Differentiation of first-episode schizophrenia patients from healthy controls using ROI-based multiple structural brain variables

Multikernel Capsule Network for Schizophrenia Identification

Schizophrenia seriously affects the quality of life. To date, both simple (e.g., linear discriminant analysis) and complex (e.g., deep neural network) machine-learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. To overcome the aforementioned drawbacks, we proposed a multikernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match partition sizes of the brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of the widely used dropout strategy in deep learning, we developed capsule dropout in the capsule layer to prevent overfitting of the model. The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. MKCapsnet is promising for schizophrenia identification. Our study first utilized the capsule neural network for analyzing functional connectivity of magnetic resonance imaging (MRI) and proposed a novel multikernel capsule structure with the consideration of brain anatomical parcellation, which could be a new way to reveal brain mechanisms. In addition, we provided useful information in the parameter setting, which is informative for further studies using a capsule network for other neurophysiological signal classification.

Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data

Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of 70 schizophrenic subjects and 70 healthy subjects. The decision-level fusion method outperformed the single-feature-type approaches and achieved a 97.85% accuracy, a 98.33% sensitivity, a 96.83% specificity. Moreover, feature-fusion scheme resulted in a 98.57% accuracy, a 99.71% sensitivity, a 97.66% specificity, and an area under the ROC curve of 0.9984. In general, decision-level and feature-level fusion schemes boosted the performance of schizophrenia detectors based on fMRI features.

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.

Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study

Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) patients from healthy controls (HC) using magnetic resonance images. Each study, however, uses different datasets, classification algorithms, and validation techniques. Here, we perform a critical appraisal of the accuracy of machine learning methodologies used in SZ/HC classifications studies by comparing three machine learning algorithms (logistic regression [LR], support vector machines [SVMs], and linear discriminant analysis [LDA]) on three independent datasets (435 subjects total) using two tissue density estimates and cortical thickness (CT). Performance is assessed using 10-fold cross-validation, as well as a held-out validation set. Classification using CT outperformed tissue densities, but there was no clear effect of dataset. LR, SVMs, and LDA each yielded the highest accuracies for a different feature set and validation paradigm, but most accuracies were between 55 and 70%, well below previously reported values. The highest accuracy achieved was 73.5% using CT data and an SVM. Taken together, these results illustrate some of the obstacles to constructing effective disease classifiers, and suggest that tissue densities and CT may not be sufficiently sensitive for SZ/HC classification given current available methodologies and sample sizes.

Brain morphologic changes in early stages of psychosis: Implications for clinical application and early intervention

To date, a large number of magnetic resonance imaging (MRI) studies have been conducted in schizophrenia, which generally demonstrate gray matter reduction, predominantly in the frontal and temporo-limbic regions, as well as gross brain abnormalities (e.g., a deviated sulcogyral pattern). Although the causes as well as timing and course of these findings remain elusive, these morphologic changes (especially gross brain abnormalities and medial temporal lobe atrophy) are likely present at illness onset, possibly reflecting early neurodevelopmental abnormalities. In addition, longitudinal MRI studies suggest that patients with schizophrenia and related psychoses also have progressive gray matter reduction during the transition period from prodrome to overt psychosis, as well as initial periods after psychosis onset, while such changes may become almost stable in the chronic stage. These active brain changes during the early phases seem to be relevant to the development of clinical symptoms in a region-specific manner (e.g., superior temporal gyrus atrophy and positive psychotic symptoms), but may be at least partly ameliorated by antipsychotic medication. Recently, increasing evidence from MRI findings in individuals at risk for developing psychosis has suggested that those who subsequently develop psychosis have baseline brain changes, which could be at least partly predictive of later transition into psychosis. In this article, we selectively review previous MRI findings during the course of psychosis and also refer to the possible clinical applicability of these neuroimaging research findings, especially in the diagnosis of schizophrenia and early intervention for psychosis.

