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van Veenhuijzen K, Westeneng HJ, Tan HHG, Nitert AD, van der Burgh HK, Gosselt I, van Es MA, Nijboer TCW, Veldink JH, van den Berg LH. Longitudinal Effects of Asymptomatic C9orf72 Carriership on Brain Morphology. Ann Neurol 2022; 93:668-680. [PMID: 36511398 DOI: 10.1002/ana.26572] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 11/18/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVE We investigated effects of C9orf72 repeat expansion and gene expression on longitudinal cerebral changes before symptom onset. METHODS We enrolled 79 asymptomatic family members (AFMs) from 9 families with C9orf72 repeat expansion. Twenty-eight AFMs carried the mutation (C9+). Participants had up to 3 magnetic resonance imaging (MRI) scans, after which we compared motor cortex and motor tracts between C9+ and C9- AFMs using mixed effects models, incorporating kinship to correct for familial relations and lessen effects of other genetic factors. We also compared cortical, subcortical, cerebellar, and connectome structural measurements in a hypothesis-free analysis. We correlated regional C9orf72 expression in donor brains with the pattern of cortical thinning in C9+ AFMs using meta-regression. For comparison, we included 42 C9+ and 439 C9- patients with amyotrophic lateral sclerosis (ALS) in this analysis. RESULTS C9+ AFM motor cortex had less gyrification and was thinner than in C9- AFMs, without differences in motor tracts. Whole brain analysis revealed thinner cortex and less gyrification in parietal, occipital, and temporal regions, smaller thalami and right hippocampus, and affected frontotemporal connections. Thinning of bilateral precentral, precuneus, and left superior parietal cortex was faster in C9+ than in C9- AFMs. Higher C9orf72 expression correlated with thinner cortex in both C9+ AFMs and C9+ ALS patients. INTERPRETATION In asymptomatic C9orf72 repeat expansion carriers, brain MRI reveals widespread features suggestive of impaired neurodevelopment, along with faster decline of motor and parietal cortex than found in normal aging. C9orf72 expression might play a role in cortical development, and consequently explain the specific brain abnormalities of mutation carriers. ANN NEUROL 2022.
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Affiliation(s)
- Kevin van Veenhuijzen
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Henk-Jan Westeneng
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Harold H G Tan
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Abram D Nitert
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Hannelore K van der Burgh
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Isabel Gosselt
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Center of Excellence for Rehabilitation Medicine, Brain Center, University Medical Center Utrecht and De Hoogstraat Rehabilitation, Utrecht, the Netherlands
| | - Michael A van Es
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Tanja C W Nijboer
- Department of Experimental Psychology, Utrecht University, Utrecht, the Netherlands
| | - Jan H Veldink
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Leonard H van den Berg
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
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Tan HHG, Westeneng H, Nitert AD, van Veenhuijzen K, Meier JM, van der Burgh HK, van Zandvoort MJE, van Es MA, Veldink JH, van den Berg LH. MRI Clustering Reveals Three ALS Subtypes With Unique Neurodegeneration Patterns. Ann Neurol 2022; 92:1030-1045. [PMID: 36054734 PMCID: PMC9826424 DOI: 10.1002/ana.26488] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 01/11/2023]
Abstract
OBJECTIVE The purpose of this study was to identify subtypes of amyotrophic lateral sclerosis (ALS) by comparing patterns of neurodegeneration using brain magnetic resonance imaging (MRI) and explore their phenotypes. METHODS We performed T1-weighted and diffusion tensor imaging in 488 clinically well-characterized patients with ALS and 338 control subjects. Measurements of whole-brain cortical thickness and white matter connectome fractional anisotropy were adjusted for disease-unrelated variation. A probabilistic network-based clustering algorithm was used to divide patients into subgroups of similar neurodegeneration patterns. Clinical characteristics and cognitive profiles were assessed for each subgroup. In total, 512 follow-up scans were used to validate clustering results longitudinally. RESULTS The clustering algorithm divided patients with ALS into 3 subgroups of 187, 163, and 138 patients. All subgroups displayed involvement of the precentral gyrus and are characterized, respectively, by (1) pure motor involvement (pure motor cluster [PM]), (2) orbitofrontal and temporal involvement (frontotemporal cluster [FT]), and (3) involvement of the posterior cingulate cortex, parietal white matter, temporal operculum, and cerebellum (cingulate-parietal-temporal cluster [CPT]). These subgroups had significantly distinct clinical profiles regarding male-to-female ratio, age at symptom onset, and frequency of bulbar symptom onset. FT and CPT revealed higher rates of cognitive impairment on the Edinburgh cognitive and behavioral ALS screen (ECAS). Longitudinally, clustering remained stable: at 90.4% of their follow-up visits, patients clustered in the same subgroup as their baseline visit. INTERPRETATION ALS can manifest itself in 3 main patterns of cerebral neurodegeneration, each associated with distinct clinical characteristics and cognitive profiles. Besides the pure motor and frontotemporal dementia (FTD)-like variants of ALS, a new neuroimaging phenotype has emerged, characterized by posterior cingulate, parietal, temporal, and cerebellar involvement. ANN NEUROL 2022;92:1030-1045.
