Combining ERP and structural MRI information in first episode schizophrenia and bipolar disorder

Macroscale EEG characteristics in antipsychotic-naïve patients with first-episode psychosis and healthy controls

Electroencephalography in patients with a first episode of psychosis (FEP) may contribute to the diagnosis and treatment response prediction. Findings in the literature vary due to small sample sizes, medication effects, and variable illness duration. We studied macroscale resting-state EEG characteristics of antipsychotic naïve patients with FEP. We tested (1) for differences between FEP patients and controls, (2) if EEG could be used to classify patients as FEP, and (3) if EEG could be used to predict treatment response to antipsychotic medication. In total, we studied EEG recordings of 62 antipsychotic-naïve patients with FEP and 106 healthy controls. Spectral power, phase-based and amplitude-based functional connectivity, and macroscale network characteristics were analyzed, resulting in 60 EEG variables across four frequency bands. Positive and Negative Symptom Scale (PANSS) were assessed at baseline and 4-6 weeks follow-up after treatment with amisulpride or aripiprazole. Mann-Whitney U tests, a random forest (RF) classifier and RF regression were used for statistical analysis. Our study found that at baseline, FEP patients did not differ from controls in any of the EEG characteristics. A random forest classifier showed chance-level discrimination between patients and controls. The random forest regression explained 23% variance in positive symptom reduction after treatment in the patient group. In conclusion, in this largest antipsychotic- naïve EEG sample to date in FEP patients, we found no differences in macroscale EEG characteristics between patients with FEP and healthy controls. However, these EEG characteristics did show predictive value for positive symptom reduction following treatment with antipsychotic medication.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

SIGNIFICANCE

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

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.

Diverse definitions of the early course of schizophrenia-a targeted literature review

Schizophrenia is a debilitating psychiatric disorder and patients experience significant comorbidity, especially cognitive and psychosocial deficits, already at the onset of disease. Previous research suggests that treatment during the earlier stages of disease reduces disease burden, and that a longer time of untreated psychosis has a negative impact on treatment outcomes. A targeted literature review was conducted to gain insight into the definitions currently used to describe patients with a recent diagnosis of schizophrenia in the early course of disease ('early' schizophrenia). A total of 483 relevant English-language publications of clinical guidelines and studies were identified for inclusion after searches of MEDLINE, MEDLINE In-Process, relevant clinical trial databases and Google for records published between January 2005 and October 2015. The extracted data revealed a wide variety of terminology and definitions used to describe patients with 'early' or 'recent-onset' schizophrenia, with no apparent consensus. The most commonly used criteria to define patients with early schizophrenia included experience of their first episode of schizophrenia or disease duration of less than 1, 2 or 5 years. These varied definitions likely result in substantial disparities of patient populations between studies and variable population heterogeneity. Better agreement on the definition of early schizophrenia could aid interpretation and comparison of studies in this patient population and consensus on definitions should allow for better identification and management of schizophrenia patients in the early course of their disease.

Neurochemical differences between bipolar disorder type I and II in superior temporal cortices: A proton magnetic resonance spectroscopy study

BACKGROUND

Despite the diagnostic challenges in categorizing bipolar disorder subtypes, bipolar I and II disorders (BD-I and BD-II respectively) are valid indices for researchers. Subtle neurobiological differences may underlie clinical differences between mood disorder subtypes. The aims of this study were to investigate neurochemical differences between bipolar disorder subtypes.

METHODS

Euthymic BD-II patients (n = 21) are compared with BD-I (n = 28) and healthy comparison subjects (HCs, n = 30). Magnetic Resonance Imaging (MRI) and proton spectroscopy (¹H MRS) were performed on a 3T Siemens Tim Trio system. MRS voxels were located in the left/right superior temporal cortices, and spectra acquired with the single voxel Point REsolved Spectroscopy Sequence (PRESS). The spectroscopic data were analyzed with LCModel (Version 6.3.0) software.

RESULTS

There were significant differences between groups in terms of glutamate [F = 6.27, p = 0.003], glutamate + glutamine [F = 6.08, p = 0.004], inositol containing compounds (Ino) (F = 9.25, p < 0.001), NAA [F = 7.63, p = 0.001] and creatine + phosphocreatine [F = 11.06, p < 0.001] in the left hemisphere and Ino [F = 5.65, p = 0.005] in the right hemisphere. Post-hoc comparisons showed that the BD-I disorder group had significantly lower metabolite levels in comparison to the BD-II and the HC groups.

LIMITATIONS

This was a cross-sectional study with a small sample size. In addition, patients were on various psychotropic medications, which may have impacted the results.

