The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Predicting psychosis risk using a specific measure of cognitive control: a 12-month longitudinal study.

BACKGROUND: Identifying risk factors of individuals in a clinical-high-risk state for psychosis are vital to prevention and early intervention efforts. Among prodromal abnormalities, cognitive functioning has shown intermediate levels of impairment in CHR relative to first-episode psychosis and healthy controls, highlighting a potential role as a risk factor for transition to psychosis and other negative clinical outcomes. The current study used the AX-CPT, a brief 15-min computerized task, to determine whether cognitive control impairments in CHR at baseline could predict clinical status at 12-month follow-up. METHODS: Baseline AX-CPT data were obtained from 117 CHR individuals participating in two studies, the Early Detection, Intervention, and Prevention of Psychosis Program (EDIPPP) and the Understanding Early Psychosis Programs (EP) and used to predict clinical status at 12-month follow-up. At 12 months, 19 individuals converted to a first episode of psychosis (CHR-C), 52 remitted (CHR-R), and 46 had persistent sub-threshold symptoms (CHR-P). Binary logistic regression and multinomial logistic regression were used to test prediction models. RESULTS: Baseline AX-CPT performance (d-prime context) was less impaired in CHR-R compared to CHR-P and CHR-C patient groups. AX-CPT predictive validity was robust (0.723) for discriminating converters v. non-converters, and even greater (0.771) when predicting CHR three subgroups. CONCLUSIONS: These longitudinal outcome data indicate that cognitive control deficits as measured by AX-CPT d-prime context are a strong predictor of clinical outcome in CHR individuals. The AX-CPT is brief, easily implemented and cost-effective measure that may be valuable for large-scale prediction efforts.

Longitudinal regional brain volume loss in schizophrenia: Relationship to antipsychotic medication and change in social function.

BACKGROUND: Progressive brain volume loss in schizophrenia has been reported in previous studies but its cause and regional distribution remains unclear. We investigated progressive regional brain reductions in schizophrenia and correlations with potential mediators. METHOD: Participants were drawn from the Northern Finland Birth Cohort 1966. A total of 33 schizophrenia individuals and 71 controls were MRI scanned at baseline (mean age=34.7, SD=0.77) and at follow-up (mean age=43.4, SD=0.44). Regional brain change differences and associations with clinical mediators were examined using FSL voxelwise SIENA. RESULTS: Schizophrenia cases exhibited greater progressive brain reductions than controls, mainly in the frontal and temporal lobes. The degree of periventricular brain volume reductions were predicted by antipsychotic medication exposure at the fourth ventricular edge and by the number of days in hospital between the scans (a proxy measure of relapse duration) at the thalamic ventricular border. Decline in social and occupational functioning was associated with right supramarginal gyrus reduction. CONCLUSION: Our findings are consistent with the possibility that antipsychotic medication exposure and time spent in relapse partially explain progressive brain reductions in schizophrenia. However, residual confounding could also account for the findings and caution must be applied before drawing causal inferences from associations demonstrated in observational studies of modest size. Less progressive brain volume loss in schizophrenia may indicate better preserved social and occupational functions.

Longitudinal changes in total brain volume in schizophrenia: relation to symptom severity, cognition and antipsychotic medication.

Studies show evidence of longitudinal brain volume decreases in schizophrenia. We studied brain volume changes and their relation to symptom severity, level of function, cognition, and antipsychotic medication in participants with schizophrenia and control participants from a general population based birth cohort sample in a relatively long follow-up period of almost a decade. All members of the Northern Finland Birth Cohort 1966 with any psychotic disorder and a random sample not having psychosis were invited for a MRI brain scan, and clinical and cognitive assessment during 1999-2001 at the age of 33-35 years. A follow-up was conducted 9 years later during 2008-2010. Brain scans at both time points were obtained from 33 participants with schizophrenia and 71 control participants. Regression models were used to examine whether brain volume changes predicted clinical and cognitive changes over time, and whether antipsychotic medication predicted brain volume changes. The mean annual whole brain volume reduction was 0.69% in schizophrenia, and 0.49% in controls (p = 0.003, adjusted for gender, educational level, alcohol use and weight gain). The brain volume reduction in schizophrenia patients was found especially in the temporal lobe and periventricular area. Symptom severity, functioning level, and decline in cognition were not associated with brain volume reduction in schizophrenia. The amount of antipsychotic medication (dose years of equivalent to 100 mg daily chlorpromazine) over the follow-up period predicted brain volume loss (p = 0.003 adjusted for symptom level, alcohol use and weight gain). In this population based sample, brain volume reduction continues in schizophrenia patients after the onset of illness, and antipsychotic medications may contribute to these reductions.