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Reduced functional magnetic resonance imaging (fMRI)-complexity in Alzheimer's disease (AD) progression has been demonstrated and found to be associated with tauopathy and cognition. However, association of fMRI-complexity with amyloid and influence of genetic risk (APOEɛ4) remain unknown. Here we investigate the association between fMRI-complexity, tau-PET, and amyloid-PET as well as influence of APOE genotype using multivariate generalized linear models. We show that fMRI-complexity has a strong association with tau but not amyloid deposition and that the presence of an APOEɛ4 allele enhances this effect. Thus fMRI-complexity provides a surrogate marker of impaired brain functionality in AD progression.

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80 % with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions., Competing Interests: The authors declare no competing financial or non-financial interests., (© 2023 Massachusetts Institute of Technology. Published under a Creative Commons CC BY-NC 4.0 license.)

The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is a nationally representative in-depth study of cognitive aging and dementia. We present a publicly available dataset of harmonized cognitive measures of 4,096 adults 60 years of age and older in India, collected across 18 states and union territories. Blood samples were obtained to carry out whole blood and serum-based assays. Results are included in a venous blood specimen datafile that can be linked to the Harmonized LASI-DAD dataset. A global screening array of 960 LASI-DAD respondents is also publicly available for download, in addition to neuroimaging data on 137 LASI-DAD participants. Altogether, these datasets provide comprehensive information on older adults in India that allow researchers to further understand risk factors associated with cognitive impairment and dementia., (© 2023. The Author(s).)

White matter hyperintensities are areas of hyperintense signal on MRI that typically represent cerebrovascular pathology. While focal white matter hyperintensities are common among older individuals, extensive white matter hyperintensities have been found to accelerate the progression of dementia. However, little is currently known about how various socioeconomic, health, lifestyle and environmental factors affect the severity of these lesions, particularly in low- and middle-income countries such as India. We investigated this question using cross-sectional MRI data ( n = 126) from a pilot neuroimaging sub-study of an ongoing, nationally representative epidemiological study of late-life cognition in India. As a screening step, we estimated white matter hyperintensity load from fluid-attenuated inversion recovery MRI using a fully automated technique and tested for associations with each factor separately, controlling for age, sex and estimated total intracranial volume in each case. A combined model of white matter hyperintensity load included five factors which were significant after multiple comparisons correction: systolic blood pressure, body mass index, urbanicity status (urban versus rural living), daily chore hours and the frequency of store trips. This model explained an additional 27% of the variance in white matter hyperintensity load (54 versus 27% for the baseline model with only age, sex and estimated total intracranial volume). We accounted for the possibility of reverse causality by additionally controlling for concurrent markers of neurodegeneration and cognitive impairment, with no substantial change in our findings. Overall, our findings suggest that controlling high blood pressure and maintaining both a healthy body mass index and high levels of physical activity may reduce white matter hyperintensity load in older Indian adults, helping to prevent or delay dementia., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Nomograms are important clinical tools applied widely in both developing and aging populations. They are generally constructed as normative models identifying cases as outliers to a distribution of healthy controls. Currently used normative models do not account for genetic heterogeneity. Hippocampal volume (HV) is a key endophenotype for many brain disorders. Here, we examine the impact of genetic adjustment on HV nomograms and the translational ability to detect dementia patients. Using imaging data from 35,686 healthy subjects aged 44-82 from the UK Biobank (UKB), we built HV nomograms using Gaussian process regression (GPR), which - compared to a previous method - extended the application age by 20 years, including dementia critical age ranges. Using HV polygenic scores (HV-PGS), we built genetically adjusted nomograms from participants stratified into the top and bottom 30% of HV-PGS. This shifted the nomograms in the expected directions by ~100 mm 3 (2.3% of the average HV), which equates to 3 years of normal aging for a person aged ~65. Clinical impact of genetically adjusted nomograms was investigated by comparing 818 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database diagnosed as either cognitively normal (CN), having mild cognitive impairment (MCI) or Alzheimer's disease (AD) patients. While no significant change in the survival analysis was found for MCI-to-AD conversion, an average of 68% relative decrease was found in intra-diagnostic-group variance, highlighting the importance of genetic adjustment in untangling phenotypic heterogeneity., Competing Interests: MJ, LA, JS, YM, AA No competing interests declared, (© 2022, Janahi et al.)

Background: The amygdala is widely implicated in both anxiety and autism spectrum disorder. However, no studies have investigated the relationship between co-occurring anxiety and longitudinal amygdala development in autism. Here, the authors characterize amygdala development across childhood in autistic children with and without traditional DSM forms of anxiety and anxieties distinctly related to autism., Methods: Longitudinal magnetic resonance imaging scans were acquired at up to four time points for 71 autistic and 55 typically developing (TD) children (∼2.5-12 years, 411 time points). Traditional DSM anxiety and anxieties distinctly related to autism were assessed at study time 4 (∼8-12 years) using a diagnostic interview tailored to autism: the Anxiety Disorders Interview Schedule-IV with the Autism Spectrum Addendum. Mixed-effects models were used to test group differences at study time 1 (3.18 years) and time 4 (11.36 years) and developmental differences (age-by-group interactions) in right and left amygdala volume between autistic children with and without DSM or autism-distinct anxieties and TD children., Results: Autistic children with DSM anxiety had significantly larger right amygdala volumes than TD children at both study time 1 (5.10% increase) and time 4 (6.11% increase). Autistic children with autism-distinct anxieties had significantly slower right amygdala growth than TD, autism-no anxiety, and autism-DSM anxiety groups and smaller right amygdala volumes at time 4 than the autism-no anxiety (-8.13% decrease) and autism-DSM anxiety (-12.05% decrease) groups., Conclusions: Disparate amygdala volumes and developmental trajectories between DSM and autism-distinct forms of anxiety suggest different biological underpinnings for these common, co-occurring conditions in autism., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background and Objectives: β-amyloid (Aβ) staging models assume a single spatial-temporal progression of amyloid accumulation. We assessed evidence for Aβ accumulation subtypes by applying the data-driven Subtype and Stage Inference (SuStaIn) model to amyloid-PET data., Methods: Amyloid-PET data of 3,010 participants were pooled from 6 cohorts (ALFA+, EMIF-AD, ABIDE, OASIS, and ADNI). Standardized uptake value ratios were calculated for 17 regions. We applied the SuStaIn algorithm to identify consistent subtypes in the pooled dataset based on the cross-validation information criterion and the most probable subtype/stage classification per scan. The effects of demographics and risk factors on subtype assignment were assessed using multinomial logistic regression., Results: Participants were mostly cognitively unimpaired (n = 1890 [62.8%]), had a mean age of 68.72 (SD 9.1) years, 42.1% were APOE ε4 carriers, and 51.8% were female. A 1-subtype model recovered the traditional amyloid accumulation trajectory, but SuStaIn identified 3 optimal subtypes, referred to as frontal, parietal, and occipital based on the first regions to show abnormality. Of the 788 (26.2%) with strong subtype assignment (>50% probability), the majority was assigned to frontal (n = 415 [52.5%]), followed by parietal (n = 199 [25.3%]) and occipital subtypes (n = 175 [22.2%]). Significant differences across subtypes included distinct proportions of APOE ε4 carriers (frontal 61.8%, parietal 57.1%, occipital 49.4%), participants with dementia (frontal 19.7%, parietal 19.1%, occipital 31.0%), and lower age for the parietal subtype (frontal/occipital 72.1 years, parietal 69.3 years). Higher amyloid (Centiloid) and CSF p-tau burden was observed for the frontal subtype; parietal and occipital subtypes did not differ. At follow-up, most participants (81.1%) maintained baseline subtype assignment and 25.6% progressed to a later stage., Discussion: Whereas a 1-trajectory model recovers the established pattern of amyloid accumulation, SuStaIn determined that 3 subtypes were optimal, showing distinct associations with Alzheimer disease risk factors. Further analyses to determine clinical utility are warranted., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Aims: Patients with idiopathic pulmonary fibrosis (IPF) receiving antifibrotic medication and patients with non-IPF fibrosing lung disease often demonstrate rates of annualised forced vital capacity (FVC) decline within the range of measurement variation (5.0%-9.9%). We examined whether change in visual CT variables could help confirm whether marginal FVC declines represented genuine clinical deterioration rather than measurement noise., Methods: In two IPF cohorts (cohort 1: n=103, cohort 2: n=108), separate pairs of radiologists scored paired volumetric CTs (acquired between 6 and 24 months from baseline). Change in interstitial lung disease, honeycombing, reticulation, ground-glass opacity extents and traction bronchiectasis severity was evaluated using a 5-point scale, with mortality prediction analysed using univariable and multivariable Cox regression analyses. Both IPF populations were then combined to determine whether change in CT variables could predict mortality in patients with marginal FVC declines., Results: On univariate analysis, change in all CT variables except ground-glass opacity predicted mortality in both cohorts. On multivariate analysis adjusted for patient age, gender, antifibrotic use and baseline disease severity (diffusing capacity for carbon monoxide), change in traction bronchiectasis severity predicted mortality independent of FVC decline. Change in traction bronchiectasis severity demonstrated good interobserver agreement among both scorer pairs. Across all study patients with marginal FVC declines, change in traction bronchiectasis severity independently predicted mortality and identified more patients with deterioration than change in honeycombing extent., Conclusions: Change in traction bronchiectasis severity is a measure of disease progression that could be used to help resolve the clinical importance of marginal FVC declines., Competing Interests: Competing interests: JJ reports personal fees from Boehringer Ingelheim outside the current work. AUW reports personal fees from Intermune, Boehringer Ingelheim, Gilead, MSD, Roche, Bayer and Chiesi outside the submitted work. SRD reports personal fees from Boehringer Ingelheim outside the submitted work. Work by CHMM, HWE, FTB and MV was supported by ZonMW TopZorg Care (grant number 842002001)., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.)

