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This week on The Free Cheese, terrifying box art mascots. We add one of the essential Nintendo DS titles to The List as we work to better our minds in the process. We discuss our time spent with Brain Age this year and whether or not we believe it's made us any more intelligent or sharp.
When it comes to aging, it hits hard for everyone — men, women, and…
When it comes to aging, it hits hard for everyone — men, women, and fruit flies. A new study from researchers in California explored how these tiny insects, with a...
When it comes to aging, it hits hard for everyone — men, women, and fruit flies. A new study from researchers in California explored how these tiny insects, with a...
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Book your first aesthetic and wellness sessions with Allure Medical. https://www.alluremedical.com/contact-us/Early detection can lead to meaningful interventions to prevent or slow down neurodegeneration.In this episode, Dr. Charles Mok discusses neuro quantitative MRI, a new way to assess your brain health. This approach helps detect potential brain diseases, like Alzheimer's, decades before its symptoms appear. Neuro quantitative MRI measures your brain mass and compares it to others of the same age and gender. By keeping your brain healthy with high brain mass and minimal shrinkage, you can lower your risk of Alzheimer's. Dr. Mok also shares his own experience of how a targeted lifestyle change and intervention significantly improved his brain health. You have the power to shape your future. Start putting your brain health first today, and give yourself the best chance at a vibrant, healthy life.Tune in to this episode of Inside The Cure podcast — Neuro Quantitative MRI: Know Your Brain Age————————————————————————————————Subscribe to Inside the Cure and leave a 5-star review! Dr. Charles Mok received his medical degree from Chicago College of Osteopathic Medicine, Chicago, Illinois in 1989. He completed his medical residency at Mount Clemens General Hospital, Mt. Clemens, Michigan. He has worked with laser manufacturing companies to improve their technologies; he has performed clinical research studies and has taught physicians from numerous other states. His professionalism and personal attention to detail have contributed to the success of one of the first medical spas in Michigan.LinkedIn: https://www.linkedin.com/in/charles-mok-4a0432114/Instagram: https://www.instagram.com/alluremedicals/Website: https://www.alluremedical.com/YouTube: https://www.youtube.com/@AllureMedicalAmazon Store: https://www.amazon.com/stores/Dr.-Charles-Mok/author/B0791M9FZQ?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true #BrainHealth #NeuroQuantitativeMRI #AlzheimersDisease #Alzheimers #Dementia #BioniccMRI #BrainMRI #BulletproofDiet #Fasting #BrainPlasticity #BrainAge #Health #Wellness #NeuroQuant #Plasmapheresis #HealthPodcast
Welcome to Dev Game Club, where this week we start a new series on 2006's Dead Rising, from Capcom. We situate the game a bit in its time and with Capcom and this generation of hardware before turning to the structure and feel of the game. Dev Game Club looks at classic video games and plays through them over several episodes, providing commentary. Sections played: A few hours Issues covered: the early 360 era, throwing lots of enemies on the screen, console wars, an entry into console for PC developers, achievements and GamerScore, coming into a time-limited game, the deluxe remaster, carrying over Prestige Points, limited time quests, production benefits, a controversial structure, pushing your luck, going to the mall, horde management, Tim shows he actually knows more about football than claimed, camp, inventory management, learning the space, the feeling of losing a person right at the end, saving people, Onigokko!, Artimage's charity. Games, people, and influences mentioned or discussed: Xbox, Gears of War, Republic Commando, Crystal Dynamics, Tomb Raider: Legend, PlayStation, Capcom, Keiji Inafune, MegaMan, Onimusha, Resident Evil, Shinji Mikami, Dwarf Fortress, LoZ: Twilight Princess, Okami, Elder Scrolls: Oblivion, Final Fantasy XII, Guitar Hero 2, Rainbow Six: Vegas, New Super Mario Bros, Wii, Elite Beat Agents, Nintendo DS, Burnout: Revenge, Brain Age!, Call of Cthulhu: Dark Corners of the Earth, Condemned: Criminal Origins, Tomb Raider: Legend, Heroes of Might and Magic V, Dark Messiah of Might and Magic, Arkane, Prey, Dishonored, Nintendo Switch, ElectroPlankton, Groundhog Day, Dark Souls, Rogue, Dawn of the Dead, Chopping Mall, Night of the Living Dead, George Romero, Day of the Dead, Tim Ramsay, Harley Baldwin, Deathloop, Tony Rowe, Artimage, Minecraft, Gwyneth Paltrow, Kirk Hamilton, Aaron Evers, Mark Garcia. Next time: More Dead Rising! Links: Artimage's email is artimage84@gmail.com Twitch: timlongojr Discord DevGameClub@gmail.com
Howie and Harlan discuss recent headlines, including the latest round of COVID and flu vaccines, a lousy report card for the U.S. healthcare system, and a rare case of swine flu. Plus: Howie investigates a mysteriously escalating pharmacy bill. Links: COVID and Flu Vaccines CDC FluView: Weekly US Influenza Surveillance Report: Key Updates for Week 36, ending September 7, 2024 CDC: Staying Up to Date with COVID-19 Vaccines “Florida discourages use of mRNA Covid vaccines in older adults” “Florida's New Covid Booster Guidance Is Straight-Up Misinformation” CDC: COVID Data Tracker A Failing Grade “Mirror, Mirror 2024: A Portrait of the Failing U.S. Health System” CMS Health Equity Conference “What is Driving Widening Racial Disparities in Life Expectancy?” Swine Flu “Minnesota reports 2 H3N2v flu infections in fairgoers” Shohei Otani “Shohei Ohtani reaches 50-50 in spectacular style as Dodgers clinch postseason berth” “The Shohei Ohtani Goal Matrix” Baseball Almanac: A Definition of 50-50 Club Drug Prices Mayo Clinic: Flecainide “Column: Less choice, higher prices feared in CVS' takeover of health insurer Aetna” “How to Save Money on Your Prescription Drugs” Brain Age “Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations” “Brain aging patterns in a large and diverse cohort of 49,482 individuals” “Associations between alcohol consumption and gray and white matter volumes in the UK Biobank” “Environmental Cardiology: Studying Mechanistic Links Between Pollution and Heart Disease” AirPods as Hearing Aids “FDA Authorizes First Over-the-Counter Hearing Aid Software” Oak Street Health Department of Justice: Oak Street Health Agrees to Pay $60M to Resolve Alleged False Claims Act Liability for Paying Kickbacks to Insurance Agents in Medicare Advantage Patient Recruitment Scheme “CVS Reaches $10.6 Billion Deal to Buy Clinic Owner Oak Street Health”
