Default mode network maturation and psychopathology in children and adolescents.

Sato, João Ricardo; Salum, Giovanni Abrahão; Gadelha, Ary; Crossley, Nicolas; Vieira, Gilson; Manfro, Gisele Gus; Zugman, André; Picon, Felipe Almeida; Pan, Pedro Mario; Hoexter, Marcelo Queiroz; Anés, Mauricio; Moura, Luciana Monteiro; Del'Aquilla, Marco Antonio Gomes; Jr, Edson Amaro; McGuire, Philip; Lacerda, Acioly Luiz Tavares; Rohde, Luis Augusto; Miguel, Euripedes Constantino; Jackowski, Andrea Parolin; Bressan, Rodrigo Affonseca

Background The human default mode ( DMN) is involved in a wide array of mental disorders. Current knowledge suggests that mental health disorders may reflect deviant trajectories of brain maturation. Method We studied 654 children using functional magnetic resonance imaging ( fMRI) scans under a resting-state protocol. A machine-learning method was used to obtain age predictions of children based on the average coefficient of fractional amplitude of low frequency fluctuations ( fALFFs) of the DMN, a measure of spontaneous local activity. The chronological ages of the children and fALFF measures from regions of this network, the response and predictor variables were considered respectively in a Gaussian Process Regression. Subsequently, we computed a network maturation status index for each subject (actual age minus predicted). We then evaluated the association between this maturation index and psychopathology scores on the Child Behavior Checklist (CBCL). Results Our hypothesis was that the maturation status of the DMN would be negatively associated with psychopathology. Consistent with previous studies, fALFF significantly predicted the age of participants ( p < .001). Furthermore, as expected, we found an association between the DMN maturation status (precocious vs. delayed) and general psychopathology scores ( p = .011). Conclusions Our findings suggest that child psychopathology seems to be associated with delayed maturation of the DMN. This delay in the neurodevelopmental trajectory may offer interesting insights into the pathophysiology of mental health disorders.

BRAIN physiology; AGE distribution; CHILD Behavior Checklist; CHILD development; STATISTICAL correlation; EMOTION regulation; MAGNETIC resonance imaging; PROBABILITY theory; PATHOLOGICAL psychology; REGRESSION analysis; RESEARCH funding; STATISTICS; DATA analysis; DESCRIPTIVE statistics

Journal of Child Psychology & Psychiatry, 2016, Vol 57, Issue 1, p55

0021-9630

Academic Journal

10.1111/jcpp.12444