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1.
Psychiatry Res ; 291: 113257, 2020 Jun 27.
Artigo em Inglês | MEDLINE | ID: mdl-32619826

RESUMO

Research suggests that the aging relates to variability of resting-state fMRI (rs-fMRI) signal and the functional connectivity. However, the association between the spatial and temporal activity of resting-state fMRI signal was less documented. We recruited 477 healthy Han Chinese participants, who were separated into young, middle and old groups to investigate the relationship between the variability and global functional connectivity (gFC) in different age ranges using standard deviation (SD) of time series and gFC, respectively. Our analysis revealed the changing patterns during healthy aging: 1) 17 brain regions(Olfactory_L, Orbital_L etc.) were identified to have significant association of age with both SD and gFC respectively by linear regression analysis; 2) Two typical associations could be observed between SD and gFC: positive and negative correlations; 3) The variation ratio of SD to gFC was changing with age at the voxel level by using unsupervised clustering method. It is the first time to combine voxel-wise variability and gFC together for the study of age-related changes with rs-fMRI signal. This study may provide a new clue for understanding the synchronization of human brain based on SD and gFC due to the effect of aging.

2.
Cereb Cortex ; 2020 Jun 23.
Artigo em Inglês | MEDLINE | ID: mdl-32572452

RESUMO

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.

3.
Int J Mol Sci ; 21(6)2020 Mar 22.
Artigo em Inglês | MEDLINE | ID: mdl-32235786

RESUMO

Major depressive disorder (MDD), which is a leading psychiatric illness across the world, severely affects quality of life and causes an increased incidence of suicide. Evidence from animal as well as clinical studies have indicated that increased peripheral or central cytokine interleukin-6 (IL-6) levels play an important role in stress reaction and depressive disorder, especially physical disorders comorbid with depression. Increased release of IL-6 in MDD has been found to be a factor associated with MDD prognosis and therapeutic response, and may affect a wide range of depressive symptomatology. However, study results of the IL6 genetic effects in MDD are controversial. Increased IL-6 activity may cause depression through activation of hypothalamic-pituitary-adrenal axis or influence of the neurotransmitter metabolism. The important role of neuroinflammation in MDD pathogenesis has created a new perspective that the combining of blood IL-6 and other depression-related cytokine levels may help to classify MDD biological subtypes, which may allow physicians to identify the optimal treatment for MDD patients. To modulate the IL-6 activity by IL-6-related agents, current antidepressive agents, herb medication, pre-/probiotics or non-pharmacological interventions may hold great promise for the MDD patients with inflammatory features.

4.
Artigo em Inglês | MEDLINE | ID: mdl-32276887

RESUMO

BACKGROUND: Mental disorders are typically defined as distinct diagnostic entities, but similar patterns of clinical and cognitive impairments are frequently found across diagnostic groups. We investigated whether these transdiagnostic deficits result from common neural substrates across disorders or various illness-specific mechanisms, or a combination of both. METHODS: Functional magnetic resonance imaging data were collected from clinically stable patients with major depressive disorder (n = 53), bipolar disorder (n = 78), or schizophrenia (n = 100) and matched healthy control subjects (n = 109) using a single scanner. Group comparisons were conducted to identify transdiagnostic and illness-specific features, and possible confounding effects of medication were considered. A multivariate approach with cross-validation was used to associate dysconnectivity features with shared cognitive deficits. RESULTS: Transdiagnostic dysconnectivities were identified within somatomotor (Cohen's d = 0.50-0.58) and salience (Cohen's d = 0.52-0.58) networks and between subcortical-limbic (Cohen's d = 0.55-0.69) and subcortical-dorsal attention (Cohen's d = 0.56-0.61) networks. The executive control network was found to be illness-specifically disconnected from the prefrontal-limbic-pallidal circuit in major depressive disorder (Cohen's d = 0.57-0.58), prefronto-striato-parietal circuit in bipolar disorder (Cohen's d = 0.48-0.53), and default mode network in schizophrenia (Cohen's d = 0.47-0.56). Working memory deficits were associated with a linear combination of 11 transdiagnostic and 5 illness-specific dysconnectivities (r = .322, p= 9.7 × 10-4, n = 340). The associations of the identified dysconnectivities with medication dosage were nonsignificant. CONCLUSIONS: Disconnectivity in the somatomotor network was a common transdiagnostic profile, while there were illness-specific patterns in different parts of the prefrontal cortex for different disorders. These findings suggest that prominent psychiatric disorders share common impairments, possibly linked to perception and motor output, as well as unique dysconnectivity profiles that hypothetically mediate the more distinctive features of the disorder-specific psychopathology.