Aberrant Prefrontal-Thalamic-Cerebellar Circuit in Schizophrenia and Depression: Evidence From a Possible Causal Connectivity

Neuroimaging studies have suggested the presence of abnormalities in the prefrontal-thalamic-cerebellar circuit in schizophrenia (SCH) and depression (DEP). However, the common and distinct structural and causal connectivity abnormalities in this circuit between the two disorders are still unclear. In the current study, structural and resting-state functional magnetic resonance imaging (fMRI) data were acquired from 20 patients with SCH, 20 depressive patients and 20 healthy controls (HC). Voxel-based morphometry analysis was first used to assess gray matter volume (GMV). Granger causality analysis, seeded at regions with altered GMVs, was subsequently conducted. To discover the differences between the groups, ANCOVA and post hoc tests were performed. Then, the relationships between the structural changes, causal connectivity and clinical variables were investigated. Finally, a leave-one-out resampling method was implemented to test the consistency. Statistical analyses showed the GMV and causal connectivity changes in the prefrontal-thalamic-cerebellar circuit. Compared with HC, both SCH and DEP exhibited decreased GMV in middle frontal gyrus (MFG), and a lower GMV in MFG and medial prefrontal cortex (MPFC) in SCH than DEP. Compared with HC, both patient groups showed increased causal flow from the right cerebellum to the MPFC (common causal connectivity abnormalities). And distinct causal connectivity abnormalities (increased causal connectivity from the left thalamus to the MPFC in SCH than HC and DEP, and increased causal connectivity from the right cerebellum to the left thalamus in DEP than HC and SCH). In addition, the structural deficits in the MPFC and its causal connectivity from the cerebellum were associated with the negative symptom severity in SCH. This study found common/distinct structural deficits and aberrant causal connectivity patterns in the prefrontal-thalamic-cerebellar circuit in SCH and DEP, which may provide a potential direction for understanding the convergent and divergent psychiatric pathological mechanisms between SCH and DEP. Furthermore, concomitant structural and causal connectivity deficits in the MPFC may jointly contribute to the negative symptoms of SCH.

Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain

OBJECTIVE

Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features.

MATERIALS AND METHODS

T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ.

RESULTS

The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005).

CONCLUSION

A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.

Effects of the Inertia Barbell Training on Lumbar Muscle T2 Relaxation Time

Sun, M-Y, Lu, J-Q, Ma, Z-C, Lü, J-J, Huang, Q, Sun, Y-N, and Liü, Y. Effects of the inertia barbell training on lumbar muscle T2 relaxation time. J Strength Cond Res 34(12): 3454-3462, 2020-The purpose of this study was to investigate variations in T2 relaxation time in normal human lumbar muscles caused by inertia barbell training. Thirty undergraduate healthy men (mean age = 19 ± 1.2 years, body mass = 72 ± 10.0 kg, and height = 1.78 ± 0.1 m) were recruited to participate in this study. Subjects were randomly assigned into 2 groups: an inertia barbell training group (IBTG) (n = 15) and a normal barbell-training group (NBTG) (n = 15). All subjects participated in lumbar flexion and extension muscle strength training for 1 hour per time, 3 times per week for a total of 8 weeks. The lumbar area of each subject was scanned before and after the experiment using a 3.0T superconductive magnetic resonance imaging system. The T2 values measured after intervention were significantly different compared with the T2 values measured before the experiment in both the IBTG and NBTG groups (p < 0.001). After intervention, there was no significant difference in T2 values between the IBTG and NBTG groups (p = 0.17). The ([INCREMENT]T2)/T2 percentage was significantly different in the IBTG group (p < 0.01). This study demonstrated that 8 weeks of strength training led to significant improvements in the values for T2 relaxation time of the lumbar muscles. Furthermore, the ([INCREMENT]T2)/T2 percentage for IBTG was higher than that for NBTG, which suggested that lumbar muscle activity increased more with inertial barbell training.

Supervised, Multivariate, Whole-Brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research

We examined how penalized linear discriminant analysis with resampling, which is a supervised, multivariate, whole-brain reduction technique, can help schizophrenia diagnostics and research. In an experiment with magnetic resonance brain images of 52 first-episode schizophrenia patients and 52 healthy controls, this method allowed us to select brain areas relevant to schizophrenia, such as the left prefrontal cortex, the anterior cingulum, the right anterior insula, the thalamus, and the hippocampus. Nevertheless, the classification performance based on such reduced data was not significantly better than the classification of data reduced by mass univariate selection using a t-test or unsupervised multivariate reduction using principal component analysis. Moreover, we found no important influence of the type of imaging features, namely local deformations or gray matter volumes, and the classification method, specifically linear discriminant analysis or linear support vector machines, on the classification results. However, we ascertained significant effect of a cross-validation setting on classification performance as classification results were overestimated even though the resampling was performed during the selection of brain imaging features. Therefore, it is critically important to perform cross-validation in all steps of the analysis (not only during classification) in case there is no external validation set to avoid optimistically biasing the results of classification studies.

