A multi-scanner study of subcortical brain volume abnormalities in schizophrenia

Alterations in the volume and shape of the basal ganglia and thalamus in schizophrenia with auditory hallucinations

Different lines of evidence indicate that the structure and physiology of the basal ganglia and the thalamus is disturbed in schizophrenia. However, it is unknown whether the volume and shape of these subcortical structures are affected in schizophrenia with auditory hallucinations (AH), a core positive symptom of the disorder. We took structural MRI from 63 patients with schizophrenia, including 36 patients with AH and 27 patients who had never experienced AH (NAH), and 51 matched healthy controls. We extracted volumes for the left and right thalamus, globus pallidus, putamen, caudate and nucleus accumbens. Shape analysis was also carried out. When comparing to controls, the volume of the right globus pallidus, thalamus, and putamen, was only affected in AH patients. The volume of the left putamen was also increased in individuals with AH, whereas the left globus pallidus was affected in both groups of patients. The shapes of right and left putamen and thalamus were also affected in both groups. The shape of the left globus pallidus was only altered in patients lacking AH, both in comparison to controls and to cases with AH. Lastly, the general PANSS subscale was correlated with the volume of the right thalamus, and the right and left putamen, in patients with AH. We have found volume and shape alterations of many basal ganglia and thalamus in patients with and without AH, suggesting in some cases a possible relationship between this positive symptom and these morphometric alterations.

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping

The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data-driven source-based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi-blind extension of SBM, called constrained source-based morphometry (constrained SBM), which enables the extraction of maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source-based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.

Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.

A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning

We have performed a morphological analysis of patients with schizophrenia and compared them with healthy controls. Our analysis includes the use of publicly available automated extraction tools to assess regional cortical thickness (inclusive of within region cortical thickness variability) from structural magnetic resonance imaging (MRI), to characterize group-wise abnormalities associated with schizophrenia based on a publicly available dataset. We have also performed a correlation analysis between the automatically extracted biomarkers and a variety of patient clinical variables available. Finally, we also present the results of a machine learning analysis. Results demonstrate regional cortical thickness abnormalities in schizophrenia. We observed a correlation (rho = 0.474) between patients’ depression and the average cortical thickness of the right medial orbitofrontal cortex. Our leading machine learning technology evaluated was the support vector machine with stepwise feature selection, yielding a sensitivity of 92% and a specificity of 74%, based on regional brain measurements, including from the insula, superior frontal, caudate, calcarine sulcus, gyrus rectus, and rostral middle frontal regions. These results imply that advanced analytic techniques combining MRI with automated biomarker extraction can be helpful in characterizing patients with schizophrenia.

Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score

Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale multisite schizophrenia classification. Our findings may provide insight into the underlying pathophysiology and risk mechanisms of schizophrenia.

Feasibility of accelerated 3D T1-weighted MRI using compressed sensing: application to quantitative volume measurements of human brain structures

OBJECTIVE

Scan time reduction is necessary for volumetric acquisitions to improve workflow productivity and to reduce motion artifacts during MRI procedures. We explored the possibility that Compressed Sensing-4 (CS-4) can be employed with 3D-turbo-field-echo T1-weighted (3D-TFE-T1W) sequence without compromising subcortical measurements on clinical 1.5 T MRI.

MATERIALS AND METHODS

Thirty-three healthy volunteers (24 females, 9 males) underwent imaging scans on a 1.5 T MRI equipped with a 12-channel head coil. 3D-TFE-T1W for whole-brain coverage was performed with different acceleration factors, including SENSE-2, SENSE-4, CS-4. Freesurfer, FSL's FIRST, and volBrain packages were utilized for subcortical segmentation. All processed data were assessed using the Wilcoxon signed-rank test.

RESULTS

The results obtained from SENSE-2 were considered as references. For SENSE-4, the maximum signal-to-noise ratio (SNR) drop was detected in the Accumbens (51.96%). For CS-4, the maximum SNR drop was detected in the Amygdala (10.55%). Since the SNR drop in CS-4 is relatively small, the SNR in all of the subcortical volumes obtained from SENSE-2 and CS-4 are not statistically different (P > 0.05), and their Pearson's correlation coefficients are larger than 0.90. The maximum biases of SENSE-4 and CS-4 were found in the Thalamus with the mean of differences of 1.60 ml and 0.18 ml, respectively.

CONCLUSION

CS-4 provided sufficient quality of 3D-TFE-T1W images for 1.5 T MRI equipped with a 12-channel receiver coil. Subcortical volumes obtained from the CS-4 images are consistent among different post-processing packages.

