Brain-performance correlates of working memory retrieval in schizophrenia: a cognitive modeling approach

Neural and neuronal discoordination in schizophrenia: From ensembles through networks to symptoms

Despite the substantial knowledge accumulated by past research, the exact mechanisms of the pathogenesis of schizophrenia and causal treatments still remain unclear. Deficits of cognition and information processing in schizophrenia are today often viewed as the primary and core symptoms of this devastating disorder. These deficits likely result from disruptions in the coordination of neuronal and neural activity. The aim of this review is to bring together convergent evidence of discoordinated brain circuits in schizophrenia at multiple levels of resolution, ranging from principal cells and interneurons, neuronal ensembles and local circuits, to large-scale brain networks. We show how these aberrations could underlie deficits in cognitive control and other higher order cognitive-behavioural functions. Converging evidence from both animal models and patients with schizophrenia is presented in an effort to gain insight into common features of deficits in the brain information processing in this disorder, marked by disruption of several neurotransmitter and signalling systems and severe behavioural outcomes.

Identification of brain regions associated with working memory deficit in schizophrenia

Background: Schizophrenia, a severe psychological disorder, shows symptoms such as hallucinations and delusions. In addition, patients with schizophrenia often exhibit a deficit in working memory which adversely impacts the attentiveness and the behavioral characteristics of a person. Although several clinical efforts have already been made to study working memory deficit in schizophrenia, in this paper, we investigate the applicability of a machine learning approach for identification of the brain regions that get affected by schizophrenia leading to the dysfunction of the working memory.

Methods: We propose a novel scheme for identification of the affected brain regions from functional magnetic resonance imaging data by deploying group independent component analysis in conjunction with feature extraction based on statistical measures, followed by sequential forward feature selection. The features that show highest accuracy during the classification between healthy and schizophrenia subjects are selected.

Results: This study reveals several brain regions like cerebellum, inferior temporal gyrus, superior temporal gyrus, superior frontal gyrus, insula, and amygdala that have been reported in the existing literature, thus validating the proposed approach. We are also able to identify some functional changes in the brain regions, such as Heschl gyrus and the vermian area, which have not been reported in the literature involving working memory studies amongst schizophrenia patients.

Conclusions: As our study confirms the results obtained in earlier studies, in addition to pointing out some brain regions not reported in earlier studies, the findings are likely to serve as a cue for clinical investigation, leading to better medical intervention.

The MCIC collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia

Expertly collected, well-curated data sets consisting of comprehensive clinical characterization and raw structural, functional and diffusion-weighted DICOM images in schizophrenia patients and sex and age-matched controls are now accessible to the scientific community through an on-line data repository (coins.mrn.org). The Mental Illness and Neuroscience Discovery Institute, now the Mind Research Network (MRN, http://www.mrn.org/ ), comprised of investigators at the University of New Mexico, the University of Minnesota, Massachusetts General Hospital, and the University of Iowa, conducted a cross-sectional study to identify quantitative neuroimaging biomarkers of schizophrenia. Data acquisition across multiple sites permitted the integration and cross-validation of clinical, cognitive, morphometric, and functional neuroimaging results gathered from unique samples of schizophrenia patients and controls using a common protocol across sites. Particular effort was made to recruit patients early in the course of their illness, at the onset of their symptoms. There is a relatively even sampling of illness duration in chronic patients. This data repository will be useful to 1) scientists who can study schizophrenia by further analysis of this cohort and/or by pooling with other data; 2) computer scientists and software algorithm developers for testing and validating novel registration, segmentation, and other analysis software; and 3) educators in the fields of neuroimaging, medical image analysis and medical imaging informatics who need exemplar data sets for courses and workshops. Sharing provides the opportunity for independent replication of already published results from this data set and novel exploration. This manuscript describes the inclusion/exclusion criteria, imaging parameters and other information that will assist those wishing to use this data repository.

A multi-site resting state fMRI study on the amplitude of low frequency fluctuations in schizophrenia

Background: This multi-site study compares resting state fMRI amplitude of low frequency fluctuations (ALFF) and fractional ALFF (fALFF) between patients with schizophrenia (SZ) and healthy controls (HC).