Multi-center MRI prediction models: Predicting sex and illness course in first episode psychosis patients

Structural Magnetic Resonance Imaging (MRI) studies have attempted to use brain measures obtained at the first-episode of psychosis to predict subsequent outcome, with inconsistent results. Thus, there is a real need to validate the utility of brain measures in the prediction of outcome using large datasets, from independent samples, obtained with different protocols and from different MRI scanners. This study had three main aims: 1) to investigate whether structural MRI data from multiple centers can be combined to create a machine-learning model able to predict a strong biological variable like sex; 2) to replicate our previous finding that an MRI scan obtained at first episode significantly predicts subsequent illness course in other independent datasets; and finally, 3) to test whether these datasets can be combined to generate multicenter models with better accuracy in the prediction of illness course. The multi-center sample included brain structural MRI scans from 256 males and 133 females patients with first episode psychosis, acquired in five centers: University Medical Center Utrecht (The Netherlands) (n=67); Institute of Psychiatry, Psychology and Neuroscience, London (United Kingdom) (n=97); University of São Paulo (Brazil) (n=64); University of Cantabria, Santander (Spain) (n=107); and University of Melbourne (Australia) (n=54). All images were acquired on 1.5-Tesla scanners and all centers provided information on illness course during a follow-up period ranging 3 to 7years. We only included in the analyses of outcome prediction patients for whom illness course was categorized as either "continuous" (n=94) or "remitting" (n=118). Using structural brain scans from all centers, sex was predicted with significant accuracy (89%; p<0.001). In the single- or multi-center models, illness course could not be predicted with significant accuracy. However, when reducing heterogeneity by restricting the analyses to male patients only, classification accuracy improved in some samples. This study provides proof of concept that combining multi-center MRI data to create a well performing classification model is possible. However, to create complex multi-center models that perform accurately, each center should contribute a sample either large or homogeneous enough to first allow accurate classification within the single-center.

Detecting Neuroimaging Biomarkers for Psychiatric Disorders: Sample Size Matters

In a recent review, it was suggested that much larger cohorts are needed to prove the diagnostic value of neuroimaging biomarkers in psychiatry. While within a sample, an increase of diagnostic accuracy of schizophrenia (SZ) with number of subjects (N) has been shown, the relationship between N and accuracy is completely different between studies. Using data from a recent meta-analysis of machine learning (ML) in imaging SZ, we found that while low-N studies can reach 90% and higher accuracy, above N/2 = 50 the maximum accuracy achieved steadily drops to below 70% for N/2 > 150. We investigate the role N plays in the wide variability in accuracy results in SZ studies (63-97%). We hypothesize that the underlying cause of the decrease in accuracy with increasing N is sample heterogeneity. While smaller studies more easily include a homogeneous group of subjects (strict inclusion criteria are easily met; subjects live close to study site), larger studies inevitably need to relax the criteria/recruit from large geographic areas. A SZ prediction model based on a heterogeneous group of patients with presumably a heterogeneous pattern of structural or functional brain changes will not be able to capture the whole variety of changes, thus being limited to patterns shared by most patients. In addition to heterogeneity (sample size), we investigate other factors influencing accuracy and introduce a ML effect size. We derive a simple model of how the different factors, such as sample heterogeneity and study setup determine this ML effect size, and explain the variation in prediction accuracies found from the literature, both in cross-validation and independent sample testing. From this, we argue that smaller-N studies may reach high prediction accuracy at the cost of lower generalizability to other samples. Higher-N studies, on the other hand, will have more generalization power, but at the cost of lower accuracy. In conclusion, when comparing results from different ML studies, the sample sizes should be taken into account. To assess the generalizability of the models, validation (by direct application) of the prediction models should be tested in independent samples. The prediction of more complex measures such as outcome, which are expected to have an underlying pattern of more subtle brain abnormalities (lower effect size), will require large samples.

