Functional near infra-red spectroscopy (fNIRS) in schizophrenia: A review

A scoping review of functional near-infrared spectroscopy biomarkers in late-life depression: Depressive symptoms, cognitive functioning, and social functioning

Late-life depression is one of the most damaging mental illnesses, disrupting the normal lives of older people by causing chronic illness and cognitive impairment. Patients with late-life depression, accompanied by changes in appetite, insomnia, fatigue and guilt, are more likely to experience irritability, anxiety and somatic symptoms. It increases the risk of suicide and dementia and is a major challenge for the public health systems. The current clinical assessment, identification and effectiveness assessment of late-life depression are primarily based on history taking, mental status examination and scale scoring, which lack subjectivity and precision. Functional near-infrared spectroscopy is a rapidly developing optical imaging technology that objectively reflects the oxygenation of hemoglobin in different cerebral regions during different tasks and assesses the functional status of the cerebral cortex. This article presents a comprehensive review of the assessment of functional near-infrared spectroscopy technology in assessing depressive symptoms, social functioning, and cognitive functioning in patients with late-life depression. The use of functional near-infrared spectroscopy provides greater insight into the neurobiological mechanisms underlying depression and helps to assess these three aspects of functionality in depressed patients. In addition, the study discusses the limitations of previous research and explores potential advances in the field.

Patients with chronic ankle instability exhibit increased sensorimotor cortex activation and correlation with poorer lateral balance control ability during single-leg stance: a FNIRS study

Introduction

Chronic Ankle Instability (CAI) is a musculoskeletal condition that evolves from acute ankle sprains, and its underlying mechanisms have yet to reach a consensus. Mounting evidence suggests that neuroplastic changes in the brain following ankle injuries play a pivotal role in the development of CAI. Balance deficits are a significant risk factor associated with CAI, yet there is a scarcity of evidence regarding the sensorimotor cortical plasticity related to balance control in affected individuals. This study aims to evaluate the differences in cortical activity and balance abilities between patients with CAI and uninjured individuals during a single-leg stance, as well as the correlation between these factors, in order to elucidate the neurophysiological alterations in balance control among patients with CAI.

Methods

The study enrolled 24 patients with CAI and 24 uninjured participants. During single-leg stance, cortical activity was measured using a functional near-infrared spectroscopy (fNIRS) system, which included assessments of the pre-motor cortex (PMC), supplementary motor area (SMA), primary motor cortex (M1), and primary somatosensory cortex (S1). Concurrently, balance parameters were tested utilizing a three-dimensional force platform.

Results

Independent sample t-tests revealed that, compared with the uninjured individuals, the patients with CAI exhibited a significant increase in the changes of oxyhemoglobin concentration (ΔHbO) during single-leg stance within the left S1 at Channel 5 (t = 2.101, p = 0.041, Cohen's d = 0.607), left M1 at Channel 6 (t = 2.363, p = 0.022, Cohen's d = 0.682), right M1 at Channel 15 (t = 2.273, p = 0.029, Cohen's d = 0.656), and right PMC/SMA at Channel 11 (t = 2.467, p = 0.018, Cohen's d = 0.712). Additionally, the center of pressure root mean square (COP-RMS) in the mediolateral (ML) direction was significantly greater (t = 2.630, p = 0.012, Cohen's d = 0.759) in the patients with CAI. Furthermore, a moderate positive correlation was found between ML direction COP-RMS and ΔHbO2 in the M1 (r = 0.436; p = 0.033) and PMC/SMA (r = 0.488, p = 0.016), as well as between anteroposterior (AP) direction COP-RMS and ΔHbO in the M1 (r = 0.483, p = 0.017).

Conclusion

Patients with CAI demonstrate increased cortical activation in the bilateral M1, ipsilateral PMC/SMA, and contralateral S1. This suggests that patients with CAI may require additional brain resources to maintain balance during single-leg stance, representing a compensatory mechanism to uphold task performance amidst diminished lateral balance ability in the ankle joint.

Mobile fNIRS for exploring inter-brain synchrony across generations and time

While still relatively rare, longitudinal hyperscanning studies are exceptionally valuable for documenting changes in inter-brain synchrony, which may in turn underpin how behaviors develop and evolve in social settings. The generalizability and ecological validity of this experimental approach hinges on the selected imaging technique being mobile-a requirement met by functional near-infrared spectroscopy (fNIRS). fNIRS has most frequently been used to examine the development of inter-brain synchrony and behavior in child-parent dyads. In this position paper, we contend that dedicating attention to longitudinal and intergenerational hyperscanning stands to benefit the fields of social and cognitive neuroscience more broadly. We argue that this approach is particularly relevant for understanding the neural mechanisms underpinning intergenerational social dynamics, and potentially for benchmarking progress in psychological and social interventions, many of which are situated in intergenerational contexts. In line with our position, we highlight areas of intergenerational research that stand to be enhanced by longitudinal hyperscanning with mobile devices, describe challenges that may arise from measuring across generations in the real world, and offer potential solutions.

Decoupling brain activations of muscle-caused activations and mental intention-cause activations using the general linear model: A functional near-infrared spectroscopy study

Integrating a brain-computer interface into a lower-limb medical rehabilitation assistive device can enhance rehabilitation efficiency. The latest research in the field focuses on the decoding performance of different motions. However, the difference between muscle-caused primitive activation and mental intention-caused activation has not been fully investigated. Thus, our study tried to decouple these two kinds of cerebral activation using a general linear model (GLM). Nine healthy and right-handed subjects were recruited for a two-section experiment. They were asked to extend or flex their knees while seated in the first section of the experiment or standing in the second section. Functional near-infrared spectroscopy (fNIR) was adopted to monitor their hemodynamic changes. Two groups of paradigms (one for circle-wise analysis, the other for full-section analysis) were constructed from the experimental paradigm. Each group consisted of three (the first intention, the second intention, and the muscle activation). The constructed paradigms were fed to the Balloon model for six desired hemodynamic responses (dHRFs). The regressor of GLM consisted of three dHRFs and the corresponding motion artifacts and drifts. The simulated physiological noises were included in the structured background matrix. The results showed that all subjects had similar cerebral activation patterns for the intention to extend or flex knees. The activation during musclecaused activation was less intense than that caused by both intentions. This finding can help further research on more efficient motion intention detection and the possibility of multiple motions decoding.

Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases

The emergence of the global coronavirus pandemic in 2019 (COVID-19 disease) created a need for remote methods to detect and continuously monitor patients with infectious respiratory diseases. Many different devices, including thermometers, pulse oximeters, smartwatches, and rings, were proposed to monitor the symptoms of infected individuals at home. However, these consumer-grade devices are typically not capable of automated monitoring during both day and night. This study aims to develop a method to classify and monitor breathing patterns in real-time using tissue hemodynamic responses and a deep convolutional neural network (CNN)-based classification algorithm. Tissue hemodynamic responses at the sternal manubrium were collected in 21 healthy volunteers using a wearable near-infrared spectroscopy (NIRS) device during three different breathing conditions. We developed a deep CNN-based classification algorithm to classify and monitor breathing patterns in real time. The classification method was designed by improving and modifying the pre-activation residual network (Pre-ResNet) previously developed to classify two-dimensional (2D) images. Three different one-dimensional CNN (1D-CNN) classification models based on Pre-ResNet were developed. By using these models, we were able to obtain an average classification accuracy of 88.79% (without Stage 1 (data size reducing convolutional layer)), 90.58% (with 1 × 3 Stage 1), and 91.77% (with 1 × 5 Stage 1).

System Derived Spatial-Temporal CNN for High-Density fNIRS BCI

An intuitive and generalisable approach to spatial-temporal feature extraction for high-density (HD) functional Near-Infrared Spectroscopy (fNIRS) brain-computer interface (BCI) is proposed, demonstrated here using Frequency-Domain (FD) fNIRS for motor-task classification. Enabled by the HD probe design, layered topographical maps of Oxy/deOxy Haemoglobin changes are used to train a 3D convolutional neural network (CNN), enabling simultaneous extraction of spatial and temporal features. The proposed spatial-temporal CNN is shown to effectively exploit the spatial relationships in HD fNIRS measurements to improve the classification of the functional haemodynamic response, achieving an average F1 score of 0.69 across seven subjects in a mixed subjects training scheme, and improving subject-independent classification as compared to a standard temporal CNN.

Utility of portable functional near-infrared spectroscopy (fNIRS) in patients with bipolar and unipolar disorders: A comparison with healthy controls

BACKGROUND

This study aimed to evaluate portable functional near-infrared spectroscopy (fNIRS) device as an adjunct diagnostic tool for bipolar and unipolar disorders while performing cognitive tasks.

METHODS

150 participants were divided into three groups including bipolar, unipolar disorder, and healthy controls (50:50:50), matched by age, gender, and family history of mood disorder. Hemodynamics in the frontal cortex were monitored by fNIRS during the Stroop Color-Word Test and Verbal Fluency Test. The GLM compared the differences in oxy-hemoglobin levels between the two groups. The Receiver Operating Characteristic (ROC) graph was generated for each neuroanatomical area.

RESULTS

For people with BD group, the area under the ROC curve (AUC) for the left orbitofrontal cortex was maximal during the VFT [AUC = 0.727, 95%CI = 0.617-0.824]. The Youden's index reached a peak (0.40) at the optimal cut-point value (HbO2 cutoff <0.180 μmol/ml for BD) in which the sensitivity was 82 %; specificity was 58 %; PPV was 0.66; NPV was 0.76 and correct classification rate was 70 %. Regarding the UD group, during VFT, the highest value AUC [AUC = 0.822, 95%CI = 0.740-0.903] was recorded in the left dorsolateral prefrontal cortex with the optimal cut-off value (HbO2cutoff ≥0.163 μmol/ml for healthy controls; <0.163 for unipolar disorder), the sensitivity was 72 %; specificity was 82 %; PPV was 0.80; NPV was 0.75, correct classification rate was 77 %, and the Youden's index was 0.54.

CONCLUSION

Assessing hemodynamics during VFT using portable fNIRS offers the potential as an adjunct diagnostic tool for mood disorders in low-resource environments.

Reduced left dorsolateral prefrontal activation in problematic smartphone users during the Stroop task: An fNIRS study

Introduction

The widespread use of smartphones has triggered concern over problematic smartphone use (PSPU), as well as the need to elucidate its underlying mechanisms. However, the correlation between cortical activation and deficient inhibitory control in PSPU remains unclear.

Methods

This study examined inhibitory control using the color-word matching Stroop task and its cortical-activation responses using functional near-infrared spectroscopy (fNIRS) in college students with PSPU (n = 56) compared with a control group (n = 54).

Results

At the behavioral level, Stroop interference, coupled with reaction time, was significantly greater in the PSPU group than in the control group. Changes in oxygenated hemoglobin (Oxy-Hb) signals associated with Stroop interference were significantly increased in the left ventrolateral prefrontal cortex, left frontopolar area, and bilateral dorsolateral prefrontal cortex (DLPFC). Moreover, the PSPU group had lower Oxy-Hb signal changes associated with Stroop interference in the left-DLPFC, relative to controls.

Discussion

These results provide first behavioral and neuroscientific evidence using event-related fNIRS method, to our knowledge, that college students with PSPU may have a deficit in inhibitory control associated with lower cortical activation in the left-DLPFC.

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.

Hotspots and trends in fNIRS disease research: A bibliometric analysis

Objective

To summarize the general information and hotspots of functional near-infrared spectroscopy (fNIRS)-based clinical disease research over the past 10 years and provide some references for future research.

Methods

The related literature published between 1 January 2011 and 31 January 2022 was retrieved from the Web of Science core database (WoS). Bibliometric visualization analysis of countries/regions, institutions, authors, journals, keywords and references were conducted by using CiteSpace 6.1.R3.

Results

A total of 467 articles were included, and the annual number of articles published over nearly a decade showed an upward trend year-by-year. These articles mainly come from 39 countries/regions and 280 institutions. The representative country and institution were the USA and the University of Tubingen. We identified 266 authors, among which Andreas J Fallgatter and Ann-Christine Ehlis were the influential authors. Neuroimage was the most co-cited journal. The major topics in fNIRS disease research included activation, prefrontal cortex, working memory, cortex, and functional magnetic resonance imaging (fMRI). In recent years, the Frontier topics were executive function, functional connectivity, performance, diagnosis, Alzheimer's disease, children, and adolescents. Based on the burst of co-cited references, gait research has received much attention.

Conclusion

This study conducted a comprehensive, objective, and visual analysis of publications, and revealed the status of relevant studies, hot topics, and trends concerning fNIRS disease research from 2011 to 2022. It is hoped that this work would help researchers to identify new perspectives on potential collaborators, important topics, and research Frontiers.

Low-dissipation optimization of the prefrontal cortex in the -12° head-down tilt position: A functional near-infrared spectroscopy study

Introduction

Our present study set out to investigate the instant state of the prefrontal cortex (PFC) in healthy subjects before and after placement in the -12°head-down tilt (HDT) position in order to explore the mechanism behind the low-dissipation optimization state of the PFC.

