ABSTRACT
BACKGROUND: Therapy processes are complex dynamical systems where several variables are constantly interacting with each other. In general, the underlying mechanisms are difficult to assess. Our approach is to identify the dependency structure of relevant variables within the therapy process using interaction graphs. These are instruments for multivariate time series which are based on the analysis of partial spectral coherences. We used interaction graphs in order to investigate the therapy process of a multimodal therapy concept for fibromyalgia patients. Our main hypothesis was that self-efficacy plays a central role in the therapy process. METHODS: Patients kept an electronic diary for 13 weeks. Pain intensity, depression, sleep quality, anxiety and self-efficacy were assessed via visual analogue scales. The resulting multivariate time series were aggregated over individuals, and partial spectral coherences between each pair of the variables were calculated. From the partial coherences, interaction graphs were plotted. RESULTS: Within the resulting graphical model, self-efficacy was strongly related to pain intensity, depression and sleep quality. All other relations were substantially weaker. There was no direct relationship between pain intensity and sleep quality. CONCLUSIONS: The relations between two variables within the therapy process are mainly induced by self-efficacy. Interaction graphs can be used to pool time series data of several patients and thus to assess the common underlying dependency structure of a group of patients. The graphical representation is easily comprehensible and allows to distinguish between direct and indirect relationships.
Subject(s)
Data Collection/methods , Data Interpretation, Statistical , Fibromyalgia/psychology , Fibromyalgia/therapy , Self Efficacy , Adult , Affect , Combined Modality Therapy , Depression , Female , Humans , Middle Aged , Pain , Pain Measurement , Psychotherapy , Sensitivity and Specificity , Sleep Wake Disorders , Time Factors , Treatment OutcomeABSTRACT
A method for the identification of direct synaptic connections in a larger neural net is presented. It is based on a conditional correlation graph for multivariate point processes. The connections are identified via the partial spectral coherence of two neurons, given all others. It is shown how these coherences can be calculated by inversion of the spectral density matrix. In simulations with GENESIS, we discuss the relevance of the method for identifying different neural ensembles including an excitatory feedback loop and networks with lateral inhibitions.
