Decreased Gray Matter Volume of Cuneus and Lingual Gyrus in Schizophrenia Patients with Tardive Dyskinesia is Associated with Abnormal Involuntary Movement

A connectome-wide association study of altered functional connectivity in schizophrenia based on resting-state fMRI

BACKGROUND

Aberrant resting-state functional connectivity is a neuropathological feature of schizophrenia (SCZ). Prior investigations into functional connectivity abnormalities have primarily employed seed-based connectivity analysis, necessitating predefined seed locations. To address this limitation, a data-driven multivariate method known as connectome-wide association study (CWAS) has been proposed for exploring whole-brain functional connectivity.

METHODS

We conducted a CWAS analysis involving 46 patients with SCZ and 40 age- and sex-matched healthy controls. Multivariate distance matrix regression (MDMR) was utilized to identify key nodes in the brain. Subsequently, we conducted a follow-up seed-based connectivity analysis to elucidate specific connectivity patterns between regions of interest (ROIs). Additionally, we explored the spatial correlation between changes in functional connectivity and underlying molecular architectures by examining correlations between neurotransmitter/transporter distribution densities and functional connectivity.

RESULTS

MDMR revealed the right medial frontal gyrus and the left calcarine sulcus as two key nodes. Follow-up analysis unveiled hypoconnectivity between the right medial frontal superior gyrus and the right fusiform gyrus, as well as hypoconnectivity between the left calcarine sulcus and the right lingual gyrus in SCZ. Notably, a significant association between functional connectivity strength and positive symptom severity was identified. Furthermore, altered functional connectivity patterns suggested potential dysfunctions in the dopamine, serotonin, and gamma-aminobutyric acid systems.

CONCLUSIONS

This study elucidated reduced functional connectivity both within and between the medial frontal regions and the occipital cortex in patients with SCZ. Moreover, it indicated potential alterations in molecular architecture, thereby expanding current knowledge regarding neurobiological changes associated with SCZ.

The Role of 3' Regulatory Region Flanking Kinectin 1 Gene in Schizophrenia

Objective

Schizophrenia is often associated with volumetric reductions in cortices and expansions in basal ganglia, particularly the putamen. Recent genome-wide association studies have highlighted the significance of variants in the 3' regulatory region adjacent to the kinectin 1 gene (KTN1) in regulating gray matter volume (GMV) of the putamen. This study aimed to comprehensively investigate the involvement of this region in schizophrenia.

Methods

We analyzed 1136 single-nucleotide polymorphisms (SNPs) covering the entire 3' regulatory region in 4 independent dbGaP samples (4604 schizophrenia patients vs. 4884 healthy subjects) and 3 independent Psychiatric Genomics Consortium samples (107 240 cases vs. 210 203 controls) to identify consistent associations. Additionally, we examined the regulatory effects of schizophrenia-associated alleles on KTN1 mRNA expression in 16 brain areas among 348 subjects, as well as GMVs of 7 subcortical nuclei in 38 258 subjects, and surface areas (SA) and thickness (TH) of the entire cortex and 34 cortical areas in 36 936 subjects.

Results

The major alleles (f > 0.5) of 25 variants increased (β > 0) the risk of schizophrenia across 2 to 5 independent samples (8.4 × 10-4 ≤ P ≤ .049). These schizophrenia-associated alleles significantly elevated (β > 0) GMVs of basal ganglia, including the putamen (6.0 × 10-11 ≤ P ≤ 1.1 × 10-4), caudate (8.7 × 10-4 ≤ P ≤ 9.4 × 10-3), pallidum (P = 6.0 × 10-4), and nucleus accumbens (P = 2.7 × 10-5). Moreover, they potentially augmented (β > 0) the SA of posterior cingulate and insular cortices, as well as the TH of frontal (pars triangularis and medial orbitofrontal), parietal (superior, precuneus, and inferior), and temporal (transverse) cortices, but potentially reduced (β < 0) the SA of the whole, frontal (medial orbitofrontal), and temporal (pole, superior, middle, and entorhinal) cortices, as well as the TH of rostral middle frontal and superior frontal cortices (8.9 × 10-4 ≤ P ≤ .050).

