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Asendorf AL, Theis H, Tittgemeyer M, Timmermann L, Fink GR, Drzezga A, Eggers C, Ruppert‐Junck MC, Pedrosa DJ, Hoenig MC, van Eimeren T. Dynamic properties in functional connectivity changes and striatal dopamine deficiency in Parkinson's disease. Hum Brain Mapp 2024; 45:e26776. [PMID: 38958131 PMCID: PMC11220510 DOI: 10.1002/hbm.26776] [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: 02/05/2024] [Revised: 06/14/2024] [Accepted: 06/19/2024] [Indexed: 07/04/2024] Open
Abstract
Recent studies in Parkinson's disease (PD) patients reported disruptions in dynamic functional connectivity (dFC, i.e., a characterization of spontaneous fluctuations in functional connectivity over time). Here, we assessed whether the integrity of striatal dopamine terminals directly modulates dFC metrics in two separate PD cohorts, indexing dopamine-related changes in large-scale brain network dynamics and its implications in clinical features. We pooled data from two disease-control cohorts reflecting early PD. From the Parkinson's Progression Marker Initiative (PPMI) cohort, resting-state functional magnetic resonance imaging (rsfMRI) and dopamine transporter (DaT) single-photon emission computed tomography (SPECT) were available for 63 PD patients and 16 age- and sex-matched healthy controls. From the clinical research group 219 (KFO) cohort, rsfMRI imaging was available for 52 PD patients and 17 age- and sex-matched healthy controls. A subset of 41 PD patients and 13 healthy control subjects additionally underwent 18F-DOPA-positron emission tomography (PET) imaging. The striatal synthesis capacity of 18F-DOPA PET and dopamine terminal quantity of DaT SPECT images were extracted for the putamen and the caudate. After rsfMRI pre-processing, an independent component analysis was performed on both cohorts simultaneously. Based on the derived components, an individual sliding window approach (44 s window) and a subsequent k-means clustering were conducted separately for each cohort to derive dFC states (reemerging intra- and interindividual connectivity patterns). From these states, we derived temporal metrics, such as average dwell time per state, state attendance, and number of transitions and compared them between groups and cohorts. Further, we correlated these with the respective measures for local dopaminergic impairment and clinical severity. The cohorts did not differ regarding age and sex. Between cohorts, PD groups differed regarding disease duration, education, cognitive scores and L-dopa equivalent daily dose. In both cohorts, the dFC analysis resulted in three distinct states, varying in connectivity patterns and strength. In the PPMI cohort, PD patients showed a lower state attendance for the globally integrated (GI) state and a lower number of transitions than controls. Significantly, worse motor scores (Unified Parkinson's Disease Rating Scale Part III) and dopaminergic impairment in the putamen and the caudate were associated with low average dwell time in the GI state and a low total number of transitions. These results were not observed in the KFO cohort: No group differences in dFC measures or associations between dFC variables and dopamine synthesis capacity were observed. Notably, worse motor performance was associated with a low number of bidirectional transitions between the GI and the lesser connected (LC) state across the PD groups of both cohorts. Hence, in early PD, relative preservation of motor performance may be linked to a more dynamic engagement of an interconnected brain state. Specifically, those large-scale network dynamics seem to relate to striatal dopamine availability. Notably, most of these results were obtained only for one cohort, suggesting that dFC is impacted by certain cohort features like educational level, or disease severity. As we could not pinpoint these features with the data at hand, we suspect that other, in our case untracked, demographical features drive connectivity dynamics in PD. PRACTITIONER POINTS: Exploring dopamine's role in brain network dynamics in two Parkinson's disease (PD) cohorts, we unraveled PD-specific changes in dynamic functional connectivity. Results in the Parkinson's Progression Marker Initiative (PPMI) and the KFO cohort suggest motor performance may be linked to a more dynamic engagement and disengagement of an interconnected brain state. Results only in the PPMI cohort suggest striatal dopamine availability influences large-scale network dynamics that are relevant in motor control.
