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Near-infrared spectroscopy (NIRS) has been applied to examine the possible functional alternations during the performance of cognitive tasks in schizophrenia. With this technique, previous studies have observed that patients with schizophrenia are often associated with reduced brain activation in the prefrontal cortex during the verbal fluency task (VFT) of the English version or the Japanese version. However, it remains unclear whether there is a brain functional impairment in Chinese-speaking patients with schizophrenia. In this study, we designed a Chinese version of the VFT and performed a multichannel NIRS study in a large group of patients with schizophrenia and healthy controls. We investigated brain activation during the task period of the Chinese version of the VFT within a schizophrenia group and a healthy group, respectively, and compared the relative changes between the two groups. Our results confirmed that Chinese-speaking patients with schizophrenia had significantly lower brain activation in the prefrontal cortex and superior temporal cortex when compared with healthy controls. Such findings based on the NIRS data provided us reliable evidences about brain functional deficits in the Chinese-speaking patients with schizophrenia. [ABSTRACT FROM AUTHOR]

Highlights: [•] The resting state fMRI was applied to investigate the Long Term Evolution (LTE) radiofrequency electromagnetic field (RF-EMF) exposure influence on spontaneous brain activity. [•] A controlled LTE RF-EMF exposure environment was designed, and the amplitude of low frequency fluctuation (ALFF) and fractional ALFF (fALFF) approaches were selected to analyze the resting state fMRI signals. [•] We found the spontaneous low frequency oscillations in brain were altered by the acute LTE RF-EMF exposure. [Copyright &y& Elsevier]

Purpose: In this study, we used magnetic resonance imaging (MRI) to investigate the anatomical alterations of cerebral cortex in children with Tourette syndrome (TS) and explore whether such deficits were related with their clinical symptoms.Methods: All subjects were scanned in a 3.0T MRI scanner with three-dimensional T1-weighted images (3DT1WI). Then, some surface-based features were extracted by using the FreeSurfer software. After that, the between-group differences of those features were assessed.Results: Sixty TS patients and 52 age- and gender-matched healthy control were included in this study. Surface-based analyses revealed altered cortical thickness, cortical sulcus, cortical curvature and local gyrification index (LGI) in TS group compared with healthy controls. The brain regions with significant-group differences in cortical thickness included postcentral gyrus, superiorparietal gyrus, rostral anterior cingulate cortex in the left hemisphere and frontal pole, lateral occipital gyrus, inferior temporal gyrus in the right hemisphere. In addition, the superior temporal gyrus, medial orbitofrontal gyrus, supramarginal gyrus, medial orbitofrontal gyrus, superiorparietal gyrus and lateral occipital gyrus showed significant between-group differences for cortical sulcus. Moreover, the brain regions with significant between-group differences in cortical curvature were located in caudal anterior cingulate cortex, supramarginal gyrus, inferior parietal gyrus and lateral occipital gyrus. The alteration of LGI were most prominent in the inferior temporal gyrus and insula. Additionally, there was no statistical difference in brain surface area for TS children compared with controls.Conclusion: The results of this study revealed that cortical thickness, sulcus, cortical curvature and LGI were changed in multiple brain regions for children with TS. [ABSTRACT FROM AUTHOR]

Background Based on near-infrared spectroscopy (NIRS), recent converging evidence has been observed that patients with schizophrenia exhibit abnormal functional activities in the prefrontal cortex during a verbal fluency task (VFT). Therefore, some studies have attempted to employ NIRS measurements to differentiate schizophrenia patients from healthy controls with different classification methods. However, no systematic evaluation was conducted to compare their respective classification performances on the same study population. New method In this study, we evaluated the classification performance of four classification methods (including linear discriminant analysis, k-nearest neighbors, Gaussian process classifier, and support vector machines) on an NIRS-aided schizophrenia diagnosis. We recruited a large sample of 120 schizophrenia patients and 120 healthy controls and measured the hemoglobin response in the prefrontal cortex during the VFT using a multichannel NIRS system. Features for classification were extracted from three types of NIRS data in each channel. We subsequently performed a principal component analysis (PCA) for feature selection prior to comparison of the different classification methods. Results We achieved a maximum accuracy of 85.83% and an overall mean accuracy of 83.37% using a PCA-based feature selection on oxygenated hemoglobin signals and support vector machine classifier. Comparison with existing methods This is the first comprehensive evaluation of different classification methods for the diagnosis of schizophrenia based on different types of NIRS signals. Conclusions Our results suggested that, using the appropriate classification method, NIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. [ABSTRACT FROM AUTHOR]

• Identifying schizophrenia by fNIRS using seed-based functional connectivity. • Classification performance outperforms most of the results of the available studies. • The method utilizes 26 channels for high-accuracy schizophrenia classification. • Flexible channel combinations enhance robustness of the classification. Schizophrenia is one of the most serious mental disorders. Currently, the diagnosis of schizophrenia mainly relies on scales and doctors' experience. Recently, functional near infrared spectroscopy (fNIRS) has been used to distinguish schizophrenia from other mental disorders. The conventional classification methods utilized time-course features from single or multiple fNIRS channels. The fNIRS data were obtained from 52 channels covering the frontotemporal cortices in 200 patients with schizophrenia and 100 healthy subjects during a Chinese verbal fluency task. The channels with significant between-group differences were selected as the seeds. Functional connectivity (FC) was calculated for each seed, and FCs with significant between-group differences were selected as the features for classification. The proposed method reduced the number of channels to 26 while achieving overall classification accuracy, sensitivity and specificity values as high as 89.67%, 93.00% and 86.00%, respectively, outperforming most of the reported results. The superior performance was attributed to the cross-scale neurological changes related to schizophrenia, which were employed by the classification method. In addition, the method provided multiple classification criteria with similar accuracy, consequently increasing the flexibility and reliability of the results. This is the first fNIRS study to classify schizophrenia based on FCs. This method integrated information from regional modulation, segregation and integration. The classification performance outperformed most of the classification methods described in previous studies. Our findings suggest a reliable method with a high level of accuracy and a low level of instrumental complexity to identify patients with schizophrenia. [ABSTRACT FROM AUTHOR]