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

Classifying Children with ADHD Based on Prefrontal Functional Near-infrared Spectroscopy Using Machine Learning

Objective

: Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adults characterized by cognitive and emotional self-control deficiencies. Previous functional near-infrared spectroscopy (fNIRS) studies found significant group differences between ADHD children and healthy controls during cognitive flexibility tasks in several brain regions. This study aims to apply a machine learning approach to identify medication-naive ADHD patients and healthy control (HC) groups using task-based fNIRS data.

Methods

: fNIRS signals from 33 ADHD children and 39 HC during the Stroop task were analyzed. In addition, regularized linear discriminant analysis (RLDA) was used to identify ADHD individuals from healthy controls, and classification performance was evaluated.

Results

: We found that participants can be correctly classified in RLDA leave-one-out cross validation, with a sensitivity of 0.67, specificity of 0.93, and accuracy of 0.82.

Conclusion

: RLDA using only fNIRS data can effectively discriminate children with ADHD from HC. This study suggests the potential utility of the fNIRS signal as a diagnostic biomarker for ADHD children.

Prefrontal cortex functional connectivity changes during verbal fluency test in adults with short-term insomnia disorder: a functional near-infrared spectroscopy study

Background

Individuals suffering from short-term insomnia disorder (SID) experience difficulties in falling or staying asleep, often leading to daytime fatigue and impaired concentration. However, the underlying mechanisms of SID remain unclear. This study aims to investigate the alterations in brain activation patterns and functional connectivity in patients with SID.

Methods

The study enrolled a total of 31 adults diagnosed with SID and 31 healthy controls (HC). Functional near-infrared spectroscopy (fNIRS) was utilized to assess the concentrations of oxyhemoglobin (Oxy-Hb) and functional connectivity in the prefrontal cortex of each participant while performing the verbal fluency test (VFT) task.

Results

In the VFT task, no significant difference was found between the SID group and the HC group in terms of integral values, centroid values, and mean Oxy-Hb variations. These findings suggest that both groups exhibit similar hemodynamic responses. However, the functional connectivity analysis revealed significant differences in inter-channel connectivity strength between the two groups. The SID group showed significantly lower average inter-channel connectivity strength compared to the HC group. Moreover, six channel pairs (right frontopolar cortex - left frontopolar cortex, left orbitofrontal cortex - left temporopolar cortex, left temporopolar cortex - left frontopolar cortex, left frontopolar cortex-Ch38, left frontopolar cortex - right pre-motor and supplementary motor cortex, and left frontopolar cortex - right dorsolateral prefrontal cortex) exhibited significantly higher connectivity strength in the HC group compared to the SID group (FDR corrected, p < 0.05). Specifically, channel 27 exhibited the highest frequency of significant connectivity across different channel pairs, occurring five times in total. The channel pair Ch27-Ch39, representing left frontopolar cortex and right dorsolateral prefrontal cortex, exhibited a negative correlation with PSQI scores (r = -0.422, p = 0.018).

Conclusion

Our findings suggest that patients with SID may exhibit altered brain connectivity during the VFT task, as measured by fNIRS. These results provide valuable insights into the functional brain differences associated with SID. Further research is needed to validate and expand upon these findings.

Alteration in brain functional connectivity in patients with post-stroke cognitive impairment during memory task: A fNIRS study

OBJECTIVE

This study attempted to evaluate the functional connectivity (FC) in relevant cortex areas during three memory tasks using the functional near-infrared spectroscopy (fNIRS) method to expound the neural mechanisms in individuals with post-stroke cognitive impairment (PSCI).

METHODS

Short-term memory and visuospatial abilities were assessed using the clock drawing test, digit span test, and Corsi Block-tapping tests with simultaneous fNIRS. The oxygenated hemoglobin concentration signals were recorded from the bilateral motor sense cortex (LMS/RMS) and prefrontal lobe (LPFT/PFT/RPFT) of 19 subjects with cognitive impairment (PSCI group), 27 stroke subjects (STR group) and 26 healthy subjects (HC group).

