Independent Replication Dataset Research Articles

Genetic variants of FER and SULF1 in the fibroblast-related genes are associated with non-small-cell lung cancer survival.

Fibroblasts are important components in the tumor microenvironment and can affect tumor progression and metastasis. However, the roles of genetic variants of the fibroblast-related genes (FRGs) in the prognosis of non-small-cell lung cancer (NSCLC) patients have not been reported. Therefore, we investigated the associations between 26,544 single nucleotide polymorphisms (SNPs) in 291 FRGs and survival of NSCLC patients from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. In Cox regression multivariable analysis, we found that 661 SNPs were associated with NSCLC overall survival (OS). Then we validated these SNPs in another independent replication dataset of 984 patients from the Harvard Lung Cancer Susceptibility (HLCS) Study. Finally, we identified two independent SNPs (i.e., FER rs7716388 A>G and SULF1 rs11785839 G>C) that remained significantly associated with NSCLC survival with hazards ratios (HRs) of 0.87 (95% confidence interval [CI] = 0.77-0.98, p = 0.018) and 0.88 (95% CI = 0.79-0.99, p = 0.033), respectively. Combined analysis for these two SNPs showed that the number of protective alleles was associated with better OS and disease-specific survival. Expression quantitative trait loci analysis indicated that the FER rs7716388 G allele was associated with the up-regulation of FER mRNA expression levels in lung tissue. Our results indicated that these two functional SNPs in the FRGs may be prognostic biomarkers for the prognosis of NSCLC patients, and the possible mechanism may be through modulating the expression of their corresponding genes.

Multi-omics profiling of DNA methylation and gene expression alterations in human cocaine use disorder

Structural and functional changes of the brain are assumed to contribute to excessive cocaine intake, craving, and relapse in cocaine use disorder (CUD). Epigenetic and transcriptional changes were hypothesized as a molecular basis for CUD-associated brain alterations. Here we performed a multi-omics study of CUD by integrating epigenome-wide methylomic (N = 42) and transcriptomic (N = 25) data from the same individuals using postmortem brain tissue of Brodmann Area 9 (BA9). Of the N = 1 057 differentially expressed genes (p < 0.05), one gene, ZFAND2A, was significantly upregulated in CUD at transcriptome-wide significance (q < 0.05). Differential alternative splicing (AS) analysis revealed N = 98 alternatively spliced transcripts enriched in axon and dendrite extension pathways. Strong convergent overlap in CUD-associated expression deregulation was found between our BA9 cohort and independent replication datasets. Epigenomic, transcriptomic, and AS changes in BA9 converged at two genes, ZBTB4 and INPP5E. In pathway analyses, synaptic signaling, neuron morphogenesis, and fatty acid metabolism emerged as the most prominently deregulated biological processes. Drug repositioning analysis revealed glucocorticoid receptor targeting drugs as most potent in reversing the CUD expression profile. Our study highlights the value of multi-omics approaches for an in-depth molecular characterization and provides insights into the relationship between CUD-associated epigenomic and transcriptomic signatures in the human prefrontal cortex.

Refining criteria for a neurodevelopmental sub-phenotype of bipolar disorders: a FondaMental Advanced Centers of Expertise for Bipolar Disorders study

Bipolar disorder (BD) is a complex and heterogeneous psychiatric disorder. It has been suggested that neurodevelopmental factors contribute to the etiology of BD, but a specific neurodevelopmental phenotype (NDP) of the disorder has not been identified. Our objective was to define and characterize an NDP in BD and validate its associations with clinical outcomes, polygenic risk scores, and treatment responses. We analyzed the FondaMental Advanced Centers of Expertise for Bipolar Disorders cohort of 4468 patients with BD, a validation cohort of 101 patients with BD, and 2 independent replication datasets of 274 and 89 patients with BD. Using factor analyses, we identified a set of criteria for defining NDP. Next, we developed a scoring system for NDP load and assessed its association with prognosis, neurological soft signs, polygenic risk scores for neurodevelopmental disorders, and responses to treatment using multiple regressions, adjusted for age and gender with bootstrap replications. Our study established an NDP in BD consisting of 9 clinical features: advanced paternal age, advanced maternal age, childhood maltreatment, attention-deficit/hyperactivity disorder, early onset of BD, early onset of substance use disorders, early onset of anxiety disorders, early onset of eating disorders, and specific learning disorders. Patients with higher NDP load showed a worse prognosis and increased neurological soft signs. Notably, these individuals exhibited a poorer response to lithium treatment. Furthermore, a significant positive correlation was observed between NDP load and polygenic risk score for attention-deficit/hyperactivity disorder, suggesting potential overlapping genetic factors or pathophysiological mechanisms between BD and attention-deficit/hyperactivity disorder. The proposed NDP constitutes a promising clinical tool for patient stratification in BD.

