AI Medical Compendium Journal:
Human brain mapping

Showing 91 to 100 of 142 articles

Predicting hemispheric dominance for language production in healthy individuals using support vector machine.

Human brain mapping
We used a Support Vector Machine (SVM) classifier to assess hemispheric pattern of language dominance of 47 individuals categorized as non-typical for language from their hemispheric functional laterality index (HFLI) measured on a sentence minus wor...

Deep learning with convolutional neural networks for EEG decoding and visualization.

Human brain mapping
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but...

Sensory tractography and robot-quantified proprioception in hemiparetic children with perinatal stroke.

Human brain mapping
Perinatal stroke causes most hemiparetic cerebral palsy, resulting in lifelong disability. We have demonstrated the ability of robots to quantify sensory dysfunction in hemiparetic children but the relationship between such deficits and sensory tract...

Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia.

Human brain mapping
Despite decades of research, the anatomical abnormalities associated with developmental dyslexia are still not fully described. Studies have focused on between-group comparisons in which different neuroanatomical measures were generally explored in i...

Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: Applying machine learning techniques to brain imaging data.

Human brain mapping
An important focus of studies of individuals at ultra-high risk (UHR) for psychosis has been to identify biomarkers to predict which individuals will transition to psychosis. However, the majority of individuals will prove to be resilient and go on t...

Disentangling disorders of consciousness: Insights from diffusion tensor imaging and machine learning.

Human brain mapping
Previous studies have suggested that disorders of consciousness (DOC) after severe brain injury may result from disconnections of the thalamo-cortical system. However, thalamo-cortical connectivity differences between vegetative state (VS), minimally...

Making use of longitudinal information in pattern recognition.

Human brain mapping
Longitudinal designs are widely used in medical studies as a means of observing within-subject changes over time in groups of subjects, thereby aiming to improve sensitivity for detecting disease effects. Paralleling an increased use of such studies ...

Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach.

Human brain mapping
Developmental dyslexia has been hypothesized to result from multiple causes and exhibit multiple manifestations, implying a distributed multidimensional effect on human brain. The disruption of specific white-matter (WM) tracts/regions has been obser...

Modality-independent representations of small quantities based on brain activation patterns.

Human brain mapping
Machine learning or MVPA (Multi Voxel Pattern Analysis) studies have shown that the neural representation of quantities of objects can be decoded from fMRI patterns, in cases where the quantities were visually displayed. Here we apply these technique...

Development of the brain's structural network efficiency in early adolescence: A longitudinal DTI twin study.

Human brain mapping
The brain is a network and our intelligence depends in part on the efficiency of this network. The network of adolescents differs from that of adults suggesting developmental changes. However, whether the network changes over time at the individual l...