Discriminate Schizophrenia Patients Research Articles

High-Efficiency Graph Measures in Discriminating Schizophrenia Patients from Healthy Controls Using Structural and Functional Connectivity

High-Efficiency Graph Measures in Discriminating Schizophrenia Patients from Healthy Controls Using Structural and Functional Connectivity

Assessing the relationship between antipsychotic drug use and prolidase enzyme activity and oxidative stress in schizophrenia patients: a case-control study.

The relationship between proline, its association with oxidative stress, and its connection to schizophrenia is a subject that has not been sufficiently investigated. The aim of this study is to evaluate the possible effects of atypical and combined (typical and atypical) antipsychotic use on serum prolidase enzyme activity (SPEA) and serum oxidative stress parameters, and to assess the relationship between SPEA and oxidative stress in patients with schizophrenia. A total of 57 patients with schizophrenia, of which 34 were using atypical (AAPG) and 23 were using combined (typical and atypical) (CAPG) antipsychotic therapy, and 28 healthy volunteers (control group) were included in this case-control study. SPEA levels of AAPG and CAPG were significantly lower than that of control group ( P = 0.003). The oxidative stress index (OSI) value of AAPG was significantly higher than the other two groups ( P = 0.001). SPEA (<1860 U/l) and OSI (≥0.54) could discriminate schizophrenia patients with antipsychotic therapy from control groups ( P = 0.001 and P = 0.007, respectively). Lower SPEA levels were associated with antipsychotic use ( P = 0.007). The SPEA values of patients with schizophrenia on antipsychotics were significantly lower compared to controls. OSI values were significantly higher in atypical antipsychotic recipients compared to those on combined antipsychotics and healthy controls.

Comparison of gut microbiome profile in patients with schizophrenia and healthy controls - A plausible non-invasive biomarker?

The human gut microbiome regulates brain function through the microbiome-gut-brain axis and is implicated in several neuropsychiatric disorders. However, the relationship between the gut microbiome and the pathogenesis of schizophrenia (SCZ) is poorly defined, and very few studies have examined the effect of antipsychotic treatment response. We aim to study the differences in the gut microbiota among drug-naïve (DN SCZ) and risperidone-treated SCZ patients (RISP SCZ), compared to healthy controls (HCs). We recruited a total of 60 participants, from the clinical services of a large neuropsychiatric hospital, which included DN SCZ, RISP SCZ and HCs (n = 20 each). Fecal samples were analyzed using 16s rRNA sequencing in this cross-sectional study. No significant differences were found in taxa richness (alpha diversity) but microbial composition differed between SCZ patients (both DN and RISP) and HCs (PERMANOVA, p = 0.02). Linear Discriminant Analysis Effect Size (LEfSe) and Random Forest model identified the top six genera, which significantly differed in abundance between the study groups. A specific genus-level microbial panel of Ruminococcus, UCG005, Clostridium_sensu_stricto_1 and Bifidobacterium could discriminate SCZ patients from HCs with an area under the curve (AUC) of 0.79, HCs vs DN SCZ (AUC: 0.68), HCs vs RISP SCZ (AUC: 0.93) and DN SCZ vs RISP SCZ (AUC: 0.87). Our study identified distinct microbial signatures that could aid in the differentiation of DN SCZ, RISP SCZ, and HCs. Our findings contribute to a better understanding of the role of the gut microbiome in SCZ pathophysiology and suggest potential targeted interventions.

Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis.

