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Shen T, Vogel JW, Duda J, Phillips JS, Cook PA, Gee J, Elman L, Quinn C, Amado DA, Baer M, Massimo L, Grossman M, Irwin DJ, McMillan CT. Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum. Transl Neurodegener 2023; 12:57. [PMID: 38062485 PMCID: PMC10701950 DOI: 10.1186/s40035-023-00389-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND TDP-43 proteinopathies represent a spectrum of neurological disorders, anchored clinically on either end by amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes, highlighting its heterogeneity. This study was aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum. METHODS We used a data-driven procedure to identify 13 anatomic clusters of brain volume for 57 behavioral variant FTD (bvFTD; with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants), 103 ALS, and 47 ALS-FTD patients with likely TDP-43. A Subtype and Stage Inference (SuStaIn) model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns, and we related subtypes and stages to clinical, genetic, and neuropathological features of disease. RESULTS SuStaIn identified three novel subtypes: two disease subtypes with predominant brain atrophy in either prefrontal/somatomotor regions or limbic-related regions, and a normal-appearing group without obvious brain atrophy. The limbic-predominant subtype tended to present with more impaired cognition, higher frequencies of pathogenic variants in TBK1 and TARDBP genes, and a higher proportion of TDP-43 types B, E and C. In contrast, the prefrontal/somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A. The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients, higher cognitive capacity, higher proportion of lower motor neuron onset, milder motor symptoms, and lower frequencies of genetic pathogenic variants. The overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration, higher King's stage, and cognitive decline. Additionally, SuStaIn stages differed across clinical phenotypes, genotypes and types of TDP-43 pathology. CONCLUSIONS Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo, each with distinct brain atrophy, clinical, genetic and pathological patterns.
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Affiliation(s)
- Ting Shen
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, 222 42, Lund, Sweden
| | - Jeffrey Duda
- Penn Image Computing and Science Lab (PICSL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jeffrey S Phillips
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Philip A Cook
- Penn Image Computing and Science Lab (PICSL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - James Gee
- Penn Image Computing and Science Lab (PICSL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lauren Elman
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Colin Quinn
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Defne A Amado
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael Baer
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lauren Massimo
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David J Irwin
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Corey T McMillan
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Shen T, Vogel JW, Duda J, Phillips JS, Cook PA, Gee J, Elman L, Quinn C, Amado DA, Baer M, Massimo L, Grossman M, Irwin DJ, McMillan CT. Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum. Res Sq 2023:rs.3.rs-3183113. [PMID: 37609205 PMCID: PMC10441467 DOI: 10.21203/rs.3.rs-3183113/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Background TDP-43 proteinopathies represents a spectrum of neurological disorders, anchored clinically on either end by amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes, highlighting its heterogeneity. This study aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum. Methods We used a data-driven procedure to identify 13 anatomic clusters of brain volumes for 57 behavioral variant FTD (bvFTD; with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants), 103 ALS, and 47 ALS-FTD patients with likely TDP-43. A Subtype and Stage Inference (SuStaIn) model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns, and we related subtypes and stages to clinical, genetic, and neuropathological features of disease. Results SuStaIn identified three novel subtypes: two disease subtypes with predominant brain atrophy either in prefrontal/somatomotor regions or limbic-related regions, and a normal-appearing group without obvious brain atrophy. The Limbic-predominant subtype tended to present with more impaired cognition, higher frequencies of pathogenic variants in TBK1 and TARDBP genes, and a higher proportion of TDP-43 type B, E and C. In contrast, the Prefrontal/Somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A. The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients, higher cognitive capacity, higher proportion of lower motor neuron onset, milder motor symptoms, and lower frequencies of genetic pathogenic variants. Overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration, higher King's stage, and cognitive decline. Additionally, SuStaIn stages differed across clinical phenotypes, genotypes and types of TDP-43 pathology. Conclusions Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo, each with distinct brain atrophy, clinical, genetic and pathological patterns.