Conclusion

Our findings identify significant and functionally relevant risk alleles in the 3' regulatory region adjacent to KTN1, implicating their crucial roles in the development of schizophrenia.

Intrinsic brain functional connectivity predicts treatment-related motor complications in early Parkinson's disease patients

BACKGROUND

Treatment-related motor complications may develop progressively over the course of Parkinson's disease (PD).

OBJECTIVE

We investigated intrinsic brain networks functional connectivity (FC) at baseline in a cohort of early PD patients which successively developed treatment-related motor complications over 4 years.

METHODS

Baseline MRI images of 88 drug-naïve PD patients and 20 healthy controls were analyzed. After the baseline assessments, all PD patients were prescribed with dopaminergic treatment and yearly clinically re-assessed. At the 4-year follow-up, 36 patients have developed treatment-related motor complications (PD-Compl) whereas 52 had not (PD-no-Compl). Single-subject and group-level independent component analyses were used to investigate FC changes within the major large-scale resting-state networks at baseline. A multivariate Cox regression model was used to explore baseline predictors of treatment-related motor complications at 4-year follow-up.

RESULTS

At baseline, an increased FC in the right middle frontal gyrus within the frontoparietal network as well as a decreased connectivity in the left cuneus within the default-mode network were detected in PD-Compl compared with PD-no-Compl. PD-Compl patients showed a preserved sensorimotor FC compared to controls. FC differences were found to be independent predictors of treatment-related motor complications over time.

CONCLUSION

Our findings demonstrated that specific FC differences may characterize drug-naïve PD patients more prone to develop treatment-related complications. These findings may reflect the presence of an intrinsic vulnerability across frontal and prefrontal circuits, which may be potentially targeted as a future biomarker in clinical trials.

ROI-based analysis of diffusion indices in healthy subjects and subjects with deficit or non-deficit syndrome schizophrenia

We analyzed DTI data involving 22 healthy subjects (HC), 15 patients with deficit syndrome schizophrenia (DSZ), and 25 patients with non-deficit syndrome schizophrenia (NDSZ). We used a 1.5-T MRI scanner to collect diffusion-weighted images and T1 images, which were employed to correct distortions and deformations within the diffusion-weighted images. For 156 regions of interest (ROI), we calculated the average fractional anisotropy (FA), mean diffusion (MD), and radial diffusion (RD). Each ROI underwent a group-wise comparison using permutation F-test, followed by post hoc pairwise comparisons with Bonferroni correction. In general, we observed lower FA in both schizophrenia groups compared to HC (i.e., HC>(DSZ=NDSZ)), while MD and RD showed the opposite pattern. Notably, specific ROIs with reduced FA in schizophrenia patients included bilateral nucleus accumbens, left fusiform area, brain stem, anterior corpus callosum, left rostral and caudal anterior cingulate, right posterior cingulate, left thalamus, left hippocampus, left inferior temporal cortex, right superior temporal cortex, left pars triangularis and right lingual gyrus. Significantly, the right cuneus exhibited lower FA in the DSZ group compared to other groups ((HC=NDSZ)>DSZ), without affecting MD and RD. These results indicate that compromised neural integrity in the cuneus may contribute to the pathophysiological distinctions between DSZ and NDSZ.

Regional Homogeneity in schizophrenia patients with tardive dyskinesia: a resting-state fMRI study

Neuronal degeneration and apoptosis may play an important role in the pathogenesis of tardive dyskinesia (TD). Previous studies suggested brain structural and functional abnormalities in patients with TD. We investigated changes in cerebral regional homogeneity (ReHo) in patients with TD using resting-state functional magnetic resonance imaging (rs-fMRI). Imaging data were collected from schizophrenia patients with TD (TD group, n=58) and without TD (non-TD group, n=66) and healthy controls (HC group, n=67), processed with SPM, and evaluated at a corrected threshold. Psychopathology and severity of TD were assessed with the Positive and Negative Syndrome Scale (PANSS) and Abnormal Involuntary Movement Scale (AIMS), respectively. Results: TD vs. non-TD group showed significantly higher ReHo in the Left Inferior Semilunar Lobule and Right Fusiform Gyrus and lower ReHo in Left Supramarginal Gyrus, Right Inferior Tempotal Gyrus, and Left Medial Frontal Gyrus. The ReHo value in the Left Inferior Semilunar Lobule was negatively correlated with AIMS upper limbs scores. Conclusions: The findings suggest altered regional neural connectivities in association with TD and may inform research of the etiology and monitor the course of TD in patients with schizophrenia and potentially other psychotic disorders.

