preclinical studies into humans is not without some need for care, but because only microdoses of these tracers are used for the human imaging experiment, Yanming, myself, and colleagues believe we can make this transition rapidly. We’re watching closely to see what happens next.   Interviewer - MSDF Pretty good. I appreciate it.   Interviewee - Dr. Matthews Thanks, Dan.   [transition music]   Thank you for listening to Episode Twenty-one of Multiple Sclerosis Discovery. This podcast was produced by the MS Discovery Forum, MSDF, the premier source of independent news and information on MS research. MSDF’s executive editor is Robert Finn. Msdiscovery.org is part of the non-profit Accelerated Cure Project for Multiple Sclerosis. Robert McBurney is our President and CEO, and Hollie Schmidt is vice president of scientific operations.   Msdiscovery.org aims to focus attention on what is known and not yet known about the causes of MS and related conditions, their pathological mechanisms, and potential ways to intervene. By communicating this information in a way that builds bridges among different disciplines, we hope to open new routes toward significant clinical advances.   We’re interested in your opinions. Please join the discussion on one of our online forums or send comments, criticisms, and suggestions to editor@msdiscovery.org.    [outro music]

A chat with the director of Wicker Kittens, a new documentary about competitive jigsaw puzzlers. Then scientist Deanna Barch talks about how the Human Connectome Project is mapping the brain and what it might mean for people dealing with depression. And an ode to the best show about adult nerds on TV: Parks and Recreation. Your homework? Get ready to binge watch HBO's True Detective.

Dr. Robert E. Hampson (neuroscientist, writer and public speaker) is today's featured guest. Part 3 of our 3 part exploration of: How memories are stored in the human brain.  Subtopics include: The Blue Brain Project, The Human Connectome Project, President Obama's BRAIN Initiative, Artificial Intelligence, Alzheimer's disease, Trends in Teaching Methods used in Colleges, Distance Learning, Prosthetics for the Brain, and the problem of obsolescence of brain prosthetics which might require replacement and thus suffering through a second session of brain surgery. Hosted by Stephen Euin Cobb, this is the June 12, 2013 episode of The Future And You. [Running time: 44 minutes] This interview was recorded using Skype on May 26, 2013. Dr. Robert E. Hampson is a researcher in the field of "neuroscience" –the structure and function of the brain.  After receiving his doctorate in Physiology and Pharmacology from Wake Forest University in 1988, his research focus has been on the hippocampus (a brain area involved in the processing of short-term memory) and in the prefrontal cortex (an area responsible for behavior and decisions involving memory).  His laboratory work includes the study of: 1) nerve cell function, 2) behavioral mechanisms, and 3) detection of the patterns of neural activity, underlying learning and memory in rodents, nonhuman primates and humans.  These studies have required the development of computer models of neural activity patterns associated with processing of memory, investigation of drugs that alter memory function, examination of the effects of sleep and sleep deprivation on cognitive (memory) processing, and comparative studies of memory across different animal species.  Dr. Hampson is also part of a multi-university team working to develop a "neural prosthetic" capable of restoring memory by connecting between different regions within the brain. Dr. Hampson's interest in public education and brain awareness has also led him to serve as a member of the Science and Entertainment Exchange (a service of the National Academy of Science) which supplies subject matter experts to the entertainment industry, and from there to the field of Science Fiction.  Writing science fact and fiction blogs and articles as "Tedd Roberts," he also gives public talks on science fiction for SF conventions, student and civic groups, and writes nonfiction articles for Baen Books.

Portraits of the Mind Portraits of the Mind, is a collection of images visualizing the brain from antiquity through to the present day. How to map the brain. The Human Connectome Project is a major new project which will map how different areas of the brain connect to each other and help understand what makes us human. Others say we would learn more about our minds by looking at the minute detail, at how brain cells communicate with each other within individual circuits. Gero Miesenbork the Wayneflete Professor of Physiology at Oxford University and Tim Behrens from the Human Connectome Project explain what each of these approaches can tell us about human behaviour. Online Psychological Support for Cancer There are 7 Maggie's Centres around the country providing a sanctuary for people with cancer, or those caring for someone with cancer. But not everyone can travel to a centre, perhaps because of distance, health reasons or work. For those people there is now a new online service which provides not only support but crucially a clinical psychologist takes part in every session.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.04.15.043315v1?rss=1 Authors: Hong, S.-J., Xu, T., Nikolaidis, A., Smallwood, J., Margulies, D. S., Bernhardt, B., Vogelstein, J., Milham, M. Abstract: Despite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent studies applying dimensionality reduction techniques to resting-state fMRI (R-fMRI) have unveiled neurocognitively meaningful connectivity gradients that are present in both human and primate brains, and appear to differ meaningfully among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209x2) and the Midnight scan club (n=9), we tested the following key biomarker traits, reliability, reproducibility, and predictive validity, of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (R-fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95-97%) and longer time-series data (at least [≥]20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.04.21.053702v1?rss=1 Authors: Seguin, C., Tian, Y., Zalesky, A. Abstract: The structure and function of the human connectome are coupled, but the correspondence is far from exact. We aimed to establish whether accounting for polysynaptic (multi-hop) paths in structural brain networks would improve prediction of interindividual variation in behavior as well as the strength of coupling with functional brain networks. Diffusion-weighted MRI and tractography were used to map structural connectomes for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic transmission, paths between unconnected pairs of regions were identified using each of 15 candidate models of brain network communication, giving rise to 15 communication matrices for each individual. Communication matrices were (i) used to perform predictions of five data-driven behavioral dimensions and (ii) correlated to interregional resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, network communication models improved the performance of structural connectivity. Communicability and navigation typically led to the most accurate behavioral predictions amongst the explored communication models. Accounting for polysynaptic communication in structural brain networks also significantly strengthened structure-function coupling in the human connectome. We observed that parcellation resolution and whether analyses were performed on individual- or population-level structural connectivity matrices had marked influence on the strength of associations to FC. Despite these effects, navigation and shortest paths produced consistently top-ranking FC predictions, leading to 35-65% improvements in structure-function coupling. Combining behavioral and functional results into a single ranking of communication models positioned navigation as the top model, suggesting that it may more faithfully recapitulate underlying neural signaling patterns. We conclude that network communication models augment the functional and behavioral predictive utility of the human structural connectome and contribute to narrowing the gap between brain structure and function. Copy rights belong to original authors. Visit the link for more info