Advanced Signal Processing Methods for Characterization of Schizophrenia.

OBJECTIVE: Schizophrenia is a severe mental disorder associated with nerobiological deficits. Auditory oddball P300 have been found to be one of the most consistent markers of schizophrenia. The goal of this study is to find quantitative features that can objectively distinguish patients with schizophrenia (SCZs) from healthy controls (HCs) based on their recorded auditory odd-ball P300 electroencephalogram (EEG) data. METHODS: Using EEG dataset, we develop a machine learning (ML) algorithm to distinguish 57 SCZs from 66 HCs. The proposed ML algorithm has three steps. In the first step, a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on EEG signals to extract source waveforms from 30 specified brain regions. In the second step, a method for estimating effective connectivity, referred to as symbolic transfer entropy (STE), is applied to the source waveforms. In the third step the ML algorithm is applied to the STE connectivity matrix to determine whether a set of features can be found that successfully discriminate SCZ from HC. RESULTS: The findings revealed that the SCZs have significantly higher effective connectivity compared to HCs and the selected STE features could achieve an accuracy of 92.68%, with a sensitivity of 92.98% and specificity of 92.42%. CONCLUSION: The findings imply that the extracted features are from the regions that are mainly affected by SCZ and can be used to distinguish SCZs from HCs. SIGNIFICANCE: The proposed ML algorithm may prove to be a promising tool for the clinical diagnosis of schizophrenia.

A Machine Learning Approach Using Effective Connectivity to Predict Response to Clozapine Treatment.

Clozapine is an anti-psychotic drug that is known to be effective in the treatment of patients with chronic treatment-resistant schizophrenia (TRS-SCZ), commonly estimated to be around one third of all cases. However, clinicians sometimes delay the initiation of this drug because of its adverse side-effects. Therefore, identification of predictive biological markers of clozapine response are extremely valuable to aid on-time initiation of treatment. In this study, we develop a machine learning (ML) algorithm based on pre-treatment electroencephalogram (EEG) data sets to predict response to clozapine treatment in 57 TRS-SCZs, where the treatment outcome, after at least one-year follow-up is determined using the positive and negative syndrome scale (PANSS). The ML algorithm has three steps: 1) a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on the EEG signals to extract source waveforms from 30 specified brain regions. 2) An effective connectivity measure named symbolic transfer entropy (STE) is applied to the source waveforms. 3) A ML algorithm is applied to the STE matrix to determine whether a set of features can be found to discriminate most-responder (MR) SCZ patients from least-responder (LR) ones. The findings of this study reveal that STE features can achieve an accuracy of 95.83%. This finding implies that analysis of pre-treatment EEG could contribute to our ability to distinguish MR from LR SCZs, and that the source STE matrix may prove to be a promising tool for the prediction of the clinical response to clozapine.

A machine learning approach using auditory odd-ball responses to investigate the effect of Clozapine therapy.

OBJECTIVE: To develop a machine learning (ML) methodology based on features extracted from odd-ball auditory evoked potentials to identify neurophysiologic changes induced by Clozapine (CLZ) treatment in responding schizophrenic (SCZ) subjects. This objective is of particular interest because CLZ, though a potentially dangerous drug, can be uniquely effective for otherwise medication-resistant SCZ subjects. We wish to determine whether ML methods can be used to identify a set of EEG-based discriminating features that can simultaneously (1) distinguish all the SCZ subjects before treatment (BT) from healthy volunteer (HV) subjects, (2) distinguish EEGs collected before CLZ treatment (BT) vs. those collected after treatment (AT) for those subjects most responsive to CLZ, (3) discriminate least responsive subjects from HV AT, and (4) no longer discriminate most responsive subjects from HVs AT. If a set of EEG-derived features satisfy these four conditions, then it may be concluded that these features normalize in responsive subjects as a result of CLZ treatment, and therefore potentially provide insight into the functioning of the drug on the SCZ brain. METHODS: Odd-ball auditory evoked potentials of 66 HVs and 47 SCZ adults both BT and AT with CLZ were derived from EEG recordings. Treatment outcome, after at least one year follow-up, was assessed through clinical rating scores assigned by an experienced clinician, blind to EEG results. Using a criterion of at least 35% improvement after CLZ treatment, subjects were divided into "most-responsive" (MR) and "least-responsive" (LR) groups. As a first step, a brain source localization (BSL) procedure was employed on the EEG signals to extract source waveforms from specified brain regions. ML methods were then applied to these source waveform signals to determine whether a set of features satisfying the four conditions outlined above could be discovered. RESULTS: A set of cross-power spectral density (CPSD) features meeting these criteria was identified. These CPSD features, consisting of a combination of brain regional source activity and connectivity measures, significantly overlap with the default mode network (DMN). All decrease with CLZ treatment in responding SCZs. CONCLUSIONS: A set of EEG-derived discriminating features which normalize as a result of CLZ treatment was identified. These discriminating features define a network that shares significant commonality with the DMN. Our findings are consistent with those of previous literature, which suggest that regions of the DMN are hyperactive and hyperconnected in SCZ subjects. Our study shows that these discriminating features decrease after treatment, consistent with portions of the DMN normalizing with CLZ therapy in responsive subjects. SIGNIFICANCE: Machine learning is proposed as a potentially powerful tool for analysis of the effect of medication on psychiatric illness. If replicated, the proposed approach could be used to gain some improved understanding of the effect of neuroleptic medications in treating psychotic illness. These results may also be useful in the development of new pharmaceuticals, since a new drug which induces changes in brain electrophysiology similar to those seen after CLZ could also have powerful antipsychotic properties.

