AIMC Topic: Brain

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Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-min resting electroencephalogram (EEG) recording from which featu...

Editorial for the Special Issue "Sensing Brain Activity Using EEG and Machine Learning".

Sensors (Basel, Switzerland)
Sensing brain activity to reveal, analyze and recognize brain activity patterns has become a topic of great interest and ongoing research [...].

Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time.

Magma (New York, N.Y.)
PURPOSE: Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete sc...

Perineuronal Net Microscopy: From Brain Pathology to Artificial Intelligence.

International journal of molecular sciences
Perineuronal nets (PNN) are a special highly structured type of extracellular matrix encapsulating synapses on large populations of CNS neurons. PNN undergo structural changes in schizophrenia, epilepsy, Alzheimer's disease, stroke, post-traumatic co...

Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping.

Magma (New York, N.Y.)
OBJECTIVE: Quantitative susceptibility mapping (QSM) provides an estimate of the magnetic susceptibility of tissue using magnetic resonance (MR) phase measurements. The tissue magnetic susceptibility (source) from the measured magnetic field distribu...

Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learning.

Computers in biology and medicine
Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL methods usually use a stationary distance, such as Euclidean distanc...

An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network.

Computers in biology and medicine
Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) si...

Viability leads to the emergence of gait transitions in learning agile quadrupedal locomotion on challenging terrains.

Nature communications
Quadruped animals are capable of seamless transitions between different gaits. While energy efficiency appears to be one of the reasons for changing gaits, other determinant factors likely play a role too, including terrain properties. In this articl...

A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions.

Scientific reports
This work presents a deep learning approach for rapid and accurate muscle water T with subject-specific fat T calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Ph...

Identifying Patients with CSF-Venous Fistula Using Brain MRI: A Deep Learning Approach.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Spontaneous intracranial hypotension is an increasingly recognized condition. Spontaneous intracranial hypotension is caused by a CSF leak, which is commonly related to a CSF-venous fistula. In patients with spontaneous intrac...