Novel genetic loci of inhibitory control in ADHD and healthy children and genetic correlations with ADHD

Cumulative evidence has showed the deficits of inhibitory control in patients with attention deficit hyperactivity disorder (ADHD), which is considered as an endophenotype of ADHD. Genetic study of inhibitory control could advance gene discovery and further facilitate the understanding of ADHD genetic basis, but the studies were limited in both the general population and ADHD patients. To reveal genetic risk variants of inhibitory control and its potential genetic relationship with ADHD, we conducted genome-wide association studies (GWAS) on inhibitory control using three datasets, which included 783 and 957 ADHD patients and 1350 healthy children. Subsequently, we employed polygenic risk scores (PRS) to explore the association of inhibitory control with ADHD and related psychiatric disorders. Firstly, we identified three significant loci for inhibitory control in the healthy dataset, two loci in the case dataset, and one locus in the meta-analysis of three datasets. Besides, we found more risk genes and variants by applying transcriptome-wide association study (TWAS) and conditional FDR method. Then, we constructed a network by connecting the genes identified in our study, leading to the identification of several vital genes. Lastly, we identified a potential relationship between inhibitory control and ADHD and autism by PRS analysis and found the direct and mediated contribution of the identified genetic loci on ADHD symptoms by mediation analysis. In conclusion, we revealed some genetic risk variants associated with inhibitory control and elucidated the benefit of inhibitory control as an endophenotype, providing valuable insights into the mechanisms underlying ADHD.

The mental health of Australians bereaved during the first two years of the COVID-19 pandemic: a latent class analysis

BACKGROUND

The COVID-19 pandemic disrupted many areas of life, including culturally accepted practices at end-of-life care, funeral rites, and access to social, community, and professional support. This survey investigated the mental health outcomes of Australians bereaved during this time to determine how these factors might have impacted bereavement outcomes.

METHODS

An online survey indexing pandemic and bereavement experiences, levels of grief, depression, anxiety, and health, work, and social impairment. Latent class analysis (LCA) was used to identify groups of individuals who shared similar symptom patterns. Multinomial regressions identified pandemic-related, loss-related, and sociodemographic correlates of class membership.

RESULTS

1911 Australian adults completed the survey. The LCA identified four classes: low symptoms (46.8%), grief (17.3%), depression/anxiety (17.7%), and grief/depression/anxiety (18.2%). The latter group reported the highest levels of health, work, and social impairment. The death of a child or partner and an inability to care for the deceased due to COVID-19 public health measures were correlated with grief symptoms (with or without depression and anxiety). Preparedness for the person's death and levels of pandemic-related loneliness and social isolation differentiated all four classes. Unemployment was associated with depression/anxiety (with or without grief).

CONCLUSIONS

COVID-19 had profound impacts for the way we lived and died, with effects that are likely to ricochet through society into the foreseeable future. These lessons learned must inform policymakers and healthcare professionals to improve bereavement care and ensure preparedness during and following future predicted pandemics to prevent negative impacts.

xWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data

BACKGROUND

Integrating quantitative trait loci (QTL) data related to molecular phenotypes with genome-wide association study (GWAS) data is an important post-GWAS strategic approach employed to identify disease-associated molecular features. Various types of molecular phenotypes have been investigated in neuropsychiatric disorders. However, these findings pertaining to distinct molecular features are often independent of each other, posing challenges for having an overview of the mapped genes.

METHODS

In this study, we comprehensively summarized published analyses focusing on four types of risk-related molecular features (gene expression, splicing transcriptome, protein abundance, and DNA methylation) across five common neuropsychiatric disorders. Subsequently, we conducted supplementary analyses with the latest GWAS dataset and corresponding deficient molecular phenotypes using Functional Summary-based Imputation (FUSION) and summary data-based Mendelian randomization (SMR). Based on the curated and supplemented results, novel reliable genes and their functions were explored.

RESULTS

Our findings revealed that eQTL exhibited superior ability in prioritizing risk genes compared to the other QTL, followed by sQTL. Approximately half of the genes associated with splicing transcriptome, protein abundance, and DNA methylation were successfully replicated by eQTL-associated genes across all five disorders. Furthermore, we identified 436 novel reliable genes, which enriched in pathways related with neurotransmitter transportation such as synaptic, dendrite, vesicles, axon along with correlations with other neuropsychiatric disorders. Finally, we identified ten multiple molecular involved regulation patterns (MMRP), which may provide valuable insights into understanding the contribution of molecular regulation network targeting these disease-associated genes.

CONCLUSIONS

The analyses prioritized novel and reliable gene sets related with five molecular features based on published and supplementary results for five common neuropsychiatric disorders, which were missed in the original GWAS analysis. Besides, the involved MMRP behind these genes could be given priority for further investigation to elucidate the pathogenic molecular mechanisms underlying neuropsychiatric disorders in future studies.

[Automated cephalometric landmark identification and location based on convolutional neural network]

Objective: To develop an automated landmark location system applicable to the case of landmark missing. Methods: Four and eighty-one lateral cephalograms, which contained 240 males and 241 females, with an average age of (24.5±5.6) years, taken from January 2015 to January 2021 in the Department of Orthodontics, Capital Medical University School of Stomatology, and met the inclusion criteria were collected. Five postgraduate orthodontic students were the annotators to manually locate 61 possible landmarks in 481 lateral cephalograms. Two assistant professors in the department as reviewers performed calibration. Two professors as arbitrators, made final decision. Data sets were established (341 were used as training set, 40 as validation set, and 100 as test set). In this paper, an automatic landmarks identification and location model based on convolutional neural networks (CNN), CephaNET, was developed. The model was trained by feeding the original image into the feature extraction module and convolutional pose machine (CPM) module to locate landmarks with high accuracy using deep supervision. Training set was enhanced to 1 684 images by histogram equalization, cropping, and adjustment of brightness. The model was trained to compare the Gaussian heat maps output from the network with the set threshold to identify landmark missing cases. Test set of 100 lateral cephalograms was used to test the accuracy of the model. The evaluation criteria used were success detection rate of missing landmark, mean radial error (MRE) and success detection rate (SDR) in the range of 2.0, 2.5, 3.0, 3.5 and 4.0 mm. Results: The model identified and located 61 commonly used landmarks in 0.13 seconds on average. It had an average accuracy of 93.5% in identifying missing landmarks. The MRE of our testing set was (1.19±0.91) mm. SDR of 2.0, 2.5, 3.0, 3.5 and 4.0 mm were 85.4%, 90.2%, 93.5%, 95.4%, 97.0% respectively. Conclusions: The model proposed in this paper could adapt to the absence of landmark in lateral cephalograms and locate 61 commonly used landmarks with high accuracy to meet the requirements of different cephalometric analysis methods.

