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

Neural dynamics underlying the cue validity effect in target conflict resolution

Cue validity significantly influences attention guidance, either facilitating or hindering the ability for conflict resolution. Previous studies have demonstrated that the validity effect and conflict resolution are associated with better/worse behavioral performance and specific neural activations; however, the underlying neural mechanism of their interaction remains unclear. We hypothesized that the effect of cue validity might sustain specific sequences of neural activities until target occurrence and throughout the subsequent conflict resolution. In this study, we recorded the scalp electroencephalography during the Attention Network Test paradigm to investigate their interactions in neural dynamics. Specifically, we performed a cluster-level channel-time-frequency analysis to explore significant time-frequency neural activity patterns associated with these interactions, in scalp regions of interest determined by a data-driven strategy. Our results revealed a string of significant neural dynamics in the frontal and parietal regions, including initial broad-band (especially the gamma-band) activations and subsequent complex cognitive processes evoked/effected by the invalid cue, that were firstly elicited. Finally, the resolution of conflict was completed by the frontal behavior-related theta-band power reduction. In summary, our findings advanced the understanding of the temporal and spectral sequences of neural dynamics, with the key regions involved in the resolution of conflict after invalid cueing.

Adolescent ADHD and electrophysiological reward responsiveness: A machine learning approach to evaluate classification accuracy and prognosis

We evaluated event-related potential (ERP) indices of reinforcement sensitivity as ADHD biomarkers by examining, in N=306 adolescents (M_age=15.78, SD=1.08), the extent to which ERP amplitude and latency variables measuring reward anticipation and response (1) differentiate, in age- and sex-matched subsamples, (i) youth with vs. without ADHD, (ii) youth at-risk for vs. not at-risk for ADHD, and, in the with ADHD subsample, (iii) youth with the inattentive vs. the hyperactive/impulsive (H/I) and combined presentations. We further examined the extent to which ERP variables (2) predict, in the ADHD subsample, substance use (i) concurrently and (ii) prospectively at 18-month follow-up. Linear support vector machine analyses indicated ERPs weakly differentiate youth with/without (65%) - and at-risk for/not at-risk for (63%) - ADHD but better differentiate ADHD presentations (78%). Regression analyses showed in adolescents with ADHD, ERPs explain a considerable proportion of variance (50%) in concurrent alcohol use and, controlling for concurrent marijuana and tobacco use, explain a considerable proportion of variance (87 and 87%) in, and predict later marijuana and tobacco use. Findings are consistent with the dual-pathway model of ADHD. Results also highlight limitations of a dichotomous, syndromic classification and indicate differences in neural reinforcement sensitivity are a promising ADHD prognostic biomarker.

Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT)

The attentional processes are conceptualized as a system of anatomical brain areas involving three specialized networks of alerting, orienting and executive control, each of which has been proven to have a relation with specified time-frequency oscillations through electrophysiological techniques. Nevertheless, at present, it is still unclear how the idea of these three independent attention networks is reflected in the specific short-time topology propagation of the brain, assembled with complexity and precision. In this study, we investigated the temporal patterns of dynamic information flow in each attention network via electroencephalograph (EEG)-based analysis. A modified version of the attention network test (ANT) with an EEG recording was adopted to probe the dynamic topology propagation in the three attention networks. First, the event-related potentials (ERP) analysis was used to extract sub-stage networks corresponding to the role of each attention network. Then, the dynamic network model of each attention network was constructed by post hoc test between conditions followed by the short-time-windows fitting model and brain network construction. We found that the alerting involved long-range interaction among the prefrontal cortex and posterior cortex of brain. The orienting elicited more sparse information flow after the target onset in the frequency band 1-30 Hz, and the executive control contained complex top-down control originating from the frontal cortex of the brain. Moreover, the switch of the activated regions in the associated time courses was elicited in attention networks contributing to diverse processing stages, which further extends our knowledge of the mechanism of attention networks.

Machine learning in biosignals processing for mental health: A narrative review

Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain-computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.

