Distinct Methylphenidate-Evoked Response Measured Using Functional Near-Infrared Spectroscopy During Go/No-Go Task as a Supporting Differential Diagnostic Tool Between Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Comorbid Children

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.

Functional near-infrared spectroscopy in pediatric clinical research: Different pathophysiologies and promising clinical applications

Over its 30 years of existence, functional near-infrared spectroscopy (fNIRS) has matured into a highly versatile tool to study brain function in infants and young children. Its advantages, amongst others, include its ease of application and portability, the option to combine it with electrophysiology, and its relatively good tolerance to movement. As shown by the impressive body of fNIRS literature in the field of cognitive developmental neuroscience, the method's strengths become even more relevant for (very) young individuals who suffer from neurological, behavioral, and/or cognitive impairment. Although a number of studies have been conducted with a clinical perspective, fNIRS cannot yet be considered as a truly clinical tool. The first step has been taken in this direction by studies exploring options in populations with well-defined clinical profiles. To foster further progress, here, we review several of these clinical approaches to identify the challenges and perspectives of fNIRS in the field of developmental disorders. We first outline the contributions of fNIRS in selected areas of pediatric clinical research: epilepsy, communicative and language disorders, and attention-deficit/hyperactivity disorder. We provide a scoping review as a framework to allow the highlighting of specific and general challenges of using fNIRS in pediatric research. We also discuss potential solutions and perspectives on the broader use of fNIRS in the clinical setting. This may be of use to future research, targeting clinical applications of fNIRS in children and adolescents.

Using functional near-infrared spectroscopy to study the early developing brain: future directions and new challenges

Significance

Functional near-infrared spectroscopy (fNIRS) is a frequently used neuroimaging tool to explore the developing brain, particularly in infancy, with studies spanning from birth to toddlerhood (0 to 2 years). We provide an overview of the challenges and opportunities that the developmental fNIRS field faces, after almost 25 years of research.

Aim

We discuss the most recent advances in fNIRS brain imaging with infants and outlines the trends and perspectives that will likely influence progress in the field in the near future.

Approach

We discuss recent progress and future challenges in various areas and applications of developmental fNIRS from methodological and technological innovations to data processing and statistical approaches.

Results and Conclusions

The major trends identified include uses of fNIRS "in the wild," such as global health contexts, home and community testing, and hyperscanning; advances in hardware, such as wearable technology; assessment of individual variation and developmental trajectories particularly while embedded in studies examining other environmental, health, and context specific factors and longitudinal designs; statistical advances including resting-state network and connectivity, machine learning and reproducibility, and collaborative studies. Standardization and larger studies have been, and will likely continue to be, a major goal in the field, and new data analysis techniques, statistical methods, and collaborative cross-site projects are emerging.

Methylphenidate for children and adolescents with attention deficit hyperactivity disorder (ADHD)

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is one of the most commonly diagnosed and treated psychiatric disorders in childhood. Typically, children and adolescents with ADHD find it difficult to pay attention and they are hyperactive and impulsive. Methylphenidate is the psychostimulant most often prescribed, but the evidence on benefits and harms is uncertain. This is an update of our comprehensive systematic review on benefits and harms published in 2015.

OBJECTIVES

To assess the beneficial and harmful effects of methylphenidate for children and adolescents with ADHD.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, three other databases and two trials registers up to March 2022. In addition, we checked reference lists and requested published and unpublished data from manufacturers of methylphenidate.

SELECTION CRITERIA

We included all randomised clinical trials (RCTs) comparing methylphenidate versus placebo or no intervention in children and adolescents aged 18 years and younger with a diagnosis of ADHD. The search was not limited by publication year or language, but trial inclusion required that 75% or more of participants had a normal intellectual quotient (IQ > 70). We assessed two primary outcomes, ADHD symptoms and serious adverse events, and three secondary outcomes, adverse events considered non-serious, general behaviour, and quality of life.

DATA COLLECTION AND ANALYSIS

Two review authors independently conducted data extraction and risk of bias assessment for each trial. Six review authors including two review authors from the original publication participated in the update in 2022. We used standard Cochrane methodological procedures. Data from parallel-group trials and first-period data from cross-over trials formed the basis of our primary analyses. We undertook separate analyses using end-of-last period data from cross-over trials. We used Trial Sequential Analyses (TSA) to control for type I (5%) and type II (20%) errors, and we assessed and downgraded evidence according to the GRADE approach.