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Affiliation(s)
- Ting Shen
- University of Pennsylvania Perelman School of Medicine
| | | | - Jeffrey Duda
- University of Pennsylvania Perelman School of Medicine
| | | | - Philip A Cook
- University of Pennsylvania Perelman School of Medicine
| | - James Gee
- University of Pennsylvania Perelman School of Medicine
| | - Lauren Elman
- University of Pennsylvania Perelman School of Medicine
| | - Colin Quinn
- University of Pennsylvania Perelman School of Medicine
| | - Defne A Amado
- University of Pennsylvania Perelman School of Medicine
| | - Michael Baer
- University of Pennsylvania Perelman School of Medicine
| | | | | | - David J Irwin
- University of Pennsylvania Perelman School of Medicine
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Chen M, Burke S, Olm CA, Irwin DJ, Massimo L, Lee EB, Trojanowski JQ, Gee JC, Grossman M. Antemortem network analysis of spreading pathology in autopsy-confirmed frontotemporal degeneration. Brain Commun 2023; 5:fcad147. [PMID: 37223129 PMCID: PMC10202556 DOI: 10.1093/braincomms/fcad147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 03/15/2023] [Accepted: 05/10/2023] [Indexed: 05/25/2023] Open
Abstract
Despite well-articulated hypotheses of spreading pathology in animal models of neurodegenerative disease, the basis for spreading neurodegenerative pathology in humans has been difficult to ascertain. In this study, we used graph theoretic analyses of structural networks in antemortem, multimodal MRI from autopsy-confirmed cases to examine spreading pathology in sporadic frontotemporal lobar degeneration. We defined phases of progressive cortical atrophy on T1-weighted MRI using a published algorithm in autopsied frontotemporal lobar degeneration with tau inclusions or with transactional DNA binding protein of ∼43 kDa inclusions. We studied global and local indices of structural networks in each of these phases, focusing on the integrity of grey matter hubs and white matter edges projecting between hubs. We found that global network measures are compromised to an equal degree in patients with frontotemporal lobar degeneration with tau inclusions and frontotemporal lobar degeneration-transactional DNA binding protein of ∼43 kDa inclusions compared to healthy controls. While measures of local network integrity were compromised in both frontotemporal lobar degeneration with tau inclusions and frontotemporal lobar degeneration-transactional DNA binding protein of ∼43 kDa inclusions, we discovered several important characteristics that distinguished between these groups. Hubs identified in controls were degraded in both patient groups, but degraded hubs were associated with the earliest phase of cortical atrophy (i.e. epicentres) only in frontotemporal lobar degeneration with tau inclusions. Degraded edges were significantly more plentiful in frontotemporal lobar degeneration with tau inclusions than in frontotemporal lobar degeneration-transactional DNA binding protein of ∼43 kDa inclusions, suggesting that the spread of tau pathology involves more significant white matter degeneration. Weakened edges were associated with degraded hubs in frontotemporal lobar degeneration with tau inclusions more than in frontotemporal lobar degeneration-transactional DNA binding protein of ∼43 kDa inclusions, particularly in the earlier phases of the disease, and phase-to-phase transitions in frontotemporal lobar degeneration with tau inclusions were characterized by weakened edges in earlier phases projecting to diseased hubs in subsequent phases of the disease. When we examined the spread of pathology from a region diseased in an earlier phase to physically adjacent regions in subsequent phases, we found greater evidence of disease spreading to adjacent regions in frontotemporal lobar degeneration-transactional DNA binding protein of ∼43 kDa inclusions than in frontotemporal lobar degeneration with tau inclusions. We associated evidence of degraded grey matter hubs and weakened white matter edges with quantitative measures of digitized pathology from direct observations of patients' brain samples. We conclude from these observations that the spread of pathology from diseased regions to distant regions via weakened long-range edges may contribute to spreading disease in frontotemporal dementia-tau, while spread of pathology to physically adjacent regions via local neuronal connectivity may play a more prominent role in spreading disease in frontotemporal lobar degeneration-transactional DNA binding protein of ∼43 kDa inclusions.
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Affiliation(s)
- Min Chen
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah Burke
- Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christopher A Olm
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David J Irwin
- Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lauren Massimo
- Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - James C Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Murray Grossman
- Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
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Olm CA, Burke SE, Peterson C, Lee EB, Trojanowski JQ, Massimo L, Irwin DJ, Grossman M, Gee JC. Event-based modeling of T1-weighted MRI is related to pathology in frontotemporal lobar degeneration due to tau and TDP. Neuroimage Clin 2022; 37:103285. [PMID: 36508888 PMCID: PMC9763503 DOI: 10.1016/j.nicl.2022.103285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND In previous studies of patients with frontotemporal lobar degeneration due to tau (FTLD-tau) and FTLD due to TDP (FTLD-TDP), cortical volumes derived from T1-weighted MRI have been used to identify a sequence of volume loss according to arbitrary volumetric criteria. Event-based modeling (EBM) is a probabilistic, generative machine learning model that determines the characteristic sequence of changes, or "events", occurring during disease progression. EBM also estimates an individual patient's disease "stage" by identifying which events have already occurred. In the present study, we use an EBM analysis to derive stages of regional anatomic atrophy in FTLD-tau and FTLD-TDP, and validated these stages against pathologic burden. METHODS Sporadic autopsy-confirmed patients with FTLD-tau (N = 42) and FTLD-TDP (N = 21), and 167 healthy controls with available T1-weighted images were identified. A subset of patients had quantitative digital histopathology of cortex performed at autopsy (FTLD-tau = 30, FTLD-TDP = 17). MRI images were processed, producing regional measures of cortical volumes. K-means clustering was used to find cortical regions with similar amounts of GM volume changes (n = 5 clusters). EBM was used to determine the characteristic sequence of cortical atrophy of identified clusters in autopsy-confirmed FTLD-tau and FTLD-TDP, and estimate each patient's disease stage by cortical volume biomarkers. Linear regressions related pathologic burden to EBM-estimated disease stages. RESULTS EBM for cortical volume biomarkers generated statistically robust characteristic sequences of cortical atrophy in each group of patients. Cortical volume-based EBM-estimated disease stage was associated with pathologic burden in FTLD-tau (R2 = 0.16, p = 0.017) and FTLD-TDP (R2 = 0.51, p = 0.0008). CONCLUSIONS We provide evidence that EBM can identify sequences of pathologically-confirmed cortical atrophy in sporadic FTLD-tau and FTLD-TDP.
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Affiliation(s)
- Christopher A Olm
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
| | - Sarah E Burke
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
| | - Claire Peterson
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States; Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States.
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Lauren Massimo
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
| | - David J Irwin
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States; Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
| | - James C Gee
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States.
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