AIMC Topic: Brain Mapping
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Robotic TMS mapping of motor cortex in the developing brain.
Journal of neuroscience methods
Aug 16, 2018
BACKGROUND: The human motor cortex can be mapped safely and painlessly with transcranial magnetic stimulation (TMS) to explore neurophysiology in health and disease. Human error likely contributes to heterogeneity of such TMS measures. Here, we aimed...
Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study.
Journal of magnetic resonance imaging : JMRI
Aug 5, 2018
BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood-brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine-learning methodolog...
Male
Middle Aged
Severity of Illness Index
Pilot Projects
Female
Magnetic Resonance Imaging
Diabetes Mellitus, Type 2
Humans
Aged
Brain Mapping
Imaging, Three-Dimensional
Aged, 80 and over
Machine Learning
Perfusion
Insulin Resistance
Cross-Sectional Studies
Prospective Studies
Cognition Disorders
Longitudinal Studies
Brain
Linear Models
Shared spatiotemporal category representations in biological and artificial deep neural networks.
PLoS computational biology
Jul 24, 2018
Visual scene category representations emerge very rapidly, yet the computational transformations that enable such invariant categorizations remain elusive. Deep convolutional neural networks (CNNs) perform visual categorization at near human-level ac...
Diagnostic model for attention-deficit hyperactivity disorder based on interregional morphological connectivity.
Neuroscience letters
Jul 19, 2018
Previous brain morphology-related diagnostic models for attention-deficit hyperactivity disorder (ADHD) were based on regional features. However, building a model of individual interregional morphological connectivity is a challenging task. This stud...
Neuro-cognitive mechanisms of global Gestalt perception in visual quantification.
NeuroImage
Jul 18, 2018
Recent neuroimaging studies identified posterior regions in the temporal and parietal lobes as neuro-functional correlates of subitizing and global Gestalt perception. Beyond notable overlap on a neuronal level both mechanisms are remarkably similar ...
Classification of ADHD with bi-objective optimization.
Journal of biomedical informatics
Jul 17, 2018
Attention Deficit Hyperactive Disorder (ADHD) is one of the most common diseases in school aged children. In this paper, we consider using fMRI data with classification techniques to aid the diagnosis of ADHD and propose a bi-objective ADHD classific...
Abnormal Low-Frequency Oscillations Reflect Trait-Like Pain Ratings in Chronic Pain Patients Revealed through a Machine Learning Approach.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Jul 16, 2018
Measures of moment-to-moment fluctuations in brain activity of an individual at rest have been shown to be a sensitive and reliable metric for studying pathological brain mechanisms across various chronic pain patient populations. However, the relati...
Language processing from the perspective of electrical stimulation mapping.
Cognitive neuropsychology
Jul 12, 2018
Electrical Stimulation (ES) is a neurostimulation technique that is used to localize language functions in the brain of people with intractable epilepsy and/or brain tumors. We reviewed 25 ES articles published between 1984 and 2018 and interpreted t...
Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy.
Seizure
Jul 10, 2018
PURPOSE: Quasi-stable electrical distribution in EEG called microstates could carry useful information on the dynamics of large scale brain networks. Using machine learning techniques we explored if abnormalities in microstates can identify patients ...
Reproducibility of importance extraction methods in neural network based fMRI classification.
NeuroImage
Jun 30, 2018
Recent advances in machine learning allow faster training, improved performance and increased interpretability of classification techniques. Consequently, their application in neuroscience is rapidly increasing. While classification approaches have p...
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