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Raimo S, Ferrazzano G, Di Vita A, Gaita M, Satriano F, Veneziano M, Torchia V, Zerella MP, Malimpensa L, Signoriello E, Lus G, Palermo L, Conte A. The multidimensional assessment of body representation and interoception in multiple sclerosis. Mult Scler Relat Disord 2024; 87:105692. [PMID: 38810419 DOI: 10.1016/j.msard.2024.105692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 11/29/2023] [Accepted: 05/20/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND The mental representation of the body (or body representation, BR) derives from the processing of multiple sensory and motor inputs and plays a crucial role in guiding our actions and in how we perceive our body. Fundamental inputs for BR construction come also from the interoceptive systems which refer to the whole bidirectional processes between the brain and the body. People with Multiple sclerosis (MS) show an abnormal multisensory integration which may compromise BR and interoception integrity. However, no study has evaluated possible deficits on distinct and dissociable dimensions of body representation (i.e., action-oriented, aBR; and a nonaction-oriented body representation, NaBR) and interoception (i.e., interoceptive accuracy, interoceptive sensibility, and interoceptive awareness) in MS. OBJECTIVE In the present study, we aimed to determine whether participants with MS present changes in BR and interoceptive dimensions. METHODS We performed comparison analyses on tasks and questionnaires tapping all BR and interoceptive dimensions between 36 people with relapsing-remitting MS (RRMS) and 42 healthy controls, and between 23 people with progressive MS (PMS) and 33 healthy controls. RESULTS Overall, patients with MS exhibited lower interoceptive accuracy than matched controls. The RRMS group also showed higher visceral interoceptive sensibility levels. No differences were found in BR accuracy measures, but the PMS reported longer response times when performing the aBR task. CONCLUSION These findings open a new issue on the role of inner-signal monitoring in the body symptomatology of MS and highlight the need for an accurate BR and interoceptive assessment in a clinical setting.
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Affiliation(s)
- Simona Raimo
- Department of Medical and Surgical Sciences, 'Magna Graecia' University of Catanzaro, Catanzaro, Italy.
| | - Gina Ferrazzano
- Department of Human Neuroscience, 'Sapienza' University of Rome, Roma, Italy
| | - Antonella Di Vita
- Department of Human Neuroscience, 'Sapienza' University of Rome, Roma, Italy
| | - Mariachiara Gaita
- Department of Psychology, University of Campania 'Luigi Vanvitelli', Caserta, Italy
| | - Federica Satriano
- Department of Human Neuroscience, 'Sapienza' University of Rome, Roma, Italy
| | - Miriam Veneziano
- Department of Psychology, University of Campania 'Luigi Vanvitelli', Caserta, Italy
| | - Valentina Torchia
- Department of Medical and Surgical Sciences, 'Magna Graecia' University of Catanzaro, Catanzaro, Italy
| | - Maria Paola Zerella
- Department of Human Neuroscience, 'Sapienza' University of Rome, Roma, Italy
| | | | - Elisabetta Signoriello
- Multiple Sclerosis Center, II Neurological Clinic, University of Campania 'Luigi Vanvitelli', Napoli, Italy; Department of Medical and Surgical Sciences, University of Campania 'Luigi Vanvitelli', Napoli, Italy
| | - Giacomo Lus
- Multiple Sclerosis Center, II Neurological Clinic, University of Campania 'Luigi Vanvitelli', Napoli, Italy; Department of Medical and Surgical Sciences, University of Campania 'Luigi Vanvitelli', Napoli, Italy
| | - Liana Palermo
- Department of Medical and Surgical Sciences, 'Magna Graecia' University of Catanzaro, Catanzaro, Italy
| | - Antonella Conte
- Department of Human Neuroscience, 'Sapienza' University of Rome, Roma, Italy; IRCCS Neuromed, Pozzilli (IS), Italy
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Bontempi P, Piccolantonio G, Busato A, Conti A, Angelini G, Lopez N, Bani A, Constantin G, Marzola P. Resting-state functional magnetic resonance imaging reveals functional connectivity alteration in the experimental autoimmune encephalomyelitis model of multiple sclerosis. NMR IN BIOMEDICINE 2024; 37:e5127. [PMID: 38450807 DOI: 10.1002/nbm.5127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 01/08/2024] [Accepted: 01/25/2024] [Indexed: 03/08/2024]
Abstract
Multiple sclerosis (MS) is an autoimmune degenerative disease targeting white matter in the central nervous system. The most common animal model that mimics MS is experimental autoimmune encephalomyelitis (EAE) and it plays a crucial role in pharmacological research, from the identification of a therapeutic target to the in vivo validation of efficacy. Magnetic resonance imaging (MRI) is largely used to detect MS lesions, and resting-state functional MRI (rsfMRI) to investigate alterations in the brain functional connectivity (FC). MRI was mainly used in EAE studies to detect lesions in the spinal cord and brain. The current longitudinal MRI study aims to validate rsfMRI as a biomarker of the disease progression in the myelin oligodendrocyte glycoprotein 35-55 induced EAE animal model of MS. MR images were acquired 14, 25, and 50 days postimmunization. Seed-based analysis was used to investigate the whole-brain FC with some predefined areas, such as the thalamic regions, cerebellum, motor and somatosensory cortex. When compared with the control group, the EAE group exhibited a slightly altered FC and a decreasing trend in the total number of activated voxels along the disease progression. The most interesting result regards the whole-brain FC with the cerebellum. A hyperconnectivity behavior was found at an early phase and a significant reduced connectivity at a late phase. Moreover, we found a negative correlation between the total number of activated voxels during the late phase and the cumulative disease index. The results obtained provide a clinically relevant experimental platform that may be pivotal for the elucidation of the key mechanisms of accumulation of irreversible disability, as well as the development of innovative therapies for MS. Moreover, the negative correlation between the disease severity and the size of the activated area suggests a possible research pathway to follow for the resolution of the clinico-radiological paradox.
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Affiliation(s)
- Pietro Bontempi
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
| | - Giusi Piccolantonio
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
| | - Alice Busato
- Department of Computer Science, University of Verona, Verona, Italy
- Evotec Company, Verona, Italy
| | - Anita Conti
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | | | - Nicola Lopez
- Department of Medicine, University of Verona, Verona, Italy
| | | | | | - Pasquina Marzola
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
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Ren W, Wang M, Wang Q, Huang Q, Feng S, Tao J, Wen C, Xu M, He J, Yang C, Zhao K, Yu X. Altered functional connectivity in patients with post-stroke fatigue: A resting-state fMRI study. J Affect Disord 2024; 350:468-475. [PMID: 38224743 DOI: 10.1016/j.jad.2024.01.129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 11/24/2023] [Accepted: 01/12/2024] [Indexed: 01/17/2024]
Abstract
BACKGROUND Post-stroke fatigue (PSF) was a common complication after stroke. This study aimed to explore the neuroimaging mechanism of PSF, which was rarely studied. METHODS Patients with the first episode of ischemic stroke were recruited from the First Affiliated Hospital of Wenzhou Medical University between March 2021 and December 2022. The fatigue severity scale (FSS) was used to assess fatigue symptoms. PSF was diagnosed by a neurologist based on the FSS score and PSF diagnostic criteria. All the patients were scanned by resting-state functional MRI (rs-fMRI). Precuneus, the posterior node of default-mode network (pDMN), was related to fatigue. Therefore, imaging data were further analyzed by the seed-based resting-state functional connectivity (FC) approach, with the left (PCUN.L) and right precuneus (PCUN.R) being the seeds. RESULTS A total of 70 patients with acute ischemic stroke were finally recruited, comprising 40 patients with PSF and 30 patients without PSF. Both the PCUN.L and PCUN.R seeds (pDMN) exhibited decreased FC with the prefrontal lobes located at the anterior part of DMN (aDMN), and the FC values were negatively correlated with FSS scores (both p < 0.001). These two seeds also exhibited increased FC with the right insula, and the FC values were positively correlated with FSS scores (both p < 0.05). CONCLUSION The abnormal FC between the aDMN and pDMN was associated with PSF. Besides, the insula, related to interoception, might also play an important role in PSF.
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Affiliation(s)
- Wenwei Ren
- Department of Psychiatry, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Mengpu Wang
- School of Mental Health, The Affiliated Wenzhou Kangning Hospital, Wenzhou Medical University, Wenzhou, China; School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Qiongzhang Wang
- School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Qiqi Huang
- Pediatric nursing unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shengchuang Feng
- Centre for Lifelong Learning and Individualised Cognition, Nanyang Technological University, Singapore
| | - Jiejie Tao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Caiyun Wen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Minjie Xu
- Lishui Second People's Hospital Affiliated to Wenzhou Medical University, Lishui, China
| | - Jincai He
- Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Chuang Yang
- Department of Psychiatry, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ke Zhao
- School of Mental Health, Wenzhou Medical University, Wenzhou, China; Lishui Second People's Hospital Affiliated to Wenzhou Medical University, Lishui, China; The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Xin Yu
- School of Mental Health, Wenzhou Medical University, Wenzhou, China; Peking University Institute of Mental Health (Sixth Hospital), Beijing, China; National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China; Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China.
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Ananthavarathan P, Sahi N, Chard DT. An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression. Expert Rev Neurother 2024; 24:201-216. [PMID: 38235594 DOI: 10.1080/14737175.2024.2304116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
INTRODUCTION While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear. AREAS COVERED The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning. EXPERT OPINION Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.
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Affiliation(s)
- Piriyankan Ananthavarathan
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Nitin Sahi
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Declan T Chard
- Clinical Research Associate & Consultant Neurologist, Institute of Neurology - Queen Square Multiple Sclerosis Centre, London, UK
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Liang X, Wang L, Zhu Y, Wang Y, He T, Wu L, Huang M, Zhou F. Altered neural intrinsic oscillations in patients with multiple sclerosis: effects of cortical thickness. Front Neurol 2023; 14:1143646. [PMID: 37818221 PMCID: PMC10560735 DOI: 10.3389/fneur.2023.1143646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 09/04/2023] [Indexed: 10/12/2023] Open
Abstract
Objective To investigate the effects of cortical thickness on the identification accuracy of fractional amplitude of low-frequency fluctuation (fALFF) in patients with multiple sclerosis (MS). Methods Resting-state functional magnetic resonance imaging data were collected from 31 remitting MS, 20 acute MS, and 42 healthy controls (HCs). After preprocessing, we first calculated two-dimensional fALFF (2d-fALFF) maps using the DPABISurf toolkit, and 2d-fALFF per unit thickness was obtained by dividing 2d-fALFF by cortical thickness. Then, between-group comparison, clinical correlation, and classification analyses were performed in 2d-fALFF and 2d-fALFF per unit thickness maps. Finally, we also examined whether the effect of cortical thickness on 2d-fALFF maps was affected by the subfrequency band. Results In contrast with 2d-fALFF, more changed regions in 2d-fALFF per unit thickness maps were detected in MS patients, such as increased region of the right inferior frontal cortex and faded regions of the right paracentral lobule, middle cingulate cortex, and right medial temporal cortex. There was a significant positive correlation between the disease duration and the 2d-fALFF values in the left early visual cortex in remitting MS patients (r = 0.517, Bonferroni-corrected, p = 0.008 × 4 < 0.05). In contrast with 2d-fALFF, we detected a positive correlation between the 2d-fALFF per unit thickness of the right ventral stream visual cortex and the modified Fatigue Impact Scale (MFIS) scores (r = 0.555, Bonferroni-corrected, p = 0.017 × 4 > 0.05). For detecting MS patients, 2d-fALFF and 2d- fALFF per unit thickness both performed remarkably well in support vector machine (SVM) analysis, especially in the remitting phase (AUC = 86, 83%). Compared with 2d-fALFF, the SVM model of 2d-fALFF per unit thickness had significantly higher classification performance in distinguishing between remitting and acute MS. More changed regions and more clinically relevant 2d-fALFF per unit thickness maps in the subfrequency band were also detected in MS patients. Conclusion By dividing the functional value by the cortical thickness, the identification accuracy of fALFF in MS patients was detected to be potentially influenced by cortical thickness. Additionally, 2d-fALFF per unit thickness is a potential diagnostic marker that can be utilized to distinguish between acute and remitting MS patients. Notably, we observed similar variations in the subfrequency band.
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Affiliation(s)
- Xiao Liang
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Lei Wang
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yao Wang
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Ting He
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Lin Wu
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Muhua Huang
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Institute of Medical Imaging, Nanchang University, Nanchang, Jiangxi, China
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Ibrahim AA, Adler W, Gaßner H, Rothhammer V, Kluge F, Eskofier BM. Association between cognition and gait in multiple sclerosis: A smartphone-based longitudinal analysis. Int J Med Inform 2023; 177:105145. [PMID: 37473657 DOI: 10.1016/j.ijmedinf.2023.105145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 07/02/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Gait and cognition impairments are common problems among People with Multiple Sclerosis (PwMS). Previous studies have investigated cross-sectional associations between gait and cognition. However, there is a lack of evidence regarding the longitudinal association between these factors in PwMS. Therefore, the objective of this study was to explore this longitudinal relationship using smartphone-based data from the Floodlight study. METHODS Using the publicly available Floodlight dataset, which contains smartphone-based longitudinal data, we used a linear mixed model to investigate the longitudinal relationship between cognition, measured by the Symbol Digit Modalities Test (SDMT), and gait, measured by the 2 Minute Walking test (2 MW) step count and Five-U-Turn Test (FUTT) turning speed. Four mixed models were fitted to explore the association between: 1) SDMT and mean step count; 2) SDMT and variability of step count; 3) SDMT and mean FUTT turning speed; and 4) SDMT and variability of FUTT turningt speed. RESULTS After controlling for age, sex, weight, and height, there were significant correlations between SDMT and the variability of 2 MW step count, the mean of FUTT turning speed. No significant correlation was observed between SDMT and the 2 MW mean step count. SIGNIFICANCE Our findings support the evidence that gait and cognition are associated in PwMS. This may support clinicians to adjust treatment and intervention programs that address both gait and cognitive impairments.