Methods

40 young, right-handed healthy subjects (male: female = 20: 20) were enrolled in this study. Three resting state positions, 0°initial position, -12°HDT position, and 0°rest position were sequentially tested, each for 10 minutes. A continuous-wave functional near-infrared spectroscopy (fNIRS) instrument was used to assess the resting state hemodynamic data of the PFC. After preprocessing the hemodynamics data, we evaluated changes in resting-state functional connectivity (rsFC) level and beta values of PFC. The subjective visual analogue scale (VAS) was applied before and after the experiment. The presence of sleep changes or adverse reactions were also recorded.

Results

Pairwise comparisons of the concentrations of oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and hemoglobin (HbT) revealed significant differences in the aforementioned positions. Specifically, the average rsFC of PFC showed a gradual increase throughout the whole process. In addition, based on graph theory, the topological properties of brain network, such as small-world network and nodal degree centrality were analyzed. The results show that global efficiency and small-world sigma (σ) value were differences between 0°initial and 0°rest.

Discussion

In this study, placement in the -12°HDT had a significant effect on PFC function, mainly manifested as self-inhibition, decreased concentration of HbO in the PFC, and improved rsFC, which may provide ideas to the understanding and explanation of neurological diseases.

The right prefrontal cortex (PFC) can distinguish anxious depression from non-anxious depression: A promising functional near infrared spectroscopy study (fNIRS)

BACKGROUND

Anxious depression is a serious mental disorder characterized by comorbidity of anxiety and depression, and its symptoms are similar to those of non-anxious depression. This study aimed to use functional near-infrared spectroscopy (fNIRS) as a tool to distinguish between patients with anxious and non-anxious depression based on differences in hemodynamic changes in the right prefrontal cortex during the verbal fluency task. It is helpful to improve the diagnostic accuracy of the two disorders to further promote their therapeutic effect and prognosis.

METHODS

A total of 105 subjects, comprising 39 patients with anxious depression, 32 patients with non-anxious depression, and 32 healthy controls, were evaluated using 53-channel fNIRS and the Depression and Anxiety Clinical Scale.

RESULTS

Hemodynamic activation was significantly enhanced in the right dorsolateral prefrontal cortex (DLPFC) and right frontopole cortex (FPC) in the anxious depressed group compared with the non-anxious depressed and healthy groups.

LIMITATIONS

First, Hospital Anxiety and Depression Scale (HADS) was used to evaluate the scores of anxiety and depression among the three groups in our study. Different scales may result in different research results. Therefore, other scales (HAM, the Montgomery Asberg Depression Rating Scale, or the Beck Depression Inventory) should be used for further verification. Second, although all the samples we have chosen were patients with the diagnosis of anxious depression or no-anxious depression, we did not distinguish between different severity of anxious depression or no-anxious depression. Third, pure anxiety was not included as the control condition in our study.

CONCLUSIONS

There are significant differences in activation patterns of the right DLPFC and right FPC areas between patients with and without anxious depression. Moreover, the right FPC area is promising as a brain region to assess the severity of anxious depression. fNIRS may be a potential tool to improve diagnostic accuracy for both disorders.

Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study

Distracted driving is one of the main causes of traffic accidents. By predicting the attentional state of drivers, it is possible to prevent distractions and promote safe driving. In this study, we developed a model that could predict the degree of distracted driving based on brain activity. Changes in oxyhemoglobin concentrations were measured in drivers while driving a real car using functional near-infrared spectroscopy (fNIRS). A regression model was constructed for each participant using functional connectivity as an explanatory variable and brake reaction time to random beeps while driving as an objective variable. As a result, we were able to construct a prediction model with the mean absolute error of 5.58 × 102 ms for the BRT of the 12 participants. Furthermore, the regression model with the highest prediction accuracy for each participant was analyzed to gain a better understanding of the neural basis of distracted driving. The 11 of 12 models that showed significant accuracy were classified into five clusters by hierarchical clustering based on their functional connectivity edges used in each cluster. The results showed that the combinations of the dorsal attention network (DAN)-sensory-motor network (SMN) and DAN-ventral attention network (VAN) connections were common in all clusters and that these networks were essential to predict the degree of distraction in complex multitask driving. They also confirmed the existence of multiple types of prediction models with different within- and between-network connectivity patterns. These results indicate that it is possible to predict the degree of distracted driving based on the driver's brain activity during actual driving. These results are expected to contribute to the development of safe driving systems and elucidate the neural basis of distracted driving.

A Decrease in Hemodynamic Response in the Right Postcentral Cortex Is Associated With Treatment-Resistant Auditory Verbal Hallucinations in Schizophrenia: An NIRS Study

Background

Treatment-resistant auditory verbal hallucinations (TRAVHs) might cause an increased risk of violence, suicide, and hospitalization in patients with schizophrenia (SCZ). Although neuroimaging studies have identified the neural correlation to the symptom of AVH, functional brain activity that correlates particularly in patients with TRAVH remains limited. Functional near-infrared spectroscopy (fNIRS) is a portable and suitable measurement, particularly in exploring brain activation during related tasks. Hence, our researchers aimed to explore the differences in the cerebral hemodynamic function in SCZ-TRAVH, patients with schizophrenia without AVH (SCZ-nAVH), and healthy controls (HCs), to examine neural abnormalities associated more specifically with TRAVH.

Methods

A 52-channel functional near-infrared spectroscopy system was used to monitor hemodynamic changes in patients with SCZ-TRAVH (n = 38), patients with SCZ-nAVH (n = 35), and HC (n = 30) during a verbal fluency task (VFT). VFT performance, clinical history, and symptom severity were also noted. The original fNIRS data were analyzed using MATLAB to obtain the β values (the brain cortical activity response during the VFT task period); these were used to calculate Δβ (VFT β minus baseline β), which represents the degree of change in oxygenated hemoglobin caused by VFT task.

Result

Our results showed that there were significant differences in Δβ values among the three groups at 26 channels (ch4, ch13-15, 18, 22, ch25–29, 32, ch35–39, ch43–51, F = 1.70 to 19.10, p < 0.043, FDR-corrected) distributed over the prefrontal–temporal cortical regions. The further pairwise comparisons showed that the Δβ values of 24 channels (ch13–15, 18, 22, 25, ch26–29, ch35–39, ch43–49, ch50–51) were significantly lower in the SCZ group (SCZ-TRAVH and/or SCZ-nAVH) than in the HC group (p < 0.026, FDR-corrected). Additionally, the abnormal activation in the ch22 of right postcentral gyrus was correlated, in turn, with severity of TRAVH.

Conclusion

Our findings indicate that specific regions of the prefrontal cortex may be associated with TRAVH, which may have implications for early intervention for psychosis.

Identifying neuroimaging biomarkers of major depressive disorder from cortical hemodynamic responses using machine learning approaches

Background

Early diagnosis of major depressive disorder (MDD) could enable timely interventions and effective management which subsequently improve clinical outcomes. However, quantitative and objective assessment tools for the suspected cases who present with depressive symptoms have not been fully established.

