♻️روشی جدید برای #طبقه_بندی بیماران مبتلا به #آلزایمر
A #comprehensive #analysis of #resting #state #fMRI measures to #classify individual #patients with #Alzheimer's disease
پژوهشگران نوروساینس دانشگاه لایدن 🇱🇺 و دانشگاه گراتز 🇦🇹 در پژوهشی که به تازگی منتشر شده است، شیوه ای نوین برای طبقه بندی بیماران دارای آلزایمر ابداع نموده اند.
🔬در این روش #fMRI وضعیت #استراحت #مغزی 77 فرد مبتلا به آلزایمر با 173 نفر داوطلب سالم کنترل مقایسه شد. برای بررسی و طبقه بندی از الگوهای #ارتباط #کارکردی (FC) و پویش های ارتباط کارکردی (FCD) استفاده شد.
تحلیل ها نشانگر 10 شاخص برای طبقه بندی بیماران بودند. بر این اساس 31 طبقه مختلف از #کارکردمغزی در حالت #استراحت برای #آلزایمر بدست آمد؛ نتایج تفصیلی در لینک منبع ارائه شده اند.
Abstract
#Alzheimer's disease (#AD) patients show altered patterns of #functional #connectivity (#FC) on #resting #state #functional #magnetic #resonance #imaging (#RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with #independent #component #analysis (#ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using #dual #regression, vi) FC with the #hippocampus, vii) #eigenvector centrality, and viii) the #amplitude of #low #frequency #fluctuations (#ALFF). These eight measures were used separately as predictors in an #elastic #net #logistic #regression, and combined in a #group #lasso #logistic #regression model. We calculated the area under the receiver operating characteristic curve plots (#AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly.
لینک منبع 👇🏻(further reading)👇🏻
https://doi.org/10.1016/j.neuroimage.2017.11.025
✅(در صورت جذابیت و علاقمندی به موضوع، مطلب را برای دیگران نیز بازنشر فرمایید).
📢کانال #دکترامیرمحمدشهسوارانی
🍃🌹🌸💐🌸🌹🍃
@DrAmirMohammadShahsavarani
A #comprehensive #analysis of #resting #state #fMRI measures to #classify individual #patients with #Alzheimer's disease
پژوهشگران نوروساینس دانشگاه لایدن 🇱🇺 و دانشگاه گراتز 🇦🇹 در پژوهشی که به تازگی منتشر شده است، شیوه ای نوین برای طبقه بندی بیماران دارای آلزایمر ابداع نموده اند.
🔬در این روش #fMRI وضعیت #استراحت #مغزی 77 فرد مبتلا به آلزایمر با 173 نفر داوطلب سالم کنترل مقایسه شد. برای بررسی و طبقه بندی از الگوهای #ارتباط #کارکردی (FC) و پویش های ارتباط کارکردی (FCD) استفاده شد.
تحلیل ها نشانگر 10 شاخص برای طبقه بندی بیماران بودند. بر این اساس 31 طبقه مختلف از #کارکردمغزی در حالت #استراحت برای #آلزایمر بدست آمد؛ نتایج تفصیلی در لینک منبع ارائه شده اند.
Abstract
#Alzheimer's disease (#AD) patients show altered patterns of #functional #connectivity (#FC) on #resting #state #functional #magnetic #resonance #imaging (#RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with #independent #component #analysis (#ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using #dual #regression, vi) FC with the #hippocampus, vii) #eigenvector centrality, and viii) the #amplitude of #low #frequency #fluctuations (#ALFF). These eight measures were used separately as predictors in an #elastic #net #logistic #regression, and combined in a #group #lasso #logistic #regression model. We calculated the area under the receiver operating characteristic curve plots (#AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly.
لینک منبع 👇🏻(further reading)👇🏻
https://doi.org/10.1016/j.neuroimage.2017.11.025
✅(در صورت جذابیت و علاقمندی به موضوع، مطلب را برای دیگران نیز بازنشر فرمایید).
📢کانال #دکترامیرمحمدشهسوارانی
🍃🌹🌸💐🌸🌹🍃
@DrAmirMohammadShahsavarani
♻️#مدلسازی و #پیش_بینی #توجه #مستمر
#Connectome-based #predictive #modeling of #attention: Comparing different #functional #connectivity features and #prediction methods across #datasets
در گزارشی که به تازگی منتشر شده است، پژوهشگران نوروساینس دانشگاه ییل 🇺🇸 روشی جدید برای #مدلسازی، #طبقه بندی و #پیش بینی #الگوهای توجه در افراد یافته اند.
🔬در این پژوهش 294 داوطلب تکالیف توجهی مختلفی را در وضعیت های مختلف و حین #fMRI انجام دادند.