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Affiliation(s)
- Alzhraa A Ibrahim
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany; Computer Science Department, Faculty of Computers and Information, Assiut University, Egypt.
| | - Werner Adler
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Bavaria, Germany; Fraunhofer Institut for Integrated Circuits, Erlangen, Bavaria, Germany
| | - Veit Rothhammer
- Department of Neurology, University Hospital Erlangen, Erlangen, Bavaria, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany
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Corrêa DG, van Duinkerken E, Farinhas JGD, Pereira VC, Gasparetto EL, Alves-Leon SV, Lopes FCR. Influence of natalizumab on resting-state connectivity in patients with multiple sclerosis. J Cent Nerv Syst Dis 2023; 15:11795735231195775. [PMID: 37600237 PMCID: PMC10433731 DOI: 10.1177/11795735231195775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023] Open
Abstract
Background Changes in brain connectivity occur in patients with multiple sclerosis (MS), even in patients under disease-modifying therapies. Using magnetic resonance imaging (MRI) to asses patients treated with disease-modifying therapies, such as natalizumab, can elucidate the mechanisms involved in clinical deterioration in MS. Objectives To evaluate differences in resting-state functional connectivity among MS patients treated with natalizumab, MS patients not treated with natalizumab, and controls. Design Single-center retrospective cross-sectional study. Methods Twenty-three MS patients being treated with natalizumab were retrospectively compared with 23 MS patients who were naïve for natalizumab, and were using first-line medications (interferon-β and/or glatiramer acetate), and 17 gender- and age-matched control subjects. The MS patient groups were also matched for time since diagnosis and hyperintense lesion volume on FLAIR. All participants underwent brain MRI using a 3 Tesla scanner. Independent component analysis and dual regression were used to identify resting-state functional connectivity using the FMRIB Software Library. Results In comparison to controls, the MS patients treated with natalizumab presented decreased connectivity in the left orbitofrontal cortex, in the anterior cingulate and orbitofrontal cortex network. The patients not treated with natalizumab presented increased connectivity in the secondary visual, sensorimotor, and ventral attention networks in comparison to controls.Compared to patients treated with natalizumab, the patients not using natalizumab presented increased connectivity in the left Heschl's gyrus and in the right superior frontal gyrus in the ventral attention network. Conclusion Differences in brain connectivity between MS patients not treated with natalizumab, healthy controls, and patients treated with natalizumab may be secondary to suboptimal neuronal compensation due to prior less efficient treatments, or due to a compensation in response to maladaptive plasticity.
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Affiliation(s)
- Diogo G. Corrêa
- Department of Radiology, Clínica de Diagnóstico por Imagem (CDPI)/DASA, Barra da Tijuca, Brazil
- Department of Radiology, Federal Fluminense University, Niterói, Brazil
| | - Eelco van Duinkerken
- Department of Medical Psychology, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
- Post-Graduate Program in Neurology, Hospital Universitário Gaffrée e Guinle, Universidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
| | - João Gabriel D. Farinhas
- Post-Graduate Program in Neurology, Hospital Universitário Gaffrée e Guinle, Universidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
- Department of Neurology, Hospital Universitário Clementino Fraga Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Valéria C. Pereira
- Department of Neurology, Hospital Universitário Clementino Fraga Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Emerson L. Gasparetto
- Department of Radiology, Clínica de Diagnóstico por Imagem (CDPI)/DASA, Barra da Tijuca, Brazil
| | - Soniza V. Alves-Leon
- Post-Graduate Program in Neurology, Hospital Universitário Gaffrée e Guinle, Universidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
- Department of Neurology, Hospital Universitário Clementino Fraga Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fernanda Cristina R. Lopes
- Department of Radiology, Clínica de Diagnóstico por Imagem (CDPI)/DASA, Barra da Tijuca, Brazil
- Department of Radiology, Federal Fluminense University, Niterói, Brazil
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Margoni M, Preziosa P, Rocca MA, Filippi M. Depressive symptoms, anxiety and cognitive impairment: emerging evidence in multiple sclerosis. Transl Psychiatry 2023; 13:264. [PMID: 37468462 PMCID: PMC10356956 DOI: 10.1038/s41398-023-02555-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 06/28/2023] [Accepted: 06/30/2023] [Indexed: 07/21/2023] Open
Abstract
Neuropsychiatric abnormalities may be broadly divided in two categories: disorders of mood, affect, and behavior and abnormalities affecting cognition. Among these conditions, clinical depression, anxiety and neurocognitive disorders are the most common in multiple sclerosis (MS), with a substantial impact on patients' quality of life and adherence to treatments. Such manifestations may occur from the earliest phases of the disease but become more frequent in MS patients with a progressive disease course and more severe clinical disability. Although the pathogenesis of these neuropsychiatric manifestations has not been fully defined yet, brain structural and functional abnormalities, consistently observed with magnetic resonance imaging (MRI), together with genetic and immunologic factors, have been suggested to be key players. Even though the detrimental clinical impact of such manifestations in MS patients is a matter of crucial importance, at present, they are often overlooked in the clinical setting. Moreover, the efficacy of pharmacologic and non-pharmacologic approaches for their amelioration has been poorly investigated, with the majority of studies showing marginal or no beneficial effect of different therapeutic approaches, possibly due to the presence of multiple and heterogeneous underlying pathological mechanisms and intrinsic methodological limitations. A better evaluation of these manifestations in the clinical setting and improvements in the understanding of their pathophysiology may offer the potential to develop tools for differentiating these mechanisms in individual patients and ultimately provide a principled basis for treatment selection. This review provides an updated overview regarding the pathophysiology of the most common neuropsychiatric symptoms in MS, the clinical and MRI characteristics that have been associated with mood disorders (i.e., depression and anxiety) and cognitive impairment, and the treatment approaches currently available or under investigation.
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Affiliation(s)
- Monica Margoni
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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Rouault M, Pereira I, Galioulline H, Fleming SM, Stephan KE, Manjaly ZM. Interoceptive and metacognitive facets of fatigue in multiple sclerosis. Eur J Neurosci 2023; 58:2603-2622. [PMID: 37208934 DOI: 10.1111/ejn.16048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/28/2023] [Accepted: 05/15/2023] [Indexed: 05/21/2023]
Abstract
Numerous disorders are characterised by fatigue as a highly disabling symptom. Fatigue plays a particularly important clinical role in multiple sclerosis (MS) where it exerts a profound impact on quality of life. Recent concepts of fatigue grounded in computational theories of brain-body interactions emphasise the role of interoception and metacognition in the pathogenesis of fatigue. So far, however, for MS, empirical data on interoception and metacognition are scarce. This study examined interoception and (exteroceptive) metacognition in a sample of 71 persons with a diagnosis of MS. Interoception was assessed by prespecified subscales of a standard questionnaire (Multidimensional Assessment of Interoceptive Awareness [MAIA]), while metacognition was investigated with computational models of choice and confidence data from a visual discrimination paradigm. Additionally, autonomic function was examined by several physiological measurements. Several hypotheses were tested based on a preregistered analysis plan. In brief, we found the predicted association of interoceptive awareness with fatigue (but not with exteroceptive metacognition) and an association of autonomic function with exteroceptive metacognition (but not with fatigue). Furthermore, machine learning (elastic net regression) showed that individual fatigue scores could be predicted out-of-sample from our measurements, with questionnaire-based measures of interoceptive awareness and sleep quality as key predictors. Our results support theoretical concepts of interoception as an important factor for fatigue and demonstrate the general feasibility of predicting individual levels of fatigue from simple questionnaire-based measures of interoception and sleep.
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Affiliation(s)
- Marion Rouault
- Institut du Cerveau et de la Moelle Épinière (ICM), Centre National de la Recherche Scientifique (CNRS), Hôpital Pitié Salpêtrière, Paris, France
- Département d'Études Cognitives, École Normale Supérieure, Université Paris Sciences et Lettres (PSL University), Paris, France
| | - Inês Pereira
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH, Zurich, Switzerland
| | - Herman Galioulline
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH, Zurich, Switzerland
| | - Stephen M Fleming
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Department of Experimental Psychology, University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH, Zurich, Switzerland
- Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Zina-Mary Manjaly
- Department of Neurology, Schulthess Clinic, Zurich, Switzerland
- Department of Health Sciences and Technology, ETH, Zurich, Switzerland
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10
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Nair G, Nair SS, Arun KM, Camacho P, Bava E, Ajayaghosh P, Menon RN, Nair M, Kesavadas C, Anteraper SA. Resting-State Functional Connectivity in Relapsing-Remitting Multiple Sclerosis with Mild Disability: A Data-Driven, Whole-Brain Multivoxel Pattern Analysis Study. Brain Connect 2023; 13:89-96. [PMID: 36006365 DOI: 10.1089/brain.2021.0182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Multivoxel pattern analysis (MVPA) has emerged as a powerful unbiased approach for generating seed regions of interest (ROIs) in resting-state functional connectivity (RSFC) analysis in a data-driven manner. Studies exploring RSFC in multiple sclerosis have produced diverse and often incongruent results. Objectives: The aim of the present study was to investigate RSFC differences between people with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC). Methods: We performed a whole-brain connectome-wide MVPA in 50 RRMS patients with expanded disability status scale ≤4 and 50 age and gender-matched HCs. Results: Significant group differences were noted in RSFC in three clusters distributed in the following regions: anterior cingulate gyrus, right middle frontal gyrus, and frontal medial cortex. Whole-brain seed-to-voxel RSFC characterization of these clusters as seed ROIs revealed network-specific abnormalities, specifically in the anterior cingulate cortex and the default mode network. Conclusions: The network-wide RSFC abnormalities we report agree with the previous findings in RRMS, the cognitive and clinical implications of which are discussed herein. Impact statement This study investigated resting-state functional connectivity (RSFC) in relapsing-remitting multiple sclerosis (RRMS) people with mild disability (expanded disability status scale ≤4). Whole-brain connectome-wide multivoxel pattern analysis was used for assessing RSFC. Compared with healthy controls, we were able to identify three regions of interest for significant differences in connectivity patterns, which were then extracted as a mask for whole-brain seed-to-voxel analysis. A reduced connectivity was noted in the RRMS group, particularly in the anterior cingulate cortex and the default mode network regions, providing insights into the RSFC abnormalities in RRMS.
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Affiliation(s)
- Gowthami Nair
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Sruthi S Nair
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Karumattu Manattu Arun
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Paul Camacho
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Bioengineering, Interdisciplinary Health Science Institute, Urbana, Illinois, USA
| | - Elshal Bava
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Priya Ajayaghosh
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Ramshekhar N Menon
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Muralidharan Nair
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Chandrasekharan Kesavadas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
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11
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Baldini S, Morelli ME, Sartori A, Pasquin F, Dinoto A, Bratina A, Bosco A, Manganotti P. Microstates in multiple sclerosis: an electrophysiological signature of altered large-scale networks functioning? Brain Commun 2022; 5:fcac255. [PMID: 36601622 PMCID: PMC9806850 DOI: 10.1093/braincomms/fcac255] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/07/2022] [Accepted: 11/02/2022] [Indexed: 11/24/2022] Open
Abstract
Multiple sclerosis has a highly variable course and disabling symptoms even in absence of associated imaging data. This clinical-radiological paradox has motivated functional studies with particular attention to the resting-state networks by functional MRI. The EEG microstates analysis might offer advantages to study the spontaneous fluctuations of brain activity. This analysis investigates configurations of voltage maps that remain stable for 80-120 ms, termed microstates. The aim of our study was to investigate the temporal dynamic of microstates in patients with multiple sclerosis, without reported cognitive difficulties, and their possible correlations with clinical and neuropsychological parameters. We enrolled fifty relapsing-remitting multiple sclerosis patients and 24 healthy subjects, matched for age and sex. Demographic and clinical data were collected. All participants underwent to high-density EEG in resting-state and analyzed 15 min free artefact segments. Microstates analysis consisted in two processes: segmentation, to identify specific templates, and back-fitting, to quantify their temporal dynamic. A neuropsychological assessment was performed by the Brief International Cognitive Assessment for Multiple Sclerosis. Repeated measures two-way ANOVA was run to compare microstates parameters of patients versus controls. To evaluate association between clinical, neuropsychological and microstates data, we performed Pearsons' correlation and stepwise multiple linear regression to estimate possible predictions. The alpha value was set to 0.05. We found six templates computed across all subjects. Significant differences were found in most of the parameters (global explained variance, time coverage, occurrence) for the microstate Class A (P < 0.001), B (P < 0.001), D (P < 0.001), E (P < 0.001) and F (P < 0.001). In particular, an increase of temporal dynamic of Class A, B and E and a decrease of Class D and F were observed. A significant positive association of disease duration with the mean duration of Class A was found. Eight percent of patients with multiple sclerosis were found cognitive impaired, and the multiple linear regression analysis showed a strong prediction of Symbol Digit Modalities Test score by global explained variance of Class A. The EEG microstate analysis in patients with multiple sclerosis, without overt cognitive impairment, showed an increased temporal dynamic of the sensory-related microstates (Class A and B), a reduced presence of the cognitive-related microstates (Class D and F), and a higher activation of a microstate (Class E) associated to the default mode network. These findings might represent an electrophysiological signature of brain reorganization in multiple sclerosis. Moreover, the association between Symbol Digit Modalities Test and Class A may suggest a possible marker of overt cognitive dysfunctions.
