AIMC Topic: Connectome
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Modeling Task fMRI Data Via Deep Convolutional Autoencoder.
IEEE transactions on medical imaging
Jun 15, 2017
Task-based functional magnetic resonance imaging (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neura...
Mechanisms of Connectome Development.
Trends in cognitive sciences
Jun 10, 2017
At the centenary of D'Arcy Thompson's seminal work 'On Growth and Form', pioneering the description of principles of morphological changes during development and evolution, recent experimental advances allow us to study change in anatomical brain net...
Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning.
Scientific reports
Mar 28, 2017
There is growing interest in the potential of neuroimaging to help develop non-invasive biomarkers in neurodegenerative diseases. In this study, connection-wise patterns of functional connectivity were used to distinguish Parkinson's disease patients...
Multistability of the Brain Network for Self-other Processing.
Scientific reports
Mar 3, 2017
Early fMRI studies suggested that brain areas processing self-related and other-related information were highly overlapping. Hypothesising functional localisation of the cortex, researchers have tried to locate "self-specific" and "other-specific" re...
Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning.
The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry
Feb 8, 2017
OBJECTIVES: One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity. In this study, we propose a n...
Metrics of brain network architecture capture the impact of disease in children with epilepsy.
NeuroImage. Clinical
Dec 12, 2016
BACKGROUND AND OBJECTIVE: Epilepsy is associated with alterations in the structural framework of the cerebral network. The aim of this study was to measure the potential of global metrics of network architecture derived from resting state functional ...
Detection of Mild Traumatic Brain Injury by Machine Learning Classification Using Resting State Functional Network Connectivity and Fractional Anisotropy.
Journal of neurotrauma
Nov 21, 2016
Traumatic brain injury (TBI) may adversely affect a person's thinking, memory, personality, and behavior. While mild TBI (mTBI) diagnosis is challenging, there is a risk for long-term psychiatric, neurologic, and psychosocial problems in some patient...
Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networks.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Oct 2, 2016
High-grade glioma (HGG) is a lethal cancer, which is characterized by very poor prognosis. To help optimize treatment strategy, accurate preoperative prediction of HGG patient's outcome (i.e., survival time) is of great clinical value. However, there...
Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study.
Psychological medicine
Jul 25, 2016
BACKGROUND: Early diagnosis of schizophrenia could improve the outcomes and limit the negative effects of untreated illness. Although participants with schizophrenia show aberrant functional connectivity in brain networks, these between-group differe...
The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA.
NeuroImage
Mar 23, 2016
Resting state functional network connectivity (rsFNC) derived from functional magnetic resonance (fMRI) imaging is emerging as a possible biomarker to identify several brain disorders. Recently it has been pointed out that methods used to preprocess ...
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