From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics

Psychiatric disorders are increasingly being recognised as having a biological basis, but their diagnosis is made exclusively behaviourally. A promising approach for 'biomarker' discovery has been based on pattern recognition methods applied to neuroimaging data, which could yield clinical utility in future. In this review we survey the literature on pattern recognition for making diagnostic predictions in psychiatric disorders, and evaluate progress made in translating such findings towards clinical application. We evaluate studies on many criteria, including data modalities used, the types of features extracted and algorithm applied. We identify problems common to many studies, such as a relatively small sample size and a primary focus on estimating generalisability within a single study. Furthermore, we highlight challenges that are not widely acknowledged in the field including the importance of accommodating disease prevalence, the necessity of more extensive validation using large carefully acquired samples, the need for methodological innovations to improve accuracy and to discriminate between multiple disorders simultaneously. Finally, we identify specific clinical contexts in which pattern recognition can add value in the short to medium term.

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases.

[Personalised medicine in psychiatry and psychotherapy. A review of the current state-of-the-art in the biomarker-based early recognition of psychoses]

The main goal of psychiatric high-risk research--the personalised early recognition and intervention of schizophrenic and affective psychoses--is one of the biggest challenges of current clinical psychiatry due to the immense socioeconomic burden of these disorders. In this regard, this review discusses the prospects and caveats of new clinical, neuropsychological, neurophysiological and imaging-based concepts aimed at optimising the current state-of-the-art of early recognition. Finally, multivariate modelling and machine learning methods are presented as a novel methodological framework facilitating the decoding of early psychosis into different intermediate phenotypes. In the future, these phenotypes could be employed for a more objective risk stratification that operates at the single-subject level. This could allow us to generate clinically applicable prognostic biomarkers for these disorders that would propel the individualised prevention of disease transition, chronification and psychopharmacological treatment resistance of psychotic disorders.

Discrimination between schizophrenia and major depressive disorder by magnetic resonance imaging of the female brain

BACKGROUND

Although schizophrenia and major depressive disorder (MDD) differ on a variety of neuroanatomical measures, a diagnostic tool to discriminate these disorders has not yet been established. We tried to identify structural changes of the brain that best discriminate between schizophrenia and MDD on the basis of gray matter volume, ventricle volume, and diffusion tensor imaging (DTI).

METHOD

The first exploration sample consisted of 25 female patients with schizophrenia and 25 females with MDD. Regional brain volumes and fractional anisotropy (FA) values were entered into a discriminant analysis. The second validation sample consisted of 18 female schizophrenia and 16 female MDD patients.

RESULTS

The stepwise discriminant analysis resulted in correct classification rates of 0.80 in the schizophrenic group and 0.76 in MDD. In the second validation sample, the obtained model yielded correct classification rates of 0.72 in the schizophrenia group and 0.88 in the MDD group.

CONCLUSION

Our results suggest that schizophrenia and MDD have differential structural changes in the examined brain regions and that the obtained discriminant score may be useful to discriminate the two disorders.

Classification of schizophrenia patients based on resting-state functional network connectivity

There is a growing interest in automatic classification of mental disorders based on neuroimaging data. Small training data sets (subjects) and very large amount of high dimensional data make it a challenging task to design robust and accurate classifiers for heterogeneous disorders such as schizophrenia. Most previous studies considered structural MRI, diffusion tensor imaging and task-based fMRI for this purpose. However, resting-state data has been rarely used in discrimination of schizophrenia patients from healthy controls. Resting data are of great interest, since they are relatively easy to collect, and not confounded by behavioral performance on a task. Several linear and non-linear classification methods were trained using a training dataset and evaluate with a separate testing dataset. Results show that classification with high accuracy is achievable using simple non-linear discriminative methods such as k-nearest neighbors (KNNs) which is very promising. We compare and report detailed results of each classifier as well as statistical analysis and evaluation of each single feature. To our knowledge our effects represent the first use of resting-state functional network connectivity (FNC) features to classify schizophrenia.

