634 Results Found

It is becoming increasingly common to collect multiple related neuroimaging datasets either from different modalities or from different tasks and conditions. In addition, we have non-imaging data such as cognitive or behavioral variables, and it is t...

Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In r...

Multimodal neuroimaging has gained traction in Alzheimer’s Disease (AD) diagnosis by integrating information from multiple imaging modalities to enhance classification accuracy. However, effectively handling heterogeneous data sources and overc...

As the number of people diagnosed with movement disorders is increasing, it becomes vital to design techniques that allow the better understanding of human brain in naturalistic settings. There are many brain imaging methods such as fMRI, SPECT, and...

In this paper, we propose a novel Machine Learning Model based on Bayesian Linear Regression intended to deal with the low sample-to-variable ratio typically found in neuroimaging studies and focusing on mental disorders. The proposed model combines...

We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of t...

Background: To date, no one has prospectively evaluated yoga intervention-induced changes in brain structure or function in adults with acquired brain injuries (ABI). Thus, this study was conducted to test the feasibility of acquiring neuroimaging da...

The prognosis and treatment of patients suffering from Alzheimer’s disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify...

Using computers to numerically simulate large-scale neuronal networks has become a common method for studying the mechanism of the human brain, and neuroimaging has brought forth multimodal brain data. Determining how to fully consider these multimod...

Mucolipidosis type II (MLII), an ultra-rare lysosomal storage disorder, manifests as a fatal multi-systemic disease. Mental inhibition and progressive neurodegeneration are commonly reported disease manifestations. Nevertheless, longitudinal data on...

Background/Objectives: The growing incidence of cognitive impairment among older adults has a significant impact on individuals, family members, caregivers, and society. Current conventional cognitive assessment tools are faced with some limitations....

Deep learning has shown significant value in automating radiological diagnostics but can be limited by a lack of generalizability to external datasets. Leveraging the geometric principles of non-Euclidean space, certain geometric deep learning approa...

The development of in vivo neuroimaging technology has led to an incredible amount of digital information concerning the brain [...]

This study explores the potential of graph neural networks (GNNs) in analyzing brain networks of children and adolescents exposed to trauma, addressing limitations in traditional neuroimaging approaches. MRI-based brain data from trauma-exposed and c...

In the field of medical imaging, one of the most extended research setups consists of the comparison between two groups of images, a pathological set against a control set, in order to search for statistically significant differences in brain activit...

Stereotactic brain tumor segmentation based on 3D neuroimaging data is a challenging task due to the complexity of the brain architecture, extreme heterogeneity of tumor malformations, and the extreme variability of intensity signal and noise distrib...

Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundatio...

The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI’s cutting-edge algorithms—ranging...

Background and Objective: Dementia is a broad term for a complex range of conditions that affect the brain, such as Alzheimer’s disease (AD). Dementia affects a lot of people in the elderly community, hence there is a huge demand to better unde...

Background/Objectives: Autobiographical memory (AM) is typically viewed in terms of comprising episodic (EAM) and semantic (SAM) components. Despite the emergence of numerous meta-analyses, the literature on these constructs remains fragmented. We ai...

Objective: This study aimed to investigate potential specific molecular and neuroimaging biomarkers for stroke subtype recurrence to improve secondary stroke prevention. Methods: A retrospective analysis was conducted on a prospective stroke biobank....

Background: A clearer understanding is needed about the use of brain MRI in non-HIV patients with cryptococcal meningitis. Methods: Cerebral CT and MRI were studied in 62 patients in a multicenter study of cryptococcal meningitis in non-HIV patients....

Background/Objectives: This study presents a survey-based evaluation of an explainable AI (Feature-Augmented) approach, which was designed to support the diagnosis of Alzheimer’s disease (AD) by integrating clinical data, MMSE scores, and MRI s...

Appropriately handling and switching exploration of novel knowledge and exploitation of existing knowledge is a fundamental element of genuine innovation in society. Moreover, a mounting number of studies have suggested that such “ambidexterity...

Background/Objectives: Artificial intelligence (AI) models for Alzheimer’s disease (AD) diagnosis often face the challenge of limited explainability, hindering their clinical adoption. Previous studies have relied on full-scale MRI, which incre...

We developed a novel quantification method named “shape feature” by combining the features of amyloid positron emission tomography (PET) and brain magnetic resonance imaging (MRI) and evaluated its significance in predicting the conversio...