Non-coding RNAs expression in SARS-CoV-2 infection: pathogenesis, clinical significance, and therapeutic targets

The coronavirus disease 2019 (COVID-19) pandemic has been looming globally for three years, yet the diagnostic and treatment methods for COVID-19 are still undergoing extensive exploration, which holds paramount importance in mitigating future epidemics. Host non-coding RNAs (ncRNAs) display aberrations in the context of COVID-19. Specifically, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) exhibit a close association with viral infection and disease progression. In this comprehensive review, an overview was presented of the expression profiles of host ncRNAs following SARS-CoV-2 invasion and of the potential functions in COVID-19 development, encompassing viral invasion, replication, immune response, and multiorgan deficits which include respiratory system, cardiac system, central nervous system, peripheral nervous system as well as long COVID. Furthermore, we provide an overview of several promising host ncRNA biomarkers for diverse clinical scenarios related to COVID-19, such as stratification biomarkers, prognostic biomarkers, and predictive biomarkers for treatment response. In addition, we also discuss the therapeutic potential of ncRNAs for COVID-19, presenting ncRNA-based strategies to facilitate the development of novel treatments. Through an in-depth analysis of the interplay between ncRNA and COVID-19 combined with our bioinformatic analysis, we hope to offer valuable insights into the stratification, prognosis, and treatment of COVID-19.

Treatment Tolerance and Toxicity in Elderly Oropharyngeal Cancer Patients and Implication on Outcomes

PURPOSE/OBJECTIVE(S)

To investigate the tolerance level and toxicity for standard of care treatment for oropharyngeal cancer (OP) in elderly patients and their impact on outcomes.

MATERIALS/METHODS

Using our in-house head and neck cancer database, we looked for non-metastatic OP cases that received definitive treatment between 1/2009-6/2020. All patients received either definitive radiation therapy (RT) +/- concomitant systemic therapy (ST), or surgery followed by adjuvant RT or RT-ST. For the elderly (age at diagnosis ≥65 years) and young (<65 years) patients, we compared treatment package time (TPT) (time from surgery to RT conclusion) for adjuvant RT, total RT duration and unplanned RT interruptions. ST details and dose/protocol modifications were also compared. We evaluated worst grade of pain and mucositis, hospitalization for non-hydration causes and febrile neutropenia (FN) during RT. Feeding tube (FT) use and weight loss were compared. The independent effect of these indicators on locoregional (LRFS), distant (DRFS) recurrence free and overall (OS) survival was assessed using multivariate analyses (MVA).

RESULTS

A cohort of 326 patients was included: 36% elderly (n = 118) and 64% young (n = 208), with no differences in AJCC stage distribution (8th), treatment received and HPV status (HPV+ve: 73% vs 74.6%; p = 0.86). In 23.6 % who received adjuvant RT, median TPT was 86 (range 72-128) and 81 (65-137) days for elderly vs young (p = 0.27); whereas in the definitive RT cases 76.4%, total RT duration was 49 days for both age groups. Overall, prescribed RT course was not completed in 4% and unplanned RT interruptions occurred in 22.8% and both were non-significant between age groups. Among the 261 patients that received ST, elderly utilized more cetuximab (26 vs 12%; p = 0.007). For those who received cisplatin, 20% of elderly had cumulative dose <200 mg/m2 compared to 6% among the younger age group (p = 0.006); and cisplatin was changed to carboplatin or cetuximab in 18% vs 8% (p = 0.019). Delayed/cancelled cycles and dose reductions were similar. There were more hospitalizations (47% vs 27%; p<0.001) and a trend for more FN (9% vs 3%; p = 0.09) with older age, while worst pain and mucositis was similar. FTs were used more in elderly patients (64% vs 50%; p = 0.02), for a median of 97 vs 64 days (p = 0.31); of which 19.5% vs 11% (p = 0.28) were inserted before RT start. However, % weight loss was non-significant. On MVA, longer RT duration, FT use and hospitalizations predicted worse LRFS and DRFS; and they were prognostic for OS in addition to TPT >90 days (p<0.05 for all). Nevertheless, elderly vs young had non-significant 3-year LRFS (91% vs 90% and 67% vs 69%), DRFS (86% vs 90% and 79% vs 81%) & OS (85% vs 81% and 39% vs 52%) for HPV+ve and HPV-ve respectively (p>0.05).

CONCLUSION

Elderly patients with OP need more multi-disciplinary supportive care when receiving RT and concurrent ST. However, survival outcomes are equivalent to younger patients. Ongoing studies should enroll more elderly candidates and stratify endpoints with age.

Sexual Orientation and Gender Identity (SOGI) Data Collection: Opportunities to Advance Best Clinical Practices for LGBTQ+ Patients in Radiation Oncology

PURPOSE/OBJECTIVE(S)

A long-standing barrier to progress against health disparities is the lack of data regarding cancer risks, prevalence, treatment, and outcomes for sexual and gender minority (SGM) patients. Sexual orientation and gender identity (SOGI) data are not routinely collected by individual oncologists, cancer centers, or most non-federal hospital systems. Alarmingly high proportions of SGM patients report discrimination in healthcare or avoid routine care due to perceived lack of acceptance in the healthcare system. For these and other reasons, healthcare institutions must adopt practices that promote an inclusive environment for all patients including those self-identified from SGM groups. One strategy to achieve this aim is through SOGI data collection. The purpose of this study was to pilot new procedures and training for SOGI data collection, the aims of this project were to standardize the collection of SOGI data for all new patients referred to the Division of Radiation Oncology; promote clinical staff awareness of SGM health disparities and strategies for fostering an inclusive hospital environment; and to provide SGM patients and caregivers educational resources and support systems tailored to their needs.

MATERIALS/METHODS

We designed a Quality Improvement program for collecting SOGI data, which was approved by our institution's QIAB. Patient access specialists (PAS) were trained to collect SOGI data from newly registered patients and enter the data into the electronic health record. Radiation Oncology staff completed surveys before and after SOGI training to estimate its impact on the provision of patient care. A Fisher's exact test was utilized to evaluate associations between training and provider-reported outcomes.

