AIMC Topic: Brain

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Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.

NeuroImage Apr 20, 2023
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding ...
Humans Magnetic Resonance Imaging Brain Benchmarking Reproducibility of Results Deep Learning
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Accelerated submillimeter wave-encoded magnetic resonance imaging via deep untrained neural network.

Medical physics Apr 19, 2023
BACKGROUND: Wave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) metho...
Neural Networks, Computer Brain Magnetic Resonance Imaging Humans Algorithms Motion Image Processing, Computer-Assisted
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Complex computation from developmental priors.

Nature communications Apr 19, 2023
Machine learning (ML) models have long overlooked innateness: how strong pressures for survival lead to the encoding of complex behaviors in the nascent wiring of a brain. Here, we derive a neurodevelopmental encoding of artificial neural networks th...
Biological Evolution Neural Networks, Computer Brain Machine Learning Neurons
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From local counterfactuals to global feature importance: efficient, robust, and model-agnostic explanations for brain connectivity networks.

Computer methods and programs in biomedicine Apr 16, 2023
BACKGROUND: Explainable artificial intelligence (XAI) is a technology that can enhance trust in mental state classifications by providing explanations for the reasoning behind artificial intelligence (AI) models outputs, especially for high-dimension...
Technology Brain Humans Artificial Intelligence
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Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation.

European radiology Apr 15, 2023
OBJECTIVES: To verify the reliability of the volumes automatically segmented using a new artificial intelligence (AI)-based application and evaluate changes in the brain and CSF volume with healthy aging.
Magnetic Resonance Imaging Cerebrospinal Fluid Adult Aging Humans Artificial Intelligence Brain Reproducibility of Results
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Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.

Journal of neurosurgery Apr 14, 2023
OBJECTIVE: Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state...
Brain Mapping Rest Deep Learning Magnetic Resonance Imaging Humans Brain Adult Brain Neoplasms
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Benchmarking explanation methods for mental state decoding with deep learning models.

NeuroImage Apr 13, 2023
Deep learning (DL) models find increasing application in mental state decoding, where researchers seek to understand the mapping between mental states (e.g., experiencing anger or joy) and brain activity by identifying those spatial and temporal feat...
Brain Deep Learning Humans Artificial Intelligence Benchmarking Magnetic Resonance Imaging Brain Mapping
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Learning long-term motor timing/patterns on an orthogonal basis in random neural networks.

Neural networks : the official journal of the International Neural Network Society Apr 12, 2023
The ability of the brain to generate complex spatiotemporal patterns with specific timings is essential for motor learning and temporal processing. An approach that can model this function, using the spontaneous activity of a random neural network (R...
Time Factors Neural Networks, Computer Brain Nerve Net Learning
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Deep learning classification of reading disability with regional brain volume features.

NeuroImage Apr 11, 2023
Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contribute to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited ...
Comprehension Humans Child Brain Dyslexia Neuroimaging Deep Learning
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Stereology neuron counts correlate with deep learning estimates in the human hippocampal subregions.

Scientific reports Apr 11, 2023
Hippocampal subregions differ in specialization and vulnerability to cell death. Neuron death and hippocampal atrophy have been a marker for the progression of Alzheimer's disease. Relatively few studies have examined neuronal loss in the human brain...
Humans Brain Neurons Hippocampus Deep Learning Image Processing, Computer-Assisted
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