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

Brain plasticity and adolescent HIV: A randomised controlled trial protocol investigating behavioural and hemodynamic responses in attention cognitive rehabilitation therapy

Despite advances in antiretroviral pharmacology, neuroHIV in the central nervous system (CNS), causes neuronal dysregulation, which is associated with compromised neurocognition. Non-pharmaceutical interventions such as HIV cognitive rehabilitation training (HIV-CRT), have shown potential to partially reverse cognitive deficits, sequent HIV neuroinvasion. Nonetheless, no studies exist pairing cognitive outcomes with objective neuroimaging biomarkers in adolescent HIV-CRT. This longitudinal pre-post-quasi-experimental protocol examined cognitive outcomes, paired with optimal neuroimaging outcomes following customised attention training in adolescent HIV. Twenty-six adolescents living with HIV were randomly assigned to either the treatment group, which received attention CRT using ACTIVATE™, (n = 13), or to the treatment as usual group (n = 13). Cognitive outcomes were examined using the NEPSY-II, and BRIEF; whilst neuroimaging outcomes were determined by changes in oxygenated haemoglobin (HbO), as determined by functional near-infrared spectrometry (fNIRS). Functional connectivity fNIRS measures were evaluated using seed-based correlation analysis, located in the central executive network (CEN). This study serves to guide the development and identification of objective biomarkers for adolescent neuroHIV, sequent CRT amongst children living with HIV in Sub-Saharan Africa.

Discriminative analysis of schizophrenia and major depressive disorder using fNIRS

BACKGROUND

Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions.

METHODS

A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists.

RESULTS

Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively.

CONCLUSION

This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.

Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review

Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n = 12), attention deficit and hyperactivity disorder (n = 7), and autism spectrum disorder (n = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n = 11) and ΔHbO-based functional connections (n = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.

A scoping review of utilization of the verbal fluency task in Chinese and Japanese clinical settings with near-infrared spectroscopy

This review targets the application of the Verbal Fluency Task (VFT) in conjunction with functional near-infrared spectroscopy (fNIRS) for diagnosing psychiatric disorders, specifically in the contexts of China and Japan. These two countries are at the forefront of integrating fNIRS with VFT in clinical psychiatry, often employing this combination as a complementary tool alongside traditional psychiatric examinations. Our study aims to synthesize research findings on the hemodynamic responses elicited by VFT task in clinical settings of the two countries, analyzing variations in task design (phonological versus semantic), stimulus modality (auditory versus visual), and the impact of language typology. The focus on China and Japan is crucial, as it provides insights into the unique applications and adaptations of VFT in these linguistically and culturally distinct environments. By exploring these specific cases, our review underscores the importance of tailoring VFT to fit the linguistic and cultural context, thereby enhancing its validity and utility in cross-cultural psychiatric assessments.

The effect of seed location on functional connectivity: evidence from an image-based meta-analysis

Introduction

Default mode network (DMN) is the most involved network in the study of brain development and brain diseases. Resting-state functional connectivity (rsFC) is the most used method to study DMN, but different studies are inconsistent in the selection of seed. To evaluate the effect of different seed selection on rsFC, we conducted an image-based meta-analysis (IBMA).

Methods

We identified 59 coordinates of seed regions of interest (ROIs) within the default mode network (DMN) from 11 studies (retrieved from Web of Science and Pubmed) to calculate the functional connectivity; then, the uncorrected t maps were obtained from the statistical analyses. The IBMA was performed with the t maps.

Results

We demonstrate that the overlap of meta-analytic maps across different seeds' ROIs within DMN is relatively low, which cautions us to be cautious with seeds' selection.

Discussion

Future studies using the seed-based functional connectivity method should take the reproducibility of different seeds into account. The choice of seed may significantly affect the connectivity results.

Differentiating people with schizophrenia from healthy controls in a developing Country: An evaluation of portable functional near infrared spectroscopy (fNIRS) as an adjunct diagnostic tool

INTRODUCTION

This study aimed to evaluate portable functional near-infrared spectroscopy (fNIRS) device as an adjunct diagnostic tool in Vietnam to assess hemodynamics when people with schizophrenia and healthy controls performed cognitive tasks.

METHODS

One hundred fifty-seven participants were divided into schizophrenia (n = 110) and healthy controls group (n = 47), which were recruited by match of age, and gender. Hemodynamic responses in the frontal cortex were monitored with a 48-channel portable device during the Stroop Color-Word Test (SCWT) and Verbal Fluency Test (VFT). General linear model compared the differences in oxyhemoglobin (HbO₂) levels between the two groups. The Receiver Operating Characteristic (ROC) graph was generated for each neuroanatomical area.