A hybrid decision support system for automatic detection of Schizophrenia using EEG signals

BACKGROUND

Schizophrenia (SCZ) is a serious neurological condition in which people suffer with distorted perception of reality. SCZ may result in a combination of delusions, hallucinations, disordered thinking, and behavior. This causes permanent disability and hampers routine functioning. Trained neurologists use interviewing and visual inspection techniques for the detection and diagnosis of SCZ. These techniques are manual, time-consuming, subjective, and error-prone. Therefore, there is a need to develop an automatic model for SCZ classification. The aim of this study is to develop an automated SCZ classification model using electroencephalogram (EEG) signals. The EEG signals can capture the changes in neural dynamics of human cognition during SCZ.

METHOD

Based on the nature of the SCZ condition, the EEG signals must be examined. For accurate interpretation of EEG signals during SCZ, an automated model integrating a robust variational mode decomposition (RVMD) and an optimized extreme learning machine (OELM) classifier is developed. Traditional VMD suffers from noisy mode generation, mode duplication, under segmentation, and mode discarding. These problems are suppressed in RVMD by automating the selection of quadratic penalty factor (α) and a number of modes (L). The hyperparameters (HPM) of the OELM classifier are automatically selected to ensure maximum accuracy for each mode without overfitting or underfitting. For the selection of α and L in RVMD and HPM in the OELM classifier, a whale optimization algorithm is used. The root mean square error is minimized for RVMD and classification accuracy of each mode is maximized for the OELM classifier. The EEG signals of three conditions performing basic sensory tasks have been analyzed to detect SCZ.

RESULTS

The Kruskal Wallis test is used to select different features extracted from the modes produced by RVMD. An OELM classifier is tested using a ten-fold cross-validation technique. An accuracy, precision, specificity, F-1 measure, sensitivity, and Cohen's Kappa of 92.93%, 93.94%, 91.06% 94.07%, 97.15%, and 85.32% are obtained.

CONCLUSION

The third mode's chaotic features helped to capture the significant changes that occurred during the SCZ state. The findings of the RVMD-OELM-based hybrid decision support system can help neuro-experts for the accurate identification of SCZ in real-time scenarios.

Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.

Hierarchical Structured Sparse Learning for Schizophrenia Identification

Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based schizophrenia (SZ) diagnosis. However, previous studies usually measure the fALFF within low-frequency fluctuation (from 0.01 to 0.08Hz), which cannot fully cover the complex neural activity pattern in the resting-state brain. In addition, existing studies usually ignore the fact that each specific frequency band can delineate the unique spontaneous fluctuations of neural activities in the brain. Accordingly, in this paper, we propose a novel hierarchical structured sparse learning method to sufficiently utilize the specificity and complementary structure information across four different frequency bands (from 0.01Hz to 0.25Hz) for SZ diagnosis. The proposed method can help preserve the partial group structures among multiple frequency bands and the specific characters in each frequency band. We further develop an efficient optimization algorithm to solve the proposed objective function. We validate the efficacy of our proposed method on a real SZ dataset. Also, to demonstrate the generality of the method, we apply our proposed method on a subset of Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results on both datasets demonstrate that our proposed method achieves promising performance in brain disease classification, compared with several state-of-the-art methods.

Impact of Chronic Stress on Attention Control: Evidence from Behavioral and Event-Related Potential Analyses

Chronic stress affects brain function, so assessing its hazards is important for mental health. To overcome the limitations of behavioral data, we combined behavioral and event-related potentials (ERPs) in an attention network task. This task allowed us to differentiate between three specific aspects of attention: alerting, orienting, and execution. Forty-one participants under chronic stress and 31 non-stressed participants were enrolled. On the performance level, the chronically stressed group showed a significantly slower task response and lower accuracy. Concerning ERP measures, smaller cue-N1, cue-N2, and larger cue-P3 amplitudes were found in the stressed group, indicating that this group was less able to assign attention to effective information, i.e., they made inefficient use of cues and had difficulty in maintaining alerting. In addition, the stressed group showed larger target-N2 amplitudes, indicating that this group needed to allocate more cognitive resources to deal with the conflict targets task. Subgroup analysis revealed lower target-P3 amplitudes in the stressed than in the non-stressed group. Group differences associated with the attention networks were found at the ERP level. In the stressed group, excessive depletion of resources led to changes in attention control. In this study, we examined the effects of chronic stress on individual executive function from a neurological perspective. The results may benefit the development of interventions to improve executive function in chronically stressed individuals.

Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls

Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning techniques, we explored whether abnormalities in microstates can be used to classify patients with schizophrenia and healthy controls. We applied multivariate pattern analysis of microstate features to create a specified feature set to represent microstate characteristics. Machine learning approaches using these features for classification of patients with schizophrenia were compared with prior EEG based machine learning studies. Our microstate segmentation in both patients with schizophrenia and healthy controls yielded topographies that were similar to the normative database established earlier by Koenig et al. Our machine learning model was based on large sample size, low number of features and state-of-art K-fold cross-validation technique. The multivariate analysis revealed three patterns of correlated features, which yielded an AUC of 0.84 for the group separation (accuracy: 82.7%, sensitivity/specificity: 83.5%/85.3%). Microstate segmentation of resting state EEG results in informative features to discriminate patients with schizophrenia from healthy individuals. Moreover, alteration in microstate measures may represent disturbed activity of networks in patients with schizophrenia.

Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography

BACKGROUND

When many features and a small number of clinical data exist, previous studies have used a few top-ranked features from the Fisher's discriminant ratio (FDR) for feature selection. However, there are many similarities between selected features. New method: To reduce the redundant features, we applied a technique employing FDR in conjunction with feature correlation. We performed an attention network test on schizophrenic patients and normal subjects with a 152-channel magnetoencephalograph. P300m amplitudes of event-related fields (ERFs) were used as features at the sensor level and P300m amplitudes of ERFs for 500 nodes on the cortex surface were used as features at the source level. Features were ranked using FDR criterion and cross-correlation measure, and then the highest ranked 10 features were selected and an exhaustive search was used to find combination having the maximum accuracy.

RESULTS

At the sensor level, we found a single channel of the occipital region that distinguished the two groups with an accuracy of 89.7 %. At source level, we obtained an accuracy of 96.2 % using two features, the left superior frontal region and the left inferior temporal region.

COMPARISON WITH EXISTING METHOD

At source level, we obtained a higher accuracy than traditional method using only FDR criterion (accuracy = 88.5 %). We used only the P300 m amplitude (not latency) on a single channel and two brain regions at a fairly high rate.

Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control

Efficient action control is indispensable for goal-directed behaviour. Different theories have stressed the importance of either attention or response selection sub-processes for action control. Yet, it is unclear to what extent these processes can be identified in the dynamics of neurophysiological (EEG) processes at the single-trial level and be used to predict the presence of conflicts in a given moment. Applying deep learning, which was blind to cognitive theory, on single-trial EEG data allowed to predict the presence of conflict in ~95% of subjects ~33% above chance level. Neurophysiological features related to attentional and motor response selection processes in the occipital cortex and the superior frontal gyrus contributed most to prediction accuracy. Importantly, deep learning was able to identify predictive neurophysiological processes in single-trial neural dynamics. Hence, mathematical (artificial intelligence) approaches may be used to foster the validation and development of links between cognitive theory and neurophysiology of human behavior.

Vahid et al. use a deep-learning approach to analyze single-trial EEG data to examine theories on action control. Their approach enables the identification of spatial and temporal neurophysiological features that are predictive of the response control during the Simon task. The results confirm cognitive theory-driven approaches on the relationship between neurophysiology and human behavior.

Electrophysiological Processes on Motor Imagery Mediate the Association Between Increased Gray Matter Volume and Cognition in Amnestic Mild Cognitive Impairment

Motor imagery is considered as an ideal window to observe neural processes of action representations. Behavioral evidence has indicated an alteration of motor imagery in amnestic mild cognitive impairment (aMCI). However, it still remains unclear on the altered neurophysiological processing mechanism of motor imagery and whether this mechanism links the abnormal biological basis of motor imagery with impaired cognition in aMCI. This study was to investigate the altered neurophysiological processing mechanism of motor imagery and to examine the relationships between this knowledge and the altered structural basis of motor imagery with impaired cognition in aMCI. A hand mental rotation paradigm was used to manipulate the processing of motor imagery while event-related brain potentials (ERPs) were recorded and gray matter (GM) voxel-based morphometry was performed in 20 aMCI and 29 healthy controls. Compared with controls, aMCI exhibited lower ERP amplitudes in parietal cortex and higher ERP amplitudes in frontal cortex during motor imagery. In addition, aMCI showed reduced GM volumes in cerebellum posterior lobe, insula and hippocampus/parahippocampal gyrus, and increased GM volumes in middle cingulate gyrus and superior frontal gyrus. Most importantly, increased ERP amplitude significantly mediated the association between increased GM and cognition. This study provided a novel evidence for the relationships between the electrophysiological processing mechanism and structural basis of motor imagery with impaired cognition in aMCI. It suggests that improving neural activity by stimulating the frontal lobe can potentially contribute to acquire motor imagery skills for neurological rehabilitation in aMCI subjects.