Air Pollution-Related Brain Metal Dyshomeostasis as a Potential Risk Factor for Neurodevelopmental Disorders and Neurodegenerative Diseases

Increasing evidence links air pollution (AP) exposure to effects on the central nervous system structure and function. Particulate matter AP, especially the ultrafine (nanoparticle) components, can carry numerous metal and trace element contaminants that can reach the brain in utero and after birth. Excess brain exposure to either essential or non-essential elements can result in brain dyshomeostasis, which has been implicated in both neurodevelopmental disorders (NDDs; autism spectrum disorder, schizophrenia, and attention deficit hyperactivity disorder) and neurodegenerative diseases (NDGDs; Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and amyotrophic lateral sclerosis). This review summarizes the current understanding of the extent to which the inhalational or intranasal instillation of metals reproduces in vivo the shared features of NDDs and NDGDs, including enlarged lateral ventricles, alterations in myelination, glutamatergic dysfunction, neuronal cell death, inflammation, microglial activation, oxidative stress, mitochondrial dysfunction, altered social behaviors, cognitive dysfunction, and impulsivity. Although evidence is limited to date, neuronal cell death, oxidative stress, and mitochondrial dysfunction are reproduced by numerous metals. Understanding the specific contribution of metals/trace elements to this neurotoxicity can guide the development of more realistic animal exposure models of human AP exposure and consequently lead to a more meaningful approach to mechanistic studies, potential intervention strategies, and regulatory requirements.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

SIGNIFICANCE

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

Dentate gyrus volume deficit in schizophrenia

BACKGROUND

Schizophrenia is associated with robust hippocampal volume deficits but subregion volume deficits, their associations with cognition, and contributing genes remain to be determined.

METHODS

Hippocampal formation (HF) subregion volumes were obtained using FreeSurfer 6.0 from individuals with schizophrenia (n = 176, mean age ± s.d. = 39.0 ± 11.5, 132 males) and healthy volunteers (n = 173, mean age ± s.d. = 37.6 ± 11.3, 123 males) with similar mean age, gender, handedness, and race distributions. Relationships between the HF subregion volume with the largest between group difference, neuropsychological performance, and single-nucleotide polymorphisms were assessed.

RESULTS

This study found a significant group by region interaction on hippocampal subregion volumes. Compared to healthy volunteers, individuals with schizophrenia had significantly smaller dentate gyrus (DG) (Cohen's d = -0.57), Cornu Ammonis (CA) 4, molecular layer of the hippocampus, hippocampal tail, and CA 1 volumes, when statistically controlling for intracranial volume; DG (d = -0.43) and CA 4 volumes remained significantly smaller when statistically controlling for mean hippocampal volume. DG volume showed the largest between group difference and significant positive associations with visual memory and speed of processing in the overall sample. Genome-wide association analysis with DG volume as the quantitative phenotype identified rs56055643 (β = 10.8, p < 5 × 10-8, 95% CI 7.0-14.5) on chromosome 3 in high linkage disequilibrium with MOBP. Gene-based analyses identified associations between SLC25A38 and RPSA and DG volume.

CONCLUSIONS

This study suggests that DG dysfunction is fundamentally involved in schizophrenia pathophysiology, that it may contribute to cognitive abnormalities in schizophrenia, and that underlying biological mechanisms may involve contributions from MOBP, SLC25A38, and RPSA.

The Amygdala in Schizophrenia and Bipolar Disorder: A Synthesis of Structural MRI, Diffusion Tensor Imaging, and Resting-State Functional Connectivity Findings

Frequently implicated in psychotic spectrum disorders, the amygdala serves as an important hub for elucidating the convergent and divergent neural substrates in schizophrenia and bipolar disorder, the two most studied groups of psychotic spectrum conditions. A systematic search of electronic databases through December 2017 was conducted to identify neuroimaging studies of the amygdala in schizophrenia and bipolar disorder, focusing on structural MRI, diffusion tensor imaging (DTI), and resting-state functional connectivity studies, with an emphasis on cross-diagnostic studies. Ninety-four independent studies were selected for the present review (49 structural MRI, 27 DTI, and 18 resting-state functional MRI studies). Also selected, and analyzed in a separate meta-analysis, were 33 volumetric studies with the amygdala as the region-of-interest. Reduced left, right, and total amygdala volumes were found in schizophrenia, relative to both healthy controls and bipolar subjects, even when restricted to cohorts in the early stages of illness. No volume abnormalities were observed in bipolar subjects relative to healthy controls. Shape morphometry studies showed either amygdala deformity or no differences in schizophrenia, and no abnormalities in bipolar disorder. In contrast to the volumetric findings, DTI studies of the uncinate fasciculus tract (connecting the amygdala with the medial- and orbitofrontal cortices) largely showed reduced fractional anisotropy (a marker of white matter microstructure abnormality) in both schizophrenia and bipolar patients, with no cross-diagnostic differences. While decreased amygdalar-orbitofrontal functional connectivity was generally observed in schizophrenia, varying patterns of amygdalar-orbitofrontal connectivity in bipolar disorder were found. Future studies can consider adopting longitudinal approaches with multimodal imaging and more extensive clinical subtyping to probe amygdalar subregional changes and their relationship to the sequelae of psychotic disorders.

