A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation

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), 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 DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB/ VIS 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 centroids 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.

Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets

Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.

Auditory oddball hypoactivation in schizophrenia

Individuals with schizophrenia (SZ) show aberrant activations, assessed via functional magnetic resonance imaging (fMRI), during auditory oddball tasks. However, associations with cognitive performance and genetic contributions remain unknown. This study compares individuals with SZ to healthy volunteers (HVs) using two cross-sectional data sets from multi-center brain imaging studies. It examines brain activation to auditory oddball targets, and their associations with cognitive domain performance, schizophrenia polygenic risk scores (PRS), and genetic variation (loci). Both sample 1 (137 SZ vs. 147 HV) and sample 2 (91 SZ vs. 98 HV), showed hypoactivation in SZ in the left-frontal pole, and right frontal orbital, frontal pole, paracingulate, intracalcarine, precuneus, supramarginal and hippocampal cortices, and right thalamus. In SZ, precuneus activity was positively related to cognitive performance. Schizophrenia PRS showed a negative correlation with brain activity in the right-supramarginal cortex. GWA analyses revealed significant single-nucleotide polymorphisms associated with right-supramarginal gyrus activity. RPL36 also predicted right-supramarginal gyrus activity. In addition to replicating hypoactivation for oddball targets in SZ, this study identifies novel relationships between regional activity, cognitive performance, and genetic loci that warrant replication, emphasizing the need for continued data sharing and collaborative efforts.

Alterations in grey matter structure linked to frequency-specific cortico-subcortical connectivity in schizophrenia via multimodal data fusion

Schizophrenia (SZ) is a complex psychiatric disorder that is currently defined by symptomatic and behavioral, rather than biological, criteria. Neuroimaging is an appealing avenue for SZ biomarker development, as several neuroimaging-based studies comparing individuals with SZ to healthy controls (HC) have shown measurable group differences in brain structure, as well as functional brain alterations in both static and dynamic functional network connectivity (sFNC and dFNC, respectively). The recently proposed filter-banked connectivity (FBC) method extends the standard dFNC sliding-window approach to estimate FNC within an arbitrary number of distinct frequency bands. The initial implementation used a set of filters spanning the full connectivity spectral range, providing a unified approach to examine both sFNC and dFNC in a single analysis. Initial FBC results found that individuals with SZ spend more time in a less structured, more disconnected low-frequency (i.e., static) FNC state than HC, as well as preferential SZ occupancy in high-frequency connectivity states, suggesting a frequency-specific component underpinning the functional dysconnectivity observed in SZ. Building on these findings, we sought to link such frequency-specific patterns of FNC to covarying data-driven structural brain networks in the context of SZ. Specifically, we employ a multi-set canonical correlation analysis + joint independent components analysis (mCCA + jICA) data fusion framework to study the connection between grey matter volume (GMV) maps and FBC states across the full connectivity frequency spectrum. Our multimodal analysis identified two joint sources that captured co-varying patterns of frequency-specific functional connectivity and alterations in GMV with significant group differences in loading parameters between the SZ group and HC. The first joint source linked frequency-modulated connections between the subcortical and sensorimotor networks and GMV alterations in the frontal and temporal lobes, while the second joint source identified a relationship between low-frequency cerebellar-sensorimotor connectivity and structural changes in both the cerebellum and motor cortex. Together, these results show a strong connection between cortico-subcortical functional connectivity at both high and low frequencies and alterations in cortical GMV that may be relevant to the pathogenesis and pathophysiology of SZ.

Aperiodic Neural Activity is a Better Predictor of Schizophrenia than Neural Oscillations

Diagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/fX) shape in the power spectrum. In this paper, we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic components revealed that the steepness of the power spectrum better-predicted group status than traditional band-limited oscillatory power in classification analysis. Aperiodic activity also outperformed the predictions made using participants' behavioral responses. Additionally, the differences in aperiodic activity were highly consistent across all electrodes. In sum, compared to oscillations the aperiodic activity appears to be a more accurate and more robust way to differentiate patients with schizophrenia from healthy controls.

Impaired mnemonic discrimination in children and adolescents at risk for schizophrenia

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.

Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study

Brain functional networks identified from resting functional magnetic resonance imaging (fMRI) data have the potential to reveal biomarkers for brain disorders, but studies of complex mental illnesses such as schizophrenia (SZ) often yield mixed results across replication studies. This is likely due in part to the complexity of the disorder, the short data acquisition time, and the limited ability of the approaches for brain imaging data mining. Therefore, the use of analytic approaches which can both capture individual variability while offering comparability across analyses is highly preferred. Fully blind data-driven approaches such as independent component analysis (ICA) are hard to compare across studies, and approaches that use fixed atlas-based regions can have limited sensitivity to individual sensitivity. By contrast, spatially constrained ICA (scICA) provides a hybrid, fully automated solution that can incorporate spatial network priors while also adapting to new subjects. However, scICA has thus far only been used with a single spatial scale (ICA dimensionality, i.e., ICA model order). In this work, we present an approach using multi-objective optimization scICA with reference algorithm (MOO-ICAR) to extract subject-specific intrinsic connectivity networks (ICNs) from fMRI data at multiple spatial scales, which also enables us to study interactions across spatial scales. We evaluate this approach using a large N (N > 1,600) study of schizophrenia divided into separate validation and replication sets. A multi-scale ICN template was estimated and labeled, then used as input into scICA which was computed on an individual subject level. We then performed a subsequent analysis of multiscale functional network connectivity (msFNC) to evaluate the patient data, including group differences and classification. Results showed highly consistent group differences in msFNC in regions including cerebellum, thalamus, and motor/auditory networks. Importantly, multiple msFNC pairs linking different spatial scales were implicated. The classification model built on the msFNC features obtained up to 85% F1 score, 83% precision, and 88% recall, indicating the strength of the proposed framework in detecting group differences between schizophrenia and the control group. Finally, we evaluated the relationship of the identified patterns to positive symptoms and found consistent results across datasets. The results verified the robustness of our framework in evaluating brain functional connectivity of schizophrenia at multiple spatial scales, implicated consistent and replicable brain networks, and highlighted a promising approach for leveraging resting fMRI data for brain biomarker development.

Five negative symptom domains are differentially associated with resting state amplitude of low frequency fluctuations in Schizophrenia

This study examined associations between resting-state amplitude of low frequency fluctuations (ALFF) and negative symptoms represented by total scores, second-order dimension (motivation and pleasure, expressivity), and first-order domain (anhedonia, avolition, asociality, alogia, blunted affect) factor scores in schizophrenia (n = 57). Total negative symptom scores showed positive associations with ALFF in temporal and frontal brain regions. Negative symptom domain scores showed predominantly stronger associations with regional ALFF compared to total scores, suggesting domain scores may better map to neural signatures than total scores. Improving our understanding of the neuropathology underlying negative symptoms may aid in addressing this unmet therapeutic need in schizophrenia.

