Albert C. Yang

Risk of Parkinson disease after depression A nationwide population-based study

Objective: To evaluate the risk of Parkinson disease (PD) among patients with depression by using the Taiwan National Health Insurance Research Database (NHIRD). Methods: We conducted a retrospective study of a matched cohort of 23,180 participants (4,634 patients with depression and 18,544 control patients) who were selected from the NHIRD. Patients were observed for a maximum of 10 years to determine the rates of new-onset PD, and Cox regression was used to identify the predictors of PD. We also examined the risk of PD after excluding patients who were diagnosed with PD within 2 or 5 years after their depression diagnosis. A logistic regression model was used to identify risk factors associated with PD onset in patients with depression. Results: During the 10-year follow-up period, 66 patients with depression (1.42%) and 97 control patients (0.52%) were diagnosed with PD. After adjusting for age and sex, patients with depression were 3.24 times more likely to develop PD (95% confidence interval 2.36–4.44, p < 0.001) compared with the control patients. After excluding patients who were diagnosed with PD within 2 or 5 years after their depression diagnosis, patients with depression had a higher hazard ratio for developing PD than the control patients. The odds ratios for age (1.09) and difficult-to-treat depression (2.18) showed that each is an independent risk factor for PD in patients with depression. Conclusion: The likelihood of developing PD is greater among patients with depression than patients without depression. Depression may be an independent risk factor for PD.

Complexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: a multiscale entropy analysis

The nonlinear properties of spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals remain unexplored. We test the hypothesis that complexity of BOLD activity is reduced with aging and is correlated with cognitive performance in the elderly. A total of 99 normal older and 56 younger male subjects were included. Cognitive function was assessed using Cognitive Abilities Screening Instrument and Wechsler Digit Span Task. We employed a complexity measure, multiscale entropy (MSE) analysis, and investigated appropriate parameters for MSE calculation from relatively short BOLD signals. We then compared the complexity of BOLD signals between the younger and older groups, and examined the correlation between cognitive test scores and complexity of BOLD signals in various brain regions. Compared with the younger group, older subjects had the most significant reductions in MSE of BOLD signals in posterior cingulate gyrus and hippocampal cortex. For older subjects, MSE of BOLD signals from default mode network areas, including hippocampal cortex, cingulate cortex, superior and middle frontal gyrus, and middle temporal gyrus, were found to be positively correlated with major cognitive functions, such as attention, orientation, short-term memory, mental manipulation, and language. MSE from subcortical regions, such as amygdala and putamen, were found to be positively correlated with abstract thinking and list-generating fluency, respectively. Our findings confirmed the hypothesis that complexity of BOLD activity was correlated with aging and cognitive performance based on MSE analysis, and may provide insights on how dynamics of spontaneous brain activity relates to aging and cognitive function in specific brain regions.

Multimodal pressure-flow method to assess dynamics of cerebral autoregulation in stroke and hypertension

BackgroundThis study evaluated the effects of stroke on regulation of cerebral blood flow in response to fluctuations in systemic blood pressure (BP). The autoregulatory dynamics are difficult to assess because of the nonstationarity and nonlinearity of the component signals.MethodsWe studied 15 normotensive, 20 hypertensive and 15 minor stroke subjects (48.0 ± 1.3 years). BP and blood flow velocities (BFV) from middle cerebral arteries (MCA) were measured during the Valsalva maneuver (VM) using transcranial Doppler ultrasound.ResultsA new technique, multimodal pressure-flow analysis (MMPF), was implemented to analyze these short, nonstationary signals. MMPF analysis decomposes complex BP and BFV signals into multiple empirical modes, representing their instantaneous frequency-amplitude modulation. The empirical mode corresponding to the VM BP profile was used to construct the continuous phase diagram and to identify the minimum and maximum values from the residual BP (BPR) and BFV (BFVR) signals. The BP-BFV phase shift was calculated as the difference between the phase corresponding to the BPR and BFVR minimum (maximum) values. BP-BFV phase shifts were significantly different between groups. In the normotensive group, the BFVR minimum and maximum preceded the BPR minimum and maximum, respectively, leading to large positive values of BP-BFV shifts.ConclusionIn the stroke and hypertensive groups, the resulting BP-BFV phase shift was significantly smaller compared to the normotensive group. A standard autoregulation index did not differentiate the groups. The MMPF method enables evaluation of autoregulatory dynamics based on instantaneous BP-BFV phase analysis. Regulation of BP-BFV dynamics is altered with hypertension and after stroke, rendering blood flow dependent on blood pressure.

