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Calabrese M, Preziosa P, Scalfari A, Colato E, Marastoni D, Absinta M, Battaglini M, De Stefano N, Di Filippo M, Hametner S, Howell OW, Inglese M, Lassmann H, Martin R, Nicholas R, Reynolds R, Rocca MA, Tamanti A, Vercellino M, Villar LM, Filippi M, Magliozzi R. Determinants and Biomarkers of Progression Independent of Relapses in Multiple Sclerosis. Ann Neurol 2024; 96:1-20. [PMID: 38568026 DOI: 10.1002/ana.26913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/04/2024] [Accepted: 02/15/2024] [Indexed: 06/20/2024]
Abstract
Clinical, pathological, and imaging evidence in multiple sclerosis (MS) suggests that a smoldering inflammatory activity is present from the earliest stages of the disease and underlies the progression of disability, which proceeds relentlessly and independently of clinical and radiological relapses (PIRA). The complex system of pathological events driving "chronic" worsening is likely linked with the early accumulation of compartmentalized inflammation within the central nervous system as well as insufficient repair phenomena and mitochondrial failure. These mechanisms are partially lesion-independent and differ from those causing clinical relapses and the formation of new focal demyelinating lesions; they lead to neuroaxonal dysfunction and death, myelin loss, glia alterations, and finally, a neuronal network dysfunction outweighing central nervous system (CNS) compensatory mechanisms. This review aims to provide an overview of the state of the art of neuropathological, immunological, and imaging knowledge about the mechanisms underlying the smoldering disease activity, focusing on possible early biomarkers and their translation into clinical practice. ANN NEUROL 2024;96:1-20.
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Affiliation(s)
- Massimiliano Calabrese
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, 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
| | - Antonio Scalfari
- Centre of Neuroscience, Department of Medicine, Imperial College, London, UK
| | - Elisa Colato
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Damiano Marastoni
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Martina Absinta
- Translational Neuropathology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Battaglini
- Siena Imaging S.r.l., Siena, Italy
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Massimiliano Di Filippo
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Simon Hametner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Owain W Howell
- Institute of Life Sciences, Swansea University Medical School, Swansea, UK
| | - Matilde Inglese
- Dipartimento di neuroscienze, riabilitazione, oftalmologia, genetica e scienze materno-infantili - DINOGMI, University of Genova, Genoa, Italy
| | - Hans Lassmann
- Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Roland Martin
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- Therapeutic Design Unit, Center for Molecular Medicine, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
- Cellerys AG, Schlieren, Switzerland
| | - Richard Nicholas
- Department of Brain Sciences, Faculty of Medicine, Burlington Danes, Imperial College London, London, UK
| | - Richard Reynolds
- Division of Neuroscience, Department of Brain Sciences, Imperial College London, London, UK
| | - 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
| | - Agnese Tamanti
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
| | - Marco Vercellino
- Multiple Sclerosis Center & Neurologia I U, Department of Neuroscience, University Hospital AOU Città della Salute e della Scienza di Torino, Turin, Italy
| | - Luisa Maria Villar
- Department of Immunology, Ramon y Cajal University Hospital. IRYCIS. REI, Madrid, Spain
| | - Massimo Filippi
- 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
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Roberta Magliozzi
- Department of Neurosciences and Biomedicine and Movement, The Multiple Sclerosis Center of University Hospital of Verona, Verona, Italy
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Wittens MMJ, Denissen S, Sima DM, Fransen E, Niemantsverdriet E, Bastin C, Benoit F, Bergmans B, Bier JC, de Deyn PP, Deryck O, Hanseeuw B, Ivanoiu A, Picard G, Ribbens A, Salmon E, Segers K, Sieben A, Struyfs H, Thiery E, Tournoy J, van Binst AM, Versijpt J, Smeets D, Bjerke M, Nagels G, Engelborghs S. Brain age as a biomarker for pathological versus healthy ageing - a REMEMBER study. Alzheimers Res Ther 2024; 16:128. [PMID: 38877568 PMCID: PMC11179390 DOI: 10.1186/s13195-024-01491-y] [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: 04/02/2024] [Accepted: 06/04/2024] [Indexed: 06/16/2024]
Abstract
OBJECTIVES This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. METHODS The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict 'brain age' and 'brain predicted age difference' (BPAD = brain age-chronological age) for every subject. RESULTS MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers. CONCLUSIONS Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health.
