MRI Morphometry of the Brain and Neurological Diseases

Котов, С. В.

doi:10.5772/intechopen.69098

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…Image intensity measures include intensity properties of T1w, T2w, and T1w and T2w cortical contrast. The alterations in morphometry and intensity indicate the abnormal development and changed composition of brain tissue (Kotov, 2017;Bloem et al, 2018), which are common features of many neurological diseases. Cortical contrast has also been confirmed to serve as a sensitive cortical marker of brain development and psychopathology (Lewis et al, 2018;Norbom et al, 2019).…”

Section: Imaging Measuresmentioning

confidence: 99%

Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

et al. 2021

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Attention-deficit/hyperactivity disorder (ADHD) is one of the most common brain diseases among children. The current criteria of ADHD diagnosis mainly depend on behavior analysis, which is subjective and inconsistent, especially for children. The development of neuroimaging technologies, such as magnetic resonance imaging (MRI), drives the discovery of brain abnormalities in structure and function by analyzing multimodal neuroimages for computer-aided diagnosis of brain diseases. This paper proposes a multimodal machine learning framework that combines the Boruta based feature selection and Multiple Kernel Learning (MKL) to integrate the multimodal features of structural and functional MRIs and Diffusion Tensor Images (DTI) for the diagnosis of early adolescent ADHD. The rich and complementary information of the macrostructural features, microstructural properties, and functional connectivities are integrated at the kernel level, followed by a support vector machine classifier for discriminating ADHD from healthy children. Our experiments were conducted on the comorbidity-free ADHD subjects and covariable-matched healthy children aged 9–10 chosen from the Adolescent Brain and Cognitive Development (ABCD) study. This paper is the first work to combine structural and functional MRIs with DTI for early adolescents of the ABCD study. The results indicate that the kernel-level fusion of multimodal features achieves 0.698 of AUC (area under the receiver operating characteristic curves) and 64.3% of classification accuracy for ADHD diagnosis, showing a significant improvement over the early feature fusion and unimodal features. The abnormal functional connectivity predictors, involving default mode network, attention network, auditory network, and sensorimotor mouth network, thalamus, and cerebellum, as well as the anatomical regions in basal ganglia, are found to encode the most discriminative information, which collaborates with macrostructure and diffusion alterations to boost the performances of disorder diagnosis.

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Section: Imaging Measuresmentioning

confidence: 99%

Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

et al. 2021

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“…Generally, the decrease in brain volume as a result of cerebral atrophy is one of the common characteristics associated with structural abnormalities of brain for many neurological disorders. As an example, patients diagnosed with Parkinson’s disease show a slight decrease in substantia nigra in comparison with normal control subjects [ 2 ]. However, the degeneration of neurons of substantia nigra is significantly higher in patients with chronic Parkinson’s disease in comparison to the early stage of the disease.…”

Section: Introductionmentioning

confidence: 99%

A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications

Fred¹,

Kumar²,

Haridhas³

et al. 2022

Brain Sciences

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Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal-to-noise ratio (SNRMEG =2.2 db, SNREEG <1 db) and spatial resolution (SRMEG =2–3 mm, SREEG =7–10 mm) is higher for MEG than EEG, thus MEG potentially facilitates accurate monitoring of cortical activity. We found that the direct electrophysiological MEG signals reflected the physiological status of neurological disorders and play a vital role in disease diagnosis. Single-channel connectivity, as well as brain network analysis, using MEG data acquired during resting state and a given task has been used for the diagnosis of neurological disorders such as epilepsy, Alzheimer’s, Parkinsonism, autism, and schizophrenia. The workflow of MEG and its potential applications in the diagnosis of disease and therapeutic planning are also discussed. We forecast that computer-aided algorithms will play a prominent role in the diagnosis and prediction of neurological diseases in the future. The outcome of this narrative review will aid researchers to utilise MEG in diagnostics.

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Neural correlates of dual-task walking in people with central neurological disorders: a systematic review

Kim

Fraser

2022

J Neurol

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MRI Morphometry of the Brain and Neurological Diseases

Cited by 3 publications

References 41 publications

Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications

Neural correlates of dual-task walking in people with central neurological disorders: a systematic review

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