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Affiliation(s)
- Adrian L. Asendorf
- Department of Nuclear MedicineUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
| | - Hendrik Theis
- Department of Nuclear MedicineUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
- Department of NeurologyUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Translational Neurocircuitry GroupCologneGermany
- University of Cologne, Cologne Excellence Cluster on Cellular Stress Responses in Aging‐Associated Diseases (CECAD)CologneGermany
| | | | - Gereon R. Fink
- Department of NeurologyUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
- Research Centre Juelich, Institute of Neuroscience and Medicine III, Cognitive NeuroscienceJuelichGermany
| | - Alexander Drzezga
- Department of Nuclear MedicineUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
| | - Carsten Eggers
- Department of NeurologyMarburgGermany
- Department of NeurologyUniversity of Duisburg‐Essen, Knappschaftskrankenhaus BottropBottropGermany
| | | | - David J. Pedrosa
- Universities of Marburg and Gießen, Center for Mind, Brain, and Behavior‐CMBBMarburgGermany
| | - Merle C. Hoenig
- Department of Nuclear MedicineUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
- Research Center Juelich, Institute of Neuroscience and Medicine II, Molecular Organization of the BrainJuelichGermany
| | - Thilo van Eimeren
- Department of Nuclear MedicineUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
- Department of NeurologyUniversity of Cologne, Faculty of Medicine and University Hospital CologneCologneGermany
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2
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Foster MA, Prados F, Collorone S, Kanber B, Cawley N, Davagnanam I, Yiannakas MC, Ogunbowale L, Burke A, Barkhof F, Wheeler-Kingshott CAMG, Ciccarelli O, Brownlee W, Toosy AT. Improving explanation of motor disability with diffusion-based graph metrics at onset of the first demyelinating event. Mult Scler 2024; 30:800-811. [PMID: 38751221 PMCID: PMC11134971 DOI: 10.1177/13524585241247785] [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: 01/10/2024] [Revised: 03/20/2024] [Accepted: 03/31/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Conventional magnetic resonance imaging (MRI) does not account for all disability in multiple sclerosis. OBJECTIVE The objective was to assess the ability of graph metrics from diffusion-based structural connectomes to explain motor function beyond conventional MRI in early demyelinating clinically isolated syndrome (CIS). METHODS A total of 73 people with CIS underwent conventional MRI, diffusion-weighted imaging and clinical assessment within 3 months from onset. A total of 28 healthy controls underwent MRI. Structural connectomes were produced. Differences between patients and controls were explored; clinical associations were assessed in patients. Linear regression models were compared to establish relevance of graph metrics over conventional MRI. RESULTS Local efficiency (p = 0.045), clustering (p = 0.034) and transitivity (p = 0.036) were reduced in patients. Higher assortativity was associated with higher Expanded Disability Status Scale (EDSS) (β = 74.9, p = 0.026) scores. Faster timed 25-foot walk (T25FW) was associated with higher assortativity (β = 5.39, p = 0.026), local efficiency (β = 27.1, p = 0.041) and clustering (β = 36.1, p = 0.032) and lower small-worldness (β = -3.27, p = 0.015). Adding graph metrics to conventional MRI improved EDSS (p = 0.045, ΔR2 = 4) and T25FW (p < 0.001, ΔR2 = 13.6) prediction. CONCLUSION Graph metrics are relevant early in demyelination. They show differences between patients and controls and have relationships with clinical outcomes. Segregation (local efficiency, clustering, transitivity) was particularly relevant. Combining graph metrics with conventional MRI better explained disability.