A significant, functional and replicable risk KTN1 variant block for schizophrenia

Cortical and subcortical structural alteration has been extensively reported in schizophrenia, including the unusual expansion of gray matter volumes (GMVs) of basal ganglia (BG), especially putamen. Previous genome-wide association studies pinpointed kinectin 1 gene (KTN1) as the most significant gene regulating the GMV of putamen. In this study, the role of KTN1 variants in risk and pathogenesis of schizophrenia was explored. A dense set of SNPs (n = 849) covering entire KTN1 was analyzed in three independent European- or African-American samples (n = 6704) and one mixed European and Asian Psychiatric Genomics Consortium sample (n = 56,418 cases vs. 78,818 controls), to identify replicable SNP-schizophrenia associations. The regulatory effects of schizophrenia-associated variants on the KTN1 mRNA expression in 16 cortical or subcortical regions in two European cohorts (n = 138 and 210, respectively), the total intracranial volume (ICV) in 46 European cohorts (n = 18,713), the GMVs of seven subcortical structures in 50 European cohorts (n = 38,258), and the surface areas (SA) and thickness (TH) of whole cortex and 34 cortical regions in 50 European cohorts (n = 33,992) and eight non-European cohorts (n = 2944) were carefully explored. We found that across entire KTN1, only 26 SNPs within the same block (r2 > 0.85) were associated with schizophrenia across ≥ 2 independent samples (7.5 × 10-5 ≤ p ≤ 0.048). The schizophrenia-risk alleles, which increased significantly risk for schizophrenia in Europeans (q < 0.05), were all minor alleles (f < 0.5), consistently increased (1) the KTN1 mRNA expression in 12 brain regions significantly (5.9 × 10-12 ≤ p ≤ 0.050; q < 0.05), (2) the ICV significantly (6.1 × 10-4 ≤ p ≤ 0.008; q < 0.05), (3) the SA of whole (9.6 × 10-3 ≤ p ≤ 0.047) and two regional cortices potentially (2.5 × 10-3 ≤ p ≤ 0.042; q > 0.05), and (4) the TH of eight regional cortices potentially (0.006 ≤ p ≤ 0.050; q > 0.05), and consistently decreased (1) the BG GMVs significantly (1.8 × 10-19 ≤ p ≤ 0.050; q < 0.05), especially putamen GMV (1.8 × 10-19 ≤ p ≤ 1.0 × 10-4; q < 0.05, (2) the SA of four regional cortices potentially (0.010 ≤ p ≤ 0.048), and (3) the TH of four regional cortices potentially (0.015 ≤ p ≤ 0.049) in Europeans. We concluded that we identified a significant, functional, and robust risk variant block covering entire KTN1 that might play a critical role in the risk and pathogenesis of schizophrenia.

Altered intrinsic cerebellar-cerebral functional connectivity is related to negative symptoms in patients with first-episode psychosis

BACKGROUND

Negative symptoms in schizophrenia include cognitive and affective dysfunction, such as diminished expression and amotivation. Although the cerebellar posterior hemisphere and vermis are involved in cognitive and affective functioning, previous studies on the neural mechanism of negative symptoms have mostly been confined to the cerebral cortex. This study aimed to investigate whether resting-state cerebellar-cerebral functional connectivity (FC) is altered in first-episode psychosis (FEP) patients and whether this connectivity is related to negative symptoms.

METHODS

Resting-state functional magnetic resonance images were obtained from 38 FEP patients and 100 healthy controls (HCs). Using the posterior hemisphere and vermis of the cerebellum as seeds, whole-brain FC was compared between FEP patients and HCs. As cerebellar-parietal cortex connectivity is associated with negative symptoms and sociocognitive dysfunctions in schizophrenia patients, its correlation with negative symptoms was explored in FEP patients.

