AIMC Topic: Default Mode Network

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Test-retest reliability of spatial patterns from resting-state functional MRI using the restricted Boltzmann machine and hierarchically organized spatial patterns from the deep belief network.

Journal of neuroscience methods Oct 15, 2019
BACKGROUND: Restricted Boltzmann machines (RBMs), including greedy layer-wise trained RBMs as part of a deep belief network (DBN), have the ability to identify spatial patterns (SPs; functional networks) in resting-state fMRI (rfMRI) data. However, t...
Default Mode Network Reproducibility of Results Adult Nerve Net Pattern Recognition, Automated Connectome Echo-Planar Imaging Neural Networks, Computer Brain Humans
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Prediction of Verbal Abilities From Brain Connectivity Data Across the Lifespan Using a Machine Learning Approach.

Human brain mapping Apr 1, 2025
Compared to nonverbal cognition such as executive or memory functions, language-related cognition generally appears to remain more stable until later in life. Nevertheless, different language-related processes, for example, verbal fluency versus voca...
Humans Machine Learning Magnetic Resonance Imaging Connectome Young Adult Aptitude Brain Middle Aged Adult Nerve Net Default Mode Network Adolescent Female Vocabulary Aged Male Aged, 80 and over Aging
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Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics.

Cerebral cortex (New York, N.Y. : 1991) Jan 5, 2021
Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to neuronal oscillations through the neurovascular coupling mechanism. Given the vascul...
Connectome Cerebrovascular Circulation Electroencephalography Rest Blood Vessels Default Mode Network Rats Magnetic Resonance Imaging Models, Neurological Brain Mapping Male Image Processing, Computer-Assisted Animals Algorithms Female Young Adult Adult Humans Neural Networks, Computer
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Identification of the Neural Circuit Underlying Episodic Memory Deficit in Amnestic Mild Cognitive Impairment via Machine Learning on Gray Matter Volume.

Journal of Alzheimer's disease : JAD Jan 1, 2021
Based on whole-brain gray matter volume (GMV), we used relevance vector regression to predict the Rey's Auditory Verbal Learning Test Delayed Recall (AVLT-DR) scores of individual amnestic mild cognitive impairment (aMCI) patient. The whole-brain GMV...
Gray Matter Humans Cerebral Cortex Female Neuropsychological Tests Machine Learning Aged Memory, Episodic Brain Amnesia Male Cognitive Dysfunction Default Mode Network China Magnetic Resonance Imaging
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