Replicated evidence for an accelerated rate of whole-body aging in schizophrenia

Psychological Medicine · 2026-01-01

BACKGROUND: People with schizophrenia develop more chronic diseases at a younger age and die younger than people in the general population. It has been hypothesized that this excess morbidity and mortality could be partially due to accelerated aging in schizophrenia. If true, this would motivate the development of 'gero-protective' interventions to reduce chronic disease burden in schizophrenia. However, it has been difficult to test this hypothesis, in part, due to the limited ability to measure aging in samples of people with schizophrenia. METHODS: = 2,096, 48% female) accessed through the Lieber Institute for Brain Development, the University of Bari Aldo Moro, and the North American Prodrome Longitudinal Study - 3. RESULTS: We found consistent evidence of faster DunedinPACNI in schizophrenia compared with controls. In contrast, youth at clinical-high risk for psychosis did not have faster DunedinPACNI compared to controls. Unaffected siblings of patients also did not have faster DunedinPACNI than controls. Faster DunedinPACNI in schizophrenia was not explained by tobacco smoking or antipsychotic medication use. CONCLUSIONS: The results support the hypothesis that schizophrenia is accompanied by accelerated aging. Results were inconsistent with some of the most obvious explanations for accelerated aging in schizophrenia (familial risk, smoking, and iatrogenic medication effects). Research should aim to uncover why people who have schizophrenia age rapidly, as well as the utility of early disease-risk monitoring and anti-aging interventions in schizophrenia.

Precision estimates of longitudinal brain aging capture unexpected individual differences in one year

Nature Communications · 2026-02-05

Longitudinal studies are required to measure individual differences in human brain aging, but are challenging over short intervals due to measurement error. Using cluster scanning, an approach that reduces error by densely repeating rapid structural scans, we assess brain aging in individuals across three timepoints in one year. Cluster scanning substantially improves the precision of individualized estimates, revealing previously undetectable individual differences in brain change. In just one year, we detect expected differences in the rates of brain aging between younger and older individuals, as well as differences between cognitively unimpaired and impaired individuals. Cognitively unimpaired older individuals variably reveal relative brain maintenance, unexpectedly rapid decline, and asymmetrical changes. We observe these atypical brain aging trajectories across structures and verify them in independent within-individual test-retest data. Cluster scanning promises to advance our understanding of the marked heterogeneity in brain aging by affording better short-term tracking of individual variability in structural change. Repeated rapid structural MRI scans substantially improve the precision of individual differences in brain change. This allowed for individualized insight into subtle differences in the detected rates of brain aging and neurodegeneration to be detected.

Reconsidering Brain Age: Why Age-Prediction Models Fail as Measures of Brain Aging

medRxiv · 2026-05-08

Abstract Brain age models - machine-learning predictions of chronological age from brain imaging - are widely interpreted as markers of accelerated brain aging. Here we show that this interpretation cannot be supported. Because these models are trained to predict chronological age, they prioritize features that change similarly across people and actively downweight features that capture differences in individual trajectories, precisely the property an aging-rate biomarker must have. In effect, brain age models are optimized to ignore the very signal they are used to study, thereby risking converting stable between-person differences into apparent accelerated aging. Using theoretical analysis, simulations, and longitudinal MRI, we confirm both predicted failure modes: brain age models indicated “accelerated aging” in participants with low birth weight despite no longitudinal evidence, while a single hippocampal volume measurement was more sensitive than the brain age gap to tau-related neurodegeneration. Across much of the brain age literature, it is therefore not possible to determine whether reported effects reflect brain aging or stable anatomical differences, and the brain age gap should not be interpreted as a marker of brain aging or brain health. We propose alternative strategies that reorient prediction targets from shared age-related patterns to individual differences in change.

Detecting short-interval longitudinal cortical atrophy in neurodegenerative dementias via cluster scanning: A proof of concept