Morphometry and gyrification in bipolar disorder and schizophrenia: A comparative MRI study

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Increased right globus pallidus is a consistent marker in schizophrenia (SCZ).

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Left supramarginal gyrification increases in bipolar disorder (BPD) in contrast with SCZ.

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Gyrification analysis may help distinguish early phases of BPD and SCZ.

Schizophrenia is believed to be a neurodevelopmental disease with high heritability. Differential diagnosis is often challenging, especially in early phases, namely with other psychotic disorders or even mood disorders. such as bipolar disorder with psychotic symptoms. Key pathophysiological changes separating these two classical psychoses remain poorly understood, and current evidence favors a more dimensional than categorical differentiation between schizophrenia and bipolar disorder. While established biomarkers like cortical thickness and grey matter volume are heavily influenced by post-onset changes and thus provide limited possibility of accessing early pathologies, gyrification is assumed to be more specifically determined by genetic and early developmental factors. The aim of our study was to compare both classical and novel morphometric features in these two archetypal psychiatric disorders. We included 20 schizophrenia patients, 20 bipolar disorder patients and 20 age- and gender-matched healthy controls. Data analyses were performed with CAT12/SPM12 applying general linear models for four morphometric measures: gyrification and cortical thickness (surface-based morphometry), and whole-brain grey matter/grey matter volume (voxel-based morphometry - VBM). Group effects were tested using age and gender as covariates (and total intracranial volume for VBM). Voxel-based morphometry analysis revealed a schizophrenia vs. control group effect on regional grey matter volume (p < 0.05, familywise error correction) in the right globus pallidus. There was no group effect on white matter volume when correcting for multiple comparisons neither on cortical thickness. Gyrification changes in clinical samples were found in the left supramarginal gyrus (BA40) – increased and reduced gyrification, respectively, in BPD and SCZ patients - and in the right inferior frontal gyrus (BA47), with a reduction in gyrification of the SCZ group when compared with controls. The joint analysis of different morphometric features, namely measures such as gyrification, provides a promising strategy for the elucidation of distinct phenotypes in psychiatric disorders. Different morphological change patterns, highlighting specific disease trajectories, could potentially generate neuroimaging-derived biomarkers, helping to discriminate schizophrenia from bipolar disorder in early phases, such as first-episode psychosis patients.

Schizophrenia-related microdeletion causes defective ciliary motility and brain ventricle enlargement via microRNA-dependent mechanisms in mice

Progressive ventricular enlargement, a key feature of several neurologic and psychiatric diseases, is mediated by unknown mechanisms. Here, using murine models of 22q11-deletion syndrome (22q11DS), which is associated with schizophrenia in humans, we found progressive enlargement of lateral and third ventricles and deceleration of ciliary beating on ependymal cells lining the ventricular walls. The cilia-beating deficit observed in brain slices and in vivo is caused by elevated levels of dopamine receptors (Drd1), which are expressed in motile cilia. Haploinsufficiency of the microRNA-processing gene Dgcr8 results in Drd1 elevation, which is brought about by a reduction in Drd1-targeting microRNAs miR-382-3p and miR-674-3p. Replenishing either microRNA in 22q11DS mice normalizes ciliary beating and ventricular size. Knocking down the microRNAs or deleting their seed sites on Drd1 mimicked the cilia-beating and ventricular deficits. These results suggest that the Dgcr8-miR-382-3p/miR-674-3p-Drd1 mechanism contributes to deceleration of ciliary motility and age-dependent ventricular enlargement in 22q11DS.

N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia

OBJECTIVE

We propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g., symptoms). We applied it to structural MRI data from patients with schizophrenia.

METHODS

It uses a source-based morphometry approach [i.e., independent component analysis of gray matter segmentation maps] to decompose the data into a set of spatial maps, each of which includes regions that covary among individuals. Then, the loading parameters for components of interest are entered to an exhaustive search, which incorporates a modified depth-first search technique to carry out the biclustering, with the goal of obtaining submatrices where the selected rows (individuals) show homogeneity in their expressions of selected columns (components) and vice versa.

RESULTS

Findings demonstrate that multiple biclusters have an evident association with distinct brain networks for the different types of symptoms in schizophrenia. The study identifies two components: inferior temporal gyrus (16) and brainstem (7), which are related to positive (distortion/excess of normal function) and negative (diminution/loss of normal function) symptoms in schizophrenia, respectively.

CONCLUSION

N-BiC is a data-driven method of biclustering MRI data that can exhaustively explore relationships/substructures from a dataset without any prior information with a higher degree of robustness than earlier biclustering applications.

