AIMC Topic: Connectome
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Brain dynamics and temporal trajectories during task and naturalistic processing.
NeuroImage
Nov 16, 2018
Human functional Magnetic Resonance Imaging (fMRI) data are acquired while participants engage in diverse perceptual, motor, cognitive, and emotional tasks. Although data are acquired temporally, they are most often treated in a quasi-static manner. ...
Recognizing Brain States Using Deep Sparse Recurrent Neural Network.
IEEE transactions on medical imaging
Oct 23, 2018
Brain activity is a dynamic combination of different sensory responses and thus brain activity/state is continuously changing over time. However, the brain's dynamical functional states recognition at fast time-scales in task fMRI data have been rare...
Deep Learning Models Unveiled Functional Difference Between Cortical Gyri and Sulci.
IEEE transactions on bio-medical engineering
Sep 28, 2018
It is largely unknown whether there is functional role difference between cortical gyral and sulcal regions. Recent advancements in neuroimaging studies demonstrate clear difference of structural connection profiles in gyral and sulcal areas, suggest...
Resting state dynamics meets anatomical structure: Temporal multiple kernel learning (tMKL) model.
NeuroImage
Sep 27, 2018
Over the last decade there has been growing interest in understanding the brain activity, in the absence of any task or stimulus, captured by the resting-state functional magnetic resonance imaging (rsfMRI). The resting state patterns have been obser...
Predictive connectome subnetwork extraction with anatomical and connectivity priors.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Aug 25, 2018
We present a new method to identify anatomical subnetworks of the human connectome that are optimally predictive of targeted clinical variables, developmental outcomes or disease states. Given a training set of structural or functional brain networks...
Deep learning applied to whole-brain connectome to determine seizure control after epilepsy surgery.
Epilepsia
Aug 10, 2018
OBJECTIVE: We evaluated whether deep learning applied to whole-brain presurgical structural connectomes could be used to predict postoperative seizure outcome more accurately than inference from clinical variables in patients with mesial temporal lob...
Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity.
Scientific reports
Aug 7, 2018
Attention deficit hyperactivity disorder (ADHD) is a common disorder that emerges in school-age children. The diagnostic model based on neuroimaging features could be beneficial for ADHD in twofold: identifying individuals with ADHD and discovering t...
Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients.
Acta psychiatrica Scandinavica
Aug 6, 2018
OBJECTIVE: This study determined the clinical utility of an fMRI classification algorithm predicting medication-class of response in patients with challenging mood diagnoses.
TractSeg - Fast and accurate white matter tract segmentation.
NeuroImage
Aug 4, 2018
The individual course of white matter fiber tracts is an important factor for analysis of white matter characteristics in healthy and diseased brains. Diffusion-weighted MRI tractography in combination with region-based or clustering-based selection ...
Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia.
NeuroImage
Jul 25, 2018
This work presents a novel approach to finding linkage/association between multimodal brain imaging data, such as structural MRI (sMRI) and functional MRI (fMRI). Motivated by the machine translation domain, we employ a deep learning model, and consi...
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