Recent studies in human brain connectomics with multimodal magnetic resonance imaging (MRI) data have widely reported abnormalities in brain structure, function and connectivity associated with schizophrenia (SZ). However, most previous discriminative studies of SZ patients were based on MRI features of brain regions, ignoring the complex relationships within brain networks. We applied a graph convolutional network (GCN) to discriminating SZ patients using the features of brain region and connectivity derived from a combined multimodal MRI and connectomics analysis. Structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 140 SZ patients and 205 normal controls. Eighteen types of brain graphs were constructed for each subject using 3 types of node features, 3 types of edge features, and 2 brain atlases. We investigated the performance of 18 brain graphs and used the TopK pooling layers to highlight salient brain regions (nodes in the graph). The GCN model, which used functional connectivity as edge features and multimodal features (sMRI + fMRI) of brain regions as node features, obtained the highest average accuracy of 95.8%, and outperformed other existing classification studies in SZ patients. In the explainability analysis, we reported that the top 10 salient brain regions, predominantly distributed in the prefrontal and occipital cortices, were mainly involved in the systems of emotion and visual processing. Our findings demonstrated that GCN with a combined multimodal MRI and connectomics analysis can effectively improve the classification of SZ at an individual level, indicating a promising direction for the diagnosis of SZ patients. The code is available at https://github.com/CXY-scut/GCN-SZ.git.

Classification of schizophrenia-associated brain regions in resting-state fMRI

Recently, advances in neuroscience have attracted attention to the diagnosis, treatment, and damage to schizophrenia-associated brain regions using resting-state functional magnetic resonance imaging (rs-fMRI). This research is immersed in the endowment of machine learning approaches for discriminating schizophrenia patients to provide a viable solution. Toward these goals, firstly, we implemented a two sample t-tests to find the activation difference between schizophrenia patients and healthy controls. The average activation in control is higher than the average activation of the patient. Secondly, we implemented the correlation technique to find variations on presumably hidden associations between brain structure and its associated function. Moreover, current results support the viewpoint that the resting-state function integration is helpful to gain insight into the pathological mechanism of schizophrenia. Finally, Lasso regression is used to find a low-dimensional integration of the rs-fMRI and their experimental results showed that SVM classifier surpasses nine algorithms provided the best results with good accuracy of 94%.

Investigating cognitive flexibility deficit in schizophrenia using task-based whole-brain functional connectivity.

Cognitive flexibility is a core cognitive control function supported by the brain networks of the whole-brain. Schizophrenic patients show deficits in cognitive flexibility in conditions such as task-switching. A large number of neuroimaging studies have revealed abnormalities in local brain activations associated with deficits in cognitive flexibility in schizophrenia, but the relationship between impaired cognitive flexibility and the whole-brain functional connectivity (FC) pattern is unclear. We investigated the task-based functional connectivity of the whole-brain in patients with schizophrenia and healthy controls during task-switching. Multivariate pattern analysis (MVPA) was utilized to investigate whether the FC pattern can be used as a feature to discriminate schizophrenia patients from healthy controls. Graph theory analysis was further used to quantify the degrees of integration and segregation in the whole-brain networks to interpret the different reconfiguration patterns of brain networks in schizophrenia patients and healthy controls. The results showed that the FC pattern classified schizophrenia patients and healthy controls with significant accuracy. Moreover, the altered whole-brain functional connectivity pattern was driven by a lower degree of network integration and segregation in schizophrenia, indicating that both global and local information transfers at the entire-network level were less efficient in schizophrenia patients than in healthy controls during task-switching processing. These results investigated the group differences in FC profiles during task-switching and not only elucidated that FC patterns are changed in schizophrenic patients, suggesting that task-based FC could be used as a potential neuromarker to discriminate schizophrenia patients from healthy controls in cognitive flexibility but also provide increased insight into the brain network organization that may contribute to impaired cognitive flexibility.

Morphological fingerprinting: Identifying patients with first-episode schizophrenia using auto-encoded morphological patterns.

