Individual patterns of abnormality in resting-state functional connectivity reveal two data-driven PTSD subgroups

Adi Maron-Katz ^1,2,3 Yu Zhang ^1,2,3 Manjari Narayan ^1,2,3 Sharon Naparstek ^1,2,3 Wei Wu ^1,2,3,4 Russell T. Toll ^1,2,3,5 Carlo De Los Angeles ^1,2,3 Parker Longwell ^1,2,3 Emmanuel Shpigel ^1,2,3 Jennifer Newman ^6,7 Duna Abu Amara ^6,7 Charles Marmar ^6,7 Amit Etkin ^1,2,3

¹Psychiatry and Behavioral Sciences, Stanford University School of Medicine, USA
²Wu Tsai Neurosciences Institute, Stanford University, USA
³Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (Mirecc), USA
⁴School of Automation Science and Engineering, South China University of Technology, Guangzhou
⁵Department of Bioengineering, Stanford University
⁶Steven and Alexandra Cohen Veterans Center for Post-Traumatic Stress and Traumatic Brain Injury, New York University School of Medicine
⁷Department of Psychiatry, New York University School of Medicine

Objective:

A major challenge in understanding and treating PTSD is its clinical heterogeneity, which is likely determined by various neurobiological perturbations. This heterogeneity likely also reduces the effectiveness of standard group-comparison approaches. Here we tested whether a statistical approach aimed at identifying individual-level neuroimaging abnormalities that are more prevalent in cases than in controls could reveal new clinically-meaningful insights into the heterogeneity of PTSD.

Method:

Resting-state functional magnetic resonance imaging was recorded from 87 unmedicated PTSD cases and 105 warzone-exposed healthy controls. Abnormalities were modeled using tolerance intervals, which referenced the distribution of healthy controls as the “normative population.” Out-of-norm functional-connectivity values were examined for enrichment in cases, and then used in a clustering analysis to identify biologically-defined PTSD subgroups based on their abnormality profiles.

Results:

We identified two subgroups among PTSD cases, each with a distinct pattern of functional connectivity abnormalities with respect to healthy controls. Subgroups differed clinically on levels of reexperiencing symptoms, improved case-control discriminability and were detectable using independently recorded resting-state EEG data.

Conclusions:

Our results provide a proof-of-concept for the utility of abnormality-based approaches for studying heterogeneity within clinical populations. Such approaches, applied not only to neuroimaging data, may allow detection of subpopulations with distinct biological signatures so that further clinical and mechanistic investigations can be focused on more biologically homogeneous subgroups.