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Thalamus

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A Deep-Learning Enabled Automatic Fetal Thalamus Diameter Measurement Algorithm.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The analysis of maternal factors that impact the normal development of the fetal thalamus is an emerging field of research and requires the retrospective measurement of fetal thalamus diameter (FTD). Unfortunately, FTD is not measured in routine 2D u...

Altered Blood Oxygen Level-Dependent Signal Stability in the Brain of Patients with Major Depressive Disorder Undergoing Resting-State Functional Magnetic Resonance Imaging.

Neuropsychobiology
INTRODUCTION: Major depressive disorder (MDD) is a common, relapse-prone psychiatric disorder with unknown pathogenesis. Previous studies on resting-state functional magnetic resonance imaging of MDD have mostly focused on the spontaneous activity of...

Machine learning decoding of single neurons in the thalamus for speech brain-machine interfaces.

Journal of neural engineering
. Our goal is to decode firing patterns of single neurons in the left ventralis intermediate nucleus (Vim) of the thalamus, related to speech production, perception, and imagery. For realistic speech brain-machine interfaces (BMIs), we aim to charact...

An rs-fMRI based neuroimaging marker for adult absence epilepsy.

Epilepsy research
OBJECTIVE: Approximately 20-30 % of epilepsy patients exhibit negative findings on routine magnetic resonance imaging, and this condition is known as nonlesional epilepsy. Absence epilepsy (AE) is a prevalent form of nonlesional epilepsy. This study ...

Predicting tremor improvement after MRgFUS thalamotomy in essential tremor from preoperative spontaneous brain activity: A machine learning approach.

Science bulletin
Magnetic resonance-guided focused ultrasound surgery (MRgFUS) thalamotomy is an emerging technique for medication-refractory essential tremor (ET), but with variable outcomes. This study used pattern regression analysis to identify brain signatures p...

An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits.

IEEE transactions on pattern analysis and machine intelligence
Audio-visual approaches involving visual inputs have laid the foundation for recent progress in speech separation. However, the optimization of the concurrent usage of auditory and visual inputs is still an active research area. Inspired by the corti...

Predicting cortical-thalamic functional connectivity using functional near-infrared spectroscopy and graph convolutional networks.

Scientific reports
Functional near-infrared spectroscopy (fNIRS) measures cortical hemodynamic changes, yet it cannot collect this information from subcortical structures, such as the thalamus, which is involved in several key functional networks. To address this drawb...

Understanding the Spatio-Temporal Coupling of Spikes and Spindles in Focal Epilepsy Through a Network-Level Computational Model.

International journal of neural systems
The electrophysiological findings have shown that epileptiform spikes triggering sleep spindles within 1[Formula: see text]s across multiple channels are commonly observed during sleep in focal epilepsy (FE). Such spatio-temporal couplings of spikes ...

Interpretable deep learning for deconvolutional analysis of neural signals.

Neuron
The widespread adoption of deep learning to model neural activity often relies on "black-box" approaches that lack an interpretable connection between neural activity and network parameters. Here, we propose using algorithm unrolling, a method for in...

Deep learning-based classification of dementia using image representation of subcortical signals.

BMC medical informatics and decision making
BACKGROUND: Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. Early and accurate diagnosis of dementi...