RESULTS

MMSE scores were positively correlated with the clock drawing test and digit span test scores but not with Corsi Block-tapping scores. During each test, functional connectivity between the bilateral MS (LMS/RMS) was highest within each group, but the functional connectivity between motor sense cortex and frontal lobe was lowest. PSCI group showed decreased FC between bilateral motor sense cortex (P < 0.05) and between motor sense cortex and frontal lobe (P > 0.05) during clock drawing test and Corsi Block-tapping test while decreased FC between each region of interest during digit span test with no significant difference. Functional connectivity levels were closely related to MMSE scores.

CONCLUSIONS

Decreased functional connectivity level may be a marker of impaired cognitive function in post-stroke cognitive impairment. The fNIRS-based functional connectivity provides a non-invasive method to recognize cognitive impairment post-stroke. Functional connectivity changes may help to further understand the neural mechanisms of cognitive impairment post stroke.

Upper limb intelligent feedback robot training significantly activates the cerebral cortex and promotes the functional connectivity of the cerebral cortex in patients with stroke: A functional near-infrared spectroscopy study

Background

Upper limb intelligence robots are widely used to improve the upper limb function of patients with stroke, but the treatment mechanism is still not clear. In this study, functional near-infrared spectroscopy (fNIRS) was used to evaluate the concentration changes of oxygenated hemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) in different brain regions and functional connectivity (FC) of the cerebral cortex in patients with stroke.

Method

Twenty post-stroke patients with upper limb dysfunction were included in the study. They all received three different types of shoulder joint training, namely, active intelligent feedback robot training (ACT), upper limb suspension training (SUS), and passive intelligent feedback robot training (PAS). During the training, activation of the cerebral cortex was detected by fNIRS to obtain the concentration changes of hemoglobin and FC of the cerebral cortex. The fNIRS signals were recorded over eight ROIs: bilateral prefrontal cortices (PFC), bilateral primary motor cortices (M1), bilateral primary somatosensory cortices (S1), and bilateral premotor and supplementary motor cortices (PM). For easy comparison, we defined the right hemisphere as the ipsilesional hemisphere and flipped the lesional right hemisphere in the Nirspark.

Result

Compared with the other two groups, stronger cerebral cortex activation was observed during ACT. One-way repeated measures ANOVA revealed significant differences in mean oxy-Hb changes among conditions in the four ROIs: contralesional PFC [F_{(2, 48)} = 6,798, p < 0.01], ipsilesional M1 [F_{(2, 48)} = 6.733, p < 0.01], ipsilesional S1 [F_{(2, 48)} = 4,392, p < 0.05], and ipsilesional PM [F_{(2, 48)} = 3.658, p < 0.05]. Oxy-Hb responses in the contralesional PFC region were stronger during ACT than during SUS (p < 0.01) and PAS (p < 0.05). Cortical activation in the ipsilesional M1 was significantly greater during ACT than during SUS (p < 0.01) and PAS (p < 0.05). Oxy-Hb responses in the ipsilesional S1 (p < 0.05) and ipsilesional PM (p < 0.05) were significantly higher during ACT than during PAS, and there is no significant difference in mean deoxy-Hb changes among conditions. Compared with SUS, the FC increased during ACT, which was characterized by the enhanced function of the ipsilesional cortex (p < 0.05), and there was no significant difference in FC between the ACT and PAS.

Conclusion

The study found that cortical activation during ACT was higher in the contralesional PFC, and ipsilesional M1 than during SUS, and showed tighter cortical FC between the cortices. The activation of the cerebral cortex of ACT was significantly higher than that of PAS, but there was no significant difference in FC. Our research helps to understand the difference in cerebral cortex activation between upper limb intelligent feedback robot rehabilitation and other rehabilitation training and provides an objective basis for the further application of upper limb intelligent feedback robots in the field of stroke rehabilitation.

Brain Activation During Working Memory Task in Amnestic Mild Cognitive Impairment Patients and Its Association with Memory and Attention

BACKGROUND

Amnestic mild cognitive impairment (aMCI) is regarded as a transitional state of Alzheimer's disease, with working memory (WM) impairment.

OBJECTIVE

To investigate the brain activity in aMCI patients during WM tasks with the functional near-infrared spectroscopy (fNIRS) technique, as well as explore the association between brain activity and cognitive function in multiple domains.

