Centres of Excellence in Neurodegeneration.

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10−4) or temporal stage (p = 3.96 × 10−5). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.

ObjectiveTo evaluate cerebrospinal fluid (CSF) and serum neurofilament light chain (NfL) levels in genetic frontotemporal dementia (FTD) as a potential biomarker in the presymptomatic stage and during the conversion into the symptomatic stage. Additionally, to correlate NfL levels to clinical and neuroimaging parameters.MethodsIn this multicenter case–control study, we investigated CSF NfL in 174 subjects (48 controls, 40 presymptomatic carriers and 86 patients with microtubule‐associated protein tau (MAPT), progranulin (GRN), and chromosome 9 open reading frame 72 (C9orf72) mutations), and serum NfL in 118 subjects (39 controls, 44 presymptomatic carriers, 35 patients). In 55 subjects both CSF and serum was determined. In two subjects CSF was available before and after symptom onset (converters). Additionally, NfL levels were correlated with clinical parameters, survival, and regional brain atrophy.Results CSF NfL levels in patients (median 6762 pg/mL, interquartile range 3186–9309 pg/mL) were strongly elevated compared with presymptomatic carriers (804 pg/mL, 627–1173 pg/mL, P < 0.001), resulting in a good diagnostic performance to discriminate both groups. Serum NfL correlated highly with CSF NfL (r s= 0.87, P < 0.001) and was similarly elevated in patients. Longitudinal samples in the converters showed a three‐ to fourfold increase in CSF NfL after disease onset. Additionally, NfL levels in patients correlated with disease severity, brain atrophy, annualized brain atrophy rate and survival.InterpretationNfL in both serum and CSF has the potential to serve as a biomarker for clinical disease onset and has a prognostic value in genetic FTD.

The factors driving clinical heterogeneity in Alzheimer's disease are not well understood. This study assessed the relationship between amyloid deposition, glucose metabolism and clinical phenotype in Alzheimer's disease, and investigated how these relate to the involvement of functional networks. The study included 17 patients with early-onset Alzheimer's disease (age at onset <65 years), 12 patients with logopenic variant primary progressive aphasia and 13 patients with posterior cortical atrophy [whole Alzheimer's disease group: age = 61.5 years (standard deviation 6.5 years), 55% male]. Thirty healthy control subjects [age = 70.8 (3.3) years, 47% male] were also included. Subjects underwent positron emission tomography with (11)C-labelled Pittsburgh compound B and (18)F-labelled fluorodeoxyglucose. All patients met National Institute on Ageing-Alzheimer's Association criteria for probable Alzheimer's disease and showed evidence of amyloid deposition on (11)C-labelled Pittsburgh compound B positron emission tomography. We hypothesized that hypometabolism patterns would differ across variants, reflecting involvement of specific functional networks, whereas amyloid patterns would be diffuse and similar across variants. We tested these hypotheses using three complimentary approaches: (i) mass-univariate voxel-wise group comparison of (18)F-labelled fluorodeoxyglucose and (11)C-labelled Pittsburgh compound B; (ii) generation of covariance maps across all subjects with Alzheimer's disease from seed regions of interest specifically atrophied in each variant, and comparison of these maps to functional network templates; and (iii) extraction of (11)C-labelled Pittsburgh compound B and (18)F-labelled fluorodeoxyglucose values from functional network templates. Alzheimer's disease clinical groups showed syndrome-specific (18)F-labelled fluorodeoxyglucose patterns, with greater parieto-occipital involvement in posterior cortical atrophy, and asymmetric involvement of left temporoparietal regions in logopenic variant primary progressive aphasia. In contrast, all Alzheimer's disease variants showed diffuse patterns of (11)C-labelled Pittsburgh compound B binding, with posterior cortical atrophy additionally showing elevated uptake in occipital cortex compared with early-onset Alzheimer's disease. The seed region of interest covariance analysis revealed distinct (18)F-labelled fluorodeoxyglucose correlation patterns that greatly overlapped with the right executive-control network for the early-onset Alzheimer's disease region of interest, the left language network for the logopenic variant primary progressive aphasia region of interest, and the higher visual network for the posterior cortical atrophy region of interest. In contrast, (11)C-labelled Pittsburgh compound B covariance maps for each region of interest were diffuse. Finally, (18)F-labelled fluorodeoxyglucose was similarly reduced in all Alzheimer's disease variants in the dorsal and left ventral default mode network, whereas significant differences were found in t...