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Pattern recognition approaches to the analysis of neuroimaging data have brought new applications such as the classification of patients and healthy controls within reach. In our view, the reliance on expensive neuroimaging techniques which are not well tolerated by many patient groups and the inability of most current biomarker algorithms to accommodate information about prior class frequencies (such as a disorder's prevalence in the general population) are key factors limiting practical application. To overcome both limitations, we propose a probabilistic pattern recognition approach based on cheap and easy-to-use multi-channel near-infrared spectroscopy (fNIRS) measurements. We show the validity of our method by applying it to data from healthy controls (n = 14) enabling differentiation between the conditions of a visual checkerboard task. Second, we show that high-accuracy single subject classification of patients with schizophrenia (n = 40) and healthy controls (n = 40) is possible based on temporal patterns of fNIRS data measured during a working memory task. For classification, we integrate spatial and temporal information at each channel to estimate overall classification accuracy. This yields an overall accuracy of 76% which is comparable to the highest ever achieved in biomarker-based classification of patients with schizophrenia. In summary, the proposed algorithm in combination with fNIRS measurements enables the analysis of sub-second, multivariate temporal patterns of BOLD responses and high-accuracy predictions based on low-cost, easy-to-use fNIRS patterns. In addition, our approach can easily compensate for variable class priors, which is highly advantageous in making predictions in a wide range of clinical neuroimaging applications. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.
Epigenetic signatures such as methylation of the monoamine oxidase A (MAOA) gene have been found to be altered in panic disorder (PD). Hypothesizing temporal plasticity of epigenetic processes as a mechanism of successful fear extinction, the present psychotherapy-epigenetic study for we believe the first time investigated MAOA methylation changes during the course of exposure-based cognitive behavioral therapy (CBT) in PD. MAOA methylation was compared between N=28 female Caucasian PD patients (discovery sample) and N=28 age- and sex-matched healthy controls via direct sequencing of sodium bisulfite-treated DNA extracted from blood cells. MAOA methylation was furthermore analyzed at baseline (T0) and after a 6-week CBT (T1) in the discovery sample parallelized by a waiting time in healthy controls, as well as in an independent sample of female PD patients (N=20). Patients exhibited lower MAOA methylation than healthy controls (P<0.001), and baseline PD severity correlated negatively with MAOA methylation (P=0.01). In the discovery sample, MAOA methylation increased up to the level of healthy controls along with CBT response (number of panic attacks; T0–T1: +3.37±2.17%), while non-responders further decreased in methylation (−2.00±1.28%; P=0.001). In the replication sample, increases in MAOA methylation correlated with agoraphobic symptom reduction after CBT (P=0.02–0.03). The present results support previous evidence for MAOA hypomethylation as a PD risk marker and suggest reversibility of MAOA hypomethylation as a potential epigenetic correlate of response to CBT. The emerging notion of epigenetic signatures as a mechanism of action of psychotherapeutic interventions may promote epigenetic patterns as biomarkers of lasting extinction effects.