Until relatively recently, long-acting injectable (LAI) formulations were only available for first-generation antipsychotics and their utilization decreased as use of oral second-generation antipsychotics (SGA) increased. Although registry-based naturalistic studies show LAIs reduce rehospitalization more than oral medications in clinical practice, this is not seen in recent randomized clinical trials. PROACTIVE (Preventing Relapse Oral Antipsychotics Compared to Injectables Evaluating Efficacy) relapse prevention study incorporated efficacy and effectiveness features. At 8 US academic centers, 305 patients with schizophrenia or schizoaffective disorder were randomly assigned to LAI risperidone (LAI-R) or physician's choice oral SGAs. Patients were evaluated during the 30-month study by masked, centralized assessors using 2-way video, and monitored biweekly by on-site clinicians and assessors who knew treatment assignment. Relapse was evaluated by a masked Relapse Monitoring Board. Differences between LAI-R and oral SGA treatment in time to first relapse and hospitalization were not significant. Psychotic symptoms and Brief Psychiatric Rating Scale total score improved more in the LAI-R group. In contrast, the LAI group had higher Scale for Assessment of Negative Symptoms Alogia scale scores. There were no other between-group differences in symptoms or functional improvement. Despite the advantage for psychotic symptoms, LAI-R did not confer an advantage over oral SGAs for relapse or rehospitalization. Biweekly monitoring, not focusing specifically on patients with demonstrated nonadherence to treatment and greater flexibility in changing medication in the oral treatment arm, may contribute to the inability to detect differences between LAI and oral SGA treatment in clinical trials.
Introduction:While auditory verbal hallucinations (AH) are a cardinal symptom of schizophrenia, people with a diagnosis of schizophrenia (SZ) may also experience visual hallucinations (VH). In a retrospective analysis of a large sample of SZ and healthy controls (HC) studied as part of the functional magnetic resonance imaging (fMRI) Biomedical Informatics Research Network (FBIRN), we asked if SZ who endorsed experiencing VH during clinical interviews had greater connectivity between visual cortex and limbic structures than SZ who did not endorse experiencing VH.Methods:We analyzed resting state fMRI data from 162 SZ and 178 age- and gender-matched HC. SZ were sorted into groups according to clinical ratings on AH and VH: SZ with VH (VH-SZ; n = 45), SZ with AH but no VH (AH-SZ; n = 50), and SZ with neither AH nor VH (NoH-SZ; n = 67). Our primary analysis was seed based, extracting connectivity between visual cortex and the amygdala (because of its role in fear and negative emotion) and visual cortex and the hippocampus (because of its role in memory). Results:Compared with the other groups, VH-SZ showed hyperconnectivity between the amygdala and visual cortex, specifically BA18, with no differences in connectivity among the other groups. In a voxel-wise, whole brain analysis comparing VH-SZ with AH-SZ, the amygdala was hyperconnected to left temporal pole and inferior frontal gyrus in VH-SZ, likely due to their more severe thought broadcasting.Conclusions:VH-SZ have hyperconnectivity between subcortical areas subserving emotion and cortical areas subserving higher order visual processing, providing biological support for distressing VH in schizophrenia.
Although a number of recent studies have examined functional connectivity at rest, few have assessed differences between connectivity both during rest and across active task paradigms. Therefore, the question of whether cortical connectivity patterns remain stable or change with task engagement continues to be unaddressed. We collected multi-scan fMRI data on healthy controls (N=53) and schizophrenia patients (N=42) during rest and across paradigms arranged hierarchically by sensory load. We measured functional network connectivity among 45 non-artifactual distinct brain networks. Then, we applied a novel analysis to assess cross paradigm connectivity patterns applied to healthy controls and patients with schizophrenia. To detect these patterns, we fit a group by task full factorial ANOVA model to the group average functional network connectivity values. Our approach identified both stable (static effects) and state-based differences (dynamic effects) in brain connectivity providing a better understanding of how individuals' reactions to simple sensory stimuli are conditioned by the context within which they are presented. Our findings suggest that not all group differences observed during rest are detectable in other cognitive states. In addition, the stable differences of heightened connectivity between multiple brain areas with thalamus across tasks underscore the importance of the thalamus as a gateway to sensory input and provide new insight into schizophrenia.