Methods: Eyes-closed resting fMRI scans (5:38 min; n = 306, 146 SZ) were collected from 6 Siemens 3T scanners and one GE 3T scanner. Imaging data were pre-processed using an SPM pipeline. Power in the low frequency band (0.01–0.08 Hz) was calculated both for the original pre-processed data as well as for the pre-processed data after regressing out the six rigid-body motion parameters, mean white matter (WM) and cerebral spinal fluid (CSF) signals. Both original and regressed ALFF and fALFF measures were modeled with site, diagnosis, age, and diagnosis × age interactions.

Results: Regressing out motion and non-gray matter signals significantly decreased fALFF throughout the brain as well as ALFF in the cortical edge, but significantly increased ALFF in subcortical regions. Regression had little effect on site, age, and diagnosis effects on ALFF, other than to reduce diagnosis effects in subcortical regions. There were significant effects of site across the brain in all the analyses, largely due to vendor differences. HC showed greater ALFF in the occipital, posterior parietal, and superior temporal lobe, while SZ showed smaller clusters of greater ALFF in the frontal and temporal/insular regions as well as in the caudate, putamen, and hippocampus. HC showed greater fALFF compared with SZ in all regions, though subcortical differences were only significant for original fALFF.

Conclusions: SZ show greater eyes-closed resting state low frequency power in frontal cortex, and less power in posterior lobes than do HC; fALFF, however, is lower in SZ than HC throughout the cortex. These effects are robust to multi-site variability. Regressing out physiological noise signals significantly affects both total and fALFF measures, but does not affect the pattern of case/control differences.

Limbic hyperactivity associated to verbal memory deficit in schizophrenia

In schizophrenia there seems to be an inefficient activation of prefrontal and hippocampal regions. Patients tend to show worse cognitive performance in functions subserved by those regions as compared to healthy controls in spite of higher regional activation. However, the association between activation abnormalities and cognitive deficits remains without being understood. In the present study, we compared cerebral perfusion using single-photon emission tomography (SPECT) in patients and controls to study the association between activation patterns and cognitive performance in this disease. The SPECT studies were simultaneously obtained with an electrophysiological recording during a P300 paradigm to elicit P3a and P3b components. We included 23 stable patients with paranoid schizophrenia and 29 healthy controls that underwent clinical and cognitive assessments. Patients with schizophrenia showed an increased perfusion in the right hippocampus with respect to healthy controls, they also displayed a statistically significant inverse association between perfusion in the left hippocampus and verbal memory performance. Healthy controls showed an inverse association between perfusion in the left dorsolateral prefrontal (DLPFC) region and working memory performance. P3b but not P3a amplitude was significantly lower in patients. The limbic overactivation in the patients may contribute to their cognitive deficits in verbal memory.

State-space analysis of working memory in schizophrenia: an fBIRN study

The neural correlates of working memory (WM) in schizophrenia (SZ) have been extensively studied using the multisite fMRI data acquired by the Functional Biomedical Informatics Research Network (fBIRN) consortium. Although univariate and multivariate analysis methods have been variously employed to localize brain responses under differing task conditions, important hypotheses regarding the representation of mental processes in the spatio-temporal patterns of neural recruitment and the differential organization of these mental processes in patients versus controls have not been addressed in this context. This paper uses a multivariate state-space model (SSM) to analyze the differential representation and organization of mental processes of controls and patients performing the Sternberg Item Recognition Paradigm (SIRP) WM task. The SSM is able to not only predict the mental state of the subject from the data, but also yield estimates of the spatial distribution and temporal ordering of neural activity, along with estimates of the hemodynamic response. The dynamical Bayesian modeling approach used in this study was able to find significant differences between the predictability and organization of the working memory processes of SZ patients versus healthy subjects. Prediction of some stimulus types from imaging data in the SZ group was significantly lower than controls, reflecting a greater level of disorganization/heterogeneity of their mental processes. Moreover, the changes in accuracy of predicting the mental state of the subject with respect to parametric modulations, such as memory load and task duration, may have important implications on the neurocognitive models for WM processes in both SZ and healthy adults. Additionally, the SSM was used to compare the spatio-temporal patterns of mental activity across subjects, in a holistic fashion and to derive a low-dimensional representation space for the SIRP task, in which subjects were found to cluster according to their diagnosis.