Methods

Based on a large-scale dataset (n = 363 subjects) collected with functional near-infrared spectroscopy (fNIRS) measurements during the verbal fluency task (VFT), this study proposed a data representation method for extracting spatiotemporal characteristics of NIRS signals, which emerged as candidate predictors in a two-phase machine learning framework to detect distinctive biomarkers for MDD. Supervised classifiers (e.g., support vector machine (SVM), k-nearest neighbors (KNN)) cooperated with cross-validation were implemented to evaluate the predictive capability of selected features in a training set. Another test set that was not involved in developing the algorithms enabled the independent assessment of the model's generalization.

Findings

For the classification with the optimal fusion features, the SVM classifier achieved the highest accuracy of 75.6% ± 4.7% in the nested cross-validation, and the correct prediction rate of 78.0% with a sensitivity of 75.0% and a specificity of 81.4% in the test set. Moreover, the multiway ANOVA test on clinical and demographic factors confirmed that twenty out of 39 optimal features were significantly correlated with the MDD-distinctive consequence.

Interpretation

The abnormal prefrontal activity of MDD may be quantified as diminished relative intensity and inappropriate activation timing of hemodynamic response, resulting in an objectively measurable biomarker for assessing cognitive deficits and screening MDD at the early stage.

Funding

This study was funded by NUS iHeathtech Other Operating Expenses (R-722-000-004-731).

Reduced temporal activation during a verbal fluency test in clinical high risk of psychosis: a functional near-infrared spectroscopy-based study

Background

Clinical high risk (CHR) of psychosis is a state in which positive symptoms cause the subjects distress but do not approach a severity level that fulfils the criteria for a psychotic episode. CHR exhibits cognitive deficits; however, the underlying neurobiological mechanisms remain unclear. This study aimed to investigate whether brain activation measured by the levels of oxygenated hemoglobin (oxy-Hb) in CHR subjects could be correlated with cognitive deficits.

Methods

Fifty-eight CHR individuals who fulfilled the criteria for attenuated positive syndrome as specified in the Structured Interview for Prodromal Syndrome (SIPS) and the Scale of Prodromal Syndrome (SOPS) and 58 age- and sex-matched healthy participants were included in the study. All subjects completed the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) that includes tests measuring attention, verbal memory, verbal fluency, executive function, and general intelligence. Functional near-infrared spectroscopy (fNIRS) was used to measure the level of oxy-Hb in the dorsolateral prefrontal and frontotemporal cortices.

Results

We observed significantly decreased oxy-Hb levels in channel 32 (located in the right superior temporal gyrus, rSTG)) within the CHR individuals compared with that in the healthy controls (HCs) (t=−3.44, Bonferroni-corrected p=0.002), indicating lower brain activity. A significant positive correlation was observed between task-related β values and working memory in the CHR group (r=0.35, p=0.008).

Conclusions

The brain activation of rSTG is abnormal among subjects at clinicial high risk for psychosis. This abnormality is probably associated with the neural mechanisms of deficits in the working memory during the early stage of psychosis.

MOCA: a systematic toolbox for designing and assessing modular functional near-infrared brain imaging probes

SIGNIFICANCE

The expansion of functional near-infrared spectroscopy (fNIRS) systems toward broader utilities has led to the emergence of modular fNIRS systems composed of repeating optical source/detector modules. Compared to conventional fNIRS systems, modular fNIRS systems are more compact and flexible, making wearable and long-term monitoring possible. However, the large number of design parameters makes understanding their impact on a probe's performance a daunting task.

AIM

We aim to create a systematic software platform to facilitate the design, characterization, and comparison of modular fNIRS probes.

APPROACH

Our software-modular optode configuration analyzer (MOCA)-implements semi-automatic algorithms that assist in tessellating user-specified regions-of-interest, in interconnecting modules of various shapes, and in quantitatively comparing probe performance using metrics, such as spatial channel distributions and average brain sensitivity of the resulting probes. There is also support for limited parameter sweeping capabilities.

RESULTS

Through several examples, we show that users can use MOCA to design and optimize modular fNIRS probes, study trade-offs between several module shapes, improve brain sensitivity in probes via module re-orientation, and enhance probe performance via adjusting module spatial layouts.

CONCLUSION

Despite its simplicity, our modular probe design platform offers a framework to describe and quantitatively assess probes made by modules, opening a new door for the growing fNIRS user community to approach the challenging problem of module- and probe-parameter selection and fine-tuning.

Classification of Individual Finger Movements from Right Hand Using fNIRS Signals

Functional near-infrared spectroscopy (fNIRS) is a comparatively new noninvasive, portable, and easy-to-use brain imaging modality. However, complicated dexterous tasks such as individual finger-tapping, particularly using one hand, have been not investigated using fNIRS technology. Twenty-four healthy volunteers participated in the individual finger-tapping experiment. Data were acquired from the motor cortex using sixteen sources and sixteen detectors. In this preliminary study, we applied standard fNIRS data processing pipeline, i.e., optical densities conversation, signal processing, feature extraction, and classification algorithm implementation. Physiological and non-physiological noise is removed using 4th order band-pass Butter-worth and 3rd order Savitzky–Golay filters. Eight spatial statistical features were selected: signal-mean, peak, minimum, Skewness, Kurtosis, variance, median, and peak-to-peak form data of oxygenated haemoglobin changes. Sophisticated machine learning algorithms were applied, such as support vector machine (SVM), random forests (RF), decision trees (DT), AdaBoost, quadratic discriminant analysis (QDA), Artificial neural networks (ANN), k-nearest neighbors (kNN), and extreme gradient boosting (XGBoost). The average classification accuracies achieved were 0.75±0.04, 0.75±0.05, and 0.77±0.06 using k-nearest neighbors (kNN), Random forest (RF) and XGBoost, respectively. KNN, RF and XGBoost classifiers performed exceptionally well on such a high-class problem. The results need to be further investigated. In the future, a more in-depth analysis of the signal in both temporal and spatial domains will be conducted to investigate the underlying facts. The accuracies achieved are promising results and could open up a new research direction leading to enrichment of control commands generation for fNIRS-based brain-computer interface applications.

