Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis.

People with psychosis exhibit thalamo-cortical hyperconnectivity and cortico-cortical hypoconnectivity with sensory networks, however, it remains unclear if this applies to all sensory networks, whether it arises from other illness factors, or whether such differences could form the basis of a viable biomarker. To address the foregoing, we harnessed data from the Human Connectome Early Psychosis Project and computed resting-state functional connectivity (RSFC) matrices for 54 healthy controls and 105 psychosis patients. Primary visual, secondary visual ("visual2"), auditory, and somatomotor networks were defined via a recent brain network partition. RSFC was determined for 718 regions via regularized partial correlation. Psychosis patients-both affective and non-affective-exhibited cortico-cortical hypoconnectivity and thalamo-cortical hyperconnectivity in somatomotor and visual2 networks but not in auditory or primary visual networks. When we averaged the visual2 and somatomotor network connections and subtracted the thalamo-cortical and cortico-cortical connectivity values, a robust psychosis biomarker emerged (p=2e-10, Hedges' g=1.05). This "somato-visual" biomarker was present in antipsychotic-naive patients and did not depend on confounds such as psychiatric comorbidities, substance/nicotine use, stress, or anxiety. It had moderate test-retest reliability (ICC=.61) and could be recovered in five-minute scans. The marker could discriminate groups in leave-one-site-out cross-validation (AUC=.79) and improve group classification upon being added to a well-known neurocognition task. Finally, it could differentiate later-stage psychosis patients from healthy or ADHD controls in two independent data sets. These results introduce a simple and robust RSFC biomarker that can distinguish psychosis patients from controls by the early illness stages.

Increased whole-brain functional heterogeneity in psychosis during rest and task.

Past work has shown that people with schizophrenia exhibit more cross-subject heterogeneity in their functional connectivity patterns. However, it remains unclear whether specific brain networks are implicated, whether common confounds could explain the results, or whether task activations might also be more heterogeneous. Unambiguously establishing the existence and extent of functional heterogeneity constitutes a first step toward understanding why it emerges and what it means clinically. METHODS: We first leveraged data from the HCP Early Psychosis project. Functional connectivity (FC) was extracted from 718 parcels via principal components regression. Networks were defined via a brain network partition (Ji et al., 2019). We also examined an independent data set with controls, later-stage schizophrenia patients, and ADHD patients during rest and during a working memory task. We quantified heterogeneity by averaging the Pearson correlation distance of each subject's FC or task activity pattern to that of every other subject of the same cohort. RESULTS: Affective and non-affective early psychosis patients exhibited more cross-subject whole-brain heterogeneity than healthy controls (ps < 0.001, Hedges' g > 0.74). Increased heterogeneity could be found in up to seven networks. In-scanner motion, medication, nicotine, and comorbidities could not explain the results. Later-stage schizophrenia patients exhibited heterogeneous connectivity patterns and task activations compared to ADHD and control subjects. Interestingly, individual connection weights, parcel-wise task activations, and network averages thereof were not more variable in patients, suggesting that heterogeneity becomes most obvious over large-scale patterns. CONCLUSION: Whole-brain cross-subject functional heterogeneity characterizes psychosis during rest and task. Developmental and pathophysiological consequences are discussed.

When do contrast sensitivity deficits (or enhancements) depend on spatial frequency? Two ways to avoid spurious interactions.

Studies across a broad range of disciplines-from psychiatry to cognitive science to behavioural neuroscience-have reported on whether the magnitude of contrast sensitivity alterations in one group or condition varies with spatial frequency. Significant interactions have often gone unexplained or have been used to argue for impairments in specific processing streams. Here, we show that interactions with spatial frequency may need to be re-evaluated if the inherent skew/heteroscedasticity was not taken into account or if visual acuity could plausibly differ across groups or conditions. By re-analysing a publicly available data set, we show that-when using raw contrast sensitivity data-schizophrenia patients exhibit an apparent contrast sensitivity impairment that lessens with spatial frequency, but that when using log-transformed data or when using generalized estimating equations, this interaction reversed. The reversed interaction, but not the overall contrast sensitivity deficit, disappeared when groups were matched on visual acuity. An analysis of the contrast threshold data yielded similar results. A caveat is that matching groups on acuity is probably only defensible if acuity differences arise from non-neural factors such as optical blur. Taken together, these analyses reconcile seemingly discrepant findings in the literature and demonstrate that reporting contrast sensitivity interactions with spatial frequency requires properly accounting for visual acuity and skew/heteroscedasticity.