METHODS

This study is a case-control study of 54 aMCI patients and 33 cognitively healthy elderly (NC). All participants underwent neuropsychological assessments. fNIRS was applied to examine the brain activation during the WM task. Multivariable linear regression analysis was applied to evaluate associations between brain activation and cognitive function in multiple domains.

RESULTS

Compared to NC subjects, aMCI patients had lower activation in the bilateral prefrontal, parietal, and occipital cortex during the WM task. Additionally, activation in the left prefrontal, bilateral parietal, and occipital cortex during the encoding and maintenance phase was positively associated with memory function. During memory retrieval, higher activity in the left prefrontal, parietal, and occipital cortex were correlated with higher memory scores. Besides, a positive association also formed between attention function and the activation in the left prefrontal, parietal, and occipital cortex during the WM task.

CONCLUSION

These findings demonstrated that reduced activation in the prefrontal, parietal and occipital cortex during WM might reflect the risk of cognitive impairment, especially memory and attention function in aMCI patients. Given the brain activation visualization, fNIRS may be a convenient and alternative tool for screening the risk of Alzheimer's disease.

Early screening model for mild cognitive impairment based on resting-state functional connectivity: a functional near-infrared spectroscopy study

SIGNIFICANCE

As an early stage of Alzheimer's disease (AD), the diagnosis of amnestic mild cognitive impairment (aMCI) has important clinical value for timely intervention of AD. Functional near-infrared spectroscopy (fNIRS)-based resting-state brain connectivity analysis, which could provide an economic and quick screening strategy for aMCI, remains to be extensively investigated.

AIM

This study aimed to verify the feasibility of fNIRS-based resting-state brain connectivity for evaluating brain function in patients with aMCI, and to determine an early screening model for auxiliary diagnosis.

APPROACH

The resting-state fNIRS was utilized for exploring the changes in functional connectivity of 64 patients with aMCI. The region of interest (ROI)-based and channel-based connections with significant inter-group differences have been extracted through the two-sample t -tests and the receiver operating characteristic (ROC). These connections with specificity and sensitivity were then taken as features for classification.

RESULTS

Compared with healthy controls, connections of the MCI group were significantly reduced between the bilateral prefrontal, parietal, occipital, and right temporal lobes. Specifically, the long-range connections from prefrontal to occipital lobe, and from prefrontal to parietal lobe, exhibited stronger identifiability (area under the ROC curve > 0.65 , ** p < 0.01 ). Subsequently, the optimal classification accuracy of ROI-based connections was 71.59%. Furthermore, the most responsive connections were located between the right dorsolateral prefrontal lobe and the left occipital lobe, concomitant with the highest classification accuracy of 73.86%.

CONCLUSION

Our findings indicate that fNIRS-based resting-state functional connectivity analysis could support MCI diagnosis. Notably, long-range connections involving the prefrontal and occipital lobes have the potential to be efficient biomarkers.

Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers

Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naïve bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81% and specificities above 94%. SVM had the highest performance in terms of accuracy (85.1 ± 1.77%), sensitivity (84 ± 1.7%), specificity (95 ± 0.5%), precision (86 ± 1.6%), and F1-score (85 ± 1.7%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction.

Transcutaneous electrical acupoint stimulation for children with attention-deficit/hyperactivity disorder: a randomized clinical trial

Little is known about the effects of transcutaneous electrical acupoint stimulation (TEAS) for children with attention-deficit/hyperactivity disorder (ADHD). Here, we carried out a 4 week randomized clinical trial in which patients aged 6-12 years old with an ADHD diagnosis received TEAS or sham TEAS. The primary outcome measure was the investigator-rated Clinical Global Impression-Improvement (CGI-I) score at week 4. Secondary outcomes included changes from baseline to week 4 in the investigator-rated Clinical Global Impression-Severity of Illness (CGI-S) score, the Conners' Parent/Teacher Rating Scales-Revised: Short Form (CPRS-R: S/CTRS-R: S) score, go/no-go task performance, and functional near-infrared spectroscopy (fNIRS)-based oxygenated hemoglobin level within the prefrontal cortex. At week 4, the CGI-I score indicated improvement in 33.3% of the TEAS group compared with 7.7% of the sham group (P = 0.005). The TEAS group had a greater decrease in the mean CGI-S score (-0.87) than the sham TEAS group (-0.28) (P = 0.003). A greater enhancement in the mean cerebral oxygenated hemoglobin within the prefrontal cortex was found in the TEAS group (0.099 mM mm) compared with the sham TEAS group (0.005 mM mm) (P < 0.001). CPRS-R: S score, CTRS-R: S score, and go/no-go performance exhibited no significant improvement after TEAS treatment. The manipulation-associated adverse events were uncommon in both groups, and events were very mild. Our results show that noninvasive TEAS significantly improved general symptoms and increased prefrontal cortex blood flow within 4 weeks for children with ADHD. Further clinical trials are required to understand the long-term efficacy in a larger clinical sample. This trial was registered on ClinicalTrials.gov (NCT03917953).