Howie and Harlan discuss recent headlines, including the latest round of COVID and flu vaccines, a lousy report card for the U.S. healthcare system, and a rare case of swine flu. Plus: Howie investigates a mysteriously escalating pharmacy bill. Links: COVID and Flu Vaccines CDC FluView: Weekly US Influenza Surveillance Report: Key Updates for Week 36, ending September 7, 2024 CDC: Staying Up to Date with COVID-19 Vaccines “Florida discourages use of mRNA Covid vaccines in older adults” “Florida's New Covid Booster Guidance Is Straight-Up Misinformation” CDC: COVID Data Tracker A Failing Grade “Mirror, Mirror 2024: A Portrait of the Failing U.S. Health System” CMS Health Equity Conference “What is Driving Widening Racial Disparities in Life Expectancy?” Swine Flu “Minnesota reports 2 H3N2v flu infections in fairgoers” Shohei Ohtani “Shohei Ohtani reaches 50-50 in spectacular style as Dodgers clinch postseason berth” “The Shohei Ohtani Goal Matrix” Baseball Almanac: A Definition of 50-50 Club Drug Prices Mayo Clinic: Flecainide “Column: Less choice, higher prices feared in CVS' takeover of health insurer Aetna” “How to Save Money on Your Prescription Drugs” Brain Age “Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations” “Brain aging patterns in a large and diverse cohort of 49,482 individuals” “Associations between alcohol consumption and gray and white matter volumes in the UK Biobank” “Environmental Cardiology: Studying Mechanistic Links Between Pollution and Heart Disease” AirPods as Hearing Aids “FDA Authorizes First Over-the-Counter Hearing Aid Software” Oak Street Health Department of Justice: Oak Street Health Agrees to Pay $60M to Resolve Alleged False Claims Act Liability for Paying Kickbacks to Insurance Agents in Medicare Advantage Patient Recruitment Scheme “CVS Reaches $10.6 Billion Deal to Buy Clinic Owner Oak Street Health”
Show Notes 13 September 2024Story 1: This new AI modeler can turn pictures into 3D sculptures in seconds Source: TechRadar.com Story by Eric Hal SchwartzLink: https://www.techradar.com/computing/artificial-intelligence/this-new-ai-modeler-can-turn-pictures-into-3d-sculptures-in-secondsSee also: https://arxiv.org/abs/2408.00653See also a Tom's Hardware feature about VFusion3D: https://www.tomshardware.com/tech-industry/artificial-intelligence/ai-driven-technique-can-generate-quality-3d-assets-from-2d-images-in-secondsStory 2: Researchers built an ‘AI Scientist' — what can it do? -- The large language model does everything from reading the literature to writing and reviewing its own papers, but it has a limited range of applicability so far.Source: Nature.com Story by Davide CastelvecchiLink: https://www.nature.com/articles/d41586-024-02842-3Story 3: New memory tech unveiled that reduces AI processing energy requirements by 1,000 times or more - New CRAM technology gives RAM chips the power to process data, not just store it.Source: Tomshardware.com Story by Jeff ButtsLink: https://www.tomshardware.com/tech-industry/artificial-intelligence/researchers-detail-new-technology-for-reducing-ai-processing-energy-requirements-by-1000-times-or-betterSee also: https://www.nature.com/articles/s44335-024-00003-3See also: https://www.digitaltrends.com/computing/cram-in-memory-reduces-ai-energy-1000x/See also: https://www.techno-science.net/en/news/thanks-to-this-technique-ai-could-consume-2500-times-less-energy-N25401.htmlStory 4: Cleaning up the aging brain: Scientists restore brain's trash disposal systemSource: ScienceBlog.com Story by University of RochesterLink: https://scienceblog.com/546904/cleaning-up-the-aging-brain-scientists-restore-brains-trash-disposal-system/See also: https://neurosciencenews.com/csf-neurology-aging-27549/For more info, interviews, reviews, news, radio, podcasts, video, and more, check out ComputerAmerica.com!
Dr Nathan Stevenson and Dr Kartik Iyer have developed a tool to accurately pinpoint the brain age of babies and children, helping clinicians spot neurodevelopmental delays earlier. This exciting development could lead to more effective therapeutic intervention and management, at a time when babies and children need all the support they can get. The doctors tell us how the tool came about and the exciting possibilities of implementing into clinical care.
The human brain is a complex organ, and its aging process is influenced by a plethora of factors, both genetic and environmental. Aging-related changes in the brain can lead to cognitive decline and susceptibility to neurodegenerative diseases. Therefore, understanding the molecular mechanisms underlying these changes is crucial for developing therapeutic strategies to delay or prevent age-related cognitive decline. Over the past few years, a myriad of scientific studies have been conducted to understand the intricate relationship between our genes and the aging process. In a new study, researchers Joseph A. Zarrella and Amy Tsurumi from Harvard T.H. Chan School of Public Health, Massachusetts General Hospital, Harvard Medical School, and Shriner's Hospitals for Children-Boston explored the concept of genome brain age prediction, a groundbreaking area of study that employs advanced bioinformatics tools to analyze changes in gene expression associated with aging. On February 28, 2024, their research paper was published and chosen as the cover paper for Aging's Volume 16, Issue 5, entitled, “Genome-wide transcriptome profiling and development of age prediction models in the human brain.” “[…] we aimed to profile transcriptome changes in the aging PFC [prefrontal cortex] overall and compare females and males, and develop prediction models for age.” Full blog - https://aging-us.org/2024/03/predicting-brain-age-with-machine-learning-and-transcriptome-profiling/ Paper DOI - https://doi.org/10.18632/aging.205609 Corresponding author - Amy Tsurumi - atsurumi@mgh.harvard.edu Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.205609 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, machine learning, prediction model, biomarker, transcriptome About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at https://www.Aging-US.com and connect with us: Facebook - https://www.facebook.com/AgingUS/ X - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@AgingJournal LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM
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This week on The Free Cheese, what did you learn? We chronicle the rough timeline of educational software and share key lessons we've learned in video games.
