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Background: Autocorrelation (AC) in fMRI time-series is a well-known phenomenon, typically attributed to colored noise and therefore removed from the data. We hypothesize that AC reflects systematic and meaningful signal fluctuations that may be tied to neural activity and provide evidence to support this hypothesis., New Method: Each fMRI time-series is modeled as an autoregressive process from which the autocorrelation is quantified. Then, autocorrelation during resting-state fMRI and auditory oddball (AOD) task in schizophrenia and healthy volunteers is examined., Results: During resting-state, AC was higher in the visual cortex while during AOD task, frontal part of the brain exhibited higher AC in both groups. AC values were significantly lower in specific brain regions in schizophrenia patients (such as thalamus during resting-state) compared to healthy controls in two independent datasets. Moreover, AC values had significant negative correlation with patients' symptoms. AC differences discriminated patients from healthy controls with high accuracy (resting-state)., Comparison With Existing Methods: Contrary to most prior works, the results suggest AC shows meaningful patterns that are discriminative between patients and controls. Our results are in line with recent works attributing autocorrelation to feedback loop of brain's regulatory circuit., Conclusions: Autoconnectivity is cognitive state dependent (resting-state vs. task) and mental state dependent (healthy vs. schizophrenia). The concept of autoconnectivity resembles a recurrent neural network and provides a new perspective of functional integration in the brain. These findings may have important implications for understanding of brain function in health and disease as well as for analysis of fMRI time-series., (Copyright © 2019. Published by Elsevier B.V.)

Response inhibition (RI) is a component of the cognitive control systems that support optimal cognition. Cognitive control deficits are well-described in schizophrenia, but are not well characterized in individuals at clinical high risk (CHR) for developing psychosis. Functional magnetic resonance imaging during Go/NoGo task performance was collected from 30 CHR youth, 23 early illness schizophrenia patients (ESZ), and 72 healthy adolescents and young adults (HC). Voxelwise main effects of group were examined (P < .005 height threshold, family-wise error-corrected cluster threshold, P < .05) for correct NoGo-Go contrast values and task-based functional connectivity. CHR and ESZ groups had slower and more variable reaction times (RT) on Go trials compared to HCs. Significant main effects of group in bilateral dorsal anterior cingulate (dACC) and right inferior frontal cortex stemmed from CHR and ESZ groups showing significantly less NoGo-Go activation, relative to HCs. Faster responding HCs had less functional coupling between dACC and medial prefrontal cortex, a default mode network (DMN) region during NoGo vs Go trials. This functional connectivity-performance relationship was not present in ESZ or CHR groups. The pattern of findings suggests CHR and ESZ groups were deficient in developing strong and consistent prepotent responding, based on their slow and variable motor responses and decreased engagement of dACC and right inferior frontal regions implicated in inhibitory control. Furthermore, only the control group showed a functional connectivity relationship consistent with greater response prepotency requiring more decoupling of inhibitory control regions from DMN regions during RI.

Background: Individuals with schizophrenia demonstrate deficits in context processing. These deficits can be characterized by examining the influence of auditory context on ERP responses to rare target tones. Previous studies demonstrate that target ERP deficits in schizophrenia depend on the number of non-targets that precede the target ERP. Our goal was to extend these findings by examining whether patients with schizophrenia demonstrate a reduced sensitivity to subtle differences in the auditory context preceding rare target stimuli, as quantified by Itti and Baldi's Bayesian prediction error model., Methods: Cortical responses to auditory oddball tones were measured within 59 individuals with schizophrenia (SZ) and 59 controls (HC). Individual trial amplitudes were estimated by conducting group ICA on the EEG time series and analyzing the reconstructed individual temporal sources. We quantified the auditory context of target tones using the Bayesian prediction error model and determined whether ERP amplitudes to tones were sensitive to this measure of context, or the number of preceding non-targets directly, within HC and SZ., Results: Individuals with schizophrenia show a significant reduction in ERP response amplitudes to targets approximately 244-412 ms following target onsets. Individual amplitudes within this window showed significantly greater sensitivity to the modeled prediction error within the controls than in individuals with schizophrenia. These differences approached significance when examining differences in amplitudes as a function of the number of preceding non-targets., Conclusions: These findings further clarify differences in HC and SZ with regard to their attentional and perceptual sensitivity to subtle environmental regularities., (Copyright © 2014 Elsevier B.V. All rights reserved.)

