AIMC Topic: Amygdala
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Deep learning-based classification of dementia using image representation of subcortical signals.
BMC medical informatics and decision making
Mar 6, 2025
BACKGROUND: Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. Early and accurate diagnosis of dementi...
Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children.
Brain research bulletin
Jan 9, 2025
BACKGROUND: Due to the importance and the consequences of anxiety, the goals of the current study are brain mapping, biomarker identification and the use of an assessment method for diagnosis of anxiety during emotional face in preschool children.
Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI.
Progress in neuro-psychopharmacology & biological psychiatry
May 11, 2024
It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted r...
Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study.
Human brain mapping
Nov 8, 2022
Previous studies have explored resting-state functional connectivity (rs-FC) of the amygdala in patients with autism spectrum disorder (ASD). However, it remains unclear whether there are frequency-specific FC alterations of the amygdala in ASD and w...
Predicting pediatric anxiety from the temporal pole using neural responses to emotional faces.
Scientific reports
Aug 18, 2021
A prominent cognitive aspect of anxiety is dysregulation of emotional interpretation of facial expressions, associated with neural activity from the amygdala and prefrontal cortex. We report machine learning analysis of fMRI results supporting a key ...
High resolution automated labeling of the hippocampus and amygdala using a 3D convolutional neural network trained on whole brain 700 μm isotropic 7T MP2RAGE MRI.
Human brain mapping
Jan 25, 2021
Image labeling using convolutional neural networks (CNNs) are a template-free alternative to traditional morphometric techniques. We trained a 3D deep CNN to label the hippocampus and amygdala on whole brain 700 μm isotropic 3D MP2RAGE MRI acquired a...
Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning.
Cell
Dec 16, 2020
Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which ha...
Prediction of 7-year's conversion from subjective cognitive decline to mild cognitive impairment.
Human brain mapping
Oct 8, 2020
Subjective cognitive decline (SCD) is a high-risk yet less understood status before developing Alzheimer's disease (AD). This work included 76 SCD individuals with two (baseline and 7 years later) neuropsychological evaluations and a baseline T1-weig...
Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning.
NeuroImage. Clinical
Apr 22, 2020
Intrinsic connectivity networks (ICNs), including the default mode network (DMN), the central executive network (CEN), and the salience network (SN) have been shown to be aberrant in patients with posttraumatic stress disorder (PTSD). The purpose of ...
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...
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