Podcasts about magnetic resonance imaging fmri

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.06.547919v1?rss=1 Authors: Gim, S., Lee, D. H., Lee, S., Woo, C.-W. Abstract: In a recent article, Hoeppli et al. reported that sociodemographic and psychological factors were not associated with interindividual differences in reported pain intensity. In addition, the interindividual differences in pain could not be detected by thermal pain-evoked brain activities measured by functional Magnetic Resonance Imaging (fMRI). Their comprehensive analyses provided convincing evidence for these null findings, but here we provide another look at their conclusions by analyzing their behavioral data and a large-scale fMRI dataset involving thermal pain (N = 124). Our main findings are as follows: First, a multiple regression model incorporating all available sociodemographic and psychological measures could significantly predict the interindividual differences in reported pain intensity. The key to achieving a significant prediction was including multiple individual difference measures in a single model. Second, with fMRI data from a relatively homogeneous group of 124 participants, we could identify brain regions and a multivariate pattern-based predictive model significantly correlated with the interindividual differences in reported pain intensity. Our results, along with Hoeppli et al. findings, highlight the challenge of predicting interindividual differences in pain, but also suggest that it is not an impossible task. 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/2023.01.01.522416v1?rss=1 Authors: Ohad, T., Yeshurun, Y. Abstract: We can all agree that a good story engages us, however, agreeing which story is good is far more debatable. In this study, we explored whether engagement with a narrative synchronizes listeners' brain responses, by examining individual differences in engagement to the same story. To do so, we pre-registered and re-analyzed a previously collected dataset by Chang et al. (2021) of functional Magnetic Resonance Imaging (fMRI) scans of 25 participants who listened to a one-hour story and answered questionnaires. We assessed the degree of their overall engagement with the story and their engagement with the main characters. The questionnaires revealed individual differences in engagement with the story, as well as different valence towards specific characters. Neuroimaging data showed that the auditory cortex, the default mode network (DMN) and language regions were involved in processing the story. Increased engagement with the story was correlated with increased neural synchronization within regions in the DMN (especially the medial prefrontal cortex), as well as regions outside the DMN such as the dorso-lateral prefrontal cortex and the reward system. Interestingly, positively and negatively engaging characters elicited different patterns of neural synchronization. Finally, engagement increased functional connectivity within and between the DMN, the dorsal attention network and the control network. Taken together, these findings suggest that engagement with a narrative synchronizes listeners' responses in regions involved in mentalizing, reward, working memory and attention. By examining individual differences in engagement, we revealed that these synchronization patterns are due to engagement, and not due to differences in the narrative's content. 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/2022.12.20.521084v1?rss=1 Authors: Dolfen, N., Reverberi, S., Op de beeck, H., King, B. R., Albouy, G. Abstract: A plethora of daily motor tasks consist of sequences of movements or steps that need to be performed in a specific order. Yet, it remains unclear how the brain represents sequential motor actions in a way that preserves their temporal order. Here, we used multivoxel pattern similarity analysis of functional Magnetic Resonance Imaging (fMRI) data acquired during motor sequence practice to investigate whether the hippocampus, a brain region known to support temporal order in the non-motor memory domain, represents information about the temporal order of sequential motor actions. We also examined such representation in other regions of the motor network (i.e., the premotor cortex (PMC), supplementary motor area (SMA), anterior superior parietal lobule (aSPL) and striatum) known for their critical role in motor sequence learning. Our results show that hippocampal activation patterns carried information about movements in their learned temporal position in the sequence, but not about movements or positions in random movement patterns. A similar pattern of results was observed in the striatum. Our data also indicate that multivoxel patterns in M1, SMA and aSPL carried movement-based information while the PMC represented both movement- and position-based information. Altogether, our findings provide novel insight into the role of the hippocampus in the motor memory domain and point to its capacity to bind movements to their temporal position in a motor sequence. Our results also deepen our understanding of how striatal and cortical regions contribute to motor sequence learning via position and movement coding. 