Schizophrenia patients show significant subcortical brain abnormalities. We examined these abnormalities using automated image analysis software and provide effect size estimates for prospective multi-scanner schizophrenia studies. Subcortical and intracranial volumes were obtained using FreeSurfer 5.0.0 from high-resolution structural imaging scans from 186 schizophrenia patients (mean age±S.D.=38.9±11.6, 78% males) and 176 demographically similar controls (mean age±S.D.=37.5±11.2, 72% males). Scans were acquired from seven 3-Tesla scanners. Univariate mixed model regression analyses compared between-group volume differences. Weighted mean effect sizes (and number of subjects needed for 80% power at ?=0.05) were computed based on the individual single site studies as well as on the overall multi-site study. Schizophrenia patients have significantly smaller intracranial, amygdala, and hippocampus volumes and larger lateral ventricle, putamen and pallidum volumes compared with healthy volunteers. Weighted mean effect sizes based on single site studies were generally larger than effect sizes computed based on analysis of the overall multi-site sample. Prospectively collected structural imaging data can be combined across sites to increase statistical power for meaningful group comparisons. Even when using similar scan protocols at each scanner, some between-site variance remains. The multi-scanner effect sizes provided by this study should help in the design of future multi-scanner schizophrenia imaging studies.
The N-methyl-d-aspartic acid receptor hypofunction model of schizophrenia predicts a paradoxical increase in synaptic glutamate release. In vivo measurement of glutamatergic neurotransmission in humans is challenging, but glutamine, the principal metabolite of synaptic glutamate, can be quantified with proton magnetic resonance spectroscopy (1H-MRS). Although a few studies have measured glutamate, glutamine, and glutamine to glutamate ratio, it is not clear which of these 1H-MRS indices of glutamatergic neurotransmission is altered in schizophrenia.
The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
Analyzing Functional Magnetic Resonance Imaging (fMRI) of resting brains to determine the spatial location and activity of intrinsic brain networks--a novel and burgeoning research field--is limited by the lack of ground truth and the tendency of analyses to overfit the data. Independent Component Analysis (ICA) is commonly used to separate the data into signal and Gaussian noise components, and then map these components on to spatial networks. Identifying noise from this data, however, is a tedious process that has proven hard to automate, particularly when data from different institutions, subjects, and scanners is used. Here we present an automated method to delineate noisy independent components in ICA using a data-driven infrastructure that queries a database of 246 spatial and temporal features to discover a computational signature of different types of noise. We evaluated the performance of our method to detect noisy components from healthy control fMRI (sensitivity?=?0.91, specificity?=?0.82, cross validation accuracy (CVA)?=?0.87, area under the curve (AUC)?=?0.93), and demonstrate its generalizability by showing equivalent performance on (1) an age- and scanner-matched cohort of schizophrenia patients from the same institution (sensitivity?=?0.89, specificity?=?0.83, CVA?=?0.86), (2) an age-matched cohort on an equivalent scanner from a different institution (sensitivity?=?0.88, specificity?=?0.88, CVA?=?0.88), and (3) an age-matched cohort on a different scanner from a different institution (sensitivity?=?0.72, specificity?=?0.92, CVA?=?0.79). We additionally compare our approach with a recently published method. Our results suggest that our method is robust to noise variations due to population as well as scanner differences, thereby making it well suited to the goal of automatically distinguishing noise from functional networks to enable investigation of human brain function.
Because of the wide availability of hardware as well as of standardized analytic quantification tools, proton magnetic resonance spectroscopy ((1)H-MRS) has become widely used to study psychiatric disorders. (1)H-MRS allows measurement of brain concentrations of more traditional singlet neurometabolites like N-acetylaspartate, choline, and creatine. More recently, quantification of the more complex multiplet spectra for glutamate, glutamine, inositol, and ?-aminobutyric acid have also been implemented. Here we review applications of (1)H-MRS in terms of informing treatment options in schizophrenia, bipolar disorder, and major depressive disorders. We first discuss recent meta-analytic studies reporting the most reliable findings. Then we evaluate the more sparse literature focused on 1H-MRS-detected neurometabolic effects of various treatment approaches in psychiatric populations. Finally we speculate on future developments that may result in translation of these tools to improve the treatment of psychiatric disorders.