The association of impulsivity with depression and anxiety symptoms: A transdiagnostic network analysis and replication

BackgroundImpulsivity increases the risk for depression and anxiety. However, the granular pathways among them remain unknown. A network approach that moves from disorder-level analysis to symptom-level analysis can provide further understanding of psychopathological mechanisms. In this study, we examined the network structure of impulsivity and separate and comorbid symptoms of depression and anxiety. MethodsRegularized partial-correlation networks were estimated using cross-sectional data from 1047 Chinese participants aged 18–26 years (main dataset, mean age = 21.45 ± 2.01 years) and 325 Chinese participants aged 18–36 years (an independent replication dataset, mean age = 21.49 ± 3.73 years), including impulsivity-depression, impulsivity-anxiety, and impulsivity-depression-anxiety networks. The datasets were collected from 1 June 2023 to 4 August 2023 and from 27 April 2022 to 16 May 2022, respectively. Impulsivity, depression, and anxiety were assessed using Barratt Impulsiveness Scale Version 11, Patient Health Questionnaire-9, and Generalized Anxiety Disorder-7, respectively. Bridge centrality was analyzed, and a network comparison test (NCT) was conducted to investigate the differences between the main dataset and replication dataset. ResultsThe motor impulsivity dimension was revealed to be closely connected with individual symptoms of depression and anxiety regardless of whether they were in separate disorder forms or comorbid forms. In all the networks, motor impulsivity was the most important bridge node. The NCT showed comparable network connectivity and network structure between the main and replication datasets. LimitationsThe use of cross-sectional data limited the inferences about the direction of causality between variables. ConclusionsThese findings elucidate the psychopathological mechanisms underlying how impulsivity functions within depression, anxiety, and comorbidity and support that motor impulsivity is an important risk factor across different mental disorders and is responsible for comorbidity. The implications of these findings are discussed.

Structural and resting-state connection abnormalities of habenula in obsessive-compulsive disorder.

Previous studies have suggested that the habenula (Hb) may be involved in the mechanism of obsessive-compulsive disorder (OCD). However, the specific role of Hb in OCD remains unclear. This study aimed to explore the structural and functional abnormalities of Hb in OCD and their relationship with the clinical symptoms. Eighty patients with OCD and 85 healthy controls (HCs) were recruited as the primary dataset. The grey matter volume, resting-state functional connectivity (FC), and effective connectivity (EC) of the Hb were calculated and compared between OCD group and HCs. An independent replication dataset was used to verify the stability and robustness of the results. Patients with OCD exhibited smaller Hb volume and increased FC of right Hb-left hippocampus than HCs. Dynamic causal model revealed an increased EC from left hippocampus to right Hb and a less inhibitory causal influence from the right Hb to left hippocampus in the OCD group compared to HCs. Similar results were found in the replication dataset. This study suggested that abnormal structure of Hb and hippocampus-Hb connectivity may contribute to the pathological basis of OCD.