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Affiliation(s)
- Michael A Foster
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ferran Prados
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Imaging Computing, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Science, University College London, London, UK
- Universitat Oberta de Catalunya, Barcelona, Spain
| | - Sara Collorone
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Baris Kanber
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Imaging Computing, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Science, University College London, London, UK
| | - Niamh Cawley
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Indran Davagnanam
- Department of Brain Repair & Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Marios C Yiannakas
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Lola Ogunbowale
- Strabismus and Neuro-Ophthalmology Service, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Ailbhe Burke
- Strabismus and Neuro-Ophthalmology Service, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Frederik Barkhof
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Imaging Computing, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Science, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Claudia AM Gandini Wheeler-Kingshott
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Olga Ciccarelli
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Wallace Brownlee
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Ahmed T Toosy
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
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Sendi M, Fu Z, Harnett N, van Rooij S, Vergara V, Pizzagalli D, Daskalakis N, House S, Beaudoin F, An X, Neylan T, Clifford G, Jovanovic T, Linnstaedt S, Germine L, Bollen K, Rauch S, Haran J, Storrow A, Lewandowski C, Musey P, Hendry P, Sheikh S, Jones C, Punches B, Swor R, Gentile N, Murty V, Hudak L, Pascual J, Seamon M, Harris E, Chang A, Pearson C, Peak D, Merchant R, Domeier R, Rathlev N, O'Neil B, Sergot P, Sanchez L, Bruce S, Sheridan J, Harte S, Kessler R, Koenen K, McLean S, Stevens J, Calhoun V, Ressler K. Brain dynamics reflecting an intra-network brain state is associated with increased posttraumatic stress symptoms in the early aftermath of trauma. RESEARCH SQUARE 2024:rs.3.rs-4004473. [PMID: 38496567 PMCID: PMC10942549 DOI: 10.21203/rs.3.rs-4004473/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
This study examines the association between brain dynamic functional network connectivity (dFNC) and current/future posttraumatic stress (PTS) symptom severity, and the impact of sex on this relationship. By analyzing 275 participants' dFNC data obtained ~2 weeks after trauma exposure, we noted that brain dynamics of an inter-network brain state link negatively with current (r=-0.179, pcorrected= 0.021) and future (r=-0.166, pcorrected= 0.029) PTS symptom severity. Also, dynamics of an intra-network brain state correlated with future symptom intensity (r = 0.192, pcorrected = 0.021). We additionally observed that the association between the network dynamics of the inter-network brain state with symptom severity is more pronounced in females (r=-0.244, pcorrected = 0.014). Our findings highlight a potential link between brain network dynamics in the aftermath of trauma with current and future PTSD outcomes, with a stronger protective effect of inter-network brain states against symptom severity in females, underscoring the importance of sex differences.
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Affiliation(s)
| | - Zening Fu
- d Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University
| | | | | | | | | | | | | | - Francesca Beaudoin
- The Alpert Medical School of Brown University, Rhode Island Hospital and The Miriam Hospital
| | - Xinming An
- University of North Carolina at Chapel Hill
| | - Thomas Neylan
- San Francisco VA Healthcare System; University of California San Francisco
| | - Gari Clifford
- Emory University School of Medicine; Georgia Institute of Technology
| | | | | | | | | | | | - John Haran
- University of Massachusetts Medical School
| | | | | | | | | | | | | | - Brittany Punches
- University of Cincinnati College of Medicine & University of Cincinnati College of Nursing
| | | | | | | | | | - Jose Pascual
- Perelman School of Medicine at the University of Pennsylvania
| | | | | | | | | | | | | | | | | | | | - Paulina Sergot
- Department of Emergency Medicine, McGovern Medical School at UTHealth
| | | | | | | | | | | | | | | | | | - Vince Calhoun
- Georgia Institute of Technology, Emory University and Georgia State University
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4
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Cai S, Liang Y, Wang Y, Fan Z, Qi Z, Liu Y, Chen F, Jiang C, Shi Z, Wang L, Zhang L. Shared and malignancy-specific functional plasticity of dynamic brain properties for patients with left frontal glioma. Cereb Cortex 2024; 34:bhad445. [PMID: 38011109 DOI: 10.1093/cercor/bhad445] [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: 06/12/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 11/29/2023] Open
Abstract
The time-varying brain activity may parallel the disease progression of cerebral glioma. Assessment of brain dynamics would better characterize the pathological profile of glioma and the relevant functional remodeling. This study aims to investigate the dynamic properties of functional networks based on sliding-window approach for patients with left frontal glioma. The generalized functional plasticity due to glioma was characterized by reduced dynamic amplitude of low-frequency fluctuation of somatosensory networks, reduced dynamic functional connectivity between homotopic regions mainly involving dorsal attention network and subcortical nuclei, and enhanced subcortical dynamic functional connectivity. Malignancy-specific functional remodeling featured a chaotic modification of dynamic amplitude of low-frequency fluctuation and dynamic functional connectivity for low-grade gliomas, and attenuated dynamic functional connectivity of the intrahemispheric cortico-subcortical connections and reduced dynamic amplitude of low-frequency fluctuation of the bilateral caudate for high-grade gliomas. Network dynamic activity was clustered into four distinct configuration states. The occurrence and dwell time of the weakly connected state were reduced in patients' brains. Support vector machine model combined with predictive dynamic features achieved an averaged accuracy of 87.9% in distinguishing low- and high-grade gliomas. In conclusion, dynamic network properties are highly predictive of the malignant grade of gliomas, thus could serve as new biomarkers for disease characterization.