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder.

OBJECTIVE: The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD). METHODS: A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a "leave-n-out" randomized permutation cross-validation procedure. RESULTS: A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%]. CONCLUSIONS: These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG. SIGNIFICANCE: The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs.

A machine learning approach using P300 responses to investigate effect of clozapine therapy.

Clozapine (CLZ) is uniquely effective as a treatment for medication resistant schizophrenia. Information regarding its mechanism of action may offer clues to the pathophysiology of the disease and to improved treatment. In this study we employ a machine learning (ML) analysis of P300 evoked potentials obtained from quantitative electroencephalography (QEEG) data to identify changes in the brain induced by CLZ treatment. We employ brain source localization (BSL) on the EEG signals to extract source waveforms from specified regions of the brain. A subset of 8 features is selected from a large set of candidate features (consisting of spectral coherences between all identified source waveforms at multiple frequencies) that discriminate (by means of a classifier) between the pre- and post-treatment data for the schizophrenics (SCZ) most responsive to CLZ. We show these same selected features also discriminate between pre-treatment most responsive SCZ and healthy volunteers (HV), but not after treatment. Of note, these same features discriminate the least responsive SCZ from HV both pre- and post-treatment. This analysis suggests that the net beneficial effects of CLZ in SCZ are reflected in a normalization of P300 brain-source generators.

Using pre-treatment electroencephalography data to predict response to transcranial magnetic stimulation therapy for major depression.

We investigate the use of machine learning methods based on the pre-treatment electroencephalograph (EEG) to predict response to repetitive transcranial magnetic stimulation (rTMS), which is a non-pharmacological form of therapy for treating major depressive disorder (MDD). The learning procedure involves the extraction of a large number of candidate features from EEG data, from which a very small subset of most statistically relevant features is selected for further processing. A statistical prediction model based on mixture of factor analysis (MFA) model is constructed from a training set that classifies the respective subject into responder and non-responder classes. A leave-2-out (L2O) cross-validation procedure is used to evaluate the prediction performance. This pilot study involves 27 subjects who received either left high-frequency (HF) active rTMS therapy or simultaneous left HF and right low-frequency active rTMS therapy. Our results indicate that it is possible to predict rTMS treatment efficacy of either treatment modality with a specificity of 83% and a sensitivity of 78%, for a combined accuracy of 80%.

Using pre-treatment EEG data to predict response to SSRI treatment for MDD.

The problem of identifying in advance the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we propose a machine learning (ML) methodology to predict the response to a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD), using pre-treatment electroencephalograph (EEG) measurements. The proposed feature selection technique is a modification of the method of Peng et al [10] that is based on a Kullback-Leibler (KL) distance measure. The classifier was realized as a kernelized partial least squares regression procedure, whose output is the predicted response. A low-dimensional kernelized principal component representation of the feature space was used for the purposes of visualization and clustering analysis. The overall method was evaluated using an 11-fold nested cross-validation procedure for which over 85% average prediction performance is obtained. The results indicate that ML methods hold considerable promise in predicting the efficacy of SSRI antidepressant therapy for major depression.

Diagnosis of psychiatric disorders using EEG data and employing a statistical decision model.

An automated diagnosis procedure based on a statistical machine learning methodology using electroencephalograph (EEG) data is proposed for diagnosis of psychiatric illness. First, a large collection of candidate features, mostly consisting of various statistical quantities, are calculated from the subject's EEG. This large set of candidate features is then reduced into a much smaller set of most relevant features using a feature selection procedure. The selected features are then used to evaluate the class likelihoods, through the use of a mixture of factor analysis (MFA) statistical model [7]. In a training set of 207 subjects, including 64 subjects with major depressive disorder (MDD), 40 subjects with chronic schizophrenia, 12 subjects with bipolar depression and 91 normal or healthy subjects, the average correct diagnosis rate attained using the proposed method is over 85%, as determined by various cross-validation experiments. The promise is that, with further development, the proposed methodology could serve as a valuable adjunctive tool for the medical practitioner.

A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy.