MAIN RESULTS

We included 212 trials (16,302 participants randomised); 55 parallel-group trials (8104 participants randomised), and 156 cross-over trials (8033 participants randomised) as well as one trial with a parallel phase (114 participants randomised) and a cross-over phase (165 participants randomised). The mean age of participants was 9.8 years ranging from 3 to 18 years (two trials from 3 to 21 years). The male-female ratio was 3:1. Most trials were carried out in high-income countries, and 86/212 included trials (41%) were funded or partly funded by the pharmaceutical industry. Methylphenidate treatment duration ranged from 1 to 425 days, with a mean duration of 28.8 days. Trials compared methylphenidate with placebo (200 trials) and with no intervention (12 trials). Only 165/212 trials included usable data on one or more outcomes from 14,271 participants. Of the 212 trials, we assessed 191 at high risk of bias and 21 at low risk of bias. If, however, deblinding of methylphenidate due to typical adverse events is considered, then all 212 trials were at high risk of bias.

PRIMARY OUTCOMES

methylphenidate versus placebo or no intervention may improve teacher-rated ADHD symptoms (standardised mean difference (SMD) -0.74, 95% confidence interval (CI) -0.88 to -0.61; I² = 38%; 21 trials; 1728 participants; very low-certainty evidence). This corresponds to a mean difference (MD) of -10.58 (95% CI -12.58 to -8.72) on the ADHD Rating Scale (ADHD-RS; range 0 to 72 points). The minimal clinically relevant difference is considered to be a change of 6.6 points on the ADHD-RS. Methylphenidate may not affect serious adverse events (risk ratio (RR) 0.80, 95% CI 0.39 to 1.67; I² = 0%; 26 trials, 3673 participants; very low-certainty evidence). The TSA-adjusted intervention effect was RR 0.91 (CI 0.31 to 2.68).

SECONDARY OUTCOMES

methylphenidate may cause more adverse events considered non-serious versus placebo or no intervention (RR 1.23, 95% CI 1.11 to 1.37; I² = 72%; 35 trials 5342 participants; very low-certainty evidence). The TSA-adjusted intervention effect was RR 1.22 (CI 1.08 to 1.43). Methylphenidate may improve teacher-rated general behaviour versus placebo (SMD -0.62, 95% CI -0.91 to -0.33; I² = 68%; 7 trials 792 participants; very low-certainty evidence), but may not affect quality of life (SMD 0.40, 95% CI -0.03 to 0.83; I² = 81%; 4 trials, 608 participants; very low-certainty evidence).

AUTHORS' CONCLUSIONS

The majority of our conclusions from the 2015 version of this review still apply. Our updated meta-analyses suggest that methylphenidate versus placebo or no-intervention may improve teacher-rated ADHD symptoms and general behaviour in children and adolescents with ADHD. There may be no effects on serious adverse events and quality of life. Methylphenidate may be associated with an increased risk of adverse events considered non-serious, such as sleep problems and decreased appetite. However, the certainty of the evidence for all outcomes is very low and therefore the true magnitude of effects remain unclear. Due to the frequency of non-serious adverse events associated with methylphenidate, the blinding of participants and outcome assessors is particularly challenging. To accommodate this challenge, an active placebo should be sought and utilised. It may be difficult to find such a drug, but identifying a substance that could mimic the easily recognised adverse effects of methylphenidate would avert the unblinding that detrimentally affects current randomised trials. Future systematic reviews should investigate the subgroups of patients with ADHD that may benefit most and least from methylphenidate. This could be done with individual participant data to investigate predictors and modifiers like age, comorbidity, and ADHD subtypes.

fNIRS-based brain state transition features to signify functional degeneration after Parkinson's disease

OBJECTIVE

Parkinson's disease (PD) is a common neurodegenerative brain disorder, and early diagnosis is of vital importance for treatment. Existing methods are mainly focused on behavior examination, while the functional neurodegeneration after PD has not been well explored. This paper aims to investigate the brain functional variation of PD patients in comparison with healthy controls.

APPROACH

In this work, we propose brain hemodynamic states and state transition features to signify functional degeneration after PD. Firstly, a functional near-infrared spectroscopy (fNIRS)-based experimental paradigm was designed to capture brain activation during dual-task walking from PD patients and healthy controls. Then, three brain states, named expansion, contraction, and intermediate states, were defined with respect to the oxyhemoglobin and deoxyhemoglobin responses. After that, two features were designed from a constructed transition factor and concurrent variations of oxy- and deoxy-hemoglobin over time, to quantify the transitions of brain states. Further, a support vector machine classifier was trained with the proposed features to distinguish PD patients and healthy controls.

RESULTS

Experimental results showed that our method with the proposed brain state transition features achieved classification accuracy of 0:8200 and F score of 0:9091, and outperformed existing fNIRS-based methods. Compared with healthy controls, PD patients had significantly smaller transition acceleration and transition angle.

