Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review

Hejazi, Sara; Karwowski, Waldemar; Farahani, Farzad V.; Marek, Tadeusz; Pa, Hancock

doi:10.3390/brainsci13020246

Cited by 4 publications

(1 citation statement)

References 128 publications

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“…Taken together, this pattern of results aligns with the cognitive rehabilitation literature in persons with MS, whereby different cognitive rehabilitation approaches are consistently associated with functional, but not structural MRI changes in persons with MS [13]. Further, this is consistent with previous hypotheses supporting integrative CNS plasticity based on changes in brain function with exercise training in MS [16], and suggests that future research efforts might focus on functional neuroimaging outcomes, potentially using graph theory-based analytical approaches [94], as potential endpoints in exercise trials that seek to provide a biological basis of exercise effects on functioning in this population.…”

Section: Discussion/future Directionssupporting

confidence: 87%

Neuroimaging Technology in Exercise Neurorehabilitation Research in Persons with MS: A Scoping Review

Sandroff

Rafizadeh

Motl

2023

Sensors

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There is increasing interest in the application of neuroimaging technology in exercise neurorehabilitation research among persons with multiple sclerosis (MS). The inclusion and focus on neuroimaging outcomes in MS exercise training research is critical for establishing a biological basis for improvements in functioning and elevating exercise within the neurologist’s clinical armamentarium alongside disease modifying therapies as an approach for treating the disease and its consequences. Indeed, the inclusion of selective neuroimaging approaches and sensor-based technology among physical activity, mobility, and balance outcomes in such MS research might further allow for detecting specific links between the brain and real-world behavior. This paper provided a scoping review on the application of neuroimaging in exercise training research among persons with MS based on searches conducted in PubMed, Web of Science, and Scopus. We identified 60 studies on neuroimaging-technology-based (primarily MRI, which involved a variety of sequences and approaches) correlates of functions, based on multiple sensor-based measures, which are typically targets for exercise training trials in MS. We further identified 12 randomized controlled trials of exercise training effects on neuroimaging outcomes in MS. Overall, there was a large degree of heterogeneity whereby we could not identify definitive conclusions regarding a consistent neuroimaging biomarker of MS-related dysfunction or singular sensor-based measure, or consistent neural adaptation for exercise training in MS. Nevertheless, the present review provides a first step for better linking correlational and randomized controlled trial research for the development of high-quality exercise training studies on the brain in persons with MS, and this is timely given the substantial interest in exercise as a potential disease-modifying and/or neuroplasticity-inducing behavior in this population.

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Section: Discussion/future Directionssupporting

confidence: 87%

Neuroimaging Technology in Exercise Neurorehabilitation Research in Persons with MS: A Scoping Review

Sandroff

Rafizadeh

Motl

2023

Sensors

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Hierarchical graph learning with convolutional network for brain disease prediction

Liu,

Wan

et al. 2023

Multimed Tools Appl

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In computer-aided diagnostic systems, the functional connectome approach has become a common method for detecting neurological disorders. However, the existing methods either ignore the uniqueness of different subjects across the functional connectivities or neglect the commonality of the same disease for the functional connectivity of each subject, resulting in a lack of capacity of capturing a comprehensive functional model. To solve the issues, we develop a hierarchical graph learning with convolutional network that not only considers the unique information of each subject, but also takes the common information across subjects into account. Specifically, the proposed method consists of two structures, one is the individual graph model which selects the representative brain regions by combining each subject feature and its related brain region-based graph. The other is the population graph model to directly conduct classification performance by updating the information of each subject which considers both the subject itself and the nearest neighbours. Experimental results indicate that the proposed method on four real datasets outperforms the state-of-the-art approaches.

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Application of Minimum Vertex Cover Problem in Functional Brain Connectivity Graphs

Candemir,

Akram

2023

2023 Innovations in Intelligent Systems and Applications Conference (ASYU)

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Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review

Cited by 4 publications

References 128 publications

Neuroimaging Technology in Exercise Neurorehabilitation Research in Persons with MS: A Scoping Review

Neuroimaging Technology in Exercise Neurorehabilitation Research in Persons with MS: A Scoping Review

Hierarchical graph learning with convolutional network for brain disease prediction

Application of Minimum Vertex Cover Problem in Functional Brain Connectivity Graphs

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