RESULTS

FEP patients showed hyperconnectivity between the cerebellum and bilateral frontal pole (FP), occipital pole, fusiform gyrus, right lingual gyrus, central opercular cortex, anterior middle temporal gyrus, precuneus, and subcallosal cortex. Hypoconnectivity was found between the cerebellum and left FP, right anterior supramarginal gyrus (aSMG), and cerebellum crus I. FC between the left crus II and right aSMG was negatively correlated with the severity of negative symptoms and diminished expression.

CONCLUSIONS

Altered FC between the cerebellum and cerebral regions related to cognitive, affective, and sensory processing was found in FEP patients and was connected to negative symptoms. These results suggest that the cerebellum plays a role in the pathophysiology of negative symptoms in schizophrenia.

Chaos analysis of the brain topology in first-episode psychosis and clinical high risk patients

Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis for the identification of brain topology differences in people with psychosis. Structural MRI were acquired from 77 FEP, 73 CHR and 44 healthy controls (HC). Chaos analysis of the gray matter distribution was performed: First, the distances of each voxel from the center of mass in the gray matter image was calculated. Next, the distances multiplied by the voxel intensity were represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts thus how the gray matter topology changes. Between-group differences were identified by (a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and (b) matching the lambda series with the Morlet wavelet, which resulted in statistically significant differences in the scalograms of FEP against CHR and HC. The proposed framework using spatial-series extraction enhances the between-group differences of FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.

Disentangling dyskinesia from parkinsonism in motor structures of patients with schizophrenia

Patients with schizophrenia frequently suffer from motor abnormalities, but underlying alterations in neuroarchitecture remain unclear. Here, we aimed to disentangle dyskinesia from parkinsonism in motor structures of patients with schizophrenia and to assess associated molecular architecture. We measured grey matter of motor regions and correlated volumetric estimates with dyskinesia and parkinsonism severity. Associations with molecular architecture were identified by cross-modal spatial correlations between ensuing maps of abnormality-related volume alterations and neurotransmitter maps from healthy populations. Both phenomena were linked to (specific) striatal and basal forebrain reductions as well as to D₁ receptor density. Dyskinesia also manifested in cerebellar decrease, while parkinsonism was associated with less motor cortex volume. The parkinsonism-related brain pattern was additionally associated with 5-HT_1A/2A and µ-opioid receptors distribution. Findings suggest the need to develop psychopharmacological compounds that display not only selectivity for receptor subtypes but also anatomical selectivity for alleviating dyskinesia without worsening parkinsonism and vice versa.

Neural Dysconnectivity in the Hippocampus Correlates With White Matter Lesions and Cognitive Measures in Patients With Coronary Artery Disease

Purpose

Recent neuroimaging reports have shown the microstructural changes in coronary artery disease (CAD) and its correlation with cognitive dysfunction while little is known about the functional characteristics of CAD. We hypothesize that functional characteristics may give clues to underlying pathology in CAD and its link with cognitive dysfunction. Degree centrality (DC), a graph-based assessment of network organization was performed to explore the neural connectivity changes in CAD patients compared with healthy controls and their correlation with cognitive measures.

Methods

Thirty CAD patients and 36 healthy controls were included in our study. All participants underwent functional magnetic resonance imaging (fMRI) of the brain. We performed DC analysis to identify voxels that showed changes in whole-brain functional connectivity with other voxels. DC was measured by the fMRI graph method and comparisons between the two groups were done. All participants underwent neuropsychological assessment (Montreal Cognitive Assessment, MoCA and Mini-Mental State Examination, MMSE).

Results

Our data analysis included 30 CAD patients (59.90 ± 7.53 years) and 36 HCs (61.61 ± 6.19 years). CAD patients showed a greater prevalence of white matter lesions using the Fazekas score than healthy controls (P &lt; 0.001). Importantly, CAD patients showed significantly lower (P &lt; 0.001) MoCA and MMSE scores compared with healthy controls. CAD patients showed significantly decreased DC value (P &lt; 0.001) in the right hippocampus (hippocampus_R), right lingual gyrus (lingual_R), and significantly increased DC value (P &lt; 0.001) in the left middle frontal gyrus (Frontal_Mid_L) when compared with healthy controls respectively. DC value in the hippocampus_R significantly correlated (P &lt; 0.00) with MMSE and MoCA scores in CAD patients. Fazekas scores in CAD patients showed a significant correlation (P &lt; 0.001) with the DC value in the hippocampus_R.

