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IEEE Transactions on Neural Systems and... 2023Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the detection of brain disorders such as autism spectrum disorder based on various...
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the detection of brain disorders such as autism spectrum disorder based on various machine/deep learning techniques. Learning-based methods typically rely on functional connectivity networks (FCNs) derived from blood-oxygen-level-dependent time series of rs-fMRI data to capture interactions between brain regions-of-interest (ROIs). Graph neural networks have been recently used to extract fMRI features from graph-structured FCNs, but cannot effectively characterize spatiotemporal dynamics of FCNs, e.g., the functional connectivity of brain ROIs is dynamically changing in a short period of time. Also, many studies usually focus on single-scale topology of FCN, thereby ignoring the potential complementary topological information of FCN at different spatial resolutions. To this end, in this paper, we propose a multi-scale dynamic graph learning (MDGL) framework to capture multi-scale spatiotemporal dynamic representations of rs-fMRI data for automated brain disorder diagnosis. The MDGL framework consists of three major components: 1) multi-scale dynamic FCN construction using multiple brain atlases to model multi-scale topological information, 2) multi-scale dynamic graph representation learning to capture spatiotemporal information conveyed in fMRI data, and 3) multi-scale feature fusion and classification. Experimental results on two datasets show that MDGL outperforms several state-of-the-art methods.
Topics: Humans; Autism Spectrum Disorder; Brain; Brain Diseases; Machine Learning; Magnetic Resonance Imaging
PubMed: 37643109
DOI: 10.1109/TNSRE.2023.3309847 -
Neurological Sciences : Official... May 2019In the last 20 years, we observed significant improvements in the use of magnetic resonance imaging (MRI) for the evaluation of patients affected by migraine. Before... (Review)
Review
In the last 20 years, we observed significant improvements in the use of magnetic resonance imaging (MRI) for the evaluation of patients affected by migraine. Before these technological advances, knowledge of the pathogenesis of migraine was particularly based on clinical assessment. Complementary to clinical evaluation, conventional MRI provides both specific information for differential diagnosis (particularly if cortical or subcortical lesions are detected in the migrainous brain) and unsurpassable opportunities in migraine research. However, the correlations between brain structural and functional alterations and both the clinical manifestation of the disease and the individual history of the patient remains uncertain. Both quantitative and functional MR-based techniques have a great potential to better provide insights into human brain structures and possible links between brain areas and complex brain networks that could be involved in the pathophysiology of migraine. Morphometric and functional MRI approaches are contributing to better elucidate the mechanisms that underlie the pain mechanisms and functional adaptation in migraine patients. All these information support the view of migraine as a complex brain disorder involving different cortical and subcortical areas.
Topics: Brain; Brain Diseases; Diagnosis, Differential; Humans; Magnetic Resonance Imaging; Migraine Disorders; Nerve Net
PubMed: 30906964
DOI: 10.1007/s10072-019-03851-1 -
Archives of Neurology Mar 1998
Topics: Brain Diseases; Humans; Myotonic Dystrophy
PubMed: 9520000
DOI: 10.1001/archneur.55.3.291 -
The International Journal of... Aug 2022The human brain is one of the most complicated biological structure in the entire universe. It is incredibly challenging to see how it functions, mainly during... (Review)
Review
OBJECTIVES
The human brain is one of the most complicated biological structure in the entire universe. It is incredibly challenging to see how it functions, mainly during scatter-brain, and when diseases occur in human beings. The notable contribution from consultants, academic institutions, researchers, and others have contributed significant findings in the field of neuroscience and made substantial changes in different dimensions. The exploration of the brain is becoming an emerging area due to the rapid advancement of neuroimaging techniques.
METHODS
Brain disorder is one such significant disease that is very difficult to diagnose, notably, which influence many people worldwide.
RESULTS
In this review, we explore various tools and software to construc and analyze brain networks and different brain scanning methods. Further, this study addresses the research work related to brain networks, including various graph theory methods. More specifically, we present the different diagnostics techniques in brain disorders.
CONCLUSION
This review provides a detailed understanding of brain scans, tools, and brain network construction methods. Lastly, applications of complex network theory will contribute new insights about brain structure and function.