The Scale for the Assessment of Positive Symptoms (SAPS), the Scale for the Assessment of Negative Symptoms (SANS), and the Positive and Negative Syndrome Scale for Schizophrenia (PANSS) are the most widely used schizophrenia symptom rating scales, but despite their co-existence for 25years no easily usable between-scale conversion mechanism exists. The aim of this study was to provide equations for between-scale symptom rating conversions. Two-hundred-and-five schizophrenia patients [mean age±SD=39.5±11.6, 156 males] were assessed with the SANS, SAPS, and PANSS. Pearsons correlations between symptom scores from each of the scales were computed. Linear regression analyses, on data from 176 randomly selected patients, were performed to derive equations for converting ratings between the scales. Intraclass correlations, on data from the remaining 29 patients, not part of the regression analyses, were performed to determine rating conversion accuracy. Between-scale positive and negative symptom ratings were highly correlated. Intraclass correlations between the original positive and negative symptom ratings and those obtained via conversion of alternative ratings using the conversion equations were moderate to high (ICCs=0.65 to 0.91). Regression-based equations may be useful for conversion between schizophrenia symptom severity as measured by the SANS/SAPS and PANSS, though additional validation is warranted. This studys conversion equations, implemented at http:/converteasy.org, may aid in the comparison of medication efficacy studies, in meta- and mega-analyses examining symptoms as moderator variables, and in retrospective combination of symptom data in multi-center data sharing projects that need to pool symptom rating data when such data are obtained using different scales.
Uncontrolled studies have suggested that increasing the dose of ziprasidone above the standard maximum daily dose of 160 mg may be more effective for some patients with schizophrenia. To test this hypothesis, we conducted an 8-week, placebo-controlled, fixed-dose escalation trial comparing ziprasidone 160 versus 320 mg/d in individuals with schizophrenia or schizoaffective disorder who remained symptomatic despite treatment with ziprasidone 160 mg/d for at least 3 weeks. Of 75 randomized patients, 42 completed the study. Serum ziprasidone concentrations increased significantly in the high-dose group compared with the standard-dose group at week 4 but did not differ between groups at week 8. Both treatment groups exhibited significant symptomatic improvement. Response did not differ between treatment groups; however, in the high-dose group, higher ziprasidone serum concentrations were associated with better response at a trend level. Higher ziprasidone concentrations were also associated with reductions in diastolic blood pressure and, at a trend level, with more prominent negative symptoms and greater QTc prolongation. In summary, increasing the ziprasidone dose to 320 mg/d did not produce a sustained elevation in serum concentrations or symptomatic improvement compared with a standard ziprasidone dose of 160 mg/d.
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.
DNA methylation, one of the main epigenetic mechanisms to regulate gene expression, appears to be involved in the development of schizophrenia (SZ). In this study, we investigated 7562 DNA methylation markers in blood from 98 SZ patients and 108 healthy controls. A linear regression model including age, gender, race, alcohol, nicotine and cannabis use status, and diagnosis was implemented to identify C-phosphate-G (CpG) sites significantly associated with diagnosis. These CpG sites were further validated using an independent data set. Sixteen CpG sites were identified with hyper- or hypomethylation in patients. A further verification of expression of the corresponding genes identified 7 genes whose expression levels were also significantly altered in patients. While such altered methylation patterns showed no correlation with disorganized symptoms and negative symptoms in patients, 11 CpG sites significantly correlated with reality distortion symptoms. The direction of the correlations indicates that methylation changes possibly play a protective mechanism to lessen delusion and hallucination symptoms in patients. Pathway analyses showed that the most significant biological function of the differentially methylated CpGs is inflammatory response with CD224, LAX1, TXK, PRF1, CD7, MPG, and MPO genes directly involved in activations of T cells, B cells, and natural killer cells or in cytotoxic reaction. Our results suggest that such methylation changes may modulate aspects of the immune response and hence protect against the neurobiological substrate of reality distortion symptoms in SZ patients.