Conclusion

These findings suggest that reduced cerebral neural connectivity in CAD may contribute to their cognitive impairment and white matter microstructural damage.

Biomarkers identification for Schizophrenia via VAE and GSDAE-based data augmentation

Deep learning has made great progress in analyzing MRI data, while the MRI data with high dimensional but small sample size (HDSSS) brings many limitations to biomarkers identification. Few-shot learning has been proposed to solve such problems and data augmentation is a typical method of it. The variational auto-encoder (VAE) is a generative method based on variational Bayesian inference that is used for data augmentation. Graph regularized sparse deep autoencoder (GSDAE) can reconstruct sparse samples and keep the manifold structure of data which will facilitate biomarkers selection greatly. To generate better HDSSS data for biomarkers identification, a data augmentation method based on VAE and GSDAE is proposed in this paper, termed GS-VDAE. Instead of utilizing the final products of GSDAE, our proposed model embeds the generation procedure into GSDAE for augmentation. In this way, the augmented samples will be rooted in the significant features extracted from the original samples, which can ensure the newly formed samples contain the most significant characteristics of the original samples. The classification accuracy of the samples generated directly from VAE is 0.74, while the classification accuracy of the samples generated from GS-VDAE is 0.84, which proves the validity of our model. Additionally, a regression feature selection method with truncated nuclear norm regularization is chosen for biomarkers selection. The biomarkers selection results of schizophrenia data reveal that the augmented samples obtained by our proposed method can get higher classification accuracy with less ranked features compared with original samples, which proves the validation of our model.

An independent, replicable, functional and significant risk variant block at intron 3 of CACNA1C for schizophrenia

OBJECTIVES

Genome-wide association studies have identified a significant risk gene, CACNA1C, for schizophrenia. In this study, we comprehensively investigated a large set of CACNA1C single-nucleotide polymorphisms (SNPs) to identify the replicable risk alleles for schizophrenia and explore their biological functions.

METHODS

One Jewish (1044 cases vs 2052 controls), one European (1350 cases vs 1378 controls) and one exploratory African American samples (98 cases vs 20 controls) were analyzed to identify replicable single-nucleotide polymorphism-schizophrenia associations. The regulatory effects of risk alleles on CACNA1C messenger RNA expression were examined. The most robust risk tagSNP (rs1006737) was meta-analyzed on 17 studies (74,122 cases vs 109,062 controls), and associated with the gray matter volumes of seven subcortical structures in 38,258 Europeans, and the surface areas and thickness of 34 cortical regions in 33,992 Europeans and 2944 non-Europeans.

RESULTS

Forty-seven replicable risk single-nucleotide polymorphisms, including a 20-single-nucleotide polymorphism haplotype block, were identified in our samples (1.8 × 10^-4 ⩽ p ⩽ 0.049). This variant block was consistently associated with schizophrenia across four independent Psychiatric Genomics Consortium cohorts (79,645 cases vs 109,590 controls; 2.5 × 10^-17 ⩽ p ⩽ 0.017). This block showed significant expression quantitative trait loci in three independent European brain cohorts (5.1 × 10^-12 ⩽ p ⩽ 8.3 × 10^-3) and could be tagged by the most significant risk single-nucleotide polymorphism rs1006737. The minor allele A of rs1006737 significantly increased risk for schizophrenia across the Jewish and European samples (p = 0.029 and 0.004, respectively), and this association was highly significant in the meta-analysis (p = 1.62 × 10^-42). This allele also significantly altered the CACNA1C messenger RNA expression in five brain regions (5.1 × 10^-12 ⩽ p ⩽ 0.05), decreased the gray matter volume of thalamus (p = 0.010), the surface area of isthmus cingulate cortex (p = 0.013) and the thickness of transverse temporal and superior temporal sulcus cortexes (0.005 ⩽ p ⩽ 0.043).

