Coping with depression: a narrative study of an online depression community in China.

PURPOSE: The goal of this study was to explore the coping strategies of depression sufferers that have worked for them based on the study of an online depression community. METHODS: We conducted a thematic narrative analysis of 120 stories posted by the members in the largest online depression community in China. MaxQDA version 18 was used to code the data, and the analytic approach was consistent with the category-centred approach of grounded theory. RESULTS: The study found that the coping strategies mainly include self-reconciliation (e.g., perceiving/accepting feelings, accepting the present self, and holding hope for the future), actions (recreational activities, physical exercise, and engaging in volunteer work), addressing the stressors and symptoms (e.g., staying away from stressors, seeing the doctor), and seeking interpersonal support (e.g., seeking support from family, friends, and peers). CONCLUSION: The findings revealed the coping strategies that were helpful and examined how they functioned for the affected members, which make up for the lack of attention to the individual experiences of depression sufferers in coping research. The findings also have practical implications for the related education and consultation, providing useful insights for doctors and patients. These ways of coping are based on depression sufferer' anonymous narratives, which can be convincing to clients.

Complaining, Regret, Superiority, and Discovery: Chinese Patients' Sense Making of Depression in an Online Forum.

Drawing on observations of a Chinese online depression community, this article explored the members' sense making of depression by analyzing their narrative accounts of depression. Four types of sense making were predominant among the depression sufferers: complaining, regret, superiority, and discovery. The complaining narrative is the members' telling about the pain caused by family (parental control or neglect), school bullying, stress from study or work, and social norms. The regret narrative is the members' reflection on their habit of perfectionism and lack of self-disclosure. The superiority narrative is the members' attribution of depression to their intelligence and morality that surpass the average people. The discovery narrative is the members' novel understanding of the self, significant others, and key events. The findings suggest that the social and psychological explanation of the causes of depression, instead of the medical model, is popular among the Chinese patients. Their stories of depression are also stories of marginalization, visions for the future, and realizing the normalization of identity as depression patients. The findings have implications for public policy around support for mental health.

Effect of information encounter on concerns over healthy eating- mediated through body comparison and moderated by body mass index or body satisfaction.

BACKGROUND: Understanding factors that influence healthy or unhealthy eating can inform intervention strategies. This study ascertained whether and how unintentional exposure to food and nutrition information influenced healthy eating concerns. The study tested body comparison, body satisfaction, and body mass index as three mechanisms that potentially link food information encounter, commonly known as information scanning, to healthy eating concerns. METHODS: A sample of 440 online participants (mean age = 29.15 years) was used to investigate: (1) how unintentional exposure to food and nutrition information, i.e., information encounter (IE), affects healthy eating concerns (HEC); (2) how the effect of IE on HEC is mediated by body comparison (BC); (3) how the paths of the mediation model are moderated by body satisfaction (BS) or body mass index (BMI). RESULTS: The findings show a positive and sizable total effect of IE on HEC - a whole-scale increase in information encounter is associated with a substantial increase in healthy eating concerns by 15 percentage points (bp = 0.150). BC is found to mediate the effect of IE on HEC in an all-positive complementary mediation. Both the indirect and the direct-and-remainder paths show sizable effects. The mediated path contributes about 20% of the total effect between IE and HEC (cp = 20%), while the direct-and-remainder path contributes the rest (cp = 80%). BS was found to moderate the relationship between IE and BC, the first leg of the mediation. The moderation effect is large - the effect of IE on BC is much smaller on the highly and the moderately satisfied than on the lowly satisfied (slope differential bp = -.60). BMI was found to moderate the direct-and-remainder effect of IE on HEC, controlling BC. That is, the effect of IE on HEC, after filtering out the mediated effect through BC, is much larger for those with high or low BMI than those with healthy BMI (slope differential bp = .32). CONCLUSIONS: Exposure, even if unintentional, to food and nutrition information is an important predictor of HEC. BC, BS, and BMI are important factors that help to explain the process through which information affects behaviors.

Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease.

Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to elucidate the core disease mechanisms. In this study, we aim to comprehensively characterize AD-associated functional brain alterations using one of the world's largest resting-state functional MRI (fMRI) biobank for the disorder. The biobank includes fMRI data from six neuroimaging centers, with a total of 252 AD patients, 221 mild cognitive impairment (MCI) patients and 215 healthy comparison individuals. Meta-analytic techniques were used to unveil reliable differences in brain function among the three groups. Relative to the healthy comparison group, AD was associated with significantly reduced functional connectivity and local activity in the default-mode network, basal ganglia and cingulate gyrus, along with increased connectivity or local activity in the prefrontal lobe and hippocampus (p < .05, Bonferroni corrected). Moreover, these functional alterations were significantly correlated with the degree of cognitive impairment (AD and MCI groups) and amyloid-ß burden. Machine learning models were trained to recognize key fMRI features to predict individual diagnostic status and clinical score. Leave-one-site-out cross-validation established that diagnostic status (mean area under the receiver operating characteristic curve: 0.85) and clinical score (mean correlation coefficient between predicted and actual Mini-Mental State Examination scores: 0.56, p < .0001) could be predicted with high accuracy. Collectively, our findings highlight the potential for a reproducible and generalizable functional brain imaging biomarker to aid the early diagnosis of AD and track its progression.

A neuroimaging biomarker for striatal dysfunction in schizophrenia.

Mounting evidence suggests that function and connectivity of the striatum is disrupted in schizophrenia1-5. We have developed a new hypothesis-driven neuroimaging biomarker for schizophrenia identification, prognosis and subtyping based on functional striatal abnormalities (FSA). FSA scores provide a personalized index of striatal dysfunction, ranging from normal to highly pathological. Using inter-site cross-validation on functional magnetic resonance images acquired from seven independent scanners (n = 1,100), FSA distinguished individuals with schizophrenia from healthy controls with an accuracy exceeding 80% (sensitivity, 79.3%; specificity, 81.5%). In two longitudinal cohorts, inter-individual variation in baseline FSA scores was significantly associated with antipsychotic treatment response. FSA revealed a spectrum of severity in striatal dysfunction across neuropsychiatric disorders, where dysfunction was most severe in schizophrenia, milder in bipolar disorder, and indistinguishable from healthy individuals in depression, obsessive-compulsive disorder and attention-deficit hyperactivity disorder. Loci of striatal hyperactivity recapitulated the spatial distribution of dopaminergic function and the expression profiles of polygenic risk for schizophrenia. In conclusion, we have developed a new biomarker to index striatal dysfunction and established its utility in predicting antipsychotic treatment response, clinical stratification and elucidating striatal dysfunction in neuropsychiatric disorders.

Polygenic effects of schizophrenia on hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity.

BACKGROUND: Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated. AIMS: To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia. METHOD: Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant. RESULTS: The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia. CONCLUSIONS: These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.

"Kindhearted People, Please Save My Family": Narrative Strategies for New Media Medical Crowdfunding.

With the development of new financing methods in the networked society, and due to the underdeveloped social security system in China, more and more patients and their families have to choose crowdfunding as an important way to raise treatment funds. Using thematic narrative analysis, this paper studied 100 texts of medical crowdfunding on Easy Fundraising from February 27, 2018 to May 1. It is found that the requests used a series of strategies including: constructing the identity of the patient in order to build a disadvantaged image worthy of help; using tragic narration based on the traditional Chinese cultural elements such as "family concept" and "filial piety" and contrast of the patients' experience before and after the illness to mobilize the sympathy of potential donors; and downplaying the needs itself in order to maintain patients' self-esteem.

Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer's disease: diagnosis, longitudinal progress and biological basis.

Hippocampal morphological change is one of the main hallmarks of Alzheimer's disease (AD). However, whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment (MCI) to AD dementia and whether these features provide any neurobiological foundation remains unclear. The primary aim of this study was to verify whether hippocampal radiomic features can serve as robust magnetic resonance imaging (MRI) markers for AD. Multivariate classifier-based support vector machine (SVM) analysis provided individual-level predictions for distinguishing AD patients (n = 261) from normal controls (NCs; n = 231) with an accuracy of 88.21% and intersite cross-validation. Further analyses of a large, independent the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 1228) reinforced these findings. In MCI groups, a systemic analysis demonstrated that the identified features were significantly associated with clinical features (e.g., apolipoprotein E (APOE) genotype, polygenic risk scores, cerebrospinal fluid (CSF) Aß, CSF Tau), and longitudinal changes in cognition ability; more importantly, the radiomic features had a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up. These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical application in AD/MCI, and further provide evidence for predicting whether an MCI subject would convert to AD based on the radiomics of the hippocampus. The results of this study are expected to have a substantial impact on the early diagnosis of AD/MCI.

Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas.

OBJECTIVE: To predict vascular endothelial growth factor (VEGF) expression in patients with diffuse gliomas using radiomic analysis. MATERIALS AND METHODS: Preoperative magnetic resonance images were retrospectively obtained from 239 patients with diffuse gliomas (World Health Organization grades II-IV). The patients were randomly assigned to a training group (n = 160) or a validation group (n = 79) at a 2:1 ratio. For each patient, a total of 431 radiomic features were extracted. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature selection. A machine-learning model for predicting VEGF status was then developed using the selected features and a support vector machine classifier. The predictive performance of the model was evaluated in both groups using receiver operating characteristic curve analysis, and correlations between selected features were assessed. RESULTS: Nine radiomic features were selected to generate a VEGF-associated radiomic signature of diffuse gliomas based on the mRMR algorithm. This radiomic signature consisted of two first-order statistics or related wavelet features (Entropy and Minimum) and seven textural features or related wavelet features (including Cluster Tendency and Long Run Low Gray Level Emphasis). The predictive efficiencies measured by the area under the curve were 74.1% in the training group and 70.2% in the validation group. The overall correlations between the 9 radiomic features were low in both groups. CONCLUSIONS: Radiomic analysis facilitated efficient prediction of VEGF status in diffuse gliomas, suggesting that using tumor-derived radiomic features for predicting genomic information is feasible.

Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.

BACKGROUND: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information. METHODS: Motivated by the ability of recurrent neural networks (RNN) in capturing dynamic information of time sequences, we propose a multi-scale RNN model, which enables classification between 558 schizophrenia and 542 healthy controls by using time courses of fMRI independent components (ICs) directly. To increase interpretability, we also propose a leave-one-IC-out looping strategy for estimating the top contributing ICs. FINDINGS: Accuracies of 83·2% and 80·2% were obtained respectively for the multi-site pooling and leave-one-site-out transfer classification. Subsequently, dorsal striatum and cerebellum components contribute the top two group-discriminative time courses, which is true even when adopting different brain atlases to extract time series. INTERPRETATION: This is the first attempt to apply a multi-scale RNN model directly on fMRI time courses for classification of mental disorders, and shows the potential for multi-scale RNN-based neuroimaging classifications. FUND: Natural Science Foundation of China, the Strategic Priority Research Program of the Chinese Academy of Sciences, National Institutes of Health Grants, National Science Foundation.

Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging.

PURPOSE: PTEN mutation status is a pivotal biomarker for glioblastoma. This study aimed to establish a radiomic signature to predict PTEN mutation status in patients with glioblastoma, and to investigate the genetic background behind this radiomic signature. METHODS: In this study, a total of 862 radiomic features were extracted from each patient. The training (n = 69) and validation (n = 40) sets were retrospectively collected from the Cancer Genome Atlas and the Chinese Glioma Genome Atlas, respectively. The minimum redundancy maximum relevance (mRMR) algorithm was used to select the best predictive features of PTEN status. A machine learning model was then built with the selected features using a support vector machine classifier. The predictive performance of each selected feature and the complete model were evaluated via the area under the curve from receiver operating characteristic analysis in both the training and validation sets. The genetic background underlying the radiomic signature was determined using radiogenomic analysis. RESULTS: Six features were selected using the mRMR algorithm, including two features derived from contrast-enhanced images and four features derived from T2-weighted images. The predictive performance of the machine learning model for the training and validation sets were 0.925 and 0.787, respectively, which were better than the individual features. Radiogenomics analysis revealed that the PTEN-associated biological processes could be described using the radiomic signature. CONCLUSION: These results show that radiomic features derived from preoperative MRI can predict PTEN mutation status in glioblastoma patients, thus providing a novel noninvasive imaging biomarker.

Pairwise Interactions among Brain Regions Organize Large-Scale Functional Connectivity during Execution of Various Tasks.