RESULTS

Within a 3-week period starting in January 2023, two 1-hour interactive training sessions were offered to twenty-five PAS. Three 1-hour training sessions were offered to twenty-seven Radiation Oncology clinical staff. (1) Confidence for incorporating SOGI classifiers around patients improved from before training (52%, 13/25) to after training (100%, 17/17) among medical providers surveyed (odds ratio (OR) 32, 95% confidence interval (CI) 0.70-1493, p = 0.005). Use of SOGI data in clinical decision making increased from before training (9/25, 36%) to after training (100%, 17/17) among medical providers (OR 60.79, 95% CI 3.271-1130, p<0.0001). (2) A clinical pathway for SGM patients was developed to facilitate referral to our institution's SGM patient support group and distribution of patient education materials focused on sexual health.

CONCLUSION

Establishing standardized SOGI data collection can facilitate the provision of tailored resources and care that meets the needs of patients and staff in a large comprehensive cancer center. Specialized training for staff developed through this initiative helps foster an inclusive and welcoming environment that promotes the integration, visibility, and advancement of SGM cancer care at our institution.

RBFOX1 and Working Memory: From Genome to Transcriptome Revealed Posttranscriptional Mechanism Separate From Attention-Deficit/Hyperactivity Disorder

Background

Many psychiatric disorders share a working memory (WM) impairment phenotype, yet the genetic causes remain unclear. Here, we generated genetic profiles of WM deficits using attention-deficit/hyperactivity disorder samples and validated the results in zebrafish models.

Methods

We used 2 relatively large attention-deficit/hyperactivity disorder cohorts, 799 and 776 cases, respectively. WM impairment was assessed using the Rey Complex Figure Test. First, association analyses were conducted at single-variant, gene-based, and gene-set levels. Deeper insights into the biological mechanism were gained from further functional exploration by bioinformatic analyses and zebrafish models.

Results

Genomic analyses identified and replicated a locus with rs75885813 as the index single nucleotide polymorphism showing significant association with WM defects but not with attention-deficit/hyperactivity disorder. Functional feature exploration found that these single nucleotide polymorphisms may regulate the expression level of RBFOX1 through chromatin interaction. Further pathway enrichment analysis of potential associated single nucleotide polymorphisms revealed the involvement of posttranscription regulation that affects messenger RNA stability and/or alternative splicing. Zebrafish with functionally knocked down or genome-edited rbfox1 exhibited WM impairment but no hyperactivity. Transcriptome profiling of rbfox1-defective zebrafish indicated that alternative exon usages of snap25a might partially lead to reduced WM learning of larval zebrafish.

Conclusions

The locus with rs75885813 in RBFOX1 was identified as associated with WM. Rbfox1 regulates synaptic and long-term potentiation-related gene snap25a to adjust WM at the posttranscriptional level.

Ocular and neural genes jointly regulate the visuospatial working memory in ADHD children

OBJECTIVE

Working memory (WM) deficits have frequently been linked to attention deficit hyperactivity disorder (ADHD). Despite previous studies suggested its high heritability, its genetic basis, especially in ADHD, remains unclear. The current study aimed to comprehensively explore the genetic basis of visual-spatial working memory (VSWM) in ADHD using wide-ranging genetic analyses.

METHODS

The current study recruited a cohort consisted of 802 ADHD individuals, all met DSM-IV ADHD diagnostic criteria. VSWM was assessed by Rey-Osterrieth complex figure test (RCFT), which is a widely used psychological test include four memory indexes: detail delayed (DD), structure delayed (SD), structure immediate (SI), detail immediate (DI). Genetic analyses were conducted at the single nucleotide polymorphism (SNP), gene, pathway, polygenic and protein network levels. Polygenic Risk Scores (PRS) were based on summary statistics of various psychiatric disorders, including ADHD, autism spectrum disorder (ASD), major depressive disorder (MDD), schizophrenia (SCZ), obsessive compulsive disorders (OCD), and substance use disorder (SUD).

RESULTS

Analyses at the single-marker level did not yield significant results (5E-08). However, the potential signals with P values less than E-05 and their mapped genes suggested the regulation of VSWM involved both ocular and neural system related genes, moreover, ADHD-related genes were also involved. The gene-based analysis found RAB11FIP1, whose encoded protein modulates several neurodevelopment processes and visual system, as significantly associated with DD scores (P = 1.96E-06, Padj = 0.036). Candidate pathway enrichment analyses (N = 53) found that forebrain neuron fate commitment significantly enriched in DD (P = 4.78E-04, Padj = 0.025), and dopamine transport enriched in SD (P = 5.90E-04, Padj = 0.031). We also observed a significant negative relationship between DD scores and ADHD PRS scores (P = 0.0025, Empirical P = 0.048).

CONCLUSIONS

Our results emphasized the joint contribution of ocular and neural genes in regulating VSWM. The study reveals a shared genetic basis between ADHD and VSWM, with GWAS indicating the involvement of ADHD-related genes in VSWM. Additionally, the PRS analysis identifies a significant relationship between ADHD-PRS and DD scores. Overall, our findings shed light on the genetic basis of VSWM deficits in ADHD, and may have important implications for future research and clinical practice.

Enabling early detection of knee osteoarthritis using diffusion-relaxation correlation spectrum imaging

AIM

To present a technique that enables detection of early stage OA of the knee using diffusion-relaxation correlation spectrum imaging (DR-CSI).

MATERIALS AND METHODS

Fifty-five early osteoarthritis patients (OA, Kellgren-Lawrence [KL] score 1 to 2; mean age, 56.4 years) and 49 healthy volunteers (mean age, 56.7 years) were underwent magnetic resonance imaging (MRI) with T2-mapping and DR-CSI techniques. Maps of mean apparent diffusion coefficient (ADC), T2 relaxation time and volume fraction Vi for DR-CSI compartment i (A, B, C, D) sensitivity, specificity, and positive and negative likelihood ratio (PLR, NLR) were assessed to determine the diagnostic accuracy for detection of early-stage degeneration of knee articular cartilage. The structural abnormalities of articular cartilage were evaluated using modified Whole-Organ MR Imaging Scores (WORMS).