Although dementia praecox or schizophrenia has been considered a unique disease for over a century, its definitions and boundaries have changed over this period and its etiology and pathophysiology remain elusive. Despite changing definitions, DSM-IV schizophrenia is reliably diagnosed, has fair validity and conveys useful clinical information. Therefore, the essence of the broad DSM-IV definition of schizophrenia is retained in DSM-5. The clinical manifestations are extremely diverse, however, with this heterogeneity being poorly explained by the DSM-IV clinical subtypes and course specifiers. Additionally, the boundaries of schizophrenia are imprecisely demarcated from schizoaffective disorder and other diagnostic categories and its special emphasis on Schneiderian "first-rank" symptoms appears misplaced. Changes in the definition of schizophrenia in DSM-5 seek to address these shortcomings and incorporate the new information about the nature of the disorder accumulated over the past two decades. Specific changes in its definition include elimination of the classic subtypes, addition of unique psychopathological dimensions, clarification of cross-sectional and longitudinal course specifiers, elimination of special treatment of Schneiderian first-rank symptoms, better delineation of schizophrenia from schizoaffective disorder, and clarification of the relationship of schizophrenia to catatonia. These changes should improve diagnosis and characterization of individuals with schizophrenia and facilitate measurement-based treatment and concurrently provide a more useful platform for research that will elucidate its nature and permit a more precise future delineation of the schizophrenias.
Although catatonia has historically been associated with schizophrenia and is listed as a subtype of the disorder, it can occur in patients with a primary mood disorder and in association with neurological diseases and other general medical conditions. Consequently, catatonia secondary to a general medical condition was included as a new condition and catatonia was added as an episode specifier of major mood disorders in DSM-IV. Different sets of criteria are utilized to diagnose catatonia in schizophrenia and primary mood disorders versus neurological/medical conditions in DSM-IV, however, and catatonia is a codable subtype of schizophrenia but a specifier for major mood disorders without coding. In part because of this discrepant treatment across the DSM-IV manual, catatonia is frequently not recognized by clinicians. Additionally, catatonia is known to occur in several conditions other than schizophrenia, major mood disorders, or secondary to a general medical condition. Four changes are therefore made in the treatment of catatonia in DSM-5. A single set of criteria will be utilized to diagnose catatonia across the diagnostic manual and catatonia will be a specifier for both schizophrenia and major mood disorders. Additionally, catatonia will also be a specifier for other psychotic disorders, including schizoaffective disorder, schizophreniform disorder, brief psychotic disorder, and substance-induced psychotic disorder. A new residual category of catatonia not otherwise specified will be added to allow for the rapid diagnosis and specific treatment of catatonia in severely ill patients for whom the underlying diagnosis is not immediately available. These changes should improve the consistent recognition of catatonia across the range of psychiatric disorders and facilitate its specific treatment.
One application of imaging genomics is to explore genetic variants associated with brain structure and function, presenting a new means of mapping genetic influences on mental disorders. While there is growing interest in performing genome-wide searches for determinants, it remains challenging to identify genetic factors of small effect size, especially in limited sample sizes. In an attempt to address this issue, we propose to take advantage of a priori knowledge, specifically to extend parallel independent component analysis (pICA) to incorporate a reference (pICA-R), aiming to better reveal relationships between hidden factors of a particular attribute. The new approach was first evaluated on simulated data for its performance under different configurations of effect size and dimensionality. Then pICA-R was applied to a 300-participant (140 schizophrenia (SZ) patients versus 160 healthy controls) dataset consisting of structural magnetic resonance imaging (sMRI) and single nucleotide polymorphism (SNP) data. Guided by a reference SNP set derived from ANK3, a gene implicated by the Psychiatric Genomic Consortium SZ study, pICA-R identified one pair of SNP and sMRI components with a significant loading correlation of 0.27 (p=1.64×10(-6)). The sMRI component showed a significant group difference in loading parameters between patients and controls (p=1.33×10(-15)), indicating SZ-related reduction in gray matter concentration in prefrontal and temporal regions. The linked SNP component also showed a group difference (p=0.04) and was predominantly contributed to by 1030 SNPs. The effect of these top contributing SNPs was verified using association test results of the Psychiatric Genomic Consortium SZ study, where the 1030 SNPs exhibited significant SZ enrichment compared to the whole genome. In addition, pathway analyses indicated the genetic component majorly relating to neurotransmitter and nervous system signaling pathways. Given the simulation and experiment results, pICA-R may prove a promising multivariate approach for use in imaging genomics to discover reliable genetic risk factors under a scenario of relatively high dimensionality and small effect size.
