Single-trial analysis of the auditory N100 improves separation of normal and schizophrenia subjects

Critical evaluation of auditory event-related potential deficits in schizophrenia: evidence from large-scale single-subject pattern classification

Event-related potential (ERP) deficits associated with auditory oddball and click-conditioning paradigms are among the most consistent findings in schizophrenia and are discussed as potential biomarkers. However, it is unclear to what extend these ERP deficits distinguish between schizophrenia patients and healthy controls on a single-subject level, which is of high importance for potential translation to clinical routine. Here, we investigated 144 schizophrenia patients and 144 matched controls with an auditory click-conditioning/oddball paradigm. P50 and N1 gating ratios as well as target-locked N1 and P3 components were submitted to conventional general linear models and to explorative machine learning algorithms. Repeated-measures ANOVAs revealed significant between-group differences for the oddball-locked N1 and P3 components but not for any gating measure. Machine learning-assisted analysis achieved 77.7% balanced classification accuracy using a combination of target-locked N1 and P3 amplitudes as classifiers. The superiority of machine learning over repeated-measures analysis for classifying schizophrenia patients was in the range of about 10% as quantified by receiver operating characteristics. For the first time, our study provides large-scale single-subject classification data on auditory click-conditioning and oddball paradigms in schizophrenia. Although our study exemplifies how automated inference may substantially improve classification accuracy, our data also show that the investigated ERP measures show comparably poor discriminatory properties in single subjects, thus illustrating the need to establish either new analytical approaches for these paradigms or other paradigms to investigate the disorder.

Neurocognitive pattern analysis reveals classificatory hierarchy of attention deficits in schizophrenia

Attention deficits, among other cognitive deficits, are frequently observed in schizophrenia. Although valid and reliable neurocognitive tasks have been established to assess attention deficits in schizophrenia, the hierarchical value of those tests as diagnostic discriminants on a single-subject level remains unclear. Thus, much research is devoted to attention deficits that are unlikely to be translated into clinical practice. On the other hand, a clear hierarchy of attention deficits in schizophrenia could considerably aid diagnostic decisions and may prove beneficial for longitudinal monitoring of therapeutic advances. To propose a diagnostic hierarchy of attention deficits in schizophrenia, we investigated several facets of attention in 86 schizophrenia patients and 86 healthy controls using a set of established attention tests. We applied state-of-the-art machine learning algorithms to determine attentive test variables that enable an automated differentiation between schizophrenia patients and healthy controls. After feature preranking, hypothesis building, and hypothesis validation, the polynomial support vector machine classifier achieved a classification accuracy of 90.70% ± 2.9% using psychomotor speed and 3 different attention parameters derived from sustained and divided attention tasks. Our study proposes, to the best of our knowledge, the first hierarchy of attention deficits in schizophrenia by identifying the most discriminative attention parameters among a variety of attention deficits found in schizophrenia patients. Our results offer a starting point for hierarchy building of schizophrenia-associated attention deficits and contribute to translating these concepts into diagnostic and therapeutic practice on a single-subject level.

Single-subject classification of schizophrenia using event-related potentials obtained during auditory and visual oddball paradigms

In the search for the biomarkers of schizophrenia, event-related potential (ERP) deficits obtained by applying the classic oddball paradigm are among the most consistent findings. However, the single-subject classification rate based on these parameters remains to be determined. Here, we present a data-driven approach by applying machine learning classifiers to relevant oddball ERPs. Twenty-four schizophrenic patients and 24 matched healthy controls finished auditory and visual oddball tasks while high-density electrophysiological recordings were applied. The N1 component in response to standards and target as well as the P3 component following targets were submitted to different machine learning algorithms and the resulting ERP features were submitted to further correlation analyses. We obtained a classification accuracy of 72.4 % using only two ERP components. Latencies of parietal N1 components to visual standard stimuli at electrode positions Pz and P1 were sufficient for classification. Further analysis revealed a high correlation of these features in controls and an intermediate correlation in schizophrenia patients. These data exemplarily show how automated inference may be applied to classify a pathological state in single subjects without prior knowledge of their diagnoses and illustrate the potential of machine learning algorithms for the identification of potential biomarkers. Moreover, this approach assesses the discriminative accuracy of one of the most consistent findings in schizophrenia research by means of single-subject classification.

Aberrant auditory evoked responses in schizophrenia: evidence from single-trial analysis

The average N100 (a negative response occurring around 100 ms poststimulus) component of the auditory evoked potential (EP) has been recently used in the study of schizophrenia. Averaging, however, eliminates all temporal variability of the recorded signals and, therefore, hampers the exploration of the temporal dynamics underlying the generation of the N100 component. In this study, we analyzed EPs on a single-trial basis using an iterative independent component analysis procedure that is capable of extracting individual components out of an entire EP waveform. This approach allowed estimation of an N100 in each single trial and measurement of its morphological features such as polarity, which could be either negative (most frequently) or positive (less frequently). In the latter case, the N100 component was termed aberrant. We analyzed responses from 23 normal controls (NC) and 15 schizophrenia (SZ) patients in a paired stimulus paradigm, where a first stimulus S(1) was followed by a second one S(2) 0.5 s later. To compare N100 responses within and across the two subject groups, we defined a negative polarity index NPI as the percentage of single trials that had a negative polarity N100. Our results show significantly higher NPI values in NC compared to SZ, for both the S(1) and S(2) responses. Additionally, the difference in NPI values between the S(1) and S(2) responses was significant in NC but not in SZ. We conclude that both normal and schizophrenia subjects exhibit aberrant N100 responses, but these events are more frequent in the SZ patient group. The higher number of aberrant responses can explain the lower amplitude EPs typically observed in schizophrenia, and may be one of the factors contributing to sensory gating deficits consistently reported in these patients.