Three dysconnectivity patterns in treatment-resistant schizophrenia patients and their unaffected siblings

Changes of soil carbon along precipitation gradients in three typical vegetation types in the Alxa desert region, China

The changes and influencing factors of soil inorganic carbon (SIC) and organic carbon (SOC) on precipitation gradients are crucial for predicting and evaluating carbon storage changes at the regional scale. However, people's understanding of the distribution characteristics of SOC and SIC reserves on regional precipitation gradients is insufficient, and the main environmental variables that affect SOC and SIC changes are also not well understood. Therefore, this study focuses on the Alxa region and selects five regions covered by three typical desert vegetation types, Zygophyllum xanthoxylon (ZX), Nitraria tangutorum (NT), and Reaumuria songarica (RS), along the climate transect where precipitation gradually increases. The study analyzes and discusses the variation characteristics of SOC and SIC under different vegetation and precipitation conditions. The results indicate that both SOC and SIC increase with the increase of precipitation, and the increase in SOC is greater with the increase of precipitation. The average SOC content in the 0-300cm profile is NT (4.13 g kg-1) > RS (3.61 g kg-1) > ZX (3.57 g kg-1); The average value of SIC content is: RS (5.78 g kg-1) > NT (5.11 g kg-1) > ZX (5.02 g kg-1). Overall, the multi-annual average precipitation (MAP) in the Alxa region is the most important environmental factor affecting SIC and SOC.

Treatment-resistant schizophrenia: Addressing white matter integrity, intracortical glutamate levels, clinical and cognitive profiles between early- and adult-onset patients

BACKGROUND

Treatment-resistance in schizophrenia is 30-40%. Its neurobiology remains unclear; to explore it, we conducted a combined spectrometry/tractography/cognitive battery and psychopathological rating study on patients with treatment-resistant schizophrenia (TRS), dividing the sample into early-onset (N = 21) and adult-onset TRS (N = 20). Previous studies did not differentiate between early- (onset 13-18 years) and adult-onset (>18 years at formal diagnosis of schizophrenia) TRS.

METHODS

We evaluated cross-sectionally 41 TRS patients (26 male and 15 female) and 20 matched healthy controls (HCs) with psychopathological and cognitive testing prior to participating in brain imaging scanning using magnetic resonance spectroscopy and diffusion tensor imaging to determine the relationship between their symptoms and their glutamate levels and white matter integrity.

RESULTS

TRS patients scored lower than HCs on all cognitive domains; early-onset patients performed better than adult-onset patients only on the Symbol Coding domain. TRS correlated with symptom severity, especially negative symptoms. Glutamate levels and glutamate/creatine were increased in anterior cingulate cortex. Diffusion tensor imaging showed low fractional anisotropy in TRS patients in specific white matter tracts compared to HCs (bilateral anterior thalamic radiation, cortico-spinal tract, forceps minor, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus, and right uncinate fasciculus).

CONCLUSIONS

We identified specific magnetic resonance spectroscopy and diffusion tensor imaging alterations in TRS patients. Adult-onset TRS differed little from early-onset TRS on most measures; this points to alterations being present since the outset of schizophrenia and may constitute a biological signature of treatment-resistance.

A preliminary study to investigate resting state fMRI as a potential group differentiator for schizophrenia