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Affiliation(s)
- Siqi Cai
- Paul. C. Lauterbur Research Centers for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuchao Liang
- Department of Neurosurgery, Beijing Tiantan Hospital of Capital Medical University, Beijing 10070, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital of Capital Medical University, Beijing 10070, China
| | - Zhen Fan
- Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China
| | - Zengxin Qi
- Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China
| | - Yufei Liu
- Department of Neurosurgery, Shenzhen Second People's Hospital, Shenzhen, Guangdong 518025, China
| | - Fanfan Chen
- Department of Neurosurgery, Shenzhen Second People's Hospital, Shenzhen, Guangdong 518025, China
| | - Chunxiang Jiang
- Paul. C. Lauterbur Research Centers for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Zhifeng Shi
- Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai 200040, China
| | - Lei Wang
- Department of Neurosurgery, Beijing Tiantan Hospital of Capital Medical University, Beijing 10070, China
| | - Lijuan Zhang
- Paul. C. Lauterbur Research Centers for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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5
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Tozlu C, Card S, Jamison K, Gauthier SA, Kuceyeski A. Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity. Netw Neurosci 2023; 7:539-556. [PMID: 37397885 PMCID: PMC10312270 DOI: 10.1162/netn_a_00292] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 11/07/2022] [Indexed: 01/10/2024] Open
Abstract
Quantifying the relationship between the brain's functional activity patterns and its structural backbone is crucial when relating the severity of brain pathology to disability in multiple sclerosis (MS). Network control theory (NCT) characterizes the brain's energetic landscape using the structural connectome and patterns of brain activity over time. We applied NCT to investigate brain-state dynamics and energy landscapes in controls and people with MS (pwMS). We also computed entropy of brain activity and investigated its association with the dynamic landscape's transition energy and lesion volume. Brain states were identified by clustering regional brain activity vectors, and NCT was applied to compute the energy required to transition between these brain states. We found that entropy was negatively correlated with lesion volume and transition energy, and that larger transition energies were associated with pwMS with disability. This work supports the notion that shifts in the pattern of brain activity in pwMS without disability results in decreased transition energies compared to controls, but, as this shift evolves over the disease, transition energies increase beyond controls and disability occurs. Our results provide the first evidence in pwMS that larger lesion volumes result in greater transition energy between brain states and decreased entropy of brain activity.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Sophie Card
- Horace Greeley High School, Chappaqua, NY, USA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Susan A. Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Judith Jaffe Multiple Sclerosis Center, Weill Cornell Medicine, New York, NY, USA
- Department of Neurology, Weill Cornell Medical College, New York, NY, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
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Rehák Bučková B, Mareš J, Škoch A, Kopal J, Tintěra J, Dineen R, Řasová K, Hlinka J. Multimodal-neuroimaging machine-learning analysis of motor disability in multiple sclerosis. Brain Imaging Behav 2023; 17:18-34. [PMID: 36396890 DOI: 10.1007/s11682-022-00737-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 11/19/2022]
Abstract
Motor disability is a dominant and restricting symptom in multiple sclerosis, yet its neuroimaging correlates are not fully understood. We apply statistical and machine learning techniques on multimodal neuroimaging data to discriminate between multiple sclerosis patients and healthy controls and to predict motor disability scores in the patients. We examine the data of sixty-four multiple sclerosis patients and sixty-five controls, who underwent the MRI examination and the evaluation of motor disability scales. The modalities used comprised regional fractional anisotropy, regional grey matter volumes, and functional connectivity. For analysis, we employ two approaches: high-dimensional support vector machines run on features selected by Fisher Score (aiming for maximal classification accuracy), and low-dimensional logistic regression on the principal components of data (aiming for increased interpretability). We apply analogous regression methods to predict symptom severity. While fractional anisotropy provides the classification accuracy of 96.1% and 89.9% with both approaches respectively, including other modalities did not bring further improvement. Concerning the prediction of motor impairment, the low-dimensional approach performed more reliably. The first grey matter volume component was significantly correlated (R = 0.28-0.46, p < 0.05) with most clinical scales. In summary, we identified the relationship between both white and grey matter changes and motor impairment in multiple sclerosis. Furthermore, we were able to achieve the highest classification accuracy based on quantitative MRI measures of tissue integrity between patients and controls yet reported, while also providing a low-dimensional classification approach with comparable results, paving the way to interpretable machine learning models of brain changes in multiple sclerosis.