0030 Insomnia is associated with lower serum antioxidants in the Hispanic Community Health Study/Study of Latinos

Abstract Introduction Insomnia is associated with neurodegenerative diseases. Concentration levels of some metabolites are associated with pathophysiologic changes, possibly in a sex-specific manner. Thus, serum metabolite levels are useful for understanding insomnia-related pathophysiological changes and how they potentially relate to neurodegenerative disease. Methods The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is a longitudinal cohort study following self-reported Hispanic/Latino individuals in the U.S. At baseline, insomnia symptoms were captured using the Women Health Initiative Insomnia Rating Scale (WHIIRS), insomnia being defined as WHIIRS≥9. Metabolites were measured using blood serum from the baseline exam in independent discovery and replication datasets. In discovery, metabolite associations with WHIIRS and with insomnia were estimated in linear regression. Significant associations (FDR p-values&amp;lt; 0.05) were tested in the replication dataset and considered replicated if their p-value&amp;lt; 0.05 and the direction of association matched with the discovery analysis. Associations were further studied for gender interaction. All analyses were adjusted for age, self-reported gender, study center, and Hispanic/Latino background. Results 3,895 individuals participated in the discovery analysis, and 2,121 in replication analysis. Female individuals were aged 46.5 on average (SD 15.1) and 35% had insomnia, while male individuals were aged 44.8 (14.8) and 24% had insomnia. After quality control, 767 metabolites were tested for association. Six metabolites were associated with WHIIRS (FDR&amp;lt; 0.05) in the gender-combined analysis. Three were xenobiotics which were on the benzoate or salicylic acid metabolism pathway. Of the 6 metabolite associations, two replicated (p-value&amp;lt; 0.05): hydrocinnamate and indolepropionate. These two metabolites had spearman correlation of 0.44. Higher levels of both metabolites were associated with lower WHIIRS, showing stronger associations in females (interaction p-value =0.04 for hydrocinnamate and p-value=0.13 for indolepropionate). Associations remained essentially unchanged in a model further adjusting for hypertension and diabetes. When removing 534 individuals who self-reported using sleeping pills at least once a week, the associations attenuated slightly: from an effect estimates of -0.0075 to -0.0069 (hydrocinnamate) and from -0.0082 to -0.0068 (indolepropionate). Conclusion Among the HCHS/SOL target population, insomnia symptom severity is associated with a lower concentrations of some plasma metabolite levels which are important antioxidant needed in preventing oxidative stress and neurodegeneration. Support (if any) R01HL161012.

Trait absorption is not reliably associated with brain structure or resting-state functional connectivity

Trait ‘absorption’ is a psychological construct with a rich history that was initially born from early work on hypnotic suggestibility. Absorption characterizes an individual's tendency to become effortlessly engrossed in the contents of experience, whether in terms of external sensory phenomena or internal imagery and fantasy, and is reliably associated with a constellation of psychological, cognitive, and behavioral traits. Here, we conducted a comprehensive neuroimaging investigation of associations between trait absorption and the brain. In particular, we assessed multivariate relationships between absorption scores and neuroimaging measures of grey matter density, as well as static and dynamic resting-state functional connectivity. We investigated these relationships using partial least squares in a discovery dataset (n = 201) and then attempted to reproduce results in an independent replication dataset (n = 68). Results revealed a lack of significant associations between absorption and grey matter density across both datasets, and a significant association between absorption and static resting-state functional connectivity in the discovery dataset which was not replicated in the replication dataset. Additional control analyses further indicated the lack of a reliable brain-absorption relationship, whereas we found a replicable association between the closely related trait of ‘openness to experience’ and resting-state functional connectivity. We conclude that absorption is not reliably associated with brain structure or function in the present datasets and discuss factors that may have contributed to this result. This study serves as the first comprehensive and adequately powered investigation of the neural correlates of absorption and motivates future studies to refine the conceptualization of this perplexing trait.

Regional superficial amygdala resting-state functional connectivity in adults infers childhood maltreatment severity.