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Affiliation(s)
- Harold H. G. Tan
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Henk‐Jan Westeneng
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Abram D. Nitert
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Kevin van Veenhuijzen
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Jil M. Meier
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Hannelore K. van der Burgh
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Martine J. E. van Zandvoort
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands,Department of Experimental PsychologyUtrecht UniversityUtrechtThe Netherlands
| | - Michael A. van Es
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Jan H. Veldink
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Leonard H. van den Berg
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
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Janse van Mantgem MR, van Eijk RPA, van der Burgh HK, Tan HHG, Westeneng HJ, van Es MA, Veldink JH, van den Berg LH. Prognostic value of weight loss in patients with amyotrophic lateral sclerosis: a population-based study. J Neurol Neurosurg Psychiatry 2020; 91:867-875. [PMID: 32576612 DOI: 10.1136/jnnp-2020-322909] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 03/24/2020] [Accepted: 04/07/2020] [Indexed: 12/18/2022]
Abstract
OBJECTIVE To determine the prevalence and prognostic value of weight loss (WL) prior to diagnosis in patients with amyotrophic lateral sclerosis (ALS). METHODS We enrolled patients diagnosed with ALS between 2010 and 2018 in a population-based setting. At diagnosis, detailed information was obtained regarding the patient's disease characteristics, anthropological changes, ALS-related genotypes and cognitive functioning. Complete survival data were obtained. Cox proportional hazard models were used to assess the association between WL and the risk of death during follow-up. RESULTS The data set comprised 2420 patients of whom 67.5% reported WL at diagnosis. WL occurred in 71.8% of the bulbar-onset and in 64.2% of the spinal-onset patients; the mean loss of body weight was 6.9% (95% CI 6.8 to 6.9) and 5.5% (95% CI 5.5 to 5.6), respectively (p<0.001). WL occurred in 35.1% of the patients without any symptom of dysphagia. WL is a strong independent predictor of survival, with a dose response relationship between the amount of WL and the risk of death: the risk of death during follow-up increased by 23% for every 10% increase in WL relative to body weight (HR 1.23, 95% CI 1.13 to 1.51, p<0.001). CONCLUSIONS This population-based study shows that two-thirds of the patients with ALS have WL at diagnosis, which also occurs independent of dysphagia, and is related to survival. Our results suggest that WL is a multifactorial process that may differ from patient to patient. Gaining further insight in its underlying factors could prove essential for future therapeutic measures.
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Affiliation(s)
| | - Ruben P A van Eijk
- Neurology, University Medical Centre Utrecht Brain Centre, Utrecht, The Netherlands.,Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands
| | | | - Harold H G Tan
- Neurology, University Medical Centre Utrecht Brain Centre, Utrecht, The Netherlands
| | - Henk-Jan Westeneng
- Neurology, University Medical Centre Utrecht Brain Centre, Utrecht, The Netherlands
| | - Michael A van Es
- Neurology, University Medical Centre Utrecht Brain Centre, Utrecht, The Netherlands
| | - Jan H Veldink
- Neurology, University Medical Centre Utrecht Brain Centre, Utrecht, The Netherlands
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Tan HHG, Westeneng HJ, van der Burgh HK, van Es MA, Bakker LA, van Veenhuijzen K, van Eijk KR, van Eijk RPA, Veldink JH, van den Berg LH. The Distinct Traits of the UNC13A Polymorphism in Amyotrophic Lateral Sclerosis. Ann Neurol 2020; 88:796-806. [PMID: 32627229 PMCID: PMC7540607 DOI: 10.1002/ana.25841] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 07/03/2020] [Accepted: 07/03/2020] [Indexed: 12/11/2022]
Abstract
Objective The rs12608932 single nucleotide polymorphism in UNC13A is associated with amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) susceptibility, and may underlie differences in treatment response. We aimed to characterize the clinical, cognitive, behavioral, and neuroimaging phenotype of UNC13A in patients with ALS. Methods We included 2,216 patients with ALS without a C9orf72 mutation to identify clinical characteristics associated with the UNC13A polymorphism. A subcohort of 428 patients with ALS was used to study cognitive and behavioral profiles, and 375 patients to study neuroimaging characteristics. Associations were analyzed under an additive genetic model. Results Genotyping rs12608932 resulted in 854 A/A, 988 A/C, and 374 C/C genotypes. The C allele was associated with a higher age at symptom onset (median years A/A 63.5, A/C 65.6, and C/C 65.5; p < 0.001), more frequent bulbar onset (A/A 29.6%, A/C 31.8%, and C/C 43.1%; p < 0.001), higher