AIMC Topic: Brain

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The phobic brain: Morphometric features correctly classify individuals with small animal phobia.

Psychophysiology
Specific phobia represents an anxiety disorder category characterized by intense fear generated by specific stimuli. Among specific phobias, small animal phobia (SAP) denotes a particular condition that has been poorly investigated in the neuroscient...

Machine learning reveals correlations between brain age and mechanics.

Acta biomaterialia
Our brain undergoes significant micro- and macroscopic changes throughout its life cycle. It is therefore crucial to understand the effect of aging on the mechanical properties of the brain in order to develop accurate personalized simulations and di...

Deep learning-based whole-brain B -mapping at 7T.

Magnetic resonance in medicine
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...

VCU-Net: a vascular convolutional network with feature splicing for cerebrovascular image segmentation.

Medical & biological engineering & computing
Cerebrovascular image segmentation is one of the crucial tasks in the field of biomedical image processing. Due to the variable morphology of cerebral blood vessels, the traditional convolutional kernel is weak in perceiving the structure of elongate...

Deep learning corrects artifacts in RASER MRI profiles.

Magnetic resonance imaging
A newly developed magnetic resonance imaging (MRI) approach is based on "Radiowave amplification by the stimulated emission of radiation" (RASER). RASER MRI potentially allows for higher resolution, is inherently background-free, and does not require...

Robust brain MRI image classification with SIBOW-SVM.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting humans. Early detection and classification of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention an...

Exploring potential ADHD biomarkers through advanced machine learning: An examination of audiovisual integration networks.

Computers in biology and medicine
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition marked by inattention and impulsivity, linked to disruptions in functional brain connectivity and structural alterations in large-scale brain networks. Although sensory...

Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN).

Biomolecules
With the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to studying cellular properties and signaling in living cells and tissues. Traditionally, microscopy image ...

Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.

Sensors (Basel, Switzerland)
The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely u...

Automated detection of motion artifacts in brain MR images using deep learning.

NMR in biomedicine
Quality assessment, including inspecting the images for artifacts, is a critical step during magnetic resonance imaging (MRI) data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep le...