Traditional methods of time series decomposition include cosinor analysis, seasonal decomposition of time series by local regression, or autoregressive algorithms. These methods require either predefined frequency of oscillations or the assumption of stationarity, which are often invalid in epidemiologic time series. EMD is empirical and adaptive and does not require any predetermined assumptions of data. Thus, it is useful in isolating physically meaningful oscillations embedded in complex raw data. For example, EMD has been applied to isolate travelling waves in dengue hemorrhagic fever incidences across Thailand and to evaluate the risk of stroke by identifying oscillations in cerebral blood flow related to cerebral auto-regulations. We also have applied this method to Folic acid delineate the association of suicide with air pollution and meteorological variables. We propose that the analysis and scope presented in this study provides a more generalized method to analyze health-related issues using an Internet search query database. The biological mechanisms underlying the seasonality of Internet search for depression are not understood at present. Several classes of mechanisms have been proposed in studying the neurobiology of depression as it relates to seasonal change. Indolamines, including tryptophan, serotonin and melatonin, have important roles of transducing light signals from the environment into cells and in signaling seasonal changes in humans. Functional imaging studies have found higher serotonin transporter binding during winter,Pantoprazole sodium which may facilitate extracellular serotonin loss and eventually lead to lower mood. Our findings that search interests of depression were higher during colder periods, with respect to corresponding time in northern and southern hemispheres, are consistent with this biological evidence. The interpretations made in this study have limitations. Individual search queries for depression cannot accurately reflect the actual mood state of Internet users. Factors other than seasonal changes, including news events, cultural differences or alcohol consumption, might influence human affect and thus Internet search behaviors. However, consistent with a prior study of detecting influenza epidemics using Internet search data, it is rational to assume that the reason people seek health information about depression on the Internet is because they or people they know may be experiencing mood disturbances. The collective phenomenon of Internet search behavior is unlikely to be consciously manipulated by a single user and can be a meaningful, robust symbol of human behaviors or disease patterns across large populations. In conclusion, our analysis provides novel, Internet-based evidence regarding the epidemiology of seasonal depression. The Internet only began about two decades ago, and public search trend databases have only recently become available; therefore, extensive analysis of Internet search data emerging over a longer time scale in relation to health, social, economic, and environ- mental factors is an important area for future research. The EMD method was developed to de-trend and identify intrinsic oscillations embedded in a complex signal ; this method has been widely applied in multiple disciplines. Unlike Fourier-based time series analysis, EMD holds no a priori assumptions for underlying structures of the time series and is therefore suitable for analyzing time series that consist of multiple periodic components.