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Affiliation(s)
- Barbora Rehák Bučková
- The Czech Technical University in Prague, Karlovo namesti 13, 121 35, Prague, Czech Republic.,Institute of Computer Science of the Czech Academy of Sciences, Pod Vodarenskou vezi 2/271, 182 00, Prague, Czech Republic.,National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Jan Mareš
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Videnska 1958, 140 21, Prague, Czech Republic
| | - Antonín Škoch
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Videnska 1958, 140 21, Prague, Czech Republic
| | - Jakub Kopal
- Institute of Computer Science of the Czech Academy of Sciences, Pod Vodarenskou vezi 2/271, 182 00, Prague, Czech Republic
| | - Jaroslav Tintěra
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Videnska 1958, 140 21, Prague, Czech Republic
| | - Robert Dineen
- University of Nottingham, Queen's Medical Centre, NG7 2UH, Nottingham, UK.,National Institute for Health Research, Nottingham Biomedical Research Centre, NG1 5DU, Nottingham, UK
| | - Kamila Řasová
- Charles University, Ruska 87, 100 00, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Pod Vodarenskou vezi 2/271, 182 00, Prague, Czech Republic. .,National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.
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7
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Barile B, Ashtari P, Stamile C, Marzullo A, Maes F, Durand-Dubief F, Van Huffel S, Sappey-Marinier D. Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome. Front Robot AI 2022; 9:926255. [PMID: 36313252 PMCID: PMC9608344 DOI: 10.3389/frobt.2022.926255] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/18/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose: The main goal of this study is to investigate the discrimination power of Grey Matter (GM) thickness connectome data between Multiple Sclerosis (MS) clinical profiles using statistical and Machine Learning (ML) methods. Materials and Methods: A dataset composed of 90 MS patients acquired at the MS clinic of Lyon Neurological Hospital was used for the analysis. Four MS profiles were considered, corresponding to Clinical Isolated Syndrome (CIS), Relapsing-Remitting MS (RRMS), Secondary Progressive MS (SPMS), and Primary Progressive MS (PPMS). Each patient was classified in one of these profiles by our neurologist and underwent longitudinal MRI examinations including T1-weighted image acquisition at each examination, from which the GM tissue was segmented and the cortical GM thickness measured. Following the GM parcellation using two different atlases (FSAverage and Glasser 2016), the morphological connectome was built and six global metrics (Betweenness Centrality (BC), Assortativity (r), Transitivity (T), Efficiency (Eg), Modularity (Q) and Density (D)) were extracted. Based on their connectivity metrics, MS profiles were first statistically compared and second, classified using four different learning machines (Logistic Regression, Random Forest, Support Vector Machine and AdaBoost), combined in a higher level ensemble model by majority voting. Finally, the impact of the GM spatial resolution on the MS clinical profiles classification was analyzed. Results: Using binary comparisons between the four MS clinical profiles, statistical differences and classification performances higher than 0.7 were observed. Good performances were obtained when comparing the two early clinical forms, RRMS and PPMS (F1 score of 0.86), and the two neurodegenerative profiles, PPMS and SPMS (F1 score of 0.72). When comparing the two atlases, slightly better performances were obtained with the Glasser 2016 atlas, especially between RRMS with PPMS (F1 score of 0.83), compared to the FSAverage atlas (F1 score of 0.69). Also, the thresholding value for graph binarization was investigated suggesting more informative graph properties in the percentile range between 0.6 and 0.8. Conclusion: An automated pipeline was proposed for the classification of MS clinical profiles using six global graph metrics extracted from the GM morphological connectome of MS patients. This work demonstrated that GM morphological connectivity data could provide good classification performances by combining four simple ML models, without the cost of long and complex MR techniques, such as MR diffusion, and/or deep learning architectures.