Despite advances in the treatment of schizophrenia over the past half-century, the illness is frequently associated with a poor outcome. This is principally related to the late identification and intervention in the course of the illness by which time patients have experienced a substantial amount of socio-occupational decline that can be difficult to reverse. The emphasis has therefore shifted to defining psychosis-risk syndromes and evaluating treatments that can prevent transition to psychosis in these ultra-high risk groups. To consider the appropriateness of adding psychosis risk syndrome to our diagnostic nomenclature, the psychotic disorders work group extensively reviewed all available data, consulted a range of experts, and carefully considered the variety of expert and public comments on the topic. It was clear that reliable methods were available to define a syndrome characterized by sub-threshold psychotic symptoms (in severity or duration) and which was associated with a very significant increase in the risk of development of a full-fledged psychotic disorder (schizophrenia spectrum, psychotic mood disorder, and other psychotic disorders) within the next year. At the same time, the majority of individuals with "attenuated psychotic symptoms" had one or more other current psychiatric comorbid conditions (usually mood or anxiety disorders, substance use disorder; Fusar-Poli 2012) and exhibited a range of psychiatric outcomes other than conversion to psychosis (significant proportions either fully recover or develop some other psychiatric disorder, with a minority developing a psychotic disorder). Although the reliability of the diagnosis is well established in academic and research settings, it was found to be less so in community and other clinical settings. Furthermore, the nosological relationship of attenuated psychosis syndrome (APS) to schizotypal personality disorder and other psychiatric conditions was unclear. Further study will hopefully resolve these questions. The work group decided to recommend the inclusion of attenuated psychosis syndrome as a category in the appendix (Section 3) of DSM-5 as a condition for further study.
Characterization of patients with both psychotic and mood symptoms, either concurrently or at different points during their illness, has always posed a nosological challenge and this is reflected in the poor reliability, low diagnostic stability, and questionable validity of DSM-IV Schizoaffective Disorder. The clinical reality of the frequent co-occurrence of psychosis and Mood Episodes has also resulted in over-utilization of a diagnostic category that was originally intended to only rarely be needed. In the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, an effort is made to improve reliability of this condition by providing more specific criteria and the concept of Schizoaffective Disorder shifts from an episode diagnosis in DSM-IV to a life-course of the illness in DSM-5. When psychotic symptoms occur exclusively during a Mood Episode, DSM-5 indicates that the diagnosis is the appropriate Mood Disorder with Psychotic Features, but when such a psychotic condition includes at least a two-week period of psychosis without prominent mood symptoms, the diagnosis may be either Schizoaffective Disorder or Schizophrenia. In the DSM-5, the diagnosis of Schizoaffective Disorder can be made only if full Mood Disorder episodes have been present for the majority of the total active and residual course of illness, from the onset of psychotic symptoms up until the current diagnosis. In earlier DSM versions the boundary between Schizophrenia and Schizoaffective Disorder was only qualitatively defined, leading to poor reliability. This change will provide a clearer separation between Schizophrenia with mood symptoms from Schizoaffective Disorder and will also likely reduce rates of diagnosis of Schizoaffective Disorder while increasing the stability of this diagnosis once made.
Work on the causes and treatment of schizophrenia and other psychotic disorders has long recognized the heterogeneity of the symptoms that can be displayed by individuals with these illnesses. Further, researchers have increasingly emphasized the ways in which the severity of different symptoms of this illness can vary across individuals, and have provided evidence that the severity of such symptoms can predict other important aspects of the illness, such as the degree of cognitive and/or neurobiological deficits. Additionally, research has increasingly emphasized that the boundaries between nosological entities may not be categorical and that the comorbidity of disorders may reflect impairments in common dimensions of genetic variation, human behavior and neurobiological function. As such, it is critical to focus on a dimensional approach to the assessment of symptoms and clinically relevant phenomena in psychosis, so as to increase attention to and understanding of the causes and consequences of such variation. In the current article, we review the logic and justification for including dimensional assessment of clinical symptoms in the evaluation of psychosis in the Fifth Edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5).