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/2022.11.29.518264v1?rss=1 Authors: Moradi, N., Goodyear, B. G., Sotero, R. C. Abstract: Brain imaging with a high-spatiotemporal resolution is crucial for accurate brain-function mapping. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two popular neuroimaging modalities with complementary features that record brain function with high temporal and spatial resolution, respectively. One popular non-invasive way to obtain data with both high spatial and temporal resolutions is to combine the fMRI activation map and EEG data to improve the spatial resolution of the EEG source localization. However, using the whole fMRI map may cause spurious results for the EEG source localization, especially for deep brain regions. Considering the head's conductivity, deep regions' dipoles with low activity are unlikely to be detected by the EEG electrodes at the scalp. In this study, we use fMRI's high spatial-frequency component to identify the local high-intensity activations that are most likely to be captured by the EEG. The 3D Empirical Mode Decomposition (3D-EMD), a data-driven method, is used to decompose the fMRI map into its spatial-frequency components. Different validation measurements for EEG source localization show improved performance for the EEG inverse-modeling informed by the fMRI's high-frequency spatial component compared to the fMRI-informed EEG source-localization methods. The level of improvement varies depending on the voxels' intensity and their distribution. 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/2020.11.20.391805v1?rss=1 Authors: Wall, M. B., Freeman, T. P., Hindocha, C., Demetriou, L., Ertl, N., Freeman, A. M., Jones, A. P. M., Lawn, W., Pope, R., Mokrysz, C., Solomons, D., Statton, B., Walker, H. R., Yamamori, Y., Yang, Z., Yim, J. L. L., Nutt, D. J., Howes, O. D., Curran, H. V., Bloomfield, M. Abstract: Cannabidiol (CBD) and {Delta}9-tetrahydrocannabinol (THC) are two major constituents of cannabis with contrasting mechanisms of action. THC is the major psychoactive, addiction-promoting, and psychotomimetic compound, while CBD may have somewhat opposite effects. The brain effects of these drugs alone and in combination are poorly understood. In particular the striatum is implicated in the pathophysiology of several psychiatric disorders, but it is unclear how THC and CBD influence striato-cortical connectivity. Across two placebo-controlled, double-blind studies, we examine the effects of THC, CBD, and THC+CBD on the functional connectivity of striatal sub-divisions (associative, limbic, and sensorimotor) using resting-state functional Magnetic Resonance Imaging (fMRI) and seed-based functional connectivity analyses. Study 1 (N=17; inhaled 8mg THC, 8mg THC+10mg CBD, placebo) showed strong disruptive effects of both THC and THC+CBD conditions on connectivity in the associative and sensorimotor networks, but a specific effect of THC in the limbic striatum, which was alleviated in the THC+CBD condition such that it did not differ from placebo. In Study 2 (N=23, oral 600mg CBD, placebo) CBD increased connectivity in the associative network, but relatively minor decreases/disruptions were found in the limbic and sensorimotor. In conclusion, THC strongly disrupts striato-cortical networks, and this effect is selectively mitigated in the limbic striatum when co-administered with CBD. When administered alone, 600mg oral CBD has a more complex effect profile of relative increases and decreases in connectivity. The insula emerges as a key region affected by cannabinoid-induced changes in functional connectivity, with potential implications for understanding cannabis related disorders, and the development of cannabinoid therapeutics. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.19.161315v1?rss=1 Authors: Zonnino, A., Farrens, A. J., Ress, D., Sergi, F. Abstract: Knowledge on the organization of motor function in the reticulospinal tract (RST) is limited by the lack of methods for measuring RST function in humans. Behavioral studies suggest the involvement of the RST in long latency responses (LLRs). LLRs, elicited by precisely controlled perturbations, can therefore act as a viable paradigm to measure motor-related RST activity using functional Magnetic Resonance Imaging (fMRI). Here we present StretchfMRI, a novel technique developed to study RST function associated with LLRs. StretchfMRI combines robotic perturbations with electromyography and fMRI to simultaneously quantify muscular and neural activity during stretch-evoked LLRs without loss of reliability. Using StretchfMRI, we established the muscle-specific organization of LLR activity in the brainstem. The observed organization is partially consistent with animal models, with activity primarily in the ipsilateral medulla for flexors and in the contralateral pons for extensors, but also include other areas, such as the midbrain and bilateral pontomedullary contributions. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.16.155499v1?rss=1 Authors: Reddy, A. J., Wehbe, L. Abstract: We are far from having a complete mechanistic understanding of the brain computations involved in language processing and of the role that syntax plays in those computations. Most language studies do not computationally model syntactic processing, and most studies that do model syntactic processing use effort-based metrics. These metrics capture the effort needed to process the syntactic information given by every word. They can reveal where in the brain syntactic processing occurs, but not what features of syntax are processed by different brain areas. In this paper, we move beyond effort-based metrics and propose explicit features capturing the syntactic structure that is incrementally built while a sentence is read one word at a time. Using these features and functional Magnetic Resonance Imaging (fMRI) recordings of participants reading a natural text, we study the brain representation of syntax. We find that our syntactic structure-based features are better than effort-based metrics at predicting brain activity in various parts of the language system. Our results suggest that the brain represents complex syntactic information such as phrase and clause structures. We see that regions well-predicted by syntactic features are distributed in the language system and are not distinguishable from those that process semantics. Our results call for a shift in the approach used for studying syntactic processing. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.16.154625v1?rss=1 Authors: Wirsich, J., Jorge, J., Iannotti, G. R., Shamshiri, E. A., Grouiller, F., Abreu, R., Lazeyras, F., Giraud, A.-L., Gruetter, R., Sadaghiani, S., Vulliemoz, S. Abstract: Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite their differences in probing brain activity, both electrophysiology and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reliability of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reliably correlates across different datasets and that the crossmodal correlation between EEG and fMRI connectivity of r{approx}0.3 can be reliably extracted in low and high-field scanners. The crossmodal correlation was strongest in the EEG-{beta} frequency band but exists across all frequency bands. Both homotopic and withing intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship. This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects being organized into reliable ICNs across different timescales. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. Alterations of this coupling could be explored as a potential clinical marker of pathological brain function. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.11.146969v1?rss=1 Authors: Venkatesh, M., JaJa, J., Pessoa, L. Abstract: Insights from functional Magnetic Resonance Imaging (fMRI), and more recently from recordings of large numbers of neurons through calcium imaging, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of neuronal populations. To capture and characterize spatiotemporal properties of brain events, we propose an architecture based on long short-term memory (LSTM) networks to uncover distributed spatiotemporal signatures during dynamic experimental conditions. We demonstrate the potential of the approach using naturalistic movie-watching fMRI data. We show that movie clips result in complex but distinct spatiotemporal patterns in brain data that can be classified using LSTMs ({approx}90% for 15-way classification), demonstrating that learned representations generalized to unseen participants. LSTMs were also superior to existing methods in predicting behavior and personality traits of individuals. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational properties of brain dynamics. Finally, we employed saliency maps to characterize spatiotemporally-varying brain-region importance. The spatiotemporal saliency maps revealed dynamic but consistent changes in fMRI activation data. We believe our approach provides a powerful framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions. Copy rights belong to original authors. Visit the link for more info

Dr. Haris Sair is an Associate Professor in the Johns Hopkins Medicine Department of Radiology and Radiological Science, and the Director of the Division of Neuroradiology. His areas of clinical expertise include functional Magnetic Resonance Imaging (fMRI) of the brain. He also has a faculty appointment in the Malone Center for Engineering in Healthcare at The Whiting School of Engineering, Johns Hopkins University, investigating the use of machine learning and artificial intelligence in imaging. Dr. Sair earned his M.D. from Duke University School of Medicine. He completed his residency at Temple University Medical Center and performed a fellowship in neuroradiology at Massachusetts General Hospital in Boston, MA.