The influence of sex on cognitive insight and neurocognitive functioning in schizophrenia

Both impaired insight and cognitive deficits in schizophrenia are core features of this disorder. Previous studies have demonstrated the complex relationships between neurocognition and cognitive insight, as well as the contribution of neurocognition in explaining cognitive insight. However, there is lack of research regarding the influences of sex on the relation of neurocognition and cognitive insight. The present study sought to elucidate sex differences in cognitive insight and neurocognition in schizophrenia. Seventy three outpatients (male=39) with DSM-IV diagnosis of schizophrenia or schizoaffective disorder were enrolled in the cross-sectional study. The participants were assessed with cognitive insight using the Beck Cognitive Insight Scale, executive functions using the Wisconsin Card Sorting Test, sustained attention using the Conner's Continuous Performance Test (Second Edition), and intelligence using the Wechsler Adult Intelligence Scale-Third version, respectively. Sex differences in demographic and clinical variables were small; nevertheless, female patients had significantly later age of illness onset and higher levels of formal education than males (p<0.05). Poor cognitive insight was attributed to impairment in performance of executive function and sustained attention. Results from hierarchical regression analyses indicated sex as a moderator only in the association between cognitive insight and executive function. Our findings support an association between poor cognitive insight and neurocognitive impairment in outpatients with schizophrenia and suggest that the relationship may be sex-specific. This study highlights potential targets for effective intervention and rehabilitation in improving patients' insight toward mental illness.

Volume reductions in frontopolar and left perisylvian cortices in methamphetamine induced psychosis

Consumption of methamphetamine disturbs dopaminergic transmission and sometimes provokes schizophrenia-like-psychosis, named methamphetamine-associated psychosis (MAP). While previous studies have repeatedly reported regional volume reductions in the frontal and temporal areas as neuroanatomical substrates for psychotic symptoms, no study has examined whether such neuroanatomical substrates exist or not in patients with MAP. Magnetic resonance images obtained from twenty patients with MAP and 20 demographically-matched healthy controls (HC) were processed for voxel-based morphometry (VBM) using Diffeomorphic Anatomical Registration using Exponentiated Lie Algebra. An analysis of covariance model was adopted to identify volume differences between subjects with MAP and HC, treating intracranial volume as a confounding covariate. The VBM analyses showed significant gray matter volume reductions in the left perisylvian structures, such as the posterior inferior frontal gyrus and the anterior superior temporal gyrus, and the frontopolar cortices, including its dorsomedial, ventromedial, dorsolateral, and ventrolateral portions, and white matter volume reduction in the orbitofrontal area in the patients with MAP compared with the HC subjects. The smaller regional gray matter volume in the medial portion of the frontopolar cortex was significantly correlated with the severe positive symptoms in the individuals with MAP. The volume reductions in the left perisylvian structure suggest that patients with MAP have a similar pathophysiology to schizophrenia, whereas those in the frontopolar cortices and orbitofrontal area suggest an association with antisocial traits or vulnerability to substance dependence.

Neuroanatomical pattern classification in a population-based sample of first-episode schizophrenia

Recent neuroanatomical pattern classification studies have attempted to individually classify cases with psychotic disorders using morphometric MRI data in an automated fashion. However, this approach has not been tested in population-based samples, in which variable patterns of comorbidity and disease course are typically found. We aimed to evaluate the diagnostic accuracy (DA) of the above technique to discriminate between incident cases of first-episode schizophrenia identified in a circumscribed geographical region over a limited period of time, in comparison with next-door healthy controls. Sixty-two cases of first-episode schizophrenia or schizophreniform disorder and 62 age, gender and educationally-matched controls underwent 1.5 T MRI scanning at baseline, and were naturalistically followed-up over 1 year. T1-weighted images were used to train a high-dimensional multivariate classifier, and to generate both spatial maps of the discriminative morphological patterns between groups and ROC curves. The spatial map discriminating first-episode schizophrenia patients from healthy controls revealed a complex pattern of regional volumetric abnormalities in the former group, affecting fronto-temporal-occipital gray and white matter regions bilaterally, including the inferior fronto-occipital fasciculus, as well as the third and lateral ventricles. However, an overall modest DA (73.4%) was observed for the individual discrimination between first-episode schizophrenia patients and controls, and the classifier failed to predict 1-year prognosis (remitting versus non-remitting course) of first-episode schizophrenia (DA=58.3%). In conclusion, using a "real world" sample recruited with epidemiological methods, the application of a neuroanatomical pattern classifier afforded only modest DA to classify first-episode schizophrenia subjects and next-door healthy controls, and poor discriminative power to predict the 1-year prognosis of first-episode schizophrenia.

