AIMC Topic: Cerebrovascular Circulation

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Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis.

Computational intelligence and neuroscience
Functional near-infrared spectroscopy (fNIRS) is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convo...

Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain.

Human brain mapping
Recent reports of multivariate machine learning (ML) techniques have highlighted their potential use to detect prognostic and diagnostic markers of pain. However, applications to date have focussed on acute experimental nociceptive stimuli rather tha...

"MR Fingerprinting for Imaging Brain Hemodynamics and Oxygenation".

Journal of magnetic resonance imaging : JMRI
Over the past decade, several studies have explored the potential of magnetic resonance fingerprinting (MRF) for the quantification of brain hemodynamics, oxygenation, and perfusion. Recent advances in simulation models and reconstruction frameworks ...

Exploring Neural Dynamics in the Auditory Telencephalon of Crows Using Functional Ultrasound Imaging.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Crows, renowned for advanced cognitive abilities and vocal communication, rely on intricate auditory systems. While the neuroanatomy of corvid auditory pathways is partially explored, the underlying neurophysiological mechanisms are largely unknown. ...

A novel MRI-based deep learning imaging biomarker for comprehensive assessment of the lenticulostriate artery-neural complex.

BMC medical imaging
OBJECTIVES: To develop a deep learning network for extracting features from the blood-supplying regions of the lenticulostriate artery (LSA) and to establish these features as an imaging biomarker for the comprehensive assessment of the lenticulostri...

Artificial Intelligence in Transcranial Doppler Ultrasonography.

Current medical imaging
Transcranial Doppler is an instrumental ultrasound method capable of providing data on various brain pathologies, in particular, the study of cerebral hemodynamics in stroke, quickly, economically, and with repeatability of the data themselves. Howev...

Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics.

Cerebral cortex (New York, N.Y. : 1991)
Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to neuronal oscillations through the neurovascular coupling mechanism. Given the vascul...

[Accuracy of Classification of Cerebral Blood Flow Reduction Patterns Using Statistical Analysis Images Generated with Simulated SPECT Datasets via Deep Learning].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: The aim of this study was to evaluate the classification accuracy of specific blood flow reduction patterns in clinical images by deep learning using simulation data.

Reducing False Alarm Rates in Neonatal Intensive Care: A New Machine Learning Approach.

Advances in experimental medicine and biology
UNLABELLED: In neonatal intensive care units (NICUs), 87.5% of alarms by the monitoring system are false alarms, often caused by the movements of the neonates. Such false alarms are not only stressful for the neonates as well as for their parents and...