FMRI data are acquired as complex-valued spatiotemporal images. Despite the fact that several studies have identified the presence of novel information in the phase images, they are usually discarded due to their noisy nature. Several approaches have been devised to incorporate magnitude and phase data, but none of them has performed between-group inference or classification. Multiple kernel learning (MKL) is a powerful field of machine learning that finds an automatic combination of kernel functions that can be applied to multiple data sources. By analyzing this combination of kernels, the most informative data sources can be found, hence providing a better understanding of the analyzed learning task. This paper presents a methodology based on a new MKL algorithm (ν-MKL) capable of achieving a tunable sparse selection of features' sets (brain regions' patterns) that improves the classification accuracy rate of healthy controls and schizophrenia patients by 5% when phase data is included. In addition, the proposed method achieves accuracy rates that are equivalent to those obtained by the state of the art lp-norm MKL algorithm on the schizophrenia dataset and we argue that it better identifies the brain regions that show discriminative activation between groups. This claim is supported by the more accurate detection achieved by ν-MKL of the degree of information present on regions of spatial maps extracted from a simulated fMRI dataset. In summary, we present an MKL-based methodology that improves schizophrenia characterization by using both magnitude and phase fMRI data and is also capable of detecting the brain regions that convey most of the discriminative information between patients and controls., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Altered topological properties of brain connectivity networks have emerged as important features of schizophrenia. The aim of this study was to investigate how the state-related modulations to graph measures of functional integration and functional segregation brain networks are disrupted in schizophrenia. Firstly, resting state and auditory oddball discrimination (AOD) fMRI data of healthy controls (HCs) and schizophrenia patients (SZs) were decomposed into spatially independent components (ICs) by group independent component analysis (ICA). Then, weighted positive and negative functional integration (inter-component networks) and functional segregation (intra-component networks) brain networks were built in each subject. Subsequently, connectivity strength, clustering coefficient, and global efficiency of all brain networks were statistically compared between groups (HCs and SZs) in each state and between states (rest and AOD) within group. We found that graph measures of negative functional integration brain network and several positive functional segregation brain networks were altered in schizophrenia during AOD task. The metrics of positive functional integration brain network and one positive functional segregation brain network were higher during the resting state than during the AOD task only in HCs. These findings imply that state-related characteristics of both functional integration and functional segregation brain networks are impaired in schizophrenia which provides new insight into the altered brain performance in this brain disorder., (© 2013.)