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 humans 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, p<0.001; Australia: r = −0.656, p<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; p<0.001; southern hemisphere: r = 0.871; p<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.

Reduced physiologic complexity is associated with poor sleep in patients with major depression and primary insomnia

BACKGROUND Depression is known to be associated with altered cardiovascular variability and increased cardiovascular comorbidity, yet it is unknown whether altered cardiac autonomic function in depression is associated with insomnia, a common symptom comorbid with depression. This study aimed to investigate the long-term diurnal profile of autonomic function as measured by heart rate variability (HRV) in both major depression and primary insomnia patients. METHOD A total of 52 non-medicated patients with major depression, 47 non-medicated patients with primary insomnia, and 88 matched controls without insomnia were recruited. Each subject was assessed by means of sleep and mood questionnaires and underwent twenty-four-hour ambulatory electrocardiogram monitoring. Standard HRV analysis and a well-validated complexity measure, multiscale entropy, were applied to comprehensively assess the diurnal profiles of autonomic function and physiologic complexity in our study sample. RESULTS Compared with the controls, the patients with major depression and those with primary insomnia exhibited significant reductions in parasympathetic-related HRV indices, and this association was mainly driven by the presence of poor sleep. Both groups of patients also exhibited significant reductions in physiologic complexity during the sleep period as compared with the healthy controls. Alterations in HRV indices were correlated with perceived sleep questionnaire scores but not with depression scales. CONCLUSIONS Our findings suggest a pivotal role of sleep disturbance in regulating cardiovascular variability in major depression and primary insomnia patients. These findings could highlight the importance of treating insomnia as an independent disease rather than a symptom.

Sleep Apnea and the Risk of Dementia: A Population-Based 5-Year Follow-Up Study in Taiwan

Background Sleep apnea (SA) has been associated with cognitive impairment. However, no data regarding the risk of dementia in patients with SA has been reported in the general population. This retrospective matched-control cohort study was designed to estimate and compare the risk of dementia in SA and non-SA patients among persons aged 40 and above over a 5-year period follow-up. Methods We conducted a nationwide 5-year population-based study using data retrieved from the Longitudinal Health Insurance Database 2005 (LHID2005) in Taiwan. The study cohort comprised 1414 patients with SA aged 40 years who had at least 1 inpatient service claim or 1 ambulatory care claim. The comparison cohort comprised 7070 randomly selected patients who were matched with the study group according to sex, age, and index year. We performed Cox proportional-hazards regressions to compute the 5-year dementia-free survival rates after adjusting for potentially confounding factors. Results The SA patients in this study had a 1.70-times greater risk of developing dementia within 5 years of diagnosis compared to non-SA age- and sex-matched patients, after adjusting for other risk factors (95% confidence interval (CI) = 1.26-2.31; P < .01). For the gender-dependent effect, only females with SA were more likely to develop dementia (adjust HR: 2.38, 95% CI =1.51–3.74; P < .001). For the age-dependent effect of different genders, males with SA aged 50-59 years had a 6.08 times greater risk for developing dementia (95% CI = 1.96-18.90), and females with SA aged ≥ 70 years had a 3.20 times greater risk of developing dementia (95% CI =1.71–6.00). For the time-dependent effect, dementia may be most likely to occur in the first 2.5 years of follow-up (adjusted HR:2.04, 95% CI =1.35-3.07). Conclusions SA may be a gender-dependent, age-dependent, and time-dependent risk factor for dementia.