SIGNIFICANCE

The use of such approaches is important to investigate the underlying biological substrates of mental illness by grouping patients into homogeneous subjects, as the schizophrenia diagnosis is known to be relatively nonspecific and heterogeneous.

A transdiagnostic neuroanatomical signature of psychiatric illness

Despite an increasing focus on transdiagnostic approaches to mental health, it remains unclear whether different diagnostic categories share a common neuronatomical basis. The current investigation sought to investigate whether a transdiagnostic set of structural alterations characterized schizophrenia, depression, post-traumatic stress disorder, and obsessive-compulsive disorder, and determine whether any such alterations reflected markers of psychiatric illness or pre-existing familial vulnerability. A total of 404 patients with a psychiatric diagnosis were recruited (psychosis, n = 129; unipolar depression, n = 92; post-traumatic stress disorder, n = 91; obsessive-compulsive disorder, n = 92) alongside n = 201 healthy controls and n = 20 unaffected first-degree relatives. We collected structural magnetic resonance imaging scans from each participant, and tested for transdiagnostic alterations using Voxel-based morphometry. Inferences were made at p < 0.05 after family-wise error correction for multiple comparisons. The four psychiatric groups relative to healthy controls were all characterized by significantly greater gray matter volume in the putamen (right: z-score: 5.97, p-value < 0.001; left: z-score: 4.97, p-value = 0.001); the volume of this region was positively correlated with severity of symptoms across groups (r = 0.313; p < 0.001). Putamen enlargement was also evident in unaffected relatives compared to healthy controls (right: z-score: 8.13, p-value < 0.001; left: z-score: 9.38, p-value < 0.001). Taken collectively, these findings indicate that increased putamen volume may reflect a transdiagnostic marker of familial vulnerability to psychopathology. This is consistent with emerging conceptualizations of psychiatric illness, in which each disorder is understood as a combination of diagnosis-specific features and a transdiagnostic factor reflecting general psychopathology.

Variation in fourteen brain structure volumes in schizophrenia: A comprehensive meta-analysis of 246 studies

Despite hundreds of structural MRI studies documenting smaller brain volumes on average in schizophrenia compared to controls, little attention has been paid to group differences in the variability of brain volumes. Examination of variability may help interpret mean group differences in brain volumes and aid in better understanding the heterogeneity of schizophrenia. Variability in 246 MRI studies was meta-analyzed for 13 structures that have shown medium to large mean effect sizes (Cohen's d≥0.4): intracranial volume, total brain volume, lateral ventricles, third ventricle, total gray matter, frontal gray matter, prefrontal gray matter, temporal gray matter, superior temporal gyrus gray matter, planum temporale, hippocampus, fusiform gyrus, insula; and a control structure, caudate nucleus. No significant differences in variability in cortical/subcortical volumes were detected in schizophrenia relative to controls. In contrast, increased variability was found in schizophrenia compared to controls for intracranial and especially lateral and third ventricle volumes. These findings highlight the need for more attention to ventricles and detailed analyses of brain volume distributions to better elucidate the pathophysiology of schizophrenia.

Neandertal Introgression Sheds Light on Modern Human Endocranial Globularity

One of the features that distinguishes modern humans from our extinct relatives and ancestors is a globular shape of the braincase [1-4]. As the endocranium closely mirrors the outer shape of the brain, these differences might reflect altered neural architecture [4, 5]. However, in the absence of fossil brain tissue, the underlying neuroanatomical changes as well as their genetic bases remain elusive. To better understand the biological foundations of modern human endocranial shape, we turn to our closest extinct relatives: the Neandertals. Interbreeding between modern humans and Neandertals has resulted in introgressed fragments of Neandertal DNA in the genomes of present-day non-Africans [6, 7]. Based on shape analyses of fossil skull endocasts, we derive a measure of endocranial globularity from structural MRI scans of thousands of modern humans and study the effects of introgressed fragments of Neandertal DNA on this phenotype. We find that Neandertal alleles on chromosomes 1 and 18 are associated with reduced endocranial globularity. These alleles influence expression of two nearby genes, UBR4 and PHLPP1, which are involved in neurogenesis and myelination, respectively. Our findings show how integration of fossil skull data with archaic genomics and neuroimaging can suggest developmental mechanisms that may contribute to the unique modern human endocranial shape.

A diffusion weighted imaging study of basal ganglia in schizophrenia

OBJECTIVES

Several magnetic resonance imaging (MRI) studies provided evidence of selective brain abnormalities in schizophrenia, both in cortical and subcortical structures. Basal ganglia are of particular interest, given not only the high concentration of dopaminergic neurons and receptors, but also for their crucial role in cognitive functions, commonly impaired in schizophrenia. To date, very few studies explored basal ganglia using diffusion imaging, which is sensitive to microstructural organization in brain tissues. The aim of our study is to explore basal ganglia structures with diffusion imaging in a sizeable sample of patients affected by schizophrenia and healthy controls.