Multimodal fusion is an effective approach to better understand brain diseases. However, most such instances have been limited to pair-wise fusion; because there are often more than two imaging modalities available per subject, there is a need for approaches that can combine multiple datasets optimally. In this paper, we extended our previous two-way fusion model called "multimodal CCA+joint ICA", to three or N-way fusion, that enables robust identification of correspondence among N data types and allows one to investigate the important question of whether certain disease risk factors are shared or distinct across multiple modalities. We compared "mCCA+jICA" with its alternatives in a 3-way fusion simulation and verified its advantages in both decomposition accuracy and modal linkage detection. We also applied it to real functional Magnetic Resonance Imaging (fMRI)-Diffusion Tensor Imaging (DTI) and structural MRI fusion to elucidate the abnormal architecture underlying schizophrenia (n=97) relative to healthy controls (n=116). Both modality-common and modality-unique abnormal regions were identified in schizophrenia. Specifically, the visual cortex in fMRI, the anterior thalamic radiation (ATR) and forceps minor in DTI, and the parietal lobule, cuneus and thalamus in sMRI were linked and discriminated between patients and controls. One fMRI component with regions of activity in motor cortex and superior temporal gyrus individually discriminated schizophrenia from controls. Finally, three components showed significant correlation with duration of illness (DOI), suggesting that lower gray matter volumes in parietal, frontal, and temporal lobes and cerebellum are associated with increased DOI, along with white matter disruption in ATR and cortico-spinal tracts. Findings suggest that the identified fractional anisotropy changes may relate to the corresponding functional/structural changes in the brain that are thought to play a role in the clinical expression of schizophrenia. The proposed "mCCA+jICA" method showed promise for elucidating the joint or coupled neuronal abnormalities underlying mental illnesses and improves our understanding of the disease process., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Altered brain connectivity has emerged as a central feature of schizophrenia. Low frequency oscillations and connectivity strength (CS) of resting state brain networks are altered in patients with schizophrenia (SZs). However, the relationship between these two measures has not yet been studied. Such work may be helpful in understanding the so-called "rich club" organization (i.e. high-CS nodes are more densely connected among themselves than are nodes of a lower CS in the human brain) in healthy controls (HCs) and SZs. Here we present a study of HCs and SZs examining low frequency oscillations and CS by first decomposing resting state fMRI (R-fMRI) data into independent components (ICs) using group independent component analysis (ICA) and computing the low frequency power ratio (LFPR) of each ICA time course. Weighted brain graphs consisting of ICs were built based on correlations between ICA time courses. Positive CS and negative CS of each node in the brain graphs were then examined. The correlations between LFPR and CSs as well as "rich club" coefficients of group mean brain graphs were assessed. Results demonstrate that the LFPR of some ICs were lower in SZs compared to HCs. In addition, LFPR was correlated with positive CS in HCs, but to a lesser extent in SZs. HCs showed higher normalized rich club parameter than SZs. The findings provide new insight into disordered intrinsic brain graphs in schizophrenia., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia by combining fMRI (collected during an auditory oddball task) and diffusion tensor imaging (DTI) data. We proposed a fusion method, "multimodal CCA+ joint ICA", which increases flexibility in statistical assumptions beyond existing approaches and can achieve higher estimation accuracy. The data collected from 164 participants (62 healthy controls, 54 schizophrenia and 48 bipolar) were extracted into "features" (contrast maps for fMRI and fractional anisotropy (FA) for DTI) and analyzed in multiple facets to investigate the group differences for each pair-wised groups and each modality. Specifically, both patient groups shared significant dysfunction in dorsolateral prefrontal cortex and thalamus, as well as reduced white matter (WM) integrity in anterior thalamic radiation and uncinate fasciculus. Schizophrenia and bipolar subjects were separated by functional differences in medial frontal and visual cortex, as well as WM tracts associated with occipital and frontal lobes. Both patients and controls showed similar spatial distributions in motor and parietal regions, but exhibited significant variations in temporal lobe. Furthermore, there were different group trends for age effects on loading parameters in motor cortex and multiple WM regions, suggesting that brain dysfunction and WM disruptions occurred in identified regions for both disorders. Most importantly, we can visualize an underlying function-structure network by evaluating the joint components with strong links between DTI and fMRI. Our findings suggest that although the two patient groups showed several distinct brain patterns from each other and healthy controls, they also shared common abnormalities in prefrontal thalamic WM integrity and in frontal brain mechanisms., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Aberrant topological properties of small-world human brain networks in patients with schizophrenia (SZ) have been documented in previous neuroimaging studies. Aberrant functional network connectivity (FNC, temporal relationships among independent component time courses) has also been found in SZ by a previous resting state functional magnetic resonance imaging (fMRI) study. However, no study has yet determined if topological properties of FNC are also altered in SZ. In this study, small-world network metrics of FNC during the resting state were examined in both healthy controls (HCs) and SZ subjects. FMRI data were obtained from 19 HCs and 19 SZ. Brain images were decomposed into independent components (ICs) by group independent component analysis (ICA). FNC maps were constructed via a partial correlation analysis of ICA time courses. A set of undirected graphs were built by thresholding the FNC maps and the small-world network metrics of these maps were evaluated. Our results demonstrated significantly altered topological properties of FNC in SZ relative to controls. In addition, topological measures of many ICs involving frontal, parietal, occipital and cerebellar areas were altered in SZ relative to controls. Specifically, topological measures of whole network and specific components in SZ were correlated with scores on the negative symptom scale of the Positive and Negative Symptom Scale (PANSS). These findings suggest that aberrant architecture of small-world brain topology in SZ consists of ICA temporally coherent brain networks.