Investigation of effect of modulation frequency on high-density diffuse optical tomography image quality

Significance: By incorporating multiple overlapping functional near-infrared spectroscopy (fNIRS) measurements, high-density diffuse optical tomography (HD-DOT) images human brain function with fidelity comparable to functional magnetic resonance imaging (fMRI). Previous work has shown that frequency domain high-density diffuse optical tomography (FD-HD-DOT) may further improve image quality over more traditional continuous wave (CW) HD-DOT. Aim: The effects of modulation frequency on image quality as obtainable with FD-HD-DOT is investigated through simulations with a realistic noise model of functional activations in human head models, arising from 11 source modulation frequencies between CW and 1000 MHz. Approach: Simulations were performed using five representative head models with an HD regular grid of 158 light sources and 166 detectors and an empirically derived noise model. Functional reconstructions were quantitatively assessed with multiple image quality metrics including the localization error (LE), success rate, full width at half maximum, and full volume at half maximum (FVHM). All metrics were evaluated against CW-based models. Results: Compared to CW, localization accuracy is improved by >40% throughout brain depths of 13 to 25 mm below the surface with 300 to 500 MHz modulation frequencies. Additionally, the reliable field of view in brain tissue is enlarged by 35% to 48% within an optimal frequency of 300 MHz after considering realistic noise, depending on the dynamic range of the system. Conclusions: These results point to the tremendous opportunities in further development of high bandwidth FD-HD-DOT system hardware for applications in human brain mapping.

Anger Experience and Anger Expression Through Drawing in Schizophrenia: An fNIRS Study

Differences in emotion experience and emotion expression between patients with schizophrenia and the healthy population have long been the focus of research and clinical attention. However, few empirical studies have addressed this topic using art-making as a tool of emotion expression. This study explores the differences in brain mechanism during the process of expressing anger between patients with schizophrenia and healthy participants using pictographic psychological techniques. We used functional near-infrared spectroscopy to fully detect changes in frontal cortex activity among participants in two groups-schizophrenia and healthy-during the process of experiencing and expressing anger. The results showed that there were no differences in the experience of anger between the two groups. In the process of anger expression, the dorsolateral prefrontal cortex, frontal pole, and other regions showed significant negative activation among patients with schizophrenia, which was significantly different from that of the healthy group. There were significant differences between patients with schizophrenia and the healthy group in the drawing features, drawing contents, and the ability to describe the contents of their drawings. Moreover, the effect size of the latter was greater than those of the former two. In terms of emotion expression, the drawing data and brain activation data were significantly correlated in each group; however, the correlation patterns differed between groups.

Differentiating between bipolar and unipolar depression using prefrontal activation patterns: Promising results from functional near infrared spectroscopy (fNIRS) findings

BACKGROUND

Bipolar depression (BD) is a unique, severe and prevalent mental illness that shares many similarities in symptoms with unipolar depression (UD). Improving precision of their diagnoses would enhance treatment outcome and prognosis for both conditions. This study aims to provide evidence from functional Near-Infrared Spectroscopy (fNIRS) as a potential tool to differentiate UD and BD based on their differences in hemodynamic change in the prefrontal cortex during verbal fluency tasks (VFT).

METHODS

We enrolled 179 participants with clinically confirmed diagnoses, including 69 UD patients, 68 BD patients and 42 healthy controls(HC). Every participant was assessed using a 45-channel fNIRS and various clinical scales.

FINDINGS

Compared with HC, region-specific fNIR leads show UD patients had significant lower hemodynamic activation in 4 particular pre-frontal regions: 1) the left dorsolateral prefrontal cortex (DLPFC), 2) orbitofrontal cortex (OFC), 3) bilateral ventrolateral prefrontal cortex (VLPFC) and 4) left inferior frontal gyrus (IFG). In contrast, BD vs. HC comparisons showed only significant lower hemodynamic activation in the LIFG area. Furthermore, compared to BD patients, UD patients showed decreased hemodynamic activation changes in the VLPFC region.

CONCLUSION

Our results show significant frontal lobe activation pattern differences between UD and BD groups. fNIRS can be a potential tool to increase diagnostic precision for these conditions. In particular, the VLPFC area holds promise to be a useful site for such differentiation for further investigations.

Prefrontal cortex activation during working memory task in schizophrenia: A fNIRS study

Neurocognitive cognitive deficits including working memory (WM) impairment is a key component of schizophrenia (SCZ). Though a prefrontal cortex (PFC) abnormality is recognised to contribute to WM impairment, the exact nature of its neurobiological basis in SCZ is not well established. Functional near infra-red spectroscopy (fNIRS) is an emerging low-cost neuroimaging tool to study neuro-hemodynamics. In this background, we examined the hemodynamic activity during a WM task in schizophrenia using fNIRS. fNIRS was acquired during computerised N-back (zero-, one- & two-back) task in 15 SCZ patients and compared with 22 healthy controls. Performance in N-back test were calculated using signal detection theory alongside the mean reaction times. Concentration and latencies of oxy-, deoxy-, and totalhaemoglobin, and oxygen saturation were computed from 8*8 optodes positioned over bilateral PFC. SCZ performed poorly as measured by most of the WM parameters (p < 0.05). Lesser deoxyhemoglobin concentration (two > zero, at right BA10, p = 0.006) was noted in the right frontopolar cortex in SCZ surviving multiple-comparison correction. In addition, olanzapine equivalent doses correlated negatively with right frontopolar cortex activation (two > zero back, BA10, ρ = 0.70, p = 0.004) and better performance in two back (false alarm rate, ρ = 0.61, p = 0.015). A delayed but compensatory hyperactivation of right frontopolar cortex noted in SCZ may underlie the WM deficit in SCZ. Future studies are recommended to replicate the role of right frontopolar cortex in WM using larger samples and systematically explore the effect of antipsychotics on them.

Individual Differences in Hemodynamic Responses Measured on the Head Due to a Long-Term Stimulation Involving Colored Light Exposure and a Cognitive Task: A SPA-fNIRS Study

When brain activity is measured by neuroimaging, the canonical hemodynamic response (increase in oxygenated hemoglobin ([O2Hb]) and decrease in deoxygenated hemoglobin ([HHb]) is not always seen in every subject. The reason for this intersubject-variability of the responses is still not completely understood. This study is performed with 32 healthy subjects, using the systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS) approach. We investigate the intersubject variability of hemodynamic and systemic physiological responses, due to a verbal fluency task (VFT) under colored light exposure (CLE; blue and red). Five and seven different hemodynamic response patterns were detected in the subgroup analysis of the blue and red light exposure, respectively. We also found that arterial oxygen saturation and mean arterial pressure were positively correlated with [O2Hb] at the prefrontal cortex during the CLE-VFT independent of the color of light and classification of the subjects. Our study finds that there is substantial intersubject-variability of cerebral hemodynamic responses, which is partially explained by subject-specific systemic physiological changes induced by the CLE-VFT. This means that both subgroup analyses and the additional assessment of systemic physiology are of crucial importance to achieve a comprehensive understanding of the effects of a CLE-VFT on human subjects.