METHODS

We enrolled 52 subjects affected by schizophrenia according to DMS-IV-R criteria and 46 healthy controls. Diffusion weighted images were obtained using a 1.5 Tesla scanner and apparent diffusion coefficient (ADC) values were determined in axial and coronal sections at the level of basal ganglia.

RESULTS

Patients affected by schizophrenia showed a significantly higher ADC compared to healthy controls in the left anterior lenticular nucleus (F = 3.9, p = .05). A significant positive correlation between right anterior lenticular nucleus and psychotropic dosages was found (r = 0.4, p = .01).

CONCLUSIONS

Our study provides evidence of lenticular nucleus microstructure alterations in schizophrenia, potentially sustaining cognitive and motor deficits in schizophrenia. Key points The basal ganglia structures was explored with diffusion imaging in a sizeable sample of patients affected by schizophrenia and healthy controls. Patients affected by schizophrenia showed a significantly higher ADC compared to healthy controls in the left anterior lenticular nucleus. Our study provides evidence of lenticular nucleus microstructure alterations in schizophrenia, potentially sustaining cognitive and motor deficits in schizophrenia.

Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

By exploiting cross-information among multiple imaging data, multimodal fusion has often been used to better understand brain diseases. However, most current fusion approaches are blind, without adopting any prior information. There is increasing interest to uncover the neurocognitive mapping of specific clinical measurements on enriched brain imaging data; hence, a supervised, goal-directed model that employs prior information as a reference to guide multimodal data fusion is much needed and becomes a natural option. Here, we proposed a fusion with reference model called "multi-site canonical correlation analysis with reference + joint-independent component analysis" (MCCAR+jICA), which can precisely identify co-varying multimodal imaging patterns closely related to the reference, such as cognitive scores. In a three-way fusion simulation, the proposed method was compared with its alternatives on multiple facets; MCCAR+jICA outperforms others with higher estimation precision and high accuracy on identifying a target component with the right correspondence. In human imaging data, working memory performance was utilized as a reference to investigate the co-varying working memory-associated brain patterns among three modalities and how they are impaired in schizophrenia. Two independent cohorts (294 and 83 subjects respectively) were used. Similar brain maps were identified between the two cohorts along with substantial overlaps in the central executive network in fMRI, salience network in sMRI, and major white matter tracts in dMRI. These regions have been linked with working memory deficits in schizophrenia in multiple reports and MCCAR+jICA further verified them in a repeatable, joint manner, demonstrating the ability of the proposed method to identify potential neuromarkers for mental disorders.

Association between the scores of the Japanese version of the Brief Assessment of Cognition in Schizophrenia and whole-brain structure in patients with chronic schizophrenia: A voxel-based morphometry and diffusion tensor imaging study

AIM

The Brief Assessment of Cognition in Schizophrenia (BACS) is a concise tool designed to evaluate cognitive deficits in schizophrenia. We examined the possible association between BACS scores and whole-brain structure, as observed using magnetic resonance imaging with a relatively large sample.

METHODS

The study sample comprised 116 patients with schizophrenia (mean age, 39.3 ± 11.1 years; 66 men) and 118 healthy controls (HC; mean age, 40.0 ± 13.6 years; 58 men) who completed the Japanese version of the BACS (BACS-J). All participants were of Japanese ethnicity. The magnetic resonance imaging volume and diffusion tensor imaging data were processed with voxel-based morphometry and tract-based spatial statistics, respectively.

RESULTS

There were significant reductions in the regional gray matter volumes and white matter fractional anisotropy values in patients with schizophrenia compared to HC. For the gray matter areas, the working memory score had a significant positive correlation with the anterior cingulate and medial frontal cortices volumes in the patients. For the white matter areas, the motor speed score had a significant positive correlation with fractional anisotropy values in the corpus callosum, internal capsule, superior corona radiata, and superior longitudinal fasciculus in the patients. However, there was no significant correlation among either the gray or white matter areas in the HC.

CONCLUSION

Our results suggest that among the BACS-J measures, the working memory and motor speed scores are associated with several structural alterations in the brains of patients with schizophrenia.

The effect of duration of illness and antipsychotics on subcortical volumes in schizophrenia: Analysis of 778 subjects

Background

The effect of duration of illness and antipsychotic medication on the volumes of subcortical structures in schizophrenia is inconsistent among previous reports. We implemented a large sample analysis utilizing clinical data from 11 institutions in a previous meta-analysis.