Should we pay attention to eye movements? The impact of bilateral eye movements on behavioral and neural responses during the Attention Network Test

Bilateral eye movements (EMs) have been associated with enhancements in episodic memory and creativity. We explored the influence of EMs on behavior and event related potential (ERP) responses during the Attention Network Test (ANT). Participants completed ANT trials after bilateral EMs or a center-fixation control manipulation. We examined condition (EM, control) and handedness (consistent, inconsistent) differences for overall task performance, as well as alerting, orienting, and executive attention networks. Behaviorally, there was a trend for inconsistent-handed participants to display faster RTs across cue types, and greater accuracy for no cue, double, and center cue trials when compared to consistent handers, yet consistent handers garnered greater improvements in behavior following altering and orienting cues than inconsistent handers. Although there were no behavioral differences between EM and control conditions, target-locked N100 and P200 ERPs were weaker in the EM than control condition for all cue types, except spatial cues for which there were no differences between groups. Because stronger N100 and P200 responses have been linked to increased selective attention, we speculate that ERP differences between EM and control conditions, in the absence of behavioral differences, may indicate that participants exposed to EMs required less selective attention to successfully complete the task.

Effects of Gender and Apolipoprotein E on Novelty MMN and P3a in Healthy Elderly and Amnestic Mild Cognitive Impairment

Background: The apolipoprotein E epsilon4 (ApoE ε4) allele and female gender may be important risk factors for the development of Alzheimer’s disease and amnestic mild cognitive impairment (aMCI). Novelty mismatch negativity (MMN) represents the pre-attentive index of deviance detection and P3a represents the attention orienting response. Furthermore, MMN and P3a components have been reported to be potential markers in aMCI. Therefore, this study will investigate the effects of gender and ApoE on auditory novelty MMN and P3a and their relationship to neuropsychological performance in aMCI.

Methods: Thirty nine aMCI subjects and 44 controls underwent neuropsychological assessment and ApoE genotyping. Novelty MMN and P3a components were investigated during an auditory novelty oddball task.

Results: Firstly, novelty MMN latency was significantly shorter in aMCI than in healthy control (HC) group. Secondly, novelty MMN latency was negatively correlated with episodic memory in aMCI, but not in HC. Novelty P3a latency was negatively correlated with information processing speed in all subjects. For gender effect, novelty MMN latency was shorter in aMCI females than in HC females. Moreover, novelty P3a amplitudes were lower in males than in females in both aMCI and HC. For the effect of ApoE status, novelty MMN latency was shorter in aMCI ApoE ε4- than HC ApoE ε4-.

Conclusion: aMCI presents altered pre-attentive processing indexed by novelty MMN components. Furthermore, there may be a compensatory mechanism on the impaired processing in aMCI. It further suggests that aMCI female and ApoE ε4- recruited the compensatory mechanism.

Staying Alert? Neural Correlates of the Association Between Grit and Attention Networks

Recent research has demonstrated that heightened motivational levels promote enhanced attention capabilities. However, the relation between attentional systems and the trait-based ability to sustain a motivational state long-term is less understood. Grit refers to one's ability and willingness to pursue long-term goals despite setbacks. This report presents the results of two studies conducted to examine the relation between facets of Grit-Consistency and Perseverance and attention networks, assessed using the Attention Network Test (ANT). Across both studies Grit-Perseverance was related to performance on the ANT. In Study 1, Grit-Perseverance was negatively related to alerting indicating that individuals who were high on Perseverance were more likely to show a smaller alerting effect. In particular, Grit-Perseverance was negatively correlated with reaction times in the no cue trials. In Study 2, we assessed ERP components associated with attention networks. Individuals with higher scores on Grit-Perseverance were more likely to demonstrate smaller mean difference in N1 amplitudes for double cue relative to no cue trials, suggesting an attenuated alerting effect. Our findings indicate that individuals high on Grit-Perseverance may have enhanced sustained attention. Specifically individuals with high Grit-Perseverance appear to exhibit a more efficient alerting system in the no cue trials. Implications of high levels of Grit on cognitive performance are discussed.