Spatially separated brain areas interact with each other to form networks with coordinated activities, supporting various brain functions. Interaction structures among brain areas have been widely investigated through pairwise measures. However, interactions among multiple (e.g., triple and quadruple) areas cannot be reduced to pairwise interactions. Such higher order interactions (HOIs), e.g., exclusive-or (XOR) operation, are widely implemented in computation systems and are crucial for effective information processing. However, it is currently unclear whether any HOIs are present in large-scale brain functional networks when subjects are executing specific tasks. Here we analyzed functional magnetic resonance imaging (fMRI) data collected from human subjects executing various perceptual, motor, and cognitive tasks. We found that HOI strength in the macroscopic functional networks was very weak for all tasks, suggesting that major brain activities do not rely on HOIs on the macroscopic level at the timescale of hundreds of milliseconds. These weak HOIs during tasks were further investigated with a neural network model activated by external inputs, which suggested that weak pairwise interactions among brain areas organized the system without involving HOIs. Taken together, these results demonstrated the dominance of pairwise interactions in organizing coordinated activities among different brain areas to support various brain functions.

ASAF: altered spontaneous activity fingerprinting in Alzheimer's disease based on multisite fMRI.

Several monocentric studies have noted alterations in spontaneous brain activity in Alzheimer's disease (AD), although there is no consensus on the altered amplitude of low-frequency fluctuations in AD patients. The main aim of the present study was to identify a reliable and reproducible abnormal brain activity pattern in AD. The amplitude of local brain activity (AM), which can provide fast mapping of spontaneous brain activity across the whole brain, was evaluated based on multisite rs-fMRI data for 688 subjects (215 normal controls (NCs), 221 amnestic mild cognitive impairment (aMCI) 252 AD). Two-sample t-tests were used to detect group differences between AD patients and NCs from the same site. Differences in the AM maps were statistically analyzed via the Stouffer's meta-analysis. Consistent regions of lower spontaneous brain activity in the default mode network and increased activity in the bilateral hippocampus/parahippocampus, thalamus, caudate nucleus, orbital part of the middle frontal gyrus and left fusiform were observed in the AD patients compared with those in NCs. Significant correlations (P < 0.05, Bonferroni corrected) between the normalized amplitude index and Mini-Mental State Examination scores were found in the identified brain regions, which indicates that the altered brain activity was associated with cognitive decline in the patients. Multivariate analysis and leave-one-site-out cross-validation led to a 78.49% prediction accuracy for single-patient classification. The altered activity patterns of the identified brain regions were largely correlated with the FDG-PET results from another independent study. These results emphasized the impaired brain activity to provide a robust and reproducible imaging signature of AD.

Linked 4-Way Multimodal Brain Differences in Schizophrenia in a Large Chinese Han Population.

Multimodal fusion has been regarded as a promising tool to discover covarying patterns of multiple imaging types impaired in brain diseases, such as schizophrenia (SZ). In this article, we aim to investigate the covarying abnormalities underlying SZ in a large Chinese Han population (307 SZs, 298 healthy controls [HCs]). Four types of magnetic resonance imaging (MRI) features, including regional homogeneity (ReHo) from resting-state functional MRI, gray matter volume (GM) from structural MRI, fractional anisotropy (FA) from diffusion MRI, and functional network connectivity (FNC) resulted from group independent component analysis, were jointly analyzed by a data-driven multivariate fusion method. Results suggest that a widely distributed network disruption appears in SZ patients, with synchronous changes in both functional and structural regions, especially the basal ganglia network, salience network (SAN), and the frontoparietal network. Such a multimodal coalteration was also replicated in another independent Chinese sample (40 SZs, 66 HCs). Our results on auditory verbal hallucination (AVH) also provide evidence for the hypothesis that prefrontal hypoactivation and temporal hyperactivation in SZ may lead to failure of executive control and inhibition, which is relevant to AVH. In addition, impaired working memory performance was found associated with GM reduction and FA decrease in SZ in prefrontal and superior temporal area, in both discovery and replication datasets. In summary, by leveraging multiple imaging and clinical information into one framework to observe brain in multiple views, we can integrate multiple inferences about SZ from large-scale population and offer unique perspectives regarding the missing links between the brain function and structure that may not be achieved by separate unimodal analyses.

Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour.