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Affiliation(s)
- Mandy M J Wittens
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
| | - Stijn Denissen
- icometrix, Leuven, Belgium
- AIMS lab, Center for Neurosciences (C4N), Vrije Universiteit Brussel, UZ Brussel, Brussels, Belgium
| | - Diana M Sima
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- icometrix, Leuven, Belgium
| | - Erik Fransen
- Centre of Medical Genetics, University of Antwerp, and Antwerp University Hospital - UZA, Edegem, Belgium
| | | | - Christine Bastin
- GIGA-CRC-IVI, Liège University, Allée du Six Août, 8, Liège, 4000, Belgium
| | - Florence Benoit
- Geriatrics Department, Brugmann University Hospital, Universite Libre de Bruxelles, Brussels, Belgium
| | - Bruno Bergmans
- Neurology Department, AZ St-Jan Brugge, Brugge, Belgium
- Ghent University Hospital, Ghent, Belgium
| | - Jean-Christophe Bier
- Neurological department H. U. B. - Erasme Hospital - Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Peter Paul de Deyn
- Laboratory of Neurochemistry and Behavior, Experimental Neurobiology Unit, University of Antwerp, Antwerp, 2610, Belgium
- Memory Clinic, Ziekenhuisnetwerk, Antwerp, Belgium
| | - Olivier Deryck
- Neurology Department, AZ St-Jan Brugge, Brugge, Belgium
- Ghent University Hospital, Ghent, Belgium
| | - Bernard Hanseeuw
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, 1200, Belgium
- Department of Neurology, Clinique Universitaires Saint-Luc, Brussels, 1200, Belgium
- WELBIO Department, WEL Research Institute, Wavre, 1300, Belgium
| | - Adrian Ivanoiu
- Department of Neurology, Cliniques Universitaires St Luc, and Institute of Neuroscience, Université Catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Gaëtane Picard
- Department of Neurology, Clinique Saint-Pierre, Ottignies, Belgium
| | | | - Eric Salmon
- GIGA-CRC-IVI, Liège University, Allée du Six Août, 8, Liège, 4000, Belgium
- Department of Neurology, Memory Clinic, Centre Hospitalier Universitaire (CHU) Liège, Liège, Belgium
| | - Kurt Segers
- Memory Clinic - Neurology and Geriatrics Department, CHU Brugmann, Van Gehuchtenplein 4, Brussels, 1020, Belgium
| | - Anne Sieben
- Neuropathology Lab, IBB-NeuroBiobank BB190113, Born Bunge Institute, Antwerp, Belgium
- Department of Pathology, Antwerp University Hospital - UZA, Antwerp, Belgium
- Laboratory of Neurology, Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Hanne Struyfs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Johnson and Johnson Innovative Medicine, Beerse, Belgium
| | - Evert Thiery
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | - Jos Tournoy
- Department of Chronic Diseases, Metabolism and Ageing, Geriatric Medicine and Memory Clinic, University Hospitals Leuven and KU Leuven, Louvain, Belgium
| | - Anne-Marie van Binst
- Radiology Department, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Jan Versijpt
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
| | - Dirk Smeets
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- icometrix, Leuven, Belgium
| | - Maria Bjerke
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- Department of Clinical Chemistry, Laboratory of Neurochemistry, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Guy Nagels
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- St. Edmund Hall, University of Oxford, Oxford, UK
- AIMS lab, Center for Neurosciences (C4N), Vrije Universiteit Brussel, UZ Brussel, Brussels, Belgium
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium.
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium.
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Tetereva A, Pat N. Brain age has limited utility as a biomarker for capturing fluid cognition in older individuals. eLife 2024; 12:RP87297. [PMID: 38869938 PMCID: PMC11175613 DOI: 10.7554/elife.87297] [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] [Indexed: 06/14/2024] Open
Abstract
One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI. To formally evaluate the utility of Brain Age for capturing fluid cognition, we built 26 age-prediction models for Brain Age based on different combinations of MRI modalities, using the Human Connectome Project in Aging (n=504, 36-100 years old). First, based on commonality analyses, we found a large overlap between Brain Age and chronological age: Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition over and above chronological age. Second, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition. Third, Brain Age missed around 11% of the total variation in fluid cognition that could have been explained by the brain variation. That is, directly predicting fluid cognition from brain MRI data (instead of relying on Brain Age and chronological age) could lead to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.