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Affiliation(s)
- Sara Baldini
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Maria Elisa Morelli
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Arianna Sartori
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Fulvio Pasquin
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Alessandro Dinoto
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Alessio Bratina
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Antonio Bosco
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Paolo Manganotti
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
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12
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Riazi AH, Rabbani H, Kafieh R. Dynamic Brain Connectivity in Resting-State FMRI Using Spectral ICA and Graph Approach: Application to Healthy Controls and Multiple Sclerosis. Diagnostics (Basel) 2022; 12:diagnostics12092263. [PMID: 36140663 PMCID: PMC9497797 DOI: 10.3390/diagnostics12092263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 09/07/2022] [Accepted: 09/10/2022] [Indexed: 11/27/2022] Open
Abstract
Multiple sclerosis (MS) is a neuroinflammatory disease that involves structural and functional damage to the brain. It changes the functional connectivity of the brain between and within networks. Resting-state functional magnetic resonance imaging (fMRI) enables us to measure functional correlation and independence between different brain regions. In recent years, statistical methods, including independent component analysis (ICA) and graph-based analysis, have been widely used in fMRI studies. Furthermore, topological properties of the brain have been appeared as significant features of neuroscience studies. Most studies are focused on graph analysis and ICA methods, rather than considering spectral approaches. Here, we developed a new framework to measure brain connectivity (in static and dynamic formats) and incorporate it to study fMRI data from MS patients and healthy controls (HCs). For this purpose, a spectral ICA method is proposed to extract the nodes of the brain graph. Spectral ICA extracts more reliable components and decreases the processing time in calculation of the static brain connectivity. Compared to Infomax ICA, dynamic range and low-frequency to high-frequency power ratio (fALFF) show better results using the proposed ICA. It is also helpful in selection of the states for dynamic connectivity. Furthermore, the dynamic connectivity-based extracted components from spectral ICA are estimated using a mutual information method and based on correlation of sliding time-windowed on selected IC time courses. First-level and second-level connectivity states are calculated using correlations of connectivity strength between graph nodes (spectral ICA components). Finally, static and dynamic connectivity are analyzed based on correlation nodes percolated by an anatomical automatic labeling (AAL) atlas. Despite static and dynamic connectivity results of AAL correlations not showing any significant changes between MS and HC, our results based on spectral ICA in static and dynamic connectivity showed significantly decreased connectivity in MS patients in the anterior cingulate cortex, whereas it was significantly weaker in the core but stronger at the periphery of the posterior cingulate cortex.
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Affiliation(s)
- Amir Hosein Riazi
- Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Hossein Rabbani
- Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Rahele Kafieh
- Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
- Department of Engineering, Durham University, South Road, Durham DH1 3LE, UK
- Correspondence:
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13
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Ruiz-Rizzo AL, Bublak P, Kluckow S, Finke K, Gaser C, Schwab M, Güllmar D, Müller HJ, Witte OW, Rupprecht S. Neural distinctiveness of fatigue and low sleep quality in multiple sclerosis. Eur J Neurol 2022; 29:3017-3027. [PMID: 35699354 DOI: 10.1111/ene.15445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/08/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND PURPOSE Fatigue and low sleep quality in multiple sclerosis (MS) are closely related symptoms. Here, the associations between the brain's functional connectivity (FC) and fatigue and low sleep quality were investigated to determine the degree of neural distinctiveness of these symptoms. METHOD A hundred and four patients with relapsing-remitting MS (age 38.9 ± 10.2 years, 66 females) completed the Modified Fatigue Impact Scale and the Pittsburgh Sleep Quality Index and underwent resting-state functional magnetic resonance imaging. FC was analyzed using independent-component analysis in sensorimotor, default-mode, fronto-parietal and basal-ganglia networks. Multiple linear regression models allowed us to test the association between FC and fatigue and sleep quality whilst controlling for one another as well as for demographic, disease-related and imaging variables. RESULTS Higher fatigue correlated with lower sleep quality (r = 0.54, p < 0.0001). Higher fatigue was associated with lower FC of the precentral gyrus in the sensorimotor network, the precuneus in the posterior default-mode network and the superior frontal gyrus in the left fronto-parietal network, independently of sleep quality. Lower sleep quality was associated with lower FC of the left intraparietal sulcus in the left fronto-parietal network, independently of fatigue. Specific associations were found between fatigue and the sensorimotor network's global FC and between low sleep quality and the left fronto-parietal network's global FC. CONCLUSION Despite the high correlation between fatigue and low sleep quality in the clinical picture, our findings clearly indicate that, on the neural level, fatigue and low sleep quality in MS are associated with decreased FC in distinct functional brain networks.
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Affiliation(s)
- Adriana L Ruiz-Rizzo
- Department of Psychology, General and Experimental Psychology Unit, LMU Munich, Munich, Germany.,Department of Neurology, Jena University Hospital, Jena, Germany
| | - Peter Bublak
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - Steffen Kluckow
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - Kathrin Finke
- Department of Psychology, General and Experimental Psychology Unit, LMU Munich, Munich, Germany.,Department of Neurology, Jena University Hospital, Jena, Germany
| | - Christian Gaser
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - Matthias Schwab
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - Daniel Güllmar
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - Hermann J Müller
- Department of Psychology, General and Experimental Psychology Unit, LMU Munich, Munich, Germany
| | - Otto W Witte
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - Sven Rupprecht
- Department of Neurology, Jena University Hospital, Jena, Germany
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14
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Chitnis T, Vandercappellen J, King M, Brichetto G. Symptom Interconnectivity in Multiple Sclerosis: A Narrative Review of Potential Underlying Biological Disease Processes. Neurol Ther 2022; 11:1043-1070. [PMID: 35680693 PMCID: PMC9338216 DOI: 10.1007/s40120-022-00368-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/16/2022] [Indexed: 11/18/2022] Open
Abstract
Introduction Fatigue, cognitive impairment, depression, and pain are highly prevalent symptoms in multiple sclerosis (MS). These often co-occur and may be explained by a common etiology. By reviewing existing literature, we aimed to identify potential underlying biological processes implicated in the interconnectivity between these symptoms. Methods A literature search was conducted to identify articles reporting research into the biological mechanisms responsible for the manifestation of fatigue, cognitive impairment, depression, and pain in MS. PubMed was used to search for articles published from July 2011 to July 2021. We reviewed and assessed findings from the literature to identify biological processes common to the symptoms of interest. Results Of 693 articles identified from the search, 252 were selected following screening of titles and abstracts and assessing reference lists of review articles. Four biological processes linked with two or more of the symptoms of interest were frequently identified from the literature: (1) direct neuroanatomical changes to brain regions linked with symptoms of interest (e.g., thalamic injury associated with cognitive impairment, fatigue, and depression), (2) pro-inflammatory cytokines associated with so-called ‘sickness behavior,’ including manifestation of fatigue, transient cognitive impairment, depression, and pain, (3) dysregulation of monoaminergic pathways leading to depressive symptoms and fatigue, and (4) hyperactivity of the hypothalamic–pituitary-adrenal (HPA) axis as a result of pro-inflammatory cytokines promoting the release of brain noradrenaline, serotonin, and tryptophan, which is associated with symptoms of depression and cognitive impairment. Conclusion The co-occurrence of fatigue, cognitive impairment, depression, and pain in MS appears to be associated with a common set of etiological factors, namely neuroanatomical changes, pro-inflammatory cytokines, dysregulation of monoaminergic pathways, and a hyperactive HPA axis. This association of symptoms and biological processes has important implications for disease management strategies and, eventually, could help find a common therapeutic pathway that will impact both inflammation and neuroprotection. Supplementary Information The online version contains supplementary material available at 10.1007/s40120-022-00368-2.
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Affiliation(s)
- Tanuja Chitnis
- Department of Neurology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
| | | | - Miriam King
- Novartis Pharma AG, Fabrikstrasse 12-2, 4056, Basel, Switzerland
| | - Giampaolo Brichetto
- Associazione Italiana Sclerosi Multipla Rehabilitation Center, Via Operai, 30, 16149, Genoa, GE, Italy
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15
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Rocca MA, Schoonheim MM, Valsasina P, Geurts JJG, Filippi M. Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective. Neuroimage Clin 2022; 35:103076. [PMID: 35691253 PMCID: PMC9194954 DOI: 10.1016/j.nicl.2022.103076] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 01/12/2023]
Abstract
Functional MRI is able to detect adaptive and maladaptive abnormalities at different MS stages. Increased fMRI activity is a feature of early MS, while progressive exhaustion of adaptive mechanisms is detected later on in the disease. Collapse of long-range connections and impaired hub integration characterize MS network reorganization. Time-varying connectivity analysis provides useful and complementary pieces of information to static functional connectivity. New perspectives might be the use of multimodal MRI and artificial intelligence.
Multiple sclerosis (MS) is a neurological disorder affecting the central nervous system and features extensive functional brain changes that are poorly understood but relate strongly to clinical impairments. Functional magnetic resonance imaging (fMRI) is a non-invasive, powerful technique able to map activity of brain regions and to assess how such regions interact for an efficient brain network. FMRI has been widely applied to study functional brain changes in MS, allowing to investigate functional plasticity consequent to disease-related structural injury. The first studies in MS using active fMRI tasks mainly aimed to study such plastic changes by identifying abnormal activity in salient brain regions (or systems) involved by the task. In later studies the focus shifted towards resting state (RS) functional connectivity (FC) studies, which aimed to map large-scale functional networks of the brain and to establish how MS pathology impairs functional integration, eventually leading to the hypothesized network collapse as patients clinically progress. This review provides a summary of the main findings from studies using task-based and RS fMRI and illustrates how functional brain alterations relate to clinical disability and cognitive deficits in this condition. We also give an overview of longitudinal studies that used task-based and RS fMRI to monitor disease evolution and effects of motor and cognitive rehabilitation. In addition, we discuss the results of studies using newer technologies involving time-varying FC to investigate abnormal dynamism and flexibility of network configurations in MS. Finally, we show some preliminary results from two recent topics (i.e., multimodal MRI analysis and artificial intelligence) that are receiving increasing attention. Together, these functional studies could provide new (conceptual) insights into disease stage-specific mechanisms underlying progression in MS, with recommendations for future research.
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Affiliation(s)
- Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy.
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
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16
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Frieske J, Pareto D, García-Vidal A, Cuypers K, Meesen RL, Alonso J, Arévalo MJ, Galán I, Renom M, Vidal-Jordana Á, Auger C, Montalban X, Rovira À, Sastre-Garriga J. Can cognitive training reignite compensatory mechanisms in advanced multiple sclerosis patients? An explorative morphological network approach. Neuroscience 2022; 495:86-96. [DOI: 10.1016/j.neuroscience.2022.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/22/2022] [Accepted: 03/24/2022] [Indexed: 10/18/2022]
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17
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Tozlu C, Jamison K, Gauthier SA, Kuceyeski A. Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis. Front Neurosci 2021; 15:763966. [PMID: 34966255 PMCID: PMC8710545 DOI: 10.3389/fnins.2021.763966] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/17/2021] [Indexed: 12/30/2022] Open
Abstract
Background: Advanced imaging techniques such as diffusion and functional MRI can be used to identify pathology-related changes to the brain's structural and functional connectivity (SC and FC) networks and mapping of these changes to disability and compensatory mechanisms in people with multiple sclerosis (pwMS). No study to date performed a comparison study to investigate which connectivity type (SC, static or dynamic FC) better distinguishes healthy controls (HC) from pwMS and/or classifies pwMS by disability status. Aims: We aim to compare the performance of SC, static FC, and dynamic FC (dFC) in classifying (a) HC vs. pwMS and (b) pwMS who have no disability vs. with disability. The secondary objective of the study is to identify which brain regions' connectome measures contribute most to the classification tasks. Materials and Methods: One hundred pwMS and 19 HC were included. Expanded Disability Status Scale (EDSS) was used to assess disability, where 67 pwMS who had EDSS<2 were considered as not having disability. Diffusion and resting-state functional MRI were used to compute the SC and FC matrices, respectively. Logistic regression with ridge regularization was performed, where the models included demographics/clinical information and either pairwise entries or regional summaries from one of the following matrices: SC, FC, and dFC. The performance of the models was assessed using the area under the receiver operating curve (AUC). Results: In classifying HC vs. pwMS, the regional SC model significantly outperformed others with a median AUC of 0.89 (p <0.05). In classifying pwMS by disability status, the regional dFC and dFC metrics models significantly outperformed others with a median AUC of 0.65 and 0.61 (p < 0.05). Regional SC in the dorsal attention, subcortical and cerebellar networks were the most important variables in the HC vs. pwMS classification task. Increased regional dFC in dorsal attention and visual networks and decreased regional dFC in frontoparietal and cerebellar networks in certain dFC states was associated with being in the group of pwMS with evidence of disability. Discussion: Damage to SCs is a hallmark of MS and, unsurprisingly, the most accurate connectomic measure in classifying patients and controls. On the other hand, dynamic FC metrics were most important for determining disability level in pwMS, and could represent functional compensation in response to white matter pathology in pwMS.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Susan A. Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
- Judith Jaffe Multiple Sclerosis Center, Weill Cornell Medicine, New York, NY, United States
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
- Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, United States
- *Correspondence: Amy Kuceyeski
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18
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Groppa S, Gonzalez-Escamilla G, Eshaghi A, Meuth SG, Ciccarelli O. Linking immune-mediated damage to neurodegeneration in multiple sclerosis: could network-based MRI help? Brain Commun 2021; 3:fcab237. [PMID: 34729480 PMCID: PMC8557667 DOI: 10.1093/braincomms/fcab237] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2021] [Indexed: 01/04/2023] Open
Abstract
Inflammatory demyelination characterizes the initial stages of multiple sclerosis, while progressive axonal and neuronal loss are coexisting and significantly contribute to the long-term physical and cognitive impairment. There is an unmet need for a conceptual shift from a dualistic view of multiple sclerosis pathology, involving either inflammatory demyelination or neurodegeneration, to integrative dynamic models of brain reorganization, where, glia-neuron interactions, synaptic alterations and grey matter pathology are longitudinally envisaged at the whole-brain level. Functional and structural MRI can delineate network hallmarks for relapses, remissions or disease progression, which can be linked to the pathophysiology behind inflammatory attacks, repair and neurodegeneration. Here, we aim to unify recent findings of grey matter circuits dynamics in multiple sclerosis within the framework of molecular and pathophysiological hallmarks combined with disease-related network reorganization, while highlighting advances from animal models (in vivo and ex vivo) and human clinical data (imaging and histological). We propose that MRI-based brain networks characterization is essential for better delineating ongoing pathology and elaboration of particular mechanisms that may serve for accurate modelling and prediction of disease courses throughout disease stages.