RESULTS

All intra- and interobserver agreements for DR-CSI compartment volume fractions and modified WORMS of cartilage were excellent. Early OA versus the controls had higher V_C, lower V_A and V_B (p<0.001), but comparable V_D (p>0.05). V_A, V_B and V_C had a moderate association with WORMS. No significant correlation was identified between V_D and WORMS. V_C had better ability than V_A,V_B, V_D, T2 and ADC to discriminate early OA patients from healthy controls (area under the curve, 0.898). Sensitivity, specificity, PLR, and NLR of V_C with a cut-off value of 29.9% were 81.8% (95% confidence interval [CI], 69.1-90.9%), 95.9% (86-99.5%), 20.05% (5.13-78.34%), and 0.19% (0.11-0.33%).

CONCLUSIONS

DR-CSI compartment volume fractions may be sensitive indicators for detecting early-stage degeneration in knee articular cartilage.

[Automated diagnostic classification with lateral cephalograms based on deep learning network model]

Objective: To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis. Methods: A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps. Results: The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions. Conclusions: In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.

[Research on multi-class orthodontic image recognition system based on deep learning network model]

Objective: To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep learning model for automatic classification of orthodontic image data. Methods: A total of 35 000 clinical orthodontic images were collected in the Department of Orthodontics, Capital Medical University School of Stomatology, from October to November 2020 and June to July 2021. The images were from 490 orthodontic patients with a male-to-female ratio of 49∶51 and the age range of 4 to 45 years. After data cleaning based on inclusion and exclusion criteria, the final image dataset included 17 453 face images (frontal, smiling, 90° right, 90° left, 45° right, and 45° left), 8 026 intraoral images [frontal occlusion, right occlusion, left occlusion, upper occlusal view (original and flipped), lower occlusal view (original and flipped) and coverage of occlusal relationship], 4 115 X-ray images [lateral skull X-ray from the left side, lateral skull X-ray from the right side, frontal skull X-ray, cone-beam CT (CBCT), and wrist bone X-ray] and 684 other non-orthodontic images. A labeling team composed of orthodontic doctoral students, associate professors, and professors used image labeling tools to classify the orthodontic images into 20 categories, including 6 face image categories, 8 intraoral image categories, 5 X-ray image categories, and other images. The data for each label were randomly divided into training, validation, and testing sets in an 8∶1∶1 ratio using the random function in the Python programming language. The improved SqueezeNet deep learning model was used for training, and 13 000 natural images from the ImageNet open-source dataset were used as additional non-orthodontic images for algorithm optimization of anomaly data processing. A multi-classification orthodontic image recognition system based on deep learning models was constructed. The accuracy of the orthodontic image classification was evaluated using precision, recall, F1 score, and confusion matrix based on the prediction results of the test set. The reliability of the model's image classification judgment logic was verified using the gradient-weighted class activation mapping (Grad-CAM) method to generate heat maps. Results: After data cleaning and labeling, a total of 30 278 orthodontic images were included in the dataset. The test set classification results showed that the precision, recall, and F1 scores of most classification labels were 100%, with only 5 misclassified images out of 3 047, resulting in a system accuracy of 99.84%(3 042/3 047). The precision of anomaly data processing was 100% (10 500/10 500). The heat map showed that the judgment basis of the SqueezeNet deep learning model in the image classification process was basically consistent with that of humans. Conclusions: This study developed a multi-classification orthodontic image recognition system for automatic classification of 20 types of orthodontic images based on the improved SqueezeNet deep learning model. The system exhibitted good accuracy in orthodontic image classification.

Understanding the heterogeneity of dynamic functional connectivity patterns in first-episode drug naïve depression using normative models

BACKGROUND

The heterogeneity of the clinical symptoms and presumptive neural pathologies has stunted progress toward identifying reproducible biomarkers and limited therapeutic interventions' effectiveness for the first episode drug-naïve major depressive disorders (FEDN-MDD). This study combined the dynamic features of fMRI data and normative modeling to quantitative and individualized metrics for delineating the biological heterogeneity of FEDN-MDD.

METHOD

Two hundred seventy-four adults with FEDN-MDD and 832 healthy controls from International Big-Data Center for Depression Research were included. Subject-specific dynamic brain networks and network fluctuation characteristics were computed for each subject using the group information-guided independent component analysis. Then, we mapped the heterogeneity of the dynamic features (network fluctuation characteristics and dynamic functional connectivity within brain networks) in the patients group via normative modeling.

RESULTS

The FEDN-MDD whose network fluctuation characteristics deviate from the normative model also showed significant differences within the default mode network, executive control network, and limbic network compared with healthy controls. Furthermore, the network fluctuation characteristics are significantly increased in patients with FEDN-MDD. About 4.74 % of the patients showed a deviation of dynamic functional connectivity, and only 3.35 % of the controls deviated from the normative model in above 100 connectivities. More patients than healthy controls showed extreme dynamic variabilities in above 100 connectivities.

CONCLUSIONS

This work evaluates the efficacy of an individualized approach based on normative modeling for understanding the heterogeneity of abnormal dynamic functional connectivity patterns in FEDN-MDD, and could be used as complementary to classical case-control comparisons.

Heterogeneous brain dynamic functional connectivity patterns in first-episode drug-naive patients with major depressive disorder

It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first-episode drug-naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big-Data Center for Depression Research. Twenty large-scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual-level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN-MDD subtypes and their associated neurobiological heterogeneity.

Associations of DNA Methylation With Behavioral Problems, Gray Matter Volumes, and Negative Life Events Across Adolescence: Evidence From the Longitudinal IMAGEN Study

BACKGROUND

Negative life events (NLEs) increase the risk for externalizing behaviors (EBs) and internalizing behaviors (IBs) in adolescence and adult psychopathology. DNA methylation associated with behavioral problems may reflect this risk and long-lasting effects of NLEs.

METHODS

To identify consistent associations between blood DNA methylation and EBs or IBs across adolescence, we conducted longitudinal epigenome-wide association studies (EWASs) using data from the IMAGEN cohort, collected at ages 14 and 19 years (n = 506). Significant findings were validated in a separate subsample (n = 823). Methylation risk scores were generated by 10-fold cross-validation and further tested for their associations with gray matter volumes and NLEs.

RESULTS

No significant findings were obtained for the IB-EWAS. The EB-EWAS identified a genome-wide significant locus in a gene linked to attention-deficit/hyperactivity disorder (ADHD) (IQSEC1, cg01460382; p = 1.26 × 10-8). Other most significant CpG sites were near ADHD-related genes and enriched for genes regulating tumor necrosis factor and interferon-γ signaling, highlighting the relevance of EB-EWAS findings for ADHD. Analyses with the EB methylation risk scores suggested that it partly reflected comorbidity with IBs in late adolescence. Specific to EBs, EB methylation risk scores correlated with smaller gray matter volumes in medial orbitofrontal and anterior/middle cingulate cortices, brain regions known to associate with ADHD and conduct problems. Longitudinal mediation analyses indicated that EB-related DNA methylation were more likely the outcomes of problematic behaviors accentuated by NLEs, and less likely the epigenetic bases of such behaviors.