Childhood maltreatment (CM) is a potential risk factor for some neuropsychiatric disorders in adulthood (e.g. depression and anxiety) and alters trajectories of brain development. Accumulating evidence suggests that functional connectivity of the limbic system, especially the amygdala, is highly associated with childhood maltreatment, although not all studies have found this. These inconsistent results may be due to differential alterations of amygdala resting-state functional connectivity (rsFC) following childhood maltreatment. Our aim was to investigate the relationship between the rsFC of amygdala subregions and CM severity, as well as to develop a stable rsFC-based model for inferring the severity of CM. In this study, we employed the Childhood Trauma Questionnaire (CTQ) to assess CM severity in each individual. We explored the relationship between the rsFC of amygdala subregions (i.e. centromedial -CMA, basolateral -BLA, superficial-SFA amygdala) and CM experience in a discovery dataset of n=110 healthy Chinese participants by linear multiple regression analysis. Subsequent dimensional and categorical approach were performed to elucidate the relationship between rsFCs and CM severity and CM subtypes, respectively. A support vector regression model was then conducted to validate the associations between rsFCs and total CTQ scores. Moreover, we also verified the model into another independent replication dataset (n=38). Our findings suggested that childhood maltreatment was negatively associated with rsFC between the right superficial amygdala and perigenual anterior cingulate cortex (pgACC)/postcentral gyrus (PCG) but not the other two amygdala subregions. Moreover, SFA-pgACC coupling was more associated with physical neglect whereas the SFA-PCG was more related to emotional neglect. In addition, supervised machine learning confirmed that using these two rsFCs as predictors could stably estimate continuous maltreatment severity in both discovery and replication datasets. The current study supports that the rsFCs of superficial amygdala are related to childhood maltreatment and which may be a potential biomarker for the effects of childhood maltreatment-related psychiatric disorders (i.e. depression and anxiety).

Context-dependent choice and evaluation in real-world consumer behavior

A body of work spanning neuroscience, economics, and psychology indicates that decision-making is context-dependent, which means that the value of an option depends not only on the option in question, but also on the other options in the choice set—or the ‘context’. While context effects have been observed primarily in small-scale laboratory studies with tightly constrained, artificially constructed choice sets, it remains to be determined whether these context effects take hold in real-world choice problems, where choice sets are large and decisions driven by rich histories of direct experience. Here, we investigate whether valuations are context-dependent in real-world choice by analyzing a massive restaurant rating dataset as well as two independent replication datasets which provide complementary operationalizations of restaurant choice. We find that users make fewer ratings-maximizing choices in choice sets with higher-rated options—a hallmark of context-dependent choice—and that post-choice restaurant ratings also varied systematically with the ratings of unchosen restaurants. Furthermore, in a follow-up laboratory experiment using hypothetical choice sets matched to the real-world data, we find further support for the idea that subjective valuations of restaurants are scaled in accordance with the choice context, providing corroborating evidence for a general mechanistic-level account of these effects. Taken together, our results provide a potent demonstration of context-dependent choice in real-world choice settings, manifesting both in decisions and subjective valuation of options.

The location of the t(4;14) translocation breakpoint within the NSD2 gene identifies a subset of patients with high-risk NDMM

AbstractAlthough translocation events between chromosome 4 (NSD2 gene) and chromosome 14 (immunoglobulin heavy chain [IgH] locus) (t(4;14)) is considered high risk in newly diagnosed multiple myeloma (NDMM), only ∼30% to 40% of t(4;14) patients are clinically high risk. We generated and compared a large whole genome sequencing (WGS) and transcriptome (RNA sequencing) from 258 t(4;14) (n = 153 discovery, n = 105 replication) and 183 non-t(4;14) NDMM patients with associated clinical data. A landmark survival analysis indicated only ∼25% of t(4;14) patients had an overall survival (OS) <24 months, and a comparative analysis of the patient subgroups identified biomarkers associated with this poor outcome, including translocation breakpoints located in the NSD2 gene and expression of IgH-NSD2 fusion transcripts. Three breakpoints were identified and are designated as: “no-disruption” (upstream of NSD2), “early-disruption” (in the 5' UTR), and “late-disruption” (within the NSD2 gene). Our results show a significant difference in OS based on the location of DNA breakpoints (median OS 28.6 “late-disruption” vs 59.2 “early disruption” vs 75.1 months “no disruption”). These findings have been replicated in an independent replication dataset. Also, univariate and multivariate analysis suggest high-risk markers such as del17p, 1p independently contribute to poor outcome in t(4;14) MM patients.

