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

Sutoko, Stephanie; Monden, Yukifumi; Tokuda, Tatsuya; Ikeda, Takahiro; Nagashima, Masako; Funane, Tsukasa; Atsumori, Hirokazu; Kiguchi, Masashi; Maki, Atsushi; Yamagata, Takanori; Dan, Ippeita

doi:10.3389/fnhum.2020.00003

Cited by 19 publications

(18 citation statements)

References 95 publications

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“…When the increase of explained variance becomes insignificant, further increasing the cluster number will not result in better clustering. In order to determine the optimum cluster number, the elbow method 7 , 8 was applied on the plot of explained variance against cluster number. A first-degree line between the most-distant points (two and ten clusters) was fