Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome

Rotem Dan; Alexis E Whitton; Michael T Treadway; Ashleigh V Rutherford; Poornima Kumar; Manon L Ironside; Roselinde H Kaiser; Boyu Ren; Diego A Pizzagalli

doi:10.1038/s41386-024-01842-1

Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome

Neuropsychopharmacology. 2024 Mar 13. doi: 10.1038/s41386-024-01842-1. Online ahead of print.

Authors

Rotem Dan^{1

2}, Alexis E Whitton^{1

2

3}, Michael T Treadway^{4

5}, Ashleigh V Rutherford¹, Poornima Kumar^{1

2}, Manon L Ironside¹, Roselinde H Kaiser⁶, Boyu Ren^{2

7}, Diego A Pizzagalli^{8

9}

Affiliations

¹ Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA.
² Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
³ Black Dog Institute, University of New South Wales, Sydney, Australia.
⁴ Department of Psychology, Emory University, Atlanta, GA, USA.
⁵ Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA.
⁶ Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.
⁷ Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA.
⁸ Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA. dap@mclean.harvard.edu.
⁹ Department of Psychiatry, Harvard Medical School, Boston, MA, USA. dap@mclean.harvard.edu.

PMID: 38480910
DOI: 10.1038/s41386-024-01842-1

Abstract

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.

Abstract

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