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Long COVID (LongC) is associated with a myriad of symptoms including cognitive impairment. We reported at the beginning of the COVID-19 pandemic that neuronal-enriched or L1CAM+ extracellular vesicles (nEVs) from people with LongC contained proteins associated with Alzheimer’s disease (AD). Since that time, a subset of people with prior COVID infection continue to report neurological problems more than three months after infection. Blood markers to better characterize LongC are elusive. To further identify neuronal proteins associated with LongC, we maximized the number of nEVs isolated from plasma by developing a hybrid EV Microfluidic Affinity Purification (EV-MAP) technique. We isolated nEVs from people with LongC and neurological complaints, AD, and HIV infection with mild cognitive impairment. Using the OLINK platform that assesses 384 neurological proteins, we identified 11 significant proteins increased in LongC and 2 decreased (BST1, GGT1). Fourteen proteins were increased in AD and forty proteins associated with HIV cognitive impairment were elevated with one decreased (IVD). One common protein (BST1) was decreased in LongC and increased in HIV. Six proteins (MIF, ENO1, MESD, NUDT5, TNFSF14 and FYB1) were expressed in both LongC and AD and no proteins were common to HIV and AD. This study begins to identify differences and similarities in the neuronal response to LongC versus AD and HIV infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) persists throughout the world with over 65 million registered cases of survivors with post-COVID-19 sequelae, also known as LongCOVID-19 (LongC). LongC survivors exhibit various symptoms that span multiple organ systems, including the nervous system. To search for neurological markers of LongC, we investigated the soluble biomolecules present in the plasma and the proteins associated with plasma neuronal-enriched extracellular vesicles (nEVs) in 33 LongC patients with neurological impairment (nLongC), 12 COVID-19 survivors without any LongC symptoms (Cov), and 28 pre-COVID-19 healthy controls (HC). COVID-19 positive participants were infected between 2020 and 2022, not hospitalized, and were vaccinated or unvaccinated before infection. IL-1β was significantly increased in both nLongC and Cov and IL-8 was elevated in only nLongC. Both brain-derived neurotrophic factor and cortisol were significantly elevated in nLongC and Cov compared to HC. nEVs from people with nLongC had significantly elevated protein markers of neuronal dysfunction, including amyloid beta 42, pTau181 and TDP-43. This study shows chronic peripheral inflammation with increased stress after COVID-19 infection. Additionally, differentially expressed nEV neurodegenerative proteins were identified in people recovering from COVID-19 regardless of persistent symptoms.

Background: Individual variation in brain aging trajectories is linked with several physical and mental health outcomes. Greater stress levels, worry, and rumination correspond with advanced brain age, while other individual characteristics, like mindfulness, may be protective of brain health. Multiple lines of evidence point to advanced brain aging in schizophrenia (i.e., neural age estimate > chronological age). Whether psychological dimensions such as mindfulness, rumination, and perceived stress contribute to brain aging in schizophrenia is unknown. Methods: We estimated brain age from high-resolution anatomical scans in 54 healthy controls (HC) and 52 individuals with schizophrenia (SZ) and computed the brain predicted age difference (BrainAGE-diff), i.e., the delta between estimated brain age and chronological age. Emotional well-being summary scores were empirically derived to reflect individual differences in trait mindfulness, rumination, and perceived stress. Core analyses evaluated relationships between BrainAGE-diff and emotional well-being, testing for slopes differences across groups. Results: HC showed higher emotional well-being (greater mindfulness and less rumination/stress), relative to SZ. We observed a significant group difference in the relationship between BrainAge-diff and emotional well-being, explained by BrainAGE-diff negatively correlating with emotional well-being scores in SZ, and not in HC. That is, SZ with younger appearing brains (predicted age