CONCLUSIONS

Our findings suggest that novel epigenetic mechanisms through which NLEs exert short and longer-term effects on behavior may contribute to ADHD.

The effect of microRNA-663b in the inhibition of interleukin-1-induced nucleus pulposus cell apoptosis and inflammatory response

The aim of this study was to explore the role and pathological mechanism of microRNA-663b in interleukin-1beta (IL-1β)-induced inflammation and apoptosis of nucleus pulposus cells. First, the best concentration and time to construct the nucleus pulposus cell inflammation model was screen out. Overexpression or inhibition of miR-663b expression was performed by adding microRNA-663b mimic or microRNA-663b inhibitor. 293T cells were transfected according to experimental requirements. The luciferase activity of each group was detected to analyze the targeted regulation of microRNA-663b on interleukin-1 receptor (IL1R1). Compared with the mimic negative control (NC) group, the expression of inflammatory factors in the microRNA-663b overexpression group was inhibited (P<0.05), and the expression of type 2 collagen and polysaccharide protein increased (P<0.05), and the apoptosis of nucleus pulposus cells was inhibited (P<0.01), and the number of TUNEL-positive cells decreased significantly (P<0.01), and the microRNA and protein expression of IL1R1, the ratio of P-P65/P65 and phospho-nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (P-IκBα)/nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (IκBα) protein expression were significantly decreased (P<0.05). The expression of inflammatory factors in the miR-663b inhibitor group was significantly higher than that in the inhibitor NC group (P<0.01), and the expression of type 2 collagen and polysaccharide protein was significantly decreased (P<0.01), and the number of apoptosis cells and TUNEL staining positive cells increased (p<0.01). The expression of IL1R1 gene and protein was significantly increased (P<0.01). The ratio of P-P65/P65 and P-IκBα/IκBα protein expression increased (P<0.05). IL1R1 is a downstream target gene of microRNA-663b. MicroRNA-663b may down-regulate the expression of IL1R1 at the transcriptional level by targeting IL1R1, inhibit the inflammatory response of nucleus pulposus cells, and slow down the degeneration of nucleus pulposus cells.

The effect of microRNA-663b in the inhibition of interleukin-1-induced nucleus pulposus cell apoptosis and inflammatory response

The aim of this study was to explore the role and pathological mechanism of microRNA-663b in interleukin-1beta (IL-1β)-induced inflammation and apoptosis of nucleus pulposus cells. First, the best concentration and time to construct the nucleus pulposus cell inflammation model was screen out. Overexpression or inhibition of miR-663b expression was performed by adding microRNA-663b mimic or microRNA-663b inhibitor. 293T cells were transfected according to experimental requirements. The luciferase activity of each group was detected to analyze the targeted regulation of microRNA-663b on interleukin-1 receptor (IL1R1). Compared with the mimic negative control (NC) group, the expression of inflammatory factors in the microRNA-663b overexpression group was inhibited (P<0.05), and the expression of type 2 collagen and polysaccharide protein increased (P<0.05), and the apoptosis of nucleus pulposus cells was inhibited (P<0.01), and the number of TUNEL-positive cells decreased significantly (P<0.01), and the microRNA and protein expression of IL1R1, the ratio of P-P65/P65 and phospho-nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (P-IκBα)/nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (IκBα) protein expression were significantly decreased (P<0.05). The expression of inflammatory factors in the miR-663b inhibitor group was significantly higher than that in the inhibitor NC group (P<0.01), and the expression of type 2 collagen and polysaccharide protein was significantly decreased (P<0.01), and the number of apoptosis cells and TUNEL staining positive cells increased (p<0.01). The expression of IL1R1 gene and protein was significantly increased (P<0.01). The ratio of P-P65/P65 and P-IκBα/IκBα protein expression increased (P<0.05). IL1R1 is a downstream target gene of microRNA-663b. MicroRNA-663b may down-regulate the expression of IL1R1 at the transcriptional level by targeting IL1R1, inhibit the inflammatory response of nucleus pulposus cells, and slow down the degeneration of nucleus pulposus cells.

Somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and its neuroimaging mechanisms

BACKGROUND

Subclinical anxiety, depressive and somatic symptoms appear closely related. However, it remains unclear whether somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and what the underlying neuroimaging mechanisms are for the mediating effect.

METHODS

Data of healthy participants (n = 466) and participants in remission of major depressive disorder (n = 53) were obtained from the Human Connectome Project. The Achenbach Adult Self-Report was adopted to assess anxiety, depressive and somatic symptoms. All participants completed four runs of resting-state functional magnetic resonance imaging. Mediation analyses were utilized to explore the interactions among these symptoms and their neuroimaging mechanisms.

RESULTS

Somatic symptoms partially mediated the association between subclinical anxiety and depressive symptoms in healthy participants (anxiety→somatic→depression: effect: 0.2785, Boot 95% CI: 0.0958-0.3729; depression→somatic→anxiety: effect: 0.0753, Boot 95% CI: 0.0232-0.1314) and participants in remission of MDD (anxiety→somatic→depression: effect: 0.2948, Boot 95% CI: 0.0357-0.7382; depression→somatic→anxiety: effect: 0.0984, Boot 95% CI: 0.0007-0.2438). Resting-state functional connectivity (FC) between the right medial superior frontal gyrus and the left thalamus and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in healthy participants (effect: 0.0020, Boot 95% CI: 0.0003-0.0043). The mean strength of common FCs of subclinical depressive and somatic symptoms, somatic symptoms, and the mean strength of common FCs of subclinical anxiety and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in remission of MDD (effect: 0.0437, Boot 95% CI: 0.0024-0.1190). These common FCs mainly involved the insula, precentral gyri, postcentral gyri and cingulate gyri. Furthermore, FC between the triangular part of the left inferior frontal gyrus and the left postcentral gyrus was positively associated with subclinical anxiety, depressive and somatic symptoms in remission of MDD (FDR-corrected p < 0.01).

CONCLUSIONS

Somatic symptoms partially mediate the interaction between subclinical anxiety and depressive symptoms. FCs involving the right medial superior frontal gyrus, left thalamus, triangular part of left inferior frontal gyrus, bilateral insula, precentral gyri, postcentral gyri and cingulate gyri maybe underlie the mediating effect of somatic symptoms.