Objective: In this paper, we develop a dynamic functional network connectivity (FNC) analysis approach using correlations between windowed time-courses of different brain networks (components) estimated via spatial independent component analysis (sICA). We apply the developed method to fMRI data to evaluate it and to study task-modulation of functional connections., Materials and Methods: We study the theoretical basis of the approach, perform a simulation analysis and apply it to fMRI data from schizophrenia patients (SP) and healthy controls (HC). Analyses on the fMRI data include: (a) group sICA to determine regions of significant task-related activity, (b) static and dynamic FNC analysis among these networks by using maximal lagged-correlation and time-frequency analysis, and (c) HC-SP group differences in functional network connections and in task-modulation of these connections., Results: This new approach enables an assessment of task-modulation of connectivity and identifies meaningful inter-component linkages and differences between the two study groups during performance of an auditory oddball task (AOT). The static FNC results revealed that connectivities involving medial visual-frontal, medial temporal-medial visual, parietal-medial temporal, parietal-medial visual and medial temporal-anterior temporal were significantly greater in HC, whereas only the right lateral fronto-parietal (RLFP)-orbitofrontal connection was significantly greater in SP. The dynamic FNC revealed that task-modulation of motor-frontal, RLFP-medial temporal and posterior default mode (pDM)-parietal connections were significantly greater in SP, and task modulation of orbitofrontal-pDM and medial temporal-frontal connections were significantly greater in HC (all P < 0.05)., Conclusion: The task-modulation of dynamic FNC provided findings and differences between the two groups that are consistent with the existing hypothesis that schizophrenia patients show less segregated motor, sensory, cognitive functions and less segregated default mode network activity when engaged with a task. Dynamic FNC, based on sICA, provided additional results which are different than, but complementary to, those of static FNC. For example, it revealed dynamic changes in default mode network connectivities with other regions which were significantly different in schizophrenia in terms of task-modulation, findings which were not possible to discover by static FNC.

Numerous studies have described attenuated event-related potential (ERP) component amplitudes in schizophrenia (e.g., P300, Mismatch Negativity (MMN), Error Negativity/Error-Related Negativity (Ne/ERN)). Functional magnetic resonance imaging (fMRI) studies have typically shown decreased recruitment of diverse brain areas during performance of tasks that elicit the above ERP components. Recent research suggests that phase-resetting of slow-oscillations (e.g., in the delta and theta bands) underlies the potentials observed in ERP averages. Several studies have reported that slow-oscillations are increased in amplitude in people with schizophrenia at rest. Few studies have examined event-related low-frequency oscillations in schizophrenia. We examined event-related evoked and induced delta and theta activity in 17 people with schizophrenia and 17 healthy controls in two go/no-go task variants. We analyzed stimulus-related and response-related oscillations associated with correct-hits, correct-rejects and false-alarms. Our results reveal a pattern of reduced delta and theta activity for task-relevant events in schizophrenia. The findings indicate that while low-frequency oscillations are increased in amplitude at rest, they are not coordinated effectively in schizophrenia during various information processing tasks including target-detection, response-inhibition and error-detection. This slow-oscillation coordination abnormality may help explain the decreased recruitment of brain areas seen in fMRI studies.

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or "spatial modes" exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder.

We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge, J., Lane, T., Link, H., Qiu, S., Clark, V.P., 2007. Discrete dynamic Bayesian network analysis of fMRI data. Hum Brain Mapp.) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge, J., Lane, T., 2005. Learning Class-Discriminative Dynamic Bayesian Networks. Proceedings of the International Conference on Machine Learning, Bonn, Germany, pp. 97-104.). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, A., 1991. Probability, random variables, and stochastic processes. McGraw-Hill, New York.). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions, including bilateral temporal, frontal, and cerebellar regions during an auditory paradigm.

Identifying intermediate phenotypes of genetically complex psychiatric illnesses such as schizophrenia is important. First-degree relatives of persons with schizophrenia have increased genetic risk for the disorder and tend to show deficits on working memory (WM) tasks. An open question is the relationship between such behavioral endophenotypes and the corresponding brain activation patterns revealed during functional imaging. We measured task performance during a Sternberg WM task and used functional magnetic resonance imaging (fMRI) to assess whether 23 non-affected first-degree relatives showed altered performance and functional activation compared to 43 matched healthy controls. We predicted that a significant proportion of unaffected first-degree relatives would show either aberrant task performance and/or abnormal related fMRI blood oxygen level dependent (BOLD) patterns. While task performance in the relatives was not different than that of controls they were significantly slower in responding to probes., Schizophrenia relatives displayed reduced activation, most markedly in bilateral dorsolateral/ventrolateral (DLPFC/VLPFC) prefrontal and posterior parietal cortex when encoding stimuli and in bilateral DLPFC and parietal areas during response selection. Additionally, fMRI differences in both conditions were modulated by load, with a parametric increase in between-group differences with load in several key regions during encoding and an opposite effect during response selection.