CONCLUSION

We identified an independent, replicable, functional, and significant risk variant block at CACNA1C for schizophrenia, which could be tagged by the most robust risk marker rs1006737, suggesting an important role of CACNA1C in the pathogenesis of schizophrenia.

Progress in sensorimotor neuroscience of schizophrenia spectrum disorders: Lessons learned and future directions

The number of neuroimaging studies on movement disorders, sensorimotor, and psychomotor functioning in schizophrenia spectrum disorders (SSD) has steadily increased over the last two decades. Accelerated by the addition of the "sensorimotor domain" to the Research Domain Criteria (RDoC) framework in January 2019, neuroscience research on the role of sensorimotor dysfunction in SSD has gained greater scientific and clinical relevance. To draw attention to recent rapid progress in the field, we performed a triennial systematic review (PubMed search from January 1st^, 2018 through December 31st^, 2020), in which we highlight recent neuroimaging findings and discuss methodological pitfalls as well as challenges for future research. The identified magnetic resonance imaging (MRI) studies suggest that sensorimotor abnormalities in SSD are related to cerebello-thalamo-cortico-cerebellar network dysfunction. Longitudinal and interventional studies highlight the translational potential of the sensorimotor domain as putative biomarkers for treatment response and as targets for non-invasive neurostimulation techniques in SSD.

Altered Dynamic Functional Connectivity of Cuneus in Schizophrenia Patients: A Resting-State fMRI Study

Objective: Schizophrenia (SZ) is a functional mental condition that has a significant impact on patients’ social lives. As a result, accurate diagnosis of SZ has attracted researchers’ interest. Based on previous research, resting-state functional magnetic resonance imaging (rsfMRI) reported neural alterations in SZ. In this study, we attempted to investigate if dynamic functional connectivity (dFC) could reveal changes in temporal interactions between SZ patients and healthy controls (HC) beyond static functional connectivity (sFC) in the cuneus, using the publicly available COBRE dataset. Methods: Sliding windows were applied to 72 SZ patients’ and 74 healthy controls’ (HC) rsfMRI data to generate temporal correlation maps and, finally, evaluate mean strength (dFC-Str), variability (dFC-SD and ALFF) in each window, and the dwelling time. The difference in functional connectivity (FC) of the cuneus between two groups was compared using a two-sample t-test. Results: Our findings demonstrated decreased mean strength connectivity between the cuneus and calcarine, the cuneus and lingual gyrus, and between the cuneus and middle temporal gyrus (TPOmid) in subjects with SZ. Moreover, no difference was detected in variability (standard deviation and the amplitude of low-frequency fluctuation), the dwelling times of all states, or static functional connectivity (sFC) between the groups. Conclusions: Our verdict suggest that dynamic functional connectivity analyses may play crucial roles in unveiling abnormal patterns that would be obscured in static functional connectivity, providing promising impetus for understanding schizophrenia disease.

Modular-level alterations of single-subject gray matter networks in schizophrenia

Schizophrenia is often regarded as a psychiatric disorder caused by disrupted connections in the brain. Evidence suggests that the gray matter of schizophrenia patients is damaged in a modular pattern. Recently, abnormal topological organization was observed in the gray matter networks of patients with schizophrenia. However, the modular-level alteration of gray matter networks in schizophrenia remains unclear. In this study, single-subject gray matter networks were constructed for a total of 217 subjects (116 patients with schizophrenia and 101 controls). We analyzed the topological characteristics of the brain network and the strengths of connections between and within modules. Compared with the outcomes in the control group, the global efficiency and participation coefficient values of the single-subject gray matter networks in schizophrenic patients were significantly reduced. The nodal participation coefficient of the regions involving the frontoparietal attention network, default mode network and subcortical network were significantly decreased in subjects with schizophrenia. The intermodule connections between the frontoparietal attention network and visual network and between the default mode network and subcortical network, in the frontoparietal attention network were significantly reduced in the patient group. In the frontoparietal attention network, the intramodule nodal connection strength of the left orbital inferior frontal gyrus and right inferior parietal gyrus was significantly decreased in schizophrenia patients. Reduced intermodule nodal connection strength between the frontoparietal attention network and visual network was associated with the severity of schizophrenia symptoms. These findings suggest that abnormal intramodule and intermodule connections in the structural brain network may a biomarker of schizophrenia symptoms.

ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia

We construct Functional Connectivity Networks (FCN) from resting state fMRI (rsfMRI) recordings towards the classification of brain activity between healthy and schizophrenic subjects using a publicly available dataset (the COBRE dataset) of 145 subjects (74 healthy controls and 71 schizophrenic subjects). First, we match the anatomy of the brain of each individual to the Desikan-Killiany brain atlas. Then, we use the conventional approach of correlating the parcellated time series to construct FCN and ISOMAP, a nonlinear manifold learning algorithm to produce low-dimensional embeddings of the correlation matrices. For the classification analysis, we computed five key local graph-theoretic measures of the FCN and used the LASSO and Random Forest (RF) algorithms for feature selection. For the classification we used standard linear Support Vector Machines. The classification performance is tested by a double cross-validation scheme (consisting of an outer and an inner loop of "Leave one out" cross-validation (LOOCV)). The standard cross-correlation methodology produced a classification rate of 73.1%, while ISOMAP resulted in 79.3%, thus providing a simpler model with a smaller number of features as chosen from LASSO and RF, namely the participation coefficient of the right thalamus and the strength of the right lingual gyrus.

Tardive Dyskinesia in Older Persons Taking Antipsychotics

Tardive dyskinesia (TD) is a hyperkinetic movement disorder caused by the use of dopamine receptor-blocking agents (DRBAs), a category of medications that includes first- and second-generation antipsychotics (APs) and agents such as metoclopramide that are used for the treatment of nausea and gastrointestinal dysmotility. While TD can affect people of all ages, older age is associated with increased risk of TD and also with the emergence of TD occurring after shorter treatment durations and lower dosages of DRBAs. TD is characterized by involuntary movements that include the face, limbs, and trunk, and is associated with increased comorbidities, social stigmatization, and impaired physical and mental health. Once present, TD tends to persist despite AP dose adjustment or discontinuation. Even with the use of US Food and Drug Administration (FDA)-approved medications for TD, symptoms may persist. Because the leading hypothesis for the pathophysiology of TD has been dysregulation of dopamine transmission due to treatment with DRBAs, APs that avoid postsynaptic dopamine receptor blockade may provide an alternative therapeutic approach for patients who require an AP. In this review, we discuss the risks, burdens, prevention, and management of TD, with a focus on older people.

Abnormal functional connectivity of motor circuit in the schizophrenic patients with tardive dyskinesia: A resting-state fMRI study

BACKGROUND

Animal and neuroimaging studies suggest that the volume of the motor-circuit region decreases in tardive dyskinesia (TD). This study examined the differences in functional connectivity within the motor circuit of patients with schizophrenia with and without TD to further clarify how the dysfunction is related to the pathogenesis of TD.

METHODS

Functional magnetic resonance images were taken of 56 schizophrenic patients with TD (TD group), 64 without TD (non-TD group), and 68 healthy controls (HC group). The motor-circuit area was selected as the seed region for a whole brain resting-state functional connectivity (rsFC) analysis. Psychopathological symptoms and TD severity were assessed with the Positive and Negative Syndrome Scale (PANSS) and Abnormal Involuntary Movement Scale (AIMS), respectively. Group differences and correlations among 18 brain regions of interest (e.g., the global strength of connectivity between two regions) were analyzed.

RESULTS

The analysis of variance results were as follows: The three groups exhibited rsFC losses in the left primary motor cortex, bilateral parietal cortices, right postcentral gyrus, right putamen, right superior parietal lobule, right supplementary motor area and bilateral thalami (false discovery rate,p < 0.05). The TD group showed a significant rsFC loss between the right postcentral gyrus and the inferior frontal gyrus of the left triangular part when compared with the non-TD group (AlphaSim, p < 0.001), which was negatively correlated with the AIMS total score (r=-0.259, p = 0.03).

CONCLUSIONS

These findings may suggest dysfunction of the postcentral and inferior frontal gyri of the triangular part in patients with schizophrenia and TD.