BackgroundCerebellar structural and functional abnormalities underlie widespread deficits in clinical, cognitive, and motor functioning that are observed in schizophrenia. Consequently, the cerebellum is a promising target for novel schizophrenia treatments. Here we conducted an updated systematic review examining the literature on cerebellar stimulation efficacy and tolerability for mitigating symptoms of schizophrenia. We discuss the purported mechanisms of cerebellar stimulation, current methods for implementing stimulation, and future directions of cerebellar stimulation for intervention development with this population.MethodsTwo independent authors identified 20 published studies (7 randomized controlled trials, 7 open-label studies, 1 pilot study, 4 case reports, 1 preclinical study) that describe the effects of cerebellar circuitry modulation in patients with schizophrenia or animal models of psychosis. Published studies up to October 11, 2022 were identified from a search within PubMed, Scopus, and PsycInfo.ResultsMost studies stimulating the cerebellum used transcranial magnetic stimulation or transcranial direct-current stimulation, specifically targeting the cerebellar vermis/midline. Accounting for levels of methodological rigor across studies, these studies detected post-cerebellar modulation in schizophrenia as indicated by the alleviation of certain clinical symptoms (mainly negative and depressive symptoms), as well as increased frontal-cerebellar connectivity and augmentation of canonical neuro-oscillations known to be abnormal in schizophrenia. In contrast to a prior review, we did not find consistent evidence for cognitive improvements following cerebellar modulation stimulation. Modern cerebellar stimulation methods appear tolerable for individuals with schizophrenia, with only mild and temporary side effects.ConclusionCerebellar stimulation is a promising intervention for individuals with schizophrenia that may be more relevant to some symptom domains than others. Initial results highlight the need for continued research using more methodologically rigorous designs, such as additional longitudinal and randomized controlled trials.Systematic review registration[https://www.crd.york.ac.uk/prospero/], identifier [CRD42022346667].

Motivation and pleasure deficits are common in schizophrenia, strongly linked with poorer functioning, and may reflect underlying alterations in brain functions governing reward processing and goal pursuit. While there is extensive research examining cognitive and reward mechanisms related to these deficits in schizophrenia, less attention has been paid to psychological characteristics that contribute to resilience against, or risk for, motivation and pleasure impairment. For example, psychological tendencies involving positive future expectancies (e.g., optimism) and effective affect management (e.g., reappraisal, mindfulness) are associated with aspects of reward anticipation and evaluation that optimally guide goal-directed behavior. Conversely, maladaptive thinking patterns (e.g., defeatist performance beliefs, asocial beliefs) and tendencies that amplify negative cognitions (e.g., rumination), may divert cognitive resources away from goal pursuit or reduce willingness to exert effort. Additionally, aspects of sociality, including the propensity to experience social connection as positive reinforcement may be particularly relevant for pursuing social goals. In the current review, we discuss the roles of several psychological characteristics with respect to motivation and pleasure in schizophrenia. We argue that individual variation in these psychological dimensions is relevant to the study of motivation and reward processing in schizophrenia, including interactions between these psychological dimensions and more well-characterized cognitive and reward processing contributors to motivation. We close by emphasizing the value of considering a broad set of modulating factors when studying motivation and pleasure functions in schizophrenia.

Deep learning techniques have been applied to electroencephalogram (EEG) signals, with promising applications in the field of psychiatry. Schizophrenia is one of the most disabling neuropsychiatric disorders, often characterized by the presence of auditory hallucinations. Auditory processing impairments have been studied using EEG-derived event-related potentials and have been associated with clinical symptoms and cognitive dysfunction in schizophrenia. Due to consistent changes in the amplitude of ERP components, such as the auditory N100, some have been proposed as biomarkers of schizophrenia. In this paper, we examine altered patterns in electrical brain activity during auditory processing and their potential to discriminate schizophrenia and healthy subjects. Using deep convolutional neural networks, we propose an architecture to perform the classification based on multi-channels auditory-related EEG single-trials, recorded during a passive listening task. We analyzed the effect of the number of electrodes used, as well as the laterality and distribution of the electrical activity over the scalp. Results show that the proposed model is able to classify schizophrenia and healthy subjects with an average accuracy of 78% using only 5 midline channels (Fz, FCz, Cz, CPz, and Pz). The present study shows the potential of deep learning methods in the study of impaired auditory processing in schizophrenia with implications for diagnosis. The proposed design can provide a base model for future developments in schizophrenia research.