Although a large number of case-control statistical and machine learning studies have been conducted to investigate structural brain changes in schizophrenia, how best to measure and characterize structural abnormalities for use in classification algorithms remains an open question. In the current study, a convolutional 3D autoencoder specifically designed for discretized volumes was constructed and trained with segmented brains from 477 healthy individuals. A cohort containing 158 first-episode schizophrenia patients and 166 matched controls was fed into the trained autoencoder to generate auto-encoded morphological patterns. A classifier discriminating schizophrenia patients from healthy controls was built using 80% of the samples in this cohort by automated machine learning and validated on the remaining 20% of the samples, and this classifier was further validated on another independent cohort containing 77 first-episode schizophrenia patients and 58 matched controls acquired at a different resolution. This specially designed autoencoder allowed a satisfactory recovery of the input. With the same feature dimension, the classifier trained with autoencoded features outperformed the classifier trained with conventional morphological features by about 10% points, achieving 73.44% accuracy and 0.8 AUC on the internal validation set and 71.85% accuracy and 0.77 AUC on the external validation set. The use of features automatically learned from the segmented brain can better identify schizophrenia patients from healthy controls, but there is still a need for further improvements to establish a clinical diagnostic marker. However, with a limited sample size, the method proposed in the current study shed insight into the application of deep learning in psychiatric disorders.

Targeted metabolomics reveals aberrant profiles of serum bile acids in patients with schizophrenia

Emerging evidence indicates that bile acids (BAs), which are signaling molecules that regulate metabolism and inflammation, appear to be dysregulated in schizophrenia (SZ). Further investigation is warranted to comprehensively characterize BA profiles in SZ. To address this, we analyzed serum BA profiles in 108 drug-free patients with SZ and in 108 healthy controls (HCs), divided into a discovery set (n = 119) and a validation set (n = 97), using ultraperformance liquid chromatography triple quadrupole mass spectrometry. Forty serum BAs were detected and absolutely quantified using calibration curves. Global BA profiling showed differences in SZ and HC groups in both discovery and validation sets. The concentrations of chenodeoxycholic acid, ursodeoxycholic acid, 3β-chenodeoxycholic acid, 7-ketolithocholic acid, 3-dehydrocholic acid, total BAs, and unconjugated BAs were significantly lower in patients with SZ compared with HCs in the two sample sets. The BA deconjugation potentials by gut microbiota and the affinity index of the farnesoid X receptor (FXR) were notably decreased in SZ patients compared to those of HCs. Conjugated BAs and BA deconjugation potentials differed in SZ patients with first versus recurrent episodes, although similar BA profiles were observed in both groups. In addition, a panel of 8 BA variables acted as a potential auxiliary diagnostic biomarker in discriminating SZ patients from HCs, with area under the curve values for receiver operating characteristic curves of 0.758 and 0.732 and for precision-recall curves of 0.750 and 0.714 in the discovery and validation sets, respectively. This study has provided compelling evidence of comprehensive characteristics of circulating BA metabolism in patients with SZ and promoted a deeper understanding of the role of BAs in the pathophysiology of this disease, possibly via the gut microbiota-FXR signaling pathway.

Using Regularized Multi-Task Learning for Schizophrenia MRI Data Classification.

Machine learning techniques and magnetic resonance imaging methods have been widely used in computer-aided diagnosis and prognosis of severe brain diseases such as schizophrenia, Alzheimer, etc. Methods: In this paper, a regularized multi-task learning method for schizophrenia classification is proposed, and three MRI datasets of schizophrenia, collected from different data centers, are investigated. Firstly, slice extraction is used in image preprocessing. Then texture features of gray-level co-occurrence matrices are extracted from the above processed images. Finally, a p-norm regularized multi-task learning method is proposed to simultaneously learn the site-specific and site-shared features of the multi-site data, which can effectively discriminate schizophrenia patients from normal controls. The classification error rate on 10 datasets can be reduced from 10% to 30%. The proposed method obtains excellent results and provides objective evidence for clinical diagnosis and treatment of schizophrenia.

Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features.

