Demographics and clinical characteristics.

<p>Values are mean ± standard deviation unless otherwise noted.</p><p>Categorical data are compared by chi-square tests, two tailed; all other <i>p</i> values are by Student's <i>t</i> test, two tailed.</p

Table_1_Exploring the role of hub and network dysfunction in brain connectomes of schizophrenia using functional magnetic resonance imaging.DOCX

IntroductionPathophysiological etiology of schizophrenia remains unclear due to the heterogeneous nature of its biological and clinical manifestations. Dysfunctional communication among large-scale brain networks and hub nodes have been reported. In this study, an exploratory approach was adopted to evaluate the dysfunctional connectome of brain in schizophrenia.MethodsTwo hundred adult individuals with schizophrenia and 200 healthy controls were recruited from Taipei Veterans General Hospital. All subjects received functional magnetic resonance imaging (fMRI) scanning. Functional connectivity (FC) between parcellated brain regions were obtained. Pair-wise brain regions with significantly different functional connectivity among the two groups were identified and further analyzed for their concurrent ratio of connectomic differences with another solitary brain region (single-FC dysfunction) or dynamically interconnected brain network (network-FC dysfunction).ResultsThe right thalamus had the highest number of significantly different pair-wise functional connectivity between schizophrenia and control groups, followed by the left thalamus and the right middle frontal gyrus. For individual brain regions, dysfunctional single-FCs and network-FCs could be found concurrently. Dysfunctional single-FCs distributed extensively in the whole brain of schizophrenia patients, but overlapped in similar groups of brain nodes. A dysfunctional module could be formed, with thalamus being the key dysfunctional hub.DiscussionThe thalamus can be a critical hub in the brain that its dysfunctional connectome with other brain regions is significant in schizophrenia patients. Interconnections between dysfunctional FCs for individual brain regions may provide future guide to identify critical brain pathology associated with schizophrenia.</p

Multiscale entropy analysis by APOE ε4 genotype.

<p>Multiscale entropy was derived from two-hours of interbeat interval time series. Symbols represent mean values of entropy for each group and the bars represent the standard error. Parameters of sample entropy calculation are m = 2 and r = 0.15. The sample entropy values for subjects with APOE ε4 allele are significantly lower (<i>p</i><0.01) on scales between 3 and 13, which are equal to oscillations at period around 10 to 40 heartbeats. <i>p</i> values were computed using Student's <i>t</i>-test at each scale factor.</p

Heart-rate variability characteristics.

<p>Values are mean ± standard deviation unless otherwise noted.</p><p>Power spectral estimates were log transformed due to skewed distributions. F ratios from analyses of covariance, controlling for age and clinical parameters.</p

A comparison of a representative interbeat interval time series and analysis of multiscale entropy (MSE) between an APOE ε4-negative subject (top panels) and an APOE ε4-positive subject (bottom panels).

<p>Time series length is 30 minutes. The APOE ε4-negative subject showed multiscale organizations in fluctuations of interbeat intervals, whereas a relatively monotonic oscillation was seen in the interbeat interval time series obtained from an APOE ε4-positive subject. By considering the impact of scale on entropy calculations, the sample entropy values for the APOE ε4-negative subject is higher than that for the APOE ε4-positive subject for scales larger than two. Of note, the sum of MSE from scale factor 1 to 20 was 28.3 for the APOE ε4-negative subject and 17.3 for the APOE ε4-positive subject.</p

Table_2_Exploring the role of hub and network dysfunction in brain connectomes of schizophrenia using functional magnetic resonance imaging.DOCX

IntroductionPathophysiological etiology of schizophrenia remains unclear due to the heterogeneous nature of its biological and clinical manifestations. Dysfunctional communication among large-scale brain networks and hub nodes have been reported. In this study, an exploratory approach was adopted to evaluate the dysfunctional connectome of brain in schizophrenia.MethodsTwo hundred adult individuals with schizophrenia and 200 healthy controls were recruited from Taipei Veterans General Hospital. All subjects received functional magnetic resonance imaging (fMRI) scanning. Functional connectivity (FC) between parcellated brain regions were obtained. Pair-wise brain regions with significantly different functional connectivity among the two groups were identified and further analyzed for their concurrent ratio of connectomic differences with another solitary brain region (single-FC dysfunction) or dynamically interconnected brain network (network-FC dysfunction).ResultsThe right thalamus had the highest number of significantly different pair-wise functional connectivity between schizophrenia and control groups, followed by the left thalamus and the right middle frontal gyrus. For individual brain regions, dysfunctional single-FCs and network-FCs could be found concurrently. Dysfunctional single-FCs distributed extensively in the whole brain of schizophrenia patients, but overlapped in similar groups of brain nodes. A dysfunctional module could be formed, with thalamus being the key dysfunctional hub.DiscussionThe thalamus can be a critical hub in the brain that its dysfunctional connectome with other brain regions is significant in schizophrenia patients. Interconnections between dysfunctional FCs for individual brain regions may provide future guide to identify critical brain pathology associated with schizophrenia.</p