incidences of ALS‐FTD (A/A 4.3%, A/C 5.2%, and C/C 9.5%; p = 0.003), lower forced vital capacity at diagnosis (median percentage A/A 92.0, A/C 90.0, and C/C 86.5; p < 0.001), and a shorter survival (median in months A/A 33.3, A.C 30.7, and C/C 26.6; p < 0.001). UNC13A was associated with lower scores on ALS‐specific cognition tests (means A/A 79.5, A/C 78.1, and C/C 76.6; p = 0.037), and more frequent behavioral disturbances (A/A 16.7%, A/C 24.4%, and C/C 27.7%; p = 0.045). Thinner left inferior temporal and right fusiform cortex were associated with the UNC13A single nucleotide polymorphism (SNP; p = 0.045 and p = 0.036). Interpretation Phenotypical distinctions associated with UNC13A make it an important factor to take into account in clinical trial design, studies on cognition and behavior, and prognostic counseling. ANN NEUROL 2020;88:796–806
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Affiliation(s)
- Harold H G Tan
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Henk-Jan Westeneng
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Hannelore K van der Burgh
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Michael A van Es
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Leonhard A Bakker
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Center of Excellence for Rehabilitation Medicine, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University and De Hoogstraat Rehabilitation, Utrecht, The Netherlands
| | - Kevin van Veenhuijzen
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Kristel R van Eijk
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Ruben P A van Eijk
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Biostatistics and Research Support, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jan H Veldink
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Leonard H van den Berg
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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van der Burgh HK, Westeneng HJ, Walhout R, van Veenhuijzen K, Tan HHG, Meier JM, Bakker LA, Hendrikse J, van Es MA, Veldink JH, van den Heuvel MP, van den Berg LH. Multimodal longitudinal study of structural brain involvement in amyotrophic lateral sclerosis. Neurology 2020; 94:e2592-e2604. [PMID: 32414878 PMCID: PMC7455328 DOI: 10.1212/wnl.0000000000009498] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Accepted: 12/05/2019] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE To understand the progressive nature of amyotrophic lateral sclerosis (ALS) by investigating differential brain patterns of gray and white matter involvement in clinically or genetically defined subgroups of patients using cross-sectional, longitudinal, and multimodal MRI. METHODS We assessed cortical thickness, subcortical volumes, and white matter connectivity from T1-weighted and diffusion-weighted MRI in 292 patients with ALS (follow-up: n = 150) and 156 controls (follow-up: n = 72). Linear mixed-effects models were used to assess changes in structural brain measurements over time in patients compared to controls. RESULTS Patients with a C9orf72 mutation (n = 24) showed widespread gray and white matter involvement at baseline, and extensive loss of white matter integrity in the connectome over time. In C9orf72-negative patients, we detected cortical thinning of motor and frontotemporal regions, and loss of white matter integrity of connections linked to the motor cortex. Patients with spinal onset displayed widespread white matter involvement at baseline and gray matter atrophy over time, whereas patients with bulbar onset started out with prominent gray matter involvement. Patients with unaffected cognition or behavior displayed predominantly motor system involvement, while widespread cerebral changes, including frontotemporal regions with progressive white matter involvement over time, were associated with impaired behavior or cognition. Progressive loss of gray and white matter integrity typically occurred in patients with shorter disease durations (<13 months), independent of progression rate. CONCLUSIONS Heterogeneity of phenotype and C9orf72 genotype relates to distinct patterns of cerebral degeneration. We demonstrate that imaging studies have the potential to monitor disease progression and early intervention may be required to limit cerebral degeneration.
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Affiliation(s)
- Hannelore K van der Burgh
- From the Department of Neurology (H.K.v.d.B., H.-J.W., R.W., K.v.V., H.H.G.T., J.M.M., L.A.B., M.A.v.E., J.H.V., L.H.v.d.B.), Center of Excellence for Rehabilitation Medicine (L.A.B.), and Department of Radiology (J.H.), UMC Utrecht Brain Center, University Medical Center Utrecht; De Hoogstraat Rehabilitation (L.A.B.), Utrecht; and Department of Complex Trait Genetics (M.P.v.d.H.), Center for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands
| | - Henk-Jan Westeneng
