Resting-State Connectivity Biomarkers of Cognitive Performance and Social Function in Individuals With Schizophrenia Spectrum Disorder and Healthy Control Subjects
Authors: Erin Dickie
Peter Kochunov
Mikko Mason
Anil K Malhotra
Judy Kwan
George Foussias
Michael Green
Sofia Chavez
Jessica Turner
Aristotle N Voineskos
Jen Zaranski
Marco Iacoboni
Peter Kingsley
Christina Plagiannakos
Eric Arbach
Laura Stefanik
Miklos Argyelan
Social Processes Initiative in Neurobiology of the Schizophrenia(s) Group
Pamela DeRosse
Nikhil Bhagwat
Philipp Homan
Robert W Buchanan
Will Carpenter
Navona Calarco
Sharon August
James M Gold
Dielle Miranda
Marzena Boczulak
Joseph D Viviano
Xiangzhi Zhou
Colin Hawco
Gary Remington
Journal: Biological Psychiatry (2018)
Discipline: NEUROSCIENCE & BEHAVIOR
Keywords: Biomarker; Functional outcomes; Imaging; Machine learning; Resting-state fMRI

Abstract:  Background: Deficits in neurocognition and social cognition are drivers of reduced functioning in schizophrenia spectrum disorders, with potentially shared neurobiological underpinnings. Many studies have sought to identify brain-based biomarkers of these clinical variables using a priori dichotomies (e.g., good vs. poor cognition, deficit vs. nondeficit syndrome). Methods: We evaluated a fully data-driven approach to do the same by building and validating a brain connectivity-based biomarker of social cognitive and neurocognitive performance in a sample using resting-state and task-based functional magnetic resonance imaging (n = 74 healthy control participants, n = 114 persons with schizophrenia spectrum disorder, 188 total). We used canonical correlation analysis followed by clustering to identify a functional connectivity signature of normal and poor social cognitive and neurocognitive performance. Results: Persons with poor social cognitive and neurocognitive performance were differentiated from those with normal performance by greater resting-state connectivity in the mirror neuron and mentalizing systems. We validated our findings by showing that poor performers also scored lower on functional outcome measures not included in the original analysis and by demonstrating neuroanatomical differences between the normal and poorly performing groups. We used a support vector machine classifier to demonstrate that functional connectivity alone is enough to distinguish normal and poorly performing participants, and we replicated our findings in an independent sample (n = 75). Conclusions: A brief functional magnetic resonance imaging scan may ultimately be useful in future studies aimed at characterizing long-term illness trajectories and treatments that target specific brain circuitry in those with impaired cognition and function. 

Retracted: No

PubPeer comments:
 There are several issues with this study. First of all: what is the rationale to lump together a group of patients with control subjects to derive brain  biotypes , when a random sample of said healthy control subjects had a mean IQ of 144 (Table S5)? One wonders if those  control subjects were recruited at MENSA. The authors then use canonical correlation, a mere overfitting machine (cf. Dinga et al.), to derive biotypes of questionable real-world relevance. Finally, while the paper claims that data and code have been shared no data can actually been found anywhere online to verify the claims of this paper. ,