Table_3_Exploring the role of hub and network dysfunction in brain connectomes of schizophrenia using functional magnetic resonance imaging.DOCX

IntroductionPathophysiological etiology of schizophrenia remains unclear due to the heterogeneous nature of its biological and clinical manifestations. Dysfunctional communication among large-scale brain networks and hub nodes have been reported. In this study, an exploratory approach was adopted to evaluate the dysfunctional connectome of brain in schizophrenia.MethodsTwo hundred adult individuals with schizophrenia and 200 healthy controls were recruited from Taipei Veterans General Hospital. All subjects received functional magnetic resonance imaging (fMRI) scanning. Functional connectivity (FC) between parcellated brain regions were obtained. Pair-wise brain regions with significantly different functional connectivity among the two groups were identified and further analyzed for their concurrent ratio of connectomic differences with another solitary brain region (single-FC dysfunction) or dynamically interconnected brain network (network-FC dysfunction).ResultsThe right thalamus had the highest number of significantly different pair-wise functional connectivity between schizophrenia and control groups, followed by the left thalamus and the right middle frontal gyrus. For individual brain regions, dysfunctional single-FCs and network-FCs could be found concurrently. Dysfunctional single-FCs distributed extensively in the whole brain of schizophrenia patients, but overlapped in similar groups of brain nodes. A dysfunctional module could be formed, with thalamus being the key dysfunctional hub.DiscussionThe thalamus can be a critical hub in the brain that its dysfunctional connectome with other brain regions is significant in schizophrenia patients. Interconnections between dysfunctional FCs for individual brain regions may provide future guide to identify critical brain pathology associated with schizophrenia.</p

Data_Sheet_2_The aging trajectories of brain functional hierarchy and its impact on cognition across the adult lifespan.xlsx

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.</p

Data_Sheet_1_The aging trajectories of brain functional hierarchy and its impact on cognition across the adult lifespan.docx

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.</p

Effects of circadian clock genes and health-related behavior on metabolic syndrome in a Taiwanese population: Evidence from association and interaction analysis

<div><p>Increased risk of developing metabolic syndrome (MetS) has been associated with the circadian clock genes. In this study, we assessed whether 29 circadian clock-related genes (including <i>ADCYAP1</i>, <i>ARNTL</i>, <i>ARNTL2</i>, <i>BHLHE40</i>, <i>CLOCK</i>, <i>CRY1</i>, <i>CRY2</i>, <i>CSNK1D</i>, <i>CSNK1E</i>, <i>GSK3B</i>, <i>HCRTR2</i>, <i>KLF10</i>, <i>NFIL3</i>, <i>NPAS2</i>, <i>NR1D1</i>, <i>NR1D2</i>, <i>PER1</i>, <i>PER2</i>, <i>PER3</i>, <i>REV1</i>, <i>RORA</i>, <i>RORB</i>, <i>RORC</i>, <i>SENP3</i>, <i>SERPINE1</i>, <i>TIMELESS</i>, <i>TIPIN</i>, <i>VIP</i>, and <i>VIPR2</i>) are associated with MetS and its individual components independently and/or through complex interactions in a Taiwanese population. We also analyzed the interactions between environmental factors and these genes in influencing MetS and its individual components. A total of 3,000 Taiwanese subjects from the Taiwan Biobank were assessed in this study. Metabolic traits such as waist circumference, triglyceride, high-density lipoprotein cholesterol, systolic and diastolic blood pressure, and fasting glucose were measured. Our data showed a nominal association of MetS with several single nucleotide polymorphisms (SNPs) in five key circadian clock genes including <i>ARNTL</i>, <i>GSK3B</i>, <i>PER3</i>, <i>RORA</i>, and <i>RORB</i>; but none of these SNPs persisted significantly after performing Bonferroni correction. Moreover, we identified the effect of <i>GSK3B</i> rs2199503 on high fasting glucose (P = 0.0002). Additionally, we found interactions among the <i>ARNTL</i> rs10832020, <i>GSK3B</i> rs2199503, <i>PER3</i> rs10746473, <i>RORA</i> rs8034880, and <i>RORB</i> rs972902 SNPs influenced MetS (P < 0.001 ~ P = 0.002). Finally, we investigated the influence of interactions between <i>ARNTL</i> rs10832020, <i>GSK3B</i> rs2199503, <i>PER3</i> rs10746473, and <i>RORB</i> rs972902 with environmental factors such as alcohol consumption, smoking status, and physical activity on MetS and its individual components (P < 0.001 ~ P = 0.002). Our study indicates that circadian clock genes such as <i>ARNTL</i>, <i>GSK3B</i>, <i>PER3</i>, <i>RORA</i>, and <i>RORB</i> genes may contribute to the risk of MetS independently as well as through gene-gene and gene-environment interactions.</p></div