AIMC Topic: Brain Mapping
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Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis.
Scientific reports
Nov 12, 2024
Positron emission tomography (PET) utilizes radiotracers like [F]fluorodeoxyglucose (FDG) to measure brain activity in health and disease. Performing behavioral tasks between the FDG injection and the PET scan allows the FDG signal to reflect task-re...
Distinct connectivity patterns between perception and attention-related brain networks characterize dyslexia: Machine learning applied to resting-state fMRI.
Cortex; a journal devoted to the study of the nervous system and behavior
Nov 8, 2024
Diagnosis of dyslexia often occurs in late schooling years, leading to academic and psychological challenges. Furthermore, diagnosis is time-consuming, costly, and reliant on arbitrary cutoffs. On the other hand, automated algorithms hold great poten...
A simple but tough-to-beat baseline for fMRI time-series classification.
NeuroImage
Nov 6, 2024
Current neuroimaging studies frequently use complex machine learning models to classify human fMRI data, distinguishing healthy and disordered brains, often to validate new methods or enhance prediction accuracy. Yet, where prediction accuracy is a c...
Modeling Functional Brain Networks for ADHD via Spatial Preservation-Based Neural Architecture Search.
IEEE journal of biomedical and health informatics
Nov 6, 2024
Modeling functional brain networks (FBNs) for attention deficit hyperactivity disorder (ADHD) has sparked significant interest since the abnormal functional connectivity is discovered in certain functional magnetic resonance imaging (fMRI)-based brai...
Deep learning-based whole-brain B -mapping at 7T.
Magnetic resonance in medicine
Oct 27, 2024
PURPOSE: This study investigates the feasibility of using complex-valued neural networks (NNs) to estimate quantitative transmit magnetic RF field (B ) maps from multi-slice localizer scans with different slice orientations in the human head at 7T, a...
Temporal dynamic alterations of regional homogeneity in major depressive disorder: a study integrating machine learning.
Neuroreport
Sep 11, 2024
Previous studies have found alterations in the local regional homogeneity of brain activity in individuals diagnosed with major depressive disorder. However, many studies have failed to consider that even during resting states, brain activity is dyna...
Prediction of anhedonia in patients with first-episode schizophrenia using a Wavelet-ALFF-based Support vector regression model.
Neuroscience
Sep 5, 2024
Anhedonia is one of the core features of the negative symptoms of schizophrenia and can be extremely burdensome. Our study applied resting-state functional magnetic resonance imaging (fMRI)-based support vector regression (SVR) to predict anhedonia i...
A Plug-In Graph Neural Network to Boost Temporal Sensitivity in fMRI Analysis.
IEEE journal of biomedical and health informatics
Sep 5, 2024
Learning-based methods offer performance leaps over traditional methods in classification analysis of high-dimensional functional MRI (fMRI) data. In this domain, deep-learning models that analyze functional connectivity (FC) features among brain reg...
SAD: semi-supervised automatic detection of BOLD activations in high temporal resolution fMRI data.
Magma (New York, N.Y.)
Aug 29, 2024
OBJECTIVE: Despite the prevalent use of the general linear model (GLM) in fMRI data analysis, assuming a pre-defined hemodynamic response function (HRF) for all voxels can lead to reduced reliability and may distort the inferences derived from it. To...
An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping.
Biomedical physics & engineering express
Aug 19, 2024
Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characteriz...
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