AIMC Topic: Brain

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Integrated multi omics and machine learning reveal mitochondrial immunometabolic networks in sepsis associated encephalopathy.

Scientific reports
Sepsis-associated encephalopathy (SAE) is a major complication in intensive care units, characterized by diffuse brain dysfunction due to systemic inflammation. Despite advances in critical care medicine, SAE remains a key factor in poor patient outc...

An efficient deep learning network for brain stroke detection using salp shuffled shepherded optimization.

Scientific reports
Brain strokes (BS) are potentially life-threatening cerebrovascular conditions and the second highest contributor to mortality. They include hemorrhagic and ischemic strokes, which vary greatly in size, shape, and location, posing significant challen...

Alzheimer's disease classification using a hybrid deep learning approach with multi-layer U-net segmentation and XAI driven analysis.

PloS one
Alzheimer's disease (AD) is a neurodegenerative illness causing a significant decrease in cognitive function, and early, accurate diagnosis is of great therapeutic and diagnostic value. Currently, there is promising potential for applying various typ...

Beyond tractography in brain connectivity mapping with dMRI morphometry and functional networks.

Brain structure & function
Traditional brain connectivity studies have focused mainly on structural connectivity, often relying on tractography with diffusion MRI (dMRI) to reconstruct white matter pathways. In parallel, studies of functional connectivity have examined correla...

Utilizing cues from developmental neurogenesis and gliogenesis for better brain models.

Artificial cells, nanomedicine, and biotechnology
With a globally ageing population, neurodegenerative disease poses an increasingly greater risk to health span, yet there are still no curative treatments. Efficient biomimetic modelling is the underlying target for improving preclinical-to-clinical ...

BrainProt v3.0: An Integrative and Simplified Omics-Based Knowledge-Base About the Human Brain and Its Associated Diseases.

Journal of proteome research
The advancements in neuroscience research and omics technologies generate extensive data for brain-related diseases and disorders that are scattered across various manuscript repositories and databases, potentially hindering global initiatives to adv...

Neuromodulation and neural networks in psychiatric disorders: current status and emerging prospects.

Psychological medicine
Psychiatric disorders lead to disability, premature mortality and economic burden, highlighting the urgent need for more effective treatments. The understanding of psychiatric disorders as conditions of large-scale brain networks has created new oppo...

Machine learning based classification of imagined speech electroencephalogram data from the amplitude and phase spectrum of frequency domain EEG signal.

Biomedical physics & engineering express
Imagined speech classification involves decoding brain signals to recognize verbalized thoughts or intentions without actual speech production. This technology has significant implications for individuals with speech impairments, offering a means to ...

Steady-state neuron-predominant LINE-1 encoded ORF1p protein and LINE-1 RNA increase with aging in the mouse and human brain.

eLife
Recent studies have established a reciprocal causal link between aging and the activation of transposable elements, characterized in particular by a de-repression of LINE-1 retrotransposons. These LINE-1 elements represent 21% of the human genome, bu...

Machine Learning-Based Classification of White Matter Functional Changes in Stroke Patients Using Resting-State fMRI.

Brain topography
Neuroimaging studies of brain function are important research methods widely applied to stroke patients. Currently, a large number of studies have focused on functional imaging of the gray matter cortex. Relevant research indicates that certain areas...