Alex A. Sergejew

Reliability and validity of a new Medication Adherence Rating Scale (MARS) for the psychoses

Medication compliance is one of the foremost problems affecting neuroleptic efficacy in psychiatric patients. To date, compliancy has most commonly been assessed with the Drug Attitude Inventory (DAI) developed by Hogan et al. (Hogan, T.P., Awad, A.G., Eastwood, R., 1983. A self-report scale predictive of drug compliance in schizophrenics: reliability and discriminative validity. Psychol. Med. 13, 177-183). The present study identified several deficiencies in the DAI. Using the partial credit version of the Item Response Theory measurement model, the DAI was refined with the aim of greater validity and clinical utility. The new inventory was administered to 66 patients, the majority of whom were diagnosed with schizophrenia. When available, lithium levels and carer ratings of compliance were also recorded and used to verify compliancy. The new inventory appears to be a valid and reliable measure of compliancy for psychoactive medications.

Classification of EEG signals using the wavelet transform

The electroencephalogram (EEG) is widely used clinically to investigate brain disorders. However, abnormalities in the EEG in serious psychiatric disorders are at times too subtle to be detected using conventional techniques. This paper describes the application of an artificial neural network (ANN) technique together with a feature extraction technique, viz., the wavelet transform, for the classification of EEG signals. The data reduction and preprocessing operations of signals are performed using the wavelet transform. Three classes of EEG signals were used: Normal, Schizophrenia (SCH), and Obsessive Compulsive Disorder (OCD). The architecture of the artificial neural network used in the classification is a three-layered feedforward network which implements the backpropagation of error learning algorithm. After training, the network with wavelet coefficients was able to correctly classify over 66% of the normal class and 71% of the schizophrenia class of EEGs, respectively. The wavelet transform thus provides a potentially powerful technique for preprocessing EEG signals prior to classification.

Reduced connectivity of the auditory cortex in patients with auditory hallucinations: a resting state functional magnetic resonance imaging study

BACKGROUND Previous research has reported auditory processing deficits that are specific to schizophrenia patients with a history of auditory hallucinations (AH). One explanation for these findings is that there are abnormalities in the interhemispheric connectivity of auditory cortex pathways in AH patients; as yet this explanation has not been experimentally investigated. We assessed the interhemispheric connectivity of both primary (A1) and secondary (A2) auditory cortices in n=13 AH patients, n=13 schizophrenia patients without auditory hallucinations (non-AH) and n=16 healthy controls using functional connectivity measures from functional magnetic resonance imaging (fMRI) data. METHOD Functional connectivity was estimated from resting state fMRI data using regions of interest defined for each participant based on functional activation maps in response to passive listening to words. Additionally, stimulus-induced responses were regressed out of the stimulus data and the functional connectivity was estimated for the same regions to investigate the reliability of the estimates. RESULTS AH patients had significantly reduced interhemispheric connectivity in both A1 and A2 when compared with non-AH patients and healthy controls. The latter two groups did not show any differences in functional connectivity. Further, this pattern of findings was similar across the two datasets, indicating the reliability of our estimates. CONCLUSIONS These data have identified a trait deficit specific to AH patients. Since this deficit was characterized within both A1 and A2 it is expected to result in the disruption of multiple auditory functions, for example, the integration of basic auditory information between hemispheres (via A1) and higher-order language processing abilities (via A2).

Nonlinear considerations in EEG signal classification

We investigate the effect of incorporating modeling of nonlinearity on the classification of electroencephalogram (EEG) signals using an artificial neural network (ANN). It is observed that the ANNs predictive ability is improved after preprocessing EEG signals using a particular nonlinear modeling technique, viz. a bilinear model, compared with those obtained by using a particular classical linear analysis method, viz. an autoregressive (AR) model. Until recently, linear time-invariant Gaussian modeling has dominated the development of time series modeling and feature extraction. The advantage of such classical models lies in the fact that a complete signal processing theory is available. In the case of EEG signals, where the underlying theory regarding the dynamical law governing the generation of these signals (e,g., the underlying physiological factors) is not completely understood, a case can be made for using improved signal processing models that are not subject to linear constraints. Such models should recognize important features of the observed data that may not be well modeled by a linear time-invariant model. It is known that EEG signals are nonstationary, and it is possible that they may be nonlinear as well. Thus, one way of gaining further insights on the structure of EEG signals is to introduce nonlinear models and higher order spectra. This paper compares the results of classification using a linear AR model with those obtained from a bilinear model. It is shown that in certain cases, the nonlinearity of the EEG signals is an important factor that ought to be taken into consideration during preprocessing of the signals prior to the classification task.

Emotional prosodic processing in auditory hallucinations

Deficits in emotional prosodic processing, the expression of emotions in voice, have been widely reported in patients with schizophrenia, not only in comprehending emotional prosody but also expressing it. Given that prosodic cues are important in memory for voice and speaker identity, Cutting has proposed that prosodic deficits may contribute to the misattribution that appears to occur in auditory hallucinations in psychosis. The present study compared hallucinating patients with schizophrenia, non-hallucinating patients and normal controls on an emotional prosodic processing task. It was hypothesised that hallucinators would demonstrate greater deficits in emotional prosodic processing than non-hallucinators and normal controls. Participants were 67 patients with a diagnosis of schizophrenia or schizoaffective disorder (hallucinating=38, non-hallucinating=29) and 31 normal controls. The prosodic processing task used in this study comprised a series of semantically neutral sentences expressed in happy, sad and neutral voices which were rated on a 7-point Likert scale from sad (-3) through neutral (0) to happy (+3). Significant deficits in the prosodic processing tasks were found in hallucinating patients compared to non-hallucinating patients and normal controls. No significant differences were observed between non-hallucinating patients and normal controls. In the present study, patients experiencing auditory hallucinations were not as successful in recognising and using prosodic cues as the non-hallucinating patients. These results are consistent with Cuttings hypothesis, that prosodic dysfunction may mediate the misattribution of auditory hallucinations.

