No abstract

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer’s disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.

Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. We investigated neuroanatomical subtypes in a multi-institutional multi-ethnic cohort, using novel semi-supervised machine learning methods designed to discover patterns associated with disease rather than normal anatomical variation. Structural MRI and clinical measures in established schizophrenia (n = 307) and healthy controls (n = 364) were analysed across three sites of PHENOM (Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging) consortium. Regional volumetric measures of grey matter, white matter, and CSF were used to identify distinct and reproducible neuroanatomical subtypes of schizophrenia. Two distinct neuroanatomical subtypes were found. Subtype 1 showed widespread lower grey matter volumes, most prominent in thalamus, nucleus accumbens, medial temporal, medial prefrontal/frontal and insular cortices. Subtype 2 showed increased volume in the basal ganglia and internal capsule, and otherwise normal brain volumes. Grey matter volume correlated negatively with illness duration in Subtype 1 (r = −0.201, P = 0.016) but not in Subtype 2 (r = −0.045, P = 0.652), potentially indicating different underlying neuropathological processes. The subtypes did not differ in age (t = −1.603, df = 305, P = 0.109), sex (chi-square = 0.013, df = 1, P = 0.910), illness duration (t = −0.167, df = 277, P = 0.868), antipsychotic dose (t = −0.439, df = 210, P = 0.521), age of illness onset (t = −1.355, df = 277, P = 0.177), positive symptoms (t = 0.249, df = 289, P = 0.803), negative symptoms (t = 0.151, df = 289, P = 0.879), or antipsychotic type (chi-square = 6.670, df = 3, P = 0.083). Subtype 1 had lower educational attainment than Subtype 2 (chi-square = 6.389, df = 2, P = 0.041). In conclusion, we discovered two distinct and highly reproducible neuroanatomical subtypes. Subtype 1 displayed widespread volume reduction correlating with illness duration, and worse premorbid functioning. Subtype 2 had normal and stable anatomy, except for larger basal ganglia and internal capsule, not explained by antipsychotic dose. These subtypes challenge the notion that brain volume loss is a general feature of schizophrenia and suggest differential aetiologies. They can facilitate strategies for clinical trial enrichment and stratification, and precision diagnostics.

Introduction: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). Methods: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. Results: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. Discussion: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

IMPORTANCE Meta-analyses of randomized clinical trials have indicated that improved hypertension control reduces the risk for cognitive impairment and dementia. However, it is unclear to what extent pathways reflective of Alzheimer disease (AD) pathology are affected by hypertension control.OBJECTIVE To evaluate the association of intensive blood pressure control on AD-related brain biomarkers. DESIGN, SETTING, AND PARTICIPANTSThis is a substudy of the Systolic Blood Pressure Intervention Trial (SPRINT MIND), a multicenter randomized clinical trial that compared the efficacy of 2 different blood pressure-lowering strategies. Potential participants (n = 1267) 50 years or older with hypertension and without a history of diabetes or stroke were approached for a brain magnetic resonance imaging (MRI) study. Of these, 205 participants were deemed ineligible and 269 did not agree to participate; 673 and 454 participants completed brain MRI at baseline and at 4-year follow-up, respectively; the final follow-up date was July 1, 2016. Analysis began September 2019 and ended November 2020.INTERVENTIONS Participants were randomized to either a systolic blood pressure goal of less than 120 mm Hg (intensive treatment: n = 356) or less than 140 mm Hg (standard treatment: n = 317). MAIN OUTCOMES AND MEASURESChanges in hippocampal volume, measures of AD regional atrophy, posterior cingulate cerebral blood flow, and mean fractional anisotropy in the cingulum bundle. RESULTS Among 673 recruited patients who had baseline MRI (mean [SD] age, 67.3 [8.2] years; 271 women [40.3%]), 454 completed the follow-up MRI at a median (interquartile range) of 3.98 (3.7-4.1) years after randomization. In the intensive treatment group, mean hippocampal volume decreased from 7.45 cm 3 to 7.39 cm 3 (difference, −0.06 cm 3 ; 95% CI, −0.08 to −0.04) vs a decrease from 7.48 cm 3 to 7.46 cm 3 (difference, −0.02 cm 3 ; 95% CI, −0.05 to −0.003) in the standard treatment group (between-group difference in change, −0.033 cm 3 ; 95% CI, −0.062 to −0.003; P = .03). There were no significant treatment group differences for measures of AD regional atrophy, cerebral blood flow, or mean fractional anisotropy.CONCLUSIONS AND RELEVANCE Intensive treatment was associated with a small but statistically significant greater decrease in hippocampal volume compared with standard treatment, consistent with the observation that intensive treatment is associated with greater decreases in total brain volume. However, intensive treatment was not associated with changes in any of the other MRI biomarkers of AD compared with standard treatment.