Aysenil Belger

Background Reduced mismatch negativity (MMN) and P300 event-related potential (ERP) components are widely replicated in schizophrenia and are also observed in individuals at clinical high risk for psychosis (CHR-P) who subsequently convert to psychosis. It is unknown whether they reflect changes in excitatory and/or inhibitory synaptic function, both implicated in schizophrenia and considered potential drug targets. Methods We analyzed baseline MMN and P300 ERPs from the NAPLS2 study, asking whether altered synaptic excitation, inhibition, or both could explain amplitude reductions in CHR-P (n=583). CHR-P participants who converted to psychosis (CHR-Converters; n=77) or remitted by 24-month follow-up (CHR-Remitters; n=94) were compared on MMN evoked by pitch+duration double-deviant tones and P300 elicited by target tones from passive and active auditory oddball paradigms, respectively. Biophysical modeling was used to infer (excitatory) pyramidal cell and (inhibitory) interneuron function from both MMN and P300 ERPs. Results MMN and P300 amplitude reductions in future CHR-Converters relative to CHR-Remitters were best explained by reduced pyramidal cell excitability (posterior probability P>.95 of a group-by-condition interaction effect). In simulations, reduced pyramidal cell excitability suppressed deviant and target ERPs. Within CHR-Converters, but not CHR-Remitters, more severe positive symptoms were associated with disinhibition of pyramidal cells (P>.99). Conclusions Results mirror previous findings in schizophrenia and suggest that reduced pyramidal cell excitability is present at baseline in future CHR-Converters, supporting the hypothesis that hypofunction of pyramidal cells is a primary pathology in schizophrenia, rather than a consequence of chronic illness. Positive symptoms among CHR-Converters may reflect compensatory downregulation of inhibition.

Individuals at clinical high risk for psychosis (CHR) have variable clinical outcomes and low conversion rates, limiting development of novel and personalized treatments. Moreover, given risks of antipsychotic drugs, safer effective medications for CHR individuals are needed. The Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ) Program was launched to address this need. Based on past CHR and schizophrenia studies, AMP SCZ assessed electroencephalography (EEG)-based event-related potential (ERP), event-related oscillation (ERO), and resting EEG power spectral density (PSD) measures, including mismatch negativity (MMN), auditory and visual P300 to target (P3b) and novel (P3a) stimuli, 40-Hz auditory steady state response, and resting EEG PSD for traditional frequency bands (eyes open/closed). Here, in an interim analysis of AMP SCZ EEG measures, we assess test-retest reliability and stability over sessions (baseline, month-2 follow-up) in CHR (n = 654) and community control (CON; n = 87) participants. Reliability was calculated as Generalizability (G)-coefficients, and changes over session were assessed with paired t-tests. G-coefficients were generally good to excellent in both groups (CHR: mean = 0.72, range = 0.49–0.85; CON: mean = 0.71, range = 0.44–0.89). Measure magnitudes significantly (p < 0.001) decreased over session (MMN, auditory and visual target P3b, visual novel P3a, 40-Hz ASSR) and/or over runs within sessions (MMN, auditory/visual novel P3a and target P3b), consistent with habituation effects. Despite these small systematic habituation effects, test-retest reliabilities of the AMP SCZ EEG-based measures are sufficiently strong to support their use in CHR studies as potential predictors of clinical outcomes, markers of illness progression, and/or target engagement or secondary outcome measures in controlled clinical trials. Watch Dr Daniel H. Mathalon discuss their work and this article: https://vimeo.com/1066564687.

Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilized fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each timepoint to optimize for a smooth gradient in the FNC (i.e., a smooth gradient among ICA connectivity values). Several methods are presented to summarize dynamic FNC gradients (dFNGs) over time, starting with static FNC gradients (sFNGs), then exploring the reordering properties as well as the dynamics of the gradients themselves. We then apply this approach to a dataset of schizophrenia (SZ) patients and healthy controls (HC). Functional dysconnectivity between different brain regions has been reported in schizophrenia, yet the neural mechanisms behind it remain elusive. Using resting state fMRI and ICA on a dataset consisting of 151 schizophrenia patients and 160 age and gender-matched healthy controls, we extracted 53 intrinsic connectivity networks (ICNs) for each subject using a fully automated spatially constrained ICA approach. We develop several summaries of our functional network connectivity gradient analysis, both in a static sense, computed as the Pearson correlation coefficient between full time series, and a dynamic sense, computed using a sliding window approach followed by reordering based on the computed gradient, and evaluate group differences. Static connectivity analysis revealed significantly stronger connectivity between subcortical (SC), auditory (AUD) and visual (VIS) networks in patients, as well as hypoconnectivity in sensorimotor (SM) network relative to controls. sFNG analysis highlighted distinctive clustering patterns in patients and HCs along cognitive control (CC)/ default mode network (DMN), as well as SC/ AUD/ SM/ cerebellar (CB), and VIS gradients. Furthermore, we observed significant differences in the sFNGs between groups in SC and CB domains. dFNG analysis suggested that SZ patients spend significantly more time in a SC/ CB state based on the first gradient, while HCs favor the SM/DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB domains, contrasting with HCs DMN engagement. The gradient synchrony analysis conveyed more shifts between SM/ SC networks and transmodal CC/ DMN networks in patients. In addition, the dFNG coupling revealed distinct connectivity patterns between SC, SM and CB domains in SZ patients compared to HCs. To recap, our results advance our understanding of brain network modulation by examining smooth connectivity trajectories. This provides a more complete spatiotemporal summary of the data, contributing to the growing body of current literature regarding the functional dysconnectivity in schizophrenia patients. By employing dFNG, we highlight a new perspective to capture large scale fluctuations across the brain while maintaining the convenience of brain networks and low dimensional summary measures.

People with schizophrenia and their high-risk, first-degree relatives report widespread episodic memory impairments that are purportedly due, at least in part, to failures of mnemonic discrimination. Here, we examined the status of mnemonic discrimination in 36 children and adolescents (aged 11–17 years) with and without familial risk for schizophrenia by employing an object-based recognition task called the Mnemonic Similarity Task (MST). The MST assesses the ability to discriminate between studied images and unstudied images that are either perceptually similar to studied images or completely novel. We compared 16 high-risk, unaffected first-degree relatives of people with schizophrenia, bipolar disorder, and/or schizoaffective disorder to 20 low-risk, control participants. High-risk participants showed worse mnemonic discrimination than low-risk participants, with no difference in recognition memory or perceptual discrimination. Our findings demonstrate that mnemonic discrimination deficits previously observed in people with schizophrenia are also present in their young, high-risk, first-degree relatives.

The FAIR principles, as applied to clinical and neuroimaging data, reflect the goal of making research products Findable, Accessible, Interoperable, and Reusable. The use of the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymized Computation (COINSTAC) platform in the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium combines the technological approach of decentralized analyses with the sociological approach of sharing data. In addition, ENIGMA + COINSTAC provides a platform to facilitate the use of machine-actionable data objects. We first present how ENIGMA and COINSTAC support the FAIR principles, and then showcase their integration with a decentralized meta-analysis of sex differences in negative symptom severity in schizophrenia, and finally present ongoing activities and plans to advance FAIR principles in ENIGMA + COINSTAC. ENIGMA and COINSTAC currently represent efforts toward improved Access, Interoperability, and Reusability. We highlight additional improvements needed in these areas, as well as future connections to other resources for expanded Findability.

In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (MI), that calculates the nonlinear correlation between different brain regions. We demonstrate our proposed approach using simulated data, then apply it to a dataset previously studied in (Damaraju et al., 2014). This resting-state fMRI data included 151 schizophrenia patients and 163 age- and gender-matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in nonlinear dependencies between schizophrenia patients and healthy controls particularly in visual cortex, with controls showing more nonlinearity in most cases. Certain domains, including cognitive control, and default mode, appeared much less nonlinear, whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Further, we propose a method that captures both linear and nonlinear effects in a ‘boosted’ approach. This method increases the sensitivity to group differences in comparison to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.