Methods

Imaging and clinical data of 778 schizophrenia subjects were taken from a prospective meta-analysis conducted by the COCORO consortium in Japan. The effect of duration of illness and daily dose and type of antipsychotics were assessed using the linear mixed effect model where the volumes of subcortical structures computed by FreeSurfer were used as a dependent variable and age, sex, duration of illness, daily dose of antipsychotics and intracranial volume were used as independent variables, and the type of protocol was incorporated as a random effect for intercept. The statistical significance of fixed-effect of dependent variable was assessed.

Results

Daily dose of antipsychotics was positively associated with left globus pallidus volume and negatively associated with right hippocampus. It was also positively associated with laterality index of globus pallidus. Duration of illness was positively associated with bilateral globus pallidus volumes. Type of antipsychotics did not have any effect on the subcortical volumes.

Discussion

A large sample size, uniform data collection methodology and robust statistical analysis are strengths of the current study. This result suggests that we need special attention to discuss about relationship between subcortical regional brain volumes and pathophysiology of schizophrenia because regional brain volumes may be affected by antipsychotic medication.

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The imaging data as well as prescription data and demographics from 778 patients with schizophrenia from 11 institutions were included.

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The effect of protocol was cooperated as random-effect in the linear mixed-effect model.

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Significant positive association were found between daily dose of antipsychotics and left globus pallidus volume.

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Significant negative association was found between daily dose of antipsychotics and right hippocampus volume.

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Significant positive associations were found between duration of illness and bilateral volumes of globus pallidus.

Discrete pre-processing step effects in registration-based pipelines, a preliminary volumetric study on T1-weighted images

Pre-processing MRI scans prior to performing volumetric analyses is common practice in MRI studies. As pre-processing steps adjust the voxel intensities, the space in which the scan exists, and the amount of data in the scan, it is possible that the steps have an effect on the volumetric output. To date, studies have compared between and not within pipelines, and so the impact of each step is unknown. This study aims to quantify the effects of pre-processing steps on volumetric measures in T1-weighted scans within a single pipeline. It was our hypothesis that pre-processing steps would significantly impact ROI volume estimations. One hundred fifteen participants from the OASIS dataset were used, where each participant contributed three scans. All scans were then pre-processed using a step-wise pipeline. Bilateral hippocampus, putamen, and middle temporal gyrus volume estimations were assessed following each successive step, and all data were processed by the same pipeline 5 times. Repeated-measures analyses tested for a main effects of pipeline step, scan-rescan (for MRI scanner consistency) and repeated pipeline runs (for algorithmic consistency). A main effect of pipeline step was detected, and interestingly an interaction between pipeline step and ROI exists. No effect for either scan-rescan or repeated pipeline run was detected. We then supply a correction for noise in the data resulting from pre-processing.

Common pattern of gray-matter abnormalities in drug-naive and medicated first-episode schizophrenia: a multimodal meta-analysis

Studies of schizophrenia at drug-naive state and on antipsychotic medication have reported a number of regions of gray-matter (GM) abnormalities but the reports have been inconsistent. The aim of this study was to conduct multimodal meta-analysis to compare the cross-sectional voxel-based morphometry studies of brain GM in antipsychotic-naive first-episode schizophrenia (AN-FES) and those with antipsychotic treatment within 1 year (AT-FES) to determine the similarities and differences in these groups. We conducted two separate meta-analyses containing 24 studies with a sample size of 801 patients and 957 healthy controls. A multimodal meta-analysis method was used to compare the findings between AN-FES and AT-FES. Meta-regression analyses were done to determine the influence of different variables including age, duration of illness, and positive and negative symptom scores. Finally, jack-knife analyses were done to test the robustness of the results. AN-FES and AT-FES showed common patterns of GM abnormalities in frontal (gyrus rectus), superior temporal, left hippocampal and insular cortex. GM in the left supramarginal gyrus and left middle temporal gyrus were found to be increased in AN-FES but decreased in AT-FES, whereas left median cingulate/paracingulate gyri and right hippocampus GM was decreased in AN-FES but increased in AT-FES. Findings suggest that both AN-FES and AT-FES share frontal, temporal and insular regions as common anatomical regions to be affected indicating these to be the primary regions of GM abnormalities in both groups.

Deficient adolescent social behavior following early-life inflammation is ameliorated by augmentation of anandamide signaling