The hallmark of resting EEG spectra are distinct rhythms emerging from a broadband, aperiodic background. This aperiodic neural signature accounts for most of total EEG power, although its significance and relation to functional neuroanatomy remains obscure. We hypothesized that aperiodic EEG reflects a significant metabolic expenditure and therefore might be associated with the default mode network while at rest. During eyes-open, resting-state recordings of simultaneous EEG-fMRI, we find that aperiodic and periodic components of EEG power are only minimally associated with activity in the default mode network. However, a whole-brain analysis identifies increases in aperiodic power correlated with hemodynamic activity in an auditory-salience-cerebellar network, and decreases in aperiodic power are correlated with hemodynamic activity in prefrontal regions. Desynchronization in residual alpha and beta power is associated with visual and sensorimotor hemodynamic activity, respectively. These findings suggest that resting-state EEG signals acquired in an fMRI scanner reflect a balance of top-down and bottom-up stimulus processing, even in the absence of an explicit task.

BackgroundA 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.MethodsWe 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.ResultsWe 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.ConclusionOur 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.

Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized.Method: Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects.Results: We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity.Conclusions: To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.

Long-term potentiation (LTP) is a form of experience-dependent synaptic plasticity mediated by glutamatergic transmission at N-methyl-D-aspartate receptors (NMDARs). Impaired neuroplasticity has been implicated in the pathophysiology of schizophrenia, possibly due to underlying NMDAR hypofunction. Analogous to the high frequency electrical stimulation used to induce LTP in vitro and in vivo in animal models, repeated high frequency presentation of a visual stimulus in humans in vivo has been shown to induce enduring LTP-like neuroplastic changes in electroencephalography (EEG)-based visual evoked potentials (VEPs) elicited by the stimulus. Using this LTP-like visual plasticity paradigm, we previously showed that visual high-frequency stimulation (VHFS) induced sustained changes in VEP amplitudes in healthy controls, but not in patients with schizophrenia. Here, we extend this prior work by re-analyzing the EEG data underlying the VEPs, focusing on neuroplastic changes in stimulus-evoked EEG oscillatory activity following VHFS. EEG data were recorded from 19 patients with schizophrenia and 21 healthy controls during the visual plasticity paradigm. Event-related EEG oscillations (total power, intertrial phase coherence; ITC) elicited by a standard black and white checkerboard stimulus (~0.83 Hz, several 2-min blocks) were assessed before and after exposure to VHFS with the same stimulus (~8.9 Hz, 2 min). A cluster-based permutation testing approach was applied to time-frequency data to examine LTP-like plasticity effects following VHFS. VHFS enhanced theta band total power and ITC in healthy controls but not in patients with schizophrenia. The magnitude and phase synchrony of theta oscillations in response to a visual stimulus were enhanced for at least 22 min following VHFS, a frequency domain manifestation of LTP-like visual cortical plasticity. These theta oscillation changes are deficient in patients with schizophrenia, consistent with hypothesized NMDA receptor dysfunction.

Although both resting and task-induced functional connectivity (FC) have been used to characterize the human brain and cognitive abilities, the potential of task-induced FCs in individualized prediction for out-of-scanner cognitive traits remains largely unexplored. A recent study Greene et al. (2018) predicted the fluid intelligence scores using FCs derived from rest and multiple task conditions, suggesting that task-induced brain state manipulation improved prediction of individual traits. Here, using a large dataset incorporating fMRI data from rest and 7 distinct task conditions, we replicated the original study by employing a different machine learning approach, and applying the method to predict two reading comprehension-related cognitive measures. Consistent with their findings, we found that task-based machine learning models often outperformed rest-based models. We also observed that combining multi-task fMRI improved prediction performance, yet, integrating the more fMRI conditions can not necessarily ensure better predictions. Compared with rest, the predictive FCs derived from language and working memory tasks were highlighted with more predictive power in predominantly default mode and frontoparietal networks. Moreover, prediction models demonstrated high stability to be generalizable across distinct cognitive states. Together, this replication study highlights the benefit of using task-based FCs to reveal brain-behavior relationships, which may confer more predictive power and promote the detection of individual differences of connectivity patterns underlying relevant cognitive traits, providing strong evidence for the validity and robustness of the original findings.