Congying Chu and David Elmenhorst discuss their paper, “Total Sleep Deprivation Increases Brain Age Prediction Reversibly in Multisite Samples of Young Healthy Adults,” published in Vol. 43, Issue 12 of JNeurosci, with Editor-in-Chief Sabine Kastner. Find our upcoming webinar schedule here. With special guests: Congying Chu and David Elmenhorst Hosted by: Sabine Kastner On Neuro Current, we delve into the stories and conversations surrounding research published in the journals of the Society for Neuroscience. Through its publications, JNeurosci, eNeuro, and the History of Neuroscience in Autobiography, SfN promotes discussion, debate, and reflection on the nature of scientific discovery, to advance the understanding of the brain and the nervous system. Find out more about SfN and connect with us on Twitter, Instagram, and LinkedIn.
Listen to this live podcast from the 2023 American Society of Clinical Oncology (ASCO) Annual Meeting with Oncology Data Advisor and Nicholas Phillips, MD!
This week, I seemingly can't escape Dragon Quest news, but I also talk about the future of Brain Age and upcoming OCP premier videos. Links OCP Ko-fi OCP Main Channel OCP Plus! Commentary Channel PC-FX Fan Club YouTube One Controller Port Website OCP Linktree Podcast Links Opening Music from Big Brain AcademyInfinity Strash - Dragon Quest: The Adventure of DaiDragon Quest Champions Pre-registration PagePanic in Sweets Land Steam PageDaiKai 6's Tweet on Panic in Sweets Land and Waku Waku Sweets ConnectionPrincess Poki Podcast
This week we continue to talk about Tears of the Kingdom and how it might be one of our alltime favorites! We also talk about Brain Age, Cassette Beasts, and more! Grubb also gets into this week's biggest gaming headlines and we dip into your emails!This show is part of the Spreaker Prime Network, if you are interested in advertising on this podcast, contact us at https://www.spreaker.com/show/5928697/advertisement
This week we continue to talk about Tears of the Kingdom and how it might be one of our alltime favorites! We also talk about Brain Age, Cassette Beasts, and more! Grubb also gets into this week's biggest gaming headlines and we dip into your emails!
Blog summary of an editorial published in Aging's Volume 15, Issue 8, on April 3, 2023, entitled, “Artificial intelligence and the aging mind.” _________________________________________ Aging is a risk factor for many diseases, including Alzheimer's disease (AD). While scientists have made some progress in understanding the physiology of aging and its relationship to AD and related disorders, our understanding remains incomplete (to say the least). It is possible that civilization is currently in the midst of an artificial intelligence (AI) and machine learning (ML) “boom.” Researchers are now using AI and ML technologies to elevate our comprehension of aging and aging-related diseases. “Artificial intelligence (AI) and machine learning (ML) technologies can help us better understand these diseases and aging itself by using biological data from the brain or other sources to create a mapping between age and biological data.” In a new editorial paper, researchers Jeyeon Lee, Leland R. Barnard and David T. Jones from the Mayo Clinic in Rochester, Minnesota, discuss a recent study they conducted and explore the potential of AI to revolutionize the field of geriatrics. Their editorial was published in Aging's Volume 15, Issue 8, on April 3, 2023, entitled, “Artificial intelligence and the aging mind.” Full blog - https://aging-us.org/2023/05/the-brain-age-gap/ Paper DOI - https://doi.org/10.18632/aging.204644 (PDF) Corresponding author - David T. Jones - Jones.David@mayo.edu Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.204644 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, brain age, artificial intelligence, Alzheimer's dementia, neurodegenerative disease, biomarker About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at https://www.Aging-US.com and connect with us: SoundCloud - https://soundcloud.com/Aging-Us Facebook - https://www.facebook.com/AgingUS/ Twitter - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@AgingJournal LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.31.535038v1?rss=1 Authors: Korbmacher, M., Wang, M.-Y., Eikeland, R., Buchert, R., Leonardsen, E., Westlye, L. T., Maximov, I. I., Specht, K. Abstract: Variability in brain age predictions complicate the metric's clinical usage, for example, as a (pre-) diagnostic tool. We presented small correlations between age and brain age when repeatedly sampling T1-weighted MRI data from the same individual in a short period of time (1-3 years). Reasons might lay in the absence of maturation effects for the age range in the presented sample, brain age model-bias (including a bimodal or trimodal age training distribution) and model error. We also find evidence for the influence of field strength and scan quality on brain age. Individual differences and the processing of such in the brain age model, might lead to variability in associations between brain age and QC metrics. The presented testing of an established brain age model on multiple single-subject short-timespan retesting data is a stricter test than the usual use-case and does not invalidate MRI group differences. However, intra-individual differences contributing to brain age require further attention in order to advance brain age as a clinical tool. 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/2023.03.02.530885v1?rss=1 Authors: Rokem, A., Qiao, J., Yeatman, J. D., Richie-Halford, A. Abstract: Multivariate measurements of human brain white matter (WM) with diffusion MRI (dMRI) provide information about the role of WM in a variety of cognitive functions and in brain health. Statistical models take advantage of the regularities in these data to make inferences about individual differences. For example, dMRI data provide the basis for accurate brain-age models: models that predict the chronological age of participants from WM tissue properties. Deep learning (DL) models are powerful machine learning models, which have been shown to provide benefits in many multivariate analysis settings. We investigated whether DL would provide substantial improvements for brain-age models based on dMRI measurements of WM in a large sample of children and adolescents. We found that some DL models fit the data better than a linear baseline, but the differences are small. In particular, recurrent neural network architectures provide up to ~6% improvement in accuracy. This suggests that information about WM development is mostly accessible with linear models, and does not require the additional invariance and non-linearity offered by DL models. However, in some applications this incremental improvement may prove critical. We provide open-source software that fits DL models to dMRI data (https://yeatmanlab.github.io/AFQ-Insight). 