Identification of impulsive adolescents with a functional near infrared spectroscopy (fNIRS) based decision support system

Background.The gold standard for diagnosing impulsivity relies on clinical interviews, behavioral questionnaires and rating scales which are highly subjective.Objective.The aim of this study was to develop a functional near infrared spectroscopy (fNIRS) based classification approach for correct identification of impulsive adolescents. Taking into account the multifaceted nature of impulsivity, we propose that combining informative features from clinical, behavioral and neurophysiological domains might better elucidate the neurobiological distinction underlying symptoms of impulsivity.Approach. Hemodynamic and behavioral information was collected from 38 impulsive adolescents and from 33 non-impulsive adolescents during a Stroop task with concurrent fNIRS recordings. Connectivity-based features were computed from the hemodynamic signals and a neural efficiency metric was computed by fusing the behavioral and connectivity-based features. We tested the efficacy of two commonly used supervised machine-learning methods, namely the support vector machines (SVM) and artificial neural networks (ANN) in discriminating impulsive adolescents from their non-impulsive peers when trained with multi-domain features. Wrapper method was adapted to identify the informative biomarkers in each domain. Classification accuracies of each algorithm were computed after 10 runs of a 10-fold cross-validation procedure, conducted for 7 different combinations of the 3-domain feature set.Main results.Both SVM and ANN achieved diagnostic accuracies above 90% when trained with Wrapper-selected clinical, behavioral and fNIRS derived features. SVM performed significantly higher than ANN in terms of the accuracy metric (92.2% and 90.16%, respectively,p= 0.005).Significance.Preliminary findings show the feasibility and applicability of both machine-learning based methods for correct identification of impulsive adolescents when trained with multi-domain data involving clinical interviews, fNIRS based biomarkers and neuropsychiatric test measures. The proposed automated classification approach holds promise for assisting the clinical practice of diagnosing impulsivity and other psychiatric disorders. Our results also pave the path for a computer-aided diagnosis perspective for rating the severity of impulsivity.

Plasma Orexin Levels Related to Altered Brain Activity During Abstinence in Patients with Alcohol Dependence

Objectives

In vivo studies have correlated brain activity with alcohol-seeking behavior, while clinical studies have identified altered brain activity in patients with alcohol dependence (AD) even during abstinence. We aimed to explore the relationship between plasma orexin levels, brain activity, and alcohol-craving scores in patients with AD.

Methods

In this pilot study, we evaluated 24 male patients with AD in remission and 25 male controls. Alcohol craving was assessed using the Obsessive Compulsive Drinking Scale (OCDS). An adapted MRI technique was used to assess global functional connectivity density (gFCD), and plasma orexin concentrations were measured by radioimmunoassay. Associations were analyzed by the Pearson correlation.

Results

Plasma orexin levels in AD patients in remission were significantly higher than those in the controls. OCDS scores correlated to orexin concentrations (r = 0.47, P < .05). Compared to the controls, all AD patients demonstrated reduced gFCD, primarily in the frontal, temporal, and parietal lobes, and increased gFCD in the accumbens nuclei and posterior insular cortex. Mean gFCD values in the accumbens nuclei significantly correlated to craving scores (r = 0.55, P < .05). Although assessed during abstinence, the reward circuits in AD patients exhibited increased activity. Orexin levels correlated to increased activity in the accumbens nuclei and craving scores.