Background: Reward processing abnormalities may underlie characteristic pleasure and motivational impairments in schizophrenia. Some neural measures of reward processing show age-related modulation, highlighting the importance of considering age effects on reward sensitivity. We compared event-related potentials (ERPs) reflecting reward anticipation (stimulus-preceding negativity, SPN) and evaluation (reward positivity, RewP; late positive potential, LPP) across individuals with schizophrenia (SZ) and healthy controls (HC), with an emphasis on examining the effects of chronological age, brain age (i.e., predicted age based on neurobiological measures), and illness phase. Methods: Subjects underwent EEG while completing a slot-machine task for which rewards were not dependent on performance accuracy, speed, or response preparation. Slot-machine task EEG responses were compared between 54 SZ and 54 HC individuals, ages 19 to 65. Reward-related ERPs were analyzed with respect to chronological age, categorically-defined illness phase (early; ESZ versus chronic schizophrenia; CSZ), and were used to model brain age relative to chronological age. Results: Illness phase-focused analyses indicated there were no group differences in average SPN or RewP amplitudes. However, a group × reward outcome interaction revealed that ESZ differed from HC in later outcome processing, reflected by greater LPP responses following loss versus reward (a reversal of the HC pattern). While brain age estimates did not differ among groups, depressive symptoms in SZ were associated with older brain age estimates while controlling for negative symptoms. Conclusions: ESZ and CSZ did not differ from HC in reward anticipation or early outcome processing during a cognitively undemanding reward task, highlighting areas of preserved functioning. However, ESZ showed altered later reward outcome evaluation, pointing to selective reward deficits during the early illness phase of schizophrenia. Further, an association between ERP-derived brain age and depressive symptoms in SZ extends prior findings linking depression with reward-related ERP blunting. Taken together, both illness phase and age may impact reward processing among SZ, and brain aging may offer a promising, novel marker of reward dysfunction that warrants further study.

Cognitive impairment is a feature of many psychiatric diseases. Here the authors aimed to identify multimodal neuromarkers that can be used to quantify and predict cognitive performance in individuals with schizophrenia using three different features of MRI and three independent cohorts.

Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic states across several resting state experiments. However, estimation of states and their temporal dynamicity still suffers from noisy and imperfect estimations. In regular dFNC implementations, states are estimated by comparing connectivity patterns through the data without considering time, in other words only zero order changes are examined. In this work we propose a method that includes first order variations of dFNC in the searching scheme of dynamic connectivity patterns. Our approach, referred to as temporal variation of functional network connectivity (tvFNC), estimates the derivative of dFNC, and then searches for reoccurring patterns of concurrent dFNC states and their derivatives. The tvFNC method is first validated using a simulated dataset and then applied to a resting-state fMRI sample including healthy controls (HC) and schizophrenia (SZ) patients and compared to the standard dFNC approach. Our dynamic approach reveals extra patterns in the connectivity derivatives complementing the already reported state patterns. State derivatives consist of additional information about increment and decrement of connectivity among brain networks not observed by the original dFNC method. The tvFNC shows more sensitivity than regular dFNC by uncovering additional FNC differences between the HC and SZ groups in each state. In summary, the tvFNC method provides a new and enhanced approach to examine time-varying functional connectivity.