Schizophrenia spectrum disorders attract great interest among clinicians, researchers, and the lay public. While the diagnostic features of schizophrenia have remained unchanged for more than 100 years, the mechanism of illness has remained elusive. There is increasing evidence that the categorical diagnosis of schizophrenia and other psychotic disorders contributes to this lack of progress. The 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) continues the categorical classification of psychiatric disorders since the research needed to establish a new nosology of equal or greater validity is lacking. However, even within a categorical system, the DSM-5 aims to capture the underlying dimensional structure of psychosis. The domains of psychopathology that define psychotic disorders are presented not simply as features of schizophrenia. The level, the number, and the duration of psychotic signs and symptoms are used to demarcate psychotic disorders from each other. Finally, the categorical assessment is complemented with a dimensional assessment of psychosis that allows for more specific and individualized assessment of patients. The structure of psychosis as outlined in the DSM-5 may serve as a stepping-stone towards a more valid classification system, as we await new data to redefine psychotic disorders.
Major depressive disorder (MDD) is associated with increased functional connectivity in specific neural networks. Electroconvulsive therapy (ECT), the gold-standard treatment for acute, treatment-resistant MDD, but temporal dependencies between networks associated with ECT response have yet to be investigated. In the present longitudinal, case-control investigation, we used independent component analysis to identify distinct networks of brain regions with temporally coherent hemodynamic signal change and functional network connectivity (FNC) to assess component time course correlations across these networks. MDD subjects completed imaging and clinical assessments immediately prior to the ECT series and a minimum of 5?days after the last ECT treatment. We focused our analysis on four networks affected in MDD: the subcallosal cingulate gyrus, default mode, dorsal lateral prefrontal cortex, and dorsal medial prefrontal cortex (DMPFC). In an older sample of ECT subjects (n?=?12) with MDD, remission associated with the ECT series reverses the relationship from negative to positive between the posterior default mode (p_DM) and two other networks: the DMPFC and left dorsal lateral prefrontal cortex (l_DLPFC). Relative to demographically healthy subjects (n?=?12), the FNC between the p_DM areas and the DMPFC normalizes with ECT response. The FNC changes following treatment did not correlate with symptom improvement; however, a direct comparison between ECT remitters and non-remitters showed the pattern of increased FNC between the p_DM and l_DLPFC following ECT to be specific to those who responded to the treatment. The differences between ECT remitters and non-remitters suggest that this increased FNC between p_DM areas and the left dorsolateral prefrontal cortex is a neural correlate and potential biomarker of recovery from a depressed episode.
Although magnetoencephalography (MEG) studies show superior temporal gyrus (STG) auditory processing abnormalities in schizophrenia at 50 and 100 ms, EEG and corticography studies suggest involvement of additional brain areas (e.g., frontal areas) during this interval. Study goals were to identify 30 to 130 ms auditory encoding processes in schizophrenia (SZ) and healthy controls (HC) and group differences throughout the cortex.
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.
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.
The hippocampus has long been known to be important for memory, with the right hippocampus particularly implicated in nonverbal/visuo-spatial memory and the left in verbal/narrative or episodic memory. Despite this hypothesized lateralized functional difference, there has not been a single task that has been shown to activate both the right and left hippocampi differentially, dissociating the two, using neuroimaging. The transverse patterning (TP) task is a strong candidate for this purpose, as it has been shown in human and nonhuman animal studies to theoretically and empirically depend on the hippocampus. In TP, participants choose between stimuli presented in pairs, with the correct choice being a function of the specific pairing. In this project, TP was used to assess lateralized hippocampal function by varying its dependence on verbal material, with the goal of dissociating the two hippocampi. Magnetoencephalographic (MEG) data were collected while controls performed verbal and nonverbal versions of TP in order to verify and validate lateralized activation within the hippocampi. Schizophrenia patients were evaluated to determine whether they exhibited a lateralized hippocampal deficit. As hypothesized, patients mean level of behavioral performance was poorer than controls on both verbal and nonverbal TP. In contrast, patients had no decrement in performance on a verbal and nonverbal non-hippocampal-dependent matched control task. Also, controls but not patients showed more right hippocampal activation during nonverbal TP and more left hippocampal activation during verbal TP. These data demonstrate the capacity to assess lateralized hippocampal function and suggest a bilateral hippocampal behavioral and activation deficit in schizophrenia.