Clinical Utility of Functional Near-Infrared Spectroscopy for Assessment and Prediction of Suicidality: A Systematic Review

Introduction: Suicide is a pressing psychiatric concern worldwide with no established biomarker. While there is some evidence of the clinical utility of functional near-infrared spectroscopy (fNIRS) in assessing and predicting suicidality, no systematic review of such evidence has been conducted to date. Therefore, this review aimed to systematically review and gather evidence from existing studies that used fNIRS signals to assess suicidality and its associated changes in the brain, and those that examined how such signals correlated with suicide symptomatology. Methods: PubMed, EMBASE, and Cochrane Library databases were used in a systematic literature search for English-language articles published between 2000 and December 19, 2020 that focused on the utility of fNIRS for (i) assessing suicidality and its associated changes in the brain, and (ii) correlating with suicide symptomatology. Studies were included if they utilised fNIRS to evaluate variations in fNIRS-measured cerebral hemodynamic responses in patients with different mental disorders (e.g., major depressive disorder, schizophrenia), as well as in healthy controls, of any age group. Quality of evidence was assessed using the Newcastle-Ottawa quality assessment scale. Results: A total of 7 cross-sectional studies were included in this review, all of which had acceptable quality. Across all studies, fNIRS demonstrated reduced cerebral hemodynamic changes in suicidal individuals when compared to non-suicidal individuals. One study also demonstrated the potential of fNIRS signals in correlating with the severity of suicidality. Conclusions: This review provides a comprehensive, updated review of evidence supporting the clinical utility of fNIRS in the assessment and prediction of suicidality. Further studies involving larger sample sizes, standardised methodology, and longitudinal follow-ups are needed.

Abnormal Cortical Activation Patterns Among Chinese-Speaking Schizophrenia Patients During Category and Letter Verbal Fluency Tasks Revealed by Multi-Channel Functional Near-Infrared Spectroscopy

Background: Functional near-infrared spectroscopy (fNIRS) has many advantages over other neuroimaging modalities for routine measurement of task-dependent cortical activation, but most fNIRS studies of schizophrenia have used letter fluency tasks (LFTs). Further, performances on category fluency tasks (CFTs) and LFTs may be distinct in Chinese patients due to the unique semantic features of Chinese written characters. To identify unique disease biomarkers measurable by fNIRS in Chinese schizophrenia patients, this study compared cortical oxygenated hemoglobin changes ([oxy-Hb]) during a Chinese LFT and CFT between patients and healthy controls. Methods: Inpatients of the Second Affiliated Hospital of Xinxiang Medical University were recruited from Match 2020 to July 2021. The Positive and Negative Symptom Scale (PANSS) was used to evaluate psychiatric symptoms. Dynamic changes in [oxy-Hb], an indicator of neural activity, were measured during CFT and LFT performance by 52-channel fNIRS. Results: Forty-seven schizophrenia inpatients and 29 healthy controls completed all tests. Schizophrenia patients showed significant cortical activation at 15 channels covering the left hemisphere and 17 channels over the right hemisphere during the CFT. During the LFT, activity was significantly increased at only six channels, all over the left hemisphere (FDR P < 0.05). In healthy controls, significant [oxy-Hb] increases were found at 24 channels over the left hemisphere and 19 channels over the right hemisphere during CFT. While during the LFT, the significant increases were found at 7 channels all over the left hemisphere (FDR P < 0.05). When years of education was included as a covariate, the schizophrenia group demonstrated no significant hypoactivation relative to healthy controls at any channel after FDR correction (FDR P < 0.05) during CFT while demonstrated significant hypoactivation at channel 11 during LFT (FDR P < 0.05). There were no significant associations between PANSS scores and [oxy-Hb] changes after FDR correction (FDR P < 0.05). Conclusions: Left lateralization during CFT was reduced among schizophrenia patients and may be related to the semantic deficit. The Chinese-CFT could be a more sensitive indicator of frontal-temporal dysfunction in schizophrenia.

New Directions in Exercise Prescription: Is There a Role for Brain-Derived Parameters Obtained by Functional Near-Infrared Spectroscopy?

In the literature, it is well established that regular physical exercise is a powerful strategy to promote brain health and to improve cognitive performance. However, exact knowledge about which exercise prescription would be optimal in the setting of exercise–cognition science is lacking. While there is a strong theoretical rationale for using indicators of internal load (e.g., heart rate) in exercise prescription, the most suitable parameters have yet to be determined. In this perspective article, we discuss the role of brain-derived parameters (e.g., brain activity) as valuable indicators of internal load which can be beneficial for individualizing the exercise prescription in exercise–cognition research. Therefore, we focus on the application of functional near-infrared spectroscopy (fNIRS), since this neuroimaging modality provides specific advantages, making it well suited for monitoring cortical hemodynamics as a proxy of brain activity during physical exercise.

Proposing a convolutional neural network for stress assessment by means of derived heart rate from functional near infrared spectroscopy

BACKGROUND

Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals.

METHOD

In this study, a method based on the Convolutional Neural Network (CNN) approach is proposed to assess stress induced by the Montreal Imaging Stress Task. The proposed model is trained on the heart rate signal derived from functional Near-Infrared Spectroscopy (fNIRS), which is referred to as HRF. In this regard, fNIRS signals of 20 healthy volunteers were recorded using a configuration of 23 channels located on the prefrontal cortex. The proposed deep learning system consists of two main parts where in the first part, the one-dimensional convolutional neural network is employed to build informative activation maps, and then in the second part, a stack of deep fully connected layers is used to predict the stress existence probability. Thereafter, the employed CNN method is compared with the Dense Neural Network, Support Vector Machine, and Random Forest regarding various classification metrics.

RESULTS

Results clearly showed the superiority of CNN over all other methods. Additionally, the trained HRF model significantly outperforms the model trained on the filtered fNIRS signals, where the HRF model could achieve 98.69 ± 0.45% accuracy, which is 10.09% greater than the accuracy obtained by the fNIRS model.

CONCLUSIONS

Employment of the proposed deep learning system trained on the HRF measurements leads to higher stress classification accuracy than the accuracy reported in the existing studies where the same experimental procedure has been done. Besides, the proposed method suggests better stability with lower variation in prediction. Furthermore, its low computational cost opens up the possibility to be applied in real-time monitoring of stress assessment.

Cortical haemodynamic response measured by functional near infrared spectroscopy during a verbal fluency task in patients with major depression and borderline personality disorder

Background

Functional near infrared spectroscopy (fNIRS) provides a direct and quantitative assessment of cortical haemodynamic function during a cognitive task. This functional neuroimaging modality may be used to elucidate the pathophysiology of psychiatric disorders, and identify neurophysiological differences between co-occurring psychiatric disorders. However, fNIRS research on borderline personality disorder (BPD) has been limited. Hence, this study aimed to compare cerebral haemodynamic function in healthy controls (HC), patients with major depressive disorder (MDD) and patients with BPD.