5.
Hum Mol Genet ; 29(7): 1218-1228, 2020 May 08.
Artigo em Inglês | MEDLINE | ID: mdl-32160288

RESUMO

Previous studies have shown that men and women have different genetic architectures across many traits. However, except waist-to-hip ratio (WHR) and waist circumference (WC), it remains unknown whether the genetic effects of a certain trait are weaker or stronger on men/women. With ~18 000 Taiwan Biobank subjects, we comprehensively investigate sexual heterogeneity in autosomal genetic effects, for traits regarding cardiovascular health, diabetes, kidney, liver, anthropometric profiles, blood, etc. 'Gene-by-sex interactions' (G $\times$ S) were detected in 18 out of 26 traits, each with an interaction P-value (${{P}}_{{INT}}$) less than $0.05/104={0.00048}$, where 104 is the number of tests conducted in this study. The most significant evidence of G $\times$ S was found in WHR (${{P}}_{{INT}}$ = 3.2 $\times{{10}}^{-{55}}$) and WC (${{P}}_{{INT}}$ = 2.3$\times{{10}}^{-{41}}$). As a novel G$\times$S investigation for other traits, we here find that the autosomal genetic effects are weaker on women than on men, for low-density lipoprotein cholesterol (LDL-C), uric acid (UA) and diabetes-related traits such as fasting glucose and glycated hemoglobin. For LDL-C and UA, the evidence of G$\times$S is especially notable in subjects aged less than 50 years, where estrogen can play a role in attenuating the autosomal genetic effects of these two traits. Men and women have systematically distinct environmental contexts caused by hormonal milieu and their specific society roles, which may trigger diverse gene expressions despite the same DNA materials. As many environmental exposures are difficult to collect and quantify, sex can serve as a good surrogate for these factors.

6.
Genes Brain Behav ; 19(5): e12639, 2020 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31925923

RESUMO

Schizophrenia and substance involvement frequently co-occur in individuals, and a bidirectional relationship between the two has been proposed; shared underlying genetic factors could be an alternative explanation. This study investigated the genetic overlap between schizophrenia and substance involvement, including tobacco, alcohol and betel nut use. The study subjects were recruited from the Taiwan Biobank, and genome-wide genotyping data was available for 18 327 participants without schizophrenia. We calculated the Psychiatric Genomics Consortium-derived polygenic risk score (PRS) for schizophrenia in each participant. The significance of the schizophrenia PRS associated with substance involvement was evaluated using a regression model with adjustments for gender, age and population stratification components. The modified effect of gender or birth decade was also explored. The schizophrenia PRS was positively associated with lifetime tobacco smoking in women (OR in per SD increase in PRS = 1.12 with 95% CI 1.04-1.20, P = .002), but not in men (OR = 0.99 with 95% CI 0.95-1.04, P = .74), and the gender-PRS interaction reached significance (P = .006). The OR between PRS and lifetime tobacco smoking increased with the birth decade (P of birth decade-PRS interaction = .0002). In women, OR increased from 0.97 (P = .85) for subjects with a birth decade before 1950 to 1.21 (P = .04) for subjects with a birth decade after 1980; in men, the corresponding OR increased from 0.88 (P = .04) to 1.13 (P = .11). There was no association between schizophrenia PRS and alcohol/betel nut use phenotypes. This study provides evidence for the genetic overlap between schizophrenia and tobacco use in women, and this overlap was stronger in the younger population.

7.
Schizophr Res ; 215: 181-189, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31706787

RESUMO

The neurobiological heterogeneity of schizophrenia is widely accepted, but it is unclear how mechanistic differences converge to produce the observed phenotype. Establishing a pathophysiological model that accounts for both neurobiological heterogeneity and phenotypic similarity is essential to inform stratified treatment approaches. In this cross-sectional diffusion tensor imaging study, we recruited 77 healthy controls, and 70 patients with DSM-IV diagnosis of schizophrenia. We first confirmed the heterogeneity in structural connectivity by showing a reduced between-individual similarity of the structural connectivity in patients compared to healthy controls. Second, at a system level, we found the diversity of the topographic distribution of the strength of structural connectivity was significantly reduced in patients (P = 7.21 × 10-7, T142 = 5.19 [95% CI: 3.37-7.52], Cohen's d = 0.91), and this affected 65 of the 90 brain regions examined (False Discovery Rate <5%). Third, when topographic diversity was used as a discriminant feature to train a model for classifying patients from controls, it significantly improved the accuracy on an independent sample (T99 = 5.54; P < 0.001). These findings suggest a highly individualized pattern of structural dysconnectivity underlies the heterogeneity of schizophrenia, but these disruptions likely converge on an emergent common pathway to generate the clinical phenotype of the disorder.

