Do Seasons Have an Influence on the Incidence of Depression? The Use of an Internet Search Engine Query Data as a Proxy of Human Affect

Background: Seasonal depression has generated considerable clinical interest in recent years. Despite a common belief that people in higher latitudes are more vulnerable to low mood during the winter, it has never been demonstrated that human's moods are subject to seasonal change on a global scale. The aim of this study was to investigate large-scale seasonal patterns of depression using Internet search query data as a signature and proxy of human affect. Methodology/Principal Findings: Our study was based on a publicly available search engine database, Google Insights for Search, which provides time series data of weekly search trends from January 1, 2004 to June 30, 2009. We applied an empirical mode decomposition method to isolate seasonal components of health-related search trends of depression in 54 geographic areas worldwide. We identified a seasonal trend of depression that was opposite between the northern and southern hemispheres; this trend was significantly correlated with seasonal oscillations of temperature (USA: r = −0.872, <0.001; Australia: r = −0.656, <0.001). Based on analyses of search trends over 54 geological locations worldwide, we found that the degree of correlation between searching for depression and temperature was latitude-dependent (northern hemisphere: r = −0.686; <0.001; southern hemisphere: r = 0.871; <0.0001). Conclusions/Significance: Our findings indicate that Internet searches for depression from people in higher latitudes are more vulnerable to seasonal change, whereas this phenomenon is obscured in tropical areas. This phenomenon exists universally across countries, regardless of language. This study provides novel, Internet-based evidence for the epidemiology of seasonal depression

The aging trajectories of brain functional hierarchy and its impact on cognition across the adult lifespan

IntroductionThe hierarchical network architecture of the human brain, pivotal to cognition and behavior, can be explored via gradient analysis using restingstate functional MRI data. Although it has been employed to understand brain development and disorders, the impact of aging on this hierarchical architecture and its link to cognitive decline remains elusive.MethodsThis study utilized resting-state functional MRI data from 350 healthy adults (aged 20–85) to investigate the functional hierarchical network using connectome gradient analysis with a cross-age sliding window approach. Gradient-related metrics were estimated and correlated with age to evaluate trajectory of gradient changes across lifespan.ResultsThe principal gradient (unimodal-to-transmodal) demonstrated a significant non-linear relationship with age, whereas the secondary gradient (visual-to-somatomotor) showed a simple linear decreasing pattern. Among the principal gradient, significant age-related changes were observed in the somatomotor, dorsal attention, limbic and default mode networks. The changes in the gradient scores of both the somatomotor and frontal–parietal networks were associated with greater working memory and visuospatial ability. Gender differences were found in global gradient metrics and gradient scores of somatomotor and default mode networks in the principal gradient, with no interaction with age effect.DiscussionOur study delves into the aging trajectories of functional connectome gradient and its cognitive impact across the adult lifespan, providing insights for future research into the biological underpinnings of brain function and pathological models of atypical aging processes

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1–3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy

Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests

The identification of gene-environment interactions (G × E) may eventually guide health-related choices and medical interventions for complex diseases. More powerful methods must be developed to identify G × E. The “adaptive combination of Bayes factors method” (ADABF) has been proposed as a powerful genome-wide polygenic approach to detect G × E. In this work, we evaluate its performance when serving as a gene-based G × E test. We compare ADABF with six tests including the “Set-Based gene-EnviRonment InterAction test” (SBERIA), “gene-environment set association test” (GESAT), etc. With extensive simulations, SBERIA and ADABF are found to be more powerful than other G × E tests. However, SBERIA suffers from a power loss when 50% SNP main effects are in the same direction with the SNP × E interaction effects while 50% are in the opposite direction. We further applied these seven G × E methods to the Taiwan Biobank data to explore gene× alcohol interactions on blood pressure levels. The ADAMTS7P1 gene at chromosome 15q25.2 was detected to interact with alcohol consumption on diastolic blood pressure (p = 9.5 × 10−7, according to the GESAT test). At this gene, the P-values provided by other six tests all reached the suggestive significance level (p &lt; 5 × 10−5). Regarding the computation time required for a genome-wide G × E analysis, SBERIA is the fastest method, followed by ADABF. Considering the validity, power performance, robustness, and computation time, ADABF is recommended for genome-wide G × E analyses

Panax notoginseng Attenuates Bleomycin-Induced Pulmonary Fibrosis in Mice

Panax notoginseng (PN) is a traditional Chinese herb experimentally proven to have anti-inflammatory effects, and it is used clinically for the treatment of atherosclerosis, cerebral infarction, and cerebral ischemia. This study aimed to determine the anti-inflammatory effects of PN against bleomycin-induced pulmonary fibrosis in mice. First, in an in vitro study, culture media containing lipopolysaccharide (LPS) was used to stimulate macrophage cells (RAW 264.7 cell line). TNF-α and IL-6 levels were then determined before and after treatment with PN extract. In an animal model (C57BL/6 mice), a single dose of PN (0.5 mg/kg) was administered orally on Day 2 or Day 7 postbleomycin treatment. The results showed that TNF-α and IL-6 levels increased in the culture media of LPS-stimulated macrophage cells, and this effect was significantly inhibited in a concentration-dependent manner by PN extract. Histopathologic examination revealed that PN administered on Day 7 postbleomycin treatment significantly decreased inflammatory cell infiltrates, fibrosis scores, and TNF-α, TGF-β, IL-1β, and IL-6 levels in bronchoalveolar lavage fluid when compared with PN given on Day 2 postbleomycin treatment. These results suggest that PN administered in the early fibrotic stage can attenuate pulmonary fibrosis in an animal model of idiopathic pulmonary fibrosis

The influence of serotonin transporter polymorphisms on cortical activity: A resting EEG study