BackgroundThe search for a method that utilizes biomarkers to identify patients with schizophrenia from healthy individuals has occupied researchers for decades. However, no single indicator can be employed to achieve the good in clinical practice. We aim to develop a comprehensive machine learning pipeline based on neurocognitive and electrophysiological combined features for distinguishing schizophrenia patients from healthy people.MethodsIn the present study, 69 patients with schizophrenia and 50 healthy controls participated. Neurocognitive (contains seven specific domains of cognition) and electrophysiological [prepulse inhibition, electroencephalography (EEG) power spectrum, detrended fluctuation analysis, and fractal dimension (FD)] features were collected, all these features were taken together to generate the identification models of schizophrenia by applying logistics, random forest, and extreme gradient boosting algorithm. The classification capabilities of these models were also evaluated.ResultsBoth the neurocognitive and electrophysiological feature sets showed a good classification effect with the highest accuracy greater than 85% and AUC greater than 90%. Specifically, the performances of the combined neurocognitive and electrophysiological feature sets achieved the highest accuracy of 93.28% and AUC of 97.91%. The extreme gradient boosting algorithm as a whole presented more stably and precisely in classification efficiency.ConclusionThe highest classification accuracy of 93.28% by combination of neurocognitive and electrophysiological features shows that both measurements are appropriate indicators to be used in discriminating schizophrenia patients and healthy individuals. Also, among three algorithms, extreme gradient boosting had better classified performances than logistics and random forest algorithms.

Construct validity for computational linguistic metrics in individuals at clinical risk for psychosis: Associations with clinical ratings

Language deficits are prevalent in psychotic illness, including its risk states, and are related to marked impairment in functioning. It is therefore important to characterize language impairment in the psychosis spectrum in order to develop potential preventive interventions. Natural language processing (NLP) metrics of semantic coherence and syntactic complexity have been used to discriminate schizophrenia patients from healthy controls (HC) and predict psychosis onset in individuals at clinical high-risk (CHR) for psychosis. To date, no studies have yet examined the construct validity of key NLP features with respect to clinical ratings of thought disorder in a CHR cohort. Herein we test the association of key NLP metrics of coherence and complexity with ratings of positive and negative thought disorder, respectively, in 60 CHR individuals, using Andreasen's Scale of Assessment of Thought, Language and Communication (TLC) Scale to measure of positive and negative thought disorder. As hypothesized, in CHR individuals, the NLP metric of semantic coherence was significantly correlated with positive thought disorder severity and the NLP metrics of complexity (sentence length and determiner use) were correlated with negative thought disorder severity. The finding of construct validity supports the premise that NLP analytics, at least in respect to core features of reduction of coherence and complexity, are capturing clinically relevant language disturbances in risk states for psychosis. Further psychometric study is required, in respect to reliability and other forms of validity.

Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study.

Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p < 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p < 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.

Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning

BackgroundIndividuals with psychiatric disorders perceive the world differently. Previous studies indicated impaired color vision and weakened color discrimination ability in psychotic patients. Examining the paintings from psychotic patients can measure the visual-motor function. However, few studies examined the potential changes in the color painting behavior in these individuals. The current study aims to discriminate schizophrenia patients from healthy controls (HCs) and predict PANSS scores of schizophrenia patients according to their paintings.MethodsIn the present study, we retrospectively analyzed the paintings colored by 281 chronic schizophrenia patients and 35 HCs. The images were scanned and processed using series of computational analyses.ResultsThe results showed that schizophrenia patients tend to use less color and exhibit different strokes compared to HCs. Using a deep learning residual neural network (ResNet), we were able to discriminate patients from HCs with over 90% accuracy. Further, we developed a novel convolutional neural network to predict PANSS positive, negative, general psychopathology, and total scores. The Root Mean Square Error (RMSE) of the prediction was low, which indicates higher accuracy of prediction.ConclusionIn conclusion, the deep learning paradigm showed the large potential to discriminate schizophrenia patients from HCs based on color paintings. Besides, this color painting-based paradigm can effectively predict clinical symptom severity for chronic schizophrenia patients. The color paintings by schizophrenia patients show potential as a tool for clinical diagnosis and prognosis. These findings show potential as a tool for clinical diagnosis and prognosis among schizophrenia patients.

