AIMC Topic: Brain

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Mapping brain-wide activity networks: brainways as a tool for neurobiological discovery.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Identifying brain-wide neural circuits and targeting these areas for neuropharmacological interventions are significant challenges in contemporary neuroscience. Traditional methods for registering and quantifying fluorescence in brain slices are labo...

Meta-Analysis Informed Functional Connectomes Representations for Depression Identification.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Meta-analyses in neuroimaging have gained popularity. However, their clinical utility remains uncertain. Convergent masks, containing repeated clusters from publications, are often focal and small, and voxel-wise features can lead to the ...

Enhancing motor imagery EEG classification with a Riemannian geometry-based spatial filtering (RSF) method.

Neural networks : the official journal of the International Neural Network Society
Motor imagery (MI) refers to the mental simulation of movements without physical execution, and it can be captured using electroencephalography (EEG). This area has garnered significant research interest due to its substantial potential in brain-comp...

SynMSE: A multimodal similarity evaluator for complex distribution discrepancy in unsupervised deformable multimodal medical image registration.

Medical image analysis
Unsupervised deformable multimodal medical image registration often confronts complex scenarios, which include intermodality domain gaps, multi-organ anatomical heterogeneity, and physiological motion variability. These factors introduce substantial ...

Parcellation of individual brains: From group level atlas to precise mapping.

Neuroscience and biobehavioral reviews
Individual brains vary greatly in morphology, connectivity, and organization. Group-level brain parcellations, which do not account for individual variations in brain parcels, are increasingly limited in their applicability, especially given the rapi...

A CVAE-based generative model for generalized B inhomogeneity corrected chemical exchange saturation transfer MRI at 5 T.

NeuroImage
Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful tool to image endogenous or exogenous macromolecules. CEST contrast highly depends on radiofrequency irradiation B level. Spatial inhomogeneity of...

Deconvolution of cell types and states in spatial multiomics utilizing TACIT.

Nature communications
Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning increasingly plays a role, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in...

Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps.

The Journal of international medical research
ObjectiveCompared with anatomical magnetic resonance imaging modalities, metabolite images from magnetic resonance spectroscopic imaging often suffer from low quality and detail due to their larger voxel sizes. Conventional interpolation techniques a...

Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes.

Medical image analysis
Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI frame...

Tailored self-supervised pretraining improves brain MRI diagnostic models.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Self-supervised learning has shown potential in enhancing deep learning methods, yet its application in brain magnetic resonance imaging (MRI) analysis remains underexplored. This study seeks to leverage large-scale, unlabeled public brain MRI datase...