Discovery and Replication of Time-Resolved Functional Network Connectivity Differences in Adolescence and Adulthood in over 50K fMRI Datasets.

There remains an open question about whether and in what context brain function varies in adolescence and adulthood. In this work, we systematically study the functional brain networks of adolescents and adults, outlining the significant differences in the developing brain detected via time-resolved functional network connectivity (trFNC) derived from a fully automated independent component analysis pipeline applied to resting-state fMRI data in over 50K individuals. We then statistically analyze the transient, recurrent, and robust brain state profiles in both groups. We confirmed the results in independent replication datasets for both groups. Our findings indicate a strengthening of a state reflecting functional coupling within the visual, motor, and auditory domains and anticorrelation with all other domains in a unique adult state profile, a pattern consistently less modular in adolescents. This new insight into possible integration, strengthening, and modularization of resting-state brain connections beyond childhood convergently indicates that the highlighted temporal dynamics likely reflect robust differences in brain function in adolescents versus adults.

An association test of the spatial distribution of rare missense variants within protein structures identifies Alzheimer's disease-related patterns.

More than 90% of genetic variants are rare in most modern sequencing studies, such as the Alzheimer's Disease Sequencing Project (ADSP) whole-exome sequencing (WES) data. Furthermore, 54% of the rare variants in ADSP WES are singletons. However, both single variant and unit-based tests are limited in their statistical power to detect an association between rare variants and phenotypes. To best use missense rare variants and investigate their biological effect, we examine their association with phenotypes in the context of protein structures. We developed a protein structure–based approach, protein optimized kernel evaluation of missense nucleotides (POKEMON), which evaluates rare missense variants based on their spatial distribution within a protein rather than their allele frequency. The hypothesis behind this test is that the three-dimensional spatial distribution of variants within a protein structure provides functional context to power an association test. POKEMON identified three candidate genes (TREM2, SORL1, and EXOC3L4) and another suggestive gene from the ADSP WES data. For TREM2 and SORL1, two known Alzheimer's disease (AD) genes, the signal from the spatial cluster is stable even if we exclude known AD risk variants, indicating the presence of additional low-frequency risk variants within these genes. EXOC3L4 is a novel AD risk gene that has a cluster of variants primarily shared by case subjects around the Sec6 domain. This cluster is also validated in an independent replication data set and a validation data set with a larger sample size.

Genome-Wide Association Study Identifies First Locus Associated with Susceptibility to Cerebral Venous Thrombosis.

Cerebral venous thrombosis (CVT) is an uncommon form of stroke affecting mostly young individuals. Although genetic factors are thought to play a role in this cerebrovascular condition, its genetic etiology is not well understood. A genome-wide association study was performed to identify genetic variants influencing susceptibility to CVT. A 2-stage genome-wide study was undertaken in 882 Europeans diagnosed with CVT and 1,205 ethnicity-matched control subjects divided into discovery and independent replication datasets. In the overall case-control cohort, we identified highly significant associations with 37 single nucleotide polymorphisms (SNPs) within the 9q34.2 region. The strongest association was with rs8176645 (combined p= 9.15 × 10-24 ; odds ratio [OR]=2.01, 95% confidence interval [CI] = 1.76-2.31). The discovery set findings were validated across an independent European cohort. Genetic risk score for this 9q34.2 region increases CVT risk by a pooled estimate OR=2.65 (95% CI=2.21-3.20, p= 2.00 × 10-16 ). SNPs within this region were in strong linkage disequilibrium (LD) with coding regions of the ABO gene. The ABO blood group was determined using allele combination of SNPs rs8176746 and rs8176645. Blood groups A, B, or AB, were at 2.85 times (95% CI=2.32-3.52, p= 2.00 × 10-16 ) increased risk of CVT compared with individuals with blood group O. We present the first chromosomal region to robustly associate with a genetic susceptibility to CVT. This region more than doubles the likelihood of CVT, a risk greater than any previously identified thrombophilia genetic risk marker. That the identified variant is in strong LD with the coding region of the ABO gene with differences in blood group prevalence provides important new insights into the pathophysiology of CVT. ANN NEUROL 2021;90:777-788.