Objective: The "default mode" has been defined as a baseline condition of brain function and is of interest because its component brain regions are believed to be abnormal in schizophrenia. It was hypothesized that the default mode network would show abnormal activation and connectivity in patients with schizophrenia., Method: Patients with schizophrenia (N=21) and healthy comparison subjects (N=22) performed an auditory oddball task during functional magnetic resonance imaging (fMRI). Independent component analysis was used to identify the default mode component. Differences in the spatial and temporal aspects of the default mode network were examined in patients versus comparison subjects., Results: Healthy comparison subjects and patients had significant spatial differences in the default mode network, most notably in the frontal, anterior cingulate, and parahippocampal gyri. In addition, activity in patients in the medial frontal, temporal, and cingulate gyri correlated with severity of positive symptoms. The patients also showed significantly higher frequency fluctuations in the temporal evolution of the default mode., Conclusions: Schizophrenia is associated with altered temporal frequency and spatial location of the default mode network. The authors hypothesized that this network may be under- or overmodulated by key regions, including the anterior and posterior cingulate cortex. In addition, the altered temporal fluctuations in patients may result from a change in the connectivity of these regions with other brain networks.

Background: Previous neuroimaging studies of working memory (WM) in schizophrenia, typically focusing on dorsolateral prefrontal cortex, yield conflicting results, possibly because of varied choice of tasks and analysis techniques. We examined neural function changes at several WM loads to derive a more complete picture of WM dysfunction in schizophrenia., Methods: We used a version of the Sternberg Item Recognition Paradigm to test WM function at five distinct loads. Eighteen schizophrenia patients and 18 matched healthy controls were scanned with functional magnetic resonance imaging at 3 Tesla., Results: Patterns of both overactivation and underactivation in patients were observed depending on WM load. Patients' activation was generally less responsive to load changes than control subjects', and different patterns of between-group differences were observed for memory encoding and retrieval. In the specific case of successful retrieval, patients recruited additional neural circuits unused by control subjects. Behavioral effects were generally consistent with these imaging results., Conclusions: Differential findings of overactivation and underactivation may be attributable to patients' decreased ability to focus and allocate neural resources at task-appropriate levels. Additionally, differences between encoding and retrieval suggest that WM dysfunction may be manifested differently during the distinct phases of encoding, maintenance, and retrieval.

It is becoming common to collect data from multiple functional magnetic resonance imaging (fMRI) paradigms on a single individual. The data from these experiments are typically analyzed separately and sometimes directly subtracted from one another on a voxel-by-voxel basis. These comparative approaches, although useful, do not directly attempt to examine potential commonalities between tasks and between voxels. To remedy this we propose a method to extract maximally spatially independent maps for each task that are "coupled" together by a shared loading parameter. We first compute an activation map for each task and each individual as "features," which are then used to perform joint independent component analysis (jICA) on the group data. We demonstrate our approach on a data set derived from healthy controls and schizophrenia patients, each of which carried out an auditory oddball task and a Sternberg working memory task. Our analysis approach revealed two interesting findings in the data that were missed with traditional analyses. First, consistent with our hypotheses, schizophrenia patients demonstrate "decreased" connectivity in a joint network including portions of regions implicated in two prevalent models of schizophrenia. A second finding is that for the voxels identified by the jICA analysis, the correlation between the two tasks was significantly higher in patients than in controls. This finding suggests that schizophrenia patients activate "more similarly" for both tasks than do controls. A possible synthesis of both findings is that patients are activating less, but also activating with a less-unique set of regions for these very different tasks. Both of the findings described support the claim that examination of joint activation across multiple tasks can enable new questions to be posed about fMRI data. Our approach can also be applied to data using more than two tasks. It thus provides a way to integrate and probe brain networks using a variety of tasks and may increase our understanding of coordinated brain networks and the impact of pathology upon them., (2005 Wiley-Liss, Inc.)

The acquisition of both structural MRI (sMRI) and functional MRI (fMRI) data for a given study is a very common practice. However, these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform independent component analysis across image modalities, specifically, gray matter images and fMRI activation images as well as a joint histogram visualization technique. Joint independent component analysis (jICA) is used to decompose a matrix with a given row consisting of an fMRI activation image resulting from auditory oddball target stimuli and an sMRI gray matter segmentation image, collected from the same individual. We analyzed data collected on a group of schizophrenia patients and healthy controls using the jICA approach. Spatially independent joint-components are estimated and resulting components were further analyzed only if they showed a significant difference between patients and controls. The main finding was that group differences in bilateral parietal and frontal as well as posterior temporal regions in gray matter were associated with bilateral temporal regions activated by the auditory oddball target stimuli. A finding of less patient gray matter and less hemodynamic activity for target detection in these bilateral anterior temporal lobe regions was consistent with previous work. An unexpected corollary to this finding was that, in the regions showing the largest group differences, gray matter concentrations were larger in patients vs. controls, suggesting that more gray matter may be related to less functional connectivity in the auditory oddball fMRI task., (Hum Brain Mapp, 2005. (c) 2005 Wiley-Liss, Inc.)