Samantaray et al [...]

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous...

Background/Objectives: Artificial intelligence (AI) is transforming neuroimaging by enhancing diagnostic precision and treatment planning. However, its applications in pediatric cancer neuroimaging remain limited. This review assesses the current sta...

Artificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders. However, traditional artificial neural networks (ANNs) face challenges in p...

Artificial Intelligence (AI) and deep learning models have revolutionized diagnosis, prognostication, and treatment planning by extracting complex patterns from medical images, enabling more accurate, personalized, and timely clinical decisions. Desp...

This perspective paper explores the untapped potential of artificial intelligence (AI), particularly machine learning-based dimension reduction techniques in multimodal neuroimaging analysis of Long COVID fatigue. The complexity and high dimensionali...

We performed a systematic literature review of neuroimaging, predominantly focusing on magnetic resonance imaging (MRI) findings associated with neurological manifestations of coronavirus disease-2019 (COVID-19). We screened articles from PubMed, Goo...

Anderson–Fabry disease (AFD) is a genetic sphingolipidosis involving virtually the entire body. Among its manifestation, the involvement of the central and peripheral nervous system is frequent. In recent decades, it has become evident that, be...

Background: Ayahuasca is a serotoninergic hallucinogen that plays a central role in the Amazonian traditional medicine. Its psychoactive effects are associated with the presence of N,N-dimethyltryptamine (DMT), and monoamine oxidase inhibitors (MAO-A...

Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pu...

Traditional psychiatric diagnosis has been overly reliant on either self-reported measures (introspection) or clinical rating scales (interviews). This produced the so-called explanatory gap with the bio-medical disciplines, such as neuroscience, whi...

Prader–Willi syndrome (PWS) is a rare disease determined by the loss of the paternal copy of the 15q11-q13 region, and it is characterized by hypotonia, hyperphagia, obesity, short stature, hypogonadism, craniofacial dysmorphisms, and cognitive and b...

Headache is a common symptom during pregnancy and in puerperium that requires careful consideration, as it may be caused by a life-threatening condition. Headaches in pregnant women and women in puerperium are classified as primary or secondary; acut...

Neuroimaging has emerged as an innovative and essential tool for understanding the intricate relationship between brain development, emotions, and behavior. Investigating the neurobiological mechanisms underlying this interaction during the critical...

Background/Objectives: Breast cancer chemotherapy patients and survivors face cognitive side effects that are not fully understood. Neuroimaging can provide a unique way to study these effects; however, it can be difficult to recruit large numbers of...

Following the recent acquisition of unprecedented anatomical details through state-of-the-art neuroimaging, stereotactic procedures such as microelectrode recording (MER) or deep brain stimulation (DBS) can now rely on direct and accurately individua...

As our understanding of the brain continues to advance, so too does the demand for sophisticated tools that can model, simulate, and interpret the intricate data generated by contemporary neuroimaging and electrophysiological techniques [...]

Clinicians often base their predictions of walking and arm recovery on multiple predictors. Multivariate prediction models may assist clinicians to make accurate predictions. Several reviews have been published on the prediction of motor recovery aft...

The availability of powerful non-invasive neuroimaging techniques has given rise to various studies that aim to map the human brain. These studies focus on not only finding brain activation signatures but also on understanding the overall organizatio...

The purpose of the present study was to conduct a systematic review of the literature, adhering to PRISMA guidelines, regarding evidence of neuroplasticity in children and adolescents in response to cognitive or sensory-motor interventions. Twenty-ei...

Multi-label brain segmentation from brain magnetic resonance imaging (MRI) provides valuable structural information for most neurological analyses. Due to the complexity of the brain segmentation algorithm, it could delay the delivery of neuroimaging...

The etiology of epilepsy is variable and sometimes multifactorial. Clinical course and response to treatment largely depend on the precise etiology of the seizures. Along with the electroencephalogram (EEG), neuroimaging techniques, in particular, ma...

Proprioceptive impairments occur in ~50% of stroke survivors, with 20–40% still impaired six months post-stroke. Early identification of those likely to have persistent impairments is key to personalizing rehabilitation strategies and reducing...

Little is known about the brain correlates of anosognosia or unawareness of disease in Parkinson’s Disease (PD) and Huntington’s Disease (HD). The presence of unawareness or impaired self-awareness (ISA) of illness has profound implicatio...