Alteration of lipids and amino acids in plasma distinguish schizophrenia patients from controls: A targeted metabolomics study.

Schizophrenia (SCZ) is a serious psychiatric disorder. Metabolite disturbance is an important pathogenic factor in schizophrenic patients. In this study, we aim to identify plasma lipid and amino acid biomarkers for SCZ using targeted metabolomics. Plasma from 76 SCZ patients and 50 matched controls were analyzed using the LC/MS-based multiple reaction monitoring (MRM) metabolomics approach. A total of 182 targeted metabolites, including 22 amino acids and 160 lipids or lipid-related metabolites were observed. We used binary logistic regression analysis to determine whether the lipid and amino acid biomarkers could discriminate SCZ patients from controls. The area under the curve (AUC) from receiver operation characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance of the biomarkers panel. We identified 19 significantly differentially expressed metabolites between the SCZ patients and the controls (false discovery rate < 0.05), including one amino acid and 18 lipids or lipid-related metabolites. The binary logistic regression-selected panel showed good diagnostic performance in the drug-naïve group (AUC = 0.936) and all SCZ patients (AUC = 0.948), especially in the drug-treated group (AUC = 0.963). Plasma lipids and amino acids showed significant dysregulation in SCZ, which could effectively discriminate SCZ patients from controls. The LC/MS/MS-based approach provides reliable data for the objective diagnosis of SCZ.

Expression of BDNF-Associated lncRNAs in Treatment-Resistant Schizophrenia Patients.

Long non-coding RNAs (lncRNAs) play a decisive role in the development of the central nervous system and modulation, differentiation, and function of neurons. Thus, any abnormal pattern of expression of these transcripts might alter normal development leading to neuropsychiatric disorders. In this regard, transcripts of brain-derived neurotrophic factor (BDNF) and four BDNF-associated lncRNAs (BDNF-AS, MIR137HG, MIAT, and PNKY) were evaluated in the peripheral blood of schizophrenia (SCZ) patients as well as normal subjects. The results indicated that the relative expression (RE) of PNKY was higher in SCZ patients as compared with controls (posterior beta of RE=2.605, P value=0.006) and in female patients compared with female controls (posterior beta of RE=2.831, P value<0.0001). BDNF expression was also higher in SCZ patients when compared with controls (posterior beta of RE=0.64, P value<0.036). Finally, a correlation was detected between the disease status and gender in terms of BDNF-AS expression (P value=0.026). An inverse correlation was also found between levels of PNKY and age in the control group (r=-0.30, P value<0.0001). Expressions of BDNF and all lncRNAs were correlated with each other in both patients and controls. PNKY had the best diagnostic power among all assessed genes in the identification of disease status (area under curve=0.78). BDNF, BDNF-AS, MIR137HG, and MIAT genes could discriminate SCZ patients from normal subjects with diagnostic power of 71%, 72%, 67%, and 68%, respectively. The current investigation suggests the possibility of the application of transcript levels of lncRNAs as an SCZ diagnostic marker. However, it warrants further studies in larger sample sizes.

Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score

Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale multisite schizophrenia classification. Our findings may provide insight into the underlying pathophysiology and risk mechanisms of schizophrenia.

A Computerized Method for Automatic Detection of Schizophrenia Using EEG Signals.

