Bilateral hippocampal dysfunction in schizophrenia

Normative model detects abnormal functional connectivity in psychiatric disorders

INTRODUCTION

The diagnosis of psychiatric disorders is mostly based on the clinical evaluation of the patient's signs and symptoms. Deep learning binary-based classification models have been developed to improve the diagnosis but have not yet reached clinical practice, in part due to the heterogeneity of such disorders. Here, we propose a normative model based on autoencoders.

METHODS

We trained our autoencoder on resting-state functional magnetic resonance imaging (rs-fMRI) data from healthy controls. The model was then tested on schizophrenia (SCZ), bipolar disorder (BD), and attention-deficit hyperactivity disorder (ADHD) patients to estimate how each patient deviated from the norm and associate it with abnormal functional brain networks' (FBNs) connectivity. Rs-fMRI data processing was conducted within the FMRIB Software Library (FSL), which included independent component analysis and dual regression. Pearson's correlation coefficients between the extracted blood oxygen level-dependent (BOLD) time series of all FBNs were calculated, and a correlation matrix was generated for each subject.

RESULTS AND DISCUSSION

We found that the functional connectivity related to the basal ganglia network seems to play an important role in the neuropathology of BD and SCZ, whereas in ADHD, its role is less evident. Moreover, the abnormal connectivity between the basal ganglia network and the language network is more specific to BD. The connectivity between the higher visual network and the right executive control and the connectivity between the anterior salience network and the precuneus networks are the most relevant in SCZ and ADHD, respectively. The results demonstrate that the proposed model could identify functional connectivity patterns that characterize different psychiatric disorders, in agreement with the literature. The abnormal connectivity patterns from the two independent SCZ groups of patients were similar, demonstrating that the presented normative model was also generalizable. However, the group-level differences did not withstand individual-level analysis implying that psychiatric disorders are highly heterogeneous. These findings suggest that a precision-based medical approach, focusing on each patient's specific functional network changes may be more beneficial than the traditional group-based diagnostic classification.

Reduced intrinsic neural timescales in schizophrenia along posterior parietal and occipital areas

We computed intrinsic neural timescales (INT) based on resting-state functional magnetic resonance imaging (rsfMRI) data of healthy controls (HC) and patients with schizophrenia spectrum disorder (SZ) from three independently collected samples. Five clusters showed decreased INT in SZ compared to HC in all three samples: right occipital fusiform gyrus (rOFG), left superior occipital gyrus (lSOG), right superior occipital gyrus (rSOG), left lateral occipital cortex (lLOC) and right postcentral gyrus (rPG). In other words, it appears that sensory information in visual and posterior parietal areas is stored for reduced lengths of time in SZ compared to HC. Finally, we found that symptom severity appears to modulate INT of these areas in SZ.

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.

Cognitive impairment in schizophrenia: relationships with cortical thickness in fronto-temporal regions, and dissociability from symptom severity

Cognitive impairments are a core and persistent characteristic of schizophrenia with implications for daily functioning. These show only limited response to antipsychotic treatment and their neural basis is not well characterised. Previous studies point to relationships between cortical thickness and cognitive performance in fronto-temporal brain regions in schizophrenia patients (SZH). There is also evidence that these relationships might be independent of symptom severity, suggesting dissociable disease processes. We set out to explore these possibilities in a sample of 70 SZH and 72 age and gender-matched healthy controls (provided by the Center of Biomedical Research Excellence (COBRE)). Cortical thickness within fronto-temporal regions implicated by previous work was considered in relation to performance across various cognitive domains (from the MATRICS Cognitive Battery). Compared to controls, SZH had thinner cortices across most fronto-temporal regions and significantly lower performance on all cognitive domains. Robust relationships with cortical thickness were found: visual learning and attention performance correlated with bilateral superior and middle frontal thickness in SZH only. Correlations between attention performance and right transverse temporal thickness were also specific to SZH. Findings point to the importance of these regions for cognitive performance in SZH, possibly reflecting compensatory processes and/or aberrant connectivity. No links to symptom severity were observed in these regions, suggesting these relationships are dissociable from underlying psychotic symptomology. Findings enhance understanding of the brain structural underpinnings and possible aetiology of cognitive impairment in SZH.

Structural Covariance Reveals Alterations in Control and Salience Network Integrity in Chronic Schizophrenia

Schizophrenia (SCZ) is recognized as a disorder of distributed brain dysconnectivity. While progress has been made delineating large-scale functional networks in SCZ, little is known about alterations in grey matter integrity of these networks. We used a multivariate approach to identify the structural covariance of the salience, default, motor, visual, fronto-parietal control, and dorsal attention networks. We derived individual scores reflecting covariance in each structural image for a given network. Seed-based multivariate analyses were conducted on structural images in a discovery (n = 90) and replication (n = 74) sample of SCZ patients and healthy controls. We first validated patterns across all networks, consistent with well-established functional connectivity reports. Next, across two SCZ samples, we found reliable and robust reductions in structural integrity of the fronto-parietal control and salience networks, but not default, dorsal attention, motor and sensory networks. Well-powered exploratory analyses failed to identify relationships with symptoms. These findings provide evidence of selective structural decline in associative networks in SCZ. Such decline may be linked with recently identified functional disturbances in associative networks, providing more sensitive multi-modal network-level probes in SCZ. Absence of symptom effects suggests that identified disturbances may underlie a trait-type marker in SCZ.