In recent years, we have increasingly heard about the use of functional Magnetic Resonance Imaging (fMRI) to find areas of the brain that may be associated with our thoughts and actions, such as when we are being deceptive, if we trust someone or are in love, or our religiosity. While this research has been very exciting, concerns were raised that there may be a fundamental flaw in how at least some of these studies were analyzed. Lisa Eyler explains the implications and meaning of these new concerns, and addresses some of the ethical implications for scientists and the general public. Series: "Exploring Ethics" [Health and Medicine] [Humanities] [Science] [Show ID: 31032]

In patients with schizophrenia, the misattribution of self-generated events to an external source is associated with self-recognition deficits and the presence of psychotic symptoms. The aim of the present study was to investigate how this misattribution is influenced by dysfunction of attentional processing, which is also impaired in schizophrenia. I conducted two different studies. In both studies participant’s expectancies were manipulated using visual cues that were either congruent (valid) or incongruent (invalid) with the speech. The source (self/alien) and the acoustic quality (undistorted/distorted) of the speech were also manipulated. First, twentythree patients with schizophrenia, with hallucinations and delusions (H/D patients) and twentythree matched healthy controls (HC) were tested for the behavioral study. Later on, twenty patients with first episode psychosis (FEP) and twenty matched healthy controls (HC) underwent functional Magnetic Resonance Imaging (fMRI) while listening to prerecorded speech. The results of the behavioral part of the study showed that H/D patients exhibited increased error rates comparing to HC, when listening to the distorted self spoken words, misidentifying their own speech as produced by others. Importantly, patients made significantly more errors across all the invalid cue conditions. This suggested not only the presence of pathological misattribution bias, but also an inadequate balance between top-down and bottom-up attentional processes in patients, which could be responsible for misattribution of the ambiguous sensory material. Analysis of fMRI data showed that FEP patients when listening to self-generated speech preceded by an invalid (alien) cue, relative to HC showed a strong trend to misidentify their own speech as an other person's. The patient group had reduced activation in the right middle temporal gyrus (MTG) and left precuneus (Pc) relative to HC. Within the FEP group, the level of activation in the right MTG was negatively correlated with the severity of their positive psychotic symptoms. I conclude that impaired attentional modulation in schizophrenia may contribute to the tendency for FEP patients to misattribute the source of self-generated material, and this may be mediated through the right MTG and Pc, regions that are involved in both self-referential processing and the integration of sensory information.

Background: An important aspect in functional imaging research employing magnetic resonance imaging (MRI) is how participants perceive the MRI scanning itself. For instance, the knowledge of how (un)comfortable MRI scanning is perceived may help institutional review boards (IRBs) or ethics committees to decide on the approval of a study, or researchers to design their experiments. Methods: We provide empirical data from our lab gained from 70 neurologically healthy mainly student subjects and from 22 mainly elderly patients suffering from motor deficits after brain damage. All participants took part in various basic research fMRI studies using a 3T MRI scanner. Directly after the scanning, all participants completed a questionnaire assessing their experience with the fMRI procedure. Results: 87.2% of the healthy subjects and 77.3% of the patients rated the MRI procedure as acceptable to comfortable. In healthy subjects, males found the procedure more comfortable, while the opposite was true for patients. 12.1% of healthy subjects considered scanning durations between 30 and 60 min as too long, while no patient considered their 30 min scanning interval as too long. 93.4% of the healthy subjects would like to participate in an fMRI study again, with a significantly lower rate for the subjects who considered the scanning as too long. Further factors, such as inclusion of a diffusion tensor imaging (DTI) scan, age, and study duration had no effect on the questionnaire responses. Of the few negative comments, the main issues were noise, the restriction to keep still for the whole time, and occasional feelings of dizziness. Conclusion: MRI scanning in the basic research setting is an acceptable procedure for elderly and patient participants as well as young healthy subjects.