The Effect of Apolipoprotein E ε4 (APOE ε4) on Visuospatial Working Memory in Healthy Elderly and Amnestic Mild Cognitive Impairment Patients: An Event-Related Potentials Study

Background: Apolipoprotein E (APOE) ε4 is the only established risk gene for late-onset, sporadic Alzheimer’s disease (AD). Previous studies have provided inconsistent evidence for the effect of APOE ε4 status on the visuospatial working memory (VSWM).

Objective: The aim was to investigate the effect of APOE ε4 on VSWM with an event-related potential (ERP) study in healthy controls (HC) and amnestic mild cognitive impairment (aMCI) patients.

Methods: The study recorded 39 aMCI patients (27 APOE ε4 non-carriers and 12 APOE ε4 carriers) and their 43 matched controls (25 APOE ε4 non-carriers and 18 APOE ε4 carriers) with an 64-channel electroencephalogram. Participants performed an N-back task, a VSWM paradigm that manipulated the number of items to be stored in memory.

Results: The present study detected reduced accuracy and delayed mean correct response time (RT) in aMCI patients compared to HC. P300, a positive component that peaks between 300 and 500 ms, was elicited by the VSWM task. In addition, aMCI patients showed decreased P300 amplitude at the central–parietal (CP1, CPz, and CP2) and parietal (P1, Pz, and P2) electrodes in 0- and 1-back task compared to HC. In both HC and aMCI patients, APOE ε4 carriers showed reduced P300 amplitude with respect to non-carriers, whereas no significant differences in accuracy or RT were detected between APOE ε4 carriers and non-carriers. Additionally, standardized low-resolution brain electromagnetic tomography analysis (s-LORETA) showed enhanced brain activation in the right parahippocampal gyrus (PHG) during P300 time range in APOE ε4 carriers with respect to non-carriers in aMCI patients.

Conclusion: It demonstrated that P300 amplitude could predict VSWM deficits in aMCI patients and contribute to early detection of VSWM deficits in APOE ε4 carriers.

Auditory prediction errors as individual biomarkers of schizophrenia

Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to develop an objective biologically-based computational tool which aids diagnosis and relies on accessible imaging technologies such as electroencephalography (EEG). To achieve this, we used machine learning techniques and a combination of paradigms designed to elicit prediction errors or Mismatch Negativity (MMN) responses. MMN, an EEG component elicited by unpredictable changes in sequences of auditory stimuli, has previously been shown to be reduced in people with schizophrenia and this is arguably one of the most reproducible neurophysiological markers of schizophrenia.

EEG data were acquired from 21 patients with schizophrenia and 22 healthy controls whilst they listened to three auditory oddball paradigms comprising sequences of tones which deviated in 10% of trials from regularly occurring standard tones. Deviant tones shared the same properties as standard tones, except for one physical aspect: 1) duration - the deviant stimulus was twice the duration of the standard; 2) monaural gap - deviants had a silent interval omitted from the standard, or 3) inter-aural timing difference, which caused the deviant location to be perceived as 90° away from the standards.

We used multivariate pattern analysis, a machine learning technique implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo) to classify images generated through statistical parametric mapping (SPM) of spatiotemporal EEG data, i.e. event-related potentials measured on the two-dimensional surface of the scalp over time. Using support vector machine (SVM) and Gaussian processes classifiers (GPC), we were able classify individual patients and controls with balanced accuracies of up to 80.48% (p-values = 0.0326, FDR corrected) and an ROC analysis yielding an AUC of 0.87. Crucially, a GP regression revealed that MMN predicted global assessment of functioning (GAF) scores (correlation = 0.73, R² = 0.53, p = 0.0006).

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The diagnostic utility of multiple auditory oddball stimulus paradigms is assessed.

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Greatest classification accuracy was achieved using a monaural gap stimulus paradigm.

