Humans can now emulate language in silica-based neural networks, but we remain ignorant about how language emerged in carbon-based neural networks in the first place. This gap represents not merely a scientific blind spot, but a unique opportunity to...
Rome IV disorders of gut-brain interaction (DGBI) subtypes are known to be unstable and demonstrate high rates of non-treatment response, likely indicating patient heterogeneity. Cluster analysis, a type of unsupervised machine learning, can identify...
Deep Learning (DL) and Machine Learning (ML) algorithms are adept at managing and classifying a wide range of data formats, including time series, text, and images, addressing challenges in both supervised and unsupervised learning. However, the prac...
Remarkably, human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and perspective. While previous studies have delved into this phenomenon, distinguishing the processing ...
. Meditation and mindfulness are increasingly recognized as important in improving mental well-being. However, electroencephalography (EEG)-based neurofeedback systems supporting these practices typically fail to generalize to unseen subjects. This s...
INTRODUCTION: T1w/T2w ratio mapping, combining voxel-wise signal intensities in T1-weighted (T1w) and T2-weighted (T2w) structural MRI, has been used to investigate cortical architecture in the brain, but has also shown promise in tissue discriminati...
BACKGROUND AND OBJECTIVES: The brain-predicted age difference (brain-PAD) is a novel marker of neurodegeneration in multiple sclerosis (MS). Brain-PAD has been associated with clinical disability in heterogeneous MS patient cohorts of varying ages an...
. Modeling neural encoding of visual stimuli often uses deep neural networks (DNNs) to predict human brain response to external stimuli. However, each DNN depends on networks tailored for computer vision tasks, resulting in suboptimal brain correspon...
Multiple sclerosis (MS) is a chronic autoimmune disease characterized by inflammatory brain lesions, making MRI-based lesion segmentation challenging due to noise, missing data, and limited availability of high-quality labeled images. This paper pres...
BACKGROUND: This study aimed to evaluate the efficacy of a convolutional neural network (CNN) model in estimating fetal brain age from MRI scans during second and third trimesters.
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