In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at bas...
International journal of neural systems
May 28, 2020
The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography...
Relatively little is known about how the human brain identifies movement of objects while the observer is also moving in the environment. This is, ecologically, one of the most fundamental motion processing problems, critical for survival. To study t...
Thalamo-cortical pathology plays key roles in both generalized and focal epilepsies, but there is little work directly comparing these syndromes at the level of whole-brain mechanisms. Using multimodal imaging, connectomics, and computational simulat...
Computational and mathematical methods in medicine
May 18, 2020
Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specif...
Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to ...
We describe our connectomics pipeline for processing anterograde tracer injection data for the brain of the common marmoset (Callithrix jacchus). Brain sections were imaged using a batch slide scanner (NanoZoomer 2.0-HT) and we used artificial intell...
International journal of neural systems
Apr 18, 2020
Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the "black box problem"). In this paper, we train a convolutional neural network (CNN)...
Acute stress induces large-scale neural reorganization with relevance to stress-related psychopathology. Here, we applied a novel supervised machine learning method, combining the strengths of a priori theoretical insights with a data-driven approach...
A 360-area surface-based cortical parcellation is extended to study mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy control (HC) using the joint human connectome project multi-modal parcellation (JHCPMMP) proposed by us. We ...
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