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/2023.01.26.525514v1?rss=1 Authors: Dorfel, R. P., Arenas-Gomez, J. M., Fisher, P. M., Ganz, M., Knudsen, G. M., Svensson, J., Plaven-Sigray, P. Abstract: Background: Brain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to assess the biological age of the human brain. The difference between a person's chronological age and the estimated brain age is thought to reflect deviations from a normal aging trajectory, indicating a slower, or accelerated, biological aging process. Several pre-trained software packages for predicting brain age are publicly available. In this study we perform a head-to-head comparison of such packages with respect to 1) predictive accuracy, 2) test-retest reliability, and 3) the ability to track age progression over time. Methods: We evaluated the five brain age prediction packages: brainageR, DeepBrainNet, brainage, ENIGMA, and pyment. The accuracy and test-retest reliability were assessed on MRI data from 372 healthy people, aged between 18.4 and 86.2 years (mean 38.7 +/- 17.5 years). Results: All packages showed significant correlations between predicted brain age and chronological age (r = 0.66 to 0.97, p less than 0.001), with pyment displaying the strongest correlation. The mean absolute error was between 3.56 (pyment) and 9.54 years (ENIGMA). brainageR and pyment were superior in terms of reliability (ICC values between 0.94 - 0.98), as well as predicting age progression over a longer time span. Conclusion: Of the five packages, pyment and brainageR consistently showed the highest accuracy and test-retest reliability. 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/2023.01.24.525361v1?rss=1 Authors: Ansari, A., Pillay, K., Baxter, L., Arasteh, E., Dereymaeker, A., Schmidt Mellado, G., Jansen, K., Naulaers, G., Bhatt, A., Van Huffel, S., Hartley, C., De Vos, M., Slater, R. Abstract: The preterm neonate can experience stressors that affect the rate of brain maturation and lead to long-term neurodevelopmental deficits. However, some neonates who are born early follow normal developmental trajectories. Extraction of data from electroencephalography (EEG) signals can be used to calculate the neonate's brain age which can be compared to their true age. Discrepancies between true age and brain age (the brain age delta) can then be used to quantify maturational deviation, which has been shown to correlate with long-term abnormal neurodevelopmental outcomes. Nevertheless, current brain age models that are based on traditional analytical techniques are less suited to clinical cot-side monitoring due to their dependency on long-duration EEG recordings, the need to record activity across multiple EEG channels, and the manual calculation of predefined EEG features which is time-consuming and may not fully capture the wealth of information in the EEG signal. In this study, we propose an alternative deep-learning approach to determine brain age, which operates directly on the EEG, using a Convolutional Neural Network (CNN) block based on the Inception architecture (called Sinc). Using this deep-learning approach on a dataset of preterm infants with normal neurodevelopmental outcomes (where we assume brain age = postmenstrual age), we can calculate infant brain age with a Mean Absolute Error (MAE) of 0.78 weeks (equivalent to a brain age estimation error for the infant within +/- 5.5 days of their true age). Importantly, this level of accuracy can be achieved by recording only 20 minutes of EEG activity from a single channel. This compares favourably to the degree of accuracy that can be achieved using traditional methods that require long duration recordings (typically greater than 2 hours of EEG activity) recorded from a higher density 8-electrode montage (MAE = 0.73 weeks). Importantly, the deep learning model's brain age deltas also distinguish between neonates with normal and severely abnormal outcomes (Normal MAE = 0.71 weeks, severely abnormal MAE = 1.27 weeks, p=0.02, one-way ANOVA), making it highly suited for potential clinical applications. Lastly, in an independent dataset collected at an independent site, we demonstrate the model's generalisability in age prediction, as accurate age predictions were also observed (MAE of 0.97 weeks). Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC
https://psychiatry.dev/wp-content/uploads/speaker/post-11376.mp3?cb=1672921078.mp3 Playback speed: 0.8x 1x 1.3x 1.6x 2x Download: Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment – PubMed Chenzhong Yin et al. PNAS. 2023. The gap betweenFull EntryAnatomically interpretable deep learning of brain age captures domain-specific cognitive impairment – PubMed
In this week's podcast, Neurology Today's editor-in-chief discusses a comparison of spinal cord stimulation with medical management for chronic pain, earlier brain aging in Black people, and the pros/cons of working for teleneurology companies.
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/2022.11.16.515405v1?rss=1 Authors: More, S., Antonopoulos, G., Hoffstaedter, F., Caspers, J., Eickhoff, S. B., Patil, K. R. Abstract: The difference between age predicted using anatomical brain scans and chronological age, i.e., the brain-age delta, provides a proxy for atypical aging. Various data representations and machine learning (ML) algorithms have been used for brain-age estimation. However, how these choices compare on performance criteria important for real-world applications, such as; (1) within-site accuracy, (2) cross-site generalization, (3) test-retest reliability, and (4) longitudinal consistency, remains uncharacterized. We evaluated 128 workflows consisting of 16 feature representations derived from gray matter (GM) images and eight ML algorithms with diverse inductive biases. Using four large neuroimaging databases covering the adult lifespan (total N = 2953, 18-88 years), we followed a systematic model selection procedure by sequentially applying stringent criteria. The 128 workflows showed a within-site mean absolute error (MAE) between 4.73-8.38 years, from which 32 broadly sampled workflows showed a cross-site MAE between 5.23-8.98 years. The test-retest reliability and longitudinal consistency of the top 10 workflows were comparable. The choice of feature representation and the ML algorithm both affected the performance. Specifically, voxel-wise feature spaces (smoothed and resampled), with and without principal components analysis, with non-linear and kernel-based ML algorithms performed well. Strikingly, the correlation of brain-age delta with behavioral measures disagreed between within-site and cross-site predictions. Application of the best-performing workflow on the ADNI sample showed a significantly higher brain-age delta in Alzheimer's and mild cognitive impairment patients. However, in the presence of age bias, the delta estimates in the diseased population varied depending on the sample used for bias correction. Taken together, brain-age shows promise, but further evaluation and improvements are needed for its real-world application. 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/2022.11.15.516349v1?rss=1 Authors: Komandur, D., Gupta, U., Chattopadhyay, T., Dhinagar, N., Thomopoulos, S. I., Chen, J.