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Affiliation(s)
- Sergiu Groppa
- Imaging and Neurostimulation, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Gabriel Gonzalez-Escamilla
- Imaging and Neurostimulation, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Arman Eshaghi
- Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London WC1E 6BT, UK.,Department of Computer Science, Centre for Medical Image Computing (CMIC), University College London, London WC1E 6BT, UK
| | - Sven G Meuth
- Department of Neurology, Medical Faculty, Heinrich Heine University, Düsseldorf 40225, Germany
| | - Olga Ciccarelli
- Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London WC1E 6BT, UK
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19
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Jandric D, Doshi A, Scott R, Paling D, Rog D, Chataway J, Schoonheim M, Parker G, Muhlert N. A systematic review of resting state functional MRI connectivity changes and cognitive impairment in multiple sclerosis. Brain Connect 2021; 12:112-133. [PMID: 34382408 DOI: 10.1089/brain.2021.0104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Cognitive impairment in multiple sclerosis (MS) is increasingly being investigated with resting state functional MRI (rs-fMRI) functional connectivity (FC) . However, results remain difficult to interpret, showing both high and low FC associated with cognitive impairment. We conducted a systematic review of rs-fMRI studies in MS to understand whether the direction of FC change relates to cognitive dysfunction, and how this may be influenced by the choice of methodology. METHODS Embase, Medline and PsycINFO were searched for studies assessing cognitive function and rs-fMRI FC in adults with MS. RESULTS Fifty-seven studies were included in a narrative synthesis. Of these, 50 found an association between cognitive impairment and FC abnormalities. Worse cognition was linked to high FC in 18 studies, and to low FC in 17 studies. Nine studies found patterns of both high and low FC related to poor cognitive performance, in different regions or for different MR metrics. There was no clear link to increased FC during early stages of MS and reduced FC in later stages, as predicted by common models of MS pathology. Throughout, we found substantial heterogeneity in study methodology, and carefully consider how this may impact on the observed findings. DISCUSSION These results indicate an urgent need for greater standardisation in the field - in terms of the choice of MRI analysis and the definition of cognitive impairment. This will allow us to use rs-fMRI FC as a biomarker in future clinical studies, and as a tool to understand mechanisms underpinning cognitive symptoms in MS.
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Affiliation(s)
- Danka Jandric
- The University of Manchester, 5292, Oxford Road, Manchester, United Kingdom of Great Britain and Northern Ireland, M13 9PL;
| | - Anisha Doshi
- University College London, 4919, London, London, United Kingdom of Great Britain and Northern Ireland;
| | - Richelle Scott
- The University of Manchester, 5292, Manchester, United Kingdom of Great Britain and Northern Ireland;
| | - David Paling
- Royal Hallamshire Hospital, 105629, Sheffield, Sheffield, United Kingdom of Great Britain and Northern Ireland;
| | - David Rog
- Salford Royal Hospital, 105621, Salford, Salford, United Kingdom of Great Britain and Northern Ireland;
| | - Jeremy Chataway
- University College London, 4919, London, London, United Kingdom of Great Britain and Northern Ireland;
| | - Menno Schoonheim
- Amsterdam UMC Locatie VUmc, 1209, Anatomy & Neurosciences, Amsterdam, Noord-Holland, Netherlands;
| | - Geoff Parker
- University College London, 4919, London, London, United Kingdom of Great Britain and Northern Ireland.,The University of Manchester, 5292, Manchester, United Kingdom of Great Britain and Northern Ireland;
| | - Nils Muhlert
- The University of Manchester, 5292, Manchester, United Kingdom of Great Britain and Northern Ireland;
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20
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Høgestøl EA, Ghezzo S, Nygaard GO, Espeseth T, Sowa P, Beyer MK, Harbo HF, Westlye LT, Hulst HE, Alnæs D. Functional connectivity in multiple sclerosis modelled as connectome stability: A 5-year follow-up study. Mult Scler 2021; 28:532-540. [PMID: 34259578 PMCID: PMC8961247 DOI: 10.1177/13524585211030212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background: Brain functional connectivity (FC) in multiple sclerosis (MS) is abnormal
compared to healthy controls (HCs). More longitudinal studies in MS are
needed to evaluate whether FC stability is clinically relevant. Objective: To compare functional magnetic resonance imaging (fMRI)-based FC between MS
and HC, and to determine the relationship between longitudinal FC changes
and structural brain damage, cognitive performance and physical
disability. Methods: T1-weighted MPRAGE and resting-state fMRI (1.5T) were acquired from 70
relapsing-remitting MS patients and 94 matched HC at baseline (mean months
since diagnosis 14.0 ± 11) and from 60 MS patients after 5 years.
Independent component analysis and network modelling were used to measure
longitudinal FC stability and cross-sectional comparisons with HC. Linear
mixed models, adjusted for age and sex, were used to calculate
correlations. Results: At baseline, patients with MS showed FC abnormalities both within networks
and in single connections compared to HC. Longitudinal analyses revealed
functional stability and no significant relationships with clinical
disability, cognitive performance, lesion or brain volume. Conclusion: FC abnormalities occur already at the first decade of MS, yet we found no
relevant clinical correlations for these network deviations. Future
large-scale longitudinal fMRI studies across a range of MS subtypes and
outcomes are required.
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Affiliation(s)
- Einar August Høgestøl
- "Department of Neurology, Neuroscience Research Unit, Multiple Sclerosis Research Group University of Oslo & Oslo University Hospital; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Samuele Ghezzo
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway/Department of Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Gro Owren Nygaard
- Department of Neurology, Neuroscience Research Unit, Multiple Sclerosis Research Group University of Oslo & Oslo University Hospital, Oslo, Norway
| | - Thomas Espeseth
- Department of Psychology, University of Oslo, Oslo, Norway; Bjørknes College, Oslo, Norway
| | - Piotr Sowa
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Mona K Beyer
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway/Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Hanne Flinstad Harbo
- "Department of Neurology, Neuroscience Research Unit, Multiple Sclerosis Research Group University of Oslo & Oslo University Hospital; Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway/Department of Psychology, University of Oslo, Oslo, Norway/KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Hanneke E Hulst
- Department of Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands/Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Bjørknes College, Oslo, Norway
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21
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Rocca MA, Valsasina P, Meani A, Pagani E, Cordani C, Cervellin C, Filippi M. Network Damage Predicts Clinical Worsening in Multiple Sclerosis: A 6.4-Year Study. NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION 2021; 8:8/4/e1006. [PMID: 34021055 PMCID: PMC8143700 DOI: 10.1212/nxi.0000000000001006] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 03/05/2021] [Indexed: 01/06/2023]
Abstract
OBJECTIVE In multiple sclerosis (MS), clinical impairment is likely due to both structural damage and abnormal brain function. We assessed the added value of integrating structural and functional network MRI measures to predict 6.4-year MS clinical disability deterioration. METHODS Baseline 3D T1-weighted and resting-state functional MRI scans were obtained from 233 patients with MS and 77 healthy controls. Patients underwent a neurologic evaluation at baseline and at 6.4-year median follow-up (interquartile range = 5.06-7.51 years). At follow-up, patients were classified as clinically stable/worsened according to disability changes. In relapsing-remitting (RR) MS, secondary progressive (SP) MS conversion was evaluated. Global brain volumetry was obtained. Furthermore, independent component analysis identified the main functional connectivity (FC) and gray matter (GM) network patterns. RESULTS At follow-up, 105/233 (45%) patients were clinically worsened; 26/157 (16%) patients with RRMS evolved to SPMS. The treatment-adjusted random forest model identified normalized GM and brain volumes, decreased FC between default-mode networks, increased FC of the left precentral gyrus in the sensorimotor network (SMN), and GM atrophy in the fronto-parietal network (false discovery rate [FDR]-corrected p = range 0.01-0.09) as predictors of clinical worsening (out-of-bag [OOB] accuracy = 0.74). An expected contribution of baseline disability was also present (FDR-p = 0.01). Baseline disability, normalized GM volume, and GM atrophy in the SMN (FDR-p = range 0.01-0.09) were independently associated with SPMS conversion (OOB accuracy = 0.84). At receiver operating characteristic analysis, including network MRI variables improved disability worsening (p = 0.05) and SPMS conversion (p = 0.02) prediction. CONCLUSIONS Integration of MRI network measures helped determining the relative contributions of global/local GM damage and functional reorganization to clinical deterioration in MS.
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Affiliation(s)
- Maria A Rocca
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Valsasina
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alessandro Meani
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Claudio Cordani
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Chiara Cervellin
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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22
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Chard DT, Alahmadi AAS, Audoin B, Charalambous T, Enzinger C, Hulst HE, Rocca MA, Rovira À, Sastre-Garriga J, Schoonheim MM, Tijms B, Tur C, Gandini Wheeler-Kingshott CAM, Wink AM, Ciccarelli O, Barkhof F. Mind the gap: from neurons to networks to outcomes in multiple sclerosis. Nat Rev Neurol 2021; 17:173-184. [PMID: 33437067 DOI: 10.1038/s41582-020-00439-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/20/2020] [Indexed: 12/21/2022]
Abstract
MRI studies have provided valuable insights into the structure and function of neural networks, particularly in health and in classical neurodegenerative conditions such as Alzheimer disease. However, such work is also highly relevant in other diseases of the CNS, including multiple sclerosis (MS). In this Review, we consider the effects of MS pathology on brain networks, as assessed using MRI, and how these changes to brain networks translate into clinical impairments. We also discuss how this knowledge can inform the targeting of MS treatments and the potential future directions for research in this area. Studying MS is challenging as its pathology involves neurodegenerative and focal inflammatory elements, both of which could disrupt neural networks. The disruption of white matter tracts in MS is reflected in changes in network efficiency, an increasingly random grey matter network topology, relative cortical disconnection, and both increases and decreases in connectivity centred around hubs such as the thalamus and the default mode network. The results of initial longitudinal studies suggest that these changes evolve rather than simply increase over time and are linked with clinical features. Studies have also identified a potential role for treatments that functionally modify neural networks as opposed to altering their structure.
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Affiliation(s)
- Declan T Chard
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK. .,National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, UK.
| | - Adnan A S Alahmadi
- Department of Diagnostic Radiology, Faculty of Applied Medical Science, King Abdulaziz University (KAU), Jeddah, Saudi Arabia
| | - Bertrand Audoin
- Aix-Marseille University, CNRS, CRMBM, Marseille, France.,AP-HM, University Hospital Timone, Department of Neurology, Marseille, France
| | - Thalis Charalambous
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Christian Enzinger
- Department of Neurology, Research Unit for Neuronal Repair and Plasticity, Medical University of Graz, Graz, Austria.,Department of Radiology, Division of Neuroradiology, Vascular and Interventional Radiology, Medical University of Graz, Graz, Austria
| | - Hanneke E Hulst
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jaume Sastre-Garriga
- Servei de Neurologia/Neuroimmunologia, Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Betty Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Carmen Tur
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Department of Neurology, Luton and Dunstable University Hospital, Luton, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Alle Meije Wink
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Olga Ciccarelli
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, UK
| | - Frederik Barkhof
- National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, UK.,Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Institutes of Neurology and Healthcare Engineering, University College London, London, UK
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23
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West KL, Sivakolundu DK, Zuppichini MD, Turner MP, Spence JS, Lu H, Okuda DT, Rypma B. Altered task-induced cerebral blood flow and oxygen metabolism underlies motor impairment in multiple sclerosis. J Cereb Blood Flow Metab 2021; 41:182-193. [PMID: 32126873 PMCID: PMC7747162 DOI: 10.1177/0271678x20908356] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 10/29/2019] [Accepted: 12/04/2019] [Indexed: 02/01/2023]
Abstract
The neural mechanisms underlying motor impairment in multiple sclerosis (MS) remain unknown. Motor cortex dysfunction is implicated in blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies, but the role of neural-vascular coupling underlying BOLD changes remains unknown. We sought to independently measure the physiologic factors (i.e., cerebral blood flow (ΔCBF), cerebral metabolic rate of oxygen (ΔCMRO2), and flow-metabolism coupling (ΔCBF/ΔCMRO2), utilizing dual-echo calibrated fMRI (cfMRI) during a bilateral finger-tapping task. We utilized cfMRI to measure physiologic responses in 17 healthy volunteers and 32 MS patients (MSP) with and without motor impairment during a thumb-button-press task in thumb-related (task-central) and surrounding primary motor cortex (task-surround) regions of interest (ROIs). We observed significant ΔCBF and ΔCMRO2 increases in all MSP compared to healthy volunteers in the task-central ROI and increased flow-metabolism coupling (ΔCBF/ΔCMRO2) in the MSP without motor impairment. In the task-surround ROI, we observed decreases in ΔCBF and ΔCMRO2 in MSP with motor impairment. Additionally, ΔCBF and ΔCMRO2 responses in the task-surround ROI were associated with motor function and white matter damage in MSP. These results suggest an important role for task-surround recruitment in the primary motor cortex to maintain motor dexterity and its dependence on intact white matter microstructure and neural-vascular coupling.
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Affiliation(s)
- Kathryn L West
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Dinesh K Sivakolundu
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Mark D Zuppichini
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Monroe P Turner
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Jeffrey S Spence
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | - Darin T Okuda
- Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bart Rypma
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
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24
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Lashkari A, Davoodi-Bojd E, Fahmy L, Li L, Nejad-Davarani SP, Chopp M, Jiang Q, Cerghet M. Impairments of white matter tracts and connectivity alterations in five cognitive networks of patients with multiple sclerosis. Clin Neurol Neurosurg 2020; 201:106424. [PMID: 33348120 DOI: 10.1016/j.clineuro.2020.106424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/04/2020] [Accepted: 12/05/2020] [Indexed: 01/01/2023]
Abstract
INTRODUCTION MS is associated with structural and functional brain alterations leading to cognitive impairments across multiple domains including attention, memory, and speed of information processing. Here, we analyzed the white matter damage and topological organization of white matter tracts in specific brain regions responsible for cognition in MS. METHODS Brain DTI, rs-fMRI, T1, T2, and T2-FLAIR were acquired for 22 MS subjects and 22 healthy controls. Automatic brain parcellation was performed on T1-weighted images. Skull-stripped T1-weighted intensity inverted images were co-registered to the b0 image. Diffusion-weighted images were processed to perform whole brain tractography. The rs-fMRI data were processed, and the connectivity matrixes were analyzed to identify significant differences in the network of nodes between the two groups using NBS analysis. In addition, diffusion entropy maps were produced from DTI data sets using in-house software. RESULTS MS subjects exhibited significantly reduced mean FA and entropy in 38 and 34 regions, respectively, out of a total of 54 regions. The connectivity values in both structural and functional analyses were decreased in most regions of the default mode network and in four other cognitive networks in MS subjects compared to healthy controls. MS also induced significant reduction in the normalized hippocampus and corpus callosum volumes; the normalized hippocampus volume was significantly correlated with EDSS scores. CONCLUSION MS subjects have significant white matter damage and reduction of FA and entropy in various brain regions involved in cognitive networks. Structural and functional connectivity within the default mode network and an additional four cognitive networks exhibited significant changes compared with healthy controls.