Schizophrenia (SZ) is found to be associated with dysconnectivity between the various regions of the brain. These aberrant connections in brain networks responsible for various mental processes in schizophrenia. We examined differences in functional connectivity among persons with SZ (n = 30) and an equal number of their unaffected relatives using resting state functional Magnetic Resonance Imaging (rsfMRI). Subjects were interviewed using the Diagnostic Interview for Genetic Studies (DIGS) and Family Interview for Genetic Studies (FIGS). Cognition was assessed using the Computerized Neuropsychological Battery (CNB) and Trail Making Tests A and B. The resting state functional data were acquired using 3.0 T Magnetic Resonance Imaging system and analysed using Statistical Package for the Social Sciences (SPSS) version 21 and FSL version 5.01 (FMRIB's) Software. The persons with SZ performed significantly worse on tasks of cognition and executive functioning. On rsfMRI, a significantly reduced connectivity was noted in the case group in right and left precentral gyri, right post central gyrus, right and left middle temporal gyrus, left paracingulate gyrus, anterior and posterior cingulate, right planum temporale, right pallidum, left cerebellum-6,7b and 8 lobules. Increased connectivity was noted between areas of right temporal pole and left hippocampus, posterior cingulate and the precuneus, right planum polare and right amygdala, right Heschl's gyrus and left posterior supramarginal gyrus, right amygdala with right insular cortex and left cerebellum 6 with bilateral postcentral gyrus in the same group. These differences in connectivity could be utilised as potential group differentiator for schizophrenia.

Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity

Patients with schizophrenia (SCZ), as well as their unaffected siblings (SIB), show functional connectivity (FC) alterations during performance of tasks involving attention. As compared with SCZ, these alterations are present in SIB to a lesser extent and are more pronounced during high cognitive demand, thus possibly representing one of the pathways in which familial risk is translated into the SCZ phenotype. Our aim is to measure the separability of SCZ and SIB from healthy controls (HC) using attentional control-dependent FC patterns, and to test to which extent these patterns span a continuum of neurofunctional alterations between HC and SCZ. 65 SCZ with 65 age and gender-matched HC and 39 SIB with 39 matched HC underwent the Variable Attentional Control (VAC) task. Load-dependent connectivity matrices were generated according to correct responses in each VAC load. Classification performances of high, intermediate and low VAC load FC on HC-SCZ and HC-SIB cohorts were tested through machine learning techniques within a repeated nested cross-validation framework. HC-SCZ classification models were applied to the HC-SIB cohort, and vice-versa. A high load-related decreased FC pattern discriminated between HC and SCZ with 66.9% accuracy and with 57.7% accuracy between HC and SIB. A high load-related increased FC network separated SIB from HC (69.6% accuracy), but not SCZ from HC (48.5% accuracy). Our findings revealed signatures of attentional FC abnormalities shared by SCZ and SIB individuals. We also found evidence for potential, SIB-specific FC signature, which may point to compensatory neurofunctional mechanisms in persons at familial risk for schizophrenia.

Functional neuroimaging in treatment resistant schizophrenia: A systematic review

Despite the availability of several drugs, about 30% of patients with schizophrenia still fail to respond properly to a course of appropriate antipsychotic treatment. Functional neuroimaging studies have shown widespread patterns of altered activation and functional connectivity in treatment-resistant schizophrenia (TRS). The aim of the present study was to examine the available functional magnetic resonance imaging studies investigating TRS and to identify common patterns of altered brain function that could predict the lack of response to antipsychotic treatment in this disorder. Alterations of activation and functional connectivity in fronto-temporal, cortico-striatal, default mode network and salience networks, and of their interplay, were associated with TRS. Our findings support the notion that large-scale network alterations present in schizophrenia lie in a continuum within treatment response with the most severe dysfunction in TRS. Few studies with small sample size and without adequate control group limit the generalizability of current literature. Future controlled longitudinal studies are needed to identify neuroimaging biomarkers of pharmacotherapy response to inform individual treatment selection and facilitate early clinical response.