Impaired theta phase coupling underlies frontotemporal dysconnectivity in schizophrenia

Frontotemporal dysconnectivity is a key pathology in schizophrenia. The specific nature of this dysconnectivity is unknown, but animal models imply dysfunctional theta phase coupling between hippocampus and medial prefrontal cortex (mPFC). We tested this hypothesis by examining neural dynamics in 18 participants with a schizophrenia diagnosis, both medicated and unmedicated; and 26 age, sex and IQ matched control subjects. All participants completed two tasks known to elicit hippocampal-prefrontal theta coupling: a spatial memory task (during magnetoencephalography) and a memory integration task. In addition, an overlapping group of 33 schizophrenia and 29 control subjects underwent PET to measure the availability of GABAARs expressing the α5 subunit (concentrated on hippocampal somatostatin interneurons). We demonstrate-in the spatial memory task, during memory recall-that theta power increases in left medial temporal lobe (mTL) are impaired in schizophrenia, as is theta phase coupling between mPFC and mTL. Importantly, the latter cannot be explained by theta power changes, head movement, antipsychotics, cannabis use, or IQ, and is not found in other frequency bands. Moreover, mPFC-mTL theta coupling correlated strongly with performance in controls, but not in subjects with schizophrenia, who were mildly impaired at the spatial memory task and no better than chance on the memory integration task. Finally, mTL regions showing reduced phase coupling in schizophrenia magnetoencephalography participants overlapped substantially with areas of diminished α5-GABAAR availability in the wider schizophrenia PET sample. These results indicate that mPFC-mTL dysconnectivity in schizophrenia is due to a loss of theta phase coupling, and imply α5-GABAARs (and the cells that express them) have a role in this process.

Decentralized distribution-sampled classification models with application to brain imaging

BACKGROUND

In this age of big data, certain models require very large data stores in order to be informative and accurate. In many cases however, the data are stored in separate locations requiring data transfer between local sites which can cause various practical hurdles, such as privacy concerns or heavy network load. This is especially true for medical imaging data, which can be constrained due to the health insurance portability and accountability act (HIPAA) which provides security protocols for medical data. Medical imaging datasets can also contain many thousands or millions of features, requiring heavy network load.

NEW METHOD

Our research expands upon current decentralized classification research by implementing a new singleshot method for both neural networks and support vector machines. Our approach is to estimate the statistical distribution of the data at each local site and pass this information to the other local sites where each site resamples from the individual distributions and trains a model on both locally available data and the resampled data. The model for each local site produces its own accuracy value which are then averaged together to produce the global average accuracy.

RESULTS

We show applications of our approach to handwritten digit classification as well as to multi-subject classification of brain imaging data collected from patients with schizophrenia and healthy controls. Overall, the results showed comparable classification accuracy to the centralized model with lower network load than multishot methods.

COMPARISON WITH EXISTING METHODS

Many decentralized classifiers are multishot, requiring heavy network traffic. Our model attempts to alleviate this load while preserving prediction accuracy.

CONCLUSIONS

We show that our proposed approach performs comparably to a centralized approach while minimizing network traffic compared to multishot methods.

Study on the sub-regions volume of hippocampus and amygdala in schizophrenia

Background

Many studies have found volume changes in the hippocampus and amygdala in patients with schizophrenia, but these findings have not reached an agreement. Particularly, few results showed the volumes of the sub-regions of the amygdala. In this research, we aim to clarify volume changes of hippocampus and amygdala sub-regions in patients with schizophrenia.

Methods

The sample consisted of 69 patients with schizophrenia and 72 control subjects aged from 18 to 65 years. FreeSurfer 6.0 software was used on T1-weighted images to assess the volumes of hippocampus and amygdala and their sub-regions. The general linear model (GLM) was used to analyze the volume changes between the two groups. False discovery rate (FDR) correction was performed, and the significance level was set at 0.05.

Results

The hippocampus volume in schizophrenia showed reduction compared to healthy control (P<0.05). Several hippocampal subfields showed smaller volume in schizophrenia patients, including bilateral presubiculum and molecular layer, left hippocampal tail, subiculum and cornus ammonis (CA)1, and right parasubiculum (P<0.05). Left amygdala volume showed a decrease as well, sub-regions including the bilateral basal nucleus, anterior-amygdaloid-area (AAA), paralaminar nucleus and left lateral nucleus (P<0.05).

Conclusions

Several sub-regions of hippocampus and amygdala showed a volumetric decline in patients group, which suggest the key roles of these regions in the pathophysiology of schizophrenia. Based on these results, we speculate that these regions could be used to assess the early finding of schizophrenia.

A symptom-based continuum of psychosis explains cognitive and real-world functional deficits better than traditional diagnoses

BACKGROUND

Patients with psychotic spectrum disorders share overlapping clinical/biological features, making it often difficult to separate them into a discrete nosology (i.e., Diagnostic and Statistical Manual of Mental Disorders [DSM]).

METHODS

The current study investigated whether a continuum classification scheme based on symptom burden would improve conceptualizations for cognitive and real-world dysfunction relative to traditional DSM nosology. Two independent samples (New Mexico [NM] and Bipolar and Schizophrenia Network on Intermediate Phenotypes [B-SNIP]) of patients with schizophrenia (NM: N = 93; B-SNIP: N = 236), bipolar disorder Type I (NM: N = 42; B-SNIP: N = 195) or schizoaffective disorder (NM: N = 15; B-SNIP: N = 148) and matched healthy controls (NM: N = 64; B-SNIP: N = 717) were examined. Linear regressions examined how variance differed as a function of classification scheme (DSM diagnosis, negative and positive symptom burden, or a three-cluster solution based on symptom burden).

RESULTS

Symptom-based classification schemes (continuous and clustered) accounted for a significantly larger portion of captured variance of real-world functioning relative to DSM diagnoses across both samples. The symptom-based classification schemes accounted for large percentages of variance for general cognitive ability and cognitive domains in the NM sample. However, in the B-SNIP sample, symptom-based classification schemes accounted for roughly equivalent variance as DSM diagnoses. A potential mediating variable across samples was the strength of the relationship between negative symptoms and impaired cognition.

CONCLUSIONS

Current results support suggestions that a continuum perspective of psychopathology may be more powerful for explaining real-world functioning than the DSM diagnostic nosology, whereas results for cognitive dysfunction were sample dependent.

Translational bioinformatics in mental health: open access data sources and computational biomarker discovery

Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data.

Kernel machine tests of association between brain networks and phenotypes

Applications of quantitative network analysis to functional brain connectivity have become popular in the last decade due to their ability to describe the general topological principles of brain networks. However, many issues arise when standard statistical analysis techniques are applied to functional magnetic resonance imaging (fMRI) connectivity maps. Frequently, summary measures of these maps, such as global efficiency and clustering coefficients, collapse the changing structures of graph topology from many scales to one. This can result in a loss of whole-brain spatio-temporal pattern information that may be significant in association and prediction analyses. Drawing from the electrical engineering field, the resistance perturbation distance is a quantification of similarity between graphs on the same vertex set that has been shown to identify changes in dynamic graphs, such as those from fMRI, while not being computationally expensive or result in a loss of information. This work proposes a novel kernel-based regression scheme that incorporates the resistance perturbation distance to better understand the association with biological phenotypes from fMRI using both simulated and real datasets.

Characterizing hippocampal dynamics with MEG: A systematic review and evidence-based guidelines

The hippocampus, a hub of activity for a variety of important cognitive processes, is a target of increasing interest for researchers and clinicians. Magnetoencephalography (MEG) is an attractive technique for imaging spectro-temporal aspects of function, for example, neural oscillations and network timing, especially in shallow cortical structures. However, the decrease in MEG signal-to-noise ratio as a function of source depth implies that the utility of MEG for investigations of deeper brain structures, including the hippocampus, is less clear. To determine whether MEG can be used to detect and localize activity from the hippocampus, we executed a systematic review of the existing literature and found successful detection of oscillatory neural activity originating in the hippocampus with MEG. Prerequisites are the use of established experimental paradigms, adequate coregistration, forward modeling, analysis methods, optimization of signal-to-noise ratios, and protocol trial designs that maximize contrast for hippocampal activity while minimizing those from other brain regions. While localizing activity to specific sub-structures within the hippocampus has not been achieved, we provide recommendations for improving the reliability of such endeavors.

Association between Thalamocortical Functional Connectivity Abnormalities and Cognitive Deficits in Schizophrenia

Cognitive deficits are considered a core component of schizophrenia and may predict functional outcome. However, the neural underpinnings of neuropsychological impairment remain to be fully elucidated. Data of 59 schizophrenia patients and 72 healthy controls from a public resting-state fMRI database was employed in our study. Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Battery was used to measure deficits of cognitive abilities in schizophrenia. Neural correlates of cognitive deficits in schizophrenia were examined by linear regression analysis of the thalamocortical network activity with scores of seven cognitive domains. We confirmed the combination of reduced prefrontal-thalamic connectivity and increased sensorimotor-thalamic connectivity in patients with schizophrenia. Correlation analysis with cognition revealed that in schizophrenia (1) the thalamic functional connectivity in the bilateral pre- and postcentral gyri was negatively correlated with attention/vigilance and speed of processing (Pearson’s r ≤ −0.443, p ≤ 0.042, FWE corrected), and positively correlated with patients’ negative symptoms (Pearson’s r ≥ 0.375, p ≤ 0.003, FWE corrected); (2) the thalamic functional connectivity in the right cerebellum was positively correlated with speed of processing (Pearson’s r = 0.388, p = 0.01, FWE corrected). Our study demonstrates that thalamic hyperconnectivity with sensorimotor areas is related to the severity of cognitive deficits and clinical symptoms, and extends our understanding of the neural underpinnings of “cognitive dysmetria” in schizophrenia.