A method for estimating and characterizing explicitly nonlinear dynamic functional network connectivity in resting-state fMRI data

The past 10 years have seen an explosion of approaches that focus on the study of time-resolved change in functional connectivity (FC). FC characterization among networks at a whole-brain level is frequently termed functional network connectivity (FNC). Time-resolved or dynamic functional network connectivity (dFNC) focuses on the estimation of transient, recurring, whole-brain patterns of FNC. While most approaches in this area have attempted to capture dynamic linear correlation, we are particularly interested in whether explicitly nonlinear relationships, above and beyond linear, are present and contain unique information. This study thus proposes an approach to assess explicitly nonlinear dynamic functional network connectivity (EN dFNC) derived from the relationship among independent component analysis time courses. Linear relationships were removed at each time point to evaluate, typically ignored, explicitly nonlinear dFNC using normalized mutual information (NMI). Simulations showed the proposed method estimated explicitly nonlinearity over time, even within relatively short windows of data. We then, applied our approach on 151 schizophrenia patients, and 163 healthy controls fMRI data and found three unique, highly structured, mostly long-range, functional states that also showed significant group differences. In particular, explicitly nonlinear relationships tend to be more widespread than linear ones. Results also highlighted a state with long range connections to the visual domain, which were significantly reduced in schizophrenia. Overall, this work suggests that quantifying EN dFNC may provide a complementary and potentially valuable tool for studying brain function by exposing relevant variation that is typically ignored.

Nonlinear functional network connectivity in resting functional magnetic resonance imaging data

In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (NMI) that calculates the nonlinear relationship between different brain regions. We demonstrate our proposed approach using simulated data and then apply it to a dataset previously studied by Damaraju et al. 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 explicitly nonlinear functional network connectivity (FNC) between schizophrenia patients and healthy controls, particularly in the visual cortex, with controls showing more nonlinearity (i.e., higher normalized mutual information between time courses with linear relationships removed) in most cases. Certain domains, including subcortical and auditory, showed relatively less nonlinear FNC (i.e., lower normalized mutual information), 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. Beyond this, we propose a method that captures both linear and nonlinear effects in a "boosted" approach. This method increases the sensitivity to group differences compared to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.

Auditory N100 amplitude deficits predict conversion to psychosis in the North American Prodrome Longitudinal Study (NAPLS-2) cohort

BACKGROUND

The auditory N100 is an event related potential (ERP) that is reduced in schizophrenia, but its status in individuals at clinical high risk for psychosis (CHR) and its ability to predict conversion to psychosis remains unclear. We examined whether N100 amplitudes are reduced in CHR subjects relative to healthy controls (HC), and this reduction predicts conversion to psychosis in CHR.

METHODS

Subjects included CHR individuals (n = 552) and demographically similar HC subjects (n = 236) from the North American Prodrome Longitudinal Study. Follow-up assessments identified CHR individuals who converted to psychosis (CHRC; n = 73) and those who did not (CHR-NC; n = 225) over 24 months. Electroencephalography data were collected during an auditory oddball task containing Standard, Novel, and Target stimuli. N100 peak amplitudes following each stimulus were measured at electrodes Cz and Fz.

RESULTS

The CHR subjects had smaller N100 absolute amplitudes than HC subjects at Fz (F(1,786) = 4.00, p 0.046). A model comparing three groups (CHRC, CHR-NC, HC) was significant for Group at the Cz electrode (F(2,531) = 3.58, p = 0.029). Both Standard (p = 0.019) and Novel (p = 0.017) stimuli showed N100 absolute amplitude reductions in CHR-C relative to HC. A smaller N100 amplitude at Cz predicted conversion to psychosis in the CHR cohort (Standard: p = 0.009; Novel: p = 0.001) and predicted shorter time to conversion (Standard: p = 0.013; Novel: p = 0.001).

CONCLUSION

N100 amplitudes are reduced in CHR individuals which precedes the onset of psychosis. N100 deficits in CHR individuals predict a greater likelihood of conversion to psychosis. Our results highlight N100's utility as a biomarker of psychosis risk.

Effects of gonadal steroids on reward circuitry function and anhedonia in women with a history of postpartum depression

BACKGROUND

Reward system dysfunction is evident across neuropsychiatric conditions. Here we present data from a double-blinded pharmaco-fMRI study investigating the triggering of anhedonia and reward circuit activity in women.

METHODS

The hormonal states of pregnancy and parturition were simulated in euthymic women with a history of postpartum depression (PPD+; n = 15) and those without such a history (PPD-; n = 15) by inducing hypogonadism, adding back estradiol and progesterone for 8 weeks ("addback"), and then withdrawing both steroids ("withdrawal"). Anhedonia was assessed using the Inventory of Depression and Anxiety Symptoms (IDAS) during each hormone phase. Those who reported a 30 % or greater increase in IDAS anhedonia, dysphoria, or ill temper during addback or withdrawal, compared with pre-treatment, were identified as hormone sensitive (HS+) and all others were identified as non-hormone sensitive (HS-). The monetary incentive delay (MID) task was administered during fMRI sessions at pre-treatment and during hormone withdrawal to assess brain activation during reward anticipation and feedback.

RESULTS

On average, anhedonia increased during addback and withdrawal in PPD+ but not PPD-. During reward feedback, both HS+ (n = 10) and HS- (n = 18) showed decreased activation in clusters in the right putamen (p < .031, FWE-corrected) and left postcentral and supramarginal gyri (p < .014, FWE-corrected) at the withdrawal scans, relative to pre-treatment scans.

LIMITATIONS

A modest sample size, stringent exclusion criteria, and relative lack of diversity in study participants limit the generalizability of results.

CONCLUSION

Although results do not explain differential hormone sensitivity in depression, they demonstrate significant effects of reproductive hormones on reward-related brain function in women.

Multimodel Order Independent Component Analysis: A Data-Driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales

Background: While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. Methods: We proposed an independent component analysis (ICA)-based approach to capture information at multiple-model orders (component numbers), and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting-state functional magnetic resonance imaging (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine-based classification. Results: In addition to consistent predictive patterns at both multiple-model orders and single-model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model orders 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared with other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. Conclusions: In sum, multimodel order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results, which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact statement Multimodel order independent component analysis (ICA) provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single-model order analysis. This work expands upon and adds to the relatively new literature on resting functional magnetic resonance imaging-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.

Triple Network Functional Connectivity During Acute Stress in Adolescents and the Influence of Polyvictimization

BACKGROUND

Exposure to both chronic and acute stressors can disrupt functional connectivity (FC) of the default mode network (DMN), salience network (SN), and central executive network (CEN), increasing risk for negative health outcomes. During adolescence, these stress-sensitive triple networks undergo critical neuromaturation that is altered by chronic exposure to general forms of trauma or victimization. However, no work has directly examined how acute stress affects triple network FC in adolescents or whether polyvictimization-exposure to multiple categories/subtypes of victimization-influences adolescent triple network neural acute stress response.

METHODS

This functional magnetic resonance imaging study examined seed-to-voxel FC of the DMN, SN, and CEN during the Montreal Imaging Stress Task. Complete data from 73 participants aged 9 to 16 years (31 female) are reported.

RESULTS

During acute stress, FC was increased between DMN and CEN regions and decreased between the SN and the DMN and CEN. Greater polyvictimization was associated with reduced FC during acute stress exposure between the DMN seed and a cluster containing the left insula of the SN.

CONCLUSIONS

These results indicate that acute stress exposure alters FC between the DMN, SN, and CEN in adolescents. In addition, FC changes during stress between the DMN and SN are further moderated by polyvictimization exposure.

Coordination of autonomic and endocrine stress responses to the Trier Social Stress Test in adolescence

Dysregulations in autonomic and endocrine stress responses are linked to the emergence of psychopathology in adolescence. However, most studies fail to consider the interplay between these systems giving rise to conflicting findings and a gap in understanding adolescent stress response regulation. A multisystem framework-investigation of parasympathetic (PNS), sympathetic (SNS), and hypothalamic pituitary adrenal (HPA) axis components and their coordination-is necessary to understand individual differences in stress response coordination which contribute to stress vulnerabilities. As the first investigation to comprehensively evaluate these three systems in adolescence, the current study employed the Trier Social Stress Test in 72 typically developing adolescents (mean age = 13) to address how PNS, SNS, and HPA stress responses are coordinated in adolescence. Hypotheses tested key predictions of the Adaptive Calibration Model (ACM) of stress response coordination. PNS and SNS responses were assessed via heart rate variability (HRV) and salivary alpha amylase (sAA) respectively. HPA responses were indexed by salivary cortisol. Analyses utilized piecewise growth curve modeling to investigate these aims. Supporting the ACM theory, there was significant hierarchical coordination between the systems such that those with low HRV had higher sAA and cortisol reactivity and those with high HRV had low-to-moderate sAA and cortisol responsivity. Our novel results reveal the necessity of studying multisystem dynamics in an integrative fashion to uncover the true mechanisms of stress response and regulation during development. Additionally, our findings support the existence of characteristic stress response profiles as predicted by the ACM model.

A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder

Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.

Mismatch Negativity in Response to Auditory Deviance and Risk for Future Psychosis in Youth at Clinical High Risk for Psychosis

Importance

Although clinical criteria for identifying youth at risk for psychosis have been validated, they are not sufficiently accurate for predicting outcomes to inform major treatment decisions. The identification of biomarkers may improve outcome prediction among individuals at clinical high risk for psychosis (CHR-P).

Objective

To examine whether mismatch negativity (MMN) event-related potential amplitude, which is deficient in schizophrenia, is reduced in young people with the CHR-P syndrome and associated with outcomes, accounting for effects of antipsychotic medication use.

Design, Setting, and Participants

MMN data were collected as part of the multisite case-control North American Prodrome Longitudinal Study (NAPLS-2) from 8 university-based outpatient research programs. Baseline MMN data were collected from June 2009 through April 2013. Clinical outcomes were assessed throughout 24 months. Participants were individuals with the CHR-P syndrome and healthy controls with MMN data. Participants with the CHR-P syndrome who developed psychosis (ie, converters) were compared with those who did not develop psychosis (ie, nonconverters) who were followed up for 24 months. Analysis took place between December 2019 and December 2021.

Main Outcomes and Measures

Electroencephalography was recorded during a passive auditory oddball paradigm. MMN elicited by duration-, pitch-, and duration + pitch double-deviant tones was measured.

Results

The CHR-P group (n = 580; mean [SD] age, 19.24 [4.39] years) included 247 female individuals (42.6%) and the healthy control group (n = 241; mean age, 20.33 [4.74] years) included 114 female individuals (47.3%). In the CHR-P group, 450 (77.6%) were not taking antipsychotic medication at baseline. Baseline MMN amplitudes, irrespective of deviant type, were deficient in future CHR-P converters to psychosis (n = 77, unmedicated n = 54) compared with nonconverters (n = 238, unmedicated n = 190) in both the full sample (d = 0.27) and the unmedicated subsample (d = 0.33). In the full sample, baseline medication status interacted with group and deviant type indicating that double-deviant MMN, compared with single deviants, was reduced in unmedicated converters compared with nonconverters (d = 0.43). Further, within the unmedicated subsample, deficits in double-deviant MMN were most strongly associated with earlier conversion to psychosis (hazard ratio, 1.40 [95% CI, 1.03-1.90]; P = .03], which persisted over and above positive symptom severity.

Conclusions and Relevance

This study found that MMN amplitude deficits were sensitive to future psychosis conversion among individuals at risk of CHR-P, particularly those not taking antipsychotic medication at baseline, although associations were modest. While MMN shows limited promise as a biomarker of psychosis onset on its own, it may contribute novel risk information to multivariate prediction algorithms and serve as a translational neurophysiological target for novel treatment development in a subgroup of at-risk individuals.

Stress-related hippocampus activation mediates the association between polyvictimization and trait anxiety in adolescents

Early life stress exposures are associated with adverse health outcomes and heightened anxiety symptoms in adolescents. Stress-sensitive brain regions like the hippocampus and amygdala are particularly impacted by early life adversities and are also implicated in the development of anxiety disorders. However, to date, no studies have specifically examined the neural correlates of polyvictimization (exposure to multiple categories of victimization) or the contribution of stress-sensitive neural nodes to polyvictimization's impact on mental health. To elucidate these relationships, the current study analyzed associations between polyvictimization, hippocampal and amygdalar activation during an acute stress task and trait anxiety in a sample of 80 children and adolescents aged 9-16 years (33 female participants). Results showed that polyvictimization was associated with higher trait anxiety as well as greater stress-related right hippocampus activation, and this greater hippocampal activity predicted heightened trait anxiety. Robust mediation analyses revealed that stress-related right hippocampus activation partially mediated the relationship between polyvictimization and trait anxiety. Our results expand upon the existing polyvictimization literature by suggesting a possible neurobiological pathway through which polyvictimization is connected to the etiology of mental illness.

Individual differences in frontal alpha asymmetry moderate the relationship between acute stress responsivity and state and trait anxiety in adolescents

Stress is a risk factor in the development and maintenance of psychopathology, particularly anxiety. Despite theory suggesting differences in stress responsivity may explain heterogeneity in anxiety, findings remain contradictory. This may be due to failure to account for individuals' neurobiological states and outdated methodologic analyses which confound conceptually and biologically distinct stress response pathways. In 145 adolescents, this study examined whether individual differences in neural activation underlying motivational states, indexed by resting frontal alpha asymmetry (FAA) before and after the Trier Social Stress Test (TSST), moderate the relationship between stress responsivity (measured by cortisol) and anxiety. Adolescents with rightward FAA activation (indexed by changes in resting FAA pre-to-post TSST) and high trait anxiety showed blunted cortisol reactivities while those with leftward FAA activation and high state anxiety showed prolonged cortisol recoveries. Our work reveals individual differences in vulnerability to psychosocial stressors and is the first study to show that FAA activation moderates the relationships between anxiety and distinct phases of the stress response in adolescents.

Multi-spatial-scale dynamic interactions between functional sources reveal sex-specific changes in schizophrenia

We introduce an extension of independent component analysis (ICA), called multiscale ICA, and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. Multiscale ICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and sex-common connectivity patterns in schizophrenia. Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (a) within the subcortical domain, (b) between the somatomotor and cerebellum domains, and (c) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial-scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial-scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.