Shared genetic basis between genetic generalized epilepsy and background electroencephalographic oscillations.

ObjectiveParoxysmal epileptiform abnormalities on electroencephalography (EEG) are the hallmark of epilepsies, but it is uncertain to what extent epilepsy and background EEG oscillations share neurobiological underpinnings. Here, we aimed to assess the genetic correlation between epilepsy and background EEG oscillations.MethodsConfounding factors, including the heterogeneous etiology of epilepsies and medication effects, hamper studies on background brain activity in people with epilepsy. To overcome this limitation, we compared genetic data from a genome‐wide association study (GWAS) on epilepsy (n = 12 803 people with epilepsy and 24 218 controls) with that from a GWAS on background EEG (n = 8425 subjects without epilepsy), in which background EEG oscillation power was quantified in four different frequency bands: alpha, beta, delta, and theta. We replicated our findings in an independent epilepsy replication dataset (n = 4851 people with epilepsy and 20 428 controls). To assess the genetic overlap between these phenotypes, we performed genetic correlation analyses using linkage disequilibrium score regression, polygenic risk scores, and Mendelian randomization analyses.ResultsOur analyses show strong genetic correlations of genetic generalized epilepsy (GGE) with background EEG oscillations, primarily in the beta frequency band. Furthermore, we show that subjects with higher beta and theta polygenic risk scores have a significantly higher risk of having generalized epilepsy. Mendelian randomization analyses suggest a causal effect of GGE genetic liability on beta oscillations.SignificanceOur results point to shared biological mechanisms underlying background EEG oscillations and the susceptibility for GGE, opening avenues to investigate the clinical utility of background EEG oscillations in the diagnostic workup of epilepsy.

Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app.

As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between 1 and 28 May 2020. Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic - area under the curve) of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.

Integrating human brain proteomes and genome‐wide association results implicates new genes in Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD) is the most common type of dementia, and it has a substantial genetic basis. Recent AD genome‐wide association studies (GWAS) continue to expand the number of loci that are significantly associated with AD; however, how those loci confer risk for AD remains unclear. Here, we addressed this problem by integrating 376 deep human brain proteomes and 888 human brain transcriptomes with recent AD GWAS results to identify genes, transcripts, and proteins that likely contribute to AD pathogenesis.MethodsWe inferred the genetically controlled portion of protein abundance using 376 deep human brain proteomes, and these results were integrated with two recent large AD GWAS results that included 455,258 participants (Jansen et al 2019), and 82,771 participants (Kunkle et al 2019), respectively, using the FUSION pipeline (Gusev et al 2016). We performed an analogous analysis using 888 human brain transcriptomes to infer the genetically controlled portion of the transcript abundance and integrated those results with the aforementioned AD GWAS results.ResultsWe identified a total of 15 genes whose genetically regulated brain protein abundances were associated with AD. They are ACE, CARHSP1, CTSH, DOC2A, EPHX2, ICA1L, LACTB, MADD, PITPNC1, PLEKHA1, PVR, RTFDC1, SNX32, STX4, and STX6. At the transcript level, we identified a total of 49 genes whose cis‐regulated mRNA expression levels were associated with AD. Of the 15 genes identified at the protein level, 33% (5 of 15) were also identified at the transcript level. Furthermore, 73% of these 15 genes identified at the protein level were highly expressed in excitatory neurons based on a reference single‐cell RNA‐sequencing dataset from human brain. In an independent replication dataset using single‐shot label‐free proteomes (n = 205), only one of the 15 proteins were measured and imputed (EPHX2). We replicated EPHX2’s association with AD in that dataset.ConclusionTogether, our findings implicate 15 human brain proteins in the pathogenesis of AD and suggest that these proteins are controlled by both transcriptional and post‐transcriptional mechanisms. Our results lay a foundation for future mechanistic studies to develop novel treatments and biomarkers for AD.