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Affiliation(s)
- Alina Tetereva
- Department of Psychology, University of OtagoDunedinNew Zealand
| | - Narun Pat
- Department of Psychology, University of OtagoDunedinNew Zealand
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Borrelli S, Guisset F, Vanden Bulcke C, Stölting A, Bugli C, Lolli V, Du Pasquier R, van Pesch V, Absinta M, Pasi M, Maggi P. Enlarged perivascular spaces are associated with brain microangiopathy and aging in multiple sclerosis. Mult Scler 2024:13524585241256881. [PMID: 38850029 DOI: 10.1177/13524585241256881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
BACKGROUND Growing evidence links brain-MRI enlarged perivascular spaces (EPVS) and multiple sclerosis (MS), but their role remains unclear. OBJECTIVE This study aimed to investigate the cross-sectional associations of EPVS with several neuroinflammatory and neurodegenerative features in a large multicentric-MS cohort. METHODS In total, 207 patients underwent 3T axial-T2-weighted brain-MRI for EPVS assessment (EPVS dichotomized into high/low according to ⩾ 2/< 2 rating categories). MRI biomarkers included brain-predicted age and brain-predicted age difference (brain-PAD), central vein sign (CVS)-positive lesion percentage (CVS%), paramagnetic rim and cortical lesions, T2-lesion load, and brain volumetry. The variable relative importance for EPVS-category prediction was explored using a classification random forest approach. RESULTS High EPVS patients were older (49 vs 44 years, p = 0.003), had ⩾ 1 vascular risk factors (VRFs; p = 0.005), lower CVS% (67% vs 78%, p < 0.001), reduced brain volumes (whole brain: 0.63 vs 0.73, p = 0.01; gray matter: 0.36 vs 0.40; p = 0.002), and older brain-predicted age (58 vs 50 years, p < 0.001). No differences were found for neuroinflammatory markers. After adjusting for age and VFRs (multivariate analyses), the high EPVS category correlated with lower CVS% (odds ratio (OR) = 0.98, 95% confidence interval (CI) = 0.96-0.99; p = 0.02), lower whole brain (OR = 0.01, 95% CI = 0.0003-0.5; p = 0.02), gray matter (OR = 0.0004, 95% CI = 0.0000004-0.4; p = 0.03) volumes, and higher brain-PAD (OR = 1.05, 95% CI = 1.01-1.09; p = 0.02). Random forest identified brain-PAD as the most important predictor of high EPVS. CONCLUSION EPVS in MS likely reflect microangiopathic disease rather than neuroinflammation, potentially contributing to accelerated neurodegeneration.
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Affiliation(s)
- Serena Borrelli
- Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium/Department of Neurology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, Université Libre de Brussels, Brussels, Belgium
| | - François Guisset
- Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium
| | - Colin Vanden Bulcke
- Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium/ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Anna Stölting
- Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium
| | - Céline Bugli
- Plateforme technologique de Support en Méthodologie et Calcul Statistique, Université catholique de Louvain, Brussels, Belgium
| | - Valentina Lolli
- Department of Radiology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, Université Libre de Brussels, Brussels, Belgium
| | - Renaud Du Pasquier
- Neurology Service, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Vincent van Pesch
- Department of Neurology, Cliniques Universitaires Saint-Luc, Université catholique de Louvain, Brussels, Belgium
| | - Martina Absinta
- Vita-Salute San Raffaele University, Milan, Italy/Translational Neuropathology Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy/Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Marco Pasi
- Stroke Unit, Department of Neurology, CIC-IT 1415, CHRU de Tours, INSERM 1253 iBrain, Tours, France
| | - Pietro Maggi
- Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium/Neurology Service, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland/Department of Neurology, Cliniques Universitaires Saint-Luc, Université catholique de Louvain, Brussels, Belgium
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Kim HW, Kim HJ, Lee H, Yang H, Rieu Z, Lee JH. Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment. Sci Rep 2023; 13:22467. [PMID: 38105274 PMCID: PMC10725862 DOI: 10.1038/s41598-023-49465-8] [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: 07/10/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023] Open
Abstract
Patients with amyloid-negative amnestic mild cognitive impairment (MCI) have a conversion rate of approximately 10% to dementia within 2 years. We aimed to investigate whether brain age is an important factor in predicting conversion to dementia in patients with amyloid-negative amnestic MCI. We conducted a retrospective cohort study of patients with amyloid-negative amnestic MCI. All participants underwent detailed neuropsychological evaluation, brain magnetic resonance imaging (MRI), and [18F]-florbetaben positron emission tomography. Brain age was determined by the volumetric assessment of 12 distinct brain regions using an automatic segmentation software. During the follow-up period, 38% of the patients converted from amnestic MCI to dementia. Further, 73% of patients had a brain age greater than their actual chronological age. When defining 'survival' as the non-conversion of MCI to dementia, these groups differed significantly in survival probability (p = 0.036). The low-educated female group with a brain age greater than their actual age had the lowest survival rate among all groups. Our findings suggest that the MRI-based brain age used in this study can contribute to predicting conversion to dementia in patients with amyloid-negative amnestic MCI.
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Affiliation(s)
- Hye Weon Kim
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - Hyung-Ji Kim
- Department of Neurology, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu, Korea
| | - Hyunji Lee
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - Hyeonsik Yang
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - ZunHyan Rieu
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea.
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Korbmacher M, Gurholt TP, de Lange AMG, van der Meer D, Beck D, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants. Front Psychol 2023; 14:1117732. [PMID: 37359862 PMCID: PMC10288151 DOI: 10.3389/fpsyg.2023.1117732] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/27/2023] [Indexed: 06/28/2023] Open
Abstract
Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Tiril P. Gurholt
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Ann-Marie G. de Lange
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Dani Beck
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Arvid Lundervold
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
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7
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Brasanac J, Chien C. A review on multiple sclerosis prognostic findings from imaging, inflammation, and mental health studies. Front Hum Neurosci 2023; 17:1151531. [PMID: 37250694 PMCID: PMC10213782 DOI: 10.3389/fnhum.2023.1151531] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/21/2023] [Indexed: 05/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) of the brain is commonly used to detect where chronic and active lesions are in multiple sclerosis (MS). MRI is also extensively used as a tool to calculate and extrapolate brain health by way of volumetric analysis or advanced imaging techniques. In MS patients, psychiatric symptoms are common comorbidities, with depression being the main one. Even though these symptoms are a major determinant of quality of life in MS, they are often overlooked and undertreated. There has been evidence of bidirectional interactions between the course of MS and comorbid psychiatric symptoms. In order to mitigate disability progression in MS, treating psychiatric comorbidities should be investigated and optimized. New research for the prediction of disease states or phenotypes of disability have advanced, primarily due to new technologies and a better understanding of the aging brain.
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Affiliation(s)
- Jelena Brasanac
- Charité – Universitätsmedizin Berlin, Klinik für Psychiatrie und Psychotherapie, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Medizinische Klinik m.S. Psychosomatik, Berlin, Germany
| | - Claudia Chien
- Charité – Universitätsmedizin Berlin, Klinik für Psychiatrie und Psychotherapie, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Medizinische Klinik m.S. Psychosomatik, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Experimental and Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Neuroscience Clinical Research Center, Berlin, Germany
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Jirsaraie RJ, Gorelik AJ, Gatavins MM, Engemann DA, Bogdan R, Barch DM, Sotiras A. A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility. PATTERNS (NEW YORK, N.Y.) 2023; 4:100712. [PMID: 37123443 PMCID: PMC10140612 DOI: 10.1016/j.patter.2023.100712] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Brain aging is a complex, multifaceted process that can be challenging to model in ways that are accurate and clinically useful. One of the most common approaches has been to apply machine learning to neuroimaging data with the goal of predicting age in a data-driven manner. Building on initial brain age studies that were derived solely from T1-weighted scans (i.e., unimodal), recent studies have incorporated features across multiple imaging modalities (i.e., "multimodal"). In this systematic review, we show that unimodal and multimodal models have distinct advantages. Multimodal models are the most accurate and sensitive to differences in chronic brain disorders. In contrast, unimodal models from functional magnetic resonance imaging were most sensitive to differences across a broad array of phenotypes. Altogether, multimodal imaging has provided us valuable insight for improving the accuracy of brain age models, but there is still much untapped potential with regard to achieving widespread clinical utility.
