ADHD and Autism Show No Evidence of Accelerated Brain Aging

While different psychiatric and neurological disorders manifest in unique behaviors, they might share a physical consequence: advanced biological aging.

Researchers at the Nanjing University of Aeronautics and Astronautics in China evaluated thousands of brain scans to map out how various conditions accelerate tissue decline across distinct neural networks.

The study found a hierarchy of accelerated aging across dementia, addiction, and mental health cohorts, while revealing that ADHD and autism did not lead to the same premature brain aging.

Measuring the baseline of structural brain aging

Tracking how fast the brain ages has become a central focus in modern neuroscience. Scientists measure this process using a metric known as Predicted Age Difference (PAD), which calculates the discrepancy between an individual's neuroimaging-predicted brain age and their chronological age.

“A positive PAD value indicates that an individual’s brain age is greater than their chronological age, while a negative PAD implies a delay from expected aging,” explained the authors of the new study.

Previous research has successfully linked accelerated brain aging to physical changes in specific conditions—such as Alzheimer's disease, schizophrenia, and substance use disorders. However, many of these studies relied on small sample sizes and traditional analytical methods tended to treat the brain as a single, uniform entity. The whole-brain approach left specific regional circuits and their underlying genetic and molecular mechanisms poorly understood.

The new study evaluated brain aging across nine common neurological and psychiatric disorders within a single, unified framework.

“Our aims are to systematically compare brain aging across multiple common brain disorders and explore the brain patterns and biological processes underlying these differences,” said the authors.

Mapping network signatures of accelerated brain aging

The researchers aggregated structural MRI data from 45,900 healthy controls and 2,698 patients. They mapped gray matter volumes across 1,016 brain regions and trained a machine learning model on the healthy cohort to predict chronological age. The team then combined these spatial patterns with post-mortem genetic data from the Allen Human Brain Atlas to identify corresponding gene expression profiles.

The analysis revealed a hierarchy of accelerated aging across the clinical groups. Neurodegenerative conditions caused the largest shifts, led by Alzheimer’s disease (d = 0.97) and mild cognitive impairment (d = 0.45). Substance use disorders followed closely behind, with tobacco (d = 0.72) and alcohol addiction (d = 0.62) advancing the brain's internal clock. Psychiatric disorders such as schizophrenia (d = 0.53), bipolar disorder (d = 0.46), and major depressive disorder (d = 0.28) also showed a clear impact.

Neurodevelopmental conditions such as ADHD and autism showed virtually no deviations from typical aging patterns.

The accelerated aging directly mirrored worse cognitive symptoms in dementia patients, although it did not align with behavioral severity scores in the psychiatric or addiction groups.

“Different neurological disorders appear to leave different signatures on the brain aging clock, which may help researchers better understand the neural and biological pathways involved in these conditions,” said the authors.

The changes also mapped onto distinct structural circuits: psychiatric conditions altered frontotemporal networks, dementia impacted fronto-occipital regions, and addiction disrupted default mode and salience networks.

The prefrontal cortex emerged as a shared vulnerability across all affected groups.

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Higher brain age predicted worse cognitive symptoms in dementia, while underlying genetic data tied these structural shifts to distinct cellular processes such as translation, synaptic signaling, and DNA damage responses.

Clinical implications and limits of tracking brain aging

The findings carry profound implications for how we understand the intersection of physical decline and mental health. The results suggest that the risk factors driving diverse brain disorders inherently converge on common aging pathways, placing the prefrontal cortex as a universal target for anatomical wear and tear.

However, the researchers relied on cross-sectional MRI data to evaluate biological aging, which is a dynamic, longitudinal process. The study also could not fully separate the high co-occurrence between substance addictions and psychiatric conditions, and the transcriptomic mapping used post-mortem tissue from only six healthy donors rather than direct tissue from the patient cohorts.

Future work is needed to verify if these structural patterns could become reliable clinical tools, such as to help predict when mild cognitive impairment will progress into full Alzheimer's disease, or to separate schizophrenia from bipolar disorder during a patient's first psychotic episode.

“The different brain aging patterns, each based around specific underlying circuits, may serve as neuroimaging biomarkers for understanding the neural aging mechanisms in commonly occurring brain disorders,” said the authors.

Reference: Liang C, Pearlson G, Bustillo J, et al. Brain aging patterns among nine neurological disorders: A case-control study. Brayne C, ed. PLoS Med. 2026;23(7):e1004860. doi: 10.1371/journal.pmed.1004860

This article is a rework of a press release issued by PLOS. Material has been edited for length and content.