Treatment-Resistant Schizophrenia: Genetic and Neuroimaging Correlates

Schizophrenia is a severe neuropsychiatric disorder that affects approximately 0.5–1% of the population. Response to antipsychotic therapy is highly variable, and it is not currently possible to predict those patients who will or will not respond to antipsychotic medication. Furthermore, a high percentage of patients, approximately 30%, are classified as treatment-resistant (treatment-resistant schizophrenia; TRS). TRS is defined as a non-response to at least two trials of antipsychotic medication of adequate dose and duration. These patients are usually treated with clozapine, the only evidence-based pharmacotherapy for TRS. However, clozapine is associated with severe adverse events. For these reasons, there is an increasing interest to identify better targets for drug development of new compounds and to establish better biomarkers for existing medications. The ability of antipsychotics to improve psychotic symptoms is dependent on their antagonist and reverse agonist activities at different neuroreceptors, and some genetic association studies of TRS have focused on different pharmacodynamic factors. Some genetic studies have shown an association between antipsychotic response or TRS and neurodevelopment candidate genes, antipsychotic mechanisms of action (such as dopaminergic, serotonergic, GABAergic, and glutamatergic) or pharmacokinetic factors (i.e., differences in the cytochrome families). Moreover, there is a growing body of literature on the structural and functional neuroimaging research into TRS. Neuroimaging studies can help to uncover the underlying neurobiological reasons for such resistance and identify resistant patients earlier. Studies examining the neuropharmacological mechanisms of antipsychotics, including clozapine, can help to improve our knowledge of their action on the central nervous system, with further implications for the discovery of biomarkers and the development of new treatments. The identification of the underlying mechanisms of TRS is a major challenge for developing personalized medicine in the psychiatric field for schizophrenia treatment. The main goal of precision medicine is to use genetic and brain-imaging information to improve the safety, effectiveness, and health outcomes of patients via more efficiently targeted risk stratification, prevention, and tailored medication and treatment management approaches. The aim of this review is to summarize the state of art of pharmacogenetic, pharmacogenomic and neuroimaging studies in TRS.

Attenuated resting-state functional connectivity in patients with childhood- and adult-onset schizophrenia

BACKGROUND

Childhood-onset schizophrenia (COS) is a rare, severe form of the adult-onset disorder (AOS). Our previous resting-state fMRI study identified attenuated functional connectivity in COS compared with controls. Here, we ask whether COS and AOS patients and their siblings exhibit similar abnormalities of functional connectivity.

METHODS

A whole-brain, data-driven approach was used to assess resting-state functional connectivity differences in COS (patients/siblings/controls, n: 26/28/33) and AOS (n: 19/28/30). There were no significant differences in age, sex, or head motion across groups in each dataset and as designed, the COS dataset has a significantly lower age than the AOS.

RESULTS

Both COS and AOS patients showed decreased functional connectivity relative to controls among a wide set of brain regions (P<0.05, corrected), but their siblings did not. Decreased connectivity in COS and AOS patients showed no amplitude differences and was not modulated by age-at-onset or medication doses. Cluster analysis revealed that these regions fell into two large-scale networks: one sensorimotor network and one centered on default-mode network regions, but including higher-order cognitive areas only in COS. Decreased connectivity between these two networks was notable (P<0.05, corrected) for both patient groups.

CONCLUSIONS

A shared pattern of attenuated functional connectivity was found in COS and AOS, supporting the continuity of childhood-onset and adult-onset schizophrenia. Connections were altered between sensorimotor areas and default-mode areas in both COS and AOS, suggesting potential abnormalities in processes of self-monitoring and sensory prediction. The absence of substantial dysconnectivity in siblings indicates that attenuation is state-related.

Integrated computational biology analysis to evaluate target genes for chronic myelogenous leukemia

Although hundreds of genes have been linked to chronic myelogenous leukemia (CML), many of the results lack reproducibility. In the present study, data across multiple modalities were integrated to evaluate 579 CML candidate genes, including literature‑based CML‑gene relation data, Gene Expression Omnibus RNA expression data and pathway‑based gene‑gene interaction data. The expression data included samples from 76 patients with CML and 73 healthy controls. For each target gene, four metrics were proposed and tested with case/control classification. The effectiveness of the four metrics presented was demonstrated by the high classification accuracy (94.63%; P<2x10‑4). Cross metric analysis suggested nine top candidate genes for CML: Epidermal growth factor receptor, tumor protein p53, catenin β 1, janus kinase 2, tumor necrosis factor, abelson murine leukemia viral oncogene homolog 1, vascular endothelial growth factor A, B‑cell lymphoma 2 and proto‑oncogene tyrosine‑protein kinase. In addition, 145 CML candidate pathways enriched with 485 out of 579 genes were identified (P<8.2x10‑11; q=0.005). In conclusion, weighted genetic networks generated using computational biology may be complementary to biological experiments for the evaluation of known or novel CML target genes.

