Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain&rsquo...
The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures...
Are your perceptions, memories and observations merely a statistical fluctuation arising from of the thermal equilibrium of the universe, bearing no correlation to the actual past state of the universe? Arguments are given in the literature for and a...
Background: Previous research has found that mindfulness-based techniques are beneficial for reducing stress in heavy-drinking individuals. However, the underlying neurobiology of these stress-reducing effects are unclear. Moreover, much of the resea...
Aim: To develop a microsimulation model to assess the potential health economic impact of software-assisted MRI in detecting disease activity or progression in relapsing-remitting multiple sclerosis (RRMS) patients. Methods: We develop a simulated de...
Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal...
Genetic Algorithms (GA) are a powerful set of tools for search and optimization that mimic the process of natural selection, and have been used successfully in a wide variety of problems, including evolving neural networks to solve cognitive tasks. D...
After more than 30 years since its inception, the utility of brain imaging for understanding and diagnosing mental illnesses is in doubt, receiving well-grounded criticisms from clinical practitioners. Symptom-based correlational approaches have stru...
Characterizing complex heterogeneous structures in high-dimensional data remains a significant challenge. Traditional approaches often rely on summary statistics such as histograms, skewness, or kurtosis, which—despite their simplicity—ar...
We aim to prove Poincaré inequalities for a class of pure jump Markov processes inspired by the model introduced by Galves and Löcherbach to describe the behavior of interacting brain neurons. In particular, we consider neurons with degen...
The detection of a brain tumor through magnetic resonance imaging (MRI) is still challenging when the image is in low quality. Image segmentation could be done to provide a clear brain tumor area as the region of interest. In this study, we propose a...
In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal imag...
In theoretical biology, we are often interested in random dynamical systems—like the brain—that appear to model their environments. This can be formalized by appealing to the existence of a (possibly non-equilibrium) steady state, whose density prese...
Convolutional neural networks (CNNs) have been used widely to predict biological brain age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on spatially local features and their aggregates and barely on the connective informa...
This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG dat...
This study develops an atlas-based automated framework for segmenting infants’ brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant’s brain at the isointense age (6–12 mont...
Background: Previous studies have demonstrated that mind wandering during incubation phases enhances post-incubation creative performance. Recent empirical evidence, however, has highlighted a specific form of mind wandering closely related to creati...
The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequenc...
Multimodal brain signal analysis has shown great potential in decoding complex cognitive processes, particularly in the challenging task of inner speech recognition. This paper introduces an innovative I nner Speech Recognition via Cross-Perception (...
The cyclic alternating pattern is the periodic electroencephalogram activity occurring during non-rapid eye movement sleep. It is a marker of sleep instability and is correlated with several sleep-related pathologies. Considering the connection betwe...
Stereoscopic displays can induce visual discomfort despite their wide application. Electroencephalography (EEG) technology has been applied to assess 3D visual discomfort, because it can capture brain activities with high temporal resolution. Previou...
In this study, we aimed to evaluate the effectiveness of a brain robot in rehabilitation that combines motor imagery (MI), robotic motor assistance, and electrical stimulation. Thirteen in-patients with severe post-stroke hemiplegia underwent electro...
Tai Chi (TC) practice has been shown to improve both cognitive and physical function in older adults. However, the neural mechanisms underlying the benefits of TC remain unclear. Our primary aims are to explore whether distinct age-related and TC-pra...
Diagnosing schizophrenia using Electroencephalograph (EEG) signals is a challenging task due to the subtle and overlapping differences between patients and healthy individuals. To overcome this difficulty, deep learning has shown strong potential, es...
Anxiety is a widespread mental health issue, and binaural beats have been explored as a potential non-invasive treatment. EEG data reveal changes in neural oscillation and connectivity linked to anxiety reduction; however, harmonics introduced during...
We describe a 7-year-old male with high functioning autism spectrum disorder (ASD) and maternally-inherited rare missense variant of Synaptotagmin-like protein 4 (SYTL4) gene (Xq22.1; c.835C>T; p.Arg279Cys) and an unknown missense variant of Trans...
Bayesian wavelet regression offers a mathematically grounded framework for modeling neuro-retinal signals that exhibit substantial noise, high dimensionality, and complex variations across temporal and spatial scales. Such characteristics are inheren...