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Affiliation(s)
- AmirEhsan Lashkari
- Department of Neurology, Henry Ford Health System, Detroit, MI, United States
| | | | - Lara Fahmy
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, United States
| | - Lian Li
- Department of Neurology, Henry Ford Health System, Detroit, MI, United States
| | | | - Michael Chopp
- Department of Neurology, Henry Ford Health System, Detroit, MI, United States; Oakland University, Department of Physics, Rochester, MI, United States; Department of Neurology, Wayne State University, Detroit, MI, United States
| | - Quan Jiang
- Department of Neurology, Henry Ford Health System, Detroit, MI, United States; Oakland University, Department of Physics, Rochester, MI, United States; Department of Neurology, Wayne State University, Detroit, MI, United States.
| | - Mirela Cerghet
- Department of Neurology, Henry Ford Health System, Detroit, MI, United States
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25
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Sjøgård M, Wens V, Van Schependom J, Costers L, D'hooghe M, D'haeseleer M, Woolrich M, Goldman S, Nagels G, De Tiège X. Brain dysconnectivity relates to disability and cognitive impairment in multiple sclerosis. Hum Brain Mapp 2020; 42:626-643. [PMID: 33242237 PMCID: PMC7814767 DOI: 10.1002/hbm.25247] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 09/10/2020] [Accepted: 09/29/2020] [Indexed: 12/27/2022] Open
Abstract
The pathophysiology of cognitive dysfunction in multiple sclerosis (MS) is still unclear. This magnetoencephalography (MEG) study investigates the impact of MS on brain resting-state functional connectivity (rsFC) and its relationship to disability and cognitive impairment. We investigated rsFC based on power envelope correlation within and between different frequency bands, in a large cohort of participants consisting of 99 MS patients and 47 healthy subjects. Correlations were investigated between rsFC and outcomes on disability, disease duration and 7 neuropsychological scores within each group, while stringently correcting for multiple comparisons and possible confounding factors. Specific dysconnections correlating with MS-induced physical disability and disease duration were found within the sensorimotor and language networks, respectively. Global network-level reductions in within- and cross-network rsFC were observed in the default-mode network. Healthy subjects and patients significantly differed in their scores on cognitive fatigue and verbal fluency. Healthy subjects and patients showed different correlation patterns between rsFC and cognitive fatigue or verbal fluency, both of which involved a shift in patients from the posterior default-mode network to the language network. Introducing electrophysiological rsFC in a regression model of verbal fluency and cognitive fatigue in MS patients significantly increased the explained variance compared to a regression limited to structural MRI markers (relative thalamic volume and lesion load). This MEG study demonstrates that MS induces distinct changes in the resting-state functional brain architecture that relate to disability, disease duration and specific cognitive functioning alterations. It highlights the potential value of electrophysiological intrinsic rsFC for monitoring the cognitive impairment in patients with MS.
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Affiliation(s)
- Martin Sjøgård
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Jeroen Van Schependom
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center, Belgium
| | - Lars Costers
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Marie D'hooghe
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center, Belgium
| | - Miguel D'haeseleer
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center, Belgium
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Guy Nagels
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center, Belgium.,St Edmund Hall, University of Oxford, Oxford, UK
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
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26
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Strik M, Chard DT, Dekker I, Meijer KA, Eijlers AJ, Pardini M, Uitdehaag BM, Kolbe SC, Geurts JJ, Schoonheim MM. Increased functional sensorimotor network efficiency relates to disability in multiple sclerosis. Mult Scler 2020; 27:1364-1373. [PMID: 33104448 PMCID: PMC8358536 DOI: 10.1177/1352458520966292] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
BACKGROUND Network abnormalities could help explain physical disability in multiple sclerosis (MS), which remains poorly understood. OBJECTIVE This study investigates functional network efficiency changes in the sensorimotor system. METHODS We included 222 MS patients, divided into low disability (LD, Expanded Disability Status Scale (EDSS) ⩽3.5, n = 185) and high disability (HD, EDSS ⩾6, n = 37), and 82 healthy controls (HC). Functional connectivity was assessed between 23 sensorimotor regions. Measures of efficiency were computed and compared between groups using general linear models corrected for age and sex. Binary logistic regression models related disability status to local functional network efficiency (LE), brain volumes and demographics. Functional connectivity patterns of regions important for disability were explored. RESULTS HD patients demonstrated significantly higher LE of the left primary somatosensory cortex (S1) and right pallidum compared to LD and HC, and left premotor cortex compared to HC only. The logistic regression model for disability (R2 = 0.38) included age, deep grey matter volume and left S1 LE. S1 functional connectivity was increased with prefrontal and secondary sensory areas in HD patients, compared to LD and HC. CONCLUSION Clinical disability in MS associates with functional sensorimotor increases in efficiency and connectivity, centred around S1, independent of structural damage.
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Affiliation(s)
- Myrte Strik
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands/Department of Radiology and Medicine, The University of Melbourne, Melbourne, VIC, Australia
| | - Declan T Chard
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK/National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Iris Dekker
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands/Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Kim A Meijer
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anand Jc Eijlers
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Matteo Pardini
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK/Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Genoa, Italy/Ospedale Policlinico San Martino-IRCCS, Genoa, Italy
| | - Bernard Mj Uitdehaag
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Scott C Kolbe
- Department of Radiology and Medicine, The University of Melbourne, Melbourne, VIC, Australia/Department of Neurosciences, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Jeroen Jg Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Vallée C, Maurel P, Corouge I, Barillot C. Acquisition Duration in Resting-State Arterial Spin Labeling. How Long Is Enough? Front Neurosci 2020; 14:598. [PMID: 32848529 PMCID: PMC7406917 DOI: 10.3389/fnins.2020.00598] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 05/15/2020] [Indexed: 12/12/2022] Open
Abstract
Resting-state Arterial Spin Labeling (rs-ASL) is a rather confidential method compared to resting-state BOLD. As ASL allows to quantify the cerebral blood flow, unlike BOLD, rs-ASL can lead to significant clinical subject-scaled applications. Despite directly impacting clinical practicability and functional networks estimation, there is no standard for rs-ASL regarding the acquisition duration. Our work here focuses on assessing the feasibility of ASL as an rs-fMRI method and on studying the effect of the acquisition duration on the estimation of functional networks. To this end, we acquired a long 24 min 30 s rs-ASL sequence and investigated how estimations of six typical functional brain networks evolved with respect to the acquisition duration. Our results show that, after a certain acquisition duration, the estimations of all functional networks reach their best and are stabilized. Since, for clinical application, the acquisition duration should be the shortest possible, we suggest an acquisition duration of 14 min, i.e., 240 volumes with our sequence parameters, as it covers the functional networks estimation stabilization.
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Affiliation(s)
- Corentin Vallée
- Université de Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228, Rennes, France
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Carotenuto A, Wilson H, Giordano B, Caminiti SP, Chappell Z, Williams SCR, Hammers A, Silber E, Brex P, Politis M. Impaired connectivity within neuromodulatory networks in multiple sclerosis and clinical implications. J Neurol 2020; 267:2042-2053. [PMID: 32219555 PMCID: PMC7320961 DOI: 10.1007/s00415-020-09806-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 03/17/2020] [Accepted: 03/18/2020] [Indexed: 11/25/2022]
Abstract
There is mounting evidence regarding the role of impairment in neuromodulatory networks for neurodegenerative diseases, such as Parkinson's and Alzheimer's disease. However, the role of neuromodulatory networks in multiple sclerosis (MS) has not been assessed. We applied resting-state functional connectivity and graph theory to investigate the changes in the functional connectivity within neuromodulatory networks including the serotonergic, noradrenergic, cholinergic, and dopaminergic systems in MS. Twenty-nine MS patients and twenty-four age- and gender-matched healthy controls performed clinical and cognitive assessments including the expanded disability status score, symbol digit modalities test, and Hamilton Depression rating scale. We demonstrated a diffuse reorganization of network topography (P < 0.01) in serotonergic, cholinergic, noradrenergic, and dopaminergic networks in patients with MS. Serotonergic, noradrenergic, and cholinergic network functional connectivity derangement was associated with disease duration, EDSS, and depressive symptoms (P < 0.01). Derangements in serotonergic, noradrenergic, cholinergic, and dopaminergic network impairment were associated with cognitive abilities (P < 0.01). Our results indicate that functional connectivity changes within neuromodulatory networks might be a useful tool in predicting disability burden over time, and could serve as a surrogate endpoint to assess efficacy for symptomatic treatments.
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Affiliation(s)
- Antonio Carotenuto
- Neurodegeneration Imaging Group, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Multiple Sclerosis Clinical Care and Research Centre, Department of Neuroscience, Federico II University, Naples, Italy
| | - Heather Wilson
- Neurodegeneration Imaging Group, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Neurodegeneration Imaging Group, University of Exeter Medical School, London, UK
| | - Beniamino Giordano
- Neurodegeneration Imaging Group, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Silvia P Caminiti
- Neurodegeneration Imaging Group, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zachary Chappell
- Neurodegeneration Imaging Group, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Steven C R Williams
- Institute of Psychiatry, Psychology and Neuroscience, Institute of Psychiatry, King's College London, London, UK
| | - Alexander Hammers
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, St Thomas' Hospital, London, UK
| | - Eli Silber
- Department of Neurology, King's College Hospital NHS Foundation Trust, London, UK
| | - Peter Brex
- Department of Neurology, King's College Hospital NHS Foundation Trust, London, UK
| | - Marios Politis
- Neurodegeneration Imaging Group, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Neurodegeneration Imaging Group, University of Exeter Medical School, London, UK.
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Tommasin S, De Giglio L, Ruggieri S, Petsas N, Giannì C, Pozzilli C, Pantano P. Multi-scale resting state functional reorganization in response to multiple sclerosis damage. Neuroradiology 2020; 62:693-704. [PMID: 32189024 DOI: 10.1007/s00234-020-02393-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 02/27/2020] [Indexed: 11/26/2022]
Abstract
PURPOSE In multiple sclerosis (MS), how brain functional changes relate to clinical conditions is still a matter of debate. The aim of this study was to investigate how functional connectivity (FC) reorganization at three different scales, ranging from local to whole brain, is related to tissue damage and disability. METHODS One-hundred-nineteen patients with MS were clinically evaluated with the Expanded Disability Status Scale and the Multiple Sclerosis Functional Composite. Patients and 42 healthy controls underwent a multimodal 3 T MRI, including resting-state functional MRI. RESULTS We identified 16 resting-state networks via independent component analysis and measured within-network, between-network, and whole-brain (global efficiency and degree centrality) FC. Within-network FC was higher in patients than in controls in default mode, frontoparietal, and executive-control networks, and corresponded to low clinical impairment (default mode network versus Expanded Disability Status Scale r = - 0.31, p < 0.01; right frontoparietal network versus Paced Auditory Serial Addition Test r = 0.33, p < 0.01). All measures of between-network and whole-brain FC, except default mode network global efficiency, were lower in patients than in controls, and corresponded to high disability (i.e., basal ganglia global efficiency versus Timed 25-Foot Walk r = - 0.25, p < 0.03; default mode global efficiency versus Expanded Disability Status Scale r = - 0.44, p < 0.001). Altered measures of within-network, between-network, and whole-brain FC were combined in functional indices that were linearly related to disease duration, Paced Auditory Serial Addition Test and lesion load and non-linearly related to Expanded Disability Status Scale. CONCLUSION We suggest that the combined evaluation of functional alterations occurring at different levels, from local to whole brain, could exhaustively describe neuroplastic changes in MS, while increased within-network FC likely represents adaptive compensatory processes, decreased between-network and whole-brain FC likely represent loss of functional network integration consequent to structural disruption.
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Affiliation(s)
- Silvia Tommasin
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy
| | - Laura De Giglio
- Medicine Department, Neurology Unit, San Filippo Neri Hospital, Via Giovanni Martinotti, 20, 00135, Rome, RM, Italy
| | - Serena Ruggieri
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy
| | - Nikolaos Petsas
- IRCCS Neuromed, (Pozzilli [IS], IT) Via Atinense, 18, 86077, Pozzilli, IS, Italy
| | - Costanza Giannì
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy
| | - Carlo Pozzilli
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy
- Sant'Andrea Hospital, MS Centre, Sapienza - University of Rome, Viale di Grottarossa 1035, 00189, Rome, Italy
| | - Patrizia Pantano
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy.
- IRCCS Neuromed, (Pozzilli [IS], IT) Via Atinense, 18, 86077, Pozzilli, IS, Italy.
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Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study. Sci Rep 2020; 10:806. [PMID: 31964982 PMCID: PMC6972853 DOI: 10.1038/s41598-020-57895-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 01/03/2020] [Indexed: 12/02/2022] Open
Abstract
Effective connectivity (EC) is able to explore causal effects between brain areas and can depict mechanisms that underlie repair and adaptation in chronic brain diseases. Thus, the application of EC techniques in multiple sclerosis (MS) has the potential to determine directionality of neuronal interactions and may provide an imaging biomarker for disease progression. Here, serial longitudinal structural and resting-state fMRI was performed at 12-week intervals over one year in twelve MS patients. Twelve healthy subjects served as controls (HC). Two approaches for EC quantification were used: Causal Bayesian Network (CBN) and Time-resolved Partial Directed Coherence (TPDC). The EC strength was correlated with the Expanded Disability Status Scale (EDSS) and Fatigue Scale for Motor and Cognitive functions (FSMC). Our findings demonstrated a longitudinal increase in EC between specific brain regions, detected in both the CBN and TPDC analysis in MS patients. In particular, EC from the deep grey matter, frontal, prefrontal and temporal regions showed a continuous increase over the study period. No longitudinal changes in EC were attested in HC during the study. Furthermore, we observed an association between clinical performance and EC strength. In particular, the EC increase in fronto-cerebellar connections showed an inverse correlation with the EDSS and FSMC. Our data depict continuous functional reorganization between specific brain regions indicated by increasing EC over time in MS, which is not detectable in HC. In particular, fronto-cerebellar connections, which were closely related to clinical performance, may provide a marker of brain plasticity and functional reserve in MS.