CONCLUSIONS

Neurochemical levels, in the superior temporal cortices, measured with ¹H-MRS discriminated between BD-II and BD-I. Although further studies are needed, one may speculate that the superior temporal cortices (particularly left hemispheric) play a critical role, whose pathology may be related to subtyping bipolar disorder.

Combining multi-modality data for searching biomarkers in schizophrenia

Identification of imaging biomarkers for schizophrenia is an important but still challenging problem. Even though considerable efforts have been made over the past decades, quantitative alterations between patients and healthy subjects have not yet provided a diagnostic measure with sufficient high sensitivity and specificity. One of the most important reasons is the lack of consistent findings, which is in part due to single-mode study, which only detects single dimensional information by each modality, and thus misses the most crucial differences between groups. Here, we hypothesize that multimodal integration of functional MRI (fMRI), structural MRI (sMRI), and diffusion tensor imaging (DTI) might yield more power for the diagnosis of schizophrenia. A novel multivariate data fusion method for combining these modalities is introduced without reducing the dimension or using the priors from 161 schizophrenia patients and 168 matched healthy controls. The multi-index feature for each ROI is constructed and summarized with Wilk's lambda by performing multivariate analysis of variance to calculate the significant difference between different groups. Our results show that, among these modalities, fMRI has the most significant featureby calculating the Jaccard similarity coefficient (0.7416) and Kappa index (0.4833). Furthermore, fusion of these modalities provides the most plentiful information and the highest predictive accuracy of 86.52%. This work indicates that multimodal integration can improve the ability of distinguishing differences between groups and might be assisting in further diagnosis of schizophrenia.

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

Early visual processing for low spatial frequency fearful face is correlated with cortical volume in patients with schizophrenia

Patients with schizophrenia present with dysfunction of the magnocellular pathway, which might impair their early visual processing. We explored the relationship between functional abnormality of early visual processing and brain volumetric changes in schizophrenia. Eighteen patients and 16 healthy controls underwent electroencephalographic recordings and high-resolution magnetic resonance imaging. During electroencephalographic recordings, participants passively viewed neutral or fearful faces with broad, high, or low spatial frequency characteristics. Voxel-based morphometry was performed to investigate brain volume correlates of visual processing deficits. Event related potential analysis suggested that patients with schizophrenia had relatively impaired P100 processing of low spatial frequency fearful face stimuli compared with healthy controls; patients' gray-matter volumes in the dorsolateral and medial prefrontal cortices positively correlated with this amplitude. In addition, patients' gray-matter volume in the right cuneus positively correlated with the P100 amplitude in the left hemisphere for the high spatial frequency neutral face condition and that in the left dorsolateral prefrontal cortex negatively correlated with the negative score of the Positive and Negative Syndrome Scale. No significant correlations were observed in healthy controls. This study suggests that the cuneus and prefrontal cortex are significantly involved with the early visual processing of magnocellular input in patients with schizophrenia.

Fusing Functional MRI and Diffusion Tensor Imaging Measures of Brain Function and Structure to Predict Working Memory and Processing Speed Performance among Inter-episode Bipolar Patients

Evidence for abnormal brain function as measured with diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) and cognitive dysfunction have been observed in inter-episode bipolar disorder (BD) patients. We aimed to create a joint statistical model of white matter integrity and functional response measures in explaining differences in working memory and processing speed among BD patients. Medicated inter-episode BD (n=26; age=45.2±10.1 years) and healthy comparison (HC; n=36; age=46.3±11.5 years) participants completed 51-direction DTI and fMRI while performing a working memory task. Participants also completed a processing speed test. Tract-based spatial statistics identified common white matter tracts where fractional anisotropy was calculated from atlas-defined regions of interest. Brain responses within regions of interest activation clusters were also calculated. Least angle regression was used to fuse fMRI and DTI data to select the best joint neuroimaging predictors of cognitive performance for each group. While there was overlap between groups in which regions were most related to cognitive performance, some relationships differed between groups. For working memory accuracy, BD-specific predictors included bilateral dorsolateral prefrontal cortex from fMRI, splenium of the corpus callosum, left uncinate fasciculus, and bilateral superior longitudinal fasciculi from DTI. For processing speed, the genu and splenium of the corpus callosum and right superior longitudinal fasciculus from DTI were significant predictors of cognitive performance selectively for BD patients. BD patients demonstrated unique brain-cognition relationships compared to HC. These findings are a first step in discovering how interactions of structural and functional brain abnormalities contribute to cognitive impairments in BD.