Early-life inflammation has been shown to exert profound effects on brain development and behavior, including altered emotional behavior, stress responsivity and neurochemical/neuropeptide receptor expression and function. The current study extends this research by examining the impact of inflammation, triggered with the bacterial compound lipopolysaccharide (LPS) on postnatal day (P) 14, on social behavior during adolescence. We investigated the role that the endocannabinoid (eCB) system plays in sociability after early-life LPS. To test this, multiple cohorts of Sprague Dawley rats were injected with LPS on P14. In adolescence, rats were subjected to behavioral testing in a reciprocal social interaction paradigm as well as the open field. We quantified eCB levels in the amygdala of P14 and adolescent animals (anandamide and 2-arachidonoylglycerol) as well as adolescent amygdaloid cannabinoid receptor 1 (CB1) binding site density and the hydrolytic activity of the enzyme fatty acid amide hydrolase (FAAH), which metabolizes the eCB anandamide. Additionally, we examined the impact of FAAH inhibition on alterations in social behavior. Our results indicate that P14 LPS decreases adolescent social behavior (play and social non-play) in males and females at P40. This behavioral alteration is accompanied by decreased CB1 binding, increased anandamide levels and increased FAAH activity. Oral administration of the FAAH inhibitor PF-04457845 (1mg/kg) prior to the social interaction task normalizes LPS-induced alterations in social behavior, while not affecting social behavior in the control group. Infusion of 10ng PF-04457845 into the basolateral amygdala normalized social behavior in LPS injected females. These data suggest that alterations in eCB signaling following postnatal inflammation contribute to impairments in social behavior during adolescence and that inhibition of FAAH could be a novel target for disorders involving social deficits such as social anxiety disorders or autism.

Simultaneous effects on parvalbumin-positive interneuron and dopaminergic system development in a transgenic rat model for sporadic schizophrenia

To date, unequivocal neuroanatomical features have been demonstrated neither for sporadic nor for familial schizophrenia. Here, we investigated the neuroanatomical changes in a transgenic rat model for a subset of sporadic chronic mental illness (CMI), which modestly overexpresses human full-length, non-mutant Disrupted-in-Schizophrenia 1 (DISC1), and for which aberrant dopamine homeostasis consistent with some schizophrenia phenotypes has previously been reported. Neuroanatomical analysis revealed a reduced density of dopaminergic neurons in the substantia nigra and reduced dopaminergic fibres in the striatum. Parvalbumin-positive interneuron occurrence in the somatosensory cortex was shifted from layers II/III to V/VI, and the number of calbindin-positive interneurons was slightly decreased. Reduced corpus callosum thickness confirmed trend-level observations from in vivo MRI and voxel-wise tensor based morphometry. These neuroanatomical changes help explain functional phenotypes of this animal model, some of which resemble changes observed in human schizophrenia post mortem brain tissues. Our findings also demonstrate how a single molecular factor, DISC1 overexpression or misassembly, can account for a variety of seemingly unrelated morphological phenotypes and thus provides a possible unifying explanation for similar findings observed in sporadic schizophrenia patients. Our anatomical investigation of a defined model for sporadic mental illness enables a clearer definition of neuroanatomical changes associated with subsets of human sporadic schizophrenia.

Alterations in the hippocampus and thalamus in individuals at high risk for psychosis

Reduction in hippocampal volume is a hallmark of schizophrenia and already present in the clinical high-risk state. Nevertheless, other subcortical structures, such as the thalamus, amygdala and pallidum can differentiate schizophrenia patients from controls. We studied the role of hippocampal and subcortical structures in clinical high-risk individuals from two cohorts. High-resolution T₁-weighted structural MRI brain scans of a total of 91 clinical high-risk individuals and 64 healthy controls were collected in two centers. The bilateral volume of the hippocampus, the thalamus, the caudate, the putamen, the pallidum, the amygdala, and the accumbens were automatically segmented using FSL-FIRST. A linear mixed-effects model and a prospective meta-analysis were applied to assess group-related volumetric differences. We report reduced hippocampal and thalamic volumes in clinical high-risk individuals compared to healthy controls. No volumetric alterations were detected for the caudate, the putamen, the pallidum, the amygdala, or the accumbens. Moreover, we found comparable medium effect sizes for group-related comparison of the thalamus in the two analytical methods. These findings underline the relevance of specific alterations in the hippocampal and subcortical volumes in the high-risk state. Further analyses may allow hippocampal and thalamic volumes to be used as biomarkers to predict psychosis.

The Function Biomedical Informatics Research Network Data Repository

The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical data sets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 data set consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 T scanners. The FBIRN Phase 2 and Phase 3 data sets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN's multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data.

Baseline putamen volume as a predictor of positive symptom reduction in patients at clinical high risk for psychosis: A preliminary study

OBJECTIVES

Illness course in individuals at clinical high risk (CHR) status for psychosis is heterogeneous, which limits effective treatment for all CHR subgroups. Baseline predictors of positive symptom trajectory in the CHR group will reduce such limitations. We singled out the putamen, thought to be involved in the generation of the key schizophrenia symptoms early in the course of disease, as a potential predictor of positive symptom trajectory in CHR patients.

METHOD

We recruited 45 CHR patients and 29 age- and gender-matched healthy controls (HC). The CHR group was divided into patients with positive symptom reduction (CHR-R) and patients without positive symptom reduction (CHR-NR) at 6 months. Comparisons were made between the baseline putamen volumes of CHR-R, CHR-NR and HC groups. The relationship between baseline putamen volumes and clinical measures was investigated.