PURPOSE: To explore the feasibility and diagnostic performance of radiomics based on anatomical, diffusion and perfusion MRI in differentiating among glioma subtypes and predicting tumour proliferation. METHODS: 220 pathology-confirmed gliomas and ten contrasts were included in the retrospective analysis. After being registered to T2FLAIR images and resampling to 1 mm3 isotropically, 431 radiomics features were extracted from each contrast map within a semi-automatic defined tumour volume. For single-contrast and the combination of all contrasts, correlations between the radiomics features and pathological biomarkers were revealed by partial correlation analysis, and multivariate models were built to identify the best predictive models with adjusted 0.632+ bootstrap AUC. RESULTS: In univariate analysis, both non-wavelet and wavelet radiomics features were correlated significantly with tumour grade and the Ki-67 labelling index. The max R was 0.557 (p = 2.04E-14) in T1C for tumour grade and 0.395 (p = 2.33E-07) in ADC for Ki-67. In the multivariate analysis, the combination of all-contrast radiomics features had the highest AUCs in both differentiating among glioma subtypes and predicting proliferation compared with those in single-contrast images. For low-/high-grade gliomas, the best AUC was 0.911. In differentiating among glioma subtypes, the best AUC was 0.896 for grades II-III, 0.997 for grades II-IV, and 0.881 for grades III-IV. In predicting proliferation levels, multicontrast features led to an AUC of 0.936. CONCLUSION: Multicontrast radiomics supplies complementary information on both geometric characters and molecular biological traits, which correlated significantly with tumour grade and proliferation. Combining all-contrast radiomics models might precisely predict glioma biological behaviour, which may be attributed to presurgical personal diagnosis. KEY POINTS: • Multicontrast MRI radiomics features are significantly correlated with tumour grade and Ki-67 LI. • Multimodality MRI provides independent but supplemental information in assessing glioma pathological behaviour. • Combined multicontrast MRI radiomics can precisely predict glioma subtypes and proliferation levels.

Functional Connectivity of Anterior Insula Predicts Recovery of Patients With Disorders of Consciousness.

Background: We hypothesize that the anterior insula is important for maintenance of awareness. Here, we explored the functional connectivity alterations of the anterior insula with changes in the consciousness level or over time in patients with disorders of consciousness (DOC) and determined potential correlation with clinical outcomes. Methods: We examined 20 participants (9 patients with DOC and 11 healthy controls). Each patient underwent resting-state functional magnetic resonance imaging (rs-fMRI) and a standardized Coma Recovery Scale-Revised (CRS-R) assessment on the same day. We categorized the patients according to the prognosis: those who emerged from a minimally conscious state (recovery group, n = 4) and those who remained in the unconscious state (unrecovery group, n = 5). Two rs-fMRI scans were obtained from all patients, and the second scan of patients in the recovery group was obtained after they regained consciousness. We performed seed-based fMRI analysis and selected the left ventral agranular insula (vAI) and dorsal agranular insula (dAI) as the regions of interest. Correlations with CRS-R were determined with the Spearman's correlation coefficient. Results: Compared with healthy controls, the functional connectivity between dAI and gyrus rectus of patients who recovered was significantly increased (p < 0.001, cluster-wise family-wise error rate [FWER] < 0.05). The second rs-fMRI scan of patients who remained with DOC showed a significant decreased functional connectivity between the dAI to contralateral insula, pallidum, bilateral inferior parietal lobule (IPL), precentral gyrus, and middle cingulate cortex (p < 0.001, cluster-wise FWER < 0.05) as well as the functional connectivity between vAI to caudate and cingulum contrast to controls (p < 0.001, cluster-wise FWER < 0.05). Finally, the functional connectivity strength of dAI-temporal pole (Spearman r = 0.491, p < 0.05) and dAI-IPL (Spearman r = 0.579, p < 0.05) were positively correlated with CRS-R scores in all DOC patients. The connectivity of dAI-IPL was also positively correlated with clinical scores in the recovery group (Spearman r = 0.807, p < 0.05). Conclusions: Our findings indicate that the recovery of consciousness is associated with an increased connectivity of the dAI to IPL and temporal pole. This possibly highlights the role of the insula in human consciousness. Moreover, longitudinal variations in dAI-IPL and dAI-temporal pole connectivity may be potential hallmarks in the outcome prediction of DOC patients.

Test-retest Reliability of Functional Connectivity and Graph Metrics in the Resting Brain Network.