OBJECTIVE: To investigate whether applying advanced machine learning (ML) methodologies to pre-treatment electroencephalography (EEG) data can predict the response to clozapine therapy in adult subjects suffering from chronic schizophrenia. METHODS: Pre-treatment EEG data are collected in 23+14 schizophrenic adults. Treatment outcome, after at least one year follow-up, is determined using clinical ratings by a trained clinician blind to EEG results. First, a feature selection scheme is employed to select a reduced subset of features extracted from the subjects' EEG that is most statistically relevant to our treatment-response prediction. These features are then entered into a classifier, which is realized in the form of a kernel partial least squares regression method that performs response prediction. Various scales, including the positive and negative syndrome scale (PANSS) are used as treatment-response indicators. RESULTS: We determined that a set of discriminating EEG features do exist. A low-dimensional representation of the feature space showed significant clustering into clozapine responder and non-responder groups. The minimum level of performance of the proposed prediction methodology, tested over a range of conditions using the leave-one-out cross-validation method using the original 23 subjects, with further testing in an independent sample of 14 subjects, was 85%. CONCLUSIONS: These findings indicate that analysis of pre-treatment EEG data can predict the clinical response to clozapine in treatment resistant schizophrenia. SIGNIFICANCE: If replicated in a larger population, this novel approach to EEG analysis may assist the clinician in determining treatment-efficacy.

Antipsychotic drug use is correlated with CRP40/mortalin mRNA expression in the dorsolateral prefrontal cortex of human postmortem brain specimens.

Heat shock proteins act as intracellular chaperones by assisting with proper protein folding in response to various cellular stresses. In doing so, these proteins protect the cell from unwanted protein aggregation, which in turn, plays an important role in the pathogenesis of numerous disorders. Previous reports from our laboratory have described a 40 kDa catecholamine regulated heat shock-like protein (CRP40), an alternate gene product of the 70 kDa mitochondrial heat shock protein, mortalin. CRP40 shares an intimate association with dopaminergic activity, specifically as it pertains to dopamine dysregulation in schizophrenia. This study investigates human CRP40/mortalin mRNA expression within dorsolateral prefrontal cortex postmortem specimens from normal control, schizophrenic and bipolar patients obtained from the Stanley Medical Research Institute. Real-time polymerase chain reaction was carried out for all patient samples (n=105; n=35 per group) in a blinded manner. No significant alterations in CRP40/mortalin mRNA expression levels were observed between control, bipolar and schizophrenic patients. However, multiple regression demonstrated a distinct positive correlation between CRP40/mortalin mRNA expression and lifetime use of antipsychotic drugs within the schizophrenic patient profile, after controlling for important confounding factors. Thus, the data suggest that human CRP40/mortalin is modulated by dopaminergic activity and may act to protect neurons from excess catecholamine activity in regions of the brain associated with psychosis.

Going beyond: an adventure- and recreation-based group intervention promotes well-being and weight loss in schizophrenia.

OBJECTIVE: To undertake a preliminary study to assess the feasibility of clinical implementation and evaluate the effectiveness of a novel adventure- and recreation-based group intervention in the rehabilitation of individuals with schizophrenia. METHODS: In a 2-year, prospective, case-control study, 23 consecutively referred, clinically stabilized schizophrenia patients received the new intervention over an 8-month period; 31 patients on the wait list, considered the control group, received standard clinical care that included some recreational activities. Symptom severity, self-esteem, self-appraised cognitive abilities, and functioning were documented for both groups with standardized rating scales administered at baseline, on completion of treatment, and at 12 months posttreatment. RESULTS: Treatment adherence was 97%, and there were no dropouts. Patients in the study group showed marginal improvement in perceived cognitive abilities and on domain-specific functioning measures but experienced a significant improvement in their self-esteem and global functioning (P < 0.05), as well as a weight loss of over 12 lb. Improvement was sustained over 1 year with further occupational and social gains. CONCLUSION: In the context of overcoming barriers to providing early intervention for youth and preventing metabolic problems among older adults with schizophrenia, adventure- and recreation-based interventions could play a useful complementary role.

Artefacting reliability in QEEG topographic maps.

OBJECTIVE: To quantify the extent of disagreement among expert artefactors, to compare their results with a 'minimalist' approach where only gross artefacts were removed, and to relate the result to frequency and to cranial location. METHODS: Raw QEEG records for 12 subjects were artefacted by 6-expert, and one 'minimalist', artefactor. Standard errors (SEs) of measurement were computed for each of 20 1.2 Hz frequency bins in each of 20 electrode positions. RESULTS: SEs declined with frequency. SEs associated with the 'minimalist' were comparable to those of the experts. The high SEs in delta were confined to the frontal and frontotemporal regions. SEs were small and uniform over the cranium for frequencies greater than 5.2 Hz. CONCLUSIONS: Artefactor unreliability is a serious problem in the delta band because of disagreement on eye movement artefacts. The success of the 'minimalist' suggests that automated methodologies may be a feasible alternative to the use of expert technicians. SIGNIFICANCE: A novel statistical procedure proves helpful in elucidating the sources of artefactor error and points to possible remedies.