<p>Abstract</p> <p>Background</p> <p>The serotonin transporter gene (<it>5-HTT</it>) is a key regulator of serotonergic neurotransmission and has been linked to various psychiatric disorders. Among the genetic variants, polymorphisms in the <it>5-HTT </it>gene-linked polymorphic region (<it>5-HTTLPR</it>) and variable-number-of-tandem-repeat in the second intron (<it>5-HTTVNTR</it>) have functional consequences. However, their genetic impact on cortical oscillation remains unclear. This study examined the modulatory effects of <it>5-HTTLPR </it>(L-allele carriers vs. non-carriers) and <it>5-HTTVNTR </it>(10-repeat allele carriers vs. non-carriers) polymorphism on regional neural activity in a young female population.</p> <p>Methods</p> <p>Blood samples and resting state eyes-closed electroencephalography (EEG) signals were collected from 195 healthy women and stratified into 2 sets of comparisons of 2 groups each: L-allele carriers (<it>N </it>= 91) vs. non-carriers for <it>5-HTTLPR </it>and 10-repeat allele carriers (<it>N </it>= 25) vs. non-carriers for <it>5-HTTVNTR</it>. The mean power of 18 electrodes across theta, alpha, beta, gamma, gamma1, and gamma2 frequencies was analyzed. Between-group statistics were performed by an independent t-test, and global trends of regional power were quantified by non-parametric analyses.</p> <p>Results</p> <p>Among <it>5-HTTVNTR </it>genotypes, 10-repeat allele carriers showed significantly low regional power at gamma frequencies across the brain. We noticed a consistent global trend that carriers with low transcription efficiency of 5-HTT possessed low regional powers, regardless of frequency bands. The non-parametric analyses confirmed this observation, with <it>P </it>values of 3.071 × 10^-8and 1.459 × 10^-12for <it>5-HTTLPR </it>and <it>5-HTTVNTR</it>, respectively.</p> <p>Conclusions and Limitations</p> <p>Our analyses showed that genotypes with low 5-HTT activity are associated with less local neural synchronization during relaxation. The implication with respect to genetic vulnerability of 5-HTT across a broad range of psychiatric disorders is discussed. Given the low frequency of 10-repeat allele of <it>5-HTTVNTR </it>in our research sample, the possibility of false positive findings should also be considered.</p

Galloway-Mowat syndrome: Prenatal ultrasound and perinatal magnetic resonance imaging findings

AbstractObjectiveTo present prenatal ultrasound and perinatal magnetic resonance imaging (MRI) findings of Galloway-Mowat syndrome.Case ReportA 31-year-old woman, gravida 3, para 2, was referred for genetic counseling at 29 weeks of gestation because of abnormal ultrasound findings and a previous child with Galloway-Mowat syndrome. During this pregnancy, microcephaly, intrauterine growth restriction (IUGR), and oligohydramnios were first noted at 27 weeks of gestation. Repeated ultrasounds showed microcephaly, IUGR, and oligohydramnios. MRI performed at 32 weeks of gestation showed reduced sulcation of the brain, pachygyria, poor myelination of the white matter, and cerebellar atrophy. A diagnosis of recurrent Galloway-Mowat syndrome was made. At 40 weeks of gestation, a 2,496-g female baby was delivered with microcephaly, a narrow slopping forehead, epicanthic folds, microphthalmos, a highly arched palate, a small midface, a beaked nose, thin lips, large low-set floppy ears, clenched hands, and arachnodactyly. Postnatal MRI findings were consistent with the prenatal diagnosis. Renal ultrasound showed enlarged bilateral kidneys with increased echogenicity. At the age of 2 weeks, the infant became edematous and developed nephrotic syndrome.ConclusionMicrocephaly, IUGR, and oligohydramnios are significant ultrasound triad of fetal Galloway-Mowat syndrome. Prenatal ultrasound diagnosis of microcephaly, IUGR, and oligohydramnios in late second trimester or in early third trimester should alert clinicians to the possibility of Galloway-Mowat syndrome and prompt a detailed search of abnormal sulcation, cortical gyral maldevelopment, and cerebellar atrophy by fetal ultrafast MRI

Diagnosis of Polypoidal Choroidal Vasculopathy From Fluorescein Angiography Using Deep Learning

Purpose: To differentiate polypoidal choroidal vasculopathy (PCV) from choroidal neovascularization (CNV) and to determine the extent of PCV from fluorescein angiography (FA) using attention-based deep learning networks. Methods: We build two deep learning networks for diagnosis of PCV using FA, one for detection and one for segmentation. Attention-gated convolutional neural network (AG-CNN) differentiates PCV from other types of wet age-related macular degeneration. Gradient-weighted class activation map (Grad-CAM) is generated to highlight important regions in the image for making the prediction, which offers explainability of the network. Attention-gated recurrent neural network (AG-PCVNet) for spatiotemporal prediction is applied for segmentation of PCV. Results: AG-CNN is validated with a dataset containing 167 FA sequences of PCV and 70 FA sequences of CNV. AG-CNN achieves a classification accuracy of 82.80% at image level, and 86.21% at patient-level for PCV. Grad-CAM shows that regions contributing to decision-making have on average 21.91% agreement with pathological regions identified by experts. AG-PCVNet is validatedwith56PCV sequences from the EVEREST-I study and achieves a balanced accuracy of 81.132% and dice score of 0.54. Conclusions: The developed software provides a means of performing detection and segmentation of PCV on FA images for the first time. This study is a promising step in changing the diagnostic procedure of PCV and therefore improving the detection rate of PCV using FA alone. Translational Relevance: The developed deep learning system enables early diagnosis of PCV using FA to assist the physician in choosing the best treatment for optimal visual prognosis. Introductio