1
|
Aarts J, Saddal SRD, Bosmans JE, de Groot V, de Jong BA, Klein M, Ruitenberg MFL, Schaafsma FG, Schippers ECF, Schoonheim MM, Uitdehaag BMJ, van der Veen S, Waskowiak PT, Widdershoven GAM, van der Hiele K, Hulst HE. Don't be late! Postponing cognitive decline and preventing early unemployment in people with multiple sclerosis: a study protocol. BMC Neurol 2024; 24:28. [PMID: 38225561 PMCID: PMC10789039 DOI: 10.1186/s12883-023-03513-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 12/17/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Up to 65% of people with multiple sclerosis (PwMS) develop cognitive deficits, which hampers their ability to work, participating in day-to-day life and ultimately reducing quality of life (QoL). Early cognitive symptoms are often less tangible to PwMS and their direct environment and are noticed only when symptoms and work functioning problems become more advanced, i.e., when (brain) damage is already advanced. Treatment of symptoms at a late stage can lead to cognitive impairment and unemployment, highlighting the need for preventative interventions in PwMS. AIMS This study aims to evaluate the (cost-) effectiveness of two innovative preventative interventions, aimed at postponing cognitive decline and work functioning problems, compared to enhanced usual care in improving health-related QoL (HRQoL). METHODS Randomised controlled trial including 270 PwMS with mild cognitive impairment, who have paid employment ≥ 12 h per week and are able to participate in physical exercise (Expanded Disability Status Scale < 6.0). Participants are randomised across three study arms: 1) 'strengthening the brain' - a lifestyle intervention combining personal fitness, mental coaching, dietary advice, and cognitive training; 2) 'strengthening the mind' - a work-focused intervention combining the capability approach and the participatory approach in one-on-one coaching by trained work coaches who have MS themselves; 3) Control group-receiving general information about cognitive impairment in MS and receiving care as usual. Intervention duration is four months, with short-term and long-term follow-up measurements at 10 and 16 months, respectively. The primary outcome measure of the Don't be late! intervention study will be HRQoL as measured with the 36-item Short Form. Secondary outcomes include cognition, work related outcomes, physical functioning, structural and functional brain changes, psychological functioning, and societal costs. Semi-structured interviews and focus groups with stakeholders will be organised to qualitatively reflect on the process and outcome of the interventions. DISCUSSION This study seeks to prevent (further) cognitive decline and job loss due to MS by introducing tailor-made interventions at an early stage of cognitive symptoms, thereby maintaining or improving HRQoL. Qualitative analyses will be performed to allow successful implementation into clinical practice. TRIAL REGISTRATION Retrospectively registered at ClinicalTrials.gov with reference number NCT06068582 on 10 October 2023.
Collapse
Affiliation(s)
- Jip Aarts
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands.
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.
| | - Shalina R D Saddal
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands
- MS Center Amsterdam, Public and Occupational Health, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Judith E Bosmans
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Vincent de Groot
- MS Center Amsterdam, Rehabilitation Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Brigit A de Jong
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Martin Klein
- Medical Psychology, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Marit F L Ruitenberg
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Frederieke G Schaafsma
- MS Center Amsterdam, Public and Occupational Health, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Esther C F Schippers
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Bernard M J Uitdehaag
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Sabina van der Veen
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands
| | - Pauline T Waskowiak
- Medical Psychology, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Guy A M Widdershoven
- Ethics, Law & Medical Humanities, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
| | - Karin van der Hiele
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Hanneke E Hulst
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Faculty of Social Sciences, Leiden University, Wassenaarseweg 52, Leiden, 2333 AK, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| |
Collapse
|
2
|
Fleischer V, Gonzalez-Escamilla G, Pareto D, Rovira A, Sastre-Garriga J, Sowa P, Høgestøl EA, Harbo HF, Bellenberg B, Lukas C, Ruggieri S, Gasperini C, Uher T, Vaneckova M, Bittner S, Othman AE, Collorone S, Toosy AT, Meuth SG, Zipp F, Barkhof F, Ciccarelli O, Groppa S. Prognostic value of single-subject grey matter networks in early multiple sclerosis. Brain 2024; 147:135-146. [PMID: 37642541 PMCID: PMC10766234 DOI: 10.1093/brain/awad288] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/17/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023] Open
Abstract
The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P < 0.001] and global efficiency [H(2) = 31.3, P < 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values < 0.05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.