In real-world settings, information from multiple sensory modalities is combined to form a complete, behaviorally salient percept - a process known as multisensory integration. While deficits in auditory and visual processing are often observed in schizophrenia, little is known about how multisensory integration is affected by the disorder. The present study examined auditory, visual, and combined audio-visual processing in schizophrenia patients using high-density electrical mapping. An ecologically relevant task was used to compare unisensory and multisensory evoked potentials from schizophrenia patients to potentials from healthy normal volunteers. Analysis of unisensory responses revealed a large decrease in the N100 component of the auditory-evoked potential, as well as early differences in the visual-evoked components in the schizophrenia group. Differences in early evoked responses to multisensory stimuli were also detected. Multisensory facilitation was assessed by comparing the sum of auditory and visual evoked responses to the audio-visual evoked response. Schizophrenia patients showed a significantly greater absolute magnitude response to audio-visual stimuli than to summed unisensory stimuli when compared to healthy volunteers, indicating significantly greater multisensory facilitation in the patient group. Behavioral responses also indicated increased facilitation from multisensory stimuli. The results represent the first report of increased multisensory facilitation in schizophrenia and suggest that, although unisensory deficits are present, compensatory mechanisms may exist under certain conditions that permit improved multisensory integration in individuals afflicted with the disorder.
As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12-71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.
Previous studies have shown that patients with schizophrenia have less modulation of the task-positive and default mode neural networks during novelty detection. The diminished modulation may be interpreted as less functional activation of the task-positive network and less functional deactivation of the default mode network. The relationship between network modulation and age has not been assessed in patients with a long duration of illness.
Cognitive deficits in schizophrenia may be related to glutamatergic dysfunction, but in vivo measurement of glutamate metabolism has been challenging. We examined the relationship between glutamate metabolism and cognitive function in schizophrenia.
Emerging evidence implicates white matter (WM) abnormalities in the pathophysiology of schizophrenia. However, there is considerable heterogeneity in the presentation of WM abnormalities in the existing studies. The object of this study was to evaluate WM integrity in a large sample of patients with first-episode (FE) and chronic schizophrenia in comparison to matched control groups. Our goal was to assess whether WM findings occurred early in the illness or whether these abnormalities developed with the illness over time.
General cognitive ability is usually lower in individuals with schizophrenia, partly due to genetic influences. However, the specific genetic features related to general cognitive ability are poorly understood. Individual variation in a specific type of mutation, uncommon genetic deletions, has recently been linked with both general cognitive ability and risk for schizophrenia.
Hippocampal (relational memory) and prefrontal cortex (PFC; working memory) impairments have been found in patients with schizophrenia (SP), possibly due to a dysfunctional connection between structures. Neuroanatomical studies that describe reduced fractional anisotropy (FA) in the uncinate fasciculus support this idea. The dysconnection hypothesis in SP was investigated by examining frontotemporal anatomical connectivity (uncinate fasciculus FA) and PFC-hippocampal memory and their relationship with each other and everyday functioning. PFC-hippocampal memory was examined with two working-relational memory tasks: transverse patterning and a virtual Morris water task. SP exhibited a performance deficit on both tasks and had lower FA in bilateral uncinate fasciculus than healthy volunteers. Lower frontotemporal anatomical connectivity was related to lower working-relational memory performance, and both predicted worse everyday functioning.
Structural brain measures are employed as endophenotypes in the search for schizophrenia susceptibility genes. We analyzed two independent structural imaging datasets with voxel-based morphometry and with source-based morphometry, a multivariate, independent components analysis, to determine the stability and heritability of regional gray matter concentration abnormalities in schizophrenia. The samples comprised 209 and 102 patients with schizophrenia and 208 and 96 healthy volunteers, respectively. The second sample additionally included non-ill siblings of participants with and without schizophrenia. A standard voxel-based analysis showed reproducible regional gray matter deficits in the affected participants compared with unrelated, unaffected controls in both datasets: patients showed significant gray matter concentration deficits in cortical frontal, temporal, and insular lobes. Source-based morphometry (SBM) was applied to the gray matter images of the entire sample to determine the effects of diagnosis on networks of covarying structures. The SBM analysis extracted 24 significant sets of covarying regions (components). Four of these components showed significantly lower gray matter concentrations in patients (p < .05). We determined the familiality of the observed SBM components based on 66 sibling pairs (25 discordant for schizophrenia). Two components, one including the medial frontal, insular, inferior frontal, and temporal lobes, and the other including the posterior occipital lobe, showed significant familiality (p < .05). We conclude that structural brain deficits in schizophrenia are replicable, and that SBM can extract unique familial and likely heritable components. SBM provides a useful data reduction technique that can provide measures that may serve as endophenotypes for schizophrenia.
