Alzheimer’s disease (AD) is a progressive chronic illness that leads to cognitive decline and dementia. Neuroimaging technologies, such as functional magnetic resonance imaging (fMRI), deep learning approaches offer promising avenues for AD classification. In this study, we investigate the use of fMRI-based connectivity (FC) measures, including Pearson correlation coefficient (PCC), maximal inf...

Diagnosing Autism spectrum disorder (ASD) is a challenging task for clinicians due to the inconsistencies in existing medical tests. The Internet of things (IoT) has been used several applications realize advancements healthcare industry. Using machine learning tandem IoT can enhance monitoring and detection ASD. To date, most ASD studies have relied primarily on operational connectivity struct...

Functional magnetic resonance imaging (fMRI) studies consistently demonstrate altered neural activation in youth experiencing anxiety and depression a way that is distinct from adult-onset disorders. However, there paucity of research systematically reviewing this, no meta-analyses have been conducted using Activation Likelihood Estimation (ALE). The present study systematic literature search t...

Presurgical brain mapping of language-eloquent cortex aims to minimize its injury during neurosurgery in patients with tumors and drug-resistant epilepsy, thereby, preserve their quality life. Two main goals language are identify the localization lateralization regions involved language. Gold standards for them intraoperative Wada test, respectively; however, due some limitations these techniqu...

Brain functional network (BFN) has become an important tool for the analysis and diagnosis of brain diseases, how to build a high-quality BFN based on resting-state magnetic resonance imaging (rs-fMRI) growing concern in neuroscience community. Although some methods have been proposed construct BFN, they only encode spatial characteristics ROIs, ignoring temporal characteristics. As result, it ...

Time-series data are amongst the most widely-used in biomedical sciences, including domains such as functional Magnetic Resonance Imaging (fMRI). Structure within time series can be captured by tools of topological analysis (TDA). Persistent homology is mostly commonly used data-analytic tool TDA, and effectively summarize complex high-dimensional into an interpretable 2-dimensional representat...

Abstract Background Functional mapping of eloquent brain areas is crucial for preoperative planning in patients with tumors. Resting state functional MRI (rs-fMRI) allows the localization without need task performance, making it well-suited pediatric population. In this study independent component analysis (ICA) rs-fMRI results are reported a group 22 supratentorial Additionally, connectivity (...

Combined arterial spin labeling (ASL) and functional magnetic resonance imaging (fMRI) can reveal more comprehensive properties of the spatiotemporal quantitative brain networks. Imaging markers end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) will be sought from these properties. The current multimodal classification methods often neglect to collect high-order relat...

Head motions during functional magnetic resonance imaging (fMRI) impair fMRI data quality and introduce systematic artifacts that can affect interpretation of fMRI results. Electroencephalography (EEG) recordings performed simultaneously with fMRI provide high-temporal-resolution information about ongoing brain activity as well as head movements. Recently, an EEG-assisted retrospective motion c...

Introduction In functional neuroimaging, it is of great interest to combine EEG and fMRI, to take advantage of the high temporal resolution of EEG and high spatial resolution of fMRI [1]. For the integration, it is desirable to acquire EEG and fMRI in a single session to avoid possible discrepancies due to different environmental and cognitive states in separate examinations. However, simultane...