A whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry.

Psychosis (including symptoms of delusions, hallucinations, and disorganized conduct/speech) is a main feature of schizophrenia and is frequently present in other major psychiatric illnesses. Studies in individuals with first-episode (FEP) and early psychosis (EP) have the potential to interpret aberrant connectivity associated with psychosis during a period with minimal influence from medication and other confounds. The current study uses a data-driven whole-brain approach to examine patterns of aberrant functional network connectivity (FNC) in a multi-site dataset comprising resting-state functional magnetic resonance images (rs-fMRI) from 117 individuals with FEP or EP and 130 individuals without a psychiatric disorder, as controls. Accounting for age, sex, race, head motion, and multiple imaging sites, differences in FNC were identified between psychosis and control participants in cortical (namely the inferior frontal gyrus, superior medial frontal gyrus, postcentral gyrus, supplementary motor area, posterior cingulate cortex, and superior and middle temporal gyri), subcortical (the caudate, thalamus, subthalamus, and hippocampus), and cerebellar regions. The prominent pattern of reduced cerebellar connectivity in psychosis is especially noteworthy, as most studies focus on cortical and subcortical regions, neglecting the cerebellum. The dysconnectivity reported here may indicate disruptions in cortical-subcortical-cerebellar circuitry involved in rudimentary cognitive functions which may serve as reliable correlates of psychosis.

Towards thoughtful planning of ERP studies: How participants, trials, and effect magnitude interact to influence statistical power across seven ERP components.

In the field of EEG, researchers generally rely on rules of thumb, rather than a priori statistical calculations, when planning the number of trials to include in an ERP study. To aid in this practice, studies have tried to establish minimum numbers of trials required to reliably isolate ERPs. However, these guidelines do not necessarily apply across different study designs, as the reliability of an ERP waveform is not the same as the statistical power of a given experiment. Experiment parameters such as number of participants, trials, and effect magnitude interact to affect power in complex ways. Both under- and over-powered ERP studies represent a waste of time and resources that impedes the progress of the field. The current study fills this gap by subsampling real ERP data to estimate the relationship between experiment design parameters and statistical power. The simulations include seven commonly studied ERP components: the ERN, LRP, N170, MMN, P3, N2pc, and N400. In the first set of experiments, we determined the probability of obtaining a statistically significant ERP effect for each component. In the second and third set of experiments, we determined the probability of obtaining a statistically significant difference in ERP amplitude within and between groups for each component. Results indicate that the rules of thumb for ERP experiment design in the literature often lead to underpowered studies. Going forward, these results provide researchers with experiment design guidelines that are specific to the component under study, allowing for the design of sufficiently powered ERP studies.