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.

Discrimination of female schizophrenia patients from healthy women using multiple structural brain measures obtained with voxel-based morphometry

AIM

Although schizophrenia and control subjects differ on a variety of neuroanatomical measures, the specificity and sensitivity of any one measure for differentiating between the two groups are low. To identify the correlative pattern of brain changes that best discriminate schizophrenia patients from healthy subjects, discriminant analysis techniques using voxel-based morphometry were applied.

METHODS

The first analysis was conducted to obtain a statistical model that classified 105 female healthy subjects and 38 female schizophrenia patients. First, the differences in gray matter and cerebrospinal fluid volume between the patients and healthy subjects were evaluated using optimized voxel-based morphometry. Then, a discriminant analysis reflecting the results of this evaluation was adopted. The second analysis was performed to prospectively validate the statistical model by successfully classifying a new group that consisted of 23 female healthy subjects and 23 female schizophrenia patients.

RESULTS

The use of these variables resulted in correct classification rates of 0.72 in the control subjects and 0.76 in the schizophrenia patients. In the second validation analysis using these variables, correct classification rates of 0.70 in the control subjects and 0.74 in the schizophrenia patients were achieved.

CONCLUSION

Schizophrenia patients have structural deviations in multiple brain areas, and a combination of structural brain measures can distinguish between patients and controls.

A novel approach to probabilistic biomarker-based classification using functional near-infrared spectroscopy

Pattern recognition approaches to the analysis of neuroimaging data have brought new applications such as the classification of patients and healthy controls within reach. In our view, the reliance on expensive neuroimaging techniques which are not well tolerated by many patient groups and the inability of most current biomarker algorithms to accommodate information about prior class frequencies (such as a disorder's prevalence in the general population) are key factors limiting practical application. To overcome both limitations, we propose a probabilistic pattern recognition approach based on cheap and easy‐to‐use multi‐channel near‐infrared spectroscopy (fNIRS) measurements. We show the validity of our method by applying it to data from healthy controls (n = 14) enabling differentiation between the conditions of a visual checkerboard task. Second, we show that high‐accuracy single subject classification of patients with schizophrenia (n = 40) and healthy controls (n = 40) is possible based on temporal patterns of fNIRS data measured during a working memory task. For classification, we integrate spatial and temporal information at each channel to estimate overall classification accuracy. This yields an overall accuracy of 76% which is comparable to the highest ever achieved in biomarker‐based classification of patients with schizophrenia. In summary, the proposed algorithm in combination with fNIRS measurements enables the analysis of sub‐second, multivariate temporal patterns of BOLD responses and high‐accuracy predictions based on low‐cost, easy‐to‐use fNIRS patterns. In addition, our approach can easily compensate for variable class priors, which is highly advantageous in making predictions in a wide range of clinical neuroimaging applications. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.

Reduced amygdala and hippocampal volumes in patients with methamphetamine psychosis

The similarity between psychotic symptoms in patients with schizophrenia such as hallucinations and delusions and those caused by administration of methamphetamine has been accepted. While the etiology of schizophrenia remains unclear, methamphetamine induced psychosis, which is obviously occurred by methamphetamine administration, had been widely considered as a human pharmaceutical model of exogenous psychosis. Although volume reductions in medial temporal lobe structure in patients with schizophrenia have repeatedly been reported, those in patients with methamphetamine psychosis have not yet been clarified. Magnetic resonance images (MRI) were obtained from 20 patients with methamphetamine psychosis and 20 age, sex, parental socio-economic background, and IQ matched healthy controls. A reliable manual tracing methodology was employed to measure the gray matter volume of the amygdala and the hippocampus from MRIs. Significant gray matter volume reductions of both the amygdala and hippocampus were found bilaterally in the subjects with methamphetamine psychosis compared with the controls. The degree of volume reduction was significantly greater in the amygdala than in hippocampus. While the total gray, white matter and intracranial volumes were also significantly smaller-than-normal in the patients; the regional gray matter volume reductions in these medial temporal structures remained statistically significant even after these global brain volumes being controlled. The prominent volume reduction in amygdala rather than that in hippocampus could be relatively specific characteristics of methamphetamine psychosis, since previous studies have shown significant volume reductions less frequently in amygdala than in hippocampus of the other psychosis such as schizophrenia.

