♻️کشف علت #نقص #توجه و #ضعف #عمکرد در بزرگسالان دارای اختلالات طیف #بیش_فعالی و #نقض_توجه (#ADHD)
Increased #default-mode #variability is related to reduced #task-performance and is evident in #adults with #ADHD
پژوهشگران روانشناسی و روانپزشکی دانشگاه اسلو 🇳🇴 و دانشکاه کینگزکالج 🇬🇧 در پژوهشی مشترک دریافتند افزایش تغییر در #وضعیت #شبکه #پایه #مغز (#DMN) منجر به کاهش #دقت و افت #عملکرد در افراد دارای ADHD شده و در بزرگسالی نیز ادامه دارد.
🔬در این پژوهش آزمایشی که بر 20 بزرگسال دارای ADHD و 27 بزرگسال سالم در سنین 18 تا 40 سال صورت گرفت، آزمون های #کارکردهای #اجرایی و نیز #fMRI درکنار #آزمایش #خون بعمل آمدند. همچنین، افراد دارای ADHD بصورت نامشخص هم بعد از مصرف #میتل_فنیدیت (#MPH) و هم بعد از مصرف #دارونما مورد ارزیابی قرار گرفتند.
📚نتایج نشان دادند که فعالیت DMN در افراد دارای ADHD بدون مصرف دارو بسیار بیشتر از افراد سالم است. همچنین ارتباط عملکردی بین شبکه های توجه و DMN به کارکرد و وضعیت کنترل داوطلب نیز وابسته بود. بر این اساس بنظر می رسد #دارودرمانی نمی تواند تمامی #مدارهای #عصبی را در افراد دارای ADHD کنترل نماید؛ بویژه فرآیندهای #کنترل #تکانه و #تصمیمگیری که متیل فنیدیت بر آنها بی تاثیر است.
Abstract
#Insufficient #suppression and #connectivity of the #default #mode #network (#DMN) is a potential #mediator of #cognitive #dysfunctions across various disorders, including #attention #deficit/#hyperactivity #disorder (#ADHD). However, it remains unclear if alterations in #sustained DMN suppression, variability and connectivity during prolonged #cognitive #engagement are implicated in #adult ADHD #pathophysiology, and to which degree #methylphenidate (#MPH) remediates any DMN #abnormalities. This #randomized, #double-blinded, #placebo-controlled, #cross-over #clinical #trial of #MPH (clinicaltrials.gov/ct2/show/NCT01831622) explored large-scale brain network dynamics in 20 adults with ADHD on and off MPH, compared to 27 healthy controls, while performing a reward based #decision-making task. DMN task-related #activation, variability, and connectivity were estimated and compared between groups and conditions using #independent #component #analysis, #dual #regression, and #Bayesian #linear #mixed #models. The results show that the DMN exhibited more variable activation patterns in unmedicated patients compared to healthy controls. Group differences in functional connectivity both between and within functional networks were evident. Further, functional connectivity between and within attention and DMN networks was sensitive both to task performance and case-control status. MPH altered within-network connectivity of the DMN and visual networks, but not between-network #connectivity or #temporal variability. This study thus provides novel #fMRI evidence of reduced sustained DMN suppression in adults with ADHD during value-based decision-making, a pattern that was not alleviated by MPH. We infer from multiple analytical approaches further support to the #default #mode #interference #hypothesis, in that higher DMN activation variability is evident in adult ADHD and associated with lower task performance.
لینک منبع 👇🏻(further reading)👇🏻
http://www.sciencedirect.com/science/article/pii/S2213158217300682?via%3Dihub
✅(در صورت جذابیت و علاقمندی به موضوع، مطلب را برای دیگران نیز بازنشر فرمایید).