8.
Schizophr Bull ; 46(2): 422-431, 2020 02 26.
Artigo em Inglês | MEDLINE | ID: mdl-31206161

RESUMO

Evidence from electrophysiological, functional, and structural research suggests that abnormal brain connectivity plays an important role in the pathophysiology of schizophrenia. However, most previous studies have focused on single modalities only, each of which is associated with its own limitations. Multimodal combinations can more effectively utilize various information, but previous multimodal research mostly focuses on extracting local features, rather than carrying out research based on network perspective. This study included 135 patients with schizophrenia and 148 sex- and age-matched healthy controls. Functional magnetic resonance imaging, diffusion tensor imaging, and structural magnetic resonance imaging data were used to construct the functional, anatomical, and morphological networks of each participant, respectively. These networks were used in combination with machine learning to identify more consistent biomarkers of brain connectivity and explore the relationships between different modalities. We found that although each modality had divergent connectivity biomarkers, the convergent pattern was that all were mostly located within the basal ganglia-thalamus-cortex circuit. Furthermore, using the biomarkers of these 3 modalities as a feature yielded the highest classification accuracy (91.75%, relative to a single modality), suggesting that the combination of multiple modalities could be effectively utilized to obtain complementary information regarding different mode networks; furthermore, this information could help distinguish patients. These findings provide direct evidence for the disconnection hypothesis of schizophrenia, suggesting that abnormalities in the basal ganglia-thalamus-cortex circuit can be used as a biomarker of schizophrenia.

9.
Cereb Cortex ; 30(3): 1213-1233, 2020 Mar 14.
Artigo em Inglês | MEDLINE | ID: mdl-31381086

RESUMO

To go beyond the disconnectivity hypothesis of schizophrenia, directed (effective) connectivity was measured between 94 brain regions, to provide evidence on the source of the changes in schizophrenia and a mechanistic model. Effective connectivity (EC) was measured in 180 participants with schizophrenia and 208 controls. For the significantly different effective connectivities in schizophrenia, on average the forward (stronger) effective connectivities were smaller, whereas the backward connectivities tended to be larger. Further, higher EC in schizophrenia was found from the precuneus and posterior cingulate cortex (PCC) to areas such as the parahippocampal, hippocampal, temporal, fusiform, and occipital cortices. These are backward effective connectivities and were positively correlated with the positive symptoms of schizophrenia. Lower effective connectivities were found from temporal and other regions and were negatively correlated with the symptoms, especially the negative and general symptoms. Further, a signal variance parameter was increased for areas that included the parahippocampal gyrus and hippocampus, consistent with the hypothesis that hippocampal overactivity is involved in schizophrenia. This investigation goes beyond the disconnectivity hypothesis by drawing attention to differences in schizophrenia between backprojections and forward connections, with the backward connections from the precuneus and PCC implicated in memory stronger in schizophrenia.

10.
J Clin Monit Comput ; 2019 Dec 23.
Artigo em Inglês | MEDLINE | ID: mdl-31872311

RESUMO

Poor sleep quality is associated with autonomic dysfunctions and altered pain perception and tolerance. To investigate whether autonomic dysregulations related to insomnia would still exist under general anesthesia, we adopt heart rate variability (HRV) analysis to evaluate ANS activity and surgical pleth index (SPI) to compare nociceptive/anti-nociceptive balance. We enrolled 61 adult females scheduled for gynecological surgeries under general anesthesia. All the subjects were ASA Class I to III without using medicines affecting HRV. We used the Insomnia Severity Index to evaluate sleep qualities. ECG data were recorded and signals which denote four different surgical stages were extracted (baseline, incision, mid-surgery, and end of surgery). We analyzed the HRV changes across the whole surgical period and differences among good and poor sleepers. We also compared the SPI differences among groups. For baseline HRV analysis, we found significant differences in the RMSSD (p = 0.043), pNN50 (p = 0.029), VLF power (p = 0.035), LF power (p = 0.004), and HF power (p = 0.037) between the good and poor sleeper groups. However, all intergroup differences disappeared after anesthesia induction. Temporal HRV changes significantly among different perioperative stages (RMSSD, p < 0.001; pNN50, p = 0.004; LF, p < 0.001; and HF, p < 0.001). Patients with different sleep qualities did not exhibit different SPI levels in all four periods. Poor sleepers exhibited attenuated parasympathetic activities at the baseline but no differences after the induction. Nociceptive/anti-nociceptive balance seems not be altered by poor sleep condition under general anesthesia.