Moving beyond the 'CAP' of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping

Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.

ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability

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.

Functional Magnetic Resonance Imaging Findings in Children and Adolescents With Chronic Kidney Disease: Preliminary Findings

This cross-sectional study provides preliminary findings from one of the first functional brain imaging studies in children with chronic kidney disease (CKD). The sample included 21 children with CKD (ages, 14.4 ± 3.0 y) and 11 healthy controls (ages, 14.5 ± 3.4 y). Using functional magnetic resonance imaging during a visual-spatial working memory task, findings showed that the CKD group and healthy controls invoked similar brain regions for encoding and retrieval phases of the task, but significant group differences were noted in the activation patterns for both components of the task. For the encoding phase, the CKD group showed lower activation in the posterior cingulate, anterior cingulate, precuneus, and middle occipital gyrus than the control group, but more activation in the superior temporal gyrus, middle frontal gyrus, middle temporal gyrus, and the insula. For the retrieval phase, the CKD group showed underactivation for brain systems involving the posterior cingulate, medial frontal gyrus, occipital lobe, and middle temporal gyrus, and greater activation than the healthy controls in the postcentral gyrus. Few group differences were noted with respect to disease severity. These preliminary findings support evidence showing a neurologic basis to the cognitive difficulties evident in pediatric CKD, and lay the foundation for future studies to explore the neural underpinnings for neurocognitive (dys)function in this population.

Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker

BACKGROUND

Dysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues.

METHODS

A subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels.

RESULTS

Multimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%.

CONCLUSIONS

Adolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS-associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.

Cross-paradigm connectivity: reliability, stability, and utility

While functional neuroimaging studies typically focus on a particular paradigm to investigate network connectivity, the human brain appears to possess an intrinsic "trait" architecture that is independent of any given paradigm. We have previously proposed the use of "cross-paradigm connectivity (CPC)" to quantify shared connectivity patterns across multiple paradigms and have demonstrated the utility of such measures in clinical studies. Here, using generalizability theory and connectome fingerprinting, we examined the reliability, stability, and individual identifiability of CPC in a group of highly-sampled healthy traveling subjects who received fMRI scans with a battery of five paradigms across multiple sites and days. Compared with single-paradigm connectivity matrices, the CPC matrices showed higher reliability in connectivity diversity, lower reliability in connectivity strength, higher stability, and higher individual identification accuracy. All of these assessments increased as a function of number of paradigms included in the CPC analysis. In comparisons involving different paradigm combinations and different brain atlases, we observed significantly higher reliability, stability, and identifiability for CPC matrices constructed from task-only data (versus those from both task and rest data), and higher identifiability but lower stability for CPC matrices constructed from the Power atlas (versus those from the AAL atlas). Moreover, we showed that multi-paradigm CPC matrices likely reflect the brain's "trait" structure that cannot be fully achieved from single-paradigm data, even with multiple runs. The present results provide evidence for the feasibility and utility of CPC in the study of functional "trait" networks and offer some methodological implications for future CPC studies.

Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics

BACKGROUND

A number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time.

METHODS

We introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set.

RESULTS

We present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern.

CONCLUSION

Our proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.

Visual cortical plasticity and the risk for psychosis: An interim analysis of the North American Prodrome Longitudinal Study

BACKGROUND

Adolescence/early adulthood coincides with accelerated pruning of cortical synapses and the onset of schizophrenia. Cortical gray matter reduction and dysconnectivity in schizophrenia are hypothesized to result from impaired synaptic plasticity mechanisms, including long-term potentiation (LTP), since deficient LTP may result in too many weak synapses that are then subject to over-pruning. Deficient plasticity has already been observed in schizophrenia. Here, we assessed whether such deficits are present in the psychosis risk syndrome (PRS), particularly those who subsequently convert to full psychosis.

METHODS

An interim analysis was performed on a sub-sample from the NAPLS-3 study, including 46 healthy controls (HC) and 246 PRS participants. All participants performed an LTP-like visual cortical plasticity paradigm involving assessment of visual evoked potentials (VEPs) elicited by vertical and horizontal line gratings before and after high frequency ("tetanizing") visual stimulation with one of the gratings to induce "input-specific" neuroplasticity (i.e., VEP changes specific to the tetanized stimulus). Non-parametric, cluster-based permutation testing was used to identify electrodes and timepoints that demonstrated input-specific plasticity effects.

RESULTS

Input-specific pre-post VEP changes (i.e., increased negative voltage) were found in a single spatio-temporal cluster covering multiple occipital electrodes in a 126-223 ms time window. This plasticity effect was deficient in PRS individuals who subsequently converted to psychosis, relative to PRS non-converters and HC.

CONCLUSIONS

Input-specific LTP-like visual plasticity can be measured from VEPs in adolescents and young adults. Interim analyses suggest that deficient visual cortical plasticity is evident in those PRS individuals at greatest risk for transition to psychosis.

Deficits in auditory predictive coding in individuals with the psychosis risk syndrome: Prediction of conversion to psychosis

The mismatch negativity (MMN) event-related potential (ERP) component is increasingly viewed as a prediction error signal elicited when a deviant sound violates the prediction that a frequent "standard" sound will repeat. Support for this predictive coding framework emerged with the identification of the repetition positivity (RP), a standard stimulus ERP component that increases with standard repetition and is thought to reflect strengthening of the standard's memory trace and associated predictive code. Using electroencephalographic recordings, we examined the RP elicited by repeating standard tones presented during a traditional "constant standard" MMN paradigm in individuals with the psychosis risk syndrome (PRS; n = 579) and healthy controls (HC; n = 241). Clinical follow-up assessments identified PRS participants who converted to a psychotic disorder (n = 77) and PRS nonconverters who were followed for the entire 24-month clinical follow-up period and either remained symptomatic (n = 144) or remitted from the PRS (n = 94). In HC, RP linearly increased from early- to late-appearing standards within local trains of repeating standards (p < .0001), consistent with auditory predictive code/memory trace strengthening. Relative to HC, PRS participants showed a reduced RP across standards (p = .0056). PRS converters showed a relatively small RP deficit for early appearing standards relative to HC (p = .0.0107) and a more prominent deficit for late-appearing standards (p = .0006) relative to both HC and PRS-remitted groups. Moreover, greater RP deficits predicted shorter time to conversion in a subsample of unmedicated PRS individuals (p = .02). Thus, auditory predictive coding/memory trace deficits precede psychosis onset and predict future psychosis risk in PRS individuals. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Abnormally Large Baseline P300 Amplitude Is Associated With Conversion to Psychosis in Clinical High Risk Individuals With a History of Autism: A Pilot Study