Putative structural neuroimaging endophenotypes in schizophrenia: a comprehensive review of the current evidence

The genetic contribution to schizophrenia etiopathogenesis is underscored by the fact that the best predictor of developing schizophrenia is having an affected first-degree relative, which increases lifetime risk by tenfold, as well as the observation that when both parents are affected, the risk of schizophrenia increases to approximately 50%, compared with 1% in the general population. The search to elucidate the complex genetic architecture of schizophrenia has employed various approaches, including twin and family studies to examine co-aggregation of brain abnormalities, studies on genetic linkage and studies using genome-wide association to identify genetic variations associated with schizophrenia. ‘Endophenotypes’, or ‘intermediate phenotypes’, are potentially narrower constructs of genetic risk. Hypothetically, they are intermediate in the pathway between genetic variation and clinical phenotypes and can supposedly be implemented to assist in the identification of genetic diathesis for schizophrenia and, possibly, in redefining clinical phenomenology.

Statistical adjustments for brain size in volumetric neuroimaging studies: some practical implications in methods

Volumetric magnetic resonance imaging (MRI) brain data provide a valuable tool for detecting structural differences associated with various neurological and psychiatric disorders. Analysis of such data, however, is not always straightforward, and complications can arise when trying to determine which brain structures are "smaller" or "larger" in light of the high degree of individual variability across the population. Several statistical methods for adjusting for individual differences in overall cranial or brain size have been used in the literature, but critical differences exist between them. Using agreement among those methods as an indication of stronger support of a hypothesis is dangerous given that each requires a different set of assumptions be met. Here we examine the theoretical underpinnings of three of these adjustment methods (proportion, residual, and analysis of covariance) and apply them to a volumetric MRI data set. These three methods used for adjusting for brain size are specific cases of a generalized approach which we propose as a recommended modeling strategy. We assess the level of agreement among methods and provide graphical tools to assist researchers in determining how they differ in the types of relationships they can unmask, and provide a useful method by which researchers may tease out important relationships in volumetric MRI data. We conclude with the recommended procedure involving the use of graphical analyses to help uncover potential relationships the ROI volumes may have with head size and give a generalized modeling strategy by which researchers can make such adjustments that include as special cases the three commonly employed methods mentioned above.

Sex differences in cognition among persons with schizophrenia and healthy first-degree relatives

Previous research suggests differences between women and men in the clinical features of schizophrenia, but studies examining sex differences in neuropsychological functioning have reached inconsistent results. In the present study, sex differences in cognition and clinical features were investigated in population-based samples of participants with schizophrenia (n=218), their healthy first-degree relatives (n=438) and controls (n=123). Sex differences in illness features were small; nevertheless, women with schizophrenia had less negative symptoms and lived independently more often than men. The schizophrenia group had impairments in all studied neuropsychological domains, and the relatives were impaired in processing speed and set-shifting. In all groups, women performed better than men in processing speed, set-shifting and verbal episodic memory, whereas men outperformed women in visual working memory. The group-by-sex interaction was significant in two variables: women outperformed men in the relatives group in immediate verbal reproduction and in the use of semantic clustering as a learning strategy, while there was no sex difference in the schizophrenia group. In conclusion, sex differences in cognition are mostly similar in schizophrenia to those among controls, despite sex differences in illness features. The preservation of sex differences also in first-degree relatives supports the conclusion.

Classification of first-episode schizophrenia patients and healthy subjects by automated MRI measures of regional brain volume and cortical thickness

BACKGROUND

Although structural magnetic resonance imaging (MRI) studies have repeatedly demonstrated regional brain structural abnormalities in patients with schizophrenia, relatively few MRI-based studies have attempted to distinguish between patients with first-episode schizophrenia and healthy controls.

METHOD

Three-dimensional MR images were acquired from 52 (29 males, 23 females) first-episode schizophrenia patients and 40 (22 males, 18 females) healthy subjects. Multiple brain measures (regional brain volume and cortical thickness) were calculated by a fully automated procedure and were used for group comparison and classification by linear discriminant function analysis.

RESULTS

Schizophrenia patients showed gray matter volume reductions and cortical thinning in various brain regions predominantly in prefrontal and temporal cortices compared with controls. The classifiers obtained from 66 subjects of the first group successfully assigned 26 subjects of the second group with accuracy above 80%.

CONCLUSION

Our results showed that combinations of automated brain measures successfully differentiated first-episode schizophrenia patients from healthy controls. Such neuroimaging approaches may provide objective biological information adjunct to clinical diagnosis of early schizophrenia.

Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects

Recent techniques of image analysis brought the possibility to recognize subjects based on discriminative image features. We performed a magnetic resonance imaging (MRI)-based classification study to assess its usefulness for outcome prediction of first-episode schizophrenia patients (FES). We included 39 FES patients and 39 healthy controls (HC) and performed the maximum-uncertainty linear discrimination analysis (MLDA) of MRI brain intensity images. The classification accuracy index (CA) was correlated with the Positive and Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning scale (GAF) at 1-year follow-up. The rate of correct classifications of patients with poor and good outcomes was analyzed using chi-square tests. MLDA classification was significantly better than classification by chance. Leave-one-out accuracy was 72%. CA correlated significantly with PANSS and GAF scores at the 1-year follow-up. Moreover, significantly more patients with poor outcome than those with good outcome were classified correctly. MLDA of brain MR intensity features is, therefore, able to correctly classify a significant number of FES patients, and the discriminative features are clinically relevant for clinical presentation 1 year after the first episode of schizophrenia. The accuracy of the current approach is, however, insufficient to be used in clinical practice immediately. Several methodological issues need to be addressed to increase the usefulness of this classification approach.

Do we have any solid evidence of clinical utility about the pathophysiology of schizophrenia?

A diagnosis of schizophrenia, as in most of psychiatric practice, is made largely by eliciting symptoms with reference to subjective, albeit operationalized, criteria. This diagnosis then provides some rationale for management. Objective diagnostic and therapeutic tests are much more desirable, provided they are reliably measured and interpreted. Definite advances have been made in our understanding of schizophrenia in recent decades, but there has been little consideration of how this information could be used in clinical practice. We review here the potential utility of the strongest and best replicated risk factors for and manifestations of schizophrenia within clinical, epidemiological, cognitive, blood biomarker and neuroimaging domains. We place particular emphasis on the sensitivity, specificity and predictive power of pathophysiological indices for making a diagnosis, establishing an early diagnosis or predicting treatment response in schizophrenia. We conclude that a number of measures currently available have the potential to increase the rigour of clinical assessments in schizophrenia. We propose that the time has come to more fully evaluate these and other well replicated abnormalities as objective potential diagnostic and prognostic guides, and to steer future clinical, therapeutic and nosological research in this direction.

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.

Volume reduction and altered sulco-gyral pattern of the orbitofrontal cortex in first-episode schizophrenia

BACKGROUND

Although clinical and neuropsychological findings have implicated functional deficits of the orbitofrontal cortex (OFC) in schizophrenia, structural magnetic resonance imaging (MRI) studies of this region have yielded inconsistent findings. In addition, it remains elusive whether the OFC morphology in first-episode patients is related to their clinical features.

METHOD

MR images were acquired from 42 (24 males, 18 females) first-episode schizophrenia patients and 35 (20 males, 15 females) age-, gender-, and parental socio-economic status (SES)-matched healthy subjects. The OFC sub-regions (orbital gyrus and straight gyrus) were measured on contiguous 1-mm-thick coronal slices. The OFC sulco-gyral pattern was also evaluated for each subject. Furthermore, the relationships between OFC morphology and clinical measures were examined.

RESULTS

The volumes of the bilateral orbital gyri were significantly reduced in schizophrenia patients compared with healthy subjects, whereas the volumes of the straight gyri did not show differences between the groups. Among the schizophrenia patients, the volume of the left orbital gyrus was inversely correlated with their SES and illness duration. The OFC sulco-gyral patterns were significantly different between the patients and controls in the right hemisphere.

CONCLUSION

This study demonstrated morphologic abnormalities of the OFC in first-episode schizophrenia patients, which may have reflected neurodevelopmental aberrations and neurodegenerative changes during the first episode of the illness. Our findings also suggest that such brain structural changes are related to the social dysfunction observed in schizophrenia.