Background: Auditory steady-state response (ASSR) paradigms have consistently demonstrated gamma band abnormalities in schizophrenia at a 40-Hz driving frequency with both electroencephalography (EEG) and magnetoencephalography (MEG). Various time-frequency measures have been used to assess the 40-Hz ASSR, including evoked power, single trial total power, phase-locking factor (PLF), and phase-locking angle (PLA). While both EEG and MEG studies have shown power and PLF ASSR measures to exhibit excellent test-retest reliability in healthy adults, the reliability of these measures in patients with schizophrenia has not been determined. Methods: ASSRs were obtained by recording EEG data during presentation of repeated 20-Hz, 30-Hz and 40-Hz auditory click trains from nine schizophrenia patients (SZ) and nine healthy controls (HC) tested on two occasions. Similar ASSR data were collected from a separate group of 30 HC on two to three test occasions. A subset of these HC subjects had EEG recordings during two tasks, passively listening and actively attending to click train stimuli. Evoked power, total power, PLF, and PLA were calculated following Morlet wavelet time-frequency decomposition of EEG data and test-retest generalizability (G) coefficients were calculated for each ASSR condition, time-frequency measure, and subject group. Results: G-coefficients ranged from good to excellent (> 0.6) for most 40-Hz time-frequency measures and participant groups, whereas 20-Hz G-coefficients were much more variable. Importantly, test-retest reliability was excellent for the various 40-Hz ASSR measures in SZ, similar to reliabilities in HC. Active attention to click train stimuli modestly reduced G-coefficients in HC relative to the passive listening condition. Discussion: The excellent test-retest reliability of 40-Hz ASSR measures replicates previous EEG and MEG studies. PLA, a relatively new time-frequency measure, was shown for the first time to have excellent reliability, comparable to power and PLF measures. Excellent reliability of 40 Hz ASSR measures in SZ supports their use in clinical trials and longitudinal observational studies. Keywords: Auditory steady-state response (ASSR), Gamma, Electroencephalography (EEG), Schizophrenia, Reliability, Generalizability, Oscillations, Power, Phase angle

Background: The N400 event-related potential (ERP) is triggered by meaningful stimuli that are incongruous, or unmatched, with their semantic context. Functional magnetic resonance imaging (fMRI) studies have identified brain regions activated by semantic incongruity, but their precise links to the N400 ERP are unclear. In schizophrenia (SZ), N400 amplitude reduction is thought to reflect overly broad associations in semantic networks, but the abnormalities in brain networks underlying deficient N400 remain unknown. We utilized joint independent component analysis (JICA) to link temporal patterns in ERPs to neuroanatomical patterns from fMRI and investigate relationships between N400 amplitude and neuroanatomical activation in SZ patients and healthy controls (HC). Methods: SZ patients (n = 24) and HC participants (n = 25) performed a picture-word matching task, in which words were either matched (APPLE→apple) by preceding pictures, or were unmatched by semantically related (in-category; IC, APPLE→lemon) or unrelated (out of category; OC, APPLE→cow) pictures, in separate ERP and fMRI sessions. A JICA “data fusion” analysis was conducted to identify the fMRI brain regions specifically associated with the ERP N400 component. SZ and HC loading weights were compared and correlations with clinical symptoms were assessed. Results: JICA identified an ERP-fMRI “fused” component that captured the N400, with loading weights that were reduced in SZ. The JICA map for the IC condition showed peaks of activation in the cingulate, precuneus, bilateral temporal poles and cerebellum, whereas the JICA map from the OC condition was linked primarily to visual cortical activation and the left temporal pole. Among SZ patients, fMRI activity from the IC condition was inversely correlated with unusual thought content. Conclusions: The neural networks associated with the N400 ERP response to semantic violations depends on conceptual relatedness. These findings are consistent with a distributed network underlying neural responses to semantic incongruity including unimodal visual areas as well as integrative, transmodal areas. Unusual thoughts in SZ may reflect impaired processing in transmodal hub regions such as the precuneus, leading to overly broad semantic associations. Keywords: Schizophrenia, Semantic priming, N400, Joint-ICA, fMRI

Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45 ± 8.9%, sensitivity 0.74 ± 0.11, specificity 0.79 ± 0.11) than STR (accuracy 67.45 ± 8.13%, sensitivity 0.65 ± 0.11, specificity 0.71 ± 0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease. Keywords: fMRI, Functional network, ICA, Classification, Schizophrenia, GIG-ICA, Spatio-temporal regression