- From the Department of Neurology (H.K.v.d.B., H.-J.W., R.W., K.v.V., H.H.G.T., J.M.M., L.A.B., M.A.v.E., J.H.V., L.H.v.d.B.), Center of Excellence for Rehabilitation Medicine (L.A.B.), and Department of Radiology (J.H.), UMC Utrecht Brain Center, University Medical Center Utrecht; De Hoogstraat Rehabilitation (L.A.B.), Utrecht; and Department of Complex Trait Genetics (M.P.v.d.H.), Center for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands
| | - Renée Walhout
- From the Department of Neurology (H.K.v.d.B., H.-J.W., R.W., K.v.V., H.H.G.T., J.M.M., L.A.B., M.A.v.E., J.H.V., L.H.v.d.B.), Center of Excellence for Rehabilitation Medicine (L.A.B.), and Department of Radiology (J.H.), UMC Utrecht Brain Center, University Medical Center Utrecht; De Hoogstraat Rehabilitation (L.A.B.), Utrecht; and Department of Complex Trait Genetics (M.P.v.d.H.), Center for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands
| | - Kevin van Veenhuijzen
- From the Department of Neurology (H.K.v.d.B., H.-J.W., R.W., K.v.V., H.H.G.T., J.M.M., L.A.B., M.A.v.E., J.H.V., L.H.v.d.B.), Center of Excellence for Rehabilitation Medicine (L.A.B.), and Department of Radiology (J.H.), UMC Utrecht Brain Center, University Medical Center Utrecht; De Hoogstraat Rehabilitation (L.A.B.), Utrecht; and Department of Complex Trait Genetics (M.P.v.d.H.), Center for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands
| | - Harold H G Tan
- From the Department of Neurology (H.K.v.d.B., H.-J.W., R.W., K.v.V., H.H.G.T., J.M.M., L.A.B., M.A.v.E., J.H.V., L.H.v.d.B.), Center of Excellence for Rehabilitation Medicine (L.A.B.), and Department of Radiology (J.H.), UMC Utrecht Brain Center, University Medical Center Utrecht; De Hoogstraat Rehabilitation (L.A.B.), Utrecht; and Department of Complex Trait Genetics (M.P.v.d.H.), Center for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands
| | - Jil M Meier
- From the Department of Neurology (H.K.v.d.B., H.-J.W., R.W., K.v.V., H.H.G.T., J.M.M., L.A.B., M.A.v.E., J.H.V., L.H.v.d.B.), Center of Excellence for Rehabilitation Medicine (L.A.B.), and Department of Radiology (J.H.), UMC Utrecht Brain Center, University Medical Center Utrecht; De Hoogstraat Rehabilitation (L.A.B.), Utrecht; and Department of Complex Trait Genetics (M.P.v.d.H.), Center for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands
| | - Leonhard A Bakker
- From the Department of Neurology (H.K.v.d.B., H.-J.W., R.W., K.v.V., H.H.G.T., J.M.M., L.A.B., M.A.v.E., J.H.V., L.H.v.d.B.), Center of Excellence for Rehabilitation Medicine (L.A.B.), and Department of Radiology (J.H.), UMC Utrecht Brain Center, University Medical Center Utrecht; De Hoogstraat Rehabilitation (L.A.B.), Utrecht; and Department of Complex Trait Genetics (M.P.v.d.H.), Center for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands
| | - Jeroen Hendrikse
- From the Department of Neurology (H.K.v.d.B., H.-J.W., R.W., K.v.V., H.H.G.T., J.M.M., L.A.B., M.A.v.E., J.H.V., L.H.v.d.B.), Center of Excellence for Rehabilitation Medicine (L.A.B.), and Department of Radiology (J.H.), UMC Utrecht Brain Center, University Medical Center Utrecht; De Hoogstraat Rehabilitation (L.A.B.), Utrecht; and Department of Complex Trait Genetics (M.P.v.d.H.), Center for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands
| | - Michael A van Es
- From the Department of Neurology (H.K.v.d.B., H.-J.W., R.W., K.v.V., H.H.G.T., J.M.M., L.A.B., M.A.v.E., J.H.V., L.H.v.d.B.), Center of Excellence for Rehabilitation Medicine (L.A.B.), and Department of Radiology (J.H.), UMC Utrecht Brain Center, University Medical Center Utrecht; De Hoogstraat Rehabilitation (L.A.B.), Utrecht; and Department of Complex Trait Genetics (M.P.v.d.H.), Center for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands
| | - Jan H Veldink
- From the Department of Neurology (H.K.v.d.B., H.-J.W., R.W., K.v.V., H.H.G.T., J.M.M., L.A.B., M.A.v.E., J.H.V., L.H.v.d.B.), Center of Excellence for Rehabilitation Medicine (L.A.B.), and Department of Radiology (J.H.), UMC Utrecht Brain Center, University Medical Center Utrecht; De Hoogstraat Rehabilitation (L.A.B.), Utrecht; and Department of Complex Trait Genetics (M.P.v.d.H.), Center for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands
| | - Martijn P van den Heuvel
- From the Department of Neurology (H.K.v.d.B., H.-J.W., R.W., K.v.V., H.H.G.T., J.M.M., L.A.B., M.A.v.E., J.H.V., L.H.v.d.B.), Center of Excellence for Rehabilitation Medicine (L.A.B.), and Department of Radiology (J.H.), UMC Utrecht Brain Center, University Medical Center Utrecht; De Hoogstraat Rehabilitation (L.A.B.), Utrecht; and Department of Complex Trait Genetics (M.P.v.d.H.), Center for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands
| | - Leonard H van den Berg
- From the Department of Neurology (H.K.v.d.B., H.-J.W., R.W., K.v.V., H.H.G.T., J.M.M., L.A.B., M.A.v.E., J.H.V., L.H.v.d.B.), Center of Excellence for Rehabilitation Medicine (L.A.B.), and Department of Radiology (J.H.), UMC Utrecht Brain Center, University Medical Center Utrecht; De Hoogstraat Rehabilitation (L.A.B.), Utrecht; and Department of Complex Trait Genetics (M.P.v.d.H.), Center for Neurogenomics and Cognitive Research, VU University Amsterdam, the Netherlands.
