AIMC Topic: Brain

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Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data.

NeuroImage
Accurate prediction of depressive symptoms in healthy individuals can enable early intervention and reduce both individual and societal costs. This study aimed to develop predictive models for depression in young adults using machine learning (ML) te...

Decoding the Narcissistic Brain.

NeuroImage
There is a substantial knowledge gap in the narcissism literature: <1 % of the nearly 12,000 articles on narcissism have addressed its neural basis. To help fill this gap, we asked whether the multifacetedness of narcissism could be decoded from spon...

Towards real time efficient and robust ECoG decoding for mobile brain-computer interface.

Journal of neural engineering
. Decoding locomotion-related brain activities from electrocorticographic (ECoG) signals is essential in brain-computer interfaces (BCIs). Most previous ECoG decoders are computationally demanding and sensitive to noises/outliers. Mobile and robust B...

Differential dementia detection from multimodal brain images in a real-world dataset.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Artificial intelligence (AI) models have been applied to differential dementia detection tasks in brain images from curated, high-quality benchmark databases, but not real-world data in hospitals.

Physiological Confounds in BOLD-fMRI and Their Correction.

NMR in biomedicine
Functional magnetic resonance imaging (fMRI) has opened new frontiers in neuroscience by instrumentally driving our understanding of brain function and development. Despite its substantial successes, fMRI studies persistently encounter obstacles stem...

Volumetric and Diffusion Tensor Imaging Abnormalities Are Associated With Behavioral Changes Post-Concussion in a Youth Pig Model of Mild Traumatic Brain Injury.

NMR in biomedicine
Mild traumatic brain injury (mTBI) caused by sports-related incidents in children and youth often leads to prolonged cognitive impairments but remains difficult to diagnose. In order to identify clinically relevant imaging and behavioral biomarkers a...

Improving Imaging Field of View of 3-D Transcranial Rat Brain Super-Resolution With Robotic Registered Compounding and Nonrigid Deformation Correction.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Large field-of-view (FOV) brain imaging with ultrasound has become increasingly achievable with the application of 2-D probes capable of volumetric imaging. However, even in small animals the skull presents a significant barrier and conventional plan...

Brain Fractal Dimension and Machine Learning can predict first-episode psychosis and risk for transition to psychosis.

Computers in biology and medicine
Although there are notable structural abnormalities in the brain associated with psychotic diseases, it is still unclear how these abnormalities relate to clinical presentation. However, the fractal dimension (FD), which offers details on the complex...

Relational Bi-level aggregation graph convolutional network with dynamic graph learning and puzzle optimization for Alzheimer's classification.

Computers in biology and medicine
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive cognitive decline, necessitating early diagnosis for effective treatment. This study presents the Relational Bi-level Aggregation Graph Convolutional Network with...

Unraveling the neural dynamics of mathematical interference in english reading: A novel approach with deep learning and fNIRS data.

Brain research bulletin
English has emerged as the predominant global language, driving efforts to optimize its acquisition through interdisciplinary cognitive research. While behavioral studies suggest a link between English learning and mathematical cognition, the neural ...