Imaging Neuroscience · 2026-01-01

Abstract Regional brain atrophy estimated from structural magnetic resonance imaging (MRI) is a widely used measure of neurodegeneration in Alzheimer’s disease (AD), Frontotemporal Lobar Degeneration (FTLD), and other dementias. Yet, traditional MRI-derived morphometric estimates are susceptible to measurement errors, posing a challenge for detecting longitudinal atrophy over short intervals. Here, we examined the utility of multiple MRI scans acquired in rapid succession (i.e., cluster scanning) for detecting longitudinal cortical atrophy over 3- and 6-month intervals within individual participants. Four individuals with mild cognitive impairment or mild dementia likely due to AD or FTLD participated in this study. At baseline, 3 months, and 6 months, structural MRI data were collected on a 3 Tesla scanner using a fast 1.2-mm T1-weighted multi-echo magnetization-prepared rapid gradient echo (MEMPRAGE) sequence (acquisition time = 2’23”). At each timepoint, participants underwent up to 32 MEMPRAGE scans acquired in four separate sessions over 2 days. Using linear mixed-effects models, we found that phenotypically vulnerable cortical (“core atrophy”) regions exhibited statistically significant longitudinal atrophy in all participants (i.e., decreased cortical thickness) by 3 months and further demonstrated preferential vulnerability compared to control regions in three of the participants over at least one of the 3-month intervals. These findings provide proof-of-concept evidence that pooling multiple morphometric estimates derived from cluster scanning can detect longitudinal cortical atrophy over short intervals in individual patients with neurodegenerative dementias.

Precision Estimates of Longitudinal Brain Aging Capture Unexpected Individual Differences in One Year

medRxiv · 2025-02-25 · 3 citations

Longitudinal studies are required to measure individual differences in human brain aging, but they are difficult to estimate over short intervals because of measurement error. Using cluster scanning, an approach that reduces error by densely repeating rapid structural scans, we assessed brain aging in individuals across three longitudinal timepoints spaced across one year. Cluster scanning substantially improved the precision of individualized estimates, revealing previously undetectable individual differences in brain change. In just one year, expected differences in the rates of brain aging between younger and older individuals were evident, as were differences between cognitively unimpaired and impaired individuals. Each person's brain change trajectory was compared to modeled normative expectations from a large cohort of age-matched UK Biobank participants. Cognitively unimpaired older individuals variably revealed relative brain maintenance, unexpectedly rapid decline, and asymmetrical changes. These atypical brain aging trajectories were found across structures and verified in independent within-individual test and retest data. Cluster scanning promises to advance our understanding of the marked heterogeneity in brain aging by affording better short-term tracking of individual variability in structural change.

Within-Individual Precision Mapping of Brain Networks Exclusively Using Task Data

bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-25 · 4 citations

Precision mapping of brain networks within individuals has become a widely used tool that prevailingly relies on functional connectivity analysis of resting-state data. Here we explored whether networks could be precisely estimated solely using data acquired during active task paradigms. The straightforward strategy involved extracting residualized data after application of a task-based general linear model (GLM) and then applying standard functional connectivity analysis. Functional correlation matrices estimated from task data were highly similar to those derived from traditional resting-state fixation data. The largest factor affecting similarity between correlation matrices was the amount of data. Networks estimated within-individual from task data displayed strong spatial overlap with those estimated from resting-state fixation data and predicted the same triple functional dissociation in independent data. The implications of these findings are that (1) existing task data can be reanalyzed to estimate within-individual network organization, (2) resting-state fixation and task data can be pooled to increase statistical power, and (3) future studies can exclusively acquire task data to both estimate networks and extract task responses. Most broadly, the present results suggest that there is an underlying, stable network architecture that is idiosyncratic to the individual and persists across task states.

DunedinPACNI estimates the longitudinal Pace of Aging from a single brain image to track health and disease

Nature Aging · 2025-07-01 · 20 citations

To understand how aging affects functional decline and increases disease risk, it is necessary to develop measures of how fast a person is aging. Using data from the Dunedin Study, we introduce an accurate and reliable measure for the rate of longitudinal aging derived from cross-sectional brain magnetic resonance imaging, that is, the Dunedin Pace of Aging Calculated from NeuroImaging (DunedinPACNI). Exporting this measure to the Alzheimer's Disease Neuroimaging Initiative, UK Biobank and BrainLat datasets revealed that faster DunedinPACNI predicted cognitive impairment, accelerated brain atrophy and conversion to diagnosed dementia. Faster DunedinPACNI also predicted physical frailty, poor health, future chronic diseases and mortality in older adults. When compared to brain age gap, DunedinPACNI was similarly or more strongly related to clinical outcomes. DunedinPACNI is a next-generation brain magnetic resonance imaging biomarker that can help researchers explore aging effects on health outcomes and evaluate the effectiveness of antiaging strategies.