Topics: Brain; Brain Diseases; Humans; Nerve Net; Neuroimaging; Neurosciences
PubMed: 33058738
DOI: 10.1080/00207454.2020.1837802 -
International Journal of Law and... 2013In some criminal law cases, the defendant is assessed by a forensic psychiatrist or psychologist within the context of an insanity defense. In this article I argue that... (Review)
Review
In some criminal law cases, the defendant is assessed by a forensic psychiatrist or psychologist within the context of an insanity defense. In this article I argue that specific neuroscientific research can be helpful in improving the quality of such a forensic psychiatric evaluation. This will be clarified in two ways. Firstly, we shall adopt the approach of understanding these forensic assessments as evaluations of the influence of a mental disorder on a defendant's decision-making process. Secondly, I shall point to the fact that researchers in neuroscience have performed various studies over recent years on the influence of specific mental disorders on a patient's decision-making. I argue that such research, especially if modified to decision-making in criminal scenarios, could be very helpful to forensic psychiatric assessments. This kind of research aims to provide insights not merely into the presence of a mental disorder, but also into the actual impact of mental disorders on the decisions defendants have made in regard to their actions.
Topics: Brain Diseases; Criminal Psychology; Decision Making; Expert Testimony; Humans; Insanity Defense; Mental Competency; Mental Disorders; Neuropsychological Tests; Violence
PubMed: 23433730
DOI: 10.1016/j.ijlp.2013.01.001 -
IEEE Journal of Biomedical and Health... Apr 2022Functional connectivity (FC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in automated identification of...
Functional connectivity (FC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in automated identification of brain disorders, such as Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). To generate compact representations of FC networks, various thresholding methods have been designed for FC network analysis. However, these studies usually use a pre-defined threshold or connection percentage to threshold whole FC networks, thus ignoring the diversity of temporal correlation (e.g., strong associations) between brain regions in subject groups. In this work, we propose a distribution-guided network thresholding learning (DNTL) method for FC network analysis in brain disorder identification with rs-fMRI. Specifically, for each connection of a pair of brain regions, we propose to determine its specific threshold based on the distribution of connection strength (i.e., temporal correlation) between subject groups (e.g., patients and normal controls). The proposed DNTL can adaptively yield an FC-specific threshold for each connection in an FC network, thus preserving diversity of temporal correlation among different brain regions. Experiment results on 365 subjects from two datasets (i.e., ADNI and ADHD-200) suggest that the DNT method outperforms state-of-the-art methods in brain disorder identification with rs-fMRI data.
Topics: Brain; Brain Diseases; Brain Mapping; Humans; Magnetic Resonance Imaging; Neural Pathways
PubMed: 34428167
DOI: 10.1109/JBHI.2021.3107305 -
Revue Neurologique Dec 2019Nodding syndrome (NS) is a progressive encephalopathy of children and adolescents characterized by seizures, including periodic vertical head nodding. Epidemic NS, which... (Review)
Review
Nodding syndrome (NS) is a progressive encephalopathy of children and adolescents characterized by seizures, including periodic vertical head nodding. Epidemic NS, which has affected parts of East Africa, appears to have clinical overlap with sub-Saharan Nakalanga syndrome (NLS), a brain disorder associated with pituitary dwarfism that appears to have a patchy distribution across sub-Sahara. Clinical stages of NS include inattention and blank stares, vertical head nodding, convulsive seizures, multiple impairments, and severe cognitive and motorsystem disability, including features suggesting parkinsonism. Head nodding episodes occur in clusters with an electrographic correlate of diffuse high-amplitude slow waves followed by an electrodecremental pattern with superimposed diffuse fast activity. Brain imaging reveals differing degrees of cerebral cortical and cerebellar atrophy. Brains of NS-affected children with mild frontotemporal cortical atrophy display neurofibrillary pathology and dystrophic neurites immunopositive for tau, consistent with a progressive neurodegenerative disorder. The etiology of NS and NLS appears to be dominated by environmental factors, including malnutrition, displacement, and nematode infection, but the specific cause is unknown.