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Has Silemek AC, Fischer L, Pöttgen J, Penner IK, Engel AK, Heesen C, Gold SM, Stellmann JP. Functional and structural connectivity substrates of cognitive performance in relapsing remitting multiple sclerosis with mild disability. Neuroimage Clin 2020; 25:102177. [PMID: 32014828 PMCID: PMC6997626 DOI: 10.1016/j.nicl.2020.102177] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 12/06/2019] [Accepted: 01/11/2020] [Indexed: 01/10/2023]
Abstract
Multiple Sclerosis (MS) is the most common chronic inflammatory and neurodegenerative disease of the central nervous system (CNS), which can lead to severe cognitive impairment over time. Magnetic resonance imaging (MRI) is currently the best available biomarker to track MS pathophysiology in vivo and examine the link to clinical disability. However, conventional MRI metrics have limited sensitivity and specificity to detect direct associations between symptoms and their underlying CNS substrates. In this study, we aimed to investigate structural and resting state functional connectomes and subnetworks associated with neuropsychological (NP) performance using a graph theoretical approach. A comprehensive NP test battery was administered in a sample of patients with relapsing remitting MS (RRMS) and mild disability [n = 33, F/M = 20/13, age = 40.9 ± 9.7, median [Expanded Disability Status Scale] (EDSS) = 2, range =0-4] and compared to healthy controls (HC) [n = 29, F/M = 19/10, age = 41.0 ± 8.5] closely matched for age, sex, and level of education. The NP battery comprised the most relevant domains of cognitive dysfunction in MS including attention, processing speed, verbal and spatial learning and memory, and executive function. While standard MRI metrics showed good correlations with TAP Alertness test, disease duration and neurological exams, structural networks showed closer associations with 9-hole peg test and cognitive performances. Decreased graph strength was associated with two out of the 5 NP tests in the spatial learning and memory domain specified by BVMT [Sum 1-3] and BVMT [Recall], and with also SDMT which is one out of the 9 NP tests in the attention/processing speed domain, while no correlation was found between these scores and functional connectivity. Nodal strength was decreased in all subnetworks based on Yeo atlas in patients compared to HC; however, no difference was observed in nodal level of functional connectivity between the groups. The difference in structural and functional nodal connectivity between the groups was also observed in the relationship between structural and functional connectivity within the groups; the relationship between nodal degree and nodal strength was reversed in patients but positive in controls. On a nodal level, structural and functional networks (mainly the default mode network) were correlated with more than one cognitive domain rather than one specific network for each domain within patients. Interestingly, poorer cognitive performance was mostly correlated with increased functional connectivity but decreased structural connectivity in patients. Increased functional connectivity in the default mode network had both positive as well as negative associations with verbal and spatial learning and memory, possibly indicating adaptive and maladaptive mechanisms. In conclusion, our results suggest that cognitive performance, even in patients with RRMS and very mild disability, may reflect a loss of structural connectivity. In contrast, widespread increases in functional connectivity may be the result of maladaptive processes.
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Affiliation(s)
- Arzu Ceylan Has Silemek
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany.
| | - Lukas Fischer
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany
| | - Jana Pöttgen
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany; Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany
| | - Iris-Katharina Penner
- Klinik für Neurologie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf 40225, Germany; COGITO Zentrum für Angewandte Neurokognition und Neuropsychologische Forschung, Düsseldorf 40225, Germany
| | - Andreas K Engel
- Institut für Neurophysiologie und Pathophysiologie, Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany
| | - Christoph Heesen
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany; Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany
| | - Stefan M Gold
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany; Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health (BIH), Klinik für Psychiatrie & Psychotherapie und Medizinische Klinik m.S. Psychosomatik, Campus Benjamin Franklin (CBF), Hindenburgdamm 30, Berlin 12203, Germany
| | - Jan-Patrick Stellmann
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany; Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf (UKE), Martinistr. 52, Hamburg 20246, Germany; APHM, Hopital de la Timone, CEMEREM, Marseille, France; Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
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Modeling Resilience to Damage in Multiple Sclerosis: Plasticity Meets Connectivity. Int J Mol Sci 2019; 21:ijms21010143. [PMID: 31878257 PMCID: PMC6981966 DOI: 10.3390/ijms21010143] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 12/05/2019] [Accepted: 12/20/2019] [Indexed: 02/03/2023] Open
Abstract
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) characterized by demyelinating white matter lesions and neurodegeneration, with a variable clinical course. Brain network architecture provides efficient information processing and resilience to damage. The peculiar organization characterized by a low number of highly connected nodes (hubs) confers high resistance to random damage. Anti-homeostatic synaptic plasticity, in particular long-term potentiation (LTP), represents one of the main physiological mechanisms underlying clinical recovery after brain damage. Different types of synaptic plasticity, including both anti-homeostatic and homeostatic mechanisms (synaptic scaling), contribute to shape brain networks. In MS, altered synaptic functioning induced by inflammatory mediators may represent a further cause of brain network collapse in addition to demyelination and grey matter atrophy. We propose that impaired LTP expression and pathologically enhanced upscaling may contribute to disrupting brain network topology in MS, weakening resilience to damage and negatively influencing the disease course.
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Van Schependom J, Guldolf K, D'hooghe MB, Nagels G, D'haeseleer M. Detecting neurodegenerative pathology in multiple sclerosis before irreversible brain tissue loss sets in. Transl Neurodegener 2019; 8:37. [PMID: 31827784 PMCID: PMC6900860 DOI: 10.1186/s40035-019-0178-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 11/07/2019] [Indexed: 12/29/2022] Open
Abstract
Background Multiple sclerosis (MS) is a complex chronic inflammatory and degenerative disorder of the central nervous system. Accelerated brain volume loss, or also termed atrophy, is currently emerging as a popular imaging marker of neurodegeneration in affected patients, but, unfortunately, can only be reliably interpreted at the time when irreversible tissue damage likely has already occurred. Timing of treatment decisions based on brain atrophy may therefore be viewed as suboptimal. Main body This Narrative Review focuses on alternative techniques with the potential of detecting neurodegenerative events in the brain of subjects with MS prior to the atrophic stage. First, metabolic and molecular imaging provide the opportunity to identify early subcellular changes associated with energy dysfunction, which is an assumed core mechanism of axonal degeneration in MS. Second, cerebral hypoperfusion has been observed throughout the entire clinical spectrum of the disorder but it remains an open question whether this serves as an alternative marker of reduced metabolic activity, or exists as an independent contributing process, mediated by endothelin-1 hyperexpression. Third, both metabolic and perfusion alterations may lead to repercussions at the level of network performance and structural connectivity, respectively assessable by functional and diffusion tensor imaging. Fourth and finally, elevated body fluid levels of neurofilaments are gaining interest as a biochemical mirror of axonal damage in a wide range of neurological conditions, with early rises in patients with MS appearing to be predictive of future brain atrophy. Conclusions Recent findings from the fields of advanced neuroradiology and neurochemistry provide the promising prospect of demonstrating degenerative brain pathology in patients with MS before atrophy has installed. Although the overall level of evidence on the presented topic is still preliminary, this Review may pave the way for further longitudinal and multimodal studies exploring the relationships between the abovementioned measures, possibly leading to novel insights in early disease mechanisms and therapeutic intervention strategies.
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Affiliation(s)
- Jeroen Van Schependom
- 1Neurology Department, Universitair Ziekenhuis Brussel; Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium.,2Radiology Department Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Kaat Guldolf
- 1Neurology Department, Universitair Ziekenhuis Brussel; Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium
| | - Marie Béatrice D'hooghe
- 1Neurology Department, Universitair Ziekenhuis Brussel; Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium.,Nationaal Multiple Sclerose Centrum, Melsbroek, Belgium
| | - Guy Nagels
- 1Neurology Department, Universitair Ziekenhuis Brussel; Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium.,Nationaal Multiple Sclerose Centrum, Melsbroek, Belgium
| | - Miguel D'haeseleer
- 1Neurology Department, Universitair Ziekenhuis Brussel; Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium.,Nationaal Multiple Sclerose Centrum, Melsbroek, Belgium
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Gonzalez Campo C, Salamone PC, Rodríguez-Arriagada N, Richter F, Herrera E, Bruno D, Pagani Cassara F, Sinay V, García AM, Ibáñez A, Sedeño L. Fatigue in multiple sclerosis is associated with multimodal interoceptive abnormalities. Mult Scler 2019; 26:1845-1853. [PMID: 31778101 DOI: 10.1177/1352458519888881] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
BACKGROUND Fatigue ranks among the most common and disabling symptoms in multiple sclerosis (MS). Recent theoretical works have surmised that this trait might be related to alterations across interoceptive mechanisms. However, this hypothesis has not been empirically evaluated. OBJECTIVES To determine whether fatigue in MS patients is associated with specific behavioral, structural, and functional disruptions of the interoceptive domain. METHODS Fatigue levels were established via the Modified Fatigue Impact Scale. Interoception was evaluated through a robust measure indexed by the heartbeat detection task. Structural and functional connectivity properties of key interoceptive hubs were tested by magnetic resonance imaging (MRI) and resting-state functional MRI. Machine learning analyses were employed to perform pairwise classifications. RESULTS Only patients with fatigue presented with decreased interoceptive accuracy alongside decreased gray matter volume and increased functional connectivity in core interoceptive regions, the insula, and the anterior cingulate cortex. Each of these alterations was positively associated with fatigue. Finally, machine-learning analysis with a combination of the above interoceptive indices (behavioral, structural, and functional) successfully discriminated (area under the curve > 90%) fatigued patients from both non-fatigued and healthy controls. CONCLUSION This study offers unprecedented evidence suggesting that disruptions of neurocognitive markers subserving interoception may constitute a signature of fatigue in MS.
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Affiliation(s)
- Cecilia Gonzalez Campo
- Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Paula C Salamone
- Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Nicolás Rodríguez-Arriagada
- Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina
| | - Fabian Richter
- Department of Psychology, University of Cologne, Cologne, Germany
| | - Eduar Herrera
- Departamento de Estudios Psicológicos, Universidad Icesi, Cali, Colombia
| | - Diana Bruno
- Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/Instituto de Investigación en Psicología Básica y Aplicada (IIPBA), Facultad de Filosofía y Humanidades, Universidad Católica de Cuyo, San Juan, Argentina
| | - Fátima Pagani Cassara
- Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/Instituto de Neurociencias de Fundación Favaloro, Buenos Aires, Argentina
| | - Vladimiro Sinay
- Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/Instituto de Neurociencias de Fundación Favaloro, Buenos Aires, Argentina
| | - Adolfo M García
- Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina/Faculty of Education, National University of Cuyo (UNCuyo), Mendoza, Argentina
| | - Agustín Ibáñez
- Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina/ Universidad Autónoma del Caribe, Barranquilla, Colombia/Department of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile/Centre of Excellence in Cognition and its Disorders, Australian Research Council (ACR), Macquarie University, Sydney, NSW, Australia
| | - Lucas Sedeño
- Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina/National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
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Pinter D, Beckmann CF, Fazekas F, Khalil M, Pichler A, Gattringer T, Ropele S, Fuchs S, Enzinger C. Morphological MRI phenotypes of multiple sclerosis differ in resting-state brain function. Sci Rep 2019; 9:16221. [PMID: 31700126 PMCID: PMC6838050 DOI: 10.1038/s41598-019-52757-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 09/29/2019] [Indexed: 11/09/2022] Open
Abstract
We aimed to assess differences in resting-state functional connectivity (FC) between distinct morphological MRI-phenotypes in multiple sclerosis (MS). Out of 180 MS patients, we identified those with high T2-hyperintense lesion load (T2-LL) and high normalized brain volume (NBV; a predominately white matter damage group, WMD; N = 37) and patients with low T2-LL and low NBV (N = 37; a predominately grey matter damage group; GMD). Independent component analysis of resting-state fMRI was used to test for differences in the sensorimotor network (SMN) between MS MRI-phenotypes and compared to 37 age-matched healthy controls (HC). The two MS groups did not differ regarding EDSS scores, disease duration and distribution of clinical phenotypes. WMD compared to GMD patients showed increased FC in all sub-units of the SMN (sex- and age-corrected). WMD patients had increased FC compared to HC and GMD patients in the central SMN (leg area). Only in the WMD group, higher EDSS scores and T2-LL correlated with decreased connectivity in SMN sub-units. MS patients with distinct morphological MRI-phenotypes also differ in brain function. The amount of focal white matter pathology but not global brain atrophy affects connectivity in the central SMN (leg area) of the SMN, consistent with the notion of a disconnection syndrome.
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Affiliation(s)
- Daniela Pinter
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, Austria
- Research Unit for Neuronal Plasticity and Repair, Medical University of Graz, Auenbruggerplatz 22, Graz, Austria
| | - Christian F Beckmann
- Donders Institute, Cognitive Neuroscience Department and Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Kapittelweg 29, Nijmegen, The Netherlands
| | - Franz Fazekas
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, Austria
| | - Michael Khalil
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, Austria
| | - Alexander Pichler
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, Austria
| | - Thomas Gattringer
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, Austria
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, Austria
| | - Siegrid Fuchs
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, Austria
| | - Christian Enzinger
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, Austria.
- Research Unit for Neuronal Plasticity and Repair, Medical University of Graz, Auenbruggerplatz 22, Graz, Austria.
- Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, Graz, Austria.
