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To better understand the pathological and phenotypic heterogeneity of progressive supranuclear palsy and the links between the two, we applied a novel unsupervised machine learning algorithm (Subtype and Stage Inference) to the largest MRI data set to date of people with clinically diagnosed progressive supranuclear palsy (including progressive supranuclear palsy-Richardson and variant progressive supranuclear palsy syndromes). Our cohort is comprised of 426 progressive supranuclear palsy cases, of which 367 had at least one follow-up scan, and 290 controls. Of the progressive supranuclear palsy cases, 357 were clinically diagnosed with progressive supranuclear palsy-Richardson, 52 with a progressive supranuclear palsy-cortical variant (progressive supranuclear palsy-frontal, progressive supranuclear palsy-speech/language, or progressive supranuclear palsy-corticobasal), and 17 with a progressive supranuclear palsy-subcortical variant (progressive supranuclear palsy-parkinsonism or progressive supranuclear palsy-progressive gait freezing). Subtype and Stage Inference was applied to volumetric MRI features extracted from baseline structural (T1-weighted) MRI scans and then used to subtype and stage follow-up scans. The subtypes and stages at follow-up were used to validate the longitudinal consistency of subtype and stage assignments. We further compared the clinical phenotypes of each subtype to gain insight into the relationship between progressive supranuclear palsy pathology, atrophy patterns, and clinical presentation. The data supported two subtypes, each with a distinct progression of atrophy: a 'subcortical' subtype, in which early atrophy was most prominent in the brainstem, ventral diencephalon, superior cerebellar peduncles, and the dentate nucleus, and a 'cortical' subtype, in which there was early atrophy in the frontal lobes and the insula alongside brainstem atrophy. There was a strong association between clinical diagnosis and the Subtype and Stage Inference subtype with 82% of progressive supranuclear palsy-subcortical cases and 81% of progressive supranuclear palsy-Richardson cases assigned to the subcortical subtype and 82% of progressive supranuclear palsy-cortical cases assigned to the cortical subtype. The increasing stage was associated with worsening clinical scores, whilst the 'subcortical' subtype was associated with worse clinical severity scores compared to the 'cortical subtype' (progressive supranuclear palsy rating scale and Unified Parkinson's Disease Rating Scale). Validation experiments showed that subtype assignment was longitudinally stable (95% of scans were assigned to the same subtype at follow-up) and individual staging was longitudinally consistent with 90% remaining at the same stage or progressing to a later stage at follow-up. In summary, we applied Subtype and Stage Inference to structural MRI data and empirically identified two distinct subtypes of spatiotemporal atrophy in progressive supranuclear palsy. These image-based subtypes were differentially enriched for progressive supranuclear palsy clinical syndromes and showed different clinical characteristics. Being able to accurately subtype and stage progressive supranuclear palsy patients at baseline has important implications for screening patients on entry to clinical trials, as well as tracking disease progression.

Data Availability: Joint ARTFL and LEFFTDS data and biospecimens and GENFI data are available to qualified investigators for replication of the present study results or further projects. Copyright © 2021 The Author(s). OBJECTIVE: We tested the hypothesis that plasma neurofilament light chain (NfL) identifies asymptomatic carriers of familial frontotemporal lobar degeneration (FTLD)-causing mutations at risk of disease progression. METHODS: Baseline plasma NfL concentrations were measured with single-molecule array in original (n = 277) and validation (n = 297) cohorts. C9orf72, GRN, and MAPT mutation carriers and noncarriers from the same families were classified by disease severity (asymptomatic, prodromal, and full phenotype) using the CDR Dementia Staging Instrument plus behavior and language domains from the National Alzheimer's Disease Coordinating Center FTLD module (CDR+NACC-FTLD). Linear mixed-effect models related NfL to clinical variables. RESULTS: In both cohorts, baseline NfL was higher in asymptomatic mutation carriers who showed phenoconversion or disease progression compared to nonprogressors (original: 11.4 ± 7 pg/mL vs 6.7 ± 5 pg/mL, p = 0.002; validation: 14.1 ± 12 pg/mL vs 8.7 ± 6 pg/mL, p = 0.035). Plasma NfL discriminated symptomatic from asymptomatic mutation carriers or those with prodromal disease (original cutoff: 13.6 pg/mL, 87.5% sensitivity, 82.7% specificity; validation cutoff: 19.8 pg/mL, 87.4% sensitivity, 84.3% specificity). Higher baseline NfL correlated with worse longitudinal CDR+NACC-FTLD sum of boxes scores, neuropsychological function, and atrophy, regardless of genotype or disease severity, including asymptomatic mutation carriers. CONCLUSIONS: Plasma NfL identifies asymptomatic carriers of FTLD-causing mutations at short-term risk of disease progression and is a potential tool to select participants for prevention clinical trials. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier: NCT02372773 and NCT02365922. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that in carriers of FTLD-causing mutations, elevation of plasma NfL predicts short-term risk of clinical progression. ALLFTD Consortium (LEFFTDS: U01 AG045390; ARTFL: U54 NS092089; ALLFTD: U19AG063911). J.C.R. is supported by National Institute on Aging–NIH: K23AG059888. AMS is supported by National Institute on Aging–NIH: K23AG061253 and Larry L. Hillblom Foundation: 2018-A-025-FEL. Work was also supported by grants U24 AG021886 and U01 AG016976 and the Bluefield Project to Cure FTD. Samples from the National Centralized Repository for Alzheimer’s Disease and Related Dementias, which receives government support under a cooperative agreement grant (U24 AG21886), were used in this study. The Dementia Research Centre is supported by Alzheimer's Research UK, Alzheimer's Society, Brain Research UK, and The Wolfson Foundation. This work was supported by the National Institute for Health Research UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre Clinical Research Facility, and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK. J.D.R. is supported by a Medical Research Council Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the National Institute for Health Research Rare Disease Translational Research Collaboration (BRC149/NS/MH). R.C. and C.G. are supported by Frontotemporal Dementia Research Studentships in Memory of David Blechner funded through The National Brain Appeal (RCN 290173). M.B. is supported by a Fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517) and by the UK Dementia Research Institute, which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK. R.L. is supported by the Canadian Institutes of Health Research and the Chaire de Recherche sur les Aphasies Primaires Progressives Fondation Famille Lemaire. C.G. is supported by the Swedish Frontotemporal Dementia Initiative Schörling Foundation, Swedish Research Council, JPND Prefrontals, 2015–02926, 2018–02754, Swedish Alzheimer Foundation, Swedish Brain Foundation, Karolinska Institutet Doctoral Funding, KI StratNeuro, Swedish Dementia Foundation, and Stockholm County Council ALF/Region Stockholm. J.L. is supported by Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (German Research Foundation, EXC 2145 SyNergy 390857198). This work was also supported by the Medical Research Council UK GENFI grant (MR/M023664/1), the Bluefield Project, the National Institute for Health Research including awards to Cambridge and UCL Biomedical Research Centres, and the JPND GENFI-PROX grant (2019–02248). Several authors of this publication are members of the European Reference Network for Rare Neurologic Diseases, project No. 739510. J.B.R. is supported by NIHR Cambridge Biomedical Research Centre (BRC-1215-20014).

Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited., Background and objective: Mutations in the MAPT gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signature of MAPT mutations have grouped all different mutations together and shown an association with focal atrophy of the temporal lobe. However, the variability in atrophy patterns between each particular MAPT mutation is less well characterised. We aimed to investigate whether there were distinct groups of MAPT mutation carriers based on their neuroanatomical signature. Methods: We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning technique that identifies groups of individuals with distinct progression patterns, to characterise patterns of regional atrophy in MAPT-associated FTD within the Genetic FTD Initiative (GENFI) cohort study. Results: 82 MAPT mutation carriers were analysed, the majority of whom had P301L, IVS10+16 or R406W mutations, along with 48 healthy non-carriers. SuStaIn identified two groups of MAPT mutation carriers with distinct atrophy patterns: a 'temporal' subtype in which atrophy was most prominent in the hippocampus, amygdala, temporal cortex and insula, and a 'frontotemporal' subtype in which atrophy was more localised to the lateral temporal lobe and anterior insula, as well as the orbitofrontal and ventromedial prefrontal cortex and anterior cingulate. There was a one-to-one mapping between IVS10+16 and R406W mutations and the temporal subtype, and a near one-to-one mapping between P301L mutations and the frontotemporal subtype. There were differences in clinical symptoms and neuropsychological test scores between subtypes: the temporal subtype was associated with amnestic symptoms, whereas the frontotemporal subtype was associated with executive dysfunction. Discussion: Our results demonstrate that different MAPT mutations give rise to distinct atrophy patterns and clinical phenotype, providing insights into the underlying disease biology, and potential utility for patient stratification in therapeutic trials., The Dementia Research Centre is supported by Alzheimer's Research UK, Brain Research Trust, and The Wolfson Foundation. This work was supported by the NIHR Queen Square Dementia Biomedical Research Unit, the NIHR UCL/H Biomedical Research Centre and the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility, NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and Kings College London, as well as an Alzheimer's Society grant (AS-PG-16-007). This work was also supported by the MRC UK GENFI grant (MR/M023664/1), the Italian Ministry of Health (CoEN015 and Ricerca Corrente), and the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant, a Canadian Institutes of Health Research operating grant and The Bluefield Project, and the JPND GENFI-PROX grant (2019-02248). This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy ID 390857198). Nonfinancial support was also provided through the European Reference Network for Rare Neurologic Diseases (ERN-RND), 1 of 24 ERNs funded by the European Commission (ERNRND: 3HP 767231).