AIMC Topic: Brain

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A novel channel reduction concept to enhance the classification of motor imagery tasks in brain-computer interface systems.

PloS one
Electroencephalogram (EEG) signals play a critical role in advancing brain-computer interface (BCI) systems, particularly for detecting motor imagery (MI) movements. However, analysing large volume of EEG datasets faces some challenges due to redunda...

EEG-SGENet: A lightweight convolutional network integrating SGE for motor imagery brain-computer interfaces.

Neuroscience
In recent years, there has been a significant increase in research activity on electroencephalography (EEG)-based motor imagery brain-computer interfaces (MI-BCI) in the field of deep learning. However, despite achieving high accuracy, the size of mo...

Breaking down the ear-brain dichotomy: the effects of age-related hearing loss on the cortical language system.

NeuroImage
Older individuals frequently experience hearing difficulties, often due to sensorineural hearing loss (HL) primarily originating in the inner ear. However, it is not uncommon for older adults with HL to also exhibit impairments in speech intelligibil...

In-silico comparison of a diffusion model with conventionally trained deep networks for translating 64mT to 3T brain FLAIR.

Scientific reports
Deep learning (DL) methods are increasingly applied to address the low signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of low-field MRI (LFMRI). This study evaluates the potential of diffusion models for LFMRI enhancement, comparing the...

Spec2VolCAMU-Net: a spectrogram-to-volume model for EEG-to-fMRI reconstruction based on Multi-directional Time-Frequency Convolutional Attention Encoder and Vision-Mamba U-Net.

Journal of neural engineering
High-resolution functional magnetic resonance imaging (fMRI) is essential for mapping human brain activity; however, it remains costly and logistically challenging. If comparable volumes could be generated directly from widely available scalp electro...

Multimodal contrastive learning on rs-fMRI to quantify whole-brain network recovery after hypothalamic hamartoma surgery.

Biomedical engineering online
INTRODUCTION: Epilepsy due to hypothalamic hamartoma (HH) is associated with epileptic encephalopathy and often requires surgical intervention, as medications are ineffective at reducing the seizures. However, the first step of disentangling the impa...

Multitarget neurostimulation of the deep brain: clinical opportunities, challenges, and emerging technologies.

Journal of neural engineering
Recent computational, pre-clinical, and clinical studies have demonstrated the potential for using neuromodulation through simultaneous targeting of multiple deep brain regions. This approach has already been used for therapeutic and systems neurosci...

Brain-derived 5-hydroxymethylcytosine epigenetic scores are related to Alzheimer's disease pathology and cognitive decline.

Acta neuropathologica
BACKGROUND: Cytosine modifications play critical roles in gene regulation and disease pathogenesis. Elucidating novel epigenetic contributions to Alzheimer's disease (AD) could advance diagnostic, prognostic, and therapeutic strategies. 5-hydroxymeth...

CoSpine open access simultaneous cortico-spinal fMRI database of thermal pain and motor tasks.

Scientific data
Simultaneous cortico-spinal functional magnetic resonance imaging (fMRI) enables non-invasive investigation of integrated central nervous system function, but acquisition challenges have restricted the availability of public datasets and slowed the d...

K-operator for Modelling Neurodegeneration: Simulations, fMRI Application, Eigenvalue Analysis and Recurrence Plots.

Journal of medical systems
The brain network damage provoked by a neurological disease can be modelled as the result of the action of an operator, K, acting on the brain, inspired by physics. Here, we explore the matrix formulation of K, analysing eigenvalues and eigenvectors,...