Collapse
Affiliation(s)
- Vinzenz Fleischer
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Alex Rovira
- Section of Neuroradiology, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Jaume Sastre-Garriga
- Department of Neurology/Neuroimmunology, Multiple Sclerosis Centre of Catalonia, Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
| | - Piotr Sowa
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, 0424 Oslo, Norway
| | - Einar A Høgestøl
- Institute of Clinical Medicine, University of Oslo, NO-0316 Oslo, Norway
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | - Hanne F Harbo
- Institute of Clinical Medicine, University of Oslo, NO-0316 Oslo, Norway
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | - Barbara Bellenberg
- Institute of Neuroradiology, St Josef Hospital, Ruhr-University Bochum, 44791 Bochum, Germany
| | - Carsten Lukas
- Institute of Neuroradiology, St Josef Hospital, Ruhr-University Bochum, 44791 Bochum, Germany
| | - Serena Ruggieri
- Department of Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Claudio Gasperini
- Department of Neurosciences, San Camillo-Forlanini Hospital, 00152 Rome, Italy
| | - Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, 121 08 Prague, Czech Republic
| | - Manuela Vaneckova
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, 121 08 Prague, Czech Republic
| | - Stefan Bittner
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Ahmed E Othman
- Department of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Sara Collorone
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
| | - Ahmed T Toosy
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
| | - Sven G Meuth
- Department of Neurology, Medical Faculty, Heinrich-Heine-University, 40225 Düsseldorf, Germany
| | - Frauke Zipp
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Frederik Barkhof
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, 1100 DD Amsterdam, Netherlands
| | - Olga Ciccarelli
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| |
Collapse
|
3
|
Maas DA, Douw L. Multiscale network neuroscience in neuro-oncology: How tumors, brain networks, and behavior connect across scales. Neurooncol Pract 2023; 10:506-517. [PMID: 38026586 PMCID: PMC10666814 DOI: 10.1093/nop/npad044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023] Open
Abstract
Network neuroscience refers to the investigation of brain networks across different spatial and temporal scales, and has become a leading framework to understand the biology and functioning of the brain. In neuro-oncology, the study of brain networks has revealed many insights into the structure and function of cells, circuits, and the entire brain, and their association with both functional status (e.g., cognition) and survival. This review connects network findings from different scales of investigation, with the combined aim of informing neuro-oncological healthcare professionals on this exciting new field and also delineating the promising avenues for future translational and clinical research that may allow for application of network methods in neuro-oncological care.
Collapse
Affiliation(s)
- Dorien A Maas
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Linda Douw
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| |
Collapse
|
4
|
Douw L, Nissen IA, Fitzsimmons SMDD, Santos FAN, Hillebrand A, van Straaten ECW, Stam CJ, De Witt Hamer PC, Baayen JC, Klein M, Reijneveld JC, Heyer DB, Verhoog MB, Wilbers R, Hunt S, Mansvelder HD, Geurts JJG, de Kock CPJ, Goriounova NA. Cellular Substrates of Functional Network Integration and Memory in Temporal Lobe Epilepsy. Cereb Cortex 2021; 32:2424-2436. [PMID: 34564728 PMCID: PMC9157285 DOI: 10.1093/cercor/bhab349] [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] [Received: 05/18/2021] [Revised: 08/19/2021] [Accepted: 08/22/2021] [Indexed: 11/12/2022] Open
Abstract
Temporal lobe epilepsy (TLE) patients are at risk of memory deficits, which have been linked to functional network disturbances, particularly of integration of the default mode network (DMN). However, the cellular substrates of functional network integration are unknown. We leverage a unique cross-scale dataset of drug-resistant TLE patients (n = 31), who underwent pseudo resting-state functional magnetic resonance imaging (fMRI), resting-state magnetoencephalography (MEG) and/or neuropsychological testing before neurosurgery. fMRI and MEG underwent atlas-based connectivity analyses. Functional network centrality of the lateral middle temporal gyrus, part of the DMN, was used as a measure of local network integration. Subsequently, non-pathological cortical tissue from this region was used for single cell morphological and electrophysiological patch-clamp analysis, assessing integration in terms of total dendritic length and action potential rise speed. As could be hypothesized, greater network centrality related to better memory performance. Moreover, greater network centrality correlated with more integrative properties at the cellular level across patients. We conclude that individual differences in cognitively relevant functional network integration of a DMN region are mirrored by differences in cellular integrative properties of this region in TLE patients. These findings connect previously separate scales of investigation, increasing translational insight into focal pathology and large-scale network disturbances in TLE.