Psychosis rates in autism spectrum disorder (ASD) are 5-35% higher than in the general population. The overlap in sensory and attentional processing abnormalities highlights the possibility of related neurobiological substrates. Previous research has shown that several electroencephalography (EEG)-derived event-related potential (ERP) components that are abnormal in schizophrenia, including P300, are also abnormal in individuals at Clinical High Risk (CHR) for psychosis and predict conversion to psychosis. Yet, it is unclear whether P300 is similarly sensitive to psychosis risk in help-seeking CHR individuals with ASD history. In this exploratory study, we leveraged data from the North American Prodrome Longitudinal Study (NAPLS2) to probe for the first time EEG markers of longitudinal psychosis profiles in ASD. Specifically, we investigated the P300 ERP component and its sensitivity to psychosis conversion across CHR groups with (ASD+) and without (ASD-) comorbid ASD. Baseline EEG data were analyzed from 304 CHR patients (14 ASD+; 290 ASD-) from the NAPLS2 cohort who were followed longitudinally over two years. We examined P300 amplitude to infrequent Target (10%; P3b) and Novel distractor (10%; P3a) stimuli from visual and auditory oddball tasks. Whereas P300 amplitude attenuation is typically characteristic of CHR and predictive of conversion to psychosis in non-ASD sample, in our sample, history of ASD moderated this relationship such that, in CHR/ASD+ individuals, enhanced - rather than attenuated - visual P300 (regardless of stimulus type) was associated with psychosis conversion. This pattern was also seen for auditory P3b amplitude to Target stimuli. Though drawn from a small sample of CHR individuals with ASD, these preliminary results point to a paradoxical effect, wherein those with both CHR and ASD history who go on to develop psychosis have a unique pattern of enhanced neural response during attention orienting to both visual and target stimuli. Such a pattern stands out from the usual finding of P300 amplitude reductions predicting psychosis in non-ASD CHR populations and warrants follow up in larger scale, targeted, longitudinal studies of those with ASD at clinical high risk for psychosis.

Neural mechanisms of acute stress and trait anxiety in adolescents

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The Montreal Imaging Stress Task is an effective acute stressor for adolescents.

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Acute stress exposure impacts fMRI measures of brain activation in adolescents.

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Hippocampal deactivation during acute stress is associated with cortisol release.

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Trait anxiety is linked to stress-related hippocampus, VS, and putamen activity.

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Males exhibit greater putamen deactivation during acute stress than females.

Adolescence is a critical period of heightened stress sensitivity and elevated vulnerability for developing mental illness, suggesting a possible association between stress exposure and the etiology of psychiatric disorders. In adults, aberrant neurobiological responses to acute stress relate to anxiety symptoms, yet less is known about the neural stress response in adolescents and how it relates to biological and psychological variables. Here we characterize the neurobiology of stress response in adolescents using multiple modalities, including neuroimaging, subjective stress ratings, heart rate, and cortisol data. We evaluated stress response in adolescents using the Montreal Imaging Stress Task (MIST), an acute psychosocial stressor commonly administered in adult functional magnetic resonance imaging (fMRI) studies but not previously utilized with this population. FMRI data were acquired from 101 adolescents (44 female; 9–16 years) exhibiting varied trait anxiety severity.

The MIST elicited decreased high-frequency heart rate variability and increased heart rate, subjective stress and cortisol. Whole-brain analyses comparing fMRI activity during experimental versus control MIST conditions revealed stress-related activation in regions including the anterior insula, dorsal anterior cingulate cortex, and dorsolateral prefrontal cortex and deactivations in the hippocampus, ventral striatum, and putamen. Region of Interest analyses found that during acute stress (a) hippocampal deactivation corresponded to heightened cortisol release, (b) trait anxiety was associated with increased hippocampal and ventral striatum activation and decreased putamen activity, and (c) males exhibited greater putamen deactivation than females. These results provide novel evidence that the MIST is an effective stressor for adolescents. Associations between the neural acute stress response, other biological factors, and trait anxiety highlight the importance of these neurobiological mechanisms in understanding anxiety disorders.

Progressive reconfiguration of resting-state brain networks as psychosis develops: Preliminary results from the North American Prodrome Longitudinal Study (NAPLS) consortium

Mounting evidence has shown disrupted brain network architecture across the psychosis spectrum. However, whether these changes relate to the development of psychosis is unclear. Here, we used graph theoretical analysis to investigate longitudinal changes in resting-state brain networks in samples of 72 subjects at clinical high risk (including 8 cases who converted to full psychosis) and 48 healthy controls drawn from the North American Prodrome Longitudinal Study (NAPLS) consortium. We observed progressive reduction in global efficiency (P = 0.006) and increase in network diversity (P = 0.001) in converters compared with non-converters and controls. More refined analysis separating nodes into nine key brain networks demonstrated that these alterations were primarily driven by progressively diminished local efficiency in the default-mode network (P = 0.004) and progressively enhanced node diversity across all networks (P < 0.05). The change rates of network efficiency and network diversity were significantly correlated (P = 0.003), suggesting these changes may reflect shared neural mechanisms. In addition, change rates of global efficiency and node diversity were significantly correlated with change rate of cortical thinning in the prefrontal cortex in converters (P < 0.03) and could be predicted by visuospatial memory scores at baseline (P < 0.04). These results provide preliminary evidence for longitudinal reconfiguration of resting-state brain networks during psychosis development and suggest that decreased network efficiency, reflecting an increase in path length between nodes, and increased network diversity, reflecting a decrease in the consistency of functional network organization, may be implicated in the progression to full psychosis.

Reliability of mismatch negativity event-related potentials in a multisite, traveling subjects study

OBJECTIVE

To determine the optimal methods for measuring mismatch negativity (MMN), an auditory event-related potential (ERP), and quantify sources of MMN variance in a multisite setting.

METHODS

Reliability of frequency, duration, and double (frequency + duration) MMN was determined from eight traveling subjects, tested on two occasions at eight laboratory sites. Deviant-specific variance components were estimated for MMN peak amplitude and latency measures using different ERP processing methods. Generalizability (G) coefficients were calculated using two-facet (site and occasion), fully-crossed models and single-facet (occasion) models within each laboratory to assess MMN reliability.

RESULTS

G-coefficients calculated from two-facet models indicated fair (0.4 < G<=0.6) duration MMN reliability at electrode Fz, but poor (G < 0.4) double and frequency MMN reliability. Single-facet G-coefficients averaged across laboratory resulted in improved reliability (G > 0.5). MMN amplitude reliability was greater than latency reliability, and reliability with mastoid referencing significantly outperformed nose-referencing.

CONCLUSIONS

EEG preprocessing methods have an impact on the reliability of MMN amplitude. Within site MMN reliability can be excellent, consistent with prior single site studies.

SIGNIFICANCE

With standardized data collection and ERP processing, MMN can be reliably obtained in multisite studies, providing larger samples sizeswithin rare patient groups.

Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia

OBJECTIVE

Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ).

METHODS

We start with estimating and visualizing links within and among extracted multimodal data features using a Gaussian graphical model (GGM). We then propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality. We use functional MRI (fMRI), diffusion MRI (dMRI), and structural MRI (sMRI) to compute the fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), and gray matter (GM) concentration maps. These three modalities are analyzed using our modularity method.

RESULTS

Our results show missing links that are only captured by the cross-modal information that may play an important role in disconnectivity between the components.