Identify novel, shared and disorder-specific genetic architecture of major depressive disorder, insomnia and chronic pain

Major depressive disorder (MDD), insomnia (INS) and chronic pain (CP) often have high comorbidity and show high genetic correlation. Here we aimed to better characterize their novel, shared and disorder-specific genetic architecture. Based on genome-wide association study (GWAS) summary data, we applied the conditional false discovery rate (condFDR) and conjunctional FDR (conjFDR) approach to investigate the novel and overlapped genetic loci for MDD, INS and CP. In addition, putative disorder-specific SNP associations were analyzed by conditioning the other two traits. The functions of the identified genomic loci were explored by performing gene set enrichment analysis (GSEA) for the loci mapped genes. We identified 22, 43 and 91 novel risk loci for MDD, INS and CP. GSEA for the loci mapped genes highlighted olfactory signaling pathway for MDD novel loci, breast cancer related gene set for both INS and CP novel loci, and nervous system related development, structure and activity for CP. Furthermore, we identified three loci jointly associated with the three disorders, including 13q14.3 locus with nearby gene OLFM4, 14q21.1 locus with nearby gene LRFN5 and 5q21.2 locus located in intergenic region. In addition, we identified one specific loci for MDD, 7 for INS and 11 for CP respectively by conditioning the other two traits, which were mapped to 68 genes for MDD, 85 for INS and 100 for CP. The MDD specific genes are enriched in immune system related pathways. This study increases understanding of the genetic architectures underlying MDD, INS and CP. The shared underlying genetic risk may help to explain the high comorbidity rates of the disorders.

P10.09.B Retroelement expression in glioma tumors exhibits subtype specific patterns

Background

There are ~3 million transposable elements in the human genome constituting about 42% of all basepairs. Retroelements (REs) are ~90% of the transposable elements present in the human genome. Active REs are considered highly mutagenic and have been implicated in multiple steps of cancer development and progression, as well as in neurologic diseases. RE activity has functional effects on the genome, including the maintenance of centromere and telomere integrity, and deleterious gene expression. Previous studies have shown that certain families of RE (HERVK, L1, Alu) are expressed in gliomas, however, their specific role as arbitrators of oncogenesis or promoters of the innate anti-tumor immune response remains uncertain. Moreover, it has been shown that a soluble form of PD-L1 (sPD-L1) that blocks its inhibitory activity is produced by exaptation of an intronic endogenous retroelement (LINE-2A) in the gene encoding PD-L1, highlighting the importance of REs as potential therapeutic targets. In this analysis we aim to identify the unique patterns of RE expression across major subtypes of glioma.

Material and Methods

We conducted a differential expression analysis of 49 RE families using RNA-seq data measured in glioma tumors from The Cancer Genome Atlas (TCGA). RE counts were produced using the software REDiscoverTE. Pairwise comparisons between glioma subtypes (defined by WHO2021) were done using in 625 tumor samples adjusting for age, sex and race.

Results

10 of the 49 considered RE families exhibited significantly different (false discovery rate, FDR, &lt;0.05) expression in at least one glioma subtype. Alu(Fold change, FC=1.5), RNA(FC=11.3), PiggyBac(FC=1.6), rRNA(FC=5.23) and Dong-R4(FC=1.8) were overexpressed in IDH-wildtype glioblastoma while Gypsy(FC=0.4) and CRP1(FC=0.26) were decreased in expression. scRNA (FC=2.7) were overexpressed in IDH-mutant oligodendroglioma compared to glioblastoma while Dong-R4 (FC = 0.53) showed decreased expression. LTR (FC=2.02) and tRNA-Deu (FC=1.46), showed increased expression in IDH-wildtype diffuse astrocytomas compared to IDH-mutant, 1p/19q-codeleted oligodendrogliomas while Gypsy (FC =0.41) showed decreased expression.

Conclusion

We have shown that expression of certain RE families within gliomas have subtype-specific patterns. While it is well established that RE expression is dysregulated in cancer, our analysis is the first at exploring a wide range of retroelements in the context of glioma by subtype. Given the important role of REs in transcriptional control, genomic instability, chromosomal rearrangements, and oncogenic activation, the identification of individual families and specific REs in glioma holds an intrinsic value to potential biomarkers and immunotherapy targets.

Genome-wide meta-analysis of alcohol use disorder in East Asians

Alcohol use disorder (AUD) is a leading cause of death and disability worldwide. Genome-wide association studies (GWAS) have identified ~30 AUD risk genes in European populations, but many fewer in East Asians. We conducted GWAS and genome-wide meta-analysis of AUD in 13,551 subjects with East Asian ancestry, using published summary data and newly genotyped data from five cohorts: (1) electronic health record (EHR)-diagnosed AUD in the Million Veteran Program (MVP) sample; (2) DSM-IV diagnosed alcohol dependence (AD) in a Han Chinese-GSA (array) cohort; (3) AD in a Han Chinese-Cyto (array) cohort; and (4) two AD Thai cohorts. The MVP and Thai samples included newly genotyped subjects from ongoing recruitment. In total, 2254 cases and 11,297 controls were analyzed. An AUD polygenic risk score was analyzed in an independent sample with 4464 East Asians (Genetic Epidemiology Research in Adult Health and Aging (GERA)). Phenotypes from survey data and ICD-9-CM diagnoses were tested for association with the AUD PRS. Two risk loci were detected: the well-known functional variant rs1229984 in ADH1B and rs3782886 in BRAP (near the ALDH2 gene locus) are the lead variants. AUD PRS was significantly associated with days per week of alcohol consumption (beta = 0.43, SE = 0.067, p = 2.47 × 10^-10) and nominally associated with pack years of smoking (beta = 0.09, SE = 0.05, p = 4.52 × 10^-2) and ever vs. never smoking (beta = 0.06, SE = 0.02, p = 1.14 × 10^-2). This is the largest GWAS of AUD in East Asians to date. Building on previous findings, we were able to analyze pleiotropy, but did not identify any new risk regions, underscoring the importance of recruiting additional East Asian subjects for alcohol GWAS.