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Affiliation(s)
- Berardino Barile
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - Pooya Ashtari
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | | | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Frederik Maes
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - Françoise Durand-Dubief
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- Hôpital Neurologique, Service de Neurologie, Hospices Civils de Lyon, Bron, France
| | | | - Dominique Sappey-Marinier
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- CERMEP–Imagerie du Vivant, Université de Lyon, Lyon, France
- *Correspondence: Dominique Sappey-Marinier,
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Martire MS, Moiola L, Rocca MA, Filippi M, Absinta M. What is the potential of paramagnetic rim lesions as diagnostic indicators in multiple sclerosis? Expert Rev Neurother 2022; 22:829-837. [PMID: 36342396 DOI: 10.1080/14737175.2022.2143265] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
INTRODUCTION In multiple sclerosis (MS), paramagnetic rim lesions (PRLs) on MRI identify a subset of chronic active lesions (CALs), which have been linked through clinical and pathological studies to more severe disease course and greater disability accumulation. Beside their prognostic relevance, increasing evidence supports the use of PRL as a diagnostic biomarker. AREAS COVERED This review summarizes the most recent updates regarding the MRI pathophysiology of PRL, their prevalence in MS (by clinical phenotypes) vs mimicking conditions, and their potential role as diagnostic MS biomarkers. We searched PubMed with terms including 'multiple sclerosis' AND 'paramagnetic rim lesions' OR 'iron rim lesions' OR 'rim lesions' for manuscripts published between January 2008 and July 2022. EXPERT OPINION Current research suggests that PRL can improve the diagnostic specificity and the overall accuracy of MS diagnosis when used together with the dissemination in space MRI criteria and the central vein sign. Nevertheless, future prospective multicenter studies should further define the real-world prevalence and specificity of PRL. International guidelines are needed to establish methodological criteria for PRL identification before its implementation into clinical practice.
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Affiliation(s)
| | - Lucia Moiola
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria Assunta Rocca
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Division of Neuroscience, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Division of Neuroscience, Vita-Salute San Raffaele University, Milan, Italy.,Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Martina Absinta
- Division of Neuroscience, Vita-Salute San Raffaele University, Milan, Italy.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Cacciaguerra L, Mistri D, Valsasina P, Martinelli V, Filippi M, Rocca MA. Time-varying connectivity of the precuneus and its association with cognition and depressive symptoms in neuromyelitis optica: A pilot MRI study. Mult Scler 2022; 28:2057-2069. [PMID: 35796514 PMCID: PMC9574904 DOI: 10.1177/13524585221107125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background: The precuneus is involved in cognition and depression; static functional
connectivity (SFC) abnormalities of this region have been observed in
neuromyelitis optica spectrum disorders (NMOSD). Time-varying functional
connectivity (TVC) underpins dynamic variations of brain connectivity. Objective: The aim of this study was to explore precuneus SFC and TVC in NMOSD patients
and their associations with neuropsychological features. Methods: This retrospective study includes 27 NMOSD patients and 30 matched healthy
controls undergoing resting state functional magnetic resonance imaging
(MRI) and a neuropsychological evaluation of cognitive performance and
depressive symptoms. A sliding-window correlation analysis using bilateral
precuneus as seed region assessed TVC, which was quantified by the standard
deviation of connectivity across windows. Mean connectivity indicated
SFC. Results: Compared to controls, patients had reduced SFC between precuneus, temporal
lobe, putamen and cerebellum, and reduced TVC between precuneus and
prefronto-parietal-temporo-occipital cortices and caudate. Patients also had
increased intra-precuneal TVC and increased TVC between the precuneus and
the temporal cortex. More severe depressive symptoms correlated with
increased TVC between the precuneus and the temporal lobe; worse cognitive
performance mainly correlated with higher TVC between the precuneus and the
parietal lobe. Conclusion: TVC rather than SFC of the precuneus correlates with NMOSD neuropsychological
features; different TVC abnormalities underlie depressive symptoms and
cognitive impairment.
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Affiliation(s)
- Laura Cacciaguerra
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy/Vita-Salute San Raffaele University, Milan, Italy
| | - Damiano Mistri
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy/Vita-Salute San Raffaele University, 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
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy/Vita-Salute San Raffaele University, Milan, Italy/Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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