SIGNIFICANCE

The proposed brain state transition features well signify functional degeneration of PD patients and may serve as promising functional biomarkers for PD diagnosis.

Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

Functional near-infrared spectroscopy in developmental psychiatry: a review of attention deficit hyperactivity disorder

Research has linked executive function (EF) deficits to many of the behavioral symptoms of attention deficit hyperactivity disorder (ADHD). Evidence of the involvement of EF impairment in ADHD is corroborated by accumulating neuroimaging studies, specifically functional magnetic resonance imaging (fMRI) studies. However, in recent years, functional near-infrared spectroscopy (fNIRS) has become increasingly popular in ADHD research due to its portability, high ecological validity, resistance to motion artifacts, and cost-effectiveness. While numerous studies throughout the past decade have used fNIRS to examine alterations in neural correlates of EF in ADHD, a qualitative review of the reliability of these findings compared with those reported using gold-standard fMRI measurements does not yet exist. The current review aims to fill this gap in the literature by comparing the results generated from a qualitative review of fNIRS studies (children and adolescents ages 6-16 years old) to a meta-analysis of comparable fMRI studies and examining the extent to which the results of these studies align in the context of EF impairment in ADHD. The qualitative analysis of fNIRS studies of ADHD shows a consistent hypoactivity in the right prefrontal cortex in multiple EF tasks. The meta-analysis of fMRI data corroborates altered activity in this region and surrounding areas during EF tasks in ADHD compared with typically developing controls. These findings indicate that fNIRS is a promising functional brain imaging technology for examining alterations in cortical activity in ADHD. We also address the disadvantages of fNIRS, including limited spatial resolution compared with fMRI.

Visualizing Neuropharmacological Effects of Guanfacine Extended Release in Attention Deficit Hyperactivity Disorder Using Functional Near-Infrared Spectroscopy

Objective: In the current study, we explored the neural substrate for acute effects of guanfacine extended release (GXR) on inhibitory control in school-aged children with attention deficit hyperactivity disorder (ADHD), using functional near-infrared spectroscopy (fNIRS). Methods: Following a GXR washout period, 12 AD HD children (6-10 years old) performed a go/no-go task before and 3 h after GXR or placebo administration, in a randomized, double-blind, placebo-controlled, crossover design study. In the primary analysis, fNIRS was used to monitor the right prefrontal cortical hemodynamics of the participants, where our former studies showed consistent dysfunction and osmotic release oral system-methylphenidate (OROS-MPH) and atomoxetine hydrochloride (ATX) elicited recovery. We examined the inter-medication contrast, comparing the effect of GXR against the placebo. In the exploratory analysis, we explored neural responses in regions other than the right prefrontal cortex (PFC). Results: In the primary analysis, we observed no significant main effects or interactions of medication type and age in month (two-way mixed ANCOVA, Fs < 0.20, all ps > .05). However, in the post-hoc analysis, we observed significant change in the oxy-Hb signal in the right angular gyrus (AG) for inter-medication (one sample t-test, p < 0.05, uncorrected, Cohen's d = 0.71). Conclusions: These results are different from the neuropharmacological effects of OROS-MPH and ATX, which, in an upregulated manner, reduced right PFC function in ADHD children during inhibitory tasks. This analysis, while limited by its secondary nature, suggested that the improved cognitive performance was associated with activation in the right AG, which might serve as a biological marker to monitor the effect of GXR in the ADHD children.

The Role of Diagnostic Instruments in Dual and Differential Diagnosis in Autism Spectrum Disorder Across the Lifespan

The heterogeneity inherent in autism spectrum disorder (ASD) makes the identification and diagnosis of ASD complex. We survey a large number of diagnostic tools, including screeners and tools designed for in-depth assessment. We also discuss the challenges presented by overlapping symptomatology between ASD and other disorders and the need to determine whether a diagnosis of ASD or another diagnosis best explains the individual's symptoms. We conclude with a call to action for the next steps necessary for meeting the diagnostic challenges presented here to improve the diagnostic process and to help understand each individual's particular ASD profile.

Using a Data-Driven Approach to Estimate Second-Language Proficiency From Brain Activation: A Functional Near-Infrared Spectroscopy Study

While non-invasive brain imaging has made substantial contributions to advance human brain science, estimation of individual state is becoming important to realize its applications in society. Brain activations were used to classify second-language proficiencies. Participants in functional near-infrared spectroscopy (fNIRS) experiment were 20/20 native Japanese speakers with high/low English abilities and 19/19 native English speakers with high/low Japanese abilities. Their cortical activities were measured by functional near-infrared spectroscopy while they were conducting Japanese/English listening comprehension tests. The data-driven method achieved classification accuracy of 77.5% in the case of Japanese speakers and 81.9% in the case of English speakers. The informative features predominantly originated from regions associated with language function. These results bring an insight of fNIRS neuroscience and its applications in society.

