Functional abnormalities in the left ventrolateral prefrontal cortex during a semantic fluency task, and their association with thought disorder in patients with schizophrenia.

Thought disorder is one of the primary symptoms in schizophrenia, yet the neural correlates and related semantic processing abnormalities remain unclear. We aimed to investigate the relationship between functional prefrontal abnormalities and thought disorder in schizophrenia using 2 types of verbal fluency tasks: the letter fluency task (LFT) and the category fluency task (CFT). Fifty-six adult patients with schizophrenia and 56 healthy controls matched for age, gender, and IQ participated in the study. During completion of the 2 types of verbal fluency tasks, we measured oxy- and deoxy-hemoglobin concentration ([oxy-Hb] and [deoxy-Hb]) signal changes over a wide area of the bilateral prefrontal cortex, using a 52-channel near-infrared spectroscopy (NIRS) system. Thought disorder scores were evaluated using the positive and negative syndrome scale. CFT performance was significantly higher than LFT performance in both groups, while there was no significant difference in any prefrontal NIRS signal changes between the 2 tasks in either group. In both versions of verbal fluency task, healthy controls exhibited a significantly greater NIRS signal change than did patients with schizophrenia. On the CFT only, left ventrolateral prefrontal NIRS [deoxy-Hb] signals were significantly associated with thought disorder scores in patients with schizophrenia. Our results suggest that left ventrolateral prefrontal abnormalities in category fluency might be related to thought disorder in schizophrenia. This could lead to an improved understanding of the neural mechanisms within the left ventrolateral prefrontal cortex involved in mediating semantic processing, as well as the relationship between semantic processing abnormalities and thought disorder in schizophrenia.

Quantitative evaluation of deep and shallow tissue layers' contribution to fNIRS signal using multi-distance optodes and independent component analysis.

To quantify the effect of absorption changes in the deep tissue (cerebral) and shallow tissue (scalp, skin) layers on functional near-infrared spectroscopy (fNIRS) signals, a method using multi-distance (MD) optodes and independent component analysis (ICA), referred to as the MD-ICA method, is proposed. In previous studies, when the signal from the shallow tissue layer (shallow signal) needs to be eliminated, it was often assumed that the shallow signal had no correlation with the signal from the deep tissue layer (deep signal). In this study, no relationship between the waveforms of deep and shallow signals is assumed, and instead, it is assumed that both signals are linear combinations of multiple signal sources, which allows the inclusion of a "shared component" (such as systemic signals) that is contained in both layers. The method also assumes that the partial optical path length of the shallow layer does not change, whereas that of the deep layer linearly increases along with the increase of the source-detector (S-D) distance. Deep- and shallow-layer contribution ratios of each independent component (IC) are calculated using the dependence of the weight of each IC on the S-D distance. Reconstruction of deep- and shallow-layer signals are performed by the sum of ICs weighted by the deep and shallow contribution ratio. Experimental validation of the principle of this technique was conducted using a dynamic phantom with two absorbing layers. Results showed that our method is effective for evaluating deep-layer contributions even if there are high correlations between deep and shallow signals. Next, we applied the method to fNIRS signals obtained on a human head with 5-, 15-, and 30-mm S-D distances during a verbal fluency task, a verbal working memory task (prefrontal area), a finger tapping task (motor area), and a tetrametric visual checker-board task (occipital area) and then estimated the deep-layer contribution ratio. To evaluate the signal separation performance of our method, we used the correlation coefficients of a laser-Doppler flowmetry (LDF) signal and a nearest 5-mm S-D distance channel signal with the shallow signal. We demonstrated that the shallow signals have a higher temporal correlation with the LDF signals and with the 5-mm S-D distance channel than the deep signals. These results show the MD-ICA method can discriminate between deep and shallow signals.

Association of decreased prefrontal hemodynamic response during a verbal fluency task with EGR3 gene polymorphism in patients with schizophrenia and in healthy individuals.

The early growth response 3 (EGR3) gene is an immediate early gene that is expressed throughout the brain and has been suggested as a potential susceptibility gene for schizophrenia (SZ). EGR3 impairment is associated with various neurodevelopmental dysfunctions, and some animal studies have reported a role for EGR3 function in the prefrontal cortex. Therefore, EGR3 genotype variation may be reflected in prefrontal function. By using multi-channel near-infrared spectroscopy (NIRS) in an imaging genetics approach, we tested for an association between the EGR3 gene polymorphism and prefrontal hemodynamic response during a cognitive task in patients with SZ. We assessed 73 chronic patients with SZ and 73 age-, gender-, and genotype-matched healthy controls (HC) who provided written informed consent. We used NIRS to measure changes in prefrontal oxygenated hemoglobin concentration (oxyHb) during the letter version of a verbal fluency task (VFT). Statistical comparisons were performed among EGR3 genotype subgroups (rs35201266, GG/GA/AA). The AA genotype group showed significantly smaller oxyHb increases in the left dorsolateral prefrontal cortex (DLPFC) during the VFT than the GG and GA genotype groups; this was true for both patients with SZ and HC. Our findings provide in vivo human evidence of a significant influence of EGR3 polymorphisms on prefrontal hemodynamic activation level in healthy adults and in patients with SZ. Genetic variation in EGR3 may affect prefrontal function through neurodevelopment. This study illustrates the usefulness of NIRS in imaging genetics investigations on psychiatric disorders.

Similarities and Differences in Brain Activation Between Patients With Schizophrenia and Obsessive-Compulsive Disorder: A Near-Infrared Spectroscopy Study.

Schizophrenia (SZ) and obsessive-compulsive disorder (OCD) share several epidemiological and clinical features, but the neurobiological substrates shared by these two diseases remain unclear. This study aimed to explore the similarities and differences in brain function between them using near-infrared spectroscopy (NIRS). Eventually, 130 SZ patients, 70 OCD and 75 normal controls (NCs) were enrolled. A 52-channel NIRS instrument was used to detect the concentration changes in oxygenated hemoglobin ([oxy-Hb]) during the verbal fluency task. Ten regions of interests (ROIs) were defined: the bilateral dorsolateral prefrontal cortex (DLPFC), frontopolar cortex (FPC), orbitofrontal cortex (OFC), inferior prefrontal gyrus (IFG) and temporal gyrus (TG). Through two different analysis strategies based on channels or ROIs, we compared the [oxy-Hb] changes in three groups by one-way analysis of variance (ANOVA) and post-hoc tests. Across 52 channels, compared to the NC group, both SZ and OCD groups exhibited reduced activity in 17 channels, including left FPC, left DLPFC, bilateral OFC, IFG, middle TG, supplementary motor cortex and Broca's area, while SZ showed lower activity in channel 35 (right OFC) than OCD patients. Across all ROIs, compared to the NC group, both SZ and OCD groups showed reduced activity in 7 ROIs, including left FPC, bilateral OFC, IFG and TG, while SZ showed lower activity in the right OFC than OCD group, which were almost consistent with the results based on channels. This study suggests SZ and OCD present with some similar neuropathological changes, while SZ shows more severe impairment in the right OFC than OCD.

Left Frontotemporal Region Plays a Key Role in Letter Fluency Task-Evoked Activation and Functional Connectivity in Normal Subjects: A Functional Near-Infrared Spectroscopy Study.

Letter fluency task (LFT) is a tool that measures memory, executive function, and language function but lacks a definite cutoff value to define abnormalities. We used the optical signals of functional near-infrared spectroscopy (fNIRS) to study the differences in power and connectivity between the high-functioning and low-functioning participants while performing three successive LFTs, as well as the relationships between the brain network/power and LFT performance. We found that the most differentiating factor between these two groups was network topology rather than activation power. The high-functional group (7 men and 10 women) displayed higher left intra-hemispheric global efficiency, nodal strength, and shorter characteristic path length in the first section. They then demonstrated a higher power over the left Broca's area than the right corresponding area in the latter two sections. The low-LFT group (9 men and 11 women) displayed less left-lateralized connectivity and activation power. LFT performance was only related to the network topology rather than the power values, which was only presented in the low-functioning group in the second section. The direct correlation between power and connectivity primarily existed in the inter-hemispheric network, with the timing relationship also seeming to be present. In conclusion, the high-functioning group presented more prominent left-lateralized intra-hemispheric network connectivity and power activation, particularly in the Broca's area. The low-functioning group seemed to prefer using other networks, like the inter-hemispheric, rather than having a single focus on left intra-hemispheric connectivity. The network topology seemed to better reflect the LFT performance than did the power values.

Optimizing functional near-infrared spectroscopy (fNIRS) channels for schizophrenic identification during a verbal fluency task using metaheuristic algorithms.

Objective: We aimed to reduce the complexity of the 52-channel functional near-infrared spectroscopy (fNIRS) system to facilitate its usage in discriminating schizophrenia during a verbal fluency task (VFT). Methods: Oxygenated hemoglobin signals obtained using 52-channel fNIRS from 100 patients with schizophrenia and 100 healthy controls during a VFT were collected and processed. Three features frequently used in the analysis of fNIRS signals, namely time average, functional connectivity, and wavelet, were extracted and optimized using various metaheuristic operators, i.e., genetic algorithm (GA), particle swarm optimization (PSO), and their parallel and serial hybrid algorithms. Support vector machine (SVM) was used as the classifier, and the performance was evaluated by ten-fold cross-validation. Results: GA and GA-dominant algorithms achieved higher accuracy compared to PSO and PSO-dominant algorithms. An optimal accuracy of 87.00% using 16 channels was obtained by GA and wavelet analysis. A parallel hybrid algorithm (the best 50% individuals assigned to GA) achieved an accuracy of 86.50% with 8 channels on the time-domain feature, comparable to the reported accuracy obtained using 52 channels. Conclusion: The fNIRS system can be greatly simplified while retaining accuracy comparable to that of the 52-channel system, thus promoting its applications in the diagnosis of schizophrenia in low-resource environments. Evolutionary algorithm-dominant optimization of time-domain features is promising in this regard.

Classification of Schizophrenia by Seed-based Functional Connectivity using Prefronto-Temporal Functional Near Infrared Spectroscopy.

BACKGROUND: 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. NEW METHOD: 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. RESULTS: 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. COMPARISON WITH EXISTING METHODS: 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. CONCLUSIONS: Our findings suggest a reliable method with a high level of accuracy and a low level of instrumental complexity to identify patients with schizophrenia.

Prefrontal cortex dysfunction during verbal fluency task after atypicalantipsychotic treatment in schizophrenia: A near-infrared spectroscopy imaging study.

A number of near-infrared spectroscopy (NIRS) studies in schizophrenia have shown that there is a significant dysfunction of the prefrontal cortex (PFC) in patients with schizophrenia, but it remains unclear how atypical antipsychotics affect the function of the PFC. To investigate the changes in brain activation patterns in schizophrenia who accepted antipsychotic treatment, we used NIRS to measure the hemodynamic changes of the PFC in patients with schizophrenia at the time of enrollment and after 4 weeks of treatment during a verbal fluency task (VFT). We included 16 schizophrenia and 17 sex- and age- matched healthy controls. Compared with the healthy controls, the VFT performance was significantly worse in patients with schizophrenia, as was the activation of the PFC. Furthermore, after 4 weeks of treatment, there was no significant improvement in VFT performance and the activation of the PFC in schizophrenia. Our results suggest that the function of the PFC and cognitive skills in schizophrenia were significantly impaired and do not improve from a short-term treatment of atypical antipsychotics.

High-dose antidepressants affect near-infrared spectroscopy signals: A retrospective study.

BACKGROUND: Recent studies have highlighted the clinical usefulness of near-infrared spectroscopy (NIRS) in psychiatry. However, the potential effects of psychotropics on NIRS signals remain unknown. METHODS: We conducted a systematic chart review of 40 depressed patients who underwent NIRS scans during a verbal fluency task to clarify the relationships between psychotropic dosage and NIRS signals. The dosage of psychotropic medications was calculated using defined daily dose (DDD). We investigated the associations between the DDD of psychotropic medications and oxygenated hemoglobin (oxy-Hb) in single channel levels. LIMITATIONS: Retrospective study design and small sample size are the main limitations. RESULTS: Multiple regression analysis revealed that one channel in the right temporoparietal region had a significant association with antidepressant DDD controlling for age, sex, depression severity, and the DDD of antipsychotics and benzodiazepines. Moreover, high doses of antidepressants had significant effects on NIRS signals compared with low doses, in group comparisons. CONCLUSIONS: The dose-dependent impact of antidepressants on NIRS signals should be taken into account when interpreting NIRS data.

Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy.

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.

Association between social functioning and prefrontal hemodynamic responses in elderly adults.

Social functioning has received widespread attention as one of the most important outcomes in psychiatric disorders and has been related to cognitive functioning and the underlying brain activity. Cognitive decline, however, appears not only in the psychiatric population but also in aged individuals. In our previous study, we demonstrated a significant relationship between social functioning and prefrontal cortex (PFC) activity in patients with depression. However, it has not been shown whether the above relationship could be extended to healthy populations. The purpose of the present study was to investigate a possible association between social functioning and prefrontal hemodynamic responses in healthy elderly adults by using a non-invasive and low-constraint functional neuroimaging technique, near-infrared spectroscopy (NIRS). Study subjects included 55 healthy, elderly volunteers. We measured hemodynamic responses over prefrontal cortical (PFC) areas during the verbal fluency task by using multi-channel NIRS and analyzed the relationship between task-associated hemodynamic responses and social functioning as measured by the social adaptation self-evaluation scale (SASS). A significant positive relationship was observed between the SASS total score and PFC activation. Our findings suggest that PFC activation is associated with social functioning in healthy elderly adults. Furthermore, hemodynamic responses assessed using non-invasive NIRS could be a useful biological marker of these characteristics.