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Affiliation(s)
- Robert J. Jirsaraie
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Aaron J. Gorelik
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Martins M. Gatavins
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Undergraduate Neuroscience Program, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Denis A. Engemann
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche, Ltd., Basel, Switzerland
- Université Paris-Saclay, Inria, CEA, Palaiseau, France
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M. Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Corresponding author
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Advances in Neurodegenerative Diseases. J Clin Med 2023; 12:jcm12051709. [PMID: 36902495 PMCID: PMC10002914 DOI: 10.3390/jcm12051709] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 02/25/2023] Open
Abstract
Neurological disorders are the leading cause of physical and cognitive disability across the globe, currently affecting approximately 15% of the worldwide population [...].
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Role of Demyelination in the Persistence of Neurological and Mental Impairments after COVID-19. Int J Mol Sci 2022; 23:ijms231911291. [PMID: 36232592 PMCID: PMC9569975 DOI: 10.3390/ijms231911291] [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: 08/22/2022] [Revised: 09/16/2022] [Accepted: 09/21/2022] [Indexed: 11/16/2022] Open
Abstract
Long-term neurological and mental complications of COVID-19, the so-called post-COVID syndrome or long COVID, affect the quality of life. The most persistent manifestations of long COVID include fatigue, anosmia/hyposmia, insomnia, depression/anxiety, and memory/attention deficits. The physiological basis of neurological and psychiatric disorders is still poorly understood. This review summarizes the current knowledge of neurological sequelae in post-COVID patients and discusses brain demyelination as a possible mechanism of these complications with a focus on neuroimaging findings. Numerous reviews, experimental and theoretical studies consider brain demyelination as one of the mechanisms of the central neural system impairment. Several factors might cause demyelination, such as inflammation, direct effect of the virus on oligodendrocytes, and cerebrovascular disorders, inducing myelin damage. There is a contradiction between the solid fundamental basis underlying demyelination as the mechanism of the neurological injuries and relatively little published clinical evidence related to demyelination in COVID-19 patients. The reason for this probably lies in the fact that most clinical studies used conventional MRI techniques, which can detect only large, clearly visible demyelinating lesions. A very limited number of studies use specific methods for myelin quantification detected changes in the white matter tracts 3 and 10 months after the acute phase of COVID-19. Future research applying quantitative MRI assessment of myelin in combination with neurological and psychological studies will help in understanding the mechanisms of post-COVID complications associated with demyelination.
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Correlates of patient-reported cognitive performance with regard to disability. Sci Rep 2022; 12:13489. [PMID: 35931796 PMCID: PMC9355954 DOI: 10.1038/s41598-022-17649-3] [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: 01/13/2022] [Accepted: 07/28/2022] [Indexed: 12/03/2022] Open
Abstract
The patient-reported form of the Multiple Sclerosis Neuropsychological Questionnaire (MSNQ) assesses perceived problems attributable to cognitive and neuropsychiatric symptoms. It is inconsistently related to objective cognitive performance in multiple sclerosis (MS), while strongly correlated with depression. We assessed whether the relationship between subjective and objective cognitive screening tools is moderated by disability. Furthermore, we investigated the MSNQ as a screening tool for both cognitive impairment and depression. 275 MS patients completed the patient-reported MSNQ, two‐question screening tool for depression and Symbol Digit Modalities Test (SDMT) and were divided into Expanded Disability Status Scale (EDSS) subgroups: Low 0.0–3.0, Medium 3.5–6.0, High 6.5–9.0. MSNQ scores correlated significantly with depression but not SDMT in all subgroups. After correcting for age, sex, education, EDSS and depression, MSNQ significantly predicted SDMT in the total group, but not the subgroups. MSNQ significantly predicted a positive depression and/or cognitive impairment screen in the total group and all subgroups. The relationship between subjective and objective cognitive screening tools is not influenced by physical disability. MSNQ scores are substantially influenced by depression, and reflect cognitive function to some degree. Patient-reported cognitive measures can be useful to identify patients requiring further (neuro)psychological assessment.
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