AIMC Topic: Brain

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Prediction of postoperative visual cognitive impairment using graph theory and machine learning based on resting-state brain networks.

BMC medical imaging
BACKGROUND: Visual cognitive impairment is among the most common postoperative cognitive dysfunctions, significantly impacting recovery and quality of life in elderly patients. However, effective preoperative prediction methods remain lacking. We dev...

Spiking dynamics of individual neurons reflect changes in the structure and function of neuronal networks.

Nature communications
Brain networks exhibit diverse topological structures to adapt and support brain functions. The changes in neuronal network architecture can lead to alterations in neuronal spiking activity, yet how individual neuronal behavior reflects network struc...

A hybrid filtering and deep learning approach for early Alzheimer's disease identification.

Scientific reports
Alzheimer's disease is a progressive neurological disorder that profoundly affects cognitive functions and daily activities. Rapid and precise identification is essential for effective intervention and improved patient outcomes. This research introdu...

Exploring Neural Idiosyncrasies in Response to Autonomous Sensory Meridian Response Videos: Naturalistic Functional Magnetic Resonance Imaging Study of Stress and Sensory Processing.

Journal of medical Internet research
BACKGROUND: Autonomous sensory meridian response (ASMR) videos have been increasingly popularized as accessible tools for stress relief. Despite widespread media coverage promoting their benefits, empirical research on the neural mechanisms underlyin...

A new low-rank adaptation method for brain structure and metastasis segmentation via decoupled principal weight direction and magnitude.

Scientific reports
Deep learning techniques have become pivotal in medical image segmentation, but their success often relies on large, manually annotated datasets, which are expensive and labor-intensive to obtain. Additionally, different segmentation tasks frequently...

A novel contrastive Dual-Branch Network (CDB-Net) for robust EEG-Based Alzheimer's disease diagnosis.

Brain research
Alzheimer's Disease (AD) is neurodegenerative disorder that causes cognitive decline, memory loss, confusion, and changes in behavior. Early and accurate detection is important for timely intervention, current diagnostic methods can be slow, expensiv...

Machine learning approach to DNA methylation and neuroimaging signatures as biomarkers for psychological resilience in young adults.

Behavioural brain research
Psychological resilience is influenced by both psychological and biological factors. However, the potential of using DNA methylation (DNAm) probes and brain imaging variables to predict psychological resilience remains unclear. This study aimed to in...

Post-stroke aphasia analysis using topological alterations in brain functional networks.

Journal of neural engineering
. Nearly one-third of stroke patients develop aphasia. Although the function of classical language areas (e.g. Broca's area, Wernicke's area) has been widely characterized, the network reorganization mechanisms behind specific language dysfunctions i...

Normal Pressure Hydrocephalus Classification using Weakly-Supervised Local Feature Extraction.

Computers in biology and medicine
Normal Pressure Hydrocephalus (NPH) presents diagnostic challenges because its symptoms often overlap with other neurological conditions. A key radiological NPH indicator is ventricular cerebrospinal fluid (CSF) volume, assessed by neuroradiologists ...

Machine learning approach effectively discriminates between Parkinson's disease and progressive supranuclear palsy: Multi-level indices of rs-fMRI.

Brain research bulletin
AIM: Parkinson's disease (PD) and progressive supranuclear palsy (PSP) present similar clinical symptoms, but their treatment options and clinical prognosis differ significantly. Therefore, we aimed to discriminate between PD and PSP based on multi-l...