Now a little Brain-Training tip from Dr. Kawashima. #BrainAge #BrainAgeConcentrationTraining #3DS #Artist
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#BrainAge
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asian man with glasses looking very dissapointed he says, oh dear your brain age is a little underwhelming from the nintendo ds lite game brain age
Well, ok. So what else is new? #meme #nintendodslite #brainage
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🧠 Not all brain age algorithms are equal!
In our new study, one algorithm (Pyment) outperformed other models in youth. Algorithms fail when your sample's age range is narrow or mismatched with training data. So, choose wisely...
onlinelibrary.wiley.com/doi/10.1002/...
#BrainAge #Neuroimaging
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Of course today, on a day when I’m absolutely getting the hots for fat, wobbly bellies, does this word show up while playing Brain Age… #Fat #Vore #BrainAge #DrKawashimasBrainTraining #NintendoSwitch #NintendoSwitch2
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#dsdecember #Nintendo #nintendods #nds #dslite #advancewars #animalcrossing #brainage #castlevania #kirby #legendofzelda #mariovsdonkeykong #metroid #newsupermariobros #nintendogs #professorlayton #warioware
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Pleased to share our new paper in collaboration with William Hopkins, University of Texas, showing that the brain age gap is associated with cognitive abilities in captive chimpanzees www.nature.com/articles/s41... 26062-5 #brainage #chimpanzees
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Hetalia reference Brain Age 2??! Holy cow. #hetalia #feliciano #italyhetalia #italy #brainage2 #brainage #heh
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![Feature importance for brain-age prediction using Ridge regression. The heat maps illustrate the top-10 features for both the accuracy-optimized model (top panel) and the biomarker-optimized model (bottom panel), with color intensity representing the relative SHAP values. The x-axis denotes increasing regularization strength [α]. The accuracy-optimized model (α ~ 10^3), trained for maximum age prediction accuracy, highlights features such as the volume of the pons, gray-white contrast in the inferior parietal region, and volume of the cerebrospinal fluid (CSF). Conversely, the biomarker-optimized model (α ~ 10^5), trained to enhance the brain-age gap effect size for a majority of conditions vs. controls, emphasizes features like the volume of gray matter, volume of peripheral cortical gray matter, and mean intensity of the third ventricle. The dashed vertical lines indicate the regularization strength [α] at which each model was optimized. This comparison underscores the distinct feature importances between models focused on accuracy vs. those optimized for sensitivity to disease-relevant changes, supporting the manuscript’s thesis that traditional accuracy-optimized models may not provide the best biomarkers for disease detection.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:5522ztebtekoor5efelihqhb/bafkreiflbushsogb76c77gndw6ccvjg2lg52k2l6rr36zneeyufcshwapu)
Feature importance for brain-age prediction using Ridge regression. The heat maps illustrate the top-10 features for both the accuracy-optimized model (top panel) and the biomarker-optimized model (bottom panel), with color intensity representing the relative SHAP values. The x-axis denotes increasing regularization strength [α]. The accuracy-optimized model (α ~ 10^3), trained for maximum age prediction accuracy, highlights features such as the volume of the pons, gray-white contrast in the inferior parietal region, and volume of the cerebrospinal fluid (CSF). Conversely, the biomarker-optimized model (α ~ 10^5), trained to enhance the brain-age gap effect size for a majority of conditions vs. controls, emphasizes features like the volume of gray matter, volume of peripheral cortical gray matter, and mean intensity of the third ventricle. The dashed vertical lines indicate the regularization strength [α] at which each model was optimized. This comparison underscores the distinct feature importances between models focused on accuracy vs. those optimized for sensitivity to disease-relevant changes, supporting the manuscript’s thesis that traditional accuracy-optimized models may not provide the best biomarkers for disease detection.