The combination of graph theoretical approaches and neuroimaging data provides a powerful way to explore the characteristics of brain network. Recently, the temporal variability of spontaneous brain activity and functional connectivity has attracted wide attention. Thus, it is essential to evaluate the reliability of functional network connectivity and properties from the dynamic perspective. However, previous test-retest (TRT) studies have explored this reliability with a static point of view. In this study, using a large rs-fMRI dataset from Human Connectome Project (HCP), we investigated TRT reliability of functional connectivity and graph metrics derived from the most commonly used method- sliding window at three time intervals (short: 72 seconds, middle: 15 minutes and long: >24 hours). The results revealed that reliable connectivities and related brain regions are mainly distributed in primary cortex, such as visual area and sensorimotor area and default mode network. Notably, connectivity strength and global efficiency have better reliability than other metrics. Finally, short scan time interval and long scan duration can increase the TRT reliability of metrics. Findings of present study provide important guidance for searching reliable network markers in future research.

A Schizophrenia-Related Genetic-Brain-Cognition Pathway Revealed in a Large Chinese Population.

BACKGROUND: In the past decades, substantial effort has been made to explore the genetic influence on brain structural/functional abnormalities in schizophrenia, as well as cognitive impairments. In this work, we aimed to extend previous studies to explore the internal mediation pathway among genetic factor, brain features and cognitive scores in a large Chinese dataset. METHODS: Gray matter (GM) volume, fractional amplitude of low-frequency fluctuations (fALFF), and 4522 schizophrenia-susceptible single nucleotide polymorphisms (SNP) from 905 Chinese subjects were jointly analyzed, to investigate the multimodal association. Based on the identified imaging-genetic pattern, correlations with cognition and mediation analysis were then conducted to reveal the potential mediation pathways. FINDINGS: One linked imaging-genetic pattern was identified to be group discriminative, which was also associated with working memory performance. Particularly, GM reduction in thalamus, putamen and bilateral temporal gyrus in schizophrenia was associated with fALFF decrease in medial prefrontal cortex, both were also associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including CSMD1, CNTNAP2, DCC, GABBR2 etc. This linked pattern was also replicated in an independent cohort (166 subjects), which although showed certain age and clinical differences with the discovery cohort. A further mediation analysis suggested that GM alterations significantly mediated the association from SNP to fALFF, while fALFF mediated the association from SNP and GM to working memory performance. INTERPRETATION: This study has not only verified the impaired imaging-genetic association in schizophrenia, but also initially revealed a potential genetic-brain-cognition mediation pathway, indicating that polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus could further affect cognition in schizophrenia.

Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction.

OBJECTIVE: We aimed to identify a radiomic signature to be used as a noninvasive biomarker of prognosis in patients with lower-grade gliomas (LGGs) and to reveal underlying biological processes through comprehensive radiogenomic investigation. METHODS: We extracted 55 radiomic features from T2-weighted images of 233 patients with LGGs (training cohort: n = 85; validation cohort: n = 148). Univariate Cox regression and linear risk score formula were applied to generate a radiomic-based signature. Gene ontology analysis of highly expressed genes in the high-risk score group was conducted to establish a radiogenomic map. A nomogram was constructed for individualized survival prediction. RESULTS: The six-feature radiomic signature stratified patients in the training cohort into low- or high-risk groups for overall survival (P = 0.0018). This result was successfully verified in the validation cohort (P = 0.0396). Radiogenomic analysis revealed that the prognostic radiomic signature was associated with hypoxia, angiogenesis, apoptosis, and cell proliferation. The nomogram resulted in high prognostic accuracy (C-index: 0.92, C-index: 0.70) and favorable calibration for individualized survival prediction in the training and validation cohorts. CONCLUSIONS: Our results suggest a great potential for the use of radiomic signature as a biological surrogate in providing prognostic information for patients with LGGs.

A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas.

OBJECTIVE: The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature. METHODS: In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS. RESULTS: There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training (P < 0.001, multivariable Cox regression) and validation (P = 0.045, multivariable Cox regression) cohorts. Radiogenomic analysis revealed that the radiomics signature was associated with the immune response, programmed cell death, cell proliferation, and vasculature development. A nomogram established using the radiomics signature and clinicopathologic risk factors demonstrated high accuracy and good calibration for prediction of PFS in both the training (C-index, 0.684) and validation (C-index, 0.823) cohorts. CONCLUSIONS: PFS can be predicted non-invasively in patients with LGGs by a group of radiomics features that could reflect the biological processes of these tumors.