Altered small-world brain networks in schizophrenia patients during working memory performance

Impairment of working memory (WM) performance in schizophrenia patients (SZ) is well-established. Compared to healthy controls (HC), SZ patients show aberrant blood oxygen level dependent (BOLD) activations and disrupted functional connectivity during WM performance. In this study, we examined the small-world network metrics computed from functional magnetic resonance imaging (fMRI) data collected as 35 HC and 35 SZ performed a Sternberg Item Recognition Paradigm (SIRP) at three WM load levels. Functional connectivity networks were built by calculating the partial correlation on preprocessed time courses of BOLD signal between task-related brain regions of interest (ROIs) defined by group independent component analysis (ICA). The networks were then thresholded within the small-world regime, resulting in undirected binarized small-world networks at different working memory loads. Our results showed: 1) at the medium WM load level, the networks in SZ showed a lower clustering coefficient and less local efficiency compared with HC; 2) in SZ, most network measures altered significantly as the WM load level increased from low to medium and from medium to high, while the network metrics were relatively stable in HC at different WM loads; and 3) the altered structure at medium WM load in SZ was related to their performance during the task, with longer reaction time related to lower clustering coefficient and lower local efficiency. These findings suggest brain connectivity in patients with SZ was more diffuse and less strongly linked locally in functional network at intermediate level of WM when compared to HC. SZ show distinctly inefficient and variable network structures in response to WM load increase, comparing to stable highly clustered network topologies in HC.

Altered small-world brain networks in schizophrenia patients during working memory performance

Impairment of working memory (WM) performance in schizophrenia patients (SZ) is well-established. Compared to healthy controls (HC), SZ patients show aberrant blood oxygen level dependent (BOLD) activations and disrupted functional connectivity during WM performance. In this study, we examined the small-world network metrics computed from functional magnetic resonance imaging (fMRI) data collected as 35 HC and 35 SZ performed a Sternberg Item Recognition Paradigm (SIRP) at three WM load levels. Functional connectivity networks were built by calculating the partial correlation on preprocessed time courses of BOLD signal between task-related brain regions of interest (ROIs) defined by group independent component analysis (ICA). The networks were then thresholded within the small-world regime, resulting in undirected binarized small-world networks at different working memory loads. Our results showed: 1) at the medium WM load level, the networks in SZ showed a lower clustering coefficient and less local efficiency compared with HC; 2) in SZ, most network measures altered significantly as the WM load level increased from low to medium and from medium to high, while the network metrics were relatively stable in HC at different WM loads; and 3) the altered structure at medium WM load in SZ was related to their performance during the task, with longer reaction time related to lower clustering coefficient and lower local efficiency. These findings suggest brain connectivity in patients with SZ was more diffuse and less strongly linked locally in functional network at intermediate level of WM when compared to HC. SZ show distinctly inefficient and variable network structures in response to WM load increase, comparing to stable highly clustered network topologies in HC.

The cognitive paradigm ontology: design and application

We present the basic structure of the Cognitive Paradigm Ontology (CogPO) for human behavioral experiments. While the experimental psychology and cognitive neuroscience literature may refer to certain behavioral tasks by name (e.g., the Stroop paradigm or the Sternberg paradigm) or by function (a working memory task, a visual attention task), these paradigms can vary tremendously in the stimuli that are presented to the subject, the response expected from the subject, and the instructions given to the subject. Drawing from the taxonomy developed and used by the BrainMap project ( www.brainmap.org ) for almost two decades to describe key components of published functional imaging results, we have developed an ontology capable of representing certain characteristics of the cognitive paradigms used in the fMRI and PET literature. The Cognitive Paradigm Ontology is being developed to be compliant with the Basic Formal Ontology (BFO), and to harmonize where possible with larger ontologies such as RadLex, NeuroLex, or the Ontology of Biomedical Investigations (OBI). The key components of CogPO include the representation of experimental conditions focused on the stimuli presented, the instructions given, and the responses requested. The use of alternate and even competitive terminologies can often impede scientific discoveries. Categorization of paradigms according to stimulus, response, and instruction has been shown to allow advanced data retrieval techniques by searching for similarities and contrasts across multiple paradigm levels. The goal of CogPO is to develop, evaluate, and distribute a domain ontology of cognitive paradigms for application and use in the functional neuroimaging community.

Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies

This report provides practical recommendations for the design and execution of multicenter functional MRI (MC-fMRI) studies based on the collective experience of the Function Biomedical Informatics Research Network (FBIRN). The study was inspired by many requests from the fMRI community to FBIRN group members for advice on how to conduct MC-fMRI studies. The introduction briefly discusses the advantages and complexities of MC-fMRI studies. Prerequisites for MC-fMRI studies are addressed before delving into the practical aspects of carefully and efficiently setting up a MC-fMRI study. Practical multisite aspects include: (i) establishing and verifying scan parameters including scanner types and magnetic fields, (ii) establishing and monitoring of a scanner quality program, (iii) developing task paradigms and scan session documentation, (iv) establishing clinical and scanner training to ensure consistency over time, (v) developing means for uploading, storing, and monitoring of imaging and other data, (vi) the use of a traveling fMRI expert, and (vii) collectively analyzing imaging data and disseminating results. We conclude that when MC-fMRI studies are organized well with careful attention to unification of hardware, software and procedural aspects, the process can be a highly effective means for accessing a desired participant demographics while accelerating scientific discovery.

Federated web-accessible clinical data management within an extensible neuroimaging database

Managing vast datasets collected throughout multiple clinical imaging communities has become critical with the ever increasing and diverse nature of datasets. Development of data management infrastructure is further complicated by technical and experimental advances that drive modifications to existing protocols and acquisition of new types of research data to be incorporated into existing data management systems. In this paper, an extensible data management system for clinical neuroimaging studies is introduced: The Human Clinical Imaging Database (HID) and Toolkit. The database schema is constructed to support the storage of new data types without changes to the underlying schema. The complex infrastructure allows management of experiment data, such as image protocol and behavioral task parameters, as well as subject-specific data, including demographics, clinical assessments, and behavioral task performance metrics. Of significant interest, embedded clinical data entry and management tools enhance both consistency of data reporting and automatic entry of data into the database. The Clinical Assessment Layout Manager (CALM) allows users to create on-line data entry forms for use within and across sites, through which data is pulled into the underlying database via the generic clinical assessment management engine (GAME). Importantly, the system is designed to operate in a distributed environment, serving both human users and client applications in a service-oriented manner. Querying capabilities use a built-in multi-database parallel query builder/result combiner, allowing web-accessible queries within and across multiple federated databases. The system along with its documentation is open-source and available from the Neuroimaging Informatics Tools and Resource Clearinghouse (NITRC) site.

Working memory circuitry in schizophrenia shows widespread cortical inefficiency and compensation

BACKGROUND

Working memory studies in schizophrenia (SZ), using functional magnetic resonance imaging (fMRI) and univariate analyses, have led to observations of hypo- or hyperactivation of discrete cortical regions and subsequent interpretations (e.g. neural inefficiencies). We employed a data-driven, multivariate analysis to identify the patterns of brain-behavior relationships in SZ during working memory.

METHODS

fMRI scans were collected from 13 SZ and 18 healthy control (HC) participants performing a modified Sternberg item recognition paradigm with three memory loads. We applied partial least squares analysis (PLS) to assess brain activation during the task both alone and with behavioral measures (accuracy and response time, RT) as covariates.