Integrative computational approach to evaluate risk genes for postmenopausal osteoporosis

In recent years, numerous studies reported over a hundred of genes playing roles in the etiology of postmenopausal osteoporosis (PO). However, many of these candidate genes were lack of replication and results were not always consistent. Here, the authors proposed a computational workflow to curate and evaluate PO related genes. They integrate large-scale literature knowledge data and gene expression data (PO case/control: 10/10) for the marker evaluation. Pathway enrichment, sub-network enrichment, and gene-gene interaction analysis were conducted to study the pathogenic profile of the candidate genes, with four metrics proposed and validated for each gene. By using the authors' approach, a scalable PO genetic database was developed; including PO related genes, diseases, pathways, and the supporting references. The PO case/control classification supported the effectiveness of the four proposed metrics, which successfully identified eight well-studied top PO genes (e.g. TGFB1, IL6, IL1B, TNF, ESR2, IGF1, HIF1A, and COL1A1) and highlighted one recently reported PO genes (e.g. IFNG). The computational biology approach and the PO database developed in this study provide a valuable resource which may facilitate understanding the genetic profile of PO.

Integrative computational evaluation of genetic markers for Alzheimer's disease

Recent studies have reported hundreds of genes linked to Alzheimer’s Disease (AD). However, many of these candidate genes may be not identified in different studies when analyses were replicated. Moreover, results could be controversial. Here, we proposed a computational workflow to curate and evaluate AD related genes. The method integrates large scale literature knowledge data and gene expression data that were acquired from postmortem human brain regions (AD case/control: 31/32 and 22/8). Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analysis were conducted to study the pathogenic profile of the candidate genes, with 4 metrics proposed and validated for each gene. By using our approach, a scalable AD genetic database was developed, including AD related genes, pathways, diseases and info of supporting references. The AD case/control classification supported the effectiveness of the 4 proposed metrics, which successfully identified 21 well-studied AD genes (i.g. TGFB1, CTNNB1, APP, IL1B, PSEN1, PTGS2, IL6, VEGFA, SOD1, AKT1, CDK5, TNF, GSK3B, TP53, CCL2, BDNF, NGF, IGF1, SIRT1, AGER and TLR) and highlighted one recently reported AD gene (i.g. ITGB1). The computational biology approach and the AD database developed in this study provide a valuable resource which may facilitate the understanding of the AD genetic profile.

Risk and resilience brain networks in treatment-resistant schizophrenia

BACKGROUND

Genes, molecules and neural circuits that are associated with, or confer risk to developing schizophrenia have been studied and mapped. It is hypothesized that certain neural systems may counterbalance familial risk of schizophrenia, and thus confer resilience to developing the disorder. This study sought to identify resting-state functional brain connectivity (rs-FC) representing putative risk or resilience endophenotypes in schizophrenia.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) was performed in 42 individuals with treatment resistant schizophrenia (TRS), 16 unaffected first-degree family members (UFM) and 42 healthy controls. Whole-brain rs-FC networks were mapped for each individual and analysed graph theoretically to identify network markers associated with schizophrenia risk or resilience.

RESULTS

The ~900 functional connections showing between-group differences were operationalized as conferring: i) resilience, ii) risk, or iii) precipitating risk and/or illness effects. Approximately 95% of connections belonged to the latter two categories, with substantially fewer connections associated with resilience. Schizophrenia risk primarily involved reduced frontal and occipital rs-FC, with patients showing additional reduced frontal and temporal rs-FC. Functional brain networks were characterized by greater local efficiency in UFM, compared to TRS and controls.