Schizophrenia is characterised by marked disturbances of attention and information processing. Patients experience difficulty focusing on relevant cues and avoiding distraction by irrelevant stimuli. Event-related potential recordings indicate an amplitude reduction in the P3a component elicited by involuntary orienting to task-irrelevant, infrequent novel stimuli presented during auditory oddball detection in patients with schizophrenia. The goal of the present study was to elucidate the functional abnormality underlying the disturbed orienting to novel stimuli in schizophrenia. Twenty-eight stable, partially remitted, medicated patients with schizophrenia and 28 healthy control participants completed a novelty oddball variant during event-related fMRI. Relative to healthy participants, patients with schizophrenia were characterised by underactivity during novel stimulus processing in the right amygdala-hippocampus, within paralimbic cortex in the rostral anterior cingulate and posterior cingulate cortices and the right frontal operculum, and in association cortex at the right temporo-parietal-occipital junction, bilateral intraparietal sulcus, and bilateral dorsal frontal cortex. Subcortically, relative hypoactivation during novelty processing was apparent in the cerebellum, thalamus, and basal ganglia. These results suggest that patients less efficiently reorient processing resources away from the ongoing task of detecting and responding to the task-relevant target stimuli. In addition, trend results suggest that patients experienced increased distraction by novel stimuli.

Background: Schizophrenia is a heterogeneous disorder characterized by diffuse brain abnormalities that affect many facets of cognitive function. One replicated finding in schizophrenia is abnormalities in the neural systems associated with processing salient stimuli in the context of oddball tasks. This deficit in the processing of salience stimuli might be related to abnormalities in orienting, attention, and memory processes., Methods: Behavioral responses and functional magnetic resonance imaging data were collected while 18 patients with schizophrenia and 18 matched healthy control subjects performed a three-stimulus auditory oddball task., Results: Target detection by healthy participants was associated with significant activation in all 38 regions of interest embracing distributed cortical and subcortical systems. Similar reproducibility was observed in healthy participants for processing novel stimuli. Schizophrenia patients, relative to control subjects, showed diffuse cortical and subcortical hypofunctioning during target detection and novelty processing, including bilateral frontal, temporal, and parietal cortices and amygdala, thalamus, and cerebellum., Conclusions: These data replicate and extend imaging studies of target detection in schizophrenia and present new insights regarding novelty processing in the disorder. The results are consistent with the hypothesis that schizophrenia is characterized by a widespread pathologic process affecting many cerebral areas, including cortical, subcortical, and cerebellar circuits.

Background: In a recent longitudinal study of first-episode schizophrenia patients, we found that while dysfunction of the right dorsolateral prefrontal cortex (DLPFC), right thalamus, left cerebellum and cingulate gyrus normalized with antipsychotic treatment and significant reduction in symptomatology, the left DLPFC, left thalamus, and right cerebellum remained disturbed. In the present study we investigated whether these abnormalities are also present in clinically stable, relatively well-functioning schizophrenia patients in comparison to control subjects during performance of the N-back working-memory task., Method: Twelve schizophrenia and 12 control subjects completed the study. The functional images collected during scanning were analyzed using a random-effects model in a restricted set of six regions of interest (ROIs). In addition, the exploratory search in the entire brain volume was performed., Results: The ROI analyses revealed relative underactivation in the region of the left DLPFC and the right cerebellum, as well as overactivation in the left cerebellum. The exploratory whole-brain search exposed additional overactivation in the medial frontal, anterior cingulate, and left parietal cortices., Conclusions: The present study provides evidence of significant underactivations in stable schizophrenia patients in regions that we have previously observed to be dysfunctional in acutely psychotic and partially remitted patients, together with extensive overactivations in several regions that potentially reflect some compensatory mechanism or increased effort on the working-memory task.