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

The increasing use of 3-dimensional (3D) imaging by orthodontists and maxillofacial surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies a critical need for 3D cephalometric analysis. Although promising methods were suggested to localize 3D landmarks automatically, concerns about robustness and generalizability restrain their clinical use. Consequently, highly trained operators remain needed to perform manual landmarking. In this retrospective diagnostic study, we aimed to train and evaluate a deep learning (DL) pipeline based on SpatialConfiguration-Net for automatic localization of 3D cephalometric landmarks on computed tomography (CT) scans. A retrospective sample of consecutive presurgical CT scans was randomly distributed between a training/validation set (n = 160) and a test set (n = 38). The reference data consisted of 33 landmarks, manually localized once by 1 operator(n = 178) or twice by 3 operators (n = 20, test set only). After inference on the test set, 1 CT scan showed "very low" confidence level predictions; we excluded it from the overall analysis but still assessed and discussed the corresponding results. The model performance was evaluated by comparing the predictions with the reference data; the outcome set included localization accuracy, cephalometric measurements, and comparison to manual landmarking reproducibility. On the hold-out test set, the mean localization error was 1.0 ± 1.3 mm, while success detection rates for 2.0, 2.5, and 3.0 mm were 90.4%, 93.6%, and 95.4%, respectively. Mean errors were -0.3 ± 1.3° and -0.1 ± 0.7 mm for angular and linear measurements, respectively. When compared to manual reproducibility, the measurements were within the Bland-Altman 95% limits of agreement for 91.9% and 71.8% of skeletal and dentoalveolar variables, respectively. To conclude, while our DL method still requires improvement, it provided highly accurate 3D landmark localization on a challenging test set, with a reliability for skeletal evaluation on par with what clinicians obtain.

Identification of Functional CircRNA–miRNA–mRNA Regulatory Network in Dorsolateral Prefrontal Cortex Neurons of Patients With Cocaine Use Disorder

Increasing evidence has indicated that circular RNAs (circRNAs) act as competing endogenous RNAs (ceRNAs) regulatory network to regulate the expression of target genes by sponging microRNAs (miRNAs), and therefore play an essential role in many neuropsychiatric disorders, including cocaine use disorder. However, the functional roles and regulatory mechanisms of circRNAs as ceRNAs in dorsolateral prefrontal cortex (dlPFC) of patients with cocaine use disorder remain to be determined. In this study, an expression profiling for dlPFC in 19 patients with cocaine use disorder and 17 controls from Gene Expression Omnibus datasets was used for the differentially expressed circRNAs analysis and the differentially expressed mRNAs analysis. Several tools were used to predict the miRNAs targeted by the circRNAs and the miRNAs targeted mRNAs, which then overlapped with the cocaine-associated differentially expressed mRNAs to determine the functional roles of circRNAs. Functional analysis for the obtained mRNAs was performed via Gene Ontology (GO) in Metascape database. Integrated bioinformatics analysis was conducted to further characterize the circRNA–miRNA–mRNA regulatory network and identify the functions of distinct circRNAs. We found a total of 41 differentially expressed circRNAs, and 98 miRNAs were targeted by these circRNAs. The overlapped mRNAs targeted by the miRNAs and the differentially expressed mRNAs constructed a circRNA–miRNA–mRNA regulation network including 24 circRNAs, 43 miRNAs, and 82 mRNAs in the dlPFC of patients with cocaine use disorder. Functional analysis indicated the regulation network mainly participated in cell response-related, receptor signaling-related, protein modification-related and axonogenesis-related pathways, which might be involved with cocaine use disorder. Additionally, we determined four hub genes (HSP90AA1, HSPA1B, YWHAG, and RAB8A) from the protein–protein interaction network and constructed a circRNA–miRNA-hub gene subnetwork based on the four hub genes. In conclusion, our findings provide a deeper understanding of the circRNAs-related ceRNAs regulatory mechanisms in the pathogenesis of cocaine use disorder.

DNA Methylation Markers and Prediction Model for Depression and Their Contribution for Breast Cancer Risk

Background

Major depressive disorder (MDD) has become a leading cause of disability worldwide. However, the diagnosis of the disorder is dependent on clinical experience and inventory. At present, there are no reliable biomarkers to help with diagnosis and treatment. DNA methylation patterns may be a promising approach for elucidating the etiology of MDD and predicting patient susceptibility. Our overarching aim was to identify biomarkers based on DNA methylation, and then use it to propose a methylation prediction score for MDD, which we hope will help us evaluate the risk of breast cancer.

Methods

Methylation data from 533 samples were extracted from the Gene Expression Omnibus (GEO) database, of which, 324 individuals were diagnosed with MDD. Statistical difference of DNA Methylation between Promoter and Other body region (SIMPO) score for each gene was calculated based on the DNA methylation data. Based on SIMPO scores, we selected the top genes that showed a correlation with MDD in random resampling, then proposed a methylation-derived Depression Index (mDI) by combining the SIMPO of the selected genes to predict MDD. A validation analysis was then performed using additional DNA methylation data from 194 samples extracted from the GEO database. Furthermore, we applied the mDI to construct a prediction model for the risk of breast cancer using stepwise regression and random forest methods.

Results

The optimal mDI was derived from 426 genes, which included 245 positive and 181 negative correlations. It was constructed to predict MDD with high predictive power (AUC of 0.88) in the discovery dataset. In addition, we observed moderate power for mDI in the validation dataset with an OR of 1.79. Biological function assessment of the 426 genes showed that they were functionally enriched in Eph Ephrin signaling and beta-catenin Wnt signaling pathways. The mDI was then used to construct a predictive model for breast cancer that had an AUC ranging from 0.70 to 0.67.

Conclusion

Our results indicated that DNA methylation could help to explain the pathogenesis of MDD and assist with its diagnosis.

Connectivity-Based Brain Network Supports Restricted and Repetitive Behaviors in Autism Spectrum Disorder Across Development

INTRODUCTION

Autism spectrum disorder (ASD) is a lifelong condition. Autistic symptoms can persist into adulthood. Studies have reported that autistic symptoms generally improved in adulthood, especially restricted and repetitive behaviors and interests (RRBIs). We explored brain networks that are related to differences in RRBIs in individuals with ASDs among different ages.

METHODS

We enrolled 147 ASD patients from the Autism Brain Imaging Data Exchange II (ABIDEII) database. The participants were divided into four age groups: children (6-9 years old), younger adolescents (10-14 years old), older adolescents (15-19 years old), and adults (≥20 years old). RRBIs were evaluated using the Repetitive Behaviors Scale-Revised 6. We first explored differences in RRBIs between age groups using the Kruskal-Wallis test. Associations between improvements in RRBIs and age were analyzed using a general linear model. We then analyzed RRBIs associated functional connectivity (FC) links using the network-based statistic method by adjusting covariates. The association of the identified FC with age group, and mediation function of the FC on the association of age-group and RRBI were further analyzed.

