Hippocampal Structures Among Japanese Adolescents Before and After the COVID-19 Pandemic.

Importance: Few studies have used a large-sample, longitudinal, population-based cohort study to examine whether the COVID-19 pandemic as a global major life event is associated with structural plasticity of the adolescent hippocampus. Objective: To examine whether Japan's first state of emergency (SoE) during the COVID-19 pandemic was associated with alterations in the macrostructures and microstructures of the hippocampus during its development. Design, Setting, and Participants: The population-neuroscience Tokyo TEEN Cohort study is a prospective cohort study with 4 consecutive waves in Tokyo, Japan. Due to the SoE, data collection was suspended between March 27, 2020, and July 30, 2020. Analyzed data, comprising 1149 brain structural scans obtained from 479 participants, of whom 336 participants had undergone 2 or more scans, were collected between October 2013 and November 2021. Data were analyzed from August 2022 to December 2023. Exposures: Japan's first SoE (April 7 to May 25, 2020). Main Outcomes and Measures: Hippocampal volume, 12 hippocampal subfield volumes, and 7 microstructural measures of the hippocampus. Results: A total of 1060 brain scans from 459 participants (214 female participants [47%]) including 246 participants from wave 1 (median [IQR] age, 11.3 [11.1-11.7] years), 358 from wave 2 (median [IQR] age, 13.8 [13.3-14.5] years), 304 from wave 3 (median [IQR] age, 15.9 [15.4-16.5] years), and 152 from wave 4 (median [IQR] age, 17.9 [17.5-18.4] years) were included in the final main analysis. The generalized additive mixed model showed a significant associations of the SoE with the mean hippocampal volume (ß = 102.19; 95% CI, 0.61-203.77; P = .049). The generalized linear mixed models showed the main associations of the SoE with hippocampal subfield volume (granule cell and molecular layer of the dentate gyrus: ß = 18.19; 95% CI, 2.97-33.41; uncorrected P = .02; CA4: ß = 12.75; 95% CI, 0.38-25.12; uncorrected P = .04; hippocampus-amygdala transition area: ß = 5.67; 95% CI, 1.18-10.17; uncorrected P = .01), and fractional anisotropy (ß = 0.03; 95% CI, 0.00-0.06; uncorrected P = .04). Conclusions and Relevance: After the first SoE, a volumetric increase in the hippocampus and trend increase in 3 subfield volumes and microstructural integration of the hippocampus were observed, suggesting that the transient plasticity of the adolescent hippocampus was affected by a major life event along with the typical developmental trajectory.

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Resilience assessment of metro stations against rainstorm disaster based on cloud model: a case study in Chongqing, China.

Extremely heavy rainfall has posed a significant hazard to urban growth as the most common and disaster-prone natural calamity. Due to its unique geographical location, the metro system is more vulnerable to waterlogging caused by rainstorm disaster. Research on resilience to natural disasters has attracted extensive attention in recent years. However, few studies have focused on the resilience of the metro system against rainstorms. Therefore, this paper aims to develop an assessment model for evaluating metro stations' resilience levels. Twenty factors are carried out from dimensions of resistance, recovery and adaptation. The methods of ordered binary comparison, entropy weight and cloud model are proposed to build the assessment model. Then, taking Chongqing metro system in china as a case study, the resilience level of 13 metro stations is calculated. Radar charts from dimensions of resistance, recovery, and adaptation are created to propose recommendations for improving metro stations' resilience against rainstorms, providing a reference for the sustainable development of the metro system. The case study of the Chongqing metro system in china demonstrates that the assessment model can effectively evaluate the resilience level of metro stations and can be used in other infrastructures under natural disasters for resilience assessment.

Structural brain abnormalities in schizophrenia patients with a history and presence of auditory verbal hallucination.

Although many studies have demonstrated structural brain abnormalities associated with auditory verbal hallucinations (AVH) in schizophrenia, the results remain inconsistent because of the small sample sizes and the reliability of clinical interviews. We compared brain morphometries in 204 participants, including 58 schizophrenia patients with a history of AVH (AVH + ), 29 without a history of AVH (AVH-), and 117 healthy controls (HCs) based on a detailed inspection of medical records. We further divided the AVH+ group into 37 patients with and 21 patients without hallucinations at the time of the MRI scans (AVH++ and AVH+-, respectively) via clinical interviews to explore the morphological differences according to the persistence of AVH. The AVH + group had a smaller surface area in the left caudal middle frontal gyrus (F = 7.28, FDR-corrected p = 0.0008) and precentral gyrus (F = 7.68, FDR-corrected p = 0.0006) compared to the AVH- group. The AVH+ patients had a smaller surface area in the left insula (F = 7.06, FDR-corrected p = 0.001) and a smaller subcortical volume in the bilateral hippocampus (right: F = 13.34, FDR-corrected p = 0.00003; left: F = 6.80, FDR-corrected p = 0.001) compared to the HC group. Of these significantly altered areas, the AVH++ group showed significantly smaller bilateral hippocampal volumes compared to the AVH+- group, and a smaller surface area in the left precentral gyrus and caudal middle frontal gyrus compared to the AVH- group. Our findings highlighted the distinct pattern of structural alteration between the history and presence of AVH in schizophrenia, and the importance of integrating multiple criteria to elucidate the neuroanatomical mechanisms.

Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study.

BACKGROUND AND HYPOTHESIS: Machine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs). STUDY DESIGN: Total 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat. STUDY RESULTS: The accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%). CONCLUSION: We built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.

Surface area in the insula was associated with 28-month functional outcome in first-episode psychosis.

Many studies have tested the relationship between demographic, clinical, and psychobiological measurements and clinical outcomes in ultra-high risk for psychosis (UHR) and first-episode psychosis (FEP). However, no study has investigated the relationship between multi-modal measurements and long-term outcomes for >2 years. Thirty-eight individuals with UHR and 29 patients with FEP were measured using one or more modalities (cognitive battery, electrophysiological response, structural magnetic resonance imaging, and functional near-infrared spectroscopy). We explored the characteristics associated with 13- and 28-month clinical outcomes. In UHR, the cortical surface area in the left orbital part of the inferior frontal gyrus was negatively associated with 13-month disorganized symptoms. In FEP, the cortical surface area in the left insula was positively associated with 28-month global social function. The left inferior frontal gyrus and insula are well-known structural brain characteristics in schizophrenia, and future studies on the pathological mechanism of structural alteration would provide a clearer understanding of the disease.

Sustainable Development of Urban Metro System: Perspective of Coordination between Supply and Demand.

Metro systems are gradually becoming more and more crucial in promoting the economy and society in cities. However, various challenges such as financial resources and the efficiency of utilizing these metro plans bring difficulties for metro construction. Hence, accurately evaluating the urban metro system's development condition seems significant for the sustainable development of the urban metro system. Therefore, a comprehensive evaluation indicator system of metro development conditions containing 25 indicators from dimensions of demand and supply is established in this study, and a coupling coordination degree model combined with the entropy weight method and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is proposed to analyze the level of metro development conditions and coupling coordination conditions of 35 cities in China. According to the calculation results, 35 cities are divided into six categories, and radar charts are constructed to promote the sustainable development of the metro system.

Comparison of traveling-subject and ComBat harmonization methods for assessing structural brain characteristics.

Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and development. Measurement biases-caused by site differences in scanner/image-acquisition protocols-negatively influence the reliability and reproducibility of image-analysis methods. Harmonization can reduce bias and improve the reproducibility of multisite datasets. Herein, a traveling-subject (TS) dataset including 56 T1-weighted MRI scans of 20 healthy participants in three different MRI procedures-20, 19, and 17 subjects in Procedures 1, 2, and 3, respectively-was considered to compare the reproducibility of TS-GLM, ComBat, and TS-ComBat harmonization methods. The minimum participant count required for harmonization was determined, and the Cohen's d between different MRI procedures was evaluated as a measurement-bias indicator. The measurement-bias reduction realized with different methods was evaluated by comparing test-retest scans for 20 healthy participants. Moreover, the minimum subject count for harmonization was determined by comparing test-retest datasets. The results revealed that TS-GLM and TS-ComBat reduced measurement bias by up to 85 and 81.3%, respectively. Meanwhile, ComBat showed a reduction of only 59.0%. At least 6 TSs were required to harmonize data obtained from different MRI scanners, complying with the imaging protocol predetermined for multisite investigations and operated with similar scan parameters. The results indicate that TS-based harmonization outperforms ComBat for measurement-bias reduction and is optimal for MRI data in well-prepared multisite investigations. One drawback is the small sample size used, potentially limiting the applicability of ComBat. Investigation on the number of subjects needed for a large-scale study is an interesting future problem.

Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis.

Neuropsychiatric disorders are diagnosed based on behavioral criteria, which makes the diagnosis challenging. Objective biomarkers such as neuroimaging are needed, and when coupled with machine learning, can assist the diagnostic decision and increase its reliability. Sixty-four schizophrenia, 36 autism spectrum disorder (ASD), and 106 typically developing individuals were analyzed. FreeSurfer was used to obtain the data from the participant's brain scans. Six classifiers were utilized to classify the subjects. Subsequently, 26 ultra-high risk for psychosis (UHR) and 17 first-episode psychosis (FEP) subjects were run through the trained classifiers. Lastly, the classifiers' output of the patient groups was correlated with their clinical severity. All six classifiers performed relatively well to distinguish the subject groups, especially support vector machine (SVM) and Logistic regression (LR). Cortical thickness and subcortical volume feature groups were most useful for the classification. LR and SVM were highly consistent with clinical indices of ASD. When UHR and FEP groups were run with the trained classifiers, majority of the cases were classified as schizophrenia, none as ASD. Overall, SVM and LR were the best performing classifiers. Cortical thickness and subcortical volume were most useful for the classification, compared to surface area. LR, SVM, and DT's output were clinically informative. The trained classifiers were able to help predict the diagnostic category of both UHR and FEP Individuals.