-C., Beavers, D., Steeg, G. v., Thompson, P. M. Abstract: Deep learning methods trained on brain MRI data from one scanner or imaging protocol can fail catastrophically when tested on data from other sites or protocols - a problem known as domain shift. To address this, here we propose a domain adaptation method that trains a 3D CycleGAN (cycle-consistent generative adversarial network) to harmonize brain MRI data from 5 diverse sources (ADNI, WHIMS, OASIS, AIBL, and UK Biobank; total N=4,941 MRIs, age range: 46-96 years). The approach uses 2 generators and 2 discriminators to generate an image harmonized to a specific target dataset given an image from the source domain distribution and vice versa. We train the CycleGAN to jointly optimize an adversarial loss and cyclic consistency. We use a patch-based discriminator and impose identity loss to further regularize model training. To test the benefit of the harmonization, we show that brain age estimation - a common benchmarking task - is more accurate in GAN-harmonized versus raw data. t-SNE maps show the improved distributional overlap of the harmonized data in the latent space. 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/2022.10.31.514506v1?rss=1 Authors: He, L., Chen, C., Wang, Y., Fan, Q., Chu, C., Xu, J., Fan, L. Abstract: Deep learning frameworks utilizing convolutional neural networks (CNNs) have frequently been used for brain age prediction and have achieved outstanding performance. Nevertheless, deep learning remains a black box as it is hard to interpret which brain parts contribute significantly to the predictions. To tackle this challenge, we first trained a lightweight, fully CNN model for brain age estimation on a large sample data set (N = 3054, age range = [8,80 years]) and tested it on an independent data set (N = 555, mean absolute error (MAE) = 4.45 years, r = 0.96). We then developed an interpretable scheme combining network occlusion sensitivity analysis (NOSA) with a fine-grained human brain atlas to uncover the learned invariance of the model. Our findings show that the dorsolateral, dorsomedial frontal cortex, anterior cingulate cortex, and thalamus had the highest contributions to age prediction across the lifespan. More interestingly, we observed that different regions showed divergent patterns in their predictions for specific age groups and that the bilateral hemispheres contributed differently to the predictions. Regions in the frontal lobe were essential predictors in both the developmental and aging stages with the thalamus remaining relatively stable and saliently correlated with other regional changes throughout the lifespan. The lateral and medial temporal brain regions gradually became involved during the aging phase. At the network level, the frontoparietal and the default mode networks show an inverted U-shape contribution from the developmental to the aging stages. The framework could identify regional contributions to the brain age prediction model, which could help increase the model interpretability when serving as an aging biomarker. 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/2022.09.29.510029v1?rss=1 Authors: Korbmacher, M., de Lange, A. M., van der Meer, D., Beck, D., Eikefjord, E. N., Lundervold, A., Andreassen, O., Westlye, L. T., Maximov, I. I. Abstract: Identifying white matter (WM) microstructure parameters that reflect the underlying biology of the brain will advance our understanding of ageing and brain health. In this extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analysed UK Biobank diffusion Magnetic Resonance Imaging (dMRI) data across midlife and older age (N = 35,749, 44.6 to 82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age; with their WM-features similarly related to and predicted by age. However, brain age was estimated best when combining approaches, showing different aspects of WM to contribute to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix as a potential biomarker of brain age and ageing. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer
Episode Guest: Cynthia R. Green, Ph.D., leading expert in the field of brain health, author, clinical psychologist and founding director of the Memory Enhancement Program at the Mount Sinai Medical CenterEpisode Description: What do advances in neuroscience, increased worldwide longevity, and the obsession with agelessness of the baby boomers have in common? They are all factors in the emerging Brain Age, a time of unprecedented knowledge about the brain, cognition and cognitive wellness. Join Dr. Green to learn more about the revolutionary new science of cognitive health, including what the science really shows about interventions such as brain games and exercise and their impact on brain wellness, and how we can each begin to customize our own path to improved brain vitality. Leave with 9 practical steps you can begin immediately take action to boost your personal brain vitality.In this episode, you'll discover:What it means to be brain healthyHow to achieve better brain healthWhether or not the new brain fitness software programs really workAbout Cynthia R. Green:Cynthia R. Green, Ph.D., is a leading expert in the field of brain health, clinical psychologist and founding director of the Memory Enhancement Program at the Mount Sinai Medical Center, where she is currently an assistant clinical professor of Psychiatry. Dr. Green is the president of Memory Arts LLC and CEO of TBH Brands, the parent company of Total Brain Health, which offers training, products and services to improve memory and brain fitness. Core products of Total Brain Health include the Total Brain Health Toolkits line, a series of "programs in a box" designed for use in active aging, fitness and healthcare settings.Dr. Green is the author of 5 books on brain health, both on her own and in collaboration with major brands such as Prevention and National Geographic. Her collaboration with National Geographic, Your Best Brain Ever, was named a "2013 Top Guide to Life After 50" by the Wall Street Journal. In addition, Dr. Green frequently serves as a consultant to companies on memory and brain fitness (Forest Laboratories, United Healthcare, Lifecare, Marbles) and is a highly regarded keynote speaker for organizational, corporate and association events.Originally from North Carolina, Dr. Green lives with her family in Northern New Jersey. Get in touch with Cynthia R. Green:Visit Cynthia's website: https://totalbrainhealth.com/ Buy Cynthia's Book: https://revolutionizeretirement.com/totalmemory Download Cynthia's Handout: https://revolutionizeretirement.com/green Grab our free guide, 10 Key Issues to Consider as You Explore Your Retirement Transition, at https://10keyretirementissues.com/