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Meier JM, van der Burgh HK, Nitert AD, Bede P, de Lange SC, Hardiman O, van den Berg LH, van den Heuvel MP. Connectome-Based Propagation Model in Amyotrophic Lateral Sclerosis. Ann Neurol 2020; 87:725-738. [PMID: 32072667 PMCID: PMC7186838 DOI: 10.1002/ana.25706] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 02/14/2020] [Accepted: 02/15/2020] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Clinical trials in amyotrophic lateral sclerosis (ALS) continue to rely on survival or functional scales as endpoints, despite the emergence of quantitative biomarkers. Neuroimaging-based biomarkers in ALS have been shown to detect ALS-associated pathology in vivo, although anatomical patterns of disease spread are poorly characterized. The objective of this study is to simulate disease propagation using network analyses of cerebral magnetic resonance imaging (MRI) data to predict disease progression. METHODS Using brain networks of ALS patients (n = 208) and matched controls across longitudinal time points, network-based statistics unraveled progressive network degeneration originating from the motor cortex and expanding in a spatiotemporal manner. We applied a computational model to the MRI scan of patients to simulate this progressive network degeneration. Simulated aggregation levels at the group and individual level were validated with empirical impairment observed at later time points of white matter and clinical decline using both internal and external datasets. RESULTS We observe that computer-simulated aggregation levels mimic true disease patterns in ALS patients. Simulated patterns of involvement across cortical areas show significant overlap with the patterns of empirically impaired brain regions on later scans, at both group and individual levels. These findings are validated using an external longitudinal dataset of 30 patients. INTERPRETATION Our results are in accordance with established pathological staging systems and may have implications for patient stratification in future clinical trials. Our results demonstrate the utility of computational models in ALS to predict disease progression and underscore their potential as a prognostic biomarker. ANN NEUROL 2020;87:725-738.
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Affiliation(s)
- Jil M. Meier
- Department of Neurology, UMC Utrecht Brain CenterUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Hannelore K. van der Burgh
- Department of Neurology, UMC Utrecht Brain CenterUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Abram D. Nitert
- Department of Neurology, UMC Utrecht Brain CenterUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Peter Bede
- Computational Neuroimaging GroupTrinity Biomedical Sciences Institute, Trinity College DublinDublinIreland
- Department of NeurologyPitié‐Salpêtrière University HospitalParisFrance
- Biomedical Imaging Laboratory, Sorbonne University, National Center for Scientific ResearchNational Institute of Health and Medical ResearchParisFrance
| | - Siemon C. de Lange
- Dutch Connectome Lab, Center for Neurogenomics and Cognitive Research, Amsterdam NeuroscienceFree University AmsterdamAmsterdamthe Netherlands
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity Biomedical Sciences InstituteTrinity College DublinDublinIreland
- Department of NeurologyBeaumont HospitalDublinIreland
| | - Leonard H. van den Berg
- Department of Neurology, UMC Utrecht Brain CenterUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Martijn P. van den Heuvel
- Dutch Connectome Lab, Center for Neurogenomics and Cognitive Research, Amsterdam NeuroscienceFree University AmsterdamAmsterdamthe Netherlands
- Department of Clinical GeneticsAmsterdam University Medical CenterAmsterdamthe Netherlands
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van der Burgh HK, Westeneng HJ, Meier JM, van Es MA, Veldink JH, Hendrikse J, van den Heuvel MP, van den Berg LH. Cross-sectional and longitudinal assessment of the upper cervical spinal cord in motor neuron disease. Neuroimage Clin 2019; 24:101984. [PMID: 31499409 PMCID: PMC6734179 DOI: 10.1016/j.nicl.2019.101984] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 08/12/2019] [Accepted: 08/13/2019] [Indexed: 11/28/2022]
Abstract