Topics: Africa South of the Sahara; Africa, Eastern; Brain Diseases; Dwarfism, Pituitary; Electroencephalography; Humans; Nodding Syndrome; Phenotype; Syndrome
PubMed: 31753452
DOI: 10.1016/j.neurol.2019.09.005 -
Biomolecules Dec 2020The incidence of brain pathologies has increased during last decades. Better diagnosis (autism spectrum disorders) and longer life expectancy (Parkinson's disease,... (Review)
Review
The incidence of brain pathologies has increased during last decades. Better diagnosis (autism spectrum disorders) and longer life expectancy (Parkinson's disease, Alzheimer's disease) partly explain this increase, while emerging data suggest pollutant exposures as a possible but still underestimated cause of major brain disorders. Taking into account that the brain parenchyma is rich in gap junctions and that most pollutants inhibit their function; brain disorders might be the consequence of gap-junctional alterations due to long-term exposures to pollutants. In this article, this hypothesis is addressed through three complementary aspects: (1) the gap-junctional organization and connexin expression in brain parenchyma and their function; (2) the effect of major pollutants (pesticides, bisphenol A, phthalates, heavy metals, airborne particles, etc.) on gap-junctional and connexin functions; (3) a description of the major brain disorders categorized as neurodevelopmental (autism spectrum disorders, attention deficit hyperactivity disorders, epilepsy), neurobehavioral (migraines, major depressive disorders), neurodegenerative (Parkinson's and Alzheimer's diseases) and cancers (glioma), in which both connexin dysfunction and pollutant involvement have been described. Based on these different aspects, the possible involvement of pollutant-inhibited gap junctions in brain disorders is discussed for prenatal and postnatal exposures.
Topics: Air Pollutants; Alzheimer Disease; Brain; Brain Diseases; Cell Communication; Connexin 43; Depressive Disorder, Major; Female; Gap Junctions; Humans; Pregnancy
PubMed: 33396565
DOI: 10.3390/biom11010051 -
Medical Image Analysis Jul 2021Structural magnetic resonance imaging (MRI) has shown great clinical and practical values in computer-aided brain disorder identification. Multi-site MRI data increase...
Structural magnetic resonance imaging (MRI) has shown great clinical and practical values in computer-aided brain disorder identification. Multi-site MRI data increase sample size and statistical power, but are susceptible to inter-site heterogeneity caused by different scanners, scanning protocols, and subject cohorts. Multi-site MRI harmonization (MMH) helps alleviate the inter-site difference for subsequent analysis. Some MMH methods performed at imaging level or feature extraction level are concise but lack robustness and flexibility to some extent. Even though several machine/deep learning-based methods have been proposed for MMH, some of them require a portion of labeled data in the to-be-analyzed target domain or ignore the potential contributions of different brain regions to the identification of brain disorders. In this work, we propose an attention-guided deep domain adaptation (ADA) framework for MMH and apply it to automated brain disorder identification with multi-site MRIs. The proposed framework does not need any category label information of target data, and can also automatically identify discriminative regions in whole-brain MR images. Specifically, the proposed ADA is composed of three key modules: (1) an MRI feature encoding module to extract representations of input MRIs, (2) an attention discovery module to automatically locate discriminative dementia-related regions in each whole-brain MRI scan, and (3) a domain transfer module trained with adversarial learning for knowledge transfer between the source and target domains. Experiments have been performed on 2572 subjects from four benchmark datasets with T1-weighted structural MRIs, with results demonstrating the effectiveness of the proposed method in both tasks of brain disorder identification and disease progression prediction.
Topics: Attention; Brain; Brain Diseases; Humans; Machine Learning; Magnetic Resonance Imaging
PubMed: 33930828
DOI: 10.1016/j.media.2021.102076 -
Molecules and Cells Sep 2019Brain organoids are an exciting new technology with the potential to significantly change our understanding of the development and disorders of the human brain. With... (Review)
Review
Brain organoids are an exciting new technology with the potential to significantly change our understanding of the development and disorders of the human brain. With step-by-step differentiation protocols, three-dimensional neural tissues are self-organized from pluripotent stem cells, and recapitulate the major millstones of human brain development in vitro. Recent studies have shown that brain organoids can mimic the spatiotemporal dynamicity of neurogenesis, the formation of regional neural circuitry, and the integration of glial cells into a neural network. This suggests that brain organoids could serve as a representative model system to study the human brain. In this review, we will overview the development of brain organoid technology, its current progress and applications, and future prospects of this technology.
Topics: Brain; Brain Diseases; Humans; Organoids; Tissue Engineering
PubMed: 31564073
DOI: 10.14348/molcells.2019.0162