AIMC Topic: Gyrus Cinguli
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Spatial reasoning via recurrent neural dynamics in mouse retrosplenial cortex.
Nature neuroscience
Jun 6, 2025
From visual perception to language, sensory stimuli change their meaning depending on previous experience. Recurrent neural dynamics can interpret stimuli based on externally cued context, but it is unknown whether they can compute and employ interna...
Intelligent classification of major depressive disorder using rs-fMRI of the posterior cingulate cortex.
Journal of affective disorders
Apr 9, 2024
Major Depressive Disorder (MDD) is a widespread psychiatric condition that affects a significant portion of the global population. The classification and diagnosis of MDD is crucial for effective treatment. Traditional methods, based on clinical asse...
Evidence for distinct neuro-metabolic phenotypes in humans.
NeuroImage
Jan 13, 2022
Advances in magnetic resonance imaging have shown how individual differences in the structure and function of the human brain relate to health and cognition. The relationship between individual differences and the levels of neuro-metabolites, however...
Discriminating stress from rest based on resting-state connectivity of the human brain: A supervised machine learning study.
Human brain mapping
Apr 15, 2020
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...
Resting-state anticorrelated networks in Schizophrenia.
Psychiatry research. Neuroimaging
Dec 25, 2018
Converging evidences from different lines of research suggest abnormalities in functional brain connectivity in schizophrenia. While positively correlated brain networks have been well researched, anticorrelated functional connectivity remains under ...
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...
Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion.
Biological psychiatry. Cognitive neuroscience and neuroimaging
Aug 1, 2017
BACKGROUND: Successfully treating illicit drug use has become paramount, yet elusive. Devising specialized treatment interventions could increase positive outcomes, but it is necessary to identify risk factors of poor long-term outcomes to develop sp...
Multivariate representation of food preferences in the human brain.
Brain and cognition
Jan 6, 2016
One major goal in decision neuroscience is to investigate the neuronal mechanisms being responsible for the computation of product preferences. The aim of the present fMRI study was to investigate whether similar patterns of brain activity, reflectin...
Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy.
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Sep 4, 2015
BACKGROUND AND PURPOSE: We analyzed the ability of a machine learning approach that uses diffusion tensor imaging (DTI) structural connectomes to determine lateralization of epileptogenicity in temporal lobe epilepsy (TLE).
Frontal Lobe
Temporal Lobe
Globus Pallidus
Nucleus Accumbens
Occipital Lobe
Connectome
Gyrus Cinguli
Epilepsy, Temporal Lobe
Female
Diffusion Tensor Imaging
Young Adult
Case-Control Studies
Adult
Humans
Imaging, Three-Dimensional
Machine Learning
Male
Support Vector Machine
Brain
Middle Aged
Retrospective Studies
Hippocampus
Granger causality-based synaptic weights estimation for analyzing neuronal networks.
Journal of computational neuroscience
Mar 13, 2015
Granger causality (GC) analysis has emerged as a powerful analytical method for estimating the causal relationship among various types of neural activity data. However, two problems remain not very clear and further researches are needed: (1) The GC ...
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