RESULTS

Left putamen volumes of CHR-R patients were significantly smaller than those of HCs (p=0.002) and of CHR-NR patients (p=0.024). CHR-R patients had significantly reduced leftward laterality compared to HCs (p=0.007). In the CHR-R group, bilateral putamen volumes were correlated with positive symptom severity at baseline (r=-0.552, p=0.001) and at 6 months (r=-0.360, p=0.043), and predicted positive symptom score change in 6 months at a trend level (p=0.092).

CONCLUSION

Smaller left putamen volumes in CHR-R patients, and the correlation between positive symptom severity and putamen volumes suggest that putamen volume is a possible risk-stratifier and predictor of clinical course in the CHR population.

Functional and clinical insights from neuroimaging studies in childhood-onset schizophrenia

Childhood-onset schizophrenia is a rare pediatric onset psychiatric disorder continuous with and typically more severe than its adult counterpart. Neuroimaging research conducted on this population has revealed similarly severe neural abnormalities. When taken as a whole, neuroimaging research in this population shows generally decreased cortical gray matter coupled with white matter connectivity abnormalities, suggesting an anatomical basis for deficits in executive function. Subcortical abnormalities are pronounced in limbic structures, where volumetric deficits are likely related to social skill deficits, and cerebellar deficits that have been correlated to cognitive abnormalities. Structures relevant to motor processing also show a significant alteration, with volumetric increase in basal ganglia structures likely due to antipsychotic administration. Neuroimaging of this disorder shows an important clinical image of exaggerated cortical loss, altered white matter connectivity, and differences in structural development of subcortical areas during the course of development and provides important background to the disease state.

Comparison of grey matter volume and thickness for analysing cortical changes in chronic schizophrenia: a matter of surface area, grey/white matter intensity contrast, and curvature

Grey matter volume and cortical thickness are the two most widely used measures for detecting grey matter morphometric changes in various diseases such as schizophrenia. However, these two measures only share partial overlapping regions in identifying morphometric changes. Few studies have investigated the contributions of the potential factors to the differences of grey matter volume and cortical thickness. To investigate this question, 3T magnetic resonance images from 22 patients with schizophrenia and 20 well-matched healthy controls were chosen for analyses. Grey matter volume and cortical thickness were measured by VBM and Freesurfer. Grey matter volume results were then rendered onto the surface template of Freesurfer to compare the differences from cortical thickness in anatomical locations. Discrepancy regions of the grey matter volume and thickness where grey matter volume significantly decreased but without corresponding evidence of cortical thinning involved the rostral middle frontal, precentral, lateral occipital and superior frontal gyri. Subsequent region-of-interest analysis demonstrated that changes in surface area, grey/white matter intensity contrast and curvature accounted for the discrepancies. Our results suggest that the differences between grey matter volume and thickness could be jointly driven by surface area, grey/white matter intensity contrast and curvature.

The role of cannabinoid transmission in emotional memory formation: implications for addiction and schizophrenia

Emerging evidence from both basic and clinical research demonstrates an important role for endocannabinoid (ECB) signaling in the processing of emotionally salient information, learning, and memory. Cannabinoid transmission within neural circuits involved in emotional processing has been shown to modulate the acquisition, recall, and extinction of emotionally salient memories and importantly, can strongly modulate the emotional salience of incoming sensory information. Two neural regions in particular, the medial prefrontal cortex (PFC) and the basolateral nucleus of the amygdala (BLA), play important roles in emotional regulation and contain high levels of cannabinoid receptors. Furthermore, both regions show profound abnormalities in neuropsychiatric disorders such as addiction and schizophrenia. Considerable evidence has demonstrated that cannabinoid transmission functionally interacts with dopamine (DA), a neurotransmitter system that is of exceptional importance for both addictive behaviors and the neuropsychopathology of disorders like schizophrenia. Research in our laboratory has focused on how cannabinoid transmission both within and extrinsic to the mesolimbic DA system, including the BLA → mPFC circuitry, can modulate both rewarding and aversive emotional information. In this review, we will summarize clinical and basic neuroscience research demonstrating the importance of cannabinoid signaling within this neural circuitry. In particular, evidence will be reviewed emphasizing the importance of cannabinoid signaling within the BLA → mPFC circuitry in the context of emotional salience processing, memory formation and memory-related plasticity. We propose that aberrant states of hyper or hypoactive ECB signaling within the amygdala-prefrontal cortical circuit may lead to dysregulation of mesocorticolimbic DA transmission controlling the processing of emotionally salient information. These disturbances may in turn lead to emotional processing, learning, and memory abnormalities related to various neuropsychiatric disorders, including addiction and schizophrenia-related psychoses.