Computational neuroscience and neuroinformatics: Recent progress and resources

The human brain and its temporal behavior correlated with development, structure, and function is a complex natural system even for its own kind. Coding and automation are necessary for modeling, analyzing and understanding the 86.1 +/- 8.1 +/- billion neurons, an almost equal number of non-neuronal glial cells, and the neuronal networks of the human brain comprising about 100 trillion connections. 'Computational neuroscience' which is heavily dependent on biology, physics, mathematics and computation addresses such problems while the archival, retrieval and merging of the huge amount of generated data in the form of clinical records, scientific literature, and specialized databases are carried out by 'neuroinformatics' approaches. Neuroinformatics is thus an interface between computer science and experimental neuroscience. This article provides an introduction to computational neuroscience and neuroinformatics fields along with their state-ofthe- art tools, software, and resources. Furthermore, it describes a few innovative applications of these fields in predicting and detecting brain network organization, complex brain disorder diagnosis, large-scale 3D simulation of the brain, brain- computer, and brain-to-brain interfaces. It provides an integrated overview of the fields in a non-technical way, appropriate for broad general readership. Moreover, the article is an updated unified resource of the existing knowledge and sources for researchers stepping into these fields.

Altered Global Signal Topography in Schizophrenia

Schizophrenia (SCZ) is a disabling neuropsychiatric disease associated with disruptions across distributed neural systems. Resting-state functional magnetic resonance imaging has identified extensive abnormalities in the blood-oxygen level-dependent signal in SCZ patients, including alterations in the average signal over the brain-i.e. the "global" signal (GS). It remains unknown, however, if these "global" alterations occur pervasively or follow a spatially preferential pattern. This study presents the first network-by-network quantification of GS topography in healthy subjects and SCZ patients. We observed a nonuniform GS contribution in healthy comparison subjects, whereby sensory areas exhibited the largest GS component. In SCZ patients, we identified preferential GS representation increases across association regions, while sensory regions showed preferential reductions. GS representation in sensory versus association cortices was strongly anti-correlated in healthy subjects. This anti-correlated relationship was markedly reduced in SCZ. Such shifts in GS topography may underlie profound alterations in neural information flow in SCZ, informing development of pharmacotherapies.

Multimodal Neuroimaging in Schizophrenia: Description and Dissemination

In this paper we describe an open-access collection of multimodal neuroimaging data in schizophrenia for release to the community. Data were acquired from approximately 100 patients with schizophrenia and 100 age-matched controls during rest as well as several task activation paradigms targeting a hierarchy of cognitive constructs. Neuroimaging data include structural MRI, functional MRI, diffusion MRI, MR spectroscopic imaging, and magnetoencephalography. For three of the hypothesis-driven projects, task activation paradigms were acquired on subsets of ~200 volunteers which examined a range of sensory and cognitive processes (e.g., auditory sensory gating, auditory/visual multisensory integration, visual transverse patterning). Neuropsychological data were also acquired and genetic material via saliva samples were collected from most of the participants and have been typed for both genome-wide polymorphism data as well as genome-wide methylation data. Some results are also presented from the individual studies as well as from our data-driven multimodal analyses (e.g., multimodal examinations of network structure and network dynamics and multitask fMRI data analysis across projects). All data will be released through the Mind Research Network's collaborative informatics and neuroimaging suite (COINS).

Non-invasive Investigation of Human Hippocampal Rhythms Using Magnetoencephalography: A Review

Hippocampal rhythms are believed to support crucial cognitive processes including memory, navigation, and language. Due to the location of the hippocampus deep in the brain, studying hippocampal rhythms using non-invasive magnetoencephalography (MEG) recordings has generally been assumed to be methodologically challenging. However, with the advent of whole-head MEG systems in the 1990s and development of advanced source localization techniques, simulation and empirical studies have provided evidence that human hippocampal signals can be sensed by MEG and reliably reconstructed by source localization algorithms. This paper systematically reviews simulation studies and empirical evidence of the current capacities and limitations of MEG “deep source imaging” of the human hippocampus. Overall, these studies confirm that MEG provides a unique avenue to investigate human hippocampal rhythms in cognition, and can bridge the gap between animal studies and human hippocampal research, as well as elucidate the functional role and the behavioral correlates of human hippocampal oscillations.