11.
Front Neurol ; 10: 1065, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31649612

RESUMO

Evidence suggests that the neuro-inflammation mechanisms associated with interleukin-12 (IL-12) may be linked to Alzheimer's diseases and cognitive aging. In this study, we speculate that single nucleotide polymorphisms (SNPs) in IL-12-associated genes, such as IL12A, IL12B, IL12RB1, and IL12RB2 genes, could be associated with cognitive aging individually and/or via complicated interactions in the elder Taiwanese population. There were totally 3,730 Taiwanese individuals with age ≥60 years from the Taiwan Biobank. Mini-Mental State Examination (MMSE) was analyzed for all participants. We employed MMSE scores to assess cognitive functions. Our analysis revealed that the IL12A gene (including rs116910715, rs78902931, and rs78569420), the IL12B gene (including rs730691), and the IL12RB2 gene (including rs3790558, rs4655538, rs75699623, and rs1874396) were associated with cognitive aging. Among these SNPs, the association with the IL12RB2 rs3790558 SNP remained significant after performing Bonferroni correction (P = 6.87 × 10-4). Additionally, we found that interactions between the IL12A and IL12RB2 genes influenced cognitive aging (P = 0.022). Finally, we pinpointed the effects of interactions between IL12A, IL12B, and IL12RB2 with physical activity (P < 0.001, = 0.002, and < 0.001, respectively). Our study suggests that the IL-12-associated genes may contribute to susceptibility to cognitive aging independently as well as through gene-gene and gene-physical activity interactions.

12.
PLoS Genet ; 15(8): e1008277, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31369549

RESUMO

Obesity is a worldwide health problem that is closely linked to many metabolic disorders. Regular physical exercise has been found to attenuate the genetic predisposition to obesity. However, it remains unknown what kinds of exercise can modify the genetic risk of obesity. This study included 18,424 unrelated Han Chinese adults aged 30-70 years who participated in the Taiwan Biobank (TWB). A total of 5 obesity measures were investigated here, including body mass index (BMI), body fat percentage (BFP), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR). Because there have been no large genome-wide association studies on obesity for Han Chinese, we used the TWB internal weights to construct genetic risk scores (GRSs) for each obesity measure, and then test the significance of GRS-by-exercise interactions. The significance level throughout this work was set at 0.05/550 = 9.1x10-5 because a total of 550 tests were performed. Performing regular exercise was found to attenuate the genetic effects on 4 obesity measures, including BMI, BFP, WC, and HC. Among the 18 kinds of self-reported regular exercise, 6 mitigated the genetic effects on at least one obesity measure. Regular jogging blunted the genetic effects on BMI, BFP, and HC. Mountain climbing, walking, exercise walking, international standard dancing, and a longer practice of yoga also attenuated the genetic effects on BMI. Exercises such as cycling, stretching exercise, swimming, dance dance revolution, and qigong were not found to modify the genetic effects on any obesity measure. Across all 5 obesity measures, regular jogging consistently presented the most significant interactions with GRSs. Our findings show that the genetic effects on obesity measures can be decreased to various extents by performing different kinds of exercise. The benefits of regular physical exercise are more impactful in subjects who are more predisposed to obesity.


