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      BOLD signal variability as potential new biomarker of functional neurological disorders

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          Highlights

          • Higher BOLD variability in somatomotor, salience and limbic networks in FND.

          • Better clinical outcome at follow-up associated with higher variability in SMA.

          • Higher insular variability at baseline predicted a worse clinical outcome.

          • BOLD signal variability might present a prognostic and state biomarker for FND.

          Abstract

          Background

          Functional neurological disorder (FND) is a common neuropsychiatric condition with established diagnostic criteria and effective treatments but for which the underlying neuropathophysiological mechanisms remain incompletely understood. Recent neuroimaging studies have revealed FND as a multi-network brain disorder, unveiling alterations across limbic, self-agency, attentional/salience, and sensorimotor networks. However, the relationship between identified brain alterations and disease progression or improvement is less explored.

          Methods

          This study included resting-state functional magnetic resonance imaging (fMRI) data from 79 patients with FND and 74 age and sex-matched healthy controls (HC). First, voxel-wise BOLD signal variability was computed for each participant and the group-wise difference was calculated. Second, we investigated the potential of BOLD signal variability to serve as a prognostic biomarker for clinical outcome in 47 patients who attended a follow-up measurement after eight months.

          Results

          The results demonstrated higher BOLD signal variability in key networks, including the somatomotor, salience, limbic, and dorsal attention networks, in patients compared to controls. Longitudinal analysis revealed an increase in BOLD signal variability in the supplementary motor area (SMA) in FND patients who had an improved clinical outcome, suggesting SMA variability as a potential state biomarker. Additionally, higher BOLD signal variability in the left insula at baseline predicted a worse clinical outcome.

          Conclusion

          This study contributes to the understanding of FND pathophysiology, emphasizing the dynamic nature of neural activity and highlighting the potential of BOLD signal variability as a valuable research tool. The insula and SMA emerge as promising regions for further investigation as prognostic and state markers.

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          Most cited references73

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          The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

          Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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            An inventory for measuring depression.

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              Methods to detect, characterize, and remove motion artifact in resting state fMRI.

              Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects. © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                31 May 2024
                2024
                31 May 2024
                : 43
                : 103625
                Affiliations
                [a ]Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, 3012 Bern, Switzerland
                [b ]Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, 3010 Bern, Switzerland
                [c ]University of Zurich, Psychiatric University Hospital Zurich, Department of Psychiatry, Psychotherapy and Psychosomatics, 8032 Zurich, Switzerland
                [d ]Graduate School for Health Sciences (GHS), University of Bern, 3006 Bern, Switzerland
                [e ]Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
                [f ]Faculty of Science and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
                Author notes
                [* ]Corresponding author at: Faculty of Sciences and Medicine, University of Fribourg, Office 2.106d, Chemin du Musée 5, 1700 Fribourg, Switzerland. selma.aybeck@ 123456unifr.ch
                [1]

                These authors contributed equally.

                Article
                S2213-1582(24)00064-0 103625
                10.1016/j.nicl.2024.103625
                11179625
                38833899
                a0fea0ef-0067-471b-9edf-6fc3a5dce96d
                © 2024 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 5 April 2024
                : 16 May 2024
                : 29 May 2024
                Categories
                Regular Article

                conversion disorders,longitudinal,prognostic,biomarker,insula,supplementary motor area

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