📢کانال #دکترامیرمحمدشهسوارانی
🍃🌹🌸💐🌸🌹🍃
@DrAmirMohammadShahsavarani
Increased #default-mode #variability is related to reduced #task-performance and is evident in #adults with #ADHD
پژوهشگران روانشناسی و روانپزشکی دانشگاه اسلو 🇳🇴 و دانشکاه کینگزکالج 🇬🇧 در پژوهشی مشترک دریافتند افزایش تغییر در #وضعیت #شبکه #پایه #مغز (#DMN) منجر به کاهش #دقت و افت #عملکرد در افراد دارای ADHD شده و در بزرگسالی نیز ادامه دارد.
🔬در این پژوهش آزمایشی که بر 20 بزرگسال دارای ADHD و 27 بزرگسال سالم در سنین 18 تا 40 سال صورت گرفت، آزمون های #کارکردهای #اجرایی و نیز #fMRI درکنار #آزمایش #خون بعمل آمدند. همچنین، افراد دارای ADHD بصورت نامشخص هم بعد از مصرف #میتل_فنیدیت (#MPH) و هم بعد از مصرف #دارونما مورد ارزیابی قرار گرفتند.
📚نتایج نشان دادند که فعالیت DMN در افراد دارای ADHD بدون مصرف دارو بسیار بیشتر از افراد سالم است. همچنین ارتباط عملکردی بین شبکه های توجه و DMN به کارکرد و وضعیت کنترل داوطلب نیز وابسته بود. بر این اساس بنظر می رسد #دارودرمانی نمی تواند تمامی #مدارهای #عصبی را در افراد دارای ADHD کنترل نماید؛ بویژه فرآیندهای #کنترل #تکانه و #تصمیمگیری که متیل فنیدیت بر آنها بی تاثیر است.
Abstract
#Insufficient #suppression and #connectivity of the #default #mode #network (#DMN) is a potential #mediator of #cognitive #dysfunctions across various disorders, including #attention #deficit/#hyperactivity #disorder (#ADHD). However, it remains unclear if alterations in #sustained DMN suppression, variability and connectivity during prolonged #cognitive #engagement are implicated in #adult ADHD #pathophysiology, and to which degree #methylphenidate (#MPH) remediates any DMN #abnormalities. This #randomized, #double-blinded, #placebo-controlled, #cross-over #clinical #trial of #MPH (clinicaltrials.gov/ct2/show/NCT01831622) explored large-scale brain network dynamics in 20 adults with ADHD on and off MPH, compared to 27 healthy controls, while performing a reward based #decision-making task. DMN task-related #activation, variability, and connectivity were estimated and compared between groups and conditions using #independent #component #analysis, #dual #regression, and #Bayesian #linear #mixed #models. The results show that the DMN exhibited more variable activation patterns in unmedicated patients compared to healthy controls. Group differences in functional connectivity both between and within functional networks were evident. Further, functional connectivity between and within attention and DMN networks was sensitive both to task performance and case-control status. MPH altered within-network connectivity of the DMN and visual networks, but not between-network #connectivity or #temporal variability. This study thus provides novel #fMRI evidence of reduced sustained DMN suppression in adults with ADHD during value-based decision-making, a pattern that was not alleviated by MPH. We infer from multiple analytical approaches further support to the #default #mode #interference #hypothesis, in that higher DMN activation variability is evident in adult ADHD and associated with lower task performance.
لینک منبع 👇🏻(further reading)👇🏻
http://www.sciencedirect.com/science/article/pii/S2213158217300682?via%3Dihub
✅(در صورت جذابیت و علاقمندی به موضوع، مطلب را برای دیگران نیز بازنشر فرمایید).
📢کانال #دکترامیرمحمدشهسوارانی
🍃🌹🌸💐🌸🌹🍃
@DrAmirMohammadShahsavarani
clinicaltrials.gov
Influence of Stimulant Medication on Brain Processes for Decision Making in Attention Deficit Hyperactivity Disorder - Full Text…
Influence of Stimulant Medication on Brain Processes for Decision Making in Attention Deficit Hyperactivity Disorder - Full Text View.
♻️#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