Objective: The identification of sensitive biomarkers is essential to validate therapeutics for Huntington disease (HD). We directly compare structural imaging markers across the largest collective imaging HD dataset to identify a set of imaging markers robust to multicenter variation and to derive upper estimates on sample sizes for clinical trials in HD., Methods: We used 1 postprocessing pipeline to retrospectively analyze T1-weighted magnetic resonance imaging (MRI) scans from 624 participants at 3 time points, from the PREDICT-HD, TRACK-HD, and IMAGE-HD studies. We used mixed effects models to adjust regional brain volumes for covariates, calculate effect sizes, and simulate possible treatment effects in disease-affected anatomical regions. We used our model to estimate the statistical power of possible treatment effects for anatomical regions and clinical markers., Results: We identified a set of common anatomical regions that have similarly large standardized effect sizes (>0.5) between healthy control and premanifest HD (PreHD) groups. These included subcortical, white matter, and cortical regions and nonventricular cerebrospinal fluid (CSF). We also observed a consistent spatial distribution of effect size by region across the whole brain. We found that multicenter studies were necessary to capture treatment effect variance; for a 20% treatment effect, power of >80% was achieved for the caudate (n = 661), pallidum (n = 687), and nonventricular CSF (n = 939), and, crucially, these imaging markers provided greater power than standard clinical markers., Interpretation: Our findings provide the first cross-study validation of structural imaging markers in HD, supporting the use of these measurements as endpoints for both observational studies and clinical trials. ANN NEUROL 2020;87:751-762., (© 2020 The Authors. Annals of Neurology published by Wiley Periodicals, Inc. on behalf of American Neurological Association.)