Assuntos
Exercício Físico , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Obesidade/prevenção & controle , Adulto , Bancos de Espécimes Biológicos/estatística & dados numéricos , Índice de Massa Corporal , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Obesidade/diagnóstico , Obesidade/genética , Polimorfismo de Nucleotídeo Único , Fatores de Risco , Autorrelato/estatística & dados numéricos , Taiwan , Circunferência da Cintura/genética , Relação Cintura-Quadril
13.
Front Psychiatry ; 10: 481, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31354544

RESUMO

The chronic autoimmune disease myasthenia gravis (MG) is characterized by fluctuating muscle weakness, which can lead to a large amount of stress in the patient. The current investigation plans to assess the risk of depressive disorders in MG patients. A retrospective cohort study of patients ageing 20 years and older and also newly diagnosed with MG between January 1, 2000, and December 31, 2008, was conducted from the National Health Insurance Research Database (NHIRD) in Taiwan. Observations of all 349 MG patients and 1,396 control individuals were made until a diagnosis of a depressive disorder by a psychiatrist, until death, or until December 31, 2013. A range of comorbidities were found, such as coronary artery disease, hypertension, diabetes mellitus, and dyslipidemia, with cerebrovascular disease being reported more frequently in MG patients in comparison with control subjects. After adjustment of patients' sex, age, urbanization, comorbidities, and monthly income, results indicated that MG individuals are 1.94 times more at risk (95% confidence interval [CI], 1.15-3.27, P = 0.014) of developing depressive disorders than are controls. This showed an increased risk in the development of depressive disorders in people with MG. Thus, depressive symptoms in MG patients should be regularly assessed.

14.
DNA Repair (Amst) ; 78: 1-6, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-30928815

RESUMO

Evidence indicates that the age-related neuropathological mechanisms associated with DNA repair genes may contribute to cognitive aging and Alzheimer's disease. In this study, we hypothesize that single nucleotide polymorphisms (SNPs) within 155 DNA repair genes may be linked to cognitive aging independently and/or through complex interactions in an older Taiwanese population. A total of 3,730 Taiwanese subjects aged over 60 years from the Taiwan Biobank were analyzed. Mini-Mental State Examination (MMSE) was administered to all subjects, and MMSE scores were used to measure cognitive functions. Our data showed that out of 1,652 SNPs, the rs1776181 (P = 1.47 × 10-5), rs1776177 (P = 8.42 × 10-7), rs1635510 (P = 7.97 × 10-6), and rs2526698 (P = 7.06 × 10-6) SNPs in the EXO1 gene were associated with cognitive aging. The association with these SNP remained significant after performing Bonferroni correction. Additionally, we found that interactions between the EXO1 and RAD51C genes influenced cognitive aging (P = 0.002). Finally, we pinpointed the influence of interactions between EXO1 and physical activity (P < 0.001) as well as between DCLRE1C and physical activity (P < 0.001). Our study indicated that DNA repair genes may contribute to susceptibility in cognitive aging independently as well as through gene-gene and gene-physical interactions.


Assuntos
Envelhecimento Cognitivo , Enzimas Reparadoras do DNA/genética , Reparo do DNA/genética , Exodesoxirribonucleases/genética , Polimorfismo de Nucleotídeo Único , Idoso , Feminino , Genótipo , Humanos , Masculino , Pessoa de Meia-Idade , Taiwan
15.
CNS Spectr ; 24(4): 453-459, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-30604659

RESUMO

BACKGROUND: Research suggests an association between metabolic disorders, such as type 2 diabetes mellitus (T2DM), and schizophrenia. However, the risk of metabolic disorders in the unaffected siblings of patients with schizophrenia remains unclear. METHODS: Using the Taiwan National Health Insurance Research Database, 3135 unaffected siblings of schizophrenia probands and 12,540 age-/sex-matched control subjects were included and followed up to the end of 2011. Individuals who developed metabolic disorders during the follow-up period were identified. RESULTS: The unaffected siblings of schizophrenia probands had a higher prevalence of T2DM (3.4% vs. 2.6%, p = 0.010) than the controls. Logistic regression analyses with the adjustment of demographic data revealed that the unaffected siblings of patients with schizophrenia were more likely to develop T2DM (odds ratio [OR]: 1.39, 95% confidence interval [CI]: 1.10-1.75) later in life compared with the control group. Moreover, only female siblings of schizophrenia probands had an increased risk of hypertension (OR: 1.47, 95% CI: 1.07-2.01) during the follow-up compared with the controls.DiscussionThe unaffected siblings, especially sisters, of schizophrenia probands had a higher prevalence of T2DM and hypertension compared with the controls. Our study revealed a familial link between schizophrenia and T2DM in a large sample. Additional studies are required to investigate the shared pathophysiology of schizophrenia and T2DM.