Hippocampal activation and memory performance in schizophrenia depend on strategy use in a virtual maze

Different strategies may be spontaneously adopted to solve most navigation tasks. These strategies are associated with dissociable brain systems. Here, we use brain-imaging and cognitive tasks to test the hypothesis that individuals living with Schizophrenia Spectrum Disorders (SSD) have selective impairment using a hippocampal-dependent spatial navigation strategy. Brain activation and memory performance were examined using functional magnetic resonance imaging (fMRI) during the 4-on-8 virtual maze (4/8VM) task, a human analog of the rodent radial-arm maze that is amenable to both response-based (egocentric or landmark-based) and spatial (allocentric, cognitive mapping) strategies to remember and navigate to target objects. SSD (schizophrenia and schizoaffective disorder) participants who adopted a spatial strategy performed more poorly on the 4/8VM task and had less hippocampal activation than healthy comparison participants using either strategy as well as SSD participants using a response strategy. This study highlights the importance of strategy use in relation to spatial cognitive functioning in SSD. Consistent with a selective-hippocampal dependent deficit in SSD, these results support the further development of protocols to train impaired hippocampal-dependent abilities or harness non-hippocampal dependent intact abilities.

Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

Multimodal features of structural and functional magnetic resonance imaging (MRI) of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE) and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001) accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function) support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

Development of relational memory processes in monkeys

The present study tested whether relational memory processes, as measured by the transverse patterning problem, are late-developing in nonhuman primates as they are in humans. Eighteen macaques ranging from 3 to 36 months of age, were trained to solve a set of visual discriminations that formed the transverse patterning problem. Subjects were trained at 3, 4-6, 12, 15-24 or 36 months of age to solve three discriminations as follows: 1) A+ vs. B-; 2) B+ vs. C-; 3) C+ vs. A. When trained concurrently, subjects must adopt a relational strategy to perform accurately on all three problems. All 36 month old monkeys reached the criterion of 90% correct, but only one 24-month-old and one 15-month-old did, initially. Three-month-old infants performed at chance on all problems. Six and 12-month-olds performed at 75-80% correct but used a 'linear' or elemental solution (e.g. A>B>C), which only yields correct performance on two problems. Retraining the younger subjects at 12, 24 or 36 months yielded a quantitative improvement on speed of learning, and a qualitative improvement in 24-36 month old monkeys for learning strategy. The results suggest that nonspatial relational memory develops late in macaques (as in humans), maturing between 15 and 24 months of age.

Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures

Mental disorders like schizophrenia are currently diagnosed by physicians/psychiatrists through clinical assessment and their evaluation of patient's self-reported experiences as the illness emerges. There is great interest in identifying biological markers of prognosis at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity, which indicates a subject's overall level of "synchronicity" of activity between brain regions, demonstrates promise in providing individual subject predictive power. Many previous studies reported functional connectivity changes during resting-state using only functional magnetic resonance imaging (fMRI). Nevertheless, exclusive reliance on fMRI to generate such networks may limit the inference of the underlying dysfunctional connectivity, which is hypothesized to be a factor in patient symptoms, as fMRI measures connectivity via hemodynamics. Therefore, combination of connectivity assessments using fMRI and magnetoencephalography (MEG), which more directly measures neuronal activity, may provide improved classification of schizophrenia than either modality alone. Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia. In this work, we propose a new framework for extraction of important disease related features and classification of patients with schizophrenia based on using both fMRI and MEG to investigate functional network components in the resting state. Results of this study show that the integration of fMRI and MEG provides important information that captures fundamental characteristics of functional network connectivity in schizophrenia and is helpful for prediction of schizophrenia patient group membership. Combined fMRI/MEG methods, using static functional network connectivity analyses, improved classification accuracy relative to use of fMRI or MEG methods alone (by 15 and 12.45%, respectively), while combined fMRI/MEG methods using dynamic functional network connectivity analyses improved classification up to 5.12% relative to use of fMRI alone and up to 17.21% relative to use of MEG alone.

Magnetoencephalographic and functional MRI connectomics in schizophrenia via intra- and inter-network connectivity

Examination of intrinsic functional connectivity using functional MRI (fMRI) has provided important findings regarding dysconnectivity in schizophrenia. Extending these results using a complementary neuroimaging modality, magnetoencephalography (MEG), we present the first direct comparison of functional connectivity between schizophrenia patients and controls, using these two modalities combined. We developed a novel MEG approach for estimation of networks using MEG that incorporates spatial independent component analysis (ICA) and pairwise correlations between independent component timecourses, to estimate intra- and intern-network connectivity. This analysis enables group-level inference and testing of between-group differences. Resting state MEG and fMRI data were acquired from a large sample of healthy controls (n=45) and schizophrenia patients (n=46). Group spatial ICA was performed on fMRI and MEG data to extract intrinsic fMRI and MEG networks and to compensate for signal leakage in MEG. Similar, but not identical spatial independent components were detected for MEG and fMRI. Analysis of functional network connectivity (FNC; i.e., pairwise correlations in network (ICA component) timecourses) revealed a differential between-modalities pattern, with greater connectivity among occipital networks in fMRI and among frontal networks in MEG. Most importantly, significant differences between controls and patients were observed in both modalities. MEG FNC results in particular indicated dysfunctional hyperconnectivity within frontal and temporal networks in patients, while in fMRI FNC was always greater for controls than for patients. This is the first study to apply group spatial ICA as an approach to leakage correction, and as such our results may be biased by spatial leakage effects. Results suggest that combining these two neuroimaging modalities reveals additional disease-relevant patterns of connectivity that were not detectable with fMRI or MEG alone.