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Azarmi F, Miri Ashtiani SN, Shalbaf A, Behnam H, Daliri MR. Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI. Comput Biol Med 2019; 115:103495. [PMID: 31698238 DOI: 10.1016/j.compbiomed.2019.103495] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Revised: 10/10/2019] [Accepted: 10/10/2019] [Indexed: 11/30/2022]
Abstract
Several studies have already assessed brain network variations in multiple sclerosis (MS) patients and healthy controls (HCs). The underlying neural system's functioning is apparently too complicated, however. Therefore, the neural time series' analysis through new methods is the aim of any recent research. Functional magnetic resonance imaging (fMRI) is a prominent modality for investigating the human brain's neural substrate, especially when cognitive impairment occurs. The present study was an attempt to investigate the brain network's differences between MS patients and HCs using graph-theoretic measures constructed by an effective connectivity measure through statistical tests. The results of the significant measures were then evaluated through machine learning methods. To this end, we gathered blood-oxygen level dependent (BOLD) fMRI data of the participants during the execution of paced auditory serial addition test (PASAT). Granger causality analysis (GCA) was then employed between brain regions' time series on each subject in order to construct a brain network. Afterward, the Wilcoxon rank-sum test was implemented to find the alteration of brain networks between the mentioned groups. According to the results, Global flow coefficient was significantly different between HCs and patients. Moreover, MS disease impacted several areas of the brain including Hippocampus, Para Hippocampal, Thalamus, Cuneus, Superior temporal gyrus, Heschl, Caudate, Medial Frontal Superior Gyrus, Fusiform, Pallidum, and several parts of Cerebellum in centrality measures and local flow coefficient. Most of the obtained regions were related to the cognitive impacts of the disease. We also found the best subset of graph features by means of Fisher score, and classified them to evaluate the features strength for the discrimination of MS patients from HCs via several machine learning methods. Having used the combination of Wilcoxon rank-sum test and Fisher score, we were able to classify MS patients from HCs using linear support vector machine (SVM) with an accuracy of 95%. With regard to the few existing studies on brain network of MS patients, especially during a cognitive task execution, our findings showed that the selected graph measures by Wilcoxon rank-sum test and Fisher score from the GCA-based brain networks resulted in a promising classification accuracy.
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Affiliation(s)
- Farzad Azarmi
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyedeh Naghmeh Miri Ashtiani
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science & Technology (IUST), Narmak, 16846-13114, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Behnam
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science & Technology (IUST), Narmak, 16846-13114, Tehran, Iran
| | - Mohammad Reza Daliri
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science & Technology (IUST), Narmak, 16846-13114, Tehran, Iran.
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Saçmacı H, Tanık N, Ökçesiz İ, İntepe YS, Aktürk T, Çiftçi B, İnan LE. The association of brain lesion locations and sleep parameters in patients with multiple sclerosis: a pilot study. Sleep Biol Rhythms 2019. [DOI: 10.1007/s41105-019-00231-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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38
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Abstract
The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots’ capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients’ recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.
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Van Schependom J, Vidaurre D, Costers L, Sjøgård M, D'hooghe MB, D'haeseleer M, Wens V, De Tiège X, Goldman S, Woolrich M, Nagels G. Altered transient brain dynamics in multiple sclerosis: Treatment or pathology? Hum Brain Mapp 2019; 40:4789-4800. [PMID: 31361073 DOI: 10.1002/hbm.24737] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 07/13/2019] [Accepted: 07/16/2019] [Indexed: 11/07/2022] Open
Abstract
Multiple sclerosis (MS) is a demyelinating, neuroinflammatory, and -degenerative disease that affects the brain's neurophysiological functioning through brain atrophy, a reduced conduction velocity and decreased connectivity. Currently, little is known on how MS affects the fast temporal dynamics of activation and deactivation of the different large-scale, ongoing brain networks. In this study, we investigated whether these temporal dynamics are affected in MS patients and whether these changes are induced by the pathology or by the use of benzodiazepines (BZDs), an important symptomatic treatment that aims at reducing insomnia, spasticity and anxiety and reinforces the inhibitory effect of GABA. To this aim, we employed a novel method capable of detecting these fast dynamics in 90 MS patients and 46 healthy controls. We demonstrated a less dynamic frontal default mode network in male MS patients and a reduced activation of the same network in female MS patients, regardless of BZD usage. Additionally, BZDs strongly altered the brain's dynamics by increasing the time spent in the deactivating sensorimotor network and the activating occipital network. Furthermore, BZDs induced a decreased power in the theta band and an increased power in the beta band. The latter was strongly expressed in those states without activation of the sensorimotor network. In summary, we demonstrate gender-dependent changes to the brain dynamics in the frontal DMN and strong effects from BZDs. This study is the first to characterise the effect of multiple sclerosis and BZDs in vivo in a spatially, temporally and spectrally defined way.
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Affiliation(s)
- Jeroen Van Schependom
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
- Radiology, Universitair Ziekenhuis Brussel, Brussels, Belgium
- National MS Center Melsbroek, Melsbroek, Belgium
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK
- Oxford University Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Lars Costers
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Martin Sjøgård
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Marie B D'hooghe
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
- National MS Center Melsbroek, Melsbroek, Belgium
| | - Miguel D'haeseleer
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
- National MS Center Melsbroek, Melsbroek, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
- Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
- Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Brussels, Belgium
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
- Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Brussels, Belgium
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK
- Oxford University Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Guy Nagels
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
- National MS Center Melsbroek, Melsbroek, Belgium
- St Edmund Hall, University of Oxford, Oxford, UK
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40
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Wang J, Chen H, Liang H, Wang W, Liang Y, Liang Y, Zhang Y. Low-Frequency Fluctuations Amplitude Signals Exhibit Abnormalities of Intrinsic Brain Activities and Reflect Cognitive Impairment in Leukoaraiosis Patients. Med Sci Monit 2019; 25:5219-5228. [PMID: 31302662 PMCID: PMC6650186 DOI: 10.12659/msm.915528] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Background This study aimed to explore the amplitude of low-frequency fluctuations (ALFF) for whole-brain in leukoaraiosis (LA) patients suffering from cognitive decline or impairment. Material/Methods Patients were selected by employing magnetic resonance imaging (MRI) technique. According to results of the clinical dementia rating and Montreal cognitive assessment (MoCA), patients were divided into 3 groups: LA patients diagnosed as vascular mild-cognitive impairment (LA-VaMCI, n=28), LA patients diagnosed as vascular-dementia (LA-VaD, n=18), and normal individuals (NC, n=28). Executive functions were evaluated by using the Stroop test and Trail Making Test (TMT). The higher scores in TMT test mean greater impairments. Changes for the ALFF were measured by using resting-state functional MRI (rs-fMRI) technique. Correlations between ALFF and cognition scores were analyzed. Results It was found that widespread differences in ALFF were present predominantly in the posterior cingulate cortex/precuneus (PCC/PCu) and in the right inferior temporal gyrus (ITG). Compared with the NC group, ALFF values in PCC/PCu were significantly decreased (F=3.273, P=0.022) and ALFF values were significantly increased (F=2.864, P=0.033) in temporal regions of the LA-VaD patients. ALFF values in LA-VaMCI patients were significantly increased in ITG compared to that in the NC group (F=1.064, P=0.042) and the LA-VaD group (F=2.725, P=0.037). Impairment in executive functions were positively correlated with average ALFF of the left PCu. Conclusions This research showed that LA patients exhibited abnormal intrinsic-brain activities. Furthermore, altered ALFF was positively correlated with executive function scores.
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Affiliation(s)
- Jinfang Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University; China National Clinical Research Center for Neurological Diseases; Center of Stroke, Beijing Institute for Brain Disorders; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China (mainland).,Department of Neurology, General Hospital of The Yang Tze River Shipping, Wuhan Brain Hospital, Wuhan, Hubei, China (mainland)
| | - Hongyan Chen
- Department of Neurology, General Hospital of The Yang Tze River Shipping, Wuhan Brain Hospital, Wuhan, Hubei, China (mainland)
| | - Huazheng Liang
- School of Medicine, Western Sydney University, Sydney, NSW, Austria
| | - Wanming Wang
- Department of Neurology, General Hospital of The Yang Tze River Shipping, Wuhan Brain Hospital, Wuhan, Hubei, China (mainland)
| | - Yi Liang
- Department of Neurology, General Hospital of The Yang Tze River Shipping, Wuhan Brain Hospital, Wuhan, Hubei, China (mainland)
| | - Ying Liang
- School of Biomedical Engineering, Capital Medical University, Beijing, China (mainland)
| | - Yumei Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University; China National Clinical Research Center for Neurological Diseases; Center of Stroke, Beijing Institute for Brain Disorders; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China (mainland).,Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (mainland)
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41
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Conrad BN, Barry RL, Rogers BP, Maki S, Mishra A, Thukral S, Sriram S, Bhatia A, Pawate S, Gore JC, Smith SA. Multiple sclerosis lesions affect intrinsic functional connectivity of the spinal cord. Brain 2019; 141:1650-1664. [PMID: 29648581 DOI: 10.1093/brain/awy083] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 02/04/2018] [Indexed: 11/13/2022] Open
Abstract
Patients with multiple sclerosis present with focal lesions throughout the spinal cord. There is a clinical need for non-invasive measurements of spinal cord activity and functional organization in multiple sclerosis, given the cord's critical role in the disease. Recent reports of spontaneous blood oxygenation level-dependent fluctuations in the spinal cord using functional MRI suggest that, like the brain, cord activity at rest is organized into distinct, synchronized functional networks among grey matter regions, likely related to motor and sensory systems. Previous studies looking at stimulus-evoked activity in the spinal cord of patients with multiple sclerosis have demonstrated increased levels of activation as well as a more bilateral distribution of activity compared to controls. Functional connectivity studies of brain networks in multiple sclerosis have revealed widespread alterations, which may take on a dynamic trajectory over the course of the disease, with compensatory increases in connectivity followed by decreases associated with structural damage. We build upon this literature by examining functional connectivity in the spinal cord of patients with multiple sclerosis. Using ultra-high field 7 T imaging along with processing strategies for robust spinal cord functional MRI and lesion identification, the present study assessed functional connectivity within cervical cord grey matter of patients with relapsing-remitting multiple sclerosis (n = 22) compared to a large sample of healthy controls (n = 56). Patient anatomical images were rated for lesions by three independent raters, with consensus ratings revealing 19 of 22 patients presented with lesions somewhere in the imaged volume. Linear mixed models were used to assess effects of lesion location on functional connectivity. Analysis in control subjects demonstrated a robust pattern of connectivity among ventral grey matter regions as well as a distinct network among dorsal regions. A gender effect was also observed in controls whereby females demonstrated higher ventral network connectivity. Wilcoxon rank-sum tests detected no differences in average connectivity or power of low frequency fluctuations in patients compared to controls. The presence of lesions was, however, associated with local alterations in connectivity with differential effects depending on columnar location. The patient results suggest that spinal cord functional networks are generally intact in relapsing-remitting multiple sclerosis but that lesions are associated with focal abnormalities in intrinsic connectivity. These findings are discussed in light of the current literature on spinal cord functional MRI and the potential neurological underpinnings.
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Affiliation(s)
- Benjamin N Conrad
- Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Baxter P Rogers
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Satoshi Maki
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Arabinda Mishra
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Saakshi Thukral
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Subramaniam Sriram
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Aashim Bhatia
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Siddharama Pawate
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John C Gore
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Seth A Smith
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
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42
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d'Ambrosio A, Valsasina P, Gallo A, De Stefano N, Pareto D, Barkhof F, Ciccarelli O, Enzinger C, Tedeschi G, Stromillo ML, Arévalo MJ, Hulst HE, Muhlert N, Koini M, Filippi M, Rocca MA. Reduced dynamics of functional connectivity and cognitive impairment in multiple sclerosis. Mult Scler 2019; 26:476-488. [PMID: 30887862 DOI: 10.1177/1352458519837707] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
BACKGROUND In multiple sclerosis (MS), abnormalities of brain network dynamics and their relevance for cognitive impairment have never been investigated. OBJECTIVES The aim of this study was to assess the dynamic resting state (RS) functional connectivity (FC) on 62 relapsing-remitting MS patients and 65 sex-matched healthy controls enrolled at 7 European sites. METHODS MS patients underwent clinical and cognitive evaluation. Between-group network FC differences were evaluated using a dynamic approach (based on sliding-window correlation analysis) and grouping correlation matrices into recurrent FC states. RESULTS Dynamic FC analysis revealed, in healthy controls and MS patients, three recurrent FC states: two characterized by strong intra- and inter-network connectivity and one characterized by weak inter-network connectivity (State 3). A total of 23 MS patients were cognitively impaired (CI). Compared to cognitively preserved (CP), CI-MS patients had reduced RS-FC between subcortical and default-mode networks in the low-connectivity State 3 and lower dwell time (i.e. time spent in a given state) in the high-connectivity State 2. CI-MS patients also exhibited a lower number and a less frequent switching between meta-states, as well as a smaller distance traveled through connectivity states. CONCLUSION Time-varying RS-FC was markedly less dynamic in CI- versus CP-MS patients, suggesting that slow inter-network connectivity contributes to cognitive dysfunction in MS.
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Affiliation(s)
- Alessandro d'Ambrosio
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Antonio Gallo
- I Division of Neurology, Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of Campania "L. Vanvitelli," Naples, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Deborah Pareto
- Magnetic Resonance Unit, Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands/Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK
| | - Olga Ciccarelli
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK
| | | | - Gioacchino Tedeschi
- I Division of Neurology, Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of Campania "L. Vanvitelli," Naples, Italy
| | - M Laura Stromillo
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Maria J Arévalo
- Department of Neurology/Neuroimmunology, Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Hanneke E Hulst
- Department of Anatomy & Neurosciences, VU University Medical Center, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Nils Muhlert
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London (UCL), London, UK
| | - Marisa Koini
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy/Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy/Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
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Abstract
Multiple sclerosis is a chronic demyelinating disease. Since attacks are accompanied by psychiatric and physical disabilities, additional symptoms such as withdrawal from the social environment and psychiatric disorders are also observed. It is very important to evaluate patients adequately and correctly, to determine the disability, and treat them with appropriate clinical approach. For this reason, Expanded Disability Status Scale score and upper extremity capacity measurement tests are used in many studies by investigators. These tests provide detailed examination on the patient's upper limbs, and indirectly provide information about their cognitive function. A multi-disciplinary approach for multiple sclerosis patients is the most crucial factor in clinical follow-up and treatment success.