Methods

fNIRS signals during a verbal fluency task designed for clinical assessment was recorded for all participants. Demographics, clinical history and symptom severity were also noted.

Findings

Compared to HCs (n = 31), both patient groups (MDD, n = 31; BPD, n = 31) displayed diminished haemodynamic response in the frontal, temporal and parietal cortices. Moreover, haemodynamic response in the right frontal cortex is markedly lower in patients with MDD compared to patients with BPD.

Interpretation

Normal cortical function in patients with BPD is disrupted, but not as extensively as in patients with MDD. These results provide further neurophysiological evidence for the distinction of patients with MDD from patients with BPD.

A Newcomer's Guide to Functional Near Infrared Spectroscopy Experiments

This review presents a practical primer for functional near-infrared spectroscopy (fNIRS) with respect to technology, experimentation, and analysis software. Its purpose is to jump-start interested practitioners considering utilizing a non-invasive, versatile, nevertheless challenging window into the brain using optical methods. We briefly recapitulate relevant anatomical and optical foundations and give a short historical overview. We describe competing types of illumination (trans-illumination, reflectance, and differential reflectance) and data collection methods (continuous wave, time domain and frequency domain). Basic components (light sources, detection, and recording components) of fNIRS systems are presented. Advantages and limitations of fNIRS techniques are offered, followed by a list of very practical recommendations for its use. A variety of experimental and clinical studies with fNIRS are sampled, shedding light on many brain-related ailments. Finally, we describe and discuss a number of freely available analysis and presentation packages suited for data analysis. In conclusion, we recommend fNIRS due to its ever-growing body of clinical applications, state-of-the-art neuroimaging technique and manageable hardware requirements. It can be safely concluded that fNIRS adds a new arrow to the quiver of neuro-medical examinations due to both its great versatility and limited costs.

A Mini-Review on Functional Near-Infrared Spectroscopy (fNIRS): Where Do We Stand, and Where Should We Go?

This mini-review is aimed at briefly summarizing the present status of functional near-infrared spectroscopy (fNIRS) and predicting where the technique should go in the next decade. This mini-review quotes 33 articles on the different fNIRS basics and technical developments and 44 reviews on the fNIRS applications published in the last eight years. The huge number of review articles about a wide spectrum of topics in the field of cognitive and social sciences, functional neuroimaging research, and medicine testifies to the maturity achieved by this non-invasive optical vascular-based functional neuroimaging technique. Today, fNIRS has started to be utilized on healthy subjects while moving freely in different naturalistic settings. Further instrumental developments are expected to be done in the near future to fully satisfy this latter important aspect. In addition, fNIRS procedures, including correction methods for the strong extracranial interferences, need to be standardized before using fNIRS as a clinical tool in individual patients. New research avenues such as interactive neurosciences, cortical activation modulated by different type of sport performance, and cortical activation during neurofeedback training are highlighted.

High-density functional diffuse optical tomography based on frequency-domain measurements improves image quality and spatial resolution

Measurements of dynamic near-infrared (NIR) light attenuation across the human head together with model-based image reconstruction algorithms allow the recovery of three-dimensional spatial brain activation maps. Previous studies using high-density diffuse optical tomography (HD-DOT) systems have reported improved image quality over sparse arrays. These HD-DOT systems incorporated multidistance overlapping continuous wave measurements that only recover differential intensity attenuation. We investigate the potential improvement in reconstructed image quality due to the additional incorporation of phase shift measurements, which reflect the time-of-flight of the measured NIR light, within the tomographic reconstruction from high-density measurements. To evaluate image reconstruction with and without the additional phase information, we simulated point spread functions across a whole-scalp field of view in 24 subject-specific anatomical models using an experimentally derived noise model. The addition of phase information improves the image quality by reducing localization error by up to 59% and effective resolution by up to 21% as compared to using the intensity attenuation measurements alone. Furthermore, we demonstrate that the phase data enable images to be resolved at deeper brain regions where intensity data fail, which is further supported by utilizing experimental data from a single subject measurement during a retinotopic experiment.

WearLight: Toward a Wearable, Configurable Functional NIR Spectroscopy System for Noninvasive Neuroimaging

Functional near-infrared spectroscopy (fNIRS) has emerged as an effective brain monitoring technique to measure the hemodynamic response of the cortical surface. Its wide popularity and adoption in recent time attribute to its portability, ease of use, and flexibility in multimodal studies involving electroencephalography. While fNIRS is still emerging on various fronts including hardware, software, algorithm, and applications, it still requires overcoming several scientific challenges associated with brain monitoring in naturalistic environments where the human participants are allowed to move and required to perform various tasks stimulating brain behaviors. In response to these challenges and demands, we have developed a wearable fNIRS system, WearLight that was built upon an Internet-of-Things embedded architecture for onboard intelligence, configurability, and data transmission. In addition, we have pursued detailed research and comparative analysis on the design of the optodes encapsulating an near-infrared light source and a detector into 3-D printed material. We performed rigorous experimental studies on human participants to test reliability, signal-to-noise ratio, and configurability. Most importantly, we observed that WearLight has a capacity to measure hemodynamic responses in various setups including arterial occlusion on the forearm and frontal lobe brain activity during breathing exercises in a naturalistic environment. Our promising experimental results provide an evidence of preliminary clinical validation of WearLight. This encourages us to move toward intensive studies involving brain monitoring.

Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging in Exercise⁻Cognition Science: A Systematic, Methodology-Focused Review

For cognitive processes to function well, it is essential that the brain is optimally supplied with oxygen and blood. In recent years, evidence has emerged suggesting that cerebral oxygenation and hemodynamics can be modified with physical activity. To better understand the relationship between cerebral oxygenation/hemodynamics, physical activity, and cognition, the application of state-of-the art neuroimaging tools is essential. Functional near-infrared spectroscopy (fNIRS) is such a neuroimaging tool especially suitable to investigate the effects of physical activity/exercises on cerebral oxygenation and hemodynamics due to its capability to quantify changes in the concentration of oxygenated hemoglobin (oxyHb) and deoxygenated hemoglobin (deoxyHb) non-invasively in the human brain. However, currently there is no clear standardized procedure regarding the application, data processing, and data analysis of fNIRS, and there is a large heterogeneity regarding how fNIRS is applied in the field of exercise⁻cognition science. Therefore, this review aims to summarize the current methodological knowledge about fNIRS application in studies measuring the cortical hemodynamic responses during cognitive testing (i) prior and after different physical activities interventions, and (ii) in cross-sectional studies accounting for the physical fitness level of their participants. Based on the review of the methodology of 35 as relevant considered publications, we outline recommendations for future fNIRS studies in the field of exercise⁻cognition science.

Strengthening the Brain-Is Resistance Training with Blood Flow Restriction an Effective Strategy for Cognitive Improvement?