Background: A number of functional brain abnormalities have been reported in schizophrenia, but it remains to be determined which of them represent trait and state markers of the illness., Aims: To delineate regional brain dysfunctions that remain stable and those that fluctuate during the course of schizophrenia., Method: A cohort of patients with first-episode schizophrenia and a matched group of control participants underwent functional magnetic resonance imaging on two occasions 6-8 weeks apart during performance of a working memory task. The patients' disease was in partial remission at the second scan., Results: Relative to control participants, the function of the left dorsolateral prefrontal cortex, left thalamus and right cerebellum remained disturbed in the people with schizophrenia, whereas the dysfunction of the right dorsolateral prefrontal cortex, right thalamus, left cerebellum and cingulate gyrus normalised, with significant reduction in symptoms., Conclusions: These results suggest that dysfunction of the left fronto-thalamo-cerebellar circuitry is a relatively stable characteristic of schizophrenia, whereas disturbance of the right circuitry and cingulate gyrusis predominantly a state-related phenomenon.

The aim of this study was to determine if error negativity/error-related negativity (N(e)/ERN), error positivity (P(e)), correct response negativity (CRN) or correct response positivity (P(c)) amplitude are influenced by state changes in schizophrenia. Event-related potentials (ERPs) were recorded from nine schizophrenic patients while they performed a simple go/no-go task during the early stages of an acute episode and again following 6 weeks of treatment with antipsychotics. ERPs were also recorded from nine healthy participants while they performed the same task. Response-locked potentials were computed for errors of commission and for correct responses. Scores for reality distortion syndrome, psychomotor poverty syndrome and disorganization syndrome were determined for the schizophrenic participants before and after treatment using the Signs and Symptoms of Psychotic Illness (SSPI) Scale. N(e)/ERN amplitude was significantly reduced, compared with that in healthy participants, in the schizophrenic patients when acutely ill, and increased significantly following treatment. N(e)/ERN amplitude remained significantly larger in the healthy group than in the patients with schizophrenia after treatment. This study suggests that N(e)/ERN and CRN amplitude are modulated by clinical state in schizophrenia and provides further support to findings that decreased N(e)/ERN amplitude is a potentially useful trait marker for schizophrenia, while P(c) and P(e) amplitude are not abnormal.

Background: Among the most prominent features of schizophrenic brains are abnormalities in auditory cortex structure and function, particularly in the superior temporal gyrus (STG). In this study, we attempted to examine auditory cortex function using an intrinsic, task-uncorrelated measure., Methods: Using functional magnetic resonance imaging data, we calculated synchronous hemodynamic independent maps (SHIMs) of auditory cortex in patients with schizophrenia and matched healthy control subjects while they performed an auditory oddball task., Results: Patient SHIMs revealed greater synchrony in ventral and medial STG regions (including auditory association Brodmann area [BA] 42); control SHIMs had greater synchrony in dorsal and lateral STG regions (which did not include BA 42). A within-participant subtractive comparison of these two sets of regions differentiated schizophrenic from healthy control subjects with 97% accuracy initially (further validated by a retest of the healthy control subjects) and performed with 94% accuracy in a confirmatory study of new subjects scanned at a different site., Conclusions: These results shed new light on STG functional differences in schizophrenia, suggest that aberrant patterns of coherence in temporal lobe cortical regions are a cardinal abnormality in schizophrenia, and have the potential to provide a powerful, quantitative clinical tool for the assessment of schizophrenia.

Previous research has demonstrated that patients with schizophrenia have an impaired ability to monitor erroneous responses to stimuli internally. Event-related potential (ERP) studies of error-eliciting tasks indicate that, in healthy adults, the commission of an erroneous response is associated with a fronto-centrally distributed negative voltage component termed the error negativity (Ne) or error-related negativity (ERN). In patients with schizophrenia, the Ne/ERN elicited by errors of commission (EoC) is reduced in amplitude compared with that elicited in healthy participants. Functional MRI (fMRI) studies and source localization analyses of ERP data in healthy participants suggest that EoC are associated with activity in the rostral anterior cingulate cortex (ACC). Using event-related fMRI, we examined the brain activity associated with EoC in a group of 10 patients with schizophrenia and 16 matched healthy participants. Patients were stable, partially remitted, medicated out-patients recruited from the community. Participants performed a Go/NoGo task variant that was shown previously to elicit a reduced Ne/ERN during EoC in patients with schizophrenia relative to healthy participants, as well as robust rostral ACC activation during EoC in healthy participants. Patients with schizophrenia were characterized by relative underactivity in the rostral ACC compared with healthy participants. There was also evidence for more widespread underactivity in the limbic system. In contrast to these regions of relative hypoactivity, patients with schizophrenia demonstrated hyperactivity relative to healthy participants in bilateral parietal cortex during both EoC and correctly rejected NoGo trials. Our results are consistent with previous ERP research demonstrating functional abnormalities during error processing in schizophrenia. In light of the role of the rostral ACC and other limbic structures in mediating affective and motivational behaviour, our results suggest there may be a disturbed affective or motivational response to the commission of errors in schizophrenia.