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Affiliation(s)
- R Gökçen Gözübatık Çelik
- Department of Neurology, Prof. Dr. Mazhar Osman Mental Health and Nerve Diseases Training and Research Hospital, İstanbul, Turkey
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44
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Carotenuto A, Costabile T, Moccia M, Falco F, Scala M, Russo C, Saccà F, De Rosa A, Lanzillo R, Brescia Morra V. Olfactory function and cognition in relapsing–remitting and secondary-progressive multiple sclerosis. Mult Scler Relat Disord 2019; 27:1-6. [DOI: 10.1016/j.msard.2018.09.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 09/18/2018] [Accepted: 09/21/2018] [Indexed: 10/28/2022]
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Kalron A, Achiron A, Menascu S. Gait Variability, Not Walking Speed, Is Related to Cognition in Adolescents With Multiple Sclerosis. J Child Neurol 2019; 34:27-32. [PMID: 30354845 DOI: 10.1177/0883073818808034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Gait variability is associated with cognitive performance in people with central neurologic damage illnesses, which includes multiple sclerosis. However, presently, there have been no data available as to whether this association exists in adolescents with multiple sclerosis. Therefore, our objective was to investigate the association between gait variability and cognition in adolescents with multiple sclerosis encompassing 48 recently diagnosed adolescents with multiple sclerosis (26 girls; 22 boys), average age of 16.0 years (SD = 2.2), and an Expanded Disability Status Scale (EDSS) score of 1.6 (SD = 1.3). Walking speed and gait variability expressed by the coefficient of variation of the mean step time was studied using an electronic walkway. A computerized cognitive battery of tests evaluated cognition. Cognitive outcome measurements included verbal and nonverbal memory, executive function, visual spatial processing, verbal function, attention, information processing speed, and motor skills. Mean walking speed was 76.9 cm/s (SD = 57.6); mean step time variability was 3.5 (SD = 1.3) and the global cognitive score was 93.9 (SD = 12.5). According to linear regression analysis, a significant association was found between step time variability, cognitive subdomains of attention, and information processing speed. After incorporating walking speed into the model, the association remained significant. Increased gait variability, not walking speed, is suggested as a clinical marker of cognitive performance in minimally disabled adolescents with multiple sclerosis.
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Affiliation(s)
- Alon Kalron
- 1 Department of Physical Therapy, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.,2 Sagol School of Neurosciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Anat Achiron
- 2 Sagol School of Neurosciences, Tel-Aviv University, Tel-Aviv, Israel.,3 Multiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel.,4 Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Shay Menascu
- 3 Multiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel.,4 Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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The Role of fMRI in the Assessment of Neuroplasticity in MS: A Systematic Review. Neural Plast 2018; 2018:3419871. [PMID: 30693023 PMCID: PMC6332922 DOI: 10.1155/2018/3419871] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 11/05/2018] [Indexed: 11/17/2022] Open
Abstract
Neuroplasticity, which is the ability of the brain to adapt to internal and external environmental changes, physiologically occurs during growth and in response to damage. The brain's response to damage is of particular interest in multiple sclerosis, a chronic disease characterized by inflammatory and neurodegenerative damage to the central nervous system. Functional MRI (fMRI) is a tool that allows functional changes related to the disease and to its evolution to be studied in vivo. Several studies have shown that abnormal brain recruitment during the execution of a task starts in the early phases of multiple sclerosis. The increased functional activation during a specific task observed has been interpreted mainly as a mechanism of adaptive plasticity designed to contrast the increase in tissue damage. More recent fMRI studies, which have focused on the activity of brain regions at rest, have yielded nonunivocal results, suggesting that changes in functional brain connections represent mechanisms of either adaptive or maladaptive plasticity. The few longitudinal studies available to date on disease evolution have also yielded discrepant results that are likely to depend on the clinical features considered and the length of the follow-up. Lastly, fMRI has been used in interventional studies to investigate plastic changes induced by pharmacological therapy or rehabilitation, though whether such changes represent a surrogate of neuroplasticity remains unclear. The aim of this paper is to systematically review the existing literature in order to provide an overall description of both the neuroplastic process itself and the evolution in the use of fMRI techniques as a means of assessing neuroplasticity. The quantitative and qualitative approach adopted here ensures an objective analysis of published, peer-reviewed research and yields an overview of up-to-date knowledge.
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Koubiyr I, Deloire M, Besson P, Coupé P, Dulau C, Pelletier J, Tourdias T, Audoin B, Brochet B, Ranjeva JP, Ruet A. Longitudinal study of functional brain network reorganization in clinically isolated syndrome. Mult Scler 2018; 26:188-200. [PMID: 30480467 DOI: 10.1177/1352458518813108] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND There is a lack of longitudinal studies exploring the topological organization of functional brain networks at the early stages of multiple sclerosis (MS). OBJECTIVE This study aims to assess potential brain functional reorganization at rest in patients with CIS (PwCIS) after 1 year of evolution and to characterize the dynamics of functional brain networks at the early stage of the disease. METHODS We prospectively included 41 PwCIS and 19 matched healthy controls (HCs). They were scanned at baseline and after 1 year. Using graph theory, topological metrics were calculated for each region. Hub disruption index was computed for each metric. RESULTS Hub disruption indexes of degree and betweenness centrality were negative at baseline in patients (p < 0.05), suggesting brain reorganization. After 1 year, hub disruption indexes for degree and betweenness centrality were still negative (p < 0.00001), but such reorganization appeared more pronounced than at baseline. Different brain regions were driving these alterations. No global efficiency differences were observed between PwCIS and HCs either at baseline or at 1 year. CONCLUSION Dynamic changes in functional brain networks appear at the early stages of MS and are associated with the maintenance of normal global efficiency in the brain, suggesting a compensatory effect.
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Affiliation(s)
- Ismail Koubiyr
- University of Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France
| | | | - Pierre Besson
- Aix-Marseille University, CNRS, CRMBM UMR, Marseille, France/Aix-Marseille University, APHM, Hopital la Timone, CEMEREM, Marseille, France
| | - Pierrick Coupé
- Laboratoire Bordelais de Recherche en Informatique, Talence, France
| | - Cécile Dulau
- CHU Pellegrin, CHU de Bordeaux, Bordeaux, France
| | - Jean Pelletier
- Aix-Marseille University, CNRS, CRMBM UMR, Marseille, France/Aix-Marseille University, APHM, Hopital la Timone, CEMEREM, Marseille, France/APHM, Hopital la Timone, service de Neurologie, Marseille, France
| | - Thomas Tourdias
- University of Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France/CHU Pellegrin, CHU de Bordeaux, Bordeaux, France
| | - Bertrand Audoin
- Aix-Marseille University, CNRS, CRMBM UMR, Marseille, France/Aix-Marseille University, APHM, Hopital la Timone, CEMEREM, Marseille, France/APHM, Hopital la Timone, service de Neurologie, Marseille, France
| | - Bruno Brochet
- University of Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France/CHU Pellegrin, CHU de Bordeaux, Bordeaux, France
| | - Jean-Philippe Ranjeva
- Aix-Marseille University, CNRS, CRMBM UMR, Marseille, France/Aix-Marseille University, APHM, Hopital la Timone, CEMEREM, Marseille, France
| | - Aurélie Ruet
- University of Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France/CHU Pellegrin, CHU de Bordeaux, Bordeaux, France
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48
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Tahedl M, Levine SM, Greenlee MW, Weissert R, Schwarzbach JV. Functional Connectivity in Multiple Sclerosis: Recent Findings and Future Directions. Front Neurol 2018; 9:828. [PMID: 30364281 PMCID: PMC6193088 DOI: 10.3389/fneur.2018.00828] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 09/14/2018] [Indexed: 02/03/2023] Open
Abstract
Multiple sclerosis is a debilitating disorder resulting from scattered lesions in the central nervous system. Because of the high variability of the lesion patterns between patients, it is difficult to relate existing biomarkers to symptoms and their progression. The scattered nature of lesions in multiple sclerosis offers itself to be studied through the lens of network analyses. Recent research into multiple sclerosis has taken such a network approach by making use of functional connectivity. In this review, we briefly introduce measures of functional connectivity and how to compute them. We then identify several common observations resulting from this approach: (a) high likelihood of altered connectivity in deep-gray matter regions, (b) decrease of brain modularity, (c) hemispheric asymmetries in connectivity alterations, and (d) correspondence of behavioral symptoms with task-related and task-unrelated networks. We propose incorporating such connectivity analyses into longitudinal studies in order to improve our understanding of the underlying mechanisms affected by multiple sclerosis, which can consequently offer a promising route to individualizing imaging-related biomarkers for multiple sclerosis.
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Affiliation(s)
- Marlene Tahedl
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
- Institute for Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Seth M. Levine
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Mark W. Greenlee
- Institute for Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Robert Weissert
- Department of Neurology, University of Regensburg, Regensburg, Germany
| | - Jens V. Schwarzbach
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
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49
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Tommasin S, De Giglio L, Ruggieri S, Petsas N, Giannì C, Pozzilli C, Pantano P. Relation between functional connectivity and disability in multiple sclerosis: a non-linear model. J Neurol 2018; 265:2881-2892. [PMID: 30276520 DOI: 10.1007/s00415-018-9075-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 09/21/2018] [Accepted: 09/25/2018] [Indexed: 01/12/2023]
Abstract
OBJECTIVE To characterize the relation between brain functional connectivity and disability in patients with multiple sclerosis; to investigate the existence of critical values of both disability and functional connectivity corresponding to exhaustion of functional adaptive mechanisms. METHODS Hundred-and-nineteen patients with no-to-severe disability and 42 healthy subjects were studied via 3T resting state functional MRI. Out of 116 regions extracted from Automated Anatomical Labeling atlas, pairs of regions whose functional connectivity correlated with Expanded Disability Status Score were identified. In patients, mathematical modeling was applied to find the best models describing Expanded-Disability-Status-Score vs structural or functional measures. Functional vs structural models intersecting points were identified. RESULTS Disability had direct linear relation with lesion load (r = 0.40, p < 5E-6), inverse of thalamic volume (r = 0.31 p < 1E-3) and functional connectivity in bi-frontal pairs of regions (r > 0.40, p < 0.04), while being non-linearly associated with functional connectivity in cerebello-temporal and cerebello-frontal pairs of regions (F > 1.73, p < 0.02). Structural vs functional models intersecting points corresponded to Expanded Disability Status Score of 3.0. 85% of patients scoring more than 3.0 showed functional connectivity in cerebello-temporal and cerebello-frontal pairs of regions below confidence intervals (z = [2.28-2.88] 95% CI) measured in healthy subjects. CONCLUSIONS Functional brain connectivity changes may represent mechanisms of adaptation to structural damage and inflammation and may be not always clinically beneficial. Functional connectivity decreases in comparison with structural measure at Expanded Disability Status Score greater than 3.0, which may be critical and indicate exhaustion of compensatory mechanisms.
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Affiliation(s)
- Silvia Tommasin
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy
| | - Laura De Giglio
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy
- Sant'Andrea Hospital, MS Centre, Sapienza University of Rome, Viale di Grottarossa 1035, 00189, Rome, Italy
| | - Serena Ruggieri
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy
| | - Nikolaos Petsas
- IRCCS Neuromed, Via Atinense, 18, 86077, Pozzilli, IS, Italy
| | - Costanza Giannì
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy
| | - Carlo Pozzilli
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy
- Sant'Andrea Hospital, MS Centre, Sapienza University of Rome, Viale di Grottarossa 1035, 00189, Rome, Italy
| | - Patrizia Pantano
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy.
- IRCCS Neuromed, Via Atinense, 18, 86077, Pozzilli, IS, Italy.
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50
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Zurita M, Montalba C, Labbé T, Cruz JP, Dalboni da Rocha J, Tejos C, Ciampi E, Cárcamo C, Sitaram R, Uribe S. Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data. Neuroimage Clin 2018; 20:724-730. [PMID: 30238916 PMCID: PMC6148733 DOI: 10.1016/j.nicl.2018.09.002] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2018] [Revised: 07/12/2018] [Accepted: 09/02/2018] [Indexed: 01/16/2023]
Abstract
Multiple Sclerosis patients' clinical symptoms do not correlate strongly with structural assessment done with traditional magnetic resonance images. However, its diagnosis and evaluation of the disease's progression are based on a combination of this imaging analysis complemented with clinical examination. Therefore, other biomarkers are necessary to better understand the disease. In this paper, we capitalize on machine learning techniques to classify relapsing-remitting multiple sclerosis patients and healthy volunteers based on machine learning techniques, and to identify relevant brain areas and connectivity measures for characterizing patients. To this end, we acquired magnetic resonance imaging data from relapsing-remitting multiple sclerosis patients and healthy subjects. Fractional anisotropy maps, structural and functional connectivity were extracted from the scans. Each of them were used as separate input features to construct support vector machine classifiers. A fourth input feature was created by combining structural and functional connectivity. Patients were divided in two groups according to their degree of disability and, together with the control group, three group pairs were formed for comparison. Twelve separate classifiers were built from the combination of these four input features and three group pairs. The classifiers were able to distinguish between patients and healthy subjects, reaching accuracy levels as high as 89% ± 2%. In contrast, the performance was noticeably lower when comparing the two groups of patients with different levels of disability, reaching levels below 63% ± 5%. The brain regions that contributed the most to the classification were the right occipital, left frontal orbital, medial frontal cortices and lingual gyrus. The developed classifiers based on MRI data were able to distinguish multiple sclerosis patients and healthy subjects reliably. Moreover, the resulting classification models identified brain regions, and functional and structural connections relevant for better understanding of the disease.
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Affiliation(s)
- Mariana Zurita
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile; Department of Electrical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristian Montalba
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Tomás Labbé
- Interdisciplinary Center of Neurosciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Juan Pablo Cruz
- Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Josué Dalboni da Rocha
- Brain and Language Lab, Department of Clinical Neuroscience, University of Geneva, Geneva, Switzerland
| | - Cristián Tejos
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile; Department of Electrical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ethel Ciampi
- Interdisciplinary Center of Neurosciences, Pontificia Universidad Católica de Chile, Santiago, Chile; Neurology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile; Neurology, Hospital Dr. Sótero del Río, Santiago, Chile
| | - Claudia Cárcamo
- Interdisciplinary Center of Neurosciences, Pontificia Universidad Católica de Chile, Santiago, Chile; Neurology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Institute of Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Department of Psychiatry, Section of Neuroscience, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile; Laboratory for Brain-Machine Interfaces and Neuromodulation, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sergio Uribe
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile; Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile.
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