AIMC Topic: Brain

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Robust temporal knowledge inference via pathway snapshots with liquid neural network.

Methods (San Diego, Calif.)
Static graphs play a pivotal role in modeling and analyzing biological and biomedical data. However, many real-world scenarios-such as disease progression and drug pharmacokinetic processes-exhibit dynamic behaviors. Consequently, static graph method...

Self-supervised learning for MRI reconstruction through mapping resampled k-space data to resampled k-space data.

Magnetic resonance imaging
In recent years, significant advancements have been achieved in applying deep learning (DL) to magnetic resonance imaging (MRI) reconstruction, which traditionally relies on fully sampled data. However, real-world clinical scenarios often demonstrate...

A preliminary attempt to harmonize using physics-constrained deep neural networks for multisite and multiscanner MRI datasets (PhyCHarm).

NeuroImage
In magnetic resonance imaging (MRI), variations in scan parameters and scanner specifications can result in differences in image appearance. To minimize these differences, harmonization in MRI has been suggested as a crucial image processing techniqu...

Deep learning-based diffusion MRI tractography: Integrating spatial and anatomical information.

NeuroImage
Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders....

Fractal analysis for cognitive impairment classification in DAVF using machine learning.

Biomedical physics & engineering express
. Intracranial dural arteriovenous fistula (DAVF) is an acquired vascular condition involving abnormal connections between dural arteries and veins without intervening capillary beds. Cognitive impairment is a common symptom in DAVFs, often linked to...

Synergistic Pathways of Modulation Enable Robust Task Packing Within Neural Dynamics.

Neural computation
Understanding how brain networks learn and manage multiple tasks simultaneously is of interest in both neuroscience and artificial intelligence. In this regard, a recent research thread in theoretical neuroscience has focused on how recurrent neural ...

Brain Age Prediction: Deep Models Need a Hand to Generalize.

Human brain mapping
Predicting brain age from T1-weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep learning models have shown success in reducing the mean absolute error (MAE) of predicted brain age, concerns abou...

An Explainable Connectome Convolutional Transformer for Multimodal Autism Spectrum Disorder Classification.

International journal of neural systems
The diagnosis of autism spectrum disorder (ASD) is often hampered by its heterogeneity and reliance on time-consuming behavioral assessments. Automated neuroimaging-based diagnostic tools offer a promising alternative, but multi-site data integration...

The Silent Transformation of Stereotactic Brain Biopsies After the Introduction of Robotics.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: In frame-based stereotaxy, the design of the frame limits trajectory selection, e.g., to the temporal lobe and posterior fossa. We hypothesise that frame-less neuronavigation and robotic technology might have expanded these stereotactic c...

Insights on Scan-Specific Deep-Learning Strategies for Brain MRI Parallel Imaging Reconstruction.

NMR in biomedicine
Scan-specific deep learning strategies have been proposed for parallel imaging reconstruction in which auto-calibrated signals (ACS) are used for training. Here, we introduce methods to objectively optimize architecture and training details. In addit...