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The full post-stimulus epoch contains relevant discriminatory components.

Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features

Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine learning techniques applied to EEG biomarkers. Although a number of studies showed that source-level EEG features can potentially be applied to the differential diagnosis of schizophrenia, most studies have used only sensor-level EEG features such as ERP peak amplitude and power spectrum for machine learning-based diagnosis of schizophrenia. In this study, we used both sensor-level and source-level features extracted from EEG signals recorded during an auditory oddball task for the classification of patients with schizophrenia and healthy controls. EEG signals were recorded from 34 patients with schizophrenia and 34 healthy controls while each subject was asked to attend to oddball tones. Our results demonstrated higher classification accuracy when source-level features were used together with sensor-level features, compared to when only sensor-level features were used. In addition, the selected sensor-level features were mostly found in the frontal area, and the selected source-level features were mostly extracted from the temporal area, which coincide well with the well-known pathological region of cognitive processing in patients with schizophrenia. Our results suggest that our approach would be a promising tool for the computer-aided diagnosis of schizophrenia.

Single-subject prediction of response inhibition behavior by event-related potentials

Much research has been devoted to investigating response inhibition and the neuronal processes constituting this essential cognitive faculty. However, the nexus between cognitive subprocesses, behavior, and electrophysiological processes remains associative in nature. We therefore investigated whether neurophysiological correlates of inhibition subprocesses merely correlate with behavioral performance or actually provide information expedient to the prediction of behavior on a single-subject level. Tackling this question, we used different data-driven classification approaches in a sample of n = 262 healthy young subjects who completed a standard Go/Nogo task while an EEG was recorded. On the basis of median-split response inhibition performance, subjects were classified as "accurate/slow" and "less accurate/fast." Even though these behavioral group differences were associated with significant amplitude variations in classical electrophysiological correlates of response inhibition (i.e., N2 and P3), they were not predictive for group membership on a single-subject level. Instead, amplitude differences in the Go-P2 originating in the precuneus (BA7) were shown to predict group membership on a single-subject level with up to 64% accuracy. These findings strongly suggest that the behavioral outcome of response inhibition greatly depends on the amount of cognitive resources allocated to early stages of stimulus-response activation during responding. This suggests that research should focus more on early processing steps during responding when trying to understand the origin of interindividual differences in response inhibition processes.

Clozapine improves the orienting of attention in schizophrenia

Attentional deficits are prominent in the cognitive profile of patients with schizophrenia. However, it remains unclear whether treatment with clozapine, an atypical antipsychotic and first-line intervention used to reduce positive and negative symptoms of psychosis, improves the attentional functions. We used the revised attention network test to measure alerting, orienting, and executive control of attention both pre- and post-treatment with clozapine in patients with schizophrenia (n=32) and compared performance to healthy controls (n=32). Results revealed that there were deficits in all three attentional functions pre-treatment, and while clozapine improved the orienting function in patients with schizophrenia, there was no evidence for improvement in the alerting and executive control of attention. The enhancement of the orienting function by clozapine may increase the ability of patients with schizophrenia to orient towards objects and thoughts of interest.

Electrophysiological endophenotypes in rodent models of schizophrenia and psychosis

Schizophrenia is caused by a diverse array of risk factors and results in a similarly diverse set of symptoms. Electrophysiological endophenotypes lie between risks and symptoms and have the potential to link the two. Electrophysiological studies in rodent models, described here, demonstrate that widely differing risk factors result in a similar set of core electrophysiological endophenotypes, suggesting the possibility of a shared neurobiological substrate.

Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach

Electroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity. This paper concerns the diagnosis of schizophrenia using EEG, which currently suffers from several cardinal problems: it heavily depends on assumptions, conditions and prior knowledge regarding the patient. Additionally, the diagnostic experiments take hours, and the accuracy of the analysis is low or unreliable. This article presents the "TFFO" (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives. The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.

Single-subject classification of schizophrenia patients based on a combination of oddball and mismatch evoked potential paradigms

OBJECTIVE

The diagnostic process for schizophrenia is mainly clinical and has to be performed by an experienced psychiatrist, relying primarily on clinical signs and symptoms. Current neurophysiological measurements can distinguish groups of healthy controls and groups of schizophrenia patients. Individual classification based on neurophysiological measurements mostly shows moderate accuracy. We wanted to examine whether it is possible to distinguish controls and patients individually with a good accuracy. To this end we used a combination of features extracted from the auditory and visual P300 paradigms and the mismatch negativity paradigm.