Mon, 20 Jul 2009 12:00:00 +0100 https://edoc.ub.uni-muenchen.de/12591/ https://edoc.ub.uni-muenchen.de/12591/1/Doehnel_Katrin.pdf Döhnel, Katrin ddc:150, ddc:100, Fakultät für Psychologie un

"Past, Present, and Promise" is the first program in the DISCOVERING PSYCHOLOGY series. It provides an introduction to and overview of psychology, from its origins in the nineteenth century to current study of the brain’s biochemistry. You’ll explore the development of psychology in general and some of the paths scientists take to determine relationships among the mind, the brain, and behavior. Psychology is defined as the scientific study of the behavior of individuals and their mental processes. Like many sciences, psychology has evolved with technology, giving doctors and researchers new tools to measure human behavior and analyze its causes. In this program, Dr. Mahzarin Banaji from Yale University uses the Implicit Association Test (IAT) to measure how quickly positive or negative values are associated with white or black faces. Her subjects are shown a series of words and pictures and instructed to respond immediately by pushing a button to indicate their most automatic, reflex-like reactions. For example, they may be told to press a button in their right hand if the automatic association is good and to press a button in their left hand if the association is bad. The speed with which the subjects respond is an important element of the experiment because these quick, unconscious connections can reveal biases that differ from conscious beliefs. The IAT results are matched against functional Magnetic Resonance Imaging (fMRI) data to track activity in the amygdala, the region of the brain that responds to fearful or negative images. By correlating data on the buttons subjects pushed with fMRI information about activity in the amygdala, Dr. Banaji and her colleagues have found some interesting results. The majority of the white American respondents showed an unconscious association of white with good and black with bad, while the African American respondents showed mixed results. Half more quickly associated black with good, and the other half associated white with good. Tracking brain activity in controlled experiments reveals not only the region of the brain at work, but also the power of images and messages in our culture on the subconscious human psyche, bringing psychologists one step closer to understanding human behavior. For more info on this topic visit http://psy101.MyUCCedu.com

Introduction: Mild Cognitive Impairment (MCI) is a cognitive stage between normal aging and Dementia. It is a heterogeneous group of patients, where most of them develop Alzheimer’s disease (AD), others stabilize, and a few revert to normal. AD’s first clinical symptoms are related to memory, but it has been shown that AD involves also a processing disorder in the visual sensory pathways. Accurate visual function facilitates memory, attention and executive functions, so that perceptual dysfunction contributes to the severity of cognitive impairment. Objective: The objective of the work is to measure changes in activation in the visual system between MCI patients and old Healthy Control (HC) subjects, using two different visual processing tasks with functional Magnet Resonance Imaging (fMRI). This is the first study which makes such a comparison between MCI and HC using fMRI. Methods: Brain activation was measured using fMRI. The MCI group was composed of 16 subjects and the HC group was composed of 19 subjects. All subjects performed two tasks: location matching (position of objects) and face matching (characteristics of the objects), which selectively activate one of the visual system pathways in healthy people. Answers were given by pressing a single button. Results: Performance of the task was not significantly different in both groups. The HC group selectively activated ventral pathway for face matching and the dorsal pathways for location matching. In contrast the MCI subjects did not selectively activate the ventral and dorsal pathways of the visual system. Additionally they showed higher activation in the left frontal lobe compared to HC when performing the location matching Task Conclusions: The results suggest that even when behavioural performance between groups is the same, the neural system which supports performance may differ. MCI subjects compensate their deficits using additional brain areas to help them to maintain performance. In this case MCI subjects used the left frontal lobe in addition to perform the location matching task. This work presents the usability of brain imaging techniques especially fMRI to better understand the underlying pathology of MCI and its subtypes as prodromal conditions of AD.