EEG coherence measures during auditory hallucinations in schizophrenia.

We studied the change in EEG alpha-band average coherence between auditory hallucination (AH) and non-auditory hallucination (non-AH) states in seven auditory hallucinating schizophrenia patients. Four cortical regions were considered based on the existing dominant models for auditory hallucinations, the inner speech model and the central auditory processing deficit (CAPD) model. Coherences between electrodes located over Brocas area (BA 44/45) and Wernickes area (BA 22/42) and between electrodes located over left-right temporal cortices were examined. There was no significant change observed in the coherence between Brocas and Wernickes areas, but a significant increase was observed in coherence between the left and right superior temporal cortices during AHs compared with non-AHs, suggesting increased bilateral coherence between auditory cortical areas. Since coherence is a pairwise measure of functional correlation between regions, our findings suggest abnormally increased synchrony between the left and right auditory cortices during AHs in schizophrenia. Further, a significant increase in relative power was observed in the left, but not in the right auditory cortex during AHs. Thus our findings support the CAPD model and are consistent with that which postulate reduced prosodic processing during AHs.

Functional connectivity estimation in fMRI data: influence of preprocessing and time course selection

A number of techniques have been used to provide functional connectivity estimates for a given fMRI data set. In this study we compared two methods: a ‘rest‐like’ method where the functional connectivity was estimated for the whitened residuals after regressing out the task‐induced effects, and a within‐condition method where the functional connectivity was estimated separately for each experimental condition. In both cases four pre‐processing strategies were used: 1) time courses extracted from standard pre‐processed data (standard); 2) adjusted time courses extracted using the volume of interest routines in SPM2 from standard pre‐processed data (spm); 3) time courses extracted from ICA denoised data (standard denoised); and 4) adjusted time courses extracted from ICA denoised data (spm denoised). The temporal correlation between time series extracted from two cortical regions were statistically compared with the temporal correlation between a time series extracted from a cortical region and a time series extracted form a region placed in CSF. Since the later correlation is due to physiological noise and other artifacts, we used this comparison to investigate whether rest‐like and task modulated connectivity could be estimated from the same data set. The pre‐processing strategy had a significant effect on the connectivity estimates with the standard time courses providing larger connectivity values than the spm time courses for both estimation methods. The CSF comparison indicated that for our data set only rest‐like connectivity could be estimated. The rest‐like connectivity values were similar with connectivity estimated from resting state data. Hum Brain Mapp 2008.

EEG coherence and dissociative Identity disorder: Comparing EEG coherence in DID hosts, alters, controls and acted alters

Abstract This is the first study to apply EEG coherence analysis to the study of Dissociative Identity Disorder (DID). EEG coherence is argued to be an objective measure of cortical connectivity. Five DID patients were compared to five controls, who were professional actors. Fifteen dissociated DID alter states were studied, as were 15 “alters” simulated by the actor control participants. Comparisons of EEG coherence were made between DID participants and controls. Significant differences in EEG coherence were found in comparing DID host and alter personalities, with coherence found to be lower in the alter personalities. No significant differences were found in comparing DID host personalities and controls. The acted alters matched for age and gender, showed no significant differences in coherence compared to DID alter personalities. The results indicate that EEG coherence may be an objective measure of the neuronal cortical connectivity associated with DID.

Extrapyramidal symptoms and oestrogen

Objective: The present study aimed to investigate neuroleptic side‐effect severity in women with psychosis, and to investigate their putative association with variations in sex steroids over the menstrual cycle. Based on the oestrogen hypothesis, which postulates a synergistic relationship between oestrogen and neuroleptics, it was hypothesized that oestrogen would exacerbate extrapyramidal symptoms.

Estrogen affects cognition in women with psychosis

Estrogen has been reported to affect aspects of cognition and psychopathology in women, both normal and with psychosis. This study aimed to replicate and extend this research by investigating the effect of estrogen on cognition over the menstrual cycle in a group of normal women and women with psychosis. The sample consisted of 31 premenstrual normal control subjects, and 29 women with psychosis. Subjects were tested twice, 2 weeks apart on a number of cognitive tests. There was no difference in Positive and Negative Symptom Scale scores between the follicular and luteal phases of the menstrual cycle. Both groups of women performed better on the Revised Mental Rotation Test and Trails A during the follicular phase when estrogen levels were low. Contrary to expectation, during the luteal phase, when estrogen was high, the control subjects showed no significant improvement in performance on verbal articulatory-motor tasks, and the women with psychosis performed significantly worse on the Purdue Pegboard. The unexpected adverse effect of high levels of estrogen on motor performance in the psychotic women was hypothesized to be related to their disease process.