Conclusions

The potential clinical utility of plasma orexin levels to assess the risk of relapse in AD patients in treatment and prevention programs deserves further study.

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.

Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity

In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k -means clustering technique was applied to derive four recurring connectivity states. The states were characterized by both graph theory and semi-metric analysis. Occurrence probability and state transition were extracted as dynamic FC network features, and a Random Forest (RF) classifier was implemented to detect emotional sensitivity. The proposed method was trialled on 39 nursing students and 19 registered nurses during decision-making, where we assumed registered nurses have developed strategies to cope with emotional sensitivity. Emotional stimuli were selected from International Affective Digitized Sound System (IADS) database. Experiment results showed that registered nurses demonstrated single dominant connectivity state of task-relevance, while nursing students displayed in two states and had higher level of task-irrelevant state connectivity. The results also showed that students were more susceptive to emotional stimuli, and the derived dynamic FC features provided a stronger discriminating power than heart rate variability (accuracy of 81.65% vs 71.03%) as biomarkers of emotional sensitivity. This work forms the first study to demonstrate the stability of fNIRS based dynamic FC states as a biomarker. In conclusion, the results support that the state distribution of dynamic FC could help reveal the differentiating factors between the nursing students and registered nurses during decision making, and it is anticipated that the biomarkers might be used as indicators when developing professional training related to emotional sensitivity.

Resting-state brain networks in neonatal hypoxic-ischemic brain damage: a functional near-infrared spectroscopy study

Significance: There is an emerging need for convenient and continuous bedside monitoring of full-term newborns with hypoxic-ischemic brain damage (HIBD) to determine whether early intervention is required. Functional near-infrared spectroscopy (fNIRS)-based resting-state brain network analysis, which could provide an effective evaluation method, remains to be extensively studied. Aim: Our study aims to verify the feasibility of fNIRS-based resting-state brain networks for evaluating brain function in infants with HIBD to provide a new and effective means for clinical research in neonatal HIBD. Approach: Thirteen neonates with HIBD were scanned using fNIRS in the resting state. The brain network properties were explored to attempt to extract effective features as recognition indicators. Results: Compared with healthy controls, newborns with HIBD showed decreased brain functional connectivity. Specifically, there were severe losses of long-range functional connectivity of the contralateral parietal-temporal lobe, contralateral parietal-frontal lobe, and contralateral parietal lobe. The node degree showed a widespread decrease in the left frontal middle gyrus, left superior frontal gyrus dorsal, and right central posterior gyrus. However, newborns with HIBD showed a significantly higher local network efficiency (* p < 0.05 ). Subsequently, network indicators based on small-worldness, local efficiency, modularity, and normalized clustering coefficient were extracted for HIBD identification with the accuracy observed as 79.17%. Conclusions: Our findings indicate that fNIRS-based resting-state brain network analysis could support early HIBD diagnosis.

Lesions in the right Rolandic operculum are associated with self-rating affective and apathetic depressive symptoms for post-stroke patients

Stroke survivors majorly suffered from post-stroke depression (PSD). The PSD diagnosis is commonly performed based on the clinical cut-off for psychometric inventories. However, we hypothesized that PSD involves spectrum symptoms (e.g., apathy, depression, anxiety, and stress domains) and severity levels. Therefore, instead of using the clinical cut-off, we suggested a data-driven analysis to interpret patient spectrum conditions. The patients’ psychological conditions were categorized in an unsupervised manner using the k-means clustering method, and the relationships between psychological conditions and quantitative lesion degrees were evaluated. This study involved one hundred sixty-five patient data; all patients were able to understand and perform self-rating psychological conditions (i.e., no aphasia). Four severity levels—low, low-to-moderate, moderate-to-high, and high—were observed for each combination of two psychological domains. Patients with worse conditions showed the significantly greater lesion degree at the right Rolandic operculum (part of Brodmann area 43). The dissimilarities between stress and other domains were also suggested. Patients with high stress were specifically associated with lesions in the left thalamus. Impaired emotion processing and stress-affected functions have been frequently related to those lesion regions. Those lesions were also robust and localized, suggesting the possibility of an objective for predicting psychological conditions from brain lesions.