Aging is accompanied by a decrease in physical capabilities (e.g., strength loss) and cognitive decline. The observed bidirectional relationship between physical activity and brain health suggests that physical activities could be beneficial to maintain and improve brain functioning (e.g., cognitive performance). However, the exercise type (e.g., resistance training, endurance training) and their exercise variables (e.g., load, duration, frequency) for an effective physical activity that optimally enhance cognitive performance are still unknown. There is growing evidence that resistance training induces substantial brain changes which contribute to improved cognitive functions. A relative new method in the field of resistance training is blood flow restriction training (BFR). While resistance training with BFR is widely studied in the context of muscular performance, this training strategy also induces an activation of signaling pathways associated with neuroplasticity and cognitive functions. Based on this, it seems reasonable to hypothesize that resistance training with BFR is a promising new strategy to boost the effectiveness of resistance training interventions regarding cognitive performance. To support our hypothesis, we provide rationales of possible adaptation processes induced by resistance training with BFR. Furthermore, we outline recommendations for future studies planning to investigate the effects of resistance training with BFR on cognition.

STUDIES ON SCHIZOPHRENIA AND DEPRESSIVE DISEASES BASED ON FUNCTIONAL NEAR-INFRARED SPECTROSCOPY

Many investigations have been carried out on functional near infrared spectroscopy (fNIRS) applications in depression and schizophrenia patients that are most mysterious and costliest mental disorders in current society. fNIRS is a new optical method which assesses brain cortex hemodynamic and nervous activities non-invasively and it has been used in medicine as a study tool. Most of the researches of this approach have assessed the homodynamic response of frontal and temporal regions by means of various cognitive tasks. In this research, first, the cognitive task execution techniques have been explained concisely, and then some findings of fNIRS-based researches about depression and schizophrenia have been summarized and assessed. In fNIRS studies that have used various devices with different number of channels, the brain cortex functionality in schizophrenia and depressive patients has been investigated. The results demonstrate that a decrease in prefrontal regions activities can be observed in schizophrenia and depressive patients. Also more detailed studies illustrate ventrolateral, prefrontal and frontopolar region disorders. In severe depressive patients, a decrease in activities of prefrontal and temporal regions has been detected. Therefore, by paying attention to the deficiencies in these regions’ functions, it is possible to treat these diseases.

Anti-Dementia Drugs for Psychopathology and Cognitive Impairment in Schizophrenia: A Systematic Review and Meta-Analysis

BACKGROUND

We conducted a systematic review and meta-analysis of double-blind, randomized, placebo-controlled trials of anti-dementia drugs plus antipsychotics for schizophrenia.

METHODS

Primary outcomes of efficacy and safety included improving overall symptoms (Positive and Negative Syndrome Scale and Brief Psychiatric Rating Scale scores) and all-cause discontinuation, respectively. Other outcomes included psychopathology subscales (positive, negative, general, and anxiety/depressive symptoms), cognitive function (attention/vigilance, reasoning/problem solving, social cognition, speed of processing, verbal learning, visual learning, working memory, and cognitive control/executive function), Mini-Mental State Examination scores, treatment discontinuation due to adverse events and inefficacy, and individual adverse events. We evaluated the effect size using a random effects model.

RESULTS

We identified 37 studies (n=1574): 14 donepezil-based (n=568), 10 galantamine-based (n=371), 4 rivastigmine-based (n=146), and 9 memantine-based (n=489) studies. Pooled anti-dementia drugs plus antipsychotics treatments were superior to placebo plus antipsychotics in improving the overall symptoms (24 studies, 1069 patients: standardized mean difference=-0.34, 95% CI=-0.61 to -0.08, P=.01), negative symptoms (24 studies, 1077 patients: standardized mean difference =-0.62, 95% CI=-0.92 to -0.32, Pcorrected=.00018), and Mini-Mental State Examination scores (7 studies, 225 patients: standardized mean difference=-0.79, 95% CI=-1.23 to -0.34, P=.0006). No significant differences were found between anti-dementia drugs plus antipsychotics and placebo plus antipsychotics regarding other outcomes.

CONCLUSIONS

Although the results suggest that anti-dementia drugs plus antipsychotics treatment improves negative symptoms and Mini-Mental State Examination scores in schizophrenia patients, they possibly were influenced by a small-study effect and some bias. However, it was not superior to placebo plus antipsychotics in improving composite cognitive test score, which more systematically evaluates cognitive impairment than the Mini-Mental State Examination score. Overall, the anti-dementia drugs plus antipsychotics treatment was well tolerated.

Dynamic causal modelling on infant fNIRS data: A validation study on a simultaneously recorded fNIRS-fMRI dataset

Tracking the connectivity of the developing brain from infancy through childhood is an area of increasing research interest, and fNIRS provides an ideal method for studying the infant brain as it is compact, safe and robust to motion. However, data analysis methods for fNIRS are still underdeveloped compared to those available for fMRI. Dynamic causal modelling (DCM) is an advanced connectivity technique developed for fMRI data, that aims to estimate the coupling between brain regions and how this might be modulated by changes in experimental conditions. DCM has recently been applied to adult fNIRS, but not to infants. The present paper provides a proof-of-principle for the application of this method to infant fNIRS data and a demonstration of the robustness of this method using a simultaneously recorded fMRI-fNIRS single case study, thereby allowing the use of this technique in future infant studies.

fMRI and fNIRS were simultaneously recorded from a 6-month-old sleeping infant, who was presented with auditory stimuli in a block design. Both fMRI and fNIRS data were preprocessed using SPM, and analysed using a general linear model approach. The main challenges that adapting DCM for fNIRS infant data posed included: (i) the import of the structural image of the participant for spatial pre-processing, (ii) the spatial registration of the optodes on the structural image of the infant, (iii) calculation of an accurate 3-layer segmentation of the structural image, (iv) creation of a high-density mesh as well as (v) the estimation of the NIRS optical sensitivity functions. To assess our results, we compared the values obtained for variational Free Energy (F), Bayesian Model Selection (BMS) and Bayesian Model Average (BMA) with the same set of possible models applied to both the fMRI and fNIRS datasets. We found high correspondence in F, BMS, and BMA between fMRI and fNIRS data, therefore showing for the first time high reliability of DCM applied to infant fNIRS data. This work opens new avenues for future research on effective connectivity in infancy by contributing a data analysis pipeline and guidance for applying DCM to infant fNIRS data.

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Connectivity studies give important insights into infant brain development.

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fNIRS is a valuable method for infancy studies, but can we analyse connectivity?

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On fMRI-fNIRS acquired simultaneously, we estimate effective connectivity with DCM.

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We showed high correspondence of DCM values between fMRI and fNIRS data.

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We validated DCM on fNIRS infant data, providing guidance for future projects.