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Accelerated brain aging predicts impulsivity and symptom severity in depression

Neuropsychopharmacology volume 46, pages 911–919 (2021)Cite this article

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Abstract

Multiple structural and functional neuroimaging measures vary over the course of the lifespan and can be used to predict chronological age. Accelerated brain aging, as quantified by deviations in the MRI-based predicted age with respect to chronological age, is associated with risk for neurodegenerative conditions, bipolar disorder, and mortality. Whether age-related changes in resting-state functional connectivity are accelerated in major depressive disorder (MDD) is unknown, and, if so, it is unclear if these changes contribute to specific cognitive weaknesses that often occur in MDD. Here, we delineated age-related functional connectivity changes in a large sample of normal control subjects and tested whether brain aging is accelerated in MDD. Furthermore, we tested whether accelerated brain aging predicts individual differences in cognitive function. We trained a support vector regression model predicting age using resting-state functional connectivity in 710 healthy adults aged 18–89. We applied this model trained on normal aging subjects to a sample of actively depressed MDD participants (n = 109). The difference between predicted brain age and chronological age was 2.11 years greater (p = 0.015) in MDD patients compared to control participants. An older MDD brain age was significantly associated with increased impulsivity and, in males, increased depressive severity. Unexpectedly, accelerated brain aging was also associated with increased placebo response in a sham-controlled trial of high-frequency repetitive transcranial magnetic stimulation targeting the dorsomedial prefrontal cortex. Our results indicate that MDD is associated with accelerated brain aging, and that accelerated aging is selectively associated with greater impulsivity and depression severity.

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Fig. 1: Chronological age is associated with decreased within-network rsFC and increased between-network rsFC between the SN and DMN in both HC and MDD.
Fig. 2: Tuning and training a support vector regression model predicting brain age based on rsFC in HC.
Fig. 3: Input rsFC features for the final support vector regression model and brain-PAD associations in MDD.

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Affiliations

  1. Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA

    Katharine Dunlop, Lindsay W. Victoria, Faith M. Gunning & Conor Liston

  2. Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA

    Katharine Dunlop & Conor Liston

  3. Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, USA

    Lindsay W. Victoria & Faith M. Gunning

  4. Department of Psychiatry and Krembil Research Institute, University Health Network, Toronto, ON, Canada

    Jonathan Downar

  5. Department of Psychiatry, University of Toronto, Toronto, ON, Canada

    Jonathan Downar

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  1. Katharine Dunlop
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  2. Lindsay W. Victoria
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Contributions

KD: study concept, data collection, data curation, formal analysis, writing (original draft), writing (reviewing, editing, and approval); LWV: study concept, data collection, data curation, formal analysis, writing (reviewing, editing, and approval); JD: data collection, writing (reviewing, editing, and approval); FMG: study concept, writing (reviewing, editing, and approval); CL: study concept, writing (reviewing, editing, and approval).

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Correspondence to Katharine Dunlop, Faith M. Gunning or Conor Liston.

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Dunlop, K., Victoria, L.W., Downar, J. et al. Accelerated brain aging predicts impulsivity and symptom severity in depression. Neuropsychopharmacol. 46, 911–919 (2021). https://doi.org/10.1038/s41386-021-00967-x

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