Detecting short-interval longitudinal cortical atrophy in neurodegenerative dementias via cluster scanning: A proof of concept

medRxiv · 2025-03-17

Abstract Regional brain atrophy estimated from structural magnetic resonance imaging (MRI) is a widely used measure of neurodegeneration in Alzheimer’s disease (AD), Frontotemporal Lobar Degeneration (FTLD), and other dementias. Yet, traditional MRI-derived morphometric estimates are susceptible to measurement errors, posing a challenge for reliably detecting longitudinal atrophy, particularly over short intervals. Here, we examined the utility of multiple MRI scans acquired in rapid succession (i.e., cluster scanning ) for detecting longitudinal cortical atrophy over 3- and 6-month intervals within individual patients. Four individuals with mild cognitive impairment or mild dementia likely due to AD or FTLD participated in this study. At baseline, 3 months, and 6 months, structural MRI data were collected on a 3 Tesla scanner using a fast 1.2-mm T1-weighted multi-echo magnetization-prepared rapid gradient echo (MEMPRAGE) sequence (acquisition time = 2’23’’). At each timepoint, participants underwent up to 32 MEMPRAGE scans acquired in four separate sessions over two days. Using linear mixed-effects models, phenotypically vulnerable cortical (“core atrophy”) regions exhibited statistically significant longitudinal atrophy in all participants (i.e., decreased cortical thickness) by 3 months and further demonstrated preferential vulnerability compared to control regions in three of the participants over at least one of the 3-month intervals. These findings provide proof-of-concept evidence that pooling multiple morphometric estimates derived from cluster scanning can detect longitudinal cortical atrophy over short intervals in individual patients with neurodegenerative dementias.

An estimate of the pace of aging from a single brain scan predicts chronic disease, dementia, and mortality

Innovation in Aging · 2025-12-01

Abstract Neuroimaging is a common, non-invasive measure, making it a desirable target for developing aging biomarkers. Current MRI-based aging biomarkers (i.e., brain-age gap) are estimated from cross-sectional associations with chronological age - akin to “first-generation” epigenetic clocks. DunedinPACE, a “third-generation” epigenetic measure, has advantages over first-generation epigenetic clocks because it directly estimates longitudinal aging. We extend this approach to neuroimaging with a “next-generation” neuroimaging-based biomarker of the rate of biological aging. The pace of whole-body aging was determined by tracking physiological decline of 6 organ systems over 20 years in Dunedin Study members. An elastic-net regression model was trained to estimate the pace of whole-body aging using neuroimaging data collected at age 45 from 860 Study members. We call this measure the Dunedin Pace of Aging Calculated from NeuroImaging or “DunedinPACNI.” We exported DunedinPACNI to the UK Biobank and ADNI to test associations with disease and decline. DunedinPACNI estimated whole-body aging with a cross-validated accuracy of r = 0.42 in Dunedin Study members. In 1,737 ADNI participants, people with faster DunedinPACNI had greater cognitive impairment (MCI: β = 0.27; p < 0.001; AD: β = 0.81; p < 0.001) dementia risk (HR = 1.76, p < 0.001), and hippocampal atrophy (β=-0.15; p < 0.001). In 42,583 UK Biobank participants, people with faster DunedinPACNI also had greater hippocampal atrophy (β=-0.09; p < 0.001), frailty (β = 0.17; p < 0.001), disease risk (HR = 1.14, p = 0.01), and mortality risk (HR = 1.32, p < 0.001). DunedinPACNI was superior or similar to brain-age gap at predicting clinical outcomes. DunedinPACNI, a novel, exportable MRI biomarker for longitudinal whole-body aging, offers improved opportunity to measure aging from brain structure.

Within-individual precision mapping of brain networks exclusively using task data

Neuron · 2025-09-27 · 6 citations

Precision mapping of brain networks within individuals prevailingly relies on functional connectivity analysis of resting-state data. Here, we explored whether networks can be estimated using only task data. Correlation matrices estimated from task data were similar to those derived from resting-state data. The largest factor affecting similarity was the amount of data. Precision networks estimated from task data showed strong spatial overlap with those derived from resting-state data and predicted the same triple functional dissociation in independent data. To illustrate novel possibilities enabled by the present methods, we mapped the detailed organization of thalamic association zones within individuals by pooling extensive resting-state and task data. We also demonstrated how task data can be used to estimate networks while simultaneously extracting task responses. Broadly, these findings suggest that there is an underlying, stable network architecture that is idiosyncratic to the individual and persists across task states.