Investigation of genetically regulated gene expression and response to treatment in rheumatoid arthritis highlights an association between IL18RAP expression and treatment response

ObjectivesIn this study, we sought to investigate whether there was any association between genetically regulated gene expression (as predicted using various reference panels) and anti-tumour necrosis factor (anti-TNF) treatment response...

There is no single functional atlas even for a single individual: Functional parcel definitions change with task

The goal of human brain mapping has long been to delineate the functional subunits in the brain and elucidate the functional role of each of these brain regions. Recent work has focused on whole-brain parcellation of functional Magnetic Resonance Imaging (fMRI) data to identify these subunits and create a functional atlas. Functional connectivity approaches to understand the brain at the network level require such an atlas to assess connections between parcels and extract network properties. While no single functional atlas has emerged as the dominant atlas to date, there remains an underlying assumption that such an atlas exists. Using fMRI data from a highly sampled subject as well as two independent replication data sets, we demonstrate that functional parcellations based on fMRI connectivity data reconfigure substantially and in a meaningful manner, according to brain state.

Prediction of early response to overall treatment for schizophrenia: A functional magnetic resonance imaging study.

IntroductionTreatment response at an early stage of schizophrenia is of considerable value with regard to future management of the disorder; however, there are currently no biomarkers that can inform physicians about the likelihood of response.ObjectsWe aim to develop and validate regional brain activity derived from functional magnetic resonance imaging (fMRI) as a potential signature to predict early treatment response in schizophrenia.MethodsAmplitude of low‐frequency fluctuation (ALFF) was measured at the start of the first/single episode resulting in hospitalization. Inpatients were included in a principal dataset (n = 79) and a replication dataset (n = 44). Two groups of healthy controls (n = 87; n = 106) were also recruited for each dataset. The clinical response was assessed at discharge from the hospital. The predictive capacity of normalized ALFF in patients by healthy controls, ALFFratio, was evaluated based on diagnostic tests and clinical correlates.ResultsIn the principal dataset, responders exhibited increased baseline ALFF in the left postcentral gyrus/inferior parietal lobule relative to non‐responders. ALFFratio of responders before treatment was significantly higher than that of non‐responders (p < 0.001). The area under the receiver operating characteristic curve was 0.746 for baseline ALFFratio to distinguish responders from non‐responders, and the sensitivity, specificity, and accuracy were 72.7%, 68.6%, and 70.9%, respectively. Similar results were found in the independent replication dataset.ConclusionsBaseline regional activity of the brain seems to be predictive of early response to treatment for schizophrenia. This study shows that psycho‐neuroimaging holds promise for influencing the clinical treatment and management of schizophrenia.

Disease Definition for Schizophrenia by Functional Connectivity Using Radiomics Strategy.

Specific biomarker reflecting neurobiological substrates of schizophrenia (SZ) is required for its diagnosis and treatment selection of SZ. Evidence from neuroimaging has implicated disrupted functional connectivity in the pathophysiology. We aimed to develop and validate a method of disease definition for SZ by resting-state functional connectivity using radiomics strategy. This study included 2 data sets collected with different scanners. A total of 108 first-episode SZ patients and 121 healthy controls (HCs) participated in the current study, among which 80% patients and HCs (n = 183) and 20% (n = 46) were selected for training and testing in intra-data set validation and 1 of the 2 data sets was selected for training and the other for testing in inter-data set validation, respectively. Functional connectivity was calculated for both groups, features were selected by Least Absolute Shrinkage and Selection Operator (LASSO) method, and the clinical utility of its features and the generalizability of effects across samples were assessed using machine learning by training and validating multivariate classifiers in the independent samples. We found that the accuracy of intra-data set training was 87.09% for diagnosing SZ patients by applying functional connectivity features, with a validation in the independent replication data set (accuracy = 82.61%). The inter-data set validation further confirmed the disease definition by functional connectivity features (accuracy = 83.15% for training and 80.07% for testing). Our findings demonstrate a valid radiomics approach by functional connectivity to diagnose SZ, which is helpful to facilitate objective SZ individualized diagnosis using quantitative and specific functional connectivity biomarker.