16.
Neuroimage ; 188: 628-641, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30576851

RESUMO

We describe an approach to multivariate analysis, termed structured kernel principal component regression (sKPCR), to identify associations in voxel-level connectomes using resting-state functional magnetic resonance imaging (rsfMRI) data. This powerful and computationally efficient multivariate method can identify voxel-phenotype associations based on the whole-brain connectivity pattern of voxels, and it can detect linear and non-linear signals in both volume-based and surface-based rsfMRI data. For each voxel, sKPCR first extracts low-dimensional signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations by an adaptive regression model. The method's power is derived from appropriately modelling the spatial structure of the data when performing dimension reduction, and then adaptively choosing an optimal dimension for association testing using the adaptive regression strategy. Simulations based on real connectome data have shown that sKPCR can accurately control the false-positive rate and that it is more powerful than many state-of-the-art approaches, such as the connectivity-wise generalized linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). Moreover, since sKPCR can reduce the computational cost of non-parametric permutation tests, its computation speed is much faster. To demonstrate the utility of sKPCR for real data analysis, we have also compared sKPCR with the above methods based on the identification of voxel-wise differences between schizophrenic patients and healthy controls in four independent rsfMRI datasets. The results showed that sKPCR had better between-sites reproducibility and a larger proportion of overlap with existing schizophrenia meta-analysis findings. Code for our approach can be downloaded from https://github.com/weikanggong/sKPCR.


Assuntos
Encéfalo/diagnóstico por imagem , Conectoma/métodos , Imagem por Ressonância Magnética/métodos , Modelos Estatísticos , Esquizofrenia/diagnóstico por imagem , Adulto , Encéfalo/fisiologia , Humanos , Pessoa de Meia-Idade , Análise Multivariada , Análise de Componente Principal , Esquizofrenia/fisiopatologia , Adulto Jovem
17.
Front Neurosci ; 12: 758, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30425614

RESUMO

Background: EEG mu-desynchronization is an index of motor resonance (MR) and is used to study social interaction deficiencies, but finding differences in mu-desynchronization does not reveal how nonlinear brain dynamics are affected during MR. The current study explores how nonlinear brain dynamics change during MR. We hypothesized that the complexity of the mu frequency band (8-13 Hz) changes during MR, and that this change would be frequency specific. Additionally, we sought to determine whether complexity at baseline and changes in complexity during action observation would predict MR and changes in network dynamics. Methods: EEG was recorded from healthy participants (n = 45) during rest and during an action observation task. Baseline brain activity was measured followed by participants observing videos of hands squeezing stress balls. We used multiscale entropy (MSE) to quantify the complexity of the mu rhythm during MR. We then performed post-hoc graph theory analysis to explore whether nonlinear dynamics during MR affect brain network topology. Results: We found significant mu-desynchronization during the action observation task and that mu entropy was significantly increased during the task compared to rest, while gamma, beta, theta, and delta bands showed decreased entropy. Moreover, resting-state entropy was significantly predictive of the degree of mu desynchronization. We also observed a decrease in the clustering coefficient in the mu band only and a significant decrease in global alpha efficiency during action observation. MSE during action observation was strongly correlated with alpha network efficiency. Conclusions: The current findings suggest that the desynchronization of the mu wave during MR results in a local increase of mu entropy in sensorimotor areas, potentially reflecting a release from alpha inhibition. This release from inhibition may be mediated by the baseline MSE in the mu band. The dynamical complexity and network analysis of EEG may provide a useful addition for future studies of MR by incorporating measures of nonlinearity.

18.
Front Neurosci ; 12: 685, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30337850

RESUMO

Alzheimer's disease (AD) is a progressive brain disorder with gradual memory loss that correlates to cognitive deficits in the elderly population. Recent studies have shown the potentials of machine learning algorithms to identify biomarkers and functional brain activity patterns across various AD stages using electroencephalography (EEG). In this study, we aim to discover the altered spatio-temporal patterns of EEG complexity associated with AD pathology in different severity levels. We employed the multiscale entropy (MSE), a complexity measure of time series signals, as the biomarkers to characterize the nonlinear complexity at multiple temporal scales. Two regularized logistic regression methods were applied to extracted MSE features to capture the topographic pattern of MSEs of AD cohorts compared to healthy baseline. Furthermore, canonical correlation analysis was performed to evaluate the multivariate correlation between EEG complexity and cognitive dysfunction measured by the Neuropsychiatric Inventory scores. 123 participants were recruited and each participant was examined in three sessions (length = 10 seconds) to collect resting-state EEG signals. MSE features were extracted across 20 time scale factors with pre-determined parameters (m = 2, r = 0.15). The results showed that comparing to logistic regression model, the regularized learning methods performed better for discriminating severe AD cohort from normal control, very mild and mild cohorts (test accuracy ~ 80%), as well as for selecting significant biomarkers arcoss the brain regions. It was found that temporal and occipitoparietal brain regions were more discriminative in regard to classifying severe AD cohort vs. normal controls, but more diverse and distributed patterns of EEG complexity in the brain were exhibited across individuals in early stages of AD.