Decomposing the association of completed suicide with air pollution, weather, and unemployment data at different time scales

BACKGROUND Research has implicated environmental risk factors, such as meteorological variables, in suicide. However, studies have not investigated air pollution, known to induce acute medical conditions and increase mortality, in suicide. This study comprehensively assesses the temporal relationship between suicide and air pollution, weather, and unemployment variables in Taipei City from January 1 1991 to December 31 2008. METHODS This research used the empirical mode decomposition (EMD) method to de-trend the suicide data into a set of intrinsic oscillations, called intrinsic mode functions (IMFs). Multiple linear regression analysis with forward stepwise method was used to identify significant predictors of suicide from a pool of air pollution, weather, and unemployment data, and to quantify the temporal association between decomposed suicide IMFs with these predictors at different time scales. RESULTS Findings of this study predicted a classic seasonal pattern of increased suicide occurring in early summer by increased air particulates and decreased barometric pressure, in which the latter was in accordance with increased temperature during the corresponding time. Gaseous air pollutants, such as sulfur dioxide and ozone, were found to increase the risk of suicide at longer time scales. Decreased sunshine duration and sunspot activity predicted the increased suicide. After controlling for the unemployment factor, environmental risks predicted 33.7% of variance in the suicide data. CONCLUSIONS Using EMD analysis, this study found time-scale dependent associations between suicide and air pollution, weather and unemployment data. Contributing environmental risks may vary in different geographic regions and in different populations.

Is mental illness complex? From behavior to brain

A defining but elusive feature of the human brain is its astonishing complexity. This complexity arises from the interaction of numerous neuronal circuits that operate over a wide range of temporal and spatial scales, enabling the brain to adapt to the constantly changing environment and to perform various amazing mental functions. In mentally ill patients, such adaptability is often impaired, leading to either ordered or random patterns of behavior. Quantification and classification of these abnormal human behaviors exhibited during mental illness is one of the major challenges of contemporary psychiatric medicine. In the past few decades, attempts have been made to apply concepts adopted from complexity science to better understand complex human behavior. Although considerable effort has been devoted to studying the abnormal dynamic processes involved in mental illness, unfortunately, the primary features of complexity science are typically presented in a form suitable for mathematicians, physicists, and engineers; thus, they are difficult for practicing psychiatrists or neuroscientists to comprehend. Therefore, this paper introduces recent applications of methods derived from complexity science for examining mental illness. We propose that mental illness is loss of brain complexity and the complexity of mental illness can be studied under a general framework by quantifying the order and randomness of dynamic macroscopic human behavior and microscopic neuronal activity. Additionally, substantial effort is required to identify the link between macroscopic behaviors and microscopic changes in the neuronal dynamics within the brain.

Information categorization approach to literary authorship disputes

Scientific analysis of the linguistic styles of different authors has generated considerable interest. We present a generic approach to measuring the similarity of two symbolic sequences that requires minimal background knowledge about a given human language. Our analysis is based on word rank order–frequency statistics and phylogenetic tree construction. We demonstrate the applicability of this method to historic authorship questions related to the classic Chinese novel “The Dream of the Red Chamber,” to the plays of William Shakespeare, and to the Federalist papers. This method may also provide a simple approach to other large databases based on their information content.

Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer's disease.

This study assessed the utility of multiscale entropy (MSE), a complexity analysis of biological signals, to identify changes in dynamics of surface electroencephalogram (EEG) in patients with Alzheimers disease (AD) that was correlated to cognitive and behavioral dysfunction. A total of 108 AD patients were recruited and their digital EEG recordings were analyzed using MSE methods. We investigate the appropriate parameters and time scale factors for MSE calculation from EEG signals. We then assessed the within-subject consistency of MSE measures in different EEG epochs and correlations of MSE measures to cognitive and neuropsychiatric symptoms of AD patients. Increased severity of AD was associated with decreased MSE complexity as measured by short-time scales, and with increased MSE complexity as measured by long-time scales. MSE complexity in EEGs of the temporal and occipitoparietal electrodes correlated significantly with cognitive function. MSE complexity of EEGs in various brain areas was also correlated to subdomains of neuropsychiatric symptoms. MSE analysis revealed abnormal EEG complexity across short- and long-time scales that were correlated to cognitive and neuropsychiatric assessments. The MSE-based EEG complexity analysis may provide a simple and cost-effective method to quantify the severity of cognitive and neuropsychiatric symptoms in AD patients.