AIMC Topic: Brain

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Connectome-based schizophrenia prediction using structural connectivity - Deep Graph Neural Network(sc-DGNN).

Journal of X-ray science and technology
BACKGROUND: Connectome is understanding the complex organization of the human brain's structural and functional connectivity is essential for gaining insights into cognitive processes and disorders.

Super-resolution of diffusion-weighted images using space-customized learning model.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Diffusion-weighted imaging (DWI) is a noninvasive method used for investigating the microstructural properties of the brain. However, a tradeoff exists between resolution and scanning time in clinical practice. Super-resolution has been e...

Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals.

Methods in molecular biology (Clifton, N.J.)
Cell tracking is an essential step in extracting cellular signals from moving cells, which is vital for understanding the mechanisms underlying various biological functions and processes, particularly in organs such as the brain and heart. However, c...

Unveiling New Strategies Facilitating the Implementation of Artificial Intelligence in Neuroimaging for the Early Detection of Alzheimer's Disease.

Journal of Alzheimer's disease : JAD
Alzheimer's disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for A...

Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma.

Methods in molecular biology (Clifton, N.J.)
Peptide therapeutics is gaining momentum. Advances in the field of peptidomics have enabled researchers to harvest vital information from various organisms and tissue types concerning peptide existence, expression and function. The development of mas...

Fractal Geometry Meets Computational Intelligence: Future Perspectives.

Advances in neurobiology
Characterizations in terms of fractals are typically employed for systems with complex and multiscale descriptions. A prominent example of such systems is provided by the human brain, which can be idealized as a complex dynamical system made of many ...

Computational and Translational Fractal-Based Analysis in the Translational Neurosciences: An Overview.

Advances in neurobiology
After the previous sections on "Fractals: What and Why?," the last section of this book covers the software tools necessary to perform computational fractal-based analysis, with special emphasis on its applications into the neurosciences. The use of ...

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach.

Journal of Alzheimer's disease : JAD
BACKGROUND: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming...

Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operator.

Chaos (Woodbury, N.Y.)
We propose a machine-learning approach to construct reduced-order models (ROMs) to predict the long-term out-of-sample dynamics of brain activity (and in general, high-dimensional time series), focusing mainly on task-dependent high-dimensional fMRI ...

The application value of deep learning in the background of precision medicine in glioblastoma.

Science progress
Glioblastoma is a highly malignant central nervous system tumor, World Health Organization Ⅳ, glioblastoma is the most common primary malignancy, due to its own specificity and complexity, different patients often benefit from the current convention...