Aberrant Hippocampal Connectivity in Unmedicated Patients With Schizophrenia and Effects of Antipsychotic Medication: A Longitudinal Resting State Functional MRI Study

To better characterize hippocampal pathophysiology in schizophrenia, we conducted a longitudinal study evaluating hippocampal functional connectivity during resting state, using seeds prescribed in its anterior and posterior regions. We enrolled 34 unmedicated patients with schizophrenia or schizoaffective disorder (SZ) and 34 matched healthy controls. SZ were scanned while off medication, then were treated with risperidone for 6 weeks and re-scanned (n = 22). Group differences in connectivity, as well as changes in connectivity over time, were assessed on the group's participant level functional connectivity maps. We found significant dysconnectivity with anterior and posterior hippocampal seeds in unmedicated SZ. Baseline connectivity between the hippocampus and anterior cingulate cortex, caudate nucleus, auditory cortex and calcarine sulcus in SZ predicted subsequent response to antipsychotic medications. These same regions demonstrated changes over the 6-week treatment trial that were correlated with symptomatic improvement. Our findings implicate several neural networks relevant to clinical improvement with antipsychotic medications.

Emotion recognition impairment in traumatic brain injury compared with schizophrenia spectrum: similar deficits with different origins

The aim of our study was to identify the common and separate mechanisms that might underpin emotion recognition impairment in patients with traumatic brain injury (TBI) and schizophrenia (Sz) compared with healthy controls (HCs). We recruited 21 Sz outpatients, 24 severe TBI outpatients, and 38 HCs, and we used eye-tracking to compare facial emotion processing performance. Both Sz and TBI patients were significantly poorer at recognizing facial emotions compared with HC. Sz patients showed a different way of exploring the Pictures of Facial Affects stimuli and were significantly worse in recognition of neutral expressions. Selective or sustained attention deficits in TBI may reduce efficient emotion recognition, whereas in Sz, there is a more strategic deficit underlying the observed problem. There would seem to be scope for adjustment of effective rehabilitative training focused on emotion recognition.

Medial prefrontal-hippocampal connectivity and motor memory consolidation in depression and schizophrenia

BACKGROUND

Overnight memory consolidation is disturbed in both depression and schizophrenia, creating an ideal situation to investigate the mechanisms underlying sleep-related consolidation and to distinguish disease-specific processes from common elements in their pathophysiology.

METHODS

We investigated patients with depression and schizophrenia, as well as healthy control subjects (each n = 16), under a motor memory consolidation protocol with functional magnetic resonance imaging and polysomnography.

RESULTS

In a sequential finger-tapping task associated with the degree of hippocampal-prefrontal cortex functional connectivity during the task, significantly less overnight improvement was identified as a common deficit in both patient groups. A task-related overnight decrease in activation of the basal ganglia was observed in control subjects and schizophrenia patients; in contrast, patients with depression showed an increase. During the task, schizophrenia patients, in comparison with control subjects, additionally recruited adjacent cortical areas, which showed a decrease in functional magnetic resonance imaging activation overnight and were related to disease severity. Effective connectivity analyses revealed that the hippocampus was functionally connected to the motor task network, and the cerebellum decoupled from this network overnight.

CONCLUSIONS

While both patient groups showed similar deficits in consolidation associated with hippocampal-prefrontal cortex connectivity, other activity patterns more specific for disease pathology differed.

Glutamate receptor 1 phosphorylation at serine 845 contributes to the therapeutic effect of olanzapine on schizophrenia-like cognitive impairments

Schizophrenia patients exhibit a wide range of impairments in cognitive functions. Clinically, atypical antipsychotic drugs (AAPs) such as olanzapine (OLZ) have a therapeutic effect on memory function among schizophrenia patients rather than typical antipsychotics, e.g., haloperidol. To date, however, little is known about the neuroplasticity mechanism underlying the effect of AAPs on the impairment of cognitive functions. Here, we treated schizophrenia rat models with a systematic injection of MK-801 (0.1mg/kg) and chose the drug OLZ as a tool to investigate the mechanisms of AAPs when used to alter cognitive function. The results showed that the systematic administration of MK-801 results in the impairment of spatial learning and memory as well as spatial working memory in a Morris water maze task. OLZ but not HAL improved these MK-801-induced cognitive dysfunctions. After MK-801 application, the hippocampal LTP was profoundly impaired. In conjunction with the results of the behavioral test, the administration of OLZ but not of HAL resulted in a significant reversal effect on the impaired LTP induced via MK-801 application. Furthermore, we found that OLZ but not HAL can upregulate the phosphorylation of GluR1 Ser845. These data suggest that the therapeutic effect of OLZ on cognitive dysfunctions may be due to its contribution to synaptic plasticity via the ability to upregulate the state of GluR1 Ser845 phosphorylation. We therefore suggest that the upregulated state of GluR1 Ser845 phosphorylation may be a promising target for developing novel therapeutics for treating schizophrenia.