The resting state amplitude of low frequency fluctuations (ALFF) in functional magnetic resonance imaging has been shown to be reliable in healthy subjects, and to correlate with antipsychotic treatment response in antipsychotic-naïve schizophrenia patients. We found moderate to high test-retest stability of ALFF in chronically treated schizophrenia patients assessed twice over a median interval of 2.5 months.
Proton magnetic resonance spectroscopy ((1)H-MRS) clinical studies of patients with schizophrenia document prefrontal N-acetylaspartate (NAA) reductions, suggesting an effect of the disease or of antipsychotic medications. We studied in the rat the effect of prolonged exposure to a low-dose of the NMDA glutamate receptor antagonist phencyclidine (PCP) on levels of NAA, glutamate and glutamine in several brain regions where metabolite reductions have been reported in chronically medicated patients with schizophrenia.
The cortical (auditory and prefrontal) and/or subcortical (thalamic and hippocampal) generators of abnormal electrophysiological responses during sensory gating remain actively debated in the schizophrenia literature. Functional magnetic resonance imaging has the spatial resolution for disambiguating deep or simultaneous sources but has been relatively under-utilized to investigate generators of the gating response. Thirty patients with chronic schizophrenia (SP) and 30 matched controls participated in the current experiment. Hemodynamic response functions (HRFs) for single (S1) and pairs (S1 + S2) of identical ("gating-out" redundant information) or nonidentical ("gating-in" novel information) tones were generated through deconvolution. Increased or prolonged activation for patients in conjunction with deactivation for controls was observed within auditory cortex, prefrontal cortex, and thalamus in response to single tones during the late hemodynamic response, and these group differences were not associated with clinical or cognitive symptomatology. Although patient hyperactivation to paired-tones conditions was present in several regions of interest, the effects were not statistically significant for either the gating-out or gating-in conditions. Finally, abnormalities in the postundershoot of the auditory HRF were also observed for both single and paired-tones conditions in patients. In conclusion, the amalgamation of the entire electrophysiological response to both S1 and S2 stimuli may limit hemodynamic sensitivity to paired tones during sensory gating, which may be more readily overcome by paradigms that use multiple stimuli rather than pairs. Patient hyperactivation following single tones is suggestive of deficits in basic inhibition, neurovascular abnormalities, or a combination of both factors.
The majority of patients with schizophrenia smoke cigarettes. Both nicotine use and schizophrenia have been associated with alterations in brain white matter microstructure as measured by diffusion tensor imaging (DTI). The purpose of this study was to examine fractional anisotropy (FA) in smoking and non-smoking patients with schizophrenia and in healthy volunteers. A total of 43 patients (28 smoking and 15 non-smoking) with schizophrenia and 40 healthy, non-smoking participants underwent DTI. Mean FA was calculated in four global regions of interest (ROIs) (whole brain, cerebellum, brainstem, and total cortical) as well as in four regional ROIs (frontal, temporal, parietal and occipital lobes). The non-smoking patient group had a significantly higher intellectual quotient (IQ) compared with the patients who smoked, and our results varied according to whether IQ was included as a covariate. Without IQ correction, significant between-group effects for FA were found in four ROIs: total brain, total cortical, frontal lobe and the occipital lobe. In all cases the FA was lower among the smoking patient group, and highest in the control group. Smoking patients differed significantly from non-smoking patients in the frontal lobe ROI. However, these differences were no longer significant after IQ correction. FA differences between non-smoking patients and controls were not significant. Among smoking and non-smoking patients with schizophrenia but not healthy controls, FA was correlated with IQ. In conclusion, group effects of smoking on FA in schizophrenia might be mediated by IQ. Further, low FA in specific brain areas may be a neural marker for complex pathophysiology and risk for diverse problems such as schizophrenia, low IQ, and nicotine addiction.
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