CONCLUSION

We identified multimodal (fALFF, FA and GM) disconnectivity in the default mode network area in patients with SZ, which would not have been detectable in a single modality.

SIGNIFICANCE

The proposed approach provides an important new tool for capturing information that is distributed among multiple imaging modalities.

Stability of mismatch negativity event-related potentials in a multisite study

OBJECTIVES

Mismatch negativity (MMN), an auditory event-related potential sensitive to deviance detection, is smaller in schizophrenia and psychosis risk. In a multisite study, a regression approach to account for effects of site and age (12-35 years) was evaluated alongside the one-year stability of MMN.

METHODS

Stability of frequency, duration, and frequency + duration (double) deviant MMN was assessed in 167 healthy subjects, tested on two occasions, separated by 52 weeks, at one of eight sites. Linear regression models predicting MMN with age and site were validated and used to derive standardized MMN z-scores. Variance components estimated for MMN amplitude and latency measures were used to calculate Generalizability (G) coefficients within each site to assess MMN stability. Trait-like aspects of MMN were captured by averaging across occasions and correlated with subject traits.

RESULTS

Age and site accounted for less than 7% of MMN variance. G-coefficients calculated at electrode Fz were stable (G = 0.63) across deviants and sites for amplitude measured in a fixed window, but not for latency (G = 0.37). Frequency deviant MMN z-scores averaged across tests negatively correlated with averaged global assessment of functioning.

CONCLUSION

MMN amplitude is stable and can be standardized to facilitate longitudinal multisite studies of patients and clinical features.

Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality

The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying components between hemispheres across subjects. SBL is successfully implemented with simulated data with inherent differences in laterality. SBL is then compared with a voxel-wise analysis utilizing structural data from a sample of patients with schizophrenia and controls without schizophrenia. SBL group comparisons identified three distinct temporal regions and one cerebellar region with significantly altered laterality in patients with schizophrenia relative to controls. Previous work highlights reductions in laterality (ie, reduced left gray matter volume) in patients with schizophrenia compared with controls without schizophrenia. Results from this pilot SBL project are the first, to our knowledge, to identify covarying laterality differences within discrete temporal brain regions. The authors argue SBL provides a unique focus to detect covarying laterality differences in patients with schizophrenia, facilitating the discovery of laterality aspects undetected in previous work.

Dentate gyrus volume deficit in schizophrenia

BACKGROUND

Schizophrenia is associated with robust hippocampal volume deficits but subregion volume deficits, their associations with cognition, and contributing genes remain to be determined.

METHODS

Hippocampal formation (HF) subregion volumes were obtained using FreeSurfer 6.0 from individuals with schizophrenia (n = 176, mean age ± s.d. = 39.0 ± 11.5, 132 males) and healthy volunteers (n = 173, mean age ± s.d. = 37.6 ± 11.3, 123 males) with similar mean age, gender, handedness, and race distributions. Relationships between the HF subregion volume with the largest between group difference, neuropsychological performance, and single-nucleotide polymorphisms were assessed.

RESULTS

This study found a significant group by region interaction on hippocampal subregion volumes. Compared to healthy volunteers, individuals with schizophrenia had significantly smaller dentate gyrus (DG) (Cohen's d = -0.57), Cornu Ammonis (CA) 4, molecular layer of the hippocampus, hippocampal tail, and CA 1 volumes, when statistically controlling for intracranial volume; DG (d = -0.43) and CA 4 volumes remained significantly smaller when statistically controlling for mean hippocampal volume. DG volume showed the largest between group difference and significant positive associations with visual memory and speed of processing in the overall sample. Genome-wide association analysis with DG volume as the quantitative phenotype identified rs56055643 (β = 10.8, p < 5 × 10-8, 95% CI 7.0-14.5) on chromosome 3 in high linkage disequilibrium with MOBP. Gene-based analyses identified associations between SLC25A38 and RPSA and DG volume.

CONCLUSIONS

This study suggests that DG dysfunction is fundamentally involved in schizophrenia pathophysiology, that it may contribute to cognitive abnormalities in schizophrenia, and that underlying biological mechanisms may involve contributions from MOBP, SLC25A38, and RPSA.

Toward Leveraging Human Connectomic Data in Large Consortia: Generalizability of fMRI-Based Brain Graphs Across Sites, Sessions, and Paradigms

While graph theoretical modeling has dramatically advanced our understanding of complex brain systems, the feasibility of aggregating connectomic data in large imaging consortia remains unclear. Here, using a battery of cognitive, emotional and resting fMRI paradigms, we investigated the generalizability of functional connectomic measures across sites and sessions. Our results revealed overall fair to excellent reliability for a majority of measures during both rest and tasks, in particular for those quantifying connectivity strength, network segregation and network integration. Processing schemes such as node definition and global signal regression (GSR) significantly affected resulting reliability, with higher reliability detected for the Power atlas (vs. AAL atlas) and data without GSR. While network diagnostics for default-mode and sensori-motor systems were consistently reliable independently of paradigm, those for higher-order cognitive systems were reliable predominantly when challenged by task. In addition, based on our present sample and after accounting for observed reliability, satisfactory statistical power can be achieved in multisite research with sample size of approximately 250 when the effect size is moderate or larger. Our findings provide empirical evidence for the generalizability of brain functional graphs in large consortia, and encourage the aggregation of connectomic measures using multisite and multisession data.

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.

Weighted average of shared trajectory: A new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time

BACKGROUND

Dynamic functional network connectivity (dFNC) of the brain has attracted considerable attention recently. Many approaches have been suggested to study dFNC with sliding window Pearson correlation (SWPC) being the most well-known. SWPC needs a relatively large sample size to reach a robust estimation but using large window sizes prevents us to detect rapid changes in dFNC.

NEW METHOD

Here we first calculate the gradients of each time series pair and use the magnitude of these gradients to calculate weighted average of shared trajectory (WAST) as a new estimator for dFNC.

RESULTS

Using WAST to compare healthy control and schizophrenia patients using a large dataset, we show disconnectivity between different regions associated with schizophrenia. In addition, WAST results reveals patients with schizophrenia stay longer in a connectivity state with negative connectivity between motor and sensory regions than do healthy controls.

COMPARISON WITH EXISTING METHODS

We compare WAST with SWPC and multiplication of temporal derivatives (MTD) using different simulation scenarios. We show that WAST enables us to detect very rapid changes in dFNC (undetected by SWPC) while MTD performance is generally lower.

CONCLUSIONS

As large window sizes are unable to detect short states, using shorter window size is desirable if the estimator is robust enough. We provide evidence that WAST requires fewer samples (compared to SWPC) to reach a robust estimation. As a result, we were able to identify rapidly varying dFNC patterns undetected by SWPC while still being able to robustly estimate slower dFNC patterns.