RESULTS

People with schizophrenia did not show significant activation in the frontal lobe during the SCWT and VFT as compared to pre-task. During the VFT, the area under the ROC curve of the bilateral dorsolateral prefrontal cortex, bilateral orbitofrontal cortex, bilateral frontopolar prefrontal cortex, and bilateral ventrolateral prefrontal cortex were greater than 0.7 (p < 0.001). The area under the ROC curve (AUC) for the right orbitofrontal cortex was maximal during the VFT (AUC = 0.802, 95%CI = 0.731-0.872). The Youden's index reached a peak (0.57) at the optimal cut-point value (HbO₂ cutoff <0.209 μmol/ml for schizophrenia) in which the sensitivity was 85%; specificity was 72%; positive predictive value (PPV) was 0.88; negative predictive value (NPV) was 0.68 and correct classification rate was 76%.

DISCUSSION

Assessing hemodynamics during VFT by portable fNIRS offers the potential as an adjunct diagnostic tool for schizophrenia in developing countries.

Atypical frontotemporal cortical activity in first-episode adolescent-onset schizophrenia during verbal fluency task: A functional near-infrared spectroscopy study

Background

Frontotemporal cortex dysfunction has been found to be associated with cognitive impairment in patients with schizophrenia (SCZ). In patients with adolescent-onset SCZ, a more serious type of SCZ with poorer functional outcome, cognitive impairment appeared to occur at an early stage of the disease. However, the characteristics of frontotemporal cortex involvement in adolescent patients with cognitive impairment are still unclear. In the present study, we aimed to illustrate the frontotemporal hemodynamic response during a cognitive task in adolescents with first-episode SCZ.

Methods

Adolescents with first-episode SCZ who were aged 12-17 and demographically matched healthy controls (HCs) were recruited. We used a 48-channel functional near-infrared spectroscopy (fNIRS) system to record the concentration of oxygenated hemoglobin (oxy-Hb) in the participants' frontotemporal area during a verbal fluency task (VFT) and analyzed its correlation with clinical characteristics.

Results

Data from 36 adolescents with SCZ and 38 HCs were included in the analyses. Significant differences were found between patients with SCZ and HCs in 24 channels, mainly covering the dorsolateral prefrontal cortex, superior and middle temporal gyrus and frontopolar area. Adolescents with SCZ showed no increase of oxy-Hb concentration in most channels, while the VFT performance was comparable between the two groups. In SCZ, the intensity of activation was not associated with the severity of symptoms. Finally, receiver operating characteristic analysis indicated that the changes in oxy-Hb concentration could help distinguish the two groups.

Conclusion

Adolescents with first-episode SCZ showed atypical cortical activity in the frontotemporal area during the VFT, and fNIRS features might be more sensitive indicators in cognitive assessment, indicating that the characteristic hemodynamic response pattern might be potential imaging biomarkers for this population.

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.

Effect of Shift Work on Cognitive Function in Chinese Coal Mine Workers: A Resting-State fNIRS Study

Aim: Pilot study to examine the impact of shift work on cognitive function in Chinese coal mine workers. Background: Shift work is commonly used in modern industries such as the coal industry, and there is growing concern over the impact that shift work has on miners’ work performance and personal well-being. Method: A total of 54 miners working three shifts (17 in morning shift, 18 in afternoon, and 19 in night shift) participated in this exploratory study. A resting-state fNIRS functional connectivity method was conducted to assess the cognitive ability before and after the work shift. Results: Results showed significant differences in cognitive ability between before and after the work shifts among the three-shift workers. The brain functional connectivity was reduced ranking as the night, afternoon, and morning shifts. Decreased brain functional connectivity at the end of the working shift was found compared with before in the morning and afternoon shifts. Opposite results were obtained during the night shift. The resting-state functional brain networks in the prefrontal cortex of all groups exhibited small-world properties. Significant differences in betweenness centrality and nodal local efficiency were found in the prefrontal cortex in the morning and night shifts. Conclusions: The current findings provide new insights regarding the effect of shift work on the cognitive ability of Chinese coal mine workers from the view of brain science.

Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not?