Hintergrund: Diese Studie wurde durchgeführt, um Unterschiede in der zerebralen Aktivierung zwischen zwei Gruppen von einerseits Patienten mit leichten kognitiven Störungen (LKS) und andererseits gesunden Kontrollpersonen (GK) während eines verbalen Arbeitsgedächtnistests zu untersuchen. LKS wird als Vorstufe der Alzheimer Demenz angesehen. Um eine frühe Diagnose der Demenz zu ermöglichen, ist es wichtig, diagnostische Marker für LSK und AD zu etablieren. Methoden: Acht Personen mit LKS und acht GK haben sich einer funktionellen Magnetresonanztomographie unterzogen, während sie einen verbalen Arbeitsgedächtnistest durchführten. Sie bekamen fünf Buchstaben gezeigt, die sie sich nach der Einprägungsphase sechs Sekunden lang merken mussten, währenddessen sie ein Fixierungskreuz sahen. Nach dieser Verzögerung wurde den Probanden ein einzelner Buchstabe gezeigt, und sie mussten entscheiden, ob dieser Buchstabe in der vorher gezeigten Gruppe von Buchstaben enthalten war. Die Antwort erfolgte über Tasten in der rechten und linken Hand. Statistische parametrische Karten des Gehirns, die die Gehirnaktivität für die jeweiligen Gruppen zeigen, und Karten, die die Unterschiede zwischen beiden Gruppen zeigen, wurden für beide Gruppen erstellt. Ziele: Ziele der Studie waren, die Gehirnaktivierung von Patienten mit LKS und einer Gruppe von GK während eines verbalen Arbeitsgedächtnistests zu untersuchen, und Unterschiede in der Aktivierung zwischen den beiden Gruppen zu finden. Ergebnisse: Gehirnaktivierung in der GK-Gruppe wurde in dorsolateral-präfrontalen, parietalen und temporalen Gegenden beobachtet. Diese Aktivierungen wießen linksseitige Lateralisierung auf, was für verbale Aufgaben typisch ist. Trotzdem gab es auch aktive Regionen in der rechten Hemisphäre, was einen gewissen Grad von Delateralisierung bedeutet. Dies wiederum ist ein typischer Prozess der normalen Alterung. Die LKS-Gruppe wies Aktivierung in den gleichen Regionen auf, allerdings mit einem geringeren Grad an Delateralisierung. Es gab sowohl interhemisphärische wie auch interregionale Unterschiede in der Aktivierung zwischen den Gruppen. Die GK-Gruppe zeigte höhere Aktivierung in Regionen des Frontallappens, während die LKS-Gruppe höhere Aktivierung in Regionen des Termporallappens aufwies. In beiden Gruppen fanden sich Regionen, die höhere Aktivierung während der Ruhe-Phase des Tests im Vergleich zu der tatsächlichen Aufgabe zeigten. Diese Regionen werden ‚Default’-Netzwerk genannt. Die LKS-Gruppe hatte eine ausgeprägtere ‚Deaktivierung’ als die GK-Gruppe während der Wiederholungs-Phase des Tests, und eine niedrigere ‚Deaktivierung’ als die GK-Gruppe während der Entscheidungs-Phase. Ausblick: In beiden Gruppen war die Gehirnaktivierung während der verschiedenen Teile der Aufgabe in Gegenden, die während eines verbalen Arbeitsgedächtnistests typischerweise aktiviert werden. Es fanden sich Unterschiede in den Aktivierungsmustern zwischen den beiden Gruppen. Der auffallendste Unterschied war, dass die LKS-Gruppe höhere Aktivierung als die GK-Gruppe hatte, was auf Kompensierung für neurale Degeneration und kognitiven Leistungsabfall zurückgeführt werden kann. Dieser Kompensationsprozess trat während allen Teilen des Arbeitsgedächtnistests auf.