The Role of Diagnostic Instruments in Dual and Differential Diagnosis in Autism Spectrum Disorder Across the Lifespan

The heterogeneity inherent in autism spectrum disorder (ASD) makes the identification and diagnosis of ASD complex. We survey a large number of diagnostic tools, including screeners and tools designed for in-depth assessment. We also discuss the challenges presented by overlapping symptomatology between ASD and other disorders and the need to determine whether a diagnosis of ASD or another diagnosis best explains the individual's symptoms. We conclude with a call to action for the next steps necessary for meeting the diagnostic challenges presented here to improve the diagnostic process and to help understand each individual's particular ASD profile.

Atypical Dynamic-Connectivity Recruitment in Attention-Deficit/Hyperactivity Disorder Children: An Insight Into Task-Based Dynamic Connectivity Through an fNIRS Study

Connectivity between brain regions has been redefined beyond a stationary state. Even when a person is in a resting state, brain connectivity dynamically shifts. However, shifted brain connectivity under externally evoked stimulus is still little understood. The current study, therefore, focuses on task-based dynamic functional-connectivity (FC) analysis of brain signals measured by functional near-infrared spectroscopy (fNIRS). We hypothesize that a stimulus may influence not only brain connectivity but also the occurrence probabilities of task-related and task-irrelevant connectivity states. fNIRS measurement (of the prefrontal-to-inferior parietal lobes) was conducted on 21 typically developing (TD) and 21 age-matched attention-deficit/hyperactivity disorder (ADHD) children performing an inhibitory control task, namely, the Go/No-Go (GNG) task. It has been reported that ADHD children lack inhibitory control; differences between TD and ADHD children in terms of task-based dynamic FC were also evaluated. Four connectivity states were found to occur during the temporal task course. Two dominant connectivity states (states 1 and 2) are characterized by strong connectivities within the frontoparietal network (occurrence probabilities of 40%-56% and 26%-29%), and presumptively interpreted as task-related states. A connectivity state (state 3) shows strong connectivities in the bilateral medial frontal-to-parietal cortices (occurrence probability of 7-15%). The strong connectivities were found at the overlapped regions related the default mode network (DMN). Another connectivity state (state 4) visualizes strong connectivities in all measured regions (occurrence probability of 10%-16%). A global effect coming from cerebral vascular may highly influence this connectivity state. During the GNG stimulus interval, the ADHD children tended to show decreased occurrence probability of the dominant connectivity state and increased occurrence probability of other connectivity states (states 3 and 4). Bringing a new perspective to explain neuropathophysiology, these findings suggest atypical dynamic network recruitment to accommodate task demands in ADHD children.

Exploring attentive task-based connectivity for screening attention deficit/hyperactivity disorder children: a functional near-infrared spectroscopy study

Connectivity impairment has frequently been associated with the pathophysiology of attention-deficit/hyperactivity disorder (ADHD). Although the connectivity of the resting state has mainly been studied, we expect the transition between baseline and task may also be impaired in ADHD children. Twenty-three typically developing (i.e., control) and 36 disordered (ADHD and autism-comorbid ADHD) children were subjected to connectivity analysis. Specifically, they performed an attention task, visual oddball, while their brains were measured by functional near-infrared spectroscopy. The results of the measurements revealed three key findings. First, the control group maintained attentive connectivity, even in the baseline interval. Meanwhile, the disordered group showed enhanced bilateral intra- and interhemispheric connectivities while performing the task. However, right intrahemispheric connectivity was found to be weaker than those for the control group. Second, connectivity and activation characteristics might not be positively correlated with each other. In our previous results, disordered children lacked activation in the right middle frontal gyrus. However, within region connectivity of the right middle frontal gyrus was relatively strong in the baseline interval and significantly increased in the task interval. Third, the connectivity-based biomarker performed better than the activation-based biomarker in terms of screening. Activation and connectivity features were independently optimized and cross validated to obtain the best performing threshold-based classifier. The effectiveness of connectivity features, which brought significantly higher training accuracy than the optimum activation features, was confirmed (88% versus 76%). The optimum screening features were characterized by two trends: (1) strong connectivities of right frontal, left frontal, and left parietal lobes and (2) weak connectivities of left frontal, left parietal, and right parietal lobes in the control group. We conclude that the attentive task-based connectivity effectively shows the difference between control and disordered children and may represent pathological characteristics to be feasibly implemented as a supporting tool for clinical screening.