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.

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.

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.

Single-subject classification of schizophrenia by event-related potentials during selective attention.

BACKGROUND: Executive dysfunction has repeatedly been proposed as a robust and promising substrate of analytical approaches in the research of neurocognitive markers of schizophrenia. Here, we present a mixed model- and data-driven classification approach by applying a task that targets executive dysfunction in schizophrenia and by investigating relevant event-related potential (ERP) features with machine learning classifiers. METHODS: Forty schizophrenic patients and forty matched healthy controls completed the Attention Network Test while an electroencephalogram was recorded. Target-locked N1 and P3 ERP components were constructed and submitted to different classification analyses without a priori hypotheses. Standardized source localization was applied to estimate neural sources of N1 and P3 deficits in schizophrenia. RESULTS: We obtained a classification accuracy of 79% using only very few ERP components. Central P3 components following compatible and incompatible trials and right parietal N1 latencies averaged across targets and were sufficient for classification. P3 deficits were associated with anterior cingulate cortex dysfunction, while right posterior current density deficits were observed in schizophrenia during the N1 time frame. CONCLUSIONS: The data exemplarily show how automated inference may be applied to classify a pathological state in single subjects without prior knowledge of their diagnoses. While classification accuracy may be optimized by application of other executive paradigms, this approach illustrates the potential of machine learning algorithms for the identification of biomarkers that are independent of clinical assessments. Conversely, data suggest a pathophysiological mechanism that includes early visual and late executive deficits during response inhibition in schizophrenia.

Implications of low mechanical impedance in upper limb reaching motion.

The equilibrium point hypothesis (EPH), much discussed in recent years, is central in a class of theories that posits an important role for muscular mechanical and reflex properties in the control of voluntary movement. We review briefly the findings of our studies testing the idea of equifinality, a major tenet of the EPH, which predicts that terminal limb position will be achieved regardless of transient perturbations in initial position or during ongoing movement. Our observations do not support this prediction of equifinality. We also report our findings that joint viscosity and elastic stiffness estimated during ballistic motion are unexpectedly low, limiting their potential contributions to the regulation either of limb movement trajectory or of limb stability. Taken together, our results imply that neuromuscular mechanical properties are unlikely to be used for regulating voluntary motion, and that other control strategies, most notably the use of feedforward controllers in which muscles act as force generators acting primarily on inertial loads, are more consistent with our observations.