Background Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease characterized by both upper and lower motor neuron degeneration. While neuroimaging studies of the brain can detect upper motor neuron degeneration, these brain MRI scans also include the upper part of the cervical spinal cord, which offers the possibility to expand the focus also towards lower motor neuron degeneration. Here, we set out to investigate cross-sectional and longitudinal disease effects in the upper cervical spinal cord in patients with ALS, progressive muscular atrophy (PMA: primarily lower motor neuron involvement) and primary lateral sclerosis (PLS: primarily upper motor neuron involvement), and their relation to disease severity and grey and white matter brain measurements. Methods We enrolled 108 ALS patients without C9orf72 repeat expansion (ALS C9–), 26 ALS patients with C9orf72 repeat expansion (ALS C9+), 28 PLS patients, 56 PMA patients and 114 controls. During up to five visits, longitudinal T1-weighted brain MRI data were acquired and used to segment the upper cervical spinal cord (UCSC, up to C3) and individual cervical segments (C1 to C4) to calculate cross-sectional areas (CSA). Using linear (mixed-effects) models, the CSA differences were assessed between groups and correlated with disease severity. Furthermore, a relationship between CSA and brain measurements was examined in terms of cortical thickness of the precentral gyrus and white matter integrity of the corticospinal tract. Results Compared to controls, CSAs at baseline showed significantly thinner UCSC in all groups in the MND spectrum. Over time, ALS C9– patients demonstrated significant thinning of the UCSC and, more specifically, of segment C3 compared to controls. Progressive thinning over time was also observed in C1 of PMA patients, while ALS C9+ and PLS patients did not show any longitudinal changes. Longitudinal spinal cord measurements showed a significant relationship with disease severity and we found a significant correlation between spinal cord and motor cortex thickness or corticospinal tract integrity for PLS and PMA, but not for ALS patients. Discussion Our findings demonstrate atrophy of the upper cervical spinal cord in the motor neuron disease spectrum, which was progressive over time for all but PLS patients. Cervical spinal cord imaging in ALS seems to capture different disease effects than brain neuroimaging. Atrophy of the cervical spinal cord is therefore a promising additional biomarker for both diagnosis and disease progression and could help in the monitoring of treatment effects in future clinical trials. Atrophy of upper cervical spinal cord is shown in the motor neuron disease spectrum. Progressive cervical spinal cord thinning occurs over time for all but PLS patients. Cervical spinal cord imaging is a potential biomarker for disease progression in ALS.
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Affiliation(s)
- Hannelore K van der Burgh
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Henk-Jan Westeneng
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Jil M Meier
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Michael A van Es
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Jan H Veldink
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Jeroen Hendrikse
- Department of Radiology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, The Netherlands.
| | - Leonard H van den Berg
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
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8
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van den Heuvel MP, Scholtens LH, van der Burgh HK, Agosta F, Alloza C, Arango C, Auyeung B, Baron-Cohen S, Basaia S, Benders MJNL, Beyer F, Booij L, Braun KPJ, Filho GB, Cahn W, Cannon DM, Chaim-Avancini TM, Chan SSM, Chen EYH, Crespo-Facorro B, Crone EA, Dannlowski U, de Zwarte SMC, Dietsche B, Donohoe G, Plessis SD, Durston S, Díaz-Caneja CM, Díaz-Zuluaga AM, Emsley R, Filippi M, Frodl T, Gorges M, Graff B, Grotegerd D, Gąsecki D, Hall JM, Holleran L, Holt R, Hopman HJ, Jansen A, Janssen J, Jodzio K, Jäncke L, Kaleda VG, Kassubek J, Masouleh SK, Kircher T, Koevoets MGJC, Kostic VS, Krug A, Lawrie SM, Lebedeva IS, Lee EHM, Lett TA, Lewis SJG, Liem F, Lombardo MV, Lopez-Jaramillo C, Margulies DS, Markett S, Marques P, Martínez-Zalacaín I, McDonald C, McIntosh AM, McPhilemy G, Meinert SL, Menchón JM, Montag C, Moreira PS, Morgado P, Mothersill DO, Mérillat S, Müller HP, Nabulsi L, Najt P, Narkiewicz K, Naumczyk P, Oranje B, Ortiz-Garcia de la Foz V, Peper JS, Pineda JA, Rasser PE, Redlich R, Repple J, Reuter M, Rosa PGP, Ruigrok ANV, Sabisz A, Schall U, Seedat S, Serpa MH, Skouras S, Soriano-Mas C, Sousa N, Szurowska E, Tomyshev AS, Tordesillas-Gutierrez D, Valk SL, van den Berg LH, van Erp TGM, van Haren NEM, van Leeuwen JMC, Villringer A, Vinkers CH, Vollmar C, Waller L, Walter H, Whalley HC, Witkowska M, Witte AV, Zanetti MV, Zhang R, de Lange SC. 10Kin1day: A Bottom-Up Neuroimaging Initiative. Front Neurol 2019; 10:425. [PMID: 31133958 PMCID: PMC6524614 DOI: 10.3389/fneur.2019.00425] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 04/08/2019] [Indexed: 01/11/2023] Open
Abstract
We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain.