RESULTS

While the HC primary pattern was not affected by increasing load demands, SZ participants showed an exaggerated change in the Blood Oxygenation Level Dependent (BOLD) signal from the low to moderate memory load conditions and subsequent decrease in the greatest memory load, in frontal, motor, parietal and subcortical areas. With behavioral covariates, the separate groups identified distinct brain-behavior relationships and circuits. Increased activation of the middle temporal gyrus was associated with greater accuracy and faster RT only in SZ.

CONCLUSIONS

The inverted U-shaped curves in the SZ BOLD signal in the same areas that show flat activation in the HC data indicate widespread neural inefficiency in working memory in SZ. While both groups performed the task with similar levels of accuracy, participants with schizophrenia show a compensatory network of different sub-regions of the prefrontal cortex, parietal lobule, and the temporal gyri in this working memory task.

Functional brain imaging in schizophrenia: selected results and methods

Functional brain imaging studies of patients with schizophrenia may be grouped into those that assume that the signs and symptoms of schizophrenia are due to disordered circuitry within a critical brain region and studies that assume that the signs and symptoms are due to disordered connections among brain regions. Studies have investigated the disordered functional brain anatomy of both the positive and negative symptoms of schizophrenia. Studies of spontaneous hallucinations find that although hallucinations are associated with abnormal brain activity in primary and secondary sensory areas, disordered brain activation associated with hallucinations is not limited to sensory systems. Disordered activation in non-sensory regions appear to contribute to the emotional strength and valence of hallucinations, to be a factor underlying an inability to distinguish ongoing mental processing from memories, and to reflect the brain's attempt to modulate the intensity of hallucinations and resolve conflicts with other processing demands. Brain activation studies support the view that auditory/verbal hallucinations are associated with an impaired ability of internal speech plans to modulate neural activation in sensory language areas. In early studies, negative symptoms of schizophrenia were hypothesized to be associated with impaired function in frontal brain areas. In support of this hypothesis meta-analytical studies have found that resting blood flow or metabolism in frontal cortex is reduced in schizophrenia, though the magnitude of the effect is only small to moderate. Brain activation studies of working memory (WM) functioning are typically associated with large effect sizes in the frontal cortex, whereas studies of functions other than WM generally reveal smaller effects. Findings from some functional connectivity studies have supported the hypothesis that schizophrenia patients experience impaired functional connections between frontal and temporal cortex, although the nature of the disordered connectivity is complex. More recent studies have used functional brain imaging to study neural compensation in schizophrenia, to serve as endophenotypes in genetic studies and to provide biomarkers in drug development studies. These emerging trends in functional brain imaging research are likely to help stimulate the development of a general neurobiological theory of the complex symptoms of schizophrenia.

fMRI activity correlated with auditory hallucinations during performance of a working memory task: data from the FBIRN consortium study

INTRODUCTION

Auditory hallucinations are a hallmark symptom of schizophrenia. The neural basis of auditory hallucinations was examined using data from a working memory task. Data were acquired within a multisite consortium and this unique dataset provided the opportunity to analyze data from a large number of subjects who had been tested on the same procedures across sites. We hypothesized that regions involved in verbal working memory and language processing would show activity that was associated with levels of hallucinations during a condition where subjects were rehearsing the stimuli.

METHODS

Data from the Sternberg Item Recognition Paradigm, a working memory task, were acquired during functional magnetic resonance imaging procedures. The data were collected and preprocessed by the functional imaging biomedical informatics research network consortium. Schizophrenic subjects were split into nonhallucinating and hallucinating subgroups and activity during the probe condition (in which subjects rehearsed stimuli) was examined. Levels of activation from contrast images for the probe phase (collapsed over levels of memory load) of the working memory task were also correlated with levels of auditory hallucinations from the Scale for the Assessment of Positive Symptoms scores.

RESULTS

Patients with auditory hallucinations (relative to nonhallucinating subjects) showed decreased activity during the probe condition in verbal working memory/language processing regions, including the superior temporal and inferior parietal regions. These regions also showed associations between activity and levels of hallucinations in a correlation analysis.

DISCUSSION

The association between activation and hallucinations scores in the left hemisphere language/working memory regions replicates the findings of previous studies and provides converging evidence for the association between superior temporal abnormalities and auditory hallucinations.