METHODS

We selected 54 patients and 54 controls, matched for age and gender, from the data available at the UPC Kortenberg. The EEG-data were high- and low-pass filtered, epoched and averaged. Features (latencies and amplitudes of component peaks) were extracted from the averaged signals. The resulting dataset was used to train and test classification algorithms. First on separate paradigms and then on all combinations, we applied Naïve Bayes, Support Vector Machine and Decision Tree, with two of its improvements: Adaboost and Random Forest.

RESULTS

For at least two classifiers the performance increased significantly by combining paradigms compared to single paradigms. The classification accuracy increased from at best 79.8% when trained on features from single paradigms, to 84.7% when trained on features from all three paradigms.

CONCLUSION

A combination of features originating from three evoked potential paradigms allowed us to accurately classify individual subjects as either control or patient. Classification accuracy was mostly above 80% for the machine learners evaluated in this study and close to 85% at best.

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.

Executive functioning in schizophrenia

The executive function (EF) is a set of abilities, which allows us to invoke voluntary control of our behavioral responses. These functions enable human beings to develop and carry out plans, make up analogies, obey social rules, solve problems, adapt to unexpected circumstances, do many tasks simultaneously, and locate episodes in time and place. EF includes divided attention and sustained attention, working memory (WM), set-shifting, flexibility, planning, and the regulation of goal directed behavior and can be defined as a brain function underlying the human faculty to act or think not only in reaction to external events but also in relation with internal goals and states. EF is mostly associated with dorsolateral prefrontal cortex (PFC). Besides EF, PFC is involved in self-regulation of behavior, i.e., the ability to regulate behavior according to internal goals and constraints, particularly in less structured situations. Self-regulation of behavior is subtended by ventral medial/orbital PFC. Impairment of EF is one of the most commonly observed deficits in schizophrenia through the various disease stages. Impairment in tasks measuring conceptualization, planning, cognitive flexibility, verbal fluency, ability to solve complex problems, and WM occur in schizophrenia. Disorders detected by executive tests are consistent with evidence from functional neuroimaging, which have shown PFC dysfunction in patients while performing these kinds of tasks. Schizophrenics also exhibit deficit in odor identifying, decision-making, and self-regulation of behavior suggesting dysfunction of the orbital PFC. However, impairment in executive tests is explained by dysfunction of prefronto-striato-thalamic, prefronto-parietal, and prefronto-temporal neural networks mainly. Disorders in EFs may be considered central facts with respect to schizophrenia and it has been suggested that negative symptoms may be explained by that executive dysfunction.

Identify schizophrenia using resting-state functional connectivity: an exploratory research and analysis

Background

Schizophrenia is a severe mental illness associated with the symptoms such as hallucination and delusion. The objective of this study was to investigate the abnormal resting-state functional connectivity patterns of schizophrenic patients which could identify furthest patients from healthy controls.

Methods

The whole-brain resting-state fMRI was performed on patients diagnosed with schizophrenia (n = 22) and on age- and gender-matched, healthy control subjects (n = 22). To differentiate schizophrenic individuals from healthy controls, the multivariate classification analysis was employed. The weighted brain regions were got by reconstruction arithmetic to extract highly discriminative functional connectivity information.

Results

The results showed that 93.2% (p < 0.001) of the subjects were correctly classified via the leave-one-out cross-validation method. And most of the altered functional connections identified located within the visual cortical-, default-mode-, and sensorimotor network. Furthermore, in reconstruction arithmetic, the fusiform gyrus exhibited the greatest amount of weight.

Conclusions

This study demonstrates that schizophrenic patients may be successfully differentiated from healthy subjects by using whole-brain resting-state fMRI, and the fusiform gyrus may play an important functional role in the physiological symptoms manifested by schizophrenic patients. The brain region of great weight may be the problematic region of information exchange in schizophrenia. Thus, our result may provide insights into the identification of potentially effective biomarkers for the clinical diagnosis of schizophrenia.