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.26.509522v1?rss=1 Authors: Zhang, F., Chang, H., Schaefer, S. M., Gou, J. Abstract: Multimorbidity, co-occurrence of two or more chronic conditions, is one of the top priorities in global health research and has emerged as the gold standard approach to study disease accumulation. As aging underlies the development of many chronic conditions, surrogate aging biomarkers are not disease-specific and capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Biological age has been examined in recent years as a surrogate biomarker to capture the process of aging. However, relatively few studies have investigated the relationship between biological age and multimorbidity. More research is needed to quantify biological age using a broad range of biological markers and multimorbidity based on a comprehensive set of chronic conditions. Brain age estimated by neuroimaging data and machine learning models is another surrogate aging biomarker predictive of a wide range of health outcomes. Little is known about the relationship between brain age and multimorbidity. To answer these questions, our study investigates whether elevated biological age and accelerated brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States (MIDUS) Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using convolutional neural networks and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity and presents the first effort to test whether sex moderates the relationship between these surrogate aging biomarkers and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of the accumulation of physical and mental health conditions, which may contribute to new strategies to improve the treatment of multimorbidity and detection of at-risk individuals. 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/2022.09.15.505331v1?rss=1 Authors: Cohen, J. W., Ramphal, B., Deserisy, M., Zhao, Y., Pagliaccio, D., Colcombe, S., Milham, M. P., Margolis, A. E. Abstract: Socioeconomic status (SES) has been linked to differences in brain structure and psychiatric risk across the lifespan. Despite many neuropsychiatric disorders emerging in childhood, few studies have examined the influence of SES on brain aging and psychopathology in youth. We re-analyzed relative brain age (RBA) data from the Healthy Brain Network to examine the influence of SES components (parent education, occupation, household income-to-needs ratio (INR), public assistance enrollment) on RBA. RBA was previously determined using covariation patterns for cortical morphology, white, and subcortical gray matter volumes without SES in predictive models. We also examined associations between RBA and psychiatric symptoms (child behavior checklist). Full case analysis included 470 youth (5-17 years; 61.3% male), self-identifying as White (55%), African American (15%), Hispanic (9%), or multiracial (17.2%). Mean household income was 3.95+/-2.33 (Mean+/-SD) times the federal poverty threshold. Multiple linear regression examined if 1) SES components associated with RBA, and 2) RBA associated with psychiatric symptoms. Models covaried for sex, scan location, and parent psychiatric diagnoses. RBA associated with public assistance (p = 0.03), parent occupation (p = 0.01), and parent psychiatric diagnosis (p = 0.01), but not with INR and parent education. Parent occupation (p = 0.02) and RBA (p = 0.04) associated with CBCL anxiety/depression scores. Components of SES associated with brain aging, underscoring the risk of omitting these factors in developmental brain research. Further, delayed brain aging was associated with low parental occupational prestige and child anxiety/depression scores, suggesting a possible biological pathway from SES to mental health risk. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer
On this weeks episode we're talking brain power - from childhood educational games to research and evidence about gaming that's GOOD for the brain! Get your pens and papers because it's quiz time! --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app
Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.08.506972v1?rss=1 Authors: Sanders, F., Baltramonaityte, V., Donohoe, G., Davies, N. M., Dunn, E. C., Cecil, C. A. M., Walton, E. Abstract: Recent research suggests that biological age, based on DNA methylation or neuroimaging measures, may predict health traits in adulthood more accurately than chronological age. However, whether these findings apply to earlier stages in life is unknown. We therefore aimed to characterise the performance of and interdependence between measures of biological age during adolescence, leveraging longitudinal data from a subsample of young adolescents from the population-based ALSPAC cohort (n=386). We derived four methylation age measures in late adolescence (17-19 years) and a measure of brain age derived from structural neuroimaging data (18-24 years). We then examined associations between these measures of biological age, and their relationship with five measures of physical, cognitive and mental health (8-18 years). Brain age was largely independent of different measures of methylation age, even after accounting for age, cell type composition, array and study (beta range: -0.60 to 0.17, all p greater than 0.05). Smoking and BMI predicted three measures of advanced methylation age (beta range: -0.39 to 0.52, all p less than 0.05), but not brain age. Depressive symptoms and cognitive ability were unrelated to all measures of biological age. Our findings emphasize the variability of and independence between these methylation- and brain-based measures of age in adolescents. They also highlight the importance of tracking the mosaic of ageing in younger populations. 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/2022.08.29.505635v1?rss=1 Authors: Han, L. K., Dinga, R., Leenings, R., Hahn, T., Cole, J., Aftanas, L., Amod, A., Besteher, B., Colle, R., Corruble, E., Couvy-Duchesne, B., Danilenko, K., Fuentes-Claramonte, P., Saffet Gonul, A., Gotlib, I., Goya-Maldonado, R., Groenewold, N., Hamilton, P., Ichikawa, N., Ipser, J., Itai, E., Koopowitz, S.-M., Li, M., Okada, G., Okamoto, Y., Olga, C., Osipov, E., Penninx, B., Pomarol-Clotet, E., Rogriguez-Cano, E., Sacchet, M., Shinzato, H., Sim, K., Stein, D., Uyar-Demir, A., Veltman, D., Schmaal, L. Abstract: Background: Several studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging derived brain age gap has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N=2,126 controls & N=2,675 cases; +1.08 years [SE 0.22], Cohen's d=0.14, 95% CI: 0.08 to 0.20), in independent cohorts that were not part of the original study. Methods: A previously trained brain age model (www.photon-ai.com/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18-75 years) from 13 new cohorts collected from 20 different scanners. Results: Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2=0.47, MAE=7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohen's d=0.15, 95% CI: 0.05 to 0.25) compared with controls, highly similar to our previous finding. Conclusions: This study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-scale independent mega-analyses across in total 32 cohorts and >3,400 patients and >2,800 controls worldwide show reliable but subtle effects of brain aging in adult depression. 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/2022.08.25.505251v1?rss=1 Authors: Millar, P. R., the Dominantly Inherited Alzheimer Network,, Gordon, B. A., Luckett, P. H., Benzinger, T., Cruchaga, C., Fagan, A. M., Hassenstab, J., Perrin, R. J., Schindler, S. E., Allegri, R. F., Day, G. S., Farlow, M. R., Mori, H., Nübling, G., Bateman, R., Morris, J., Ances, B. Abstract: Background: Estimates of "brain-predicted age" quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD), but has not been well explored in preclinical AD. Prior studies have typically modeled BAG with structural magnetic resonance imaging (MRI), but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods: We trained three models to predict age from FC, volumetric (Vol), or multimodal MRI (Vol+FC) in 390 control participants (18-89 years old). In independent samples of 144 older adult controls, 154 preclinical AD participants, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid, tau, and neurodegeneration, as well as a global cognitive composite. Results: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG and Vol+FC-BAG were marginally reduced in preclinical AD participants compared to controls. In CI participants only, elevated Vol-BAG and Vol+FC-BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions: Both FC-BAG and Vol-BAG are elevated in CI participants. However, FC and volumetric MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to preclinical AD pathology, while Vol-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model captures these modality-specific patterns, and further, improves sensitivity to healthy age differences. Funding: This work was supported by the National Institutes of Health (P01-AG026276, P01-AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimers Association (SG-20-690363-DIAN). Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer
In this episode, Frenchpet plays Brain Age: Concentration Training! He talks about keeping his brain sharp, dental advice, waking up, numbers, episode quality, and much more! Follow our socials for more Frenchpet madness! Facebook: https://www.facebook.com/frenchpet/ Twitter: https://www.twitter.com/ftanpodcast/ Instagram: https://www.instagram.com/frenchpetpodcast/ Join our Discord: https://discord.gg/zBaPK9xENH Linktr.ee: http://frenchpet.com/ For merch, visit: http://store.frenchpet.com/
Let's talk about the brain and how it literally is connected to everything that we do, how we act, how we feel and how we get along with others! And how all these relationships can age us faster and what we can do to help our brain age backwards
In this episode we welcome guest and house band Nate. We drink Green Apple Jones Soda, Brewtal Trewth homebrew [Thanks Tulip City Dispatch!], Expatriate from Three Weavers, and Hotel Water from Urban Artifact. RLXPs battery touched us all. We've played Pocket Planes (Android), Viva Pinata (XB), and Brain Age 2: More Training in Minutes a Day (NDS). Our reccos are Keller Williams, Grateful Dead Europe '72, and the podcasts Midnight Burger and This Planet Needs A Name. Links - This Planet Needs A Name - https://needsaname.buzzsprout.com/ Midnight Burger - https://www.weopenatsix.com/ Tulip City Dispatch - https://tulipcitydispatch.blogspot.com/ CartMart - https://www.cartmart.games/ Foundation CL - https://foundationcl.org --- Send in a voice message: https://anchor.fm/grandrapidians/message Support this podcast: https://anchor.fm/grandrapidians/support
Voice - the medium on which this and so many other types of content depend. It has the power to soothe and anger, sway and shame, enrapture and condemn. Also sometimes it causes ASMR, along with some like... crinkly plastic? Unfortunately for you, dear listeners, this episode only briefly delves into mukbang territory. This is "Voice" - we hope you enjoy! In this episode - Giovanni does his best Bubsy impression. David tells you about his favorite store on the citadel. Tess has a hard time saying colors. Greg is replaced with his vocal understudy, Grag. As always, a huge, huge thank you to our patrons. Your support means the world to us. If you want to become a patron (and get access to some exclusive podcasts) you can sign up at patreon.com/LTRT. If you don't have the cash to support us right now you can always leave us a review on your podcatcher of choice. Maybe you should head over to lefttriggerrighttrigger.com to find all our social media and other content!! Games discussed include: Forza Horizon 5, Brain Age, Bastion, and Mass Effect 2 Show notes: Is voice acting always the best option, or are some games better without it? - Samuel James Riley, Games Radar Gaming and Aging: How Accessibility Impacts Everyone Eventually - Access-Ability, Laura K Buzz Forza Horizon 5 will add on-screen sign language interpreters in a post-launch update - Kim Lyons, The Verge
Zach's Parental Leave #2 Therapy Professional Unhappiness Day of Decadence 2021 Have To Can't Driving & Urinating Singing Zach's Opinion Waterfall Zachs & Bunnies Time Outs Blippi Have To Children Have To Movies & TV Shows The Voyeurs, Succession Scenes from a Marriage, Lamb Shang Chi, Luca, How To with John Wilson Have To Albums badbadnotgood, Idles, Heart Attack Man Maxo Kream, Save Face, Finneas Outro: Heart Attack Man – Pitch Black 847-461-9598 havetopodcast@gmail.com
Former teacher turned custom jewelry maker Rachel, joins Ben for a discussion about creativity. In addition to her thoughts on the subject, Rachel shares how she discovered her creative niche through experimentation with different mediums and how a particular TikTok video she made went viral and jump started her business. Ben talks about Sigmund Freud, lightbulbs, and games that make him think of creativity. 00:20 - Rachel reveals the topic with such gusto! 00:50 - Rachel passes on the first question and then pleads the fifth 03:00 - “Body by Frost” started with a viral TikTok video 06:00 - They are called “jib-bits” (and they are trademarked) 07:00 - Creative vs. artistic 10:00 - Rachel's mom is the Helen Young Frost, renown quilter 14:30 - A lesson in American Sign Language and signing songs 17:19 - “Deaf” or “hard of hearing” is not offensive 20:42 - Fallacies and Freud 23:42 - The interesting Astrology book title 25:53 - Rachel's need and opportunity struggle with creativity 29:10 - Tom the trickster 34:08 - Two stories about boxes of stinky garbage 35:47 - “What's the deal with lightbulbs?” 38:42 - “No, she's a lawyer”, back to lightbulbs and it's not Benjamin Franklin 41:23 - Innovation versus creativity 43:30 - Creative writing and “The Vagina Monologues” 46:11 - The hookers and the honor system 47:20 - Rachel cheats in the Sims in the name of creativity 50:37 - Ben's story about his friend Michaela and the Sims 52:38 - “Brain Age” and typing games 54:50 - The cake and the companion cube 56:30 - A brief history of rhythm games 59:26 - Ben loves Katamari Damacy! 01:02:26 - Rachel's video game conversations with her students 01:07:28 - “Boring but nice,” and the last question 01:12:20 - Closing thoughts on creativity
Do your games diagnose you with dementia when you lose? How about force you to read an encyclopedia to win? How about a game that is ruined by a mini-game? Adam and Mike talk about the ten games they believe should be heralded as good and yet frustratingly hard to play. Join us as we talk about Brain Age, Where in Time is Carmen Sandiego, and Nightmare Creatures-quite possibly reasons to hate video games.
In today's episode, I stumble upon a new phrase, "fear extinction," which leads me to a novel approach for extinguishing fear and anxiety while reversing "brain age" by nearly 10 years.
This is our first episode we were able to record in person! We decided to record outside and hope you enjoy the sounds of nature behind us. We talk about the differences in generations and try not to make any harsh judgments, especially since none of us asked for the technology that may be (read: absolutely is) actively changing what it means to be human. We hope you enjoy! Please remember that if you leave a review for us on Apple Podcasts or your podcast app, you can send us a screenshot to guidanceformovement@gmail.com and Meredith would be glad to give you a tarot reading in exchange for your review. Otherwise, feel free to email us topic suggestions or connect with Meredith on Instagram https://www.instagram.com/tarotwithtux/. See y'all next week!