Brainstem alterations contribute to catatonia in schizophrenia spectrum disorders

Catatonia is a severe psychomotor syndrome that frequently occurs in patients with schizophrenia spectrum disorders (SSD). Accumulating neuroimaging evidence suggests orbitofrontal, frontoparietal and cerebellar network dysfunction in catatonia. Very little is known about contributions of brainstem regions (as part of the dopaminergic-based subcortical-cortical motor circuit) to catatonia in SSD patients. Here, we used structural magnetic resonance imaging (MRI) at 3 T to examine volumes of brainstem regions in catatonic SSD patients compared to non-catatonic SSD patients. Catatonia severity was measured with the Northoff Catatonia Rating Scale (NCRS). The segmentation of the brainstem in order to investigate the volumes of medulla oblongata, pons, superior cerebellar pedunculus, and midbrain was carried out using FreeSurfer vers. 6.0. Catatonic patients (NCRS total score ≥ 3; at least 1 point in the three different symptom categories; i.e., motor, behavioral, and affective; n = 30) had significantly smaller midbrain volumes (p = 0.004, Bonferroni corr.) when compared to non-catatonic patients (NCRS total score = 0; n = 29). In catatonic patients, significant correlations were detected between NCRS motor scores and whole brainstem (p = 0.015, Bonferroni corr.) volumes. These results support a neuromechanistically important role of brainstem structures in catatonia in SSD, particularly in motor symptom expression.

A Neural Signature of Parkinsonism in Patients With Schizophrenia Spectrum Disorders: A Multimodal MRI Study Using Parallel ICA

Motor abnormalities in schizophrenia spectrum disorders (SSD) have increasingly attracted scientific interest in the past years. However, the neural mechanisms underlying parkinsonism in SSD are unclear. The present multimodal magnetic resonance imaging (MRI) study examined SSD patients with and without parkinsonism, as defined by a Simpson and Angus Scale (SAS) total score of ≥4 (SAS group, n = 22) or <4 (non-SAS group, n = 22). Parallel independent component analysis (p-ICA) was used to examine the covarying components among gray matter volume maps computed from structural MRI (sMRI) and fractional amplitude of low-frequency fluctuations (fALFF) maps computed from resting-state functional MRI (rs-fMRI) patient data. We found a significant correlation (P = .020, false discovery rate [FDR] corrected) between an sMRI component and an rs-fMRI component, which also significantly differed between the SAS and non-SAS group (P = .042, z = -2.04). The rs-fMRI component comprised the cortical sensorimotor network, and the sMRI component included predominantly a frontothalamic/cerebellar network. Across the patient sample, correlations adjusted for the Positive and Negative Syndrome Scale (PANSS) total scores showed a significant relationship between tremor score and loadings of the cortical sensorimotor network, as well as between glabella-salivation score, frontothalamic/cerebellar and cortical sensorimotor network loadings. These data provide novel insights into neural mechanisms of parkinsonism in SSD. Aberrant bottom-up modulation of cortical motor regions may account for these specific motor symptoms, at least in patients with SSD.

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

BACKGROUND AND PURPOSE

Posttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here, we used a novel machine learning method - evolving partitions to improve classification (EPIC) - to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat-exposed military veterans.

METHODS

We used EPIC with repeated cross-validation (CV) to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n = 40) versus controls (n = 57), and classification of ELS within the PTSD (ELS+ n = 16; ELS- n = 24) and control groups (ELS+ n = 16; ELS- n = 41). Additional inputs included intracranial volume, age, sex, adult trauma, and depression.

RESULTS

On average, EPIC classified PTSD with 69% accuracy (SD = 5%), and ELS with 64% accuracy in the PTSD group (SD = 10%), and 62% accuracy in controls (SD = 6%). EPIC selected unique sets of individual features that classified each group with 75-85% accuracy in post hoc analyses; combinations of regions marginally improved classification from the individual atlas-defined brain regions. Across analyses, surface area in the right posterior cingulate was the only variable that was repeatedly selected as an important feature for classification of PTSD and ELS.

CONCLUSIONS

EPIC revealed unique patterns of features that distinguished PTSD and ELS in this sample of combat-exposed military veterans, which may represent distinct biotypes of stress-related neuropathology.