Imaging-based models are being used to assess #BrainAge, but can they predict #NeurologicalDisease? @ritterkerstin.bsky.social &co show that simpler models, with lower age prediction accuracy, are paradoxically more sensitive to disease-related brain changes @plosbiology.org 🧪 plos.io/47NcIew
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Inktober Day 29- Lesson
Inko’s failed attempt to make learning fun- Prof. Benkyo! Play a series of trivial minigames to learn Japanese and excel at Inko University. #inktober #inktober2025 #inktoberchallenge #inktoberday29 #lesson #profbenkyo #microgames #brainage #warioware #indiegames #retrogames
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![Feature importance for brain-age prediction using Ridge regression. The heat maps illustrate the top-10 features for both the accuracy-optimized model (top panel) and the biomarker-optimized model (bottom panel), with color intensity representing the relative SHAP values. The x-axis denotes increasing regularization strength [α]. The accuracy-optimized model (α ~ 10^3), trained for maximum age prediction accuracy, highlights features such as the volume of the pons, gray-white contrast in the inferior parietal region, and volume of the cerebrospinal fluid (CSF). Conversely, the biomarker-optimized model (α ~ 10^5), trained to enhance the brain-age gap effect size for a majority of conditions vs. controls, emphasizes features like the volume of gray matter, volume of peripheral cortical gray matter, and mean intensity of the third ventricle. The dashed vertical lines indicate the regularization strength [α] at which each model was optimized. This comparison underscores the distinct feature importances between models focused on accuracy vs. those optimized for sensitivity to disease-relevant changes, supporting the manuscript’s thesis that traditional accuracy-optimized models may not provide the best biomarkers for disease detection.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:5522ztebtekoor5efelihqhb/bafkreibw45hmyj52kqntfkaw4wwm35ccih3rp2dbhzeqdnrmkaznvhtgta)
Feature importance for brain-age prediction using Ridge regression. The heat maps illustrate the top-10 features for both the accuracy-optimized model (top panel) and the biomarker-optimized model (bottom panel), with color intensity representing the relative SHAP values. The x-axis denotes increasing regularization strength [α]. The accuracy-optimized model (α ~ 10^3), trained for maximum age prediction accuracy, highlights features such as the volume of the pons, gray-white contrast in the inferior parietal region, and volume of the cerebrospinal fluid (CSF). Conversely, the biomarker-optimized model (α ~ 10^5), trained to enhance the brain-age gap effect size for a majority of conditions vs. controls, emphasizes features like the volume of gray matter, volume of peripheral cortical gray matter, and mean intensity of the third ventricle. The dashed vertical lines indicate the regularization strength [α] at which each model was optimized. This comparison underscores the distinct feature importances between models focused on accuracy vs. those optimized for sensitivity to disease-relevant changes, supporting the manuscript’s thesis that traditional accuracy-optimized models may not provide the best biomarkers for disease detection.
Imaging-based models are being used to assess #BrainAge, but can they predict #NeurologicalDisease? @ritterkerstin.bsky.social &co show that simpler models, with lower age prediction accuracy, are paradoxically more sensitive to disease-related brain changes @plosbiology.org 🧪 plos.io/47NcIew
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![Feature importance for brain-age prediction using Ridge regression. The heat maps illustrate the top-10 features for both the accuracy-optimized model (top panel) and the biomarker-optimized model (bottom panel), with color intensity representing the relative SHAP values. The x-axis denotes increasing regularization strength [α]. The accuracy-optimized model (α ~ 10^3), trained for maximum age prediction accuracy, highlights features such as the volume of the pons, gray-white contrast in the inferior parietal region, and volume of the cerebrospinal fluid (CSF). Conversely, the biomarker-optimized model (α ~ 10^5), trained to enhance the brain-age gap effect size for a majority of conditions vs. controls, emphasizes features like the volume of gray matter, volume of peripheral cortical gray matter, and mean intensity of the third ventricle. The dashed vertical lines indicate the regularization strength [α] at which each model was optimized. This comparison underscores the distinct feature importances between models focused on accuracy vs. those optimized for sensitivity to disease-relevant changes, supporting the manuscript’s thesis that traditional accuracy-optimized models may not provide the best biomarkers for disease detection.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:5522ztebtekoor5efelihqhb/bafkreidbug5gkmsmbisx2efl6ibzvquaozjtdfdimluid5jw7u7h23pdqe)
Feature importance for brain-age prediction using Ridge regression. The heat maps illustrate the top-10 features for both the accuracy-optimized model (top panel) and the biomarker-optimized model (bottom panel), with color intensity representing the relative SHAP values. The x-axis denotes increasing regularization strength [α]. The accuracy-optimized model (α ~ 10^3), trained for maximum age prediction accuracy, highlights features such as the volume of the pons, gray-white contrast in the inferior parietal region, and volume of the cerebrospinal fluid (CSF). Conversely, the biomarker-optimized model (α ~ 10^5), trained to enhance the brain-age gap effect size for a majority of conditions vs. controls, emphasizes features like the volume of gray matter, volume of peripheral cortical gray matter, and mean intensity of the third ventricle. The dashed vertical lines indicate the regularization strength [α] at which each model was optimized. This comparison underscores the distinct feature importances between models focused on accuracy vs. those optimized for sensitivity to disease-relevant changes, supporting the manuscript’s thesis that traditional accuracy-optimized models may not provide the best biomarkers for disease detection.