Cognitive impairments in psychotic disorders: common mechanisms and measurement

Decades of research have provided robust evidence of cognitive impairments in psychotic disorders. Individuals with schizophrenia appear to be impaired on the majority of neuropsychological tasks, leading some researchers to argue for a "generalized deficit", in which the multitude of cognitive impairments are the result of a common neurobiological source. One such common mechanism may be an inability to actively represent goal information in working memory as a means to guide behavior, with the associated neurobiological impairment being a disturbance in the function of the dorsolateral prefrontal cortex. Here, we provide a discussion of the evidence for such impairment in schizophrenia, and how it manifests in domains typically referred to as cognitive control, working memory and episodic memory. We also briefly discuss cognitive impairment in affective psychoses, reporting that the degree of impairment is worse in schizophrenia than in bipolar disorder and psychotic major depression, but the profile of impairment is similar, possibly reflecting common mechanisms at the neural level. Given the recent release of the DSM-5, we end with a brief discussion on assessing cognition in the context of diagnosis and treatment planning in psychotic disorders.

Hippocampal lateralization and memory in children and adults

The neural organization of cognitive processes, particularly hemispheric lateralization, changes throughout childhood and adolescence. Differences in the neural basis of relational memory between children and adults are not well characterized. In this study we used magnetoencephalography to observe the lateralization differences of hippocampal activation in children and adults during performance of a relational memory task, transverse patterning (TP). The TP task was paired with an elemental control task, which does not depend upon the hippocampus. We contrasted two hypotheses; the compensation hypothesis would suggest that more bilateral activation in children would lead to better TP performance, whereas the maturation hypothesis would predict that a more adult-like right-lateralized pattern of hippocampal activation would lead to better performance. Mean-centered partial least squares analysis was used to determine unique patterns of brain activation specific to each task per group, while diminishing activation that is consistent across tasks. Our findings support the maturation hypothesis that a more adult-like pattern of increased right hippocampal lateralization in children leads to superior performance on the TP task. We also found dynamic changes of lateralization throughout the time course for all three groups, suggesting that caution is needed when interpreting conclusions about brain lateralization.

Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial

Functional network connectivity (FNC) is a method of analyzing the temporal relationship of anatomical brain components, comparing the synchronicity between patient groups or conditions. We use functional-connectivity measures between independent components to classify between Schizophrenia patients and healthy controls during resting-state. Connectivity is measured using a variety of graph-theoretic connectivity measures such as graph density, average path length, and small-worldness. The Schizophrenia patients showed significantly less clustering (transitivity) among components than healthy controls (p < 0.05, corrected) with networks less likely to be connected, and also showed lower small-world connectivity than healthy controls. Using only these connectivity measures, an SVM classifier (without parameter tuning) could discriminate between Schizophrenia patients and healthy controls with 65% accuracy, compared to 51% chance. This implies that the global functional connectivity between resting-state networks is altered in Schizophrenia, with networks more likely to be disconnected and behave dissimilarly for diseased patients. We present this research finding as a tutorial using the publicly available COBRE dataset of 146 Schizophrenia patients and healthy controls, provided as part of the 1000 Functional Connectomes Project. We demonstrate preprocessing, using independent component analysis (ICA) to nominate networks, computing graph-theoretic connectivity measures, and finally using these connectivity measures to either classify between patient groups or assess between-group differences using formal hypothesis testing. All necessary code is provided for both running command-line FSL preprocessing, and for computing all statistical measures and SVM classification within R. Collectively, this work presents not just findings of diminished FNC among resting-state networks in Schizophrenia, but also a practical connectivity tutorial.

Issues in localization of brain function: The case of lateralized frontal cortex in cognition, emotion, and psychopathology

The appeal of simple, sweeping portraits of large-scale brain mechanisms relevant to psychological phenomena competes with a rich, complex research base. As a prominent example, two views of frontal brain organization have emphasized dichotomous lateralization as a function of either emotional valence (positive/negative) or approach/avoidance motivation. Compelling findings support each. The literature has struggled to choose between them for three decades, without success. Both views are proving untenable as comprehensive models. Evidence of other frontal lateralizations, involving distinctions among dimensions of depression and anxiety, make a dichotomous view even more problematic. Recent evidence indicates that positive valence and approach motivation are associated with different areas in the left-hemisphere. Findings that appear contradictory at the level of frontal lobes as the units of analysis can be accommodated because hemodynamic and electromagnetic neuroimaging studies suggest considerable functional differentiation, in specialization and activation, of subregions of frontal cortex, including their connectivity to each other and to other regions. Such findings contribute to a more nuanced understanding of functional localization that accommodates aspects of multiple theoretical perspectives.