Collapse
Affiliation(s)
- Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands.,Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 02129 MA, Charlestown, USA
| | - Ida A Nissen
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Sophie M D D Fitzsimmons
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Fernando A N Santos
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Philip C De Witt Hamer
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center Amsterdam Brain Tumor Center Amsterdam, 1081 HV, Amsterdam, the Netherlands
| | - Johannes C Baayen
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center Amsterdam Brain Tumor Center Amsterdam, 1081 HV, Amsterdam, the Netherlands
| | - Martin Klein
- Department of Medical Psychology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center Amsterdam Brain Tumor Center Amsterdam, 1081 HV, Amsterdam, the Netherlands
| | - Jaap C Reijneveld
- Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center Amsterdam Brain Tumor Center Amsterdam, 1081 HV, Amsterdam, the Netherlands.,Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede 2103 SW, Heemstede, the Netherlands
| | - Djai B Heyer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Matthijs B Verhoog
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands.,Department of Human Biology, Division of Cell Biology, Neuroscience Institute, University of Cape Town, 7935, Cape Town, South Africa
| | - René Wilbers
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Sarah Hunt
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Christiaan P J de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Natalia A Goriounova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| |
Collapse
|
5
|
Soares GH, Ribeiro Santiago PH, Biazevic MGH, Michel-Crosato E, Jamieson L. Dynamics in oral health-related factors of Indigenous Australian children: A network analysis of a randomized controlled trial. Community Dent Oral Epidemiol 2021; 50:251-259. [PMID: 34050531 DOI: 10.1111/cdoe.12661] [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: 04/16/2021] [Revised: 04/28/2021] [Accepted: 04/30/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Network analysis is an innovative, analytic approach that enables visual representation of variables as nodes and their corresponding statistical associations as edges. It also provides a new way of framing oral health-related questions as complex systems of variables. We aimed to generate networks of oral health variables using epidemiological data of Indigenous children, and to compare network structures of oral health variables among participants who received immediate or delayed delivery of an oral health intervention. METHODS Epidemiological data from 448 mother-child dyads enrolled in a randomized controlled trial of dental caries prevention in South Australia, Australia, were obtained. Networks were estimated with nodes representing study variables and edges representing partial correlation coefficients between variables. Data included dental caries, impact on quality of life, self-rated general health, self-rated oral health, dental service utilization, knowledge of oral health, fatalism and self-efficacy in three time points. Communities of nodes, centrality, clustering coefficient and network stability were estimated. RESULTS The oral health intervention interacted with the network through self-rated general health and knowledge of oral health. Networks depicting groups shortly after receiving the intervention presented higher clustering coefficients and a similar arrangement of nodes. Networks tended to return to a preintervention state. CONCLUSION The intervention resulted in increased connectivity and changes in the structure of communities of variables in both intervention groups. Our findings contribute to elucidating dynamics between variables depicting oral health networks over time.
Collapse
Affiliation(s)
| | | | | | | | - Lisa Jamieson
- Australian Research Centre for Population Oral Health, The University of Adelaide, Adelaide, SA, Australia
| |
Collapse
|
6
|
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.
Collapse
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
| | | |
Collapse
|
7
|
Jonkman LE, Steenwijk MD, Boesen N, Rozemuller AJM, Barkhof F, Geurts JJG, Douw L, van de Berg WDJ. Relationship between β-amyloid and structural network topology in decedents without dementia. Neurology 2020; 95:e532-e544. [PMID: 32661099 PMCID: PMC7455348 DOI: 10.1212/wnl.0000000000009910] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 01/14/2020] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVE To investigate the association between β-amyloid (Aβ) load and postmortem structural network topology in decedents without dementia. METHODS Fourteen decedents (mean age at death 72.6 ± 7.2 years) without known clinical diagnosis of neurodegenerative disease and meeting pathology criteria only for no or low Alzheimer disease (AD) pathologic change were selected from the Normal Aging Brain Collection Amsterdam database. In situ brain MRI included 3D T1-weighted images for anatomical registration and diffusion tensor imaging for probabilistic tractography with subsequent structural network construction. Network topologic measures of centrality (degree), integration (global efficiency), and segregation (clustering and local efficiency) were calculated. Tissue sections from 12 cortical regions were sampled and immunostained for Aβ and hyperphosphorylated tau (p-tau), and histopathologic burden was determined. Linear mixed effect models were used to assess the relationship between Aβ and p-tau load and network topologic measures. RESULTS Aβ was present in 79% of cases and predominantly consisted of diffuse plaques; p-tau was sparsely present. Linear mixed effect models showed independent negative associations between Aβ load and global efficiency (β = -0.83 × 10-3, p = 0.014), degree (β = -0.47, p = 0.034), and clustering (β = -0.55 × 10-2, p = 0.043). A positive association was present between Aβ load and local efficiency (β = 3.16 × 10-3, p = 0.035). Regionally, these results were significant in the posterior cingulate cortex (PCC) for degree (β = -2.22, p < 0.001) and local efficiency (β = 1.01 × 10-2, p = 0.014) and precuneus for clustering (β = -0.91 × 10-2, p = 0.017). There was no relationship between p-tau and network topology. CONCLUSION This study in deceased adults with AD-related pathologic change provides evidence for a relationship among early Aβ accumulation, predominantly of the diffuse type, and structural network topology, specifically of the PCC and precuneus.
Collapse
Affiliation(s)
- Laura E Jonkman
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK.
| | - Martijn D Steenwijk
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Nicky Boesen
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Annemieke J M Rozemuller
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Frederik Barkhof
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Jeroen J G Geurts
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Linda Douw
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| | - Wilma D J van de Berg
- From the Departments of Anatomy and Neurosciences (L.E.J., M.D.S., N.B., J.J.G.G., L.D., W.D.J.v.d.B.), Pathology (A.J.M.R.), and Radiology and Nuclear Medicine (F.B.), Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and Institutes of Neurology and Healthcare Engineering (F.B.), University College London, UK
| |
Collapse
|