Objectives: To examine error-related negativity (ERN) and correct response negativity (CRN) in schizophrenia in light of two previous conflicting reports, and to determine their relation to disorganization, psychomotor poverty and reality distortion., Methods: Event-related potentials were recorded from 21 schizophrenic and 21 control participants who performed a simple go/no-go task. Response-locked potentials were computed for errors of commission and for correct-hits. Scores for reality distortion syndrome, psychomotor poverty syndrome and disorganization syndrome were determined for each schizophrenic participant using the Signs and Symptoms of Psychotic Illness (SSPI) scale., Results: ERN produced during error trials and CRN produced during correct trials were significantly larger in the control participant group than in the schizophrenic participant group. In the schizophrenic patients, ERN amplitude was negatively correlated with psychomotor poverty syndrome score and CRN amplitude was negatively correlated with disorganization syndrome score., Conclusions: Decreased ERN and CRN in the schizophrenic participant group suggests abnormal internal behavior monitoring in schizophrenic patients. Patients with high disorganization symptoms may employ an abnormal strategy for comparing actual response outcome with desired response outcome, while patients with psychomotor poverty may be less emotionally responsive to errors.

Schizophrenia is a diffuse brain disease that affects many facets of cognitive function. One of the most replicated findings in the neurobiology of schizophrenia is that the event-related potentials to auditory oddball stimuli are abnormal, effects believed to be related to abnormalities in attentional and memory processes. Although event-related potentials provide excellent resolution regarding the time course of information processing, such studies are poor at characterizing the spatial location of these abnormalities. To address this issue, we used event-related functional magnetic resonance imaging techniques to elucidate the neural areas underlying target detection in schizophrenia. Consistent with recent event-related functional magnetic resonance imaging results, target processing by control participants was associated with bilateral activation in the anterior superior temporal gyri, inferior and superior parietal lobules, and activation in anterior and posterior cingulate, thalamus, and right lateral frontal cortex. For the schizophrenic patients, selective deficits were observed in both the extent and strength of activation associated with target processing in the right lateral frontal cortex, thalamus, bilateral anterior superior temporal gyrus, anterior and posterior cingulate, and right inferior and superior parietal lobules. These findings are consistent with the evidence for abnormal processing of oddball stimuli suggested by event-related potential studies in schizophrenic patients, but provide much more detailed evidence regarding the anatomical sites implicated. These data are consistent with the hypothesis that schizophrenia is characterized by a widespread pathological process affecting many cerebral areas, including association cortex and thalamus.

Background: Schizophrenia and psychopathy are both characterized by impulsive, poorly planned behavior. This behavior may originate from a weak or poorly coordinated response inhibition system. We tested the hypothesis that schizophrenia and psychopathy are associated with abnormal neural processing during the suppression of inappropriate responses., Methods: The participants were schizophrenic patients, nonpsychotic psychopaths, and nonpsychotic, nonpsychopathic control subjects (defined by the Hare Psychopathy Checklist-Revised), all incarcerated in a maximum security psychiatric facility. We recorded behavioral responses and event-related potentials (ERPs) during a Go/No Go task., Results: Schizophrenic patients made more errors of commission than did the nonpsychopathic offenders. As expected, the nonpsychopathic nonpsychotic participants showed greater frontal ERP negativity (N275) to the No Go stimuli than to the Go stimuli. This effect was small in the schizophrenic patients and absent in the psychopaths. For the nonpsychopaths, the P375 ERP component was larger on Go than on No Go trials, a difference that was absent in schizophrenic patients and in the opposite direction in psychopaths., Conclusions: These findings support the hypothesis that the neural processes involved in response inhibition are abnormal in both schizophrenia and psychopathy; however, the nature of these processes appears to be different in the two disorders.