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

Neuropsychopharmacology (2024)Cite this article

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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.

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Fig. 1: Illustration of the brain-based graph-theoretical predictive modeling (GPM).
Fig. 2: Trajectories of anhedonia, impulsivity, and (hypo)mania symptoms.
Fig. 3: The GPM predicted anhedonia and impulsivity at baseline.
Fig. 4: The GPM predicted (hypo)mania and anhedonia at 6-month follow-up.

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Data availability

Datasets will be shared on reasonable request to the corresponding author.

Code availability

The Brain Connectivity Toolbox was used for graph theoretical analyses and is freely available at https://sites.google.com/site/bctnet. The following functions were used: weight_conversion.m, clustering_coef_wu.m, efficiency_wei.m, distance_wei.m, charpath.m, betweenness_wei.m. Connectome-based predictive modeling (CPM) was done using MATLAB scripts available by Shen and colleagues [47].

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Funding

The study was supported by the National Institute of Mental Health (NIMH) (Grant Nos. R01 MH101521 and R37 MH068376 to DAP). RD is an Awardee of the Weizmann Institute of Science – Israel National Postdoctoral Award Program for Advancing Women in Science. AEW was supported by a National Health and Medical Research Council Investigator Grant (GNT 2017521). Biostatistical consultation was provided by Harvard Catalyst.

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Authors and Affiliations

  1. Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA

    Rotem Dan, Alexis E. Whitton, Ashleigh V. Rutherford, Poornima Kumar, Manon L. Ironside & Diego A. Pizzagalli

  2. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

    Rotem Dan, Alexis E. Whitton, Poornima Kumar, Boyu Ren & Diego A. Pizzagalli

  3. Black Dog Institute, University of New South Wales, Sydney, Australia

    Alexis E. Whitton

  4. Department of Psychology, Emory University, Atlanta, GA, USA

    Michael T. Treadway

  5. Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA

    Michael T. Treadway

  6. Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA

    Roselinde H. Kaiser

  7. Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA

    Boyu Ren

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  1. Rotem Dan
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Contributions

RD: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Resources; Software; Visualization; Writing - Original draft; Writing – Review & Editing. AEW: Conceptualization; Investigation; Project administration; Methodology; Resources; Writing – Review & Editing. MTT: Investigation; Project administration; Resources; Writing – Review & Editing. AVR: Investigation; Project administration; Resources; Writing – Review & Editing. PK: Investigation; Project administration; Resources; Writing – Review & Editing. MLI: Investigation; Project administration; Resources; Writing – Review & Editing. RHK: Investigation; Project administration; Resources; Writing – Review & Editing. BR: Methodology; Writing – Review & Editing. DAP: Conceptualization; Funding acquisition; Methodology; Resources; Supervision; Writing – Review & Editing.

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Correspondence to Diego A. Pizzagalli.

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Competing interests

Over the past 3 years, Dr. Pizzagalli has received consulting fees from Boehringer Ingelheim, Compass Pathways, Engrail Therapeutics, Neumora Therapeutics (formerly BlackThorn Therapeutics), Neurocrine Biosciences, Neuroscience Software, Otsuka, Sage Therapeutics, Sama Therapeutics, Sunovion, and Takeda; he has received honoraria from the American Psychological Association, Psychonomic Society and Springer (for editorial work) and from Alkermes; he has received research funding from the Bird Foundation, Brain and Behavior Research Foundation, Dana Foundation, Wellcome Leap, Millennium Pharmaceuticals, and NIMH; he has received stock options from Compass Pathways, Engrail Therapeutics, Neumora Therapeutics, and Neuroscience Software; he has a financial interest in Neumora Therapeutics, which has licensed the copyright to the human version of the probabilistic reward task through Harvard University. No funding from these entities was used to support the current work, and all views expressed are solely those of the authors. All other authors have no conflicts of interest or relevant disclosures.

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Dan, R., Whitton, A.E., Treadway, M.T. et al. Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome. Neuropsychopharmacol. (2024). https://doi.org/10.1038/s41386-024-01842-1

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