CONCLUSIONS

TRS and UFM share frontal and occipital rs-FC deficits, representing a 'risk' endophenotype. Additional reductions in frontal and temporal rs-FC appear to be associated with risk that precipitates psychosis in vulnerable individuals, or may be due to other illness-related effects, such as medication. Functional brain networks are more topologically resilient in UFM compared to TRS, which may protect UFM from psychosis onset despite familial liability.

Treatment resistant schizophrenia and neurological soft signs may converge on the same pathology: Evidence from explanatory analysis on clinical, psychopathological, and cognitive variables

Here, we investigated neurological soft signs (NSSs) in treatment resistant schizophrenia (TRS) vs treatment responder schizophrenia (SZ) patients. TRS is a severe condition, affecting approximately one-third of schizophrenia patients and representing a relevant clinical challenge. NSSs are neurological abnormalities reportedly described in schizophrenia patients and linked to dysregulated network connections. We explored the possibility that NSSs may be: i) more severe in TRS patients; ii) differentially associated to clinical/cognitive variables in TRS vs SZ; iii) predictive of having TRS. In addition, we evaluated whether diagnosis may mediate NSSs associations with the above-mentioned variables. Consecutive patients with schizophrenia diagnosis underwent stringent assessment for TRS diagnosis. Demographics and clinical variables were recorded. Psychopathology (by Positive and Negative Syndrome Scale, PANSS), cognitive performances, and NSSs (by Neurological Evaluation Scale, NES) were tested. TRS had higher scores than SZ patients in total NES score and in almost all NES subscales, even after correction for duration of illness and antipsychotic dose (ANCOVA, p<0.05). NSSs significantly correlated with multiple clinical, psychopathological, and cognitive variables (above all: duration of disease and negative symptoms) in TRS but not in SZ patients. Two-way ANOVA showed NSS-x-diagnosis interaction in determining outcomes on multiple cognitive performances, but not in other clinical variables. However, simple main effect analysis detected a significant relationship between high severity NSSs and TRS diagnosis on multiple clinical and cognitive outcomes. Hierarchical regression analysis showed that diagnosis was among a discrete number of predictors yielding significant increases in variance explained on NES total, Sensory Integration and Other Signs subscales' scores. NSSs, together with antipsychotic dose and disease severity, were found to be significantly predictive of TRS diagnosis in a binary logistic regression model. These results suggest a stringent association between NSSs and TRS diagnosis, and may imply that NSSs association with clinical, psychopathological, and cognitive variables may be in part mediated by TRS diagnosis.

Distinguishing Between Treatment-Resistant and Non-Treatment-Resistant Schizophrenia Using Regional Homogeneity

Background: Patients with treatment-resistant schizophrenia (TRS) and non-treatment-resistant schizophrenia (NTRS) respond to antipsychotic drugs differently. Previous studies demonstrated that patients with TRS or NTRS exhibited abnormal neural activity in different brain regions. Accordingly, in the present study, we tested the hypothesis that a regional homogeneity (ReHo) approach could be used to distinguish between patients with TRS and NTRS. Methods: A total of 17 patients with TRS, 17 patients with NTRS, and 29 healthy controls (HCs) matched in sex, age, and education levels were recruited to undergo resting-state functional magnetic resonance imaging (RS-fMRI). ReHo was used to process the data. ANCOVA followed by post-hoc t-tests, receiver operating characteristic curves (ROC), and correlation analyses were applied for the data analysis. Results: ANCOVA analysis revealed widespread differences in ReHo among the three groups in the occipital, frontal, temporal, and parietal lobes. ROC results indicated that the optimal sensitivity and specificity of the ReHo values in the left postcentral gyrus, left inferior frontal gyrus/triangular part, and right fusiform could differentiate TRS from NTRS, TRS from HCs, and NTRS from HCs were 94.12 and 82.35%, 100 and 86.21%, and 82.35 and 93.10%, respectively. No correlation was found between abnormal ReHo and clinical symptoms in patients with TRS or NTRS. Conclusions: TRS and NTRS shared most brain regions with abnormal neural activity. Abnormal ReHo values in certain brain regions might be applied to differentiate TRS from NTRS, TRS from HC, and NTRS from HC with high sensitivity and specificity.

