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Current theories of schizophrenia suggest that the pathophysiology of the disorder may be the result of a deficit in the coordination of neural activity within and between areas of the brain, which may lead to impairments in basic cognitive functions such as contextual disambiguation and dynamic grouping (Phillips and Silverstein, 2003). This notion has been supported by recent studies showing that patients with schizophrenia are characterized by reduced synchronous, oscillatory activity in the gamma-frequency band during sensory processing (Spencer et al. 2003, Green et al. 2003, Wynn et al. 2005). However, it is currently unclear to what extent high-frequency gamma-band oscillations (> 60 Hz) contribute to impaired neural synchronization as research has so far focussed on gamma-band oscillations between 30 and 60 Hz. In addition, it is not known whether deficits in high-frequency oscillations are already present at the onset of the disorder and to what extent reductions may be related to the confounding influence of antipsychotic medication. Finally, the neural generators underlying impairments in synchronous oscillatory activity in schizophrenia have not been investigated yet. To address these questions, we recorded MEG activity during a visual closure task (Mooney faces task) in medicated chronic schizophrenia patients, drug-naive first-episode schizophrenia patients and healthy controls. MEG data were analysed for spectral power between 25 and 150 Hz, and beamforming techniques were used to localize the sources of oscillatory gamma-band activity. In healthy controls, we observed that the processing of Mooney faces was associated with sustained high-frequency gamma-band activity (> 60 Hz). A time-resolved analysis of the neural generators underlying perceptual closure revealed a network of distributed sources in occipito-temporal, parietal and frontal regions, which were differentially activated during specific time intervals. In chronic schizophrenia patients, we found a pronounced reduction of high-frequency gamma-band oscillatory activity that was accompanied by an impairment in perceptual organization and involved reduced source power in various brain regions associated with perceptual closure. First-episode patients were also characterized by a deficit in high-frequency gamma-band activity and reductions of source power in multiple areas; these impairments, however, were less pronounced than in chronic patients. Regarding behavioral performance, first-episode patients were not impaired in their ability to detect Mooney faces, but exhibited a loss in specificity of face detection. In conclusion, our results suggest that schizophrenia is associated with a widespread reduction in high-frequency oscillations that indicate local network abnormalities. These dysfunctions are independent of medication status and already present at illness onset, suggesting a possible progressive deficit during the course of the disorder.
Even though extensively investigated, the nature of working memory (WM) deficits in patients with schizophrenia (PSZ) is not yet fully understood. In particular, the contribution of different WM sub-processes to the severe WM deficit observed in PSZ is a matter of debate. So far, most research has focused on impaired WM maintenance. By analyzing different types of errors in a spatial delayed response task (DRT), we have recently demonstrated that incorrect yet confident responses (which we labeled as false memory errors) rather than incorrect/not-confident responses reflect failures of WM encoding, which was also impaired in PSZ. In the present study, we provide further evidence for a functional dissociation between confident and not-confident errors by manipulating the demands on WM maintenance, i.e., the length over which information has to be maintained in WM. Furthermore, we investigate whether these functionally distinguishable WM processes are impaired in PSZ. Twenty-four PSZ and 24 demographically matched healthy controls (HC) performed a spatial DRT in which the length of the delay period was varied between 1, 2, 4, and 6 s. In each trial, participants also rated their level of response confidence. Across both groups, longer delays led to increased rates of incorrect/not-confident responses, while incorrect/confident responses were not affected by delay length. This functional dissociation provides additional support for our proposal that false memory errors (i.e., confident errors) reflect problems at the level of WM encoding, while not-confident errors reflect failures of WM maintenance. Schizophrenic patients showed increased numbers of both confident and not-confident errors, suggesting that both sub-processes of WM—encoding and maintenance—are impaired in schizophrenia. Combined with the delay length-dependent functional dissociation, we propose that these impairments in schizophrenic patients are functionally distinguishable.
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.