AIMC Topic: Brain
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QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals.
Scientific reports
Nov 9, 2024
The most cost-effective data collection method is electroencephalography (EEG), which obtains meaningful information about the brain. Therefore, EEG signal processing is crucial for neuroscience and machine learning (ML). Therefore, a new EEG stress ...
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...
Automated assessment of brain MRIs in multiple sclerosis patients significantly reduces reading time.
Neuroradiology
Nov 8, 2024
INTRODUCTION: Assessment of multiple sclerosis (MS) lesions on magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. We evaluate whether assessment of new, expanding, and contrast-enhancing MS lesions can be done more time-eff...
Machine-learning based prediction of future outcome using multimodal MRI during early childhood.
Seminars in fetal & neonatal medicine
Nov 7, 2024
The human brain undergoes rapid changes from the fetal stage to two years postnatally, during which proper structural and functional maturation lays the foundation for later cognitive and behavioral development. Multimodal magnetic resonance imaging ...
Depression diagnosis: EEG-based cognitive biomarkers and machine learning.
Behavioural brain research
Nov 6, 2024
Depression is a complex mental illness that has significant effects on people as well as society. The traditional techniques for the diagnosis of depression, along with the potential of nascent biomarkers especially EEG-based biomarkers, are studied....
Improved patient identification by incorporating symptom severity in deep learning using neuroanatomic images in first episode schizophrenia.
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Nov 6, 2024
Brain alterations associated with illness severity in schizophrenia remain poorly understood. Establishing linkages between imaging biomarkers and symptom expression may enhance mechanistic understanding of acute psychotic illness. Constructing model...
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...
G-Protein Signaling in Alzheimer's Disease: Spatial Expression Validation of Semi-supervised Deep Learning-Based Computational Framework.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Nov 6, 2024
Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification of new targets for effective treatments. Recently available large-scale multiomics datasets provide ...
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...
Multi-Loss Disentangled Generative-Discriminative Learning for Multimodal Representation in Schizophrenia.
IEEE journal of biomedical and health informatics
Nov 6, 2024
Schizophrenia (SCZ) is a multifactorial mental illness, thus it will be beneficial for exploring this disease using multimodal data, including functional magnetic resonance imaging (fMRI), genes, and the gut microbiome. Previous studies reported comb...
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