An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder

Studies to date have reported hundreds of genes connected to bipolar disorder (BP). However, many studies identifying candidate genes have lacked replication, and their results have, at times, been inconsistent with one another. This paper, therefore, offers a computational workflow that can curate and evaluate BP-related genetic data. Our method integrated large-scale literature data and gene expression data that were acquired from both postmortem human brain regions (BP case/control: 45/50) and peripheral blood mononuclear cells (BP case/control: 193/593). To assess the pathogenic profiles of candidate genes, we conducted Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analyses, with 4 metrics proposed and validated for each gene. Our approach developed a scalable BP genetic database (BP_GD), including BP related genes, drugs, pathways, diseases and supporting references. The 4 metrics successfully identified frequently-studied BP genes (e.g. GRIN2A, DRD1, DRD2, HTR2A, CACNA1C, TH, BDNF, SLC6A3, P2RX7, DRD3, and DRD4) and also highlighted several recently reported BP genes (e.g. GRIK5, GRM1 and CACNA1A). The computational biology approach and the BP database developed in this study could contribute to a better understanding of the current stage of BP genetic research and assist further studies in the field.

An Integrative Computational Approach to Evaluate Genetic Markers for Chronic Lymphocytic Leukemia

Recent studies reported hundreds of genes linked to chronic lymphocytic leukemia (CLL). However, many of these candidate genes were lack of replication and results were not always consistent. Here, we proposed a computational workflow to curate and evaluate CLL-related genes. The method integrates large-scale literature knowledge data, gene expression data, and related pathways/network information for quantitative marker evaluation. Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analysis were conducted to study the pathogenic profile of the candidate genes, with four metrics proposed and validated for each gene. By using our approach, a scalable CLL genetic database was developed including CLL-related genes, pathways, diseases and information of supporting references. The CLL case/control classification supported the effectiveness of the four proposed metrics, which successfully identified nine well-studied CLL genes (i.e., TNF, BCL2, TP53, VEGFA, P2RX7, AKT1, SYK, IL4, and MDM2) and highlighted two newly reported CLL genes (i.e., PDGFRA and CSF1R). The computational biology approach and the CLL database developed in this study provide a valuable resource that may facilitate the understanding of the genetic profile of CLL.

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

Neuroimaging techniques have greatly enhanced the understanding of neurodiversity (human brain variation across individuals) in both health and disease. The ultimate goal of using brain imaging biomarkers is to perform individualized predictions. Here we proposed a generalized framework that can predict explicit values of the targeted measures by taking advantage of joint information from multiple modalities. This framework also enables whole brain voxel-wise searching by combining multivariate techniques such as ReliefF, clustering, correlation-based feature selection and multiple regression models, which is more flexible and can achieve better prediction performance than alternative atlas-based methods. For 50 healthy controls and 47 schizophrenia patients, three kinds of features derived from resting-state fMRI (fALFF), sMRI (gray matter) and DTI (fractional anisotropy) were extracted and fed into a regression model, achieving high prediction for both cognitive scores (MCCB composite r=0.7033, MCCB social cognition r=0.7084) and symptomatic scores (positive and negative syndrome scale [PANSS] positive r=0.7785, PANSS negative r=0.7804). Moreover, the brain areas likely responsible for cognitive deficits of schizophrenia, including middle temporal gyrus, dorsolateral prefrontal cortex, striatum, cuneus and cerebellum, were located with different weights, as well as regions predicting PANSS symptoms, including thalamus, striatum and inferior parietal lobule, pinpointing the potential neuromarkers. Finally, compared to a single modality, multimodal combination achieves higher prediction accuracy and enables individualized prediction on multiple clinical measures. There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making.