Drug Treated Schizophrenia, Schizoaffective and Bipolar Disorder Patients Evaluated by qEEG Absolute Spectral Power and Mean Frequency Analysis

Objective

Research of electroencephalograph (EEG) power spectrum and mean frequency has shown inconsistent results in patients with schizophrenic, schizoaffective and bipolar disorders during medication when compared to normal subjects thus; the characterization of these parameters is an important task.

Methods

We applied quantitative EEG (qEEG) to investigate 38 control, 15 schizophrenic, 7 schizoaffective and 11 bipolar disorder subjects which remaine under the administration of psychotropic drugs (except control group). Absolute spectral power (ASP), mean frequency and hemispheric electrical asymmetry were measured by 19 derivation qEEG. Group mean values were compared with non parametrical Mann-Whitney test and spectral EEG maps with z-score method at p < 0.05.

Results

Most frequent drug treatments for schizophrenic patients were neuroleptic+antiepileptic (40% of cases) or 2 neuroleptics (33.3%). Schizoaffective patients received neuroleptic+benzodiazepine (71.4%) and for bipolar disorder patients neuroleptic+antiepileptic (81.8%). Schizophrenic (at all derivations except for Fp1, Fp2, F8 and T6) and schizoaffective (only at C3) show higher values of ASP (+57.7% and +86.1% respectively) compared to control group. ASP of bipolar disorder patients did not show differences against control group. The mean frequency was higher at Fp1 (+14.2%) and Fp2 (+17.4%) in bipolar disorder patients than control group, but no differences were found in frequencies between schizophrenic or schizoaffective patients against the control group. Majority of spectral differences were found at the left hemisphere in schizophrenic and schizoaffective but not in bipolar disorder subjects.

Conclusion

The present report contributes to characterize quantitatively the qEEG in drug treated schizophrenic, schizoaffective or bipolar disorder patients.

Analysis of schizophrenia-related genes and electrophysiological measures reveals ZNF804A association with amplitude of P300b elicited by novel sounds

Several genes have recently been identified as risk factors for schizophrenia (SZ) by genome-wide association studies (GWAS), including ZNF804A which is thought to function in transcriptional regulation. However, the downstream pathophysiological changes that these genes confer remain to be elucidated. In 143 subjects (68 clinical high risk, first episode or chronic cases; 75 controls), we examined the association between 21 genetic markers previously identified by SZ GWAS or associated with putative intermediate phenotypes of SZ against three event-related potential (ERP) measures: mismatch negativity (MMN), amplitude of P300 during an auditory oddball task, and P300 amplitude during an auditory novelty oddball task. Controlling for age and sex, significant genetic association surpassing Bonferroni correction was detected between ZNF804A marker rs1344706 and P300 amplitude elicited by novel sounds (beta=4.38, P=1.03 × 10(-4)), which is thought to index orienting of attention to unexpected, salient stimuli. Subsequent analyses revealed that the association was driven by the control subjects (beta=6.35, P=9.08 × 10(-5)), and that the risk allele was correlated with higher novel P300b amplitude, in contrast to the significantly lower amplitude observed in cases compared to controls. Novel P300b amplitude was significantly correlated with a neurocognitive measure of auditory attention under interference conditions, suggesting a relationship between novel P300b amplitude and higher-order attentional processes. Our results suggest pleiotropic effects of ZNF804A on risk for SZ and neural mechanisms that are indexed by the novel P300b ERP component.

Combination of Resting State fMRI, DTI, and sMRI Data to Discriminate Schizophrenia by N-way MCCA + jICA

Multimodal brain imaging data have shown increasing utility in answering both scientifically interesting and clinically relevant questions. Each brain imaging technique provides a different view of brain function or structure, while multimodal fusion capitalizes on the strength of each and may uncover hidden relationships that can merge findings from separate neuroimaging studies. However, most current approaches have focused on pair-wise fusion and there is still relatively little work on N-way data fusion and examination of the relationships among multiple data types. We recently developed an approach called “mCCA + jICA” as a novel multi-way fusion method which is able to investigate the disease risk factors that are either shared or distinct across multiple modalities as well as the full correspondence across modalities. In this paper, we applied this model to combine resting state fMRI (amplitude of low-frequency fluctuation, ALFF), gray matter (GM) density, and DTI (fractional anisotropy, FA) data, in order to elucidate the abnormalities underlying schizophrenia patients (SZs, n = 35) relative to healthy controls (HCs, n = 28). Both modality-common and modality-unique abnormal regions were identified in SZs, which were then used for successful classification for seven modality-combinations, showing the potential for a broad applicability of the mCCA + jICA model and its results. In addition, a pair of GM-DTI components showed significant correlation with the positive symptom subscale of Positive and Negative Syndrome Scale (PANSS), suggesting that GM density changes in default model network along with white-matter disruption in anterior thalamic radiation are associated with increased positive PANSS. Findings suggest the DTI anisotropy changes in frontal lobe may relate to the corresponding functional/structural changes in prefrontal cortex and superior temporal gyrus that are thought to play a role in the clinical expression of SZ.

