AIMC Topic: Brain Mapping

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Age-Specific Diagnostic Classification of ASD Using Deep Learning Approaches.

Studies in health technology and informatics Oct 20, 2023
Autism Spectrum Disorder (ASD) is a highly heterogeneous condition, due to high variance in its etiology, comorbidity, pathogenesis, severity, genetics, and brain functional connectivity (FC). This makes it devoid of any robust universal biomarker. T...
Humans Autism Spectrum Disorder Age Factors Magnetic Resonance Imaging Brain Mapping Deep Learning Child Brain
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Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Identifying subtypes of neuropsychiatric disorders based on characteristics of their brain activity has tremendous potential to contribute to a better understanding of those disorders and to the development of new diagnostic and personalized treatmen...
Schizophrenia Magnetic Resonance Imaging Brain Mapping Brain Deep Learning Humans
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Interhemispheric connections in the maintenance of language performance and prognosis prediction: fully connected layer-based deep learning model analysis.

Neurosurgical focus Jun 1, 2023
OBJECTIVE: Language-related networks have been recognized in functional maintenance, which has also been considered the mechanism of plasticity and reorganization in patients with cerebral malignant tumors. However, the role of interhemispheric conne...
Humans Prognosis Language Transcranial Magnetic Stimulation Aphasia Glioma Brain Neoplasms Brain Mapping Deep Learning Diffusion Tensor Imaging
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Mind-reading machines are here: is it time to worry?

Nature May 1, 2023
Humans Artificial Intelligence Brain Mapping Privacy Thinking
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Interpreting mental state decoding with deep learning models.

Trends in cognitive sciences Nov 1, 2022
In mental state decoding, researchers aim to identify the set of mental states (e.g., experiencing happiness or fear) that can be reliably identified from the activity patterns of a brain region (or network). Deep learning (DL) models are highly prom...
Brain Mapping Neuroimaging Artificial Intelligence Machine Learning Reproducibility of Results Deep Learning Brain Humans
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A neural surveyor to map touch on the body.

Proceedings of the National Academy of Sciences of the United States of America Jan 4, 2022
Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Although it seems straightforward, this simple representation belies the complex link between an activation in a somatotopic map and the associated touc...
Mice Touch Perception Touch Female Models, Neurological Bayes Theorem Somatosensory Cortex Physical Stimulation Young Adult Animals Neurons Brain Mapping Humans Adult Neural Networks, Computer
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Deep learning in resting-state fMRI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Modeling the rich, dynamic spatiotemporal variations captured by human brain functional magnetic resonance imaging (fMRI) data is a complicated task. Analysis at the brain's regional and connection levels provides more straightforward biological inte...
Humans Brain Mapping Machine Learning Brain Magnetic Resonance Imaging Deep Learning
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Single feature spatio-temporal architecture for EEG Based cognitive load assessment.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
The study of electroencephalography (EEG) data for cognitive load analysis plays an important role in identification of stress-inducing tasks. This can be useful in applications such as optimal work allocation, increasing efficiency in the workplace ...
Brain Mapping Electroencephalography Spatio-Temporal Analysis Cognition Neural Networks, Computer
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A CNN and LSTM Network for Eye-Blink Classification from MRI Scanner Monitoring Videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Eye closure changes brain activity, so eye-blink tracking of subjects undergoing resting-state functional magnetic resonance imaging (fMRI) is relevant for identifying when a subject blinks, falls asleep, or keeps their eyes closed. Existing MRI eye-...
Neural Networks, Computer Prospective Studies Magnetic Resonance Imaging Brain Mapping Brain Humans
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A Bayesian optimization approach for rapidly mapping residual network function in stroke.

Brain : a journal of neurology Aug 17, 2021
Post-stroke cognitive and linguistic impairments are debilitating conditions, with limited therapeutic options. Domain-general brain networks play an important role in stroke recovery and characterizing their residual function with functional MRI has...
Image Interpretation, Computer-Assisted Adult Bayes Theorem Male Brain Magnetic Resonance Imaging Middle Aged Stroke Female Humans Brain Mapping Aged Machine Learning
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