Functional Dysconnection of the Inferior Frontal Gyrus in Young People With Bipolar Disorder or at Genetic High Risk

• Published in: Biological psychiatry (1969) 2017-04, Vol.81 (8), p.718-727
• Main Authors: Roberts, Gloria, Lord, Anton, Frankland, Andrew, Wright, Adam, Lau, Phoebe, Levy, Florence, Lenroot, Rhoshel K, Mitchell, Philip B, Breakspear, Michael
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Bipolar disorder; Bipolar Disorder - genetics; Bipolar Disorder - physiopathology; Brain - physiopathology; Brain Mapping - methods; Female; Genetic Predisposition to Disease; Genetic risk; Graph theory; Humans; Inferior frontal gyrus; Limbic System - physiopathology; Machine Learning; Magnetic Resonance Imaging; Male; Network-based statistics; Neural Pathways - physiopathology; Prefrontal Cortex - physiopathology; Psychiatry; Resting-state functional connectivity; Risk Factors; Young Adult
• ISSN: 0006-3223; 1873-2402
• DOI: 10.1016/j.biopsych.2016.08.018

Abstract Background Bipolar disorder (BD) is characterized by a dysregulation of affect and impaired integration of emotion with cognition. These traits are also expressed in probands at high genetic risk of BD. The inferior frontal gyrus (IFG) is a key cortical hub in the circuits of emotion and cognitive control, and it has been frequently associated with BD. Here, we studied resting-state functional connectivity of the left IFG in participants with BD and in those at increased genetic risk. Methods Using resting-state functional magnetic resonance imaging we compared 49 young BD participants, 71 individuals with at least one first-degree relative with BD (at-risk), and 80 control subjects. We performed between-group analyses of the functional connectivity of the left IFG and used graph theory to study its local functional network topology. We also used machine learning to study classification based solely on the functional connectivity of the IFG. Results In BD, the left IFG was functionally dysconnected from a network of regions, including bilateral insulae, ventrolateral prefrontal gyri, superior temporal gyri, and the putamen ( p < .001). A small network incorporating neighboring insular regions and the anterior cingulate cortex showed weaker functional connectivity in at-risk than control participants ( p < .006). These constellations of regions overlapped with frontolimbic regions that a machine learning classifier selected as predicting group membership with an accuracy significantly greater than chance. Conclusions Functional dysconnectivity of the IFG from regions involved in emotional regulation may represent a trait abnormality for BD and could potentially aid clinical diagnosis.