Neuromagnetic auditory response and its relation to cortical thickness in ultra-high-risk for psychosis

BACKGROUND

Higher cognitive dysfunction, lower perceptual disturbance and its relation to the structures that implicate such processes have been considered as key features in patients with schizophrenia. However, little is known about the relationship between perceptual processing and structural deficits in ultra-high-risk for psychosis.

METHODS

We investigated the dipole moment of M100 auditory evoked response using a magnetoencephalography in 18 patients with schizophrenia, 16 ultra-high-risk for psychosis and 16 healthy controls, and their relation to cortical thinning on Heschl's gyrus and planum temporale.

RESULTS

The auditory evoked M100 dipole moment was decreased in the ultra-high-risk subjects and in the patients with schizophrenia. Ultra-high-risk subjects showed impaired right M100 dipole magnitude, similar to patients with schizophrenia. Robust correlations between the cortical thickness of left Heschl's gyrus and the left M100 dipole moment were found in patients with schizophrenia. Moreover, correlations were also evident between right Heschl's gyrus and right M100 in subjects at ultra-high-risk for psychosis.

CONCLUSIONS

The primary feature of auditory perception in ultra-high-risk subjects and schizophrenia patients is an encoding deficit that manifests as a reduced M100 dipole moment. The relationship between abnormal M100, thinning of cortical generators and their symptomatology were shown to exist prior to the onset of overt psychosis and progressively worsen over time. Therefore, they may be a potential indicator of the development of schizophrenia.

Disrupted white matter connectivity is associated with reduced cortical thickness in the cingulate cortex in schizophrenia

INTRODUCTION

Both impaired white matter connectivity and alterations in gray matter morphometry have repeatedly been reported in schizophrenia. Neurodevelopmental models propose a close linkage between gray matter alterations and white matter deficits. However, there are no studies investigating alterations in cortical thickness in relation to white matter connectivity changes.

METHODS

This combined diffusion tensor imaging (DTI) - surface based morphometry study examined a potential linkage between disruption in white matter connectivity and alterations in cortical thickness. Cortical thickness was analyzed using the FreeSurfer software package (version 4.0.5, http://surfer.nmr.harvard.edu) in a sample of 19 patients with schizophrenia and 20 healthy controls.

RESULTS

Whole brain node-by-node correlational analysis revealed a highly significant association ( r= -.8, p < .0001) between disturbed white matter connectivity in the superior temporal cortex and diminished cortical thickness in the posterior part of the cingulate cortex (Brodmann area 23/31).

CONCLUSIONS

This result indicates a significant linkage between disturbed white matter connectivity and reduced cortical thickness in a relevant node of the default mode network that is held to be of high pathophysiological relevance in schizophrenia. The result moreover provides support for the assumption of a neurodevelopmental pathogenesis of the disorder.

Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model

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.

Genetics and intermediate phenotypes of the schizophrenia--bipolar disorder boundary

Categorization of psychotic illnesses into schizophrenic and affective psychoses remains an ongoing controversy. Although Kraepelinian subtyping of psychosis was historically beneficial, modern genetic and neurophysiological studies do not support dichotomous conceptualization of psychosis. Evidence suggests that schizophrenia and bipolar disorder rather present a clinical continuum with partially overlapping symptom dimensions, neurophysiology, genetics and treatment responses. Recent large scale genetic studies have produced inconsistent findings and exposed an urgent need for re-thinking phenomenology-based approach in psychiatric research. Epidemiological, linkage and molecular genetic studies, as well as studies in intermediate phenotypes (neurocognitive, neurophysiological and anatomical imaging) in schizophrenia and bipolar disorders are reviewed in order to support a dimensional conceptualization of psychosis. Overlapping and unique genetic and intermediate phenotypic signatures of the two psychoses are comprehensively recapitulated. Alternative strategies which may be implicated into genetic research are discussed.

