Abstract A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with ...Abstract A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network （DMN）, its function has been associated with memory processing, and it has been referred to as the parietal memory network （PMN）. Independent component analysis （ICA） is the most common data-driven method used to extract PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determi- nation in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high model order across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.展开更多
Attention deficit/hyperactivity disorder(ADHD)is a common disorder among children.ADHD often prevails into adulthood,unless proper treatments are facilitated to engage self-regulatory systems.Thus,there is a need for ...Attention deficit/hyperactivity disorder(ADHD)is a common disorder among children.ADHD often prevails into adulthood,unless proper treatments are facilitated to engage self-regulatory systems.Thus,there is a need for effective and reliable mechanisms for the early identification of ADHD.This paper presents a decision support system for the ADHD identification process.The proposed system uses both functional magnetic resonance imaging(fMRI)data and eye movement data.The classification processes contain enhanced pipelines,and consist of pre-processing,feature extraction,and feature selection mechanisms.fMRI data are processed by extracting seed-based correlation features in default mode network(DMN)and eye movement data using aggregated features of fixations and saccades.For the classification using eye movement data,an ensemble model is obtained with 81%overall accuracy.For the fMRI classification,a convolutional neural network(CNN)is used with 82%accuracy for the ADHD identification.Both ensemble models are proved for overfitting avoidance.展开更多
基金supported by the National Basic Research(973)Program(2015CB351702)the National Natural Science Foundation of China(81571756,81270023,81278412,81171409,81000583,81471740,81220108014)+2 种基金Beijing Nova Program(XXJH2015B079 to Z.Y.)the Outstanding Young Investigator Award of Institute of Psychology,Chinese Academy of Sciences(to Z.Y.)the Key Research Program and the Hundred Talents Program of the Chinese Academy of Sciences(KSZD-EW-TZ-002 to X.N.Z)
文摘Abstract A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network （DMN）, its function has been associated with memory processing, and it has been referred to as the parietal memory network （PMN）. Independent component analysis （ICA） is the most common data-driven method used to extract PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determi- nation in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high model order across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.
基金This work was supported by Old Dominion University,Norfolk,Virginia,USA and University of Moratuwa,Sri Lanka.We thank the participants of the system usability study.
文摘Attention deficit/hyperactivity disorder(ADHD)is a common disorder among children.ADHD often prevails into adulthood,unless proper treatments are facilitated to engage self-regulatory systems.Thus,there is a need for effective and reliable mechanisms for the early identification of ADHD.This paper presents a decision support system for the ADHD identification process.The proposed system uses both functional magnetic resonance imaging(fMRI)data and eye movement data.The classification processes contain enhanced pipelines,and consist of pre-processing,feature extraction,and feature selection mechanisms.fMRI data are processed by extracting seed-based correlation features in default mode network(DMN)and eye movement data using aggregated features of fixations and saccades.For the classification using eye movement data,an ensemble model is obtained with 81%overall accuracy.For the fMRI classification,a convolutional neural network(CNN)is used with 82%accuracy for the ADHD identification.Both ensemble models are proved for overfitting avoidance.
基金supported by the National Natural Science Foundation of China(No.8140147330600181)+1 种基金the Open Project Program of Zhejiang Province Key Laboratory of Mental Disorder’s ManagementChina(No.2014E10007)