Intact discourse cohesion and coherence following bilateral ventromedial prefrontal cortex

Discourse cohesion and coherence give communication its continuity providing the grammatical and lexical links that hold an utterance or text together and give it meaning. Researchers often link cohesion and coherence deficits to the frontal lobes by drawing attention to frontal lobe dysfunction in populations where discourse cohesion and coherence deficits are reported and through attribution of these deficits to underlying cognitive impairments putatively associated with the frontal lobes. We examined the distinct contribution of a region of the frontal lobes, the ventromedial prefrontal cortex (vmPFC), to discourse cohesion and coherence across a range of discourse tasks. We found that bilateral vmPFC damage does not impair cohesion and coherence in spoken discourse. This study provides insights into the contribution of the major anatomical subdivisions of the frontal lobes to language use and furthers our understanding of the neural and cognitive underpinnings of discourse cohesion and coherence.

Frontotemporal anatomical connectivity and working-relational memory performance predict everyday functioning in 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.

Oscillatory cortical network involved in auditory verbal hallucinations in schizophrenia

Background

Auditory verbal hallucinations (AVH), a prominent symptom of schizophrenia, are often highly distressing for patients. Better understanding of the pathogenesis of hallucinations could increase therapeutic options. Magnetoencephalography (MEG) provides direct measures of neuronal activity and has an excellent temporal resolution, offering a unique opportunity to study AVH pathophysiology.

Methods

Twelve patients (10 paranoid schizophrenia, 2 psychosis not otherwise specified) indicated the presence of AVH by button-press while lying in a MEG scanner. As a control condition, patients performed a self-paced button-press task. AVH-state and non-AVH state were contrasted in a region-of-interest (ROI) approach. In addition, the two seconds before AVH onset were contrasted with the two seconds after AVH onset to elucidate a possible triggering mechanism.

Results

AVH correlated with a decrease in beta-band power in the left temporal cortex. A decrease in alpha-band power was observed in the right inferior frontal gyrus. AVH onset was related to a decrease in theta-band power in the right hippocampus.

Conclusions

These results suggest that AVH are triggered by a short aberration in the theta band in a memory-related structure, followed by activity in language areas accompanying the experience of AVH itself.

Differential neuregulin 1 cleavage in the prefrontal cortex and hippocampus in schizophrenia and bipolar disorder: preliminary findings

Background

Neuregulin 1 (NRG1) is a key candidate susceptibility gene for both schizophrenia (SCZ) and bipolar disorder (BPD). The function of the NRG1 transmembrane proteins is regulated by cleavage. Alteration of membrane bound-NRG1 cleavage has been previously shown to be associated with behavioral impairments in mouse models lacking expression of NRG1-cleavage enzymes such as BACE1 and gamma secretase. We sought to determine whether alterations in NRG1 cleavage and associated enzymes occur in patients with SCZ and BPD.

Methodology/Principal Findings

Using human postmortem brain, we evaluated protein expression of NRG1 cleavage products and enzymes that cleave at the external (BACE1, ADAM17, ADAM19) and internal (PS1-gamma secretase) sides of the cell membrane. We used three different cohorts (Controls, SCZ and BPD) and two distinct brain regions: BA9-prefrontal cortex (Controls (n = 6), SCZ (n = 6) and BPD (n = 6)) and hippocampus (Controls (n = 5), SCZ (n = 6) and BPD (n = 6)). In BA9, the ratio of the NRG1 N-terminal fragment relative to full length was significantly upregulated in the SCZ cohort (Bonferroni test, p = 0.011). ADAM17 was negatively correlated with full length NRG1 levels in the SCZ cohort (r = –0.926, p = 0.008). In the hippocampus we found significantly lower levels of a soluble 50 kDa NRG1 fragment in the two affected groups compared the control cohort (Bonferroni test, p = 0.0018). We also examined the relationship of specific symptomatology criteria with measures of NRG1 cleavage using the Bipolar Inventory of Signs and Symptoms Scale (BISS) and the Montgomery Åsberg Depression Rating Scale (MADRS). Our results showed a positive correlation between ADAM19 and psychosis (r = 0.595 p = 0.019); PS1 and mania (r = 0.535, p = 0.040); PS1 and depression (r = 0.567, p = 0.027) in BA9, and BACE1 with anxiety (r = 0.608, p = 0.03) in the hippocampus.

Conclusion/Significance

Our preliminary findings suggest region-specific alterations in NRG1 cleavage in SCZ and BPD patients. These changes may be associated with specific symptoms in these psychiatric disorders.

The human ventromedial prefrontal cortex is critical for transitive inference

We hypothesized that the ventromedial pFC (vmPFC) is critical for making transitive inferences (e.g., the logical operation that if A > B and B > C, then A > C). To test this, participants with focal vmPFC damage, brain-damaged comparison participants, and neurologically normal participants completed a transitive inference task consisting an ordered set of arbitrary patterns. Participants first learned through trial-and-error the relationships of the patterns (e.g., Pattern A > Pattern B, Pattern B > Pattern C). After initial learning, participants were presented with novel pairings, some of which required transitive inference (e.g., Pattern A > Pattern C from the relationship above). We observed that vmPFC damage led to a specific deficit in transitive inference, suggesting that an intact vmPFC is necessary for making normal transitive inferences. Given the usefulness of transitivity in inferring social relationships, this deficit may be one of the basic features of social conduct problems associated with vmPFC damage.