19.
Neuroimage Clin ; 20: 594-602, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30186763

RESUMO

Diffusion tensor imaging and its distinct capability to detect micro-structural changes in vivo allows the exploration of white matter (WM) abnormalities in patients who have been diagnosed with schizophrenia; however, the results regarding the anatomical positions and degree of abnormalities are inconsistent. In order to obtain more robust and stable findings, we conducted a multi-level analysis to investigate WM disruption in a relatively large sample size (142 schizophrenia patients and 163 healthy subjects). Specifically, we evaluated the univariate fractional anisotropy (FA) in voxel level; the bivariate pairwise structural connectivity between regions using deterministic tractography as the network node defined by the Human Brainnetome Atlas; and the multivariate network topological properties, including the network hub, efficiency, small-worldness, and strength. Our data demonstrated callosal and subcortical WM alterations in patients with schizophrenia. These disruptions were evident in both voxel and connectivity levels and further supported by associations between FA values and illness duration. Based on the findings regarding topological properties, the structural network showed weaker global integration in patients with schizophrenia than in healthy subjects, while brain network hubs showed decreased functionality. We replicated these findings using an automated anatomical labeling atlas to define the network node. Our study indicates that callosal and subcortical WM disruptions are biomarkers for chronic schizophrenia.


Assuntos
Corpo Caloso/diagnóstico por imagem , Imagem de Tensor de Difusão/métodos , Rede Nervosa/diagnóstico por imagem , Esquizofrenia/diagnóstico por imagem , Substância Branca/diagnóstico por imagem , Adulto , Corpo Caloso/metabolismo , Imagem de Tensor de Difusão/tendências , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fibras Nervosas Mielinizadas/metabolismo , Fibras Nervosas Mielinizadas/patologia , Rede Nervosa/metabolismo , Esquizofrenia/metabolismo , Substância Branca/metabolismo
20.
PLoS One ; 13(8): e0200682, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30114239

RESUMO

INTRODUCTION: Previous studies have suggested that both bipolar disorder and atopy are associated with an increased risk of stroke. However, the role of atopic diseases/diathesis in the risk of stroke among patients with bipolar disorder remains unclear. METHODS: Using Taiwan's National Health Insurance Research Database, we selected 55,593 patients with bipolar disorder between 2002 and 2008, divided them into patients with atopic diseases/diathesis (n = 21,050) and patients without atopic diseases/diathesis (n = 34,543), and observed them until the end of 2011. Patients who experienced a stroke during the follow-up period were identified. RESULTS: Patients with bipolar disorder and atopic diseases/diathesis had an elevated risk of ischemic stroke (hazard ratio [HR]: 1.44, 95% confidence interval [CI]: 1.25-1.59) compared with patients with only bipolar disorder; a dose-dependent relationship was observed between the number of allergic comorbidities and the risk of ischemic stroke (1 atopic disease, HR: 1.30, 95% CI: 1.13-1.49; 2 atopic diseases, HR: 1.59, 95% CI: 1.33-1.91; ≥ 3 atopic diseases, HR: 2.09, 95% CI: 1.50-2.91).The role of atopic diseases in the risk of hemorrhagic stroke among patients with bipolar disorder was nonsignificant (HR: 0.84, 95% CI: 0.64-1.09). CONCLUSIONS: Patients with bipolar disorder and atopic diseases/diathesis are more prone to ischemic stroke later in life than are those without atopic diseases/diathesis. Further study is required to investigate the underlying mechanism linking atopy, bipolar disorder, and stroke.


Assuntos
Transtorno Bipolar/fisiopatologia , Isquemia Encefálica/etiologia , Dermatite Atópica/complicações , Suscetibilidade a Doenças/complicações , Acidente Vascular Cerebral/etiologia , Adulto , Comorbidade , Bases de Dados Factuais , Feminino , Humanos , Estudos Longitudinais , Masculino , Pessoa de Meia-Idade , Fatores de Risco
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