Auditory event-related potentials and associations with sensory patterns in children with autism spectrum disorder, developmental delay, and typical development

Atypical sensory response patterns are common in children with autism and developmental delay. Expanding on previous work, this observational electroencephalogram study assessed auditory event-related potentials and their associations with clinically evaluated sensory response patterns in children with autism spectrum disorder (n = 28), developmental delay (n = 17), and typical development (n = 39). Attention-orienting P3a responses were attenuated in autism spectrum disorder relative to both developmental delay and typical development, but early sensory N2 responses were attenuated in both autism spectrum disorder and developmental delay relative to typical development. Attenuated event-related potentials involving N2 or P3a components, or a P1 × N2 interaction, were related to more severe hyporesponsive or sensory-seeking response patterns across children with autism spectrum disorder and developmental delay. Thus, although attentional disruptions may be unique to autism spectrum disorder, sensory disruptions appear across developmental delay and are associated with atypical sensory behaviors.

Association Between P300 Responses to Auditory Oddball Stimuli and Clinical Outcomes in the Psychosis Risk Syndrome

IMPORTANCE

In most patients, a prodromal period precedes the onset of schizophrenia. Although clinical criteria for identifying the psychosis risk syndrome (PRS) show promising predictive validity, assessment of neurophysiologic abnormalities in at-risk individuals may improve clinical prediction and clarify the pathogenesis of schizophrenia.

OBJECTIVE

To determine whether P300 event-related potential amplitude, which is deficient in schizophrenia, is reduced in the PRS and associated with clinical outcomes.

DESIGN, SETTING, AND PARTICIPANTS

Auditory P300 data were collected as part of the multisite, case-control North American Prodrome Longitudinal Study (NAPLS-2) at 8 university-based outpatient programs. Participants included 552 individuals meeting PRS criteria and 236 healthy controls with P300 data. Auditory P300 data of participants at risk who converted to psychosis (n = 73) were compared with those of nonconverters who were followed up for 24 months and continued to be symptomatic (n = 135) or remitted from the PRS (n = 90). Data were collected from May 27, 2009, to September 17, 2014, and were analyzed from December 3, 2015, to May 1, 2019.

MAIN OUTCOMES AND MEASURES

Baseline electroencephalography was recorded during an auditory oddball task. Two P300 subcomponents were measured: P3b, elicited by infrequent target stimuli, and P3a, elicited by infrequent nontarget novel stimuli.

RESULTS

This study included 788 participants. The PRS group (n = 552) included 236 females (42.8%) (mean [SD] age, 19.21 [4.38] years), and the healthy control group (n = 236) included 111 females (47.0%) (mean [SD] age, 20.44 [4.73] years). Target P3b and novelty P3a amplitudes were reduced in at-risk individuals vs healthy controls (d = 0.37). Target P3b, but not novelty P3a, was significantly reduced in psychosis converters vs nonconverters (d = 0.26), and smaller target P3b amplitude was associated with a shorter time to psychosis onset in at-risk individuals (hazard ratio, 1.45; 95% CI, 1.04-2.00; P = .03). Participants with the PRS who remitted had baseline target P3b amplitudes that were similar to those of healthy controls and greater than those of converters (d = 0.51) and at-risk individuals who remained symptomatic (d = 0.41).

CONCLUSIONS AND RELEVANCE

In this study, deficits in P300 amplitude appeared to precede psychosis onset. Target P3b amplitudes, in particular, may be sensitive to clinical outcomes in the PRS, including both conversion to psychosis and clinical remission. Auditory target P3b amplitude shows promise as a putative prognostic biomarker of clinical outcome in the PRS.

Prenatal Nicotine Exposure Disrupts Infant Neural Markers of Orienting

Introduction

Prenatal nicotine exposure (PNE) from maternal cigarette smoking is linked to developmental deficits, including impaired auditory processing, language, generalized intelligence, attention, and sleep. Fetal brain undergoes massive growth, organization, and connectivity during gestation, making it particularly vulnerable to neurotoxic insult. Nicotine binds to nicotinic acetylcholine receptors, which are extensively involved in growth, connectivity, and function of developing neural circuitry and neurotransmitter systems. Thus, PNE may have long-term impact on neurobehavioral development. The purpose of this study was to compare the auditory K-complex, an event-related potential reflective of auditory gating, sleep preservation and memory consolidation during sleep, in infants with and without PNE and to relate these neural correlates to neurobehavioral development.

Methods

We compared brain responses to an auditory paired-click paradigm in 3- to 5-month-old infants during Stage 2 sleep, when the K-complex is best observed. We measured component amplitude and delta activity during the K-complex.

Results

Infants with PNE demonstrated significantly smaller amplitude of the N550 component and reduced delta-band power within elicited K-complexes compared to nonexposed infants and also were less likely to orient with a head turn to a novel auditory stimulus (bell ring) when awake.

Conclusions

PNE may impair auditory sensory gating, which may contribute to disrupted sleep and to reduced auditory discrimination and learning, attention re-orienting, and/or arousal during wakefulness reported in other studies.

Implications

Links between PNE and reduced K-complex amplitude and delta power may represent altered cholinergic and GABAergic synaptic programming and possibly reflect early neural bases for PNE-linked disruptions in sleep quality and auditory processing. These may pose significant disadvantage for language acquisition, attention, and social interaction necessary for academic and social success.

Altered Brain Activation During Memory Retrieval Precedes and Predicts Conversion to Psychosis in Individuals at Clinical High Risk

Memory deficits are a hallmark of psychotic disorders such as schizophrenia. However, whether the neural dysfunction underlying these deficits is present before the onset of illness and potentially predicts conversion to psychosis is unclear. In this study, we investigated brain functional alterations during memory processing in a sample of 155 individuals at clinical high risk (including 18 subjects who later converted to full psychosis) and 108 healthy controls drawn from the second phase of the North American Prodrome Longitudinal Study (NAPLS-2). All participants underwent functional magnetic resonance imaging with a paired-associate memory paradigm at the point of recruitment and were clinically followed up for approximately 2 years. We found that at baseline, subjects at high risk showed significantly higher activation during memory retrieval in the prefrontal, parietal, and bilateral temporal cortices (PFWE < .035). This effect was more pronounced in converters than nonconverters and was particularly manifested in unmedicated subjects (P < .001). The hyperactivation was significantly correlated with retrieval reaction time during scan in converters (P = .009) but not in nonconverters and controls, suggesting an exaggerated retrieval effort. These findings suggest that hyperactivation during memory retrieval may mark processes associated with conversion to psychosis, and such measures have potential as biomarkers for psychosis prediction.

Salience-Default Mode Functional Network Connectivity Linked to Positive and Negative Symptoms of Schizophrenia

Schizophrenia is a complex, debilitating mental disorder characterized by wide-ranging symptoms including delusions, hallucinations (so-called positive symptoms), and impaired motor and speech/language production (so-called negative symptoms). Salience-monitoring theorists propose that abnormal functional communication between the salience network (SN) and default mode network (DMN) begets positive and negative symptoms of schizophrenia, yet prior studies have predominately reported links between disrupted SN/DMN functional communication and positive symptoms. It remains unclear whether disrupted SN/DMN functional communication explains (1) solely positive symptoms or (2) both positive and negative symptoms of schizophrenia. To address this question, we incorporate time-lag-shifted functional network connectivity (FNC) analyses that explored coherence of the resting-state functional magnetic resonance imaging signal of 3 networks (anterior DMN, posterior DMN, and SN) with fixed time lags introduced between network time series (1 TR = 2 s; 2 TR = 4 s). Multivariate linear regression analysis revealed that severity of disordered thought and attentional deficits were negatively associated with 2 TR-shifted FNC between anterior DMN and posterior DMN. Meanwhile, severity of flat affect and bizarre behavior were positively associated with 1 TR-shifted FNC between anterior DMN and SN. These results provide support favoring the hypothesis that lagged SN/DMN functional communication is associated with both positive and negative symptoms of schizophrenia.

Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called "parallel group ICA+ICA" that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.

A method for building a genome-connectome bipartite graph model

It has been widely shown that genomic factors influence both risk for schizophrenia and variation in functional brain connectivity. Moreover, schizophrenia is characterized by disrupted brain connectivity. In this work, we proposed a genome-connectome bipartite graph model to perform imaging genomic analysis. Functional network connectivity (FNC) was estimated after decomposing resting state functional magnetic resonance imaging data from both healthy controls (HC) and patients with schizophrenia (SZ) into spatial brain components using group independent component analysis (G-ICA). Then 83 FNC connections showing a group difference (HC vs SZ) were selected as fMRI nodes, and eighty-one schizophrenia-related single nucleotide polymorphisms (SNPs) were selected as genetic nodes respectively in the bipartite graph. Edges connecting pairs of genetic and fMRI nodes were defined based on the SNP-FNC associations across subjects evaluated by a general linear model. Results show that some SNP nodes in the bipartite graph have a high degree implying they are influential in modulating brain connectivity and may be more strongly associated with the risk of schizophrenia than other SNPs. A bi-clustering analysis detected a cluster with 15 SNPs interacting with 38 FNC connections, most of which were within or between somato-motor and visual brain areas. This suggests that the activity of these brain regions may be related to common SNPs and provides insights into the pathology of schizophrenia. The findings suggest that the SNP-FNC bipartite graph approach is a novel model to investigate genetic influences on functional brain connectivity in mental illness.

Altered Domain Functional Network Connectivity Strength and Randomness in Schizophrenia

Functional connectivity is one of the most widely used tools for investigating brain changes due to schizophrenia. Previous studies have identified abnormal functional connectivity in schizophrenia patients at the resting state brain network level. This study tests the existence of functional connectivity effects at whole brain and domain levels. Domain level refers to the integration of data from several brain networks grouped by their functional relationship. Data integration provides more consistent and accurate information compared to an individual brain network. This work considers two domain level measures: functional connectivity strength and randomness. The first measure is simply an average of connectivities within the domain. The second measure assesses the unpredictability and lack of pattern of functional connectivity within the domain. Domains with less random connectivity have higher chance of exhibiting a biologically meaningful connectivity pattern. Consistent with prior observations, individuals with schizophrenia showed aberrant domain connectivity strength between subcortical, cerebellar, and sensorial brain areas. Compared to healthy volunteers, functional connectivity between cognitive and default mode domains showed less randomness, while connectivity between default mode-sensorial areas showed more randomness in schizophrenia patients. These differences in connectivity patterns suggest deleterious rewiring trade-offs among important brain networks.

A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study

Multimodal, imaging-genomics techniques offer a platform for understanding genetic influences on brain abnormalities in psychiatric disorders. Such approaches utilize the information available from both imaging and genomics data and identify their association. Particularly for complex disorders such as schizophrenia, the relationship between imaging and genomic features may be better understood by incorporating additional information provided by advanced multimodal modeling. In this study, we propose a novel framework to combine features corresponding to functional magnetic resonance imaging (functional) and single nucleotide polymorphism (SNP) data from 61 schizophrenia (SZ) patients and 87 healthy controls (HC). In particular, the features for the functional and genetic modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) features and the SNP data, respectively. The dFNC features are estimated from component time-courses, obtained using group independent component analysis (ICA), by computing sliding-window functional network connectivity, and then estimating subject specific states from this dFNC data using a k-means clustering approach. For each subject, both the functional (dFNC states) and SNP data are selected as features for a parallel ICA (pICA) based imaging-genomic framework. This analysis identified a significant association between a SNP component (defined by large clusters of functionally related SNPs statistically correlated with phenotype components) and time-varying or dFNC component (defined by clusters of related connectivity links among distant brain regions distributed across discrete dynamic states, and statistically correlated with genomic components) in schizophrenia. Importantly, the polygenetic risk score (PRS) for SZ (computed as a linearly weighted sum of the genotype profiles with weights derived from the odds ratios of the psychiatric genomics consortium (PGC)) was negatively correlated with the significant dFNC component, which were mostly present within a state that exhibited a lower occupancy rate in individuals with SZ compared with HC, hence identifying a potential dFNC imaging biomarker for schizophrenia. Taken together, the current findings provide preliminary evidence for a link between dFNC measures and genetic risk, suggesting the application of dFNC patterns as biomarkers in imaging genetic association study.

Acute stress modifies oscillatory indices of affective processing: Insight on the pathophysiology of schizophrenia spectrum disorders

OBJECTIVE

The current study evaluated the differential impact of acute psychosocial stress exposure on oscillatory correlates of affective processing in control participants and patients with schizophrenia spectrum disorders (SCZ) to elucidate the stress-mediated pathway to psychopathology.

METHODS

EEG was recorded while 21 control participants and 21 patients with SCZ performed emotional framing tasks (assessing a key aspect of emotion regulation (ER)) before and after a laboratory stress challenge (Trier Social Stress Test). EEG spectral perturbations evoked in response to neutral and aversive stimuli (presented with positive or negative contextual cues) were extracted in theta (4-8 Hz) and beta (12-30 Hz) frequencies.

RESULTS

Patients demonstrated aberrant theta and beta oscillatory activity, with impaired frontal theta-mediated framing and beta-derived motivated attention processes relative to controls. Following stress exposure, controls exhibited impaired frontal theta-mediated emotional framing, similar to the oscillatory profile observed in patients before stress.

CONCLUSIONS

The acute stress-induced oscillatory changes observed in controls were persistently present in patients, indicating an inefficiency of fronto-limbic adaptation to stress exposure.

SIGNIFICANCE

Results provide novel insight on the electrophysiological correlates of arousal and affect regulation, which are core homogeneous symptom dimensions shared across neuropsychiatric disorders, and shed light on putative mechanisms in the translation of stress into psychopathology.

Polygenic risk score, genome-wide association, and gene set analyses of cognitive domain deficits in schizophrenia

This study assessed genetic contributions to six cognitive domains, identified by the MATRICS Cognitive Consensus Battery as relevant for schizophrenia, cognition-enhancing, clinical trials. Psychiatric Genomics Consortium Schizophrenia polygenic risk scores showed significant negative correlations with each cognitive domain. Genome-wide association analyses identified loci associated with attention/vigilance (rs830786 within HNF4G), verbal memory (rs67017972 near NDUFS4), and reasoning/problem solving (rs76872642 within HDAC9). Gene set analysis identified unique and shared genes across cognitive domains. These findings suggest involvement of common and unique mechanisms across cognitive domains and may contribute to the discovery of new therapeutic targets to treat cognitive deficits in schizophrenia.