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Affiliation(s)
- Martijn P. van den Heuvel
- Connectome Lab, CTG, CNCR, VU Amsterdam, Amsterdam, Netherlands
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Lianne H. Scholtens
- Connectome Lab, CTG, CNCR, VU Amsterdam, Amsterdam, Netherlands
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Hannelore K. van der Burgh
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Clara Alloza
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
- Department of Child and Adolescent Psychiatry, IiSGM, CIBERSAM, School of Medicine, Hospital General Universitario Gregorio Marañón, Universidad Complutense, Madrid, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, IiSGM, CIBERSAM, School of Medicine, Hospital General Universitario Gregorio Marañón, Universidad Complutense, Madrid, Spain
| | - Bonnie Auyeung
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Simon Baron-Cohen
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Silvia Basaia
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Manon J. N. L. Benders
- Department of Neonatology, UMC Utrecht Brain Center, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, Netherlands
| | - Frauke Beyer
- Department of Neurology, CRC “Obesity Mechanisms”, Subproject A1, Max Planck Institute for Human Cognitive and Brain Sciences, University of Leipzig, Leipzig, Germany
| | - Linda Booij
- Department of Psychology, Concordia University, Montreal, QC, Canada
| | - Kees P. J. Braun
- Department of Child Neurology, UMC Utrecht Brain Center, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, Netherlands
| | - Geraldo Busatto Filho
- Laboratory of Psychiatric Neuroimaging (LIM21), Faculdade de Medicina, Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Wiepke Cahn
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Dara M. Cannon
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Tiffany M. Chaim-Avancini
- Laboratory of Psychiatric Neuroimaging (LIM21), Faculdade de Medicina, Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Sandra S. M. Chan
- Department of Psychiatry, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Eric Y. H. Chen
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Benedicto Crespo-Facorro
- Psychiatry Unit, Department of Medicine and Psychiatry, Hospital Universitario Marques de Valdecilla, IDIVAL, CIBERSAM, Hosptial Universitario Virgen del Rocío, Universidad de Seville, Seville, Spain
| | - Eveline A. Crone
- Brain and Development Research Center, Leiden University, Leiden, Netherlands
| | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Sonja M. C. de Zwarte
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Bruno Dietsche
- Department of Psychiatry, University of Marburg, Marburg, Germany
| | - Gary Donohoe
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and Cognitive Genomics Centre and NCBES Galway Neuroscience Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - Stefan Du Plessis
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Sarah Durston
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Covadonga M. Díaz-Caneja
- Department of Child and Adolescent Psychiatry, IiSGM, CIBERSAM, School of Medicine, Hospital General Universitario Gregorio Marañón, Universidad Complutense, Madrid, Spain
| | - Ana M. Díaz-Zuluaga
- Research Group in Psychiatry GIPSI, Department of Psychiatry, Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia
| | - Robin Emsley
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, University Hospital, Otto von Guericke University, Magdeburg, Germany
| | - Martin Gorges
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Beata Graff
- Department of Hypertension and Diabetology, Medical University of Gdańsk, Gdańsk, Poland
| | | | - Dariusz Gąsecki
- Department of Neurology of Adults, Medical University of Gdańsk, Gdańsk, Poland
| | - Julie M. Hall
- Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Laurena Holleran
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and Cognitive Genomics Centre and NCBES Galway Neuroscience Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - Rosemary Holt
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Helene J. Hopman
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
| | - Andreas Jansen
- Department of Psychiatry and Center for Mind, Brain and Behaviour, University of Marburg, Marburg, Germany
| | - Joost Janssen
- Department of Child and Adolescent Psychiatry, IiSGM, CIBERSAM, School of Medicine, Hospital General Universitario Gregorio Marañón, Universidad Complutense, Madrid, Spain
| | | | - Lutz Jäncke
- Division of Neuropsychology, University of Zurich, Zurich, Switzerland
| | - Vasiliy G. Kaleda
- Department of Endogenous Mental Disorders, Mental Health Research Center, Moscow, Russia
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
| | | | - Tilo Kircher
- Department of Psychiatry and Center for Mind, Brain and Behaviour, University of Marburg, Marburg, Germany
| | - Martijn G. J. C. Koevoets
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Vladimir S. Kostic
- Clinic of Neurology, School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Axel Krug
- Department of Psychiatry and Center for Mind, Brain and Behaviour, University of Marburg, Marburg, Germany
| | - Stephen M. Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Irina S. Lebedeva
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russia
| | - Edwin H. M. Lee
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Tristram A. Lett
- Department of Psychiatry and Psychotherapy, Division of Mind and Brain Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Simon J. G. Lewis
- Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Franziskus Liem
- University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
| | - Michael V. Lombardo
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Carlos Lopez-Jaramillo
- Mood Disorders Program, Research Group in Psychiatry GIPSI, Department of Psychiatry, Faculty of Medicine, Hospital Universitario San Vicente Fundación, Universidad de Antioquia, Medellín, Colombia
| | - Daniel S. Margulies
- Frontlab, Centre National de la Recherche Scientifique, Institut du Cerveau et de la Moelle Épinière, UMR 7225, Paris, France
| | - Sebastian Markett
- Department of Psychology, Humboldt Universität zu Berlin, Berlin, Germany
| | - Paulo Marques
- School of Medicine, Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - Ignacio Martínez-Zalacaín