📚بر اساس یافته های پژوهش حاضر، می توان در حوزه #توجه #پایدار در افراد، بر اساس #تفاوتهای #فردی 12 الگوی مختلف وجود دارد که بر اساس آن #کنشوری و #عملکرد کارکردهای اجرایی افراد با دقت بالایی قابل پیش بینی است.
Abstract
#Connectome-based predictive #modeling was recently developed to predict #individual #differences in #traits and #behaviors, including #fluid #intelligence and #sustained #attention, from #functional #brain #connectivity (#FC) measured with #fMRI. Here, using the #CPM framework, we compared the #predictive power of three different measures of FC (#Pearson's #correlation, #accordance, and #discordance) and two different #prediction #algorithms (#linear and #partial #least #square [#PLS] #regression) for attention #function. Accordance and discordance are recently proposed FC measures that respectively track #in-phase #synchronization and #out-of-phase #anti-correlation. We defined connectome-based models using task-based or #resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were #internally #validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested. The validation datasets included: 1) data collected during performance of a #stop-signal task and at rest (N = 83, including 19 participants who were administered #methylphenidate prior to scanning;) data collected during #Attention #Network #Task performance and rest (N = 41), and 3) resting-state data and #ADHD symptom severity from the #ADHD-200 Consortium (N = 113). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's < 0.05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models, it is useful to consider accordance features and PLS regression for CPM.
لینک منبع 👇🏻(further reading)👇🏻
https://doi.org/10.1016/j.neuroimage.2017.11.010
✅(در صورت جذابیت و علاقمندی به موضوع، مطلب را برای دیگران نیز بازنشر فرمایید).
📢کانال #دکترامیرمحمدشهسوارانی
🍃🌹🌸💐🌸🌹🍃
@DrAmirMohammadShahsavarani
#Connectome-based #predictive #modeling of #attention: Comparing different #functional #connectivity features and #prediction methods across #datasets
در گزارشی که به تازگی منتشر شده است، پژوهشگران نوروساینس دانشگاه ییل 🇺🇸 روشی جدید برای #مدلسازی، #طبقه بندی و #پیش بینی #الگوهای توجه در افراد یافته اند.
🔬در این پژوهش 294 داوطلب تکالیف توجهی مختلفی را در وضعیت های مختلف و حین #fMRI انجام دادند.
📚بر اساس یافته های پژوهش حاضر، می توان در حوزه #توجه #پایدار در افراد، بر اساس #تفاوتهای #فردی 12 الگوی مختلف وجود دارد که بر اساس آن #کنشوری و #عملکرد کارکردهای اجرایی افراد با دقت بالایی قابل پیش بینی است.
Abstract
#Connectome-based predictive #modeling was recently developed to predict #individual #differences in #traits and #behaviors, including #fluid #intelligence and #sustained #attention, from #functional #brain #connectivity (#FC) measured with #fMRI. Here, using the #CPM framework, we compared the #predictive power of three different measures of FC (#Pearson's #correlation, #accordance, and #discordance) and two different #prediction #algorithms (#linear and #partial #least #square [#PLS] #regression) for attention #function. Accordance and discordance are recently proposed FC measures that respectively track #in-phase #synchronization and #out-of-phase #anti-correlation. We defined connectome-based models using task-based or #resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were #internally #validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested. The validation datasets included: 1) data collected during performance of a #stop-signal task and at rest (N = 83, including 19 participants who were administered #methylphenidate prior to scanning;) data collected during #Attention #Network #Task performance and rest (N = 41), and 3) resting-state data and #ADHD symptom severity from the #ADHD-200 Consortium (N = 113). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's < 0.05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models, it is useful to consider accordance features and PLS regression for CPM.
لینک منبع 👇🏻(further reading)👇🏻
https://doi.org/10.1016/j.neuroimage.2017.11.010
✅(در صورت جذابیت و علاقمندی به موضوع، مطلب را برای دیگران نیز بازنشر فرمایید).
📢کانال #دکترامیرمحمدشهسوارانی
🍃🌹🌸💐🌸🌹🍃
@DrAmirMohammadShahsavarani
♻️ریشه های #اختلالات #زبانی در بیماری #آلزایمر (#AD)
Disruption of #Semantic #Network in #Mild #Alzheimer’s Disease Revealed by #Resting-State #fMRI
پژوهشگران نوروسانیس مرکز فرمی🇮🇹، دانشگاه کپنهاگن🇩🇰، مرکز IRCCS سانتا لوسیا🇮🇹، و دانشگاه مینه سوتا 🇺🇸در پژوهشی که به تازگی منتشر شده، تخریب های شناختی در مراکز زبانی را در بیماران مبتلا به آلزایمر توسط تحلیل #fMRI حالت #استراحت مشخص ساختند.
🔬در این پژوهش، 38 نفر مبتلا به آلزایمر خفیف به همراه 19داوطلب سالم همتا مورد بررسی fMRI در حالت استراحت قرار گرفتند.
📚نتایج تحلیل ها نشان دادند که ارتباطات کارکردی در مغز افراد دارای آلزایمر در بخش های #POp, BA44 و #pMTG, BA21 تغییر یافته اند. این نواحی نقش عمده ای در شبکه #کنترل #معنایی دارند. نقش این بخش های بویژه pMTG در پردازش معنایی #زبان و نیز ارتباط آنها با ناحیه #ورنیکه است.
Abstract
Subtle #semantic #deficits can be observed in #Alzheimer’s disease (#AD) patients even in the early stages of the illness. In this work, we tested the hypothesis that the semantic control network is deregulated in mild AD patients. We assessed the integrity of the semantic control system using #resting-state #functional #magnetic #resonance #imaging in a cohort of patients with #mild #AD (n = 38; mean mini-mental state examination = 20.5) and in a group of age-matched #healthy #controls (n = 19). #Voxel-wise analysis #spatially constrained in the left #fronto-temporal semantic control network identified two regions with altered #functional #connectivity (#FC) in AD patients, specifically in the #pars #opercularis (#POp, BA44) and in the #posterior #middle #temporal #gyrus (pMTG, BA21). Using #whole-brain #seed-based analysis, we demonstrated that these two regions have altered FC even beyond the semantic control network. In particular, the pMTG displayed a wide-distributed pattern of lower connectivity to several brain regions involved in #language-semantic #processing, along with a possibly compensatory higher connectivity to the #Wernicke’s area. We conclude that in mild AD #brain regions belonging to the semantic control network are abnormally connected not only within the network, but also to other areas known to be critical for language processing.
لینک منبع 👇🏻(further reading)👇🏻
https://doi.org/10.1016/j.neuroscience.2017.11.030
✅(در صورت جذابیت و علاقمندی به موضوع، مطلب را برای دیگران نیز بازنشر فرمایید).
📢کانال #دکترامیرمحمدشهسوارانی
🍃🌹🌸💐🌸🌹🍃
@DrAmirMohammadShahsavarani
Disruption of #Semantic #Network in #Mild #Alzheimer’s Disease Revealed by #Resting-State #fMRI
پژوهشگران نوروسانیس مرکز فرمی🇮🇹، دانشگاه کپنهاگن🇩🇰، مرکز IRCCS سانتا لوسیا🇮🇹، و دانشگاه مینه سوتا 🇺🇸در پژوهشی که به تازگی منتشر شده، تخریب های شناختی در مراکز زبانی را در بیماران مبتلا به آلزایمر توسط تحلیل #fMRI حالت #استراحت مشخص ساختند.
🔬در این پژوهش، 38 نفر مبتلا به آلزایمر خفیف به همراه 19داوطلب سالم همتا مورد بررسی fMRI در حالت استراحت قرار گرفتند.
📚نتایج تحلیل ها نشان دادند که ارتباطات کارکردی در مغز افراد دارای آلزایمر در بخش های #POp, BA44 و #pMTG, BA21 تغییر یافته اند. این نواحی نقش عمده ای در شبکه #کنترل #معنایی دارند. نقش این بخش های بویژه pMTG در پردازش معنایی #زبان و نیز ارتباط آنها با ناحیه #ورنیکه است.
Abstract
Subtle #semantic #deficits can be observed in #Alzheimer’s disease (#AD) patients even in the early stages of the illness. In this work, we tested the hypothesis that the semantic control network is deregulated in mild AD patients. We assessed the integrity of the semantic control system using #resting-state #functional #magnetic #resonance #imaging in a cohort of patients with #mild #AD (n = 38; mean mini-mental state examination = 20.5) and in a group of age-matched #healthy #controls (n = 19). #Voxel-wise analysis #spatially constrained in the left #fronto-temporal semantic control network identified two regions with altered #functional #connectivity (#FC) in AD patients, specifically in the #pars #opercularis (#POp, BA44) and in the #posterior #middle #temporal #gyrus (pMTG, BA21). Using #whole-brain #seed-based analysis, we demonstrated that these two regions have altered FC even beyond the semantic control network. In particular, the pMTG displayed a wide-distributed pattern of lower connectivity to several brain regions involved in #language-semantic #processing, along with a possibly compensatory higher connectivity to the #Wernicke’s area. We conclude that in mild AD #brain regions belonging to the semantic control network are abnormally connected not only within the network, but also to other areas known to be critical for language processing.
لینک منبع 👇🏻(further reading)👇🏻
https://doi.org/10.1016/j.neuroscience.2017.11.030
✅(در صورت جذابیت و علاقمندی به موضوع، مطلب را برای دیگران نیز بازنشر فرمایید).
📢کانال #دکترامیرمحمدشهسوارانی
🍃🌹🌸💐🌸🌹🍃
@DrAmirMohammadShahsavarani