AIMC Topic: Neural Pathways

Clear Filters Showing 131 to 140 of 163 articles

Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.

International journal of geriatric psychiatry
OBJECTIVE: Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate ...

Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI.

International journal of neural systems
Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mecha...

Bridging the gap between motor imagery and motor execution with a brain-robot interface.

NeuroImage
According to electrophysiological studies motor imagery and motor execution are associated with perturbations of brain oscillations over spatially similar cortical areas. By contrast, neuroimaging and lesion studies suggest that at least partially di...

Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques.

Cerebral cortex (New York, N.Y. : 1991)
Conventional mass-univariate analyses have been previously used to test for group differences in neural signals. However, machine learning algorithms represent a multivariate decoding approach that may help to identify neuroimaging patterns associate...

Mapping trait justice sensitivity in the Brain: Whole-brain resting-state functional connectivity as a predictor of other-oriented not self-oriented justice sensitivity.

Cognitive, affective & behavioral neuroscience
Justice sensitivity (JS) reflects personal concern and commitment to the principle of justice, showing considerable heterogeneity among the general population. Despite a growing interest in the behavioral characteristics of JS over the past decades, ...

Functional connectome-based predictive modeling of suicidal ideation.

Journal of affective disorders
Suicide represents an egregious threat to society despite major advancements in medicine, in part due to limited knowledge of the biological mechanisms of suicidal behavior. We apply a connectome predictive modeling machine learning approach to ident...

Cerebellar circuit computations for predictive motor control.

Nature reviews. Neuroscience
The rise of the deep neural network as the workhorse of artificial intelligence has brought increased attention to how network architectures serve specialized functions. The cerebellum, with its largely shallow, feedforward architecture, provides a c...

Abnormalities of brain dynamics based on large-scale cortical network modeling in autism spectrum disorder.

Neural networks : the official journal of the International Neural Network Society
Synaptic increase is a common phenomenon in the brain of autism spectrum disorder (ASD). However, the impact of increased synapses on the neurophysiological activity of ASD remains unclear. To address this, we propose a large-scale cortical network m...

Cortical-subcortical neural networks for motor learning and storing sequence memory.

Neural networks : the official journal of the International Neural Network Society
Motor sequence learning relies on the synergistic collaboration of multiple brain regions. However, most existing models for motor sequence learning primarily focus on functional-level analyses of sequence memory mechanisms, providing limited neuroph...

From resting-state functional hippocampal centrality to functional outcome: An extended neurocognitive model of psychosis.

Psychiatry research
BACKGROUND: We previously proposed a neurocognitive model of psychosis in which reduced morphometric hippocampal-cortical connectivity precedes impaired episodic memory, social cognition, negative symptoms, and functional outcome. We provided support...