RESULTS

Most subtypes of RRBIs improved with age, especially stereotyped behaviors, ritualistic behaviors, and restricted behaviors (p = 0.012, 0.014, and 0.012, respectively). Results showed that 12 FC links were closely related to overall RRBIs, 17 FC links were related to stereotyped behaviors. Among the identified 29 FC links, 15 were negatively related to age-groups. The mostly reported core brain regions included superior occipital gyrus, insula, rolandic operculum, angular, caudate, and cingulum. The decrease in FC between the left superior occipital lobe and right angular (effect = -0.125 and -0.693, respectively) and between the left insula and left caudate (effect = -0.116 and -0.664, respectively) might contribute to improvements in multiple RRBIs with age.

CONCLUSION

We identified improvements in RRBIs with age in ASD patients, especially stereotyped behaviors, ritualistic behaviors, and restricted behaviors. The decrease in FC between left superior occipital lobe and right angular and between left insula and left caudate might contribute to these improvements. Our findings improve our understanding of the pathogenesis of RRBIs and suggest potential intervention targets to improve prognosis in adulthood.

A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke

Remote monitoring of physical activity using bodyworn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data were recorded from 10 hemi paretic patients while they carried out a sequence of 11 activities of daily living (Identification tasks), and 10 activities used to evaluate misclassification errors (non-Identification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the non-Identification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of 4 ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0 %, and a mean specificity of 99.7 % for the identification tasks, and a mean misclassification error of < 10% for the non-Identification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke.

Novel Risk Loci Associated With Genetic Risk for Bipolar Disorder Among Han Chinese Individuals: A Genome-Wide Association Study and Meta-analysis

IMPORTANCE

The genetic basis of bipolar disorder (BD) in Han Chinese individuals is not fully understood.

OBJECTIVE

To explore the genetic basis of BD in the Han Chinese population.

DESIGN, SETTING, AND PARTICIPANTS

A genome-wide association study (GWAS), followed by independent replication, was conducted to identify BD risk loci in Han Chinese individuals. Individuals with BD were diagnosed based on DSM-IV criteria and had no history of schizophrenia, mental retardation, or substance dependence; individuals without any personal or family history of mental illnesses, including BD, were included as control participants. In total, discovery samples from 1822 patients and 4650 control participants passed quality control for the GWAS analysis. Replication analyses of samples from 958 patients and 2050 control participants were conducted. Summary statistics from the European Psychiatric Genomics Consortium 2 (PGC2) BD GWAS (20 352 cases and 31 358 controls) were used for the trans-ancestry genetic correlation analysis, polygenetic risk score analysis, and meta-analysis to compare BD genetic risk between Han Chinese and European individuals. The study was performed in February 2020.

MAIN OUTCOMES AND MEASURES

Single-nucleotide variations with P < 5.00 × 10-8 were considered to show genome-wide significance of statistical association.

RESULTS

The Han Chinese discovery GWAS sample included 1822 cases (mean [SD] age, 35.43 [14.12] years; 838 [46%] male) and 4650 controls (mean [SD] age, 27.48 [5.97] years; 2465 [53%] male), and the replication sample included 958 cases (mean [SD] age, 37.82 [15.54] years; 412 [43%] male) and 2050 controls (mean [SD] age, 27.50 [6.00] years; 1189 [58%] male). A novel BD risk locus in Han Chinese individuals was found near the gene encoding transmembrane protein 108 (TMEM108, rs9863544; P = 2.49 × 10-8; odds ratio [OR], 0.650; 95% CI, 0.559-0.756), which is required for dendritic spine development and glutamatergic transmission in the dentate gyrus. Trans-ancestry genetic correlation estimation (ρge = 0.652, SE = 0.106; P = 7.30 × 10-10) and polygenetic risk score analyses (maximum liability-scaled Nagelkerke pseudo R2 = 1.27%; P = 1.30 × 10-19) showed evidence of shared BD genetic risk between Han Chinese and European populations, and meta-analysis identified 2 new GWAS risk loci near VRK2 (rs41335055; P = 4.98 × 10-9; OR, 0.849; 95% CI, 0.804-0.897) and RHEBL1 (rs7969091; P = 3.12 × 10-8; OR, 0.932; 95% CI, 0.909-0.956).

CONCLUSIONS AND RELEVANCE

This GWAS study identified several loci and genes involved in the heritable risk of BD, providing insights into its genetic architecture and biological basis.

Evaluation of an accelerated 3D modulated flip-angle technique in refocused imaging with an extended echo-train sequence with compressed sensing for imaging of the knee: comparison with routine 2D MRI sequences

AIM

To accelerate the acquisition of high-resolution magnetic resonance imaging (MRI) by using the three-dimensional (3D) matrix sequence with compressed sensing and to compare it with conventional two-dimensional (2D) proton-density (PD) and fast spin-echo (FSE) sequences.

MATERIALS AND METHODS

3D matrix, 2D FSE, and PD sequences were acquired from 68 participants using 3 T magnetic resonance imaging (MRI). Two radiologists scored image quality independently on a four-point scale. The structural similarity index (SSIM), and signal- (SNRs) and contrast-to-noise ratios (CNRs) of different anatomical structures of the knee were assessed and compared between sequences using Wilcoxon signed-rank tests and Cohen's kappa.

RESULTS

The median acquisition time reduction was 44.5%. There was a substantial to perfect agreement for the rating between the 3D matrix FSE and 2D FSE or PD sequences when evaluating cartilage, subchondral bone, and ligaments (κ=0.783-872, p>0.05). The mean SSIM values between the 3D matrix FSE and 2D FSE, and between the 3D matrix PD and 2D PD sequences was 0.994 and 0.971, respectively, which are acceptable. No significant differences were found in SNR between the 3D matrix FSE and 2D FSE, and between the 3D matrix PD and 2D PD sequences, even though the SNR appeared to be higher on routine 2D sequences. The CNR of subchondral bone-meniscus, subchondral bone-joint fluid, and meniscus-joint fluid did not differentiate significantly between the 3D matrix sequence and routine 2D sequences.

CONCLUSIONS

3D matrix reduced the acquisition time in routine clinical knee MRI without the loss in image quality, SNR, and CNR.