- Department of Psychiatry, Bellvitge Biomedical Research Institute-IDIBELL and CIBERSAM, Barcelona, Spain
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Andrew M. McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Genevieve McPhilemy
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | | | - José M. Menchón
- Department of Psychiatry, Bellvitge Biomedical Research Institute-IDIBELL and CIBERSAM, Barcelona, Spain
| | - Christian Montag
- Department of Molecular Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany
| | - Pedro S. Moreira
- School of Medicine, Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - Pedro Morgado
- School of Medicine, Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - David O. Mothersill
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and Cognitive Genomics Centre and NCBES Galway Neuroscience Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - Susan Mérillat
- University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
| | | | - Leila Nabulsi
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Pablo Najt
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Krzysztof Narkiewicz
- Department of Hypertension and Diabetology, Medical University of Gdańsk, Gdańsk, Poland
| | | | - Bob Oranje
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Victor Ortiz-Garcia de la Foz
- Psychiatry Unit, Department of Medicine and Psychiatry, IDIVAL, CIBERSAM, Hospital Universitario Marques de Valdecilla, Santander, Spain
| | - Jiska S. Peper
- Brain and Development Research Center, Leiden University, Leiden, Netherlands
| | - Julian A. Pineda
- Research Group, Instituto de Alta Tecnología Médica, Universidad de Antioquia, Medellín, Colombia
| | - Paul E. Rasser
- Priority Centre for Brain and Mental Health Research, The University of Newcastle, Newcastle, NSW, Australia
| | - Ronny Redlich
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Martin Reuter
- Department of Psychology, Humboldt Universität zu Berlin, Berlin, Germany
| | - Pedro G. P. Rosa
- Laboratory of Psychiatric Neuroimaging (LIM21), Faculdade de Medicina, Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Amber N. V. Ruigrok
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Agnieszka Sabisz
- 2nd Department of Radiology, Medical University of Gdańsk, Gdańsk, Poland
| | - Ulrich Schall
- Priority Centre for Brain and Mental Health Research, The University of Newcastle, Newcastle, NSW, Australia
| | - Soraya Seedat
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Mauricio H. Serpa
- Laboratory of Psychiatric Neuroimaging (LIM21), Departamento de Psiquiatria, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Stavros Skouras
- BarcelonaBeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Carles Soriano-Mas
- Department of Psychiatry (IDIBELL and CIBERSAM) and Department of Psychobiology and Methodology in Health Sciences (UAB), Bellvitge Biomedical Research Institute-IDIBELL, CIBERSAM and Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Nuno Sousa
- School of Medicine, Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - Edyta Szurowska
- 2nd Department of Radiology, Medical University of Gdańsk, Gdańsk, Poland
| | - Alexander S. Tomyshev
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russia
| | - Diana Tordesillas-Gutierrez
- Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, CIBERSAM, Santander, Spain
| | - Sofie L. Valk
- Institute for Neuroscience and Medicine 7/Institute of Systems Neuroscience, Forschungszentrum Jülich - Heinrich Heine Universitaet Duesseldorf, Jülich, Germany
| | - Leonard H. van den Berg
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Theo G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Neeltje E. M. van Haren
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Judith M. C. van Leeuwen
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Arno Villringer
- Departments of Neurology, Cognitive Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, University of Leipzig, Leipzig, Germany
| | - Christiaan H. Vinkers
- Departments of Psychiatry, Anatomy and Neurosciences, Amsterdam UMC, Amsterdam, Netherlands
| | - Christian Vollmar
- Department of Neurology, Epilepsy Centre, University of Munich Hospital, Munich, Germany
| | - Lea Waller
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité Universitätsmedizin Berlin, Corporate Member of Berlin Institute of Health, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Berlin Institute of Health, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Heather C. Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Marta Witkowska
- Institute of Psychology, University of Gdańsk, Gdańsk, Poland
| | - A. Veronica Witte
- Department of Neurology, CRC “Obesity Mechanisms”, Subproject A1, Max Planck Institute for Human Cognitive and Brain Sciences, University of Leipzig, Leipzig, Germany
| | - Marcus V. Zanetti
- Laboratory of Psychiatric Neuroimaging (LIM21), Faculdade de Medicina, Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, São Paulo, Brazil
- Instituto de Ensino e Pesquisa, Hospital Sírio-Libanês, Universidade de São Paulo, São Paulo, Brazil
| | - Rui Zhang
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Siemon C. de Lange
- Connectome Lab, CTG, CNCR, VU Amsterdam, Amsterdam, Netherlands
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
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van der Burgh HK, Schmidt R, Westeneng HJ, de Reus MA, van den Berg LH, van den Heuvel MP. Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis. Neuroimage Clin 2016; 13:361-369. [PMID: 28070484 PMCID: PMC5219634 DOI: 10.1016/j.nicl.2016.10.008] [Citation(s) in RCA: 84] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 09/28/2016] [Accepted: 10/10/2016] [Indexed: 01/17/2023]
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.
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Affiliation(s)
- Hannelore K van der Burgh
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands
| | - Ruben Schmidt
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands
| | - Henk-Jan Westeneng
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands
| | - Marcel A de Reus
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands
| | - Leonard H van den Berg
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands
| | - Martijn P van den Heuvel
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands
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