Imaging-based models are being used to assess #BrainAge, but can they predict #NeurologicalDisease? @ritterkerstin.bsky.social &co show that simpler models, with lower age prediction accuracy, are paradoxically more sensitive to disease-related brain changes @plosbiology.org 🧪 plos.io/47NcIew
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Genomic study of brain age in 56,348 people identifies 59 genetic loci, links brain aging to mental and phys health, and suggests high blood pressure and T2D as causal factors of brain aging #brainage @pjawinski.bsky.social @humboldtuni.bsky.social @mpicbs.bsky.social www.nature.com/articles/s43...
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How Sleep Patterns Influence Your Brain’s Biological Age
A study of 27,500 adults found each point drop in a sleep score adds ~6 months to the brain‑age gap, making poor sleepers look up to a year older on MRI. Strongest in men under 60. Read more: getnews.me/how-sleep-patterns-influ... #sleep #brainage #health
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📋 Differences remained significant after controlling for age, sex, and scan quality
#MedicalAI #HealthEquity #Neuroscience #AlgorithmicBias #BrainAge
www.medrxiv.org/content/10.1... /2
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#pickups #Nintendo #nintendods #nds #brainage #brainage2 #warioware #wariowaretouched #legendofzelda #phantomhourglass #legendofzeldaphantomhourglass #nintendo3ds #3ds #monsterhunter #monsterhunter3g #monsterhunter4 #monsterhunterg
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LifeNextDaily News!
Want to keep your brain young? Research reveals how to decrease your brain age, giving you a cognitive edge! #BrainHealth #CognitiveFunction #BrainAge
Click here↓↓↓
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LifeNextDaily News!
Want to sharpen your mind? Research shows you can reverse brain aging & boost cognitive function! #BrainHealth #CognitiveFunction #BrainAge
Click here↓↓↓
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Image from article in Radiology: Artificial Intelligence
Fetal #BrainAge prediction using #MRI to identify brain abnormalities https://doi.org/10.1148/ryai.240115 @bostonchildrens.bsky.social #NeuroRad #AI #ML
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Image from article in Radiology: Artificial Intelligence
#MachineLearning to detect fetal brain abnormalities on #MRI https://doi.org/10.1148/ryai.240115 @bostonchildrens.bsky.social #BrainAge #ventriculomegaly #AI
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Image from article in Radiology: Artificial Intelligence
Fetal #BrainAge prediction using #MRI to identify brain abnormalities https://doi.org/10.1148/ryai.240115 @bostonchildrens.bsky.social #UltraCon2025 #ultrasound #USRad #radiology
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New Preprint tinyurl.com/mvjybsh5tory Adults with depression show differences in brain aging based on lifestyle. Those with balanced habits had the lowest mood symptoms and brainAGE, while those with poor diet and inactivity showed the highest. #BrainHealth #Depression #BrainAGE
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Image from article in Radiology: Artificial Intelligence
Fetal brain age prediction to identify ventriculomegaly & associated CNS abnormalities https://doi.org/10.1148/ryai.240115 @bostonchildrens.bsky.social #brain #BrainAge #AI
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Image from article in Radiology: Artificial Intelligence
Fetal #BrainAge prediction using #MRI to identify brain abnormalities https://doi.org/10.1148/ryai.240115 @bostonchildrens.bsky.social #NeuroRad #AI #ML
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Brain Age: Train Your Brain in Minutes a Day!
2006 - Nintendo DS
#retrogamingcommunity #retrogamingcollection #crttv #retrogaming #retrogames #brainage #nintendo #nintendodslite #dualscreen #DrKawashima
#sudoku
www.instagram.com/p/DFA59aQxSB...
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i loved beta 64's new video! i loved playing the 3ds brain age game as a kid #beta64 #brainage #nintendo #3ds :]
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How do #deeplearning models decode brain aging? Our study compares 7 saliency approaches for interpreting #brainage predictions from MRI scans.
Read more here: pmc.ncbi.nlm.nih.gov/articles/PMC...
#aging #TBI #AI #neuralnetworks
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Costs and benefits to advanced infant brain development news.yale.edu/2024/12/02/a... #EarlyChildhood #InfantBrain #ChildrenDevelopment #Cognition #Behavior #BrainAge #Research #YaleResearch
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La música de « #BrainAge Train your brain in minutes a day! » ha sido añadida a #NintendoMusic
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