Current source density (CSD) old/new effects during recognition memory for words and faces in schizophrenia and in healthy adults

We previously reported a preserved 'old-new effect' (enhanced parietal positivity 300-800 ms following correctly-recognized repeated words) in schizophrenia over mid-parietal sites using 31-channel nose-referenced event-related potentials (ERP) and reference-free current source densities (CSD). However, patients showed poorer word recognition memory and reduced left lateral-parietal P3 sources. The present study investigated whether these abnormalities are specific to words. High-density ERPs (67 channels) were recorded from 57 schizophrenic (24 females) and 44 healthy (26 females) right-handed adults during parallel visual continuous recognition memory tasks using common words or unknown faces. To identify and measure neuronal generator patterns underlying ERPs, unrestricted Varimax-PCA was performed using CSD estimates (spherical spline surface Laplacian). Two late source factors peaking at 442 ms (lateral parietal maximum) and 723 ms (centroparietal maximum) accounted for most of the variance between 250 and 850 ms. Poorer (76.6+/-20.0% vs. 85.7+/-12.4% correct) and slower (824+/-170 vs. 755+/-147 ms) performance in patients was accompanied by reduced stimulus-locked parietal sources. However, both controls and patients showed mid-frontal (442 ms) and left parietal (723 ms) old/new effects in both tasks. Whereas mid-frontal old/new effects were comparable across groups and tasks, later left parietal old/new effects were markedly reduced in patients over lateral temporoparietal but not mid-parietal sites, particularly for words, implicating impaired phonological processing. In agreement with prior results, ERP correlates of recognition memory deficits in schizophrenia suggest functional impairments of lateral posterior cortex (stimulus representation) associated with conscious recollection. This deficit was more pronounced for common words despite a greater difficulty to recall unknown faces, indicating that it is not due to a generalized cognitive deficit in schizophrenia.

Schizophrenia pathophysiology: are we any closer to a complete model?

Schizophrenia, a severe brain disorder that involves hallucinations, disordered thinking and deficiencies in cognition, has been studied for decades in order to determine the early events that lead to this neurological disorder. In this review, we interpret the developmental and genetic models that have been proposed and treatment options associated with these models. Schizophrenia was initially thought to be hereditary based on studies of high incidence in certain families. Additionally, studies on specific genes such as ZDHHC8 and DTNBP1 seem to suggest susceptibility to the onset of this disorder. However, no single gene variation has been linked to schizophrenia, and recent evidence on sporadic cases of schizophrenia refutes genetics as being a singular cause of the disease. In addition, current data suggests neurodevelopmental or environmental causes such as viral infections and prenatal/perinatal complications. Before any brain disorder can be understood, however, multiple cognitive neuroscientific models that accommodate evidence from many biomedical research fields should be considered, and unfortunately, many of these models are in the earliest stages of development. Consequently, it makes us question whether we are any closer to an adequate understanding of the pathophysiology of schizophrenia.

Stimulus- and response-locked neuronal generator patterns of auditory and visual word recognition memory in schizophrenia

Examining visual word recognition memory (WRM) with nose-referenced EEGs, we reported a preserved ERP 'old-new effect' (enhanced parietal positivity 300-800 ms to correctly-recognized repeated items) in schizophrenia ([Kayser, J., Bruder, G.E., Friedman, D., Tenke, C.E., Amador, X.F., Clark, S.C., Malaspina, D., Gorman, J.M., 1999. Brain event-related potentials (ERPs) in schizophrenia during a word recognition memory task. Int. J. Psychophysiol. 34(3), 249-265.]). However, patients showed reduced early negative potentials (N1, N2) and poorer WRM. Because group differences in neuronal generator patterns (i.e., sink-source orientation) may be masked by choice of EEG recording reference, the current study combined surface Laplacians and principal components analysis (PCA) to clarify ERP component topography and polarity and to disentangle stimulus- and response-related contributions. To investigate the impact of stimulus modality, 31-channel ERPs were recorded from 20 schizophrenic patients (15 male) and 20 age-, gender-, and handedness-matched healthy adults during parallel visual and auditory continuous WRM tasks. Stimulus- and response-locked reference-free current source densities (spherical splines) were submitted to unrestricted Varimax-PCA to identify and measure neuronal generator patterns underlying ERPs. Poorer (78.2+/-18.7% vs. 87.8+/-11.3% correct) and slower (958+/-226 vs. 773+/-206 ms) performance in patients was accompanied by reduced stimulus-related left-parietal P3 sources (150 ms pre-response) and vertex N2 sinks (both overall and old/new effects) but modality-specific N1 sinks were not significantly reduced. A distinct mid-frontal sink 50-ms post-response was markedly attenuated in patients. Reductions were more robust for auditory stimuli. However, patients showed increased lateral-frontotemporal sinks (T7 maximum) concurrent with auditory P3 sources. Electrophysiologic correlates of WRM deficits in schizophrenia suggest functional impairments of posterior cortex (stimulus representation) and anterior cingulate (stimulus categorization, response monitoring), primarily affecting memory for spoken words.