Perceptional abnormalities in schizophrenia are associated with hallucinations and delusions, but also with negative symptoms and poor functional outcome. Perception can be studied using EEG-derived event related potentials (ERPs). Because of their excellent temporal resolution, ERPs have been used to ask when perception is affected by schizophrenia. Because of its excellent spatial resolution, functional magnetic resonance imaging (fMRI) has been used to ask where in the brain these effects are seen. We acquired EEG and fMRI data simultaneously to explore when and where auditory perception is affected by schizophrenia. Thirty schizophrenia (SZ) patients and 23 healthy comparison subjects (HC) listened to 1000 Hz tones occurring about every second. We used joint independent components analysis (jICA) to combine EEG-based event-related potential (ERP) and fMRI responses to tones. Five ERP-fMRI joint independent components (JIC) were extracted. The “N100” JIC had temporal weights during N100 (peaking at 100 ms post-tone onset) and fMRI spatial weights in superior and middle temporal gyri (STG/MTG); however, it did not differ between groups. The “P200” JIC had temporal weights during P200 and positive fMRI spatial weights in STG/MTG and frontal areas, and negative spatial weights in the nodes of the default mode network (DMN) and visual cortex. Groups differed on the “P200” JIC: SZ had smaller “P200” JIC, especially those with more severe avolition/apathy. This is consistent with negative symptoms being related to perceptual deficits, and suggests patients with avolition/apathy may allocate too few resources to processing external auditory events and too many to processing internal events.

Schizophrenia patients have abnormal neural responses to salient, infrequent events. We integrated event-related potentials (ERP) and fMRI to examine the contributions of the ventral (salience) and dorsal (sustained) attention networks to this dysfunctional neural activation. Twenty-one schizophrenia patients and 22 healthy controls were assessed in separate sessions with ERP and fMRI during a visual oddball task. Visual P100, N100, and P300 ERP waveforms and fMRI activation were assessed. A joint independent components analysis (jICA) on the ERP and fMRI data were conducted. Patients exhibited reduced P300, but not P100 or N100, amplitudes to targets and reduced fMRI neural activation in both dorsal and ventral attentional networks compared with controls. However, the jICA revealed that the P300 was linked specifically to activation in the ventral (salience) network, including anterior cingulate, anterior insula, and temporal parietal junction, with patients exhibiting significantly lower activation. The P100 and N100 were linked to activation in the dorsal (sustained) network, with no group differences in level of activation. This joint analysis approach revealed the nature of target detection deficits that were not discernable by either imaging methodology alone, highlighting the utility of a multimodal fMRI and ERP approach to understand attentional network deficits in schizophrenia.

Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz) has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects) each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS) positive clinical symptoms (p = 0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r = 0.25; p = 0.07). The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the need for personalized treatment and targeted drug development.

In the last half-century, human neuroscience methods provided a way to study schizophrenia in vivo, and established that it is associated with subtle abnormalities in brain structure and function. However, efforts to understand the neurobiological bases of the clinical symptoms that the diagnosis is based on have been largely unsuccessful. In this paper, we provide an overview of the conceptual and methodological obstacles that undermine efforts to link the severity of specific symptoms to specific neurobiological measures. These obstacles include small samples, questionable reliability and validity of measurements, medication confounds, failure to distinguish state and trait effects, correlation-causation ambiguity, and the absence of compelling animal models of specific symptoms to test mechanistic hypotheses derived from brain-symptom correlations. We conclude with recommendations to promote progress in establishing brain-symptom relationships.

To address the statistical challenges associated with genome-wide association studies, we present an independent component analysis (ICA) with reference approach to target a specific genetic variation and associated brain networks. First, a small set of single nucleotide polymorphisms (SNPs) are empirically chosen to reflect a feature of interest and these SNPs are used as a reference when applying ICA to a full genomic SNP array. After extracting the genetic component maximally representing the characteristics of the reference, we test its association with brain networks in functional magnetic resonance imaging (fMRI) data. The method was evaluated on both real and simulated datasets. Simulation demonstrates that ICA with reference can extract a specific genetic factor, even when the variance accounted for by such a factor is so small that a regular ICA fails. Our real data application from 48 schizophrenia patients and 40 healthy controls include 300K SNPs and fMRI images in an auditory oddball task. Using SNPs with allelic frequency difference in two groups as a reference, we extracted a genetic component that maximally differentiates patients from controls (p