Diagnosis of schizophrenia (SZ) is traditionally performed through patient's interviews by a skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias. Hence the aim of this study is to develop an automatic SZ identification scheme using electroencephalogram (EEG) signals that can eradicate the aforementioned problems and support clinicians and researchers. This study introduces a methodology design involving empirical mode decomposition (EMD) technique for diagnosis of SZ from EEG signals to perfectly handle the behavior of non-stationary and nonlinear EEG signals. In this study, each EEG signal is decomposed into intrinsic mode functions (IMFs) by the EMD algorithm and then twenty-two statistical characteristics/features are calculated from these IMFs. Among them, five features are selected as significant feature applying Kruskal Wallis test. The performance of the obtained feature set is tested through several renowned classifierson a SZ EEG dataset. Among the considered classifiers, theensemble bagged tree performed as the best classifier producing 93.21% correct classification rate for SZ, with an overall accuracy of 89.59% for IMF 2. These results indicate that EEG signals discriminate SZ patients from healthy control (HC) subjects efficiently and have the potential to become a tool for the psychiatrist to support the positive diagnosis of SZ.

Classification of schizophrenia using general linear model and support vector machine via fNIRS.

Schizophrenia is a type of serious mental illness. In clinical practice, it is still a challenging problem to identify schizophrenia-related brain patterns due to the lack of objective physiological data support and a unified data analysis method, physicians can only use the subjective experience to distinguish schizophrenia patients and healthy people, which may easily lead to misdiagnosis. In this study, we designed an optimized data-preprocessing method accompanied with techniques of general linear model feature extraction, independent sample t-test feature selection and support vector machine to identify a set of robust fNIRS pattern features as a biomarker to discriminate schizophrenia patients and healthy people. Experimental results demonstrated that the proposed combination way of data preprocessing, feature extraction, feature selection and support vector machine classification can effectively identify schizophrenia patients and the healthy people with a leave-one-out-cross-validation classification accuracy of 89.5%.

Evaluation of miRNA Expression Profiles in Schizophrenia Using Principal-Component Analysis-Based Unsupervised Feature Extraction Method.

Schizophrenia (SZ) is a disease that causes mental disability and affects 1% of the total population in the world. Our aim was to identify new molecular targets that would help to diagnose and treat these patients. The GSE54578 microRNA expression profile was downloaded from the Gene Expression Omnibus database that consists of peripheral blood samples of 15 first-onset SZ patients and 15 healthy controls. Principal-component analysis-based unsupervised feature extraction (FE), protein-protein interaction network, and pathway enrichment were performed, and microRNA (miRNA)-hub gene network was established. A set of seven miRNA (hsa-miR-373-5p, hsa-miR-199a-3p, hsa-miR-22-5p, hsa-miR-4711-3p, hsa-miR-3157-3p, hsa-miR-542-5p, and hsa-miR-3615) could successfully discriminate SZ patients from healthy controls with high accuracy. We identified two miRNAs (hsa-miR-373-5p and hsa-miR-199a-3p) as a signature that mostly related to SZ. These miRNAs could be potential novel biomarkers and could contribute to the clinical treatment of this disease.

Development and testing of a web-based battery to remotely assess cognitive health in individuals with schizophrenia

Cognitive impairment in schizophrenia is often severe, enduring, and contributes significantly to chronic disability. A standardized platform for identifying cognitive impairments and measuring treatment effects in cognition is a critical aspect of comprehensive evaluation and treatment for individuals with schizophrenia. In this project, we developed and tested a suite of ten web-based, neuroscience-informed cognitive assessments that are designed to enable the interpretation of specific deficits that could signal that an individual is experiencing cognitive difficulties. The assessment suite assays speed of processing, sustained attention, executive functioning, learning and socio-affective processing in the auditory and visual modalities. We have obtained data from 283 healthy individuals who were recruited online and 104 individuals with schizophrenia who also completed formal neuropsychological testing. Our data show that the assessments 1) are acceptable and tolerable to users, with successful completion in an average of under 40 min; 2) reliably measure the distinct theoretical cognitive constructs they were designed to assess; 3) can discriminate schizophrenia patients from healthy controls with a fair degree of accuracy (AUROC > 0.70); and 4) have promising construct, convergent, and external validity. Further optimization and validation work is in progress to finalize the evaluation process prior to promoting the dissemination of these assessments in real-world settings.