In our very first episode of The Nintendo Show we celebrate Nintendo's 130th birthday by picking our favorite game of almost every Nintendo console, sorry NES. We Also give our impressions of the Switch Lite, Mario Kart Tour, and Brain Age coming to the Switch. Not to mention answering a load of your questions. Speaking of which if you would like to send in your questions please do so at podcast@nintendoshow.com, and we will be sure to include them in the show. Support the show
En éste episodio: el típico Vicio de la Semana [6m54s]. #Gaminforme [27m51s]. Comentando: - Bloodstained se retrasa. Se cancela versión PS Vita - Grandia I + Grandia II HD Remaster llegará a Nintendo Switch. Grandia HD Remaster para PC - Sekiro Die Twice tiene fecha - Ace Combat 7: Skies Unknown tiene fecha y saldrá para PS4, Xbox One y PC. - Shenmue III ya tiene fecha de lanzamiento - Fecha de lanzamiento de Luigi's Mansion para 3DS - Se anuncia Dark Souls Trilogy para PS4 y Xbox One - Fecha de Devil May Cry V - Aplicación de música de Xenogears remastered disponible para usuarios PS Plus en Japón - Edición física de Fist of the North Star Lost Paradise tendrá cubierta reversible. #Gamefemerides [1h5m18s] •New Super Mario Bros. 2 •Brain Age 2 •Jeanne D'Arc •Ratchet & Clank Future Quest for Booty •Chrono Trigger •The Legend of Zelda NES •Advance Wars Dual Strike •Devil May Cry •Final Fantasy III Remake •GoldenEye 007 N64 •Breath of Fire III PSP •Batman Arkham Asylum Recuerden dejar "me gusta" y comentarios en iVoox, y demás lugares donde nos escuchen. ¡Ahora también estamos disponibles Spotify y en Google Podcast! Facebook: https://web.facebook.com/LegionGamerRD/ Instagram: https://www.instagram.com/legiongamerrd/ Twitter: https://twitter.com/LegionGamerRD Este episodio en YouTube: Nuestro blog: https://legiongamerrd.blogspot.com/ Estamos en Apple iTunes: https:/itunes.apple.com/do/podcast/legión-gamer-podcast/id1370438088 En TuneIn: https://tunein.com/podcasts/Podcasts/Legion-Gamer-Podcast-p1121452/ Radiocasters: https://radiocasters.com/podcast/16 En Twitch: https://twitch.tv/apa_zarozo También pendientes de nuestros amigos: Twitch de Darkjuste: http://twitch.com/darkjuste Cultura Cómic RD: https://www.facebook.com/groups/culturacomicrd/ GOXP Gamers : http://www.gameoverxp.com/ VeSh Gaming: https://veshgaming.com/ Kioshop: https://www.facebook.com/kioshops/ Spinelbreaker: https://www.youtube.com/c/spinelbreaker Maguita Gaming: https://www.youtube.com/user/Maguita70s RetroAct Entertainment: https://www.facebook.com/RetroActv
The clubhouse A/C broke, and it got so hot during the recording of this episode that your nice hosts started to get a little delirious.But before they completely melted down, Mark talked about how Enemy AI can be a great tool for both level design and narrative design, Stephen collaborated on a discussion about co-op games, and Martha shared her excitement about the recently held Summer Games Done Quick speedrunning event.Discuss this episode on Reddit using this thread at r/gamedev!Stephen is on Twitter @leonyx03Some highlights from Mark's VR night!highlight 1highlight 2highlight 3 Enemy AI 0:03:00 Mark LaCroixGame DesignDesign Club - Super Mario Bros: Level 1-1 - How Super Mario Mastered Level Desi… - Extra Credits, YouTubeNobody dies on the first goomba - Sephzilla, Destructoid2007 AiGameDev.com Awards: Technical Innovation in Game AIEnemy design and enemy AI for melee combat systems - Bart Vossen, Game DeveloperAnalysing Mario to Master Super Mario Maker - Game Maker's Toolkit, YouTubeChip's Challenge:1A - Zabrush Cimahi, YouTubeSaltybet - Twitchseebotschat - TwitchThe Perfect Organism | The AI of Alien: Isolation - Tommy Thompson, AI and GamesA Thousand Tiny Details: Emergent Storytelling in Slime Rancher - Steven Lumpkin, TwitterPeople Have Discovered The Perfect Way To Mess With Your Cat - Rachael Krishna, BuzzfeedMeet the Artist Using Ritual Magic to Trap Self-Driving Cars - Beckett Mufson, Vice Co-op Games 0:29:40 Stephen McGregorGamingMartha asked Mark to cut a part out of this section, but Stephen edited this episode, so...Overcooked: How To Make Co-op Cookery Fun - Philippa Warr, Rock Paper ShotgunMiyamoto talks Super Mario Galaxy co-op - Alexander Sliwinski, Engadget Speedrunning 0:47:42 Martha MegarryGamingAwesome Games Done QuickSpeedDemosArchiveOcarina of Time - Skip 99% of Traveling Across the Field - Swordless Link, YouTubeThe Man Who Does The Impossible in Super Mario 64 - Patricia Hernandez, KotakuSpeedrunning RulesBlindfolded speedrun race - Noko Online, YouTubeI can't believe this actually happened (glitch discovered during blindfolded sp… - Tokyoboi, YouTubeKotaku's speedrun articles - KotakuIntelligent Qube by caneofpacci - Games Done Quick, YouTubeTASBot plays Brain Age by micro500, xy2_ in 20:06 - - Games Done Quick, YouTubeWorld Record Progression: Super Mario World - Summoning Salt, YouTube0:00 / 29:59 I Am Bread by Blood_Thunder in 20:50 - Games Done Quick, YouTube0:00 / 1:06:01 The Talos Principle by Azorae in 58:16 - Games Done Quick, YouTubeTomb Raider: Underworld hard mode - Jarek, Speed Demos ArchiveYes, you can speedrun in FingeanceNew Trick Has Ocarina of Time Speedrunners Debating What A Glitch Actually Is - Heather Alexandra, Kotaku
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