(1) Background: As a world-recognized high-risk occupation, coal mine workers need various cognitive functions to process the surrounding information to cope with a large number of perceived hazards or risks. Therefore, it is necessary to explore the connection between coal mine workers’ neural activity and unsafe behavior from the perspective of cognitive neuroscience. This study explored the functional brain connectivity of coal mine workers who have engaged in unsafe behaviors (EUB) and those who have not (NUB). (2) Methods: Based on functional near-infrared spectroscopy (fNIRS), a total of 106 workers from the Hongliulin coal mine of Shaanxi North Mining Group, one of the largest modern coal mines in China, completed the test. Pearson’s Correlation Coefficient (COR) analysis, brain network analysis, and two-sample t-test were used to investigate the difference in brain functional connectivity between the two groups. (3) Results: The results showed that there were significant differences in functional brain connectivity between EUB and NUB among the frontopolar area (p = 0.002325), orbitofrontal area (p = 0.02102), and pars triangularis Broca’s area (p = 0.02888). Small-world properties existed in the brain networks of both groups, and the dorsolateral prefrontal cortex had significant differences in clustering coefficient (p = 0.0004), nodal efficiency (p = 0.0384), and nodal local efficiency (p = 0.0004). (4) Conclusions: This study is the first application of fNIRS to the field of coal mine safety. The fNIRS brain functional connectivity analysis is a feasible method to investigate the neuropsychological mechanism of unsafe behavior in coal mine workers in the view of brain science.

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.

fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases

Significance: Clinical use of fNIRS-derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. We provide an algorithm to extract cognition-related features by eliminating the effect of background signal contamination, hence improving the classification accuracy. Aim: The aim in this study is to investigate the classification accuracy of an fNIRS-derived biomarker based on global efficiency (GE). To this end, fNIRS data were collected during a computerized Stroop task from healthy controls and patients with migraine, obsessive compulsive disorder, and schizophrenia. Approach: Functional connectivity (FC) maps were computed from [HbO] time series data for neutral (N), congruent (C), and incongruent (I) stimuli using the partial correlation approach. Reconstruction of FC matrices with optimal choice of principal components yielded two independent networks: cognitive mode network (CM) and default mode network (DM). Results: GE values computed for each FC matrix after applying principal component analysis (PCA) yielded strong statistical significance leading to a higher specificity and accuracy. A new index, neurocognitive ratio (NCR), was computed by multiplying the cognitive quotients (CQ) and ratio of GE of CM to GE of DM. When mean values of NCR ( N C R ¯ ) over all stimuli were computed, they showed high sensitivity (100%), specificity (95.5%), and accuracy (96.3%) for all subjects groups. Conclusions: N C R ¯ can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients.

The Role of Reward System in Dishonest Behavior: A Functional Near-Infrared Spectroscopy Study

Previous studies showed that the cortical reward system plays an important role in deceptive behavior. However, how the reward system activates during the whole course of dishonest behavior and how it affects dishonest decisions remain unclear. The current study investigated these questions. One hundred and two participants were included in the final analysis. They completed two tasks: monetary incentive delay (MID) task and an honesty task. The MID task served as the localizer task and the honesty task was used to measure participants' deceptive behaviors. Participants' spontaneous responses in the honesty task were categorized into three conditions: Correct-Truth condition (tell the truth after guessing correctly), Incorrect-Truth condition (tell the truth after guessing incorrectly), and Incorrect-Lie condition (tell lies after guessing incorrectly). To reduce contamination from neighboring functional regions as well as to increase sensitivity to small effects (Powell et al., Devel Sci 21:e12595, 2018), we adopted the individual functional channel of interest (fCOI) approach to analyze the data. Specially, we identified the channels of interest in the MID task in individual participants and then applied them to the honesty task. The result suggested that the reward system showed different activation patterns during different phases: In the pre-decision phase, the reward system was activated with the winning of the reward. During the decision and feedback phase, the reward system was activated when people made the decisions to be dishonest and when they evaluated the outcome of their decisions. Furthermore, the result showed that neural activity of the reward system toward the outcome of their decision was related to subsequent dishonest behaviors. Thus, the present study confirmed the important role of the reward system in deception. These results can also shed light on how one could use neuroimaging techniques to perform lie-detection.

Classification of Schizophrenia by Functional Connectivity Strength Using Functional Near Infrared Spectroscopy

Functional near-infrared spectroscopy (fNIRS) has been widely employed in the objective diagnosis of patients with schizophrenia during a verbal fluency task (VFT). Most of the available methods depended on the time-domain features extracted from the data of single or multiple channels. The present study proposed an alternative method based on the functional connectivity strength (FCS) derived from an individual channel. The data measured 100 patients with schizophrenia and 100 healthy controls, who were used to train the classifiers and to evaluate their performance. Different classifiers were evaluated, and support machine vector achieved the best performance. In order to reduce the dimensional complexity of the feature domain, principal component analysis (PCA) was applied. The classification results by using an individual channel, a combination of several channels, and 52 ensemble channels with and without the dimensional reduced technique were compared. It provided a new approach to identify schizophrenia, improving the objective diagnosis of this mental disorder. FCS from three channels on the medial prefrontal and left ventrolateral prefrontal cortices rendered accuracy as high as 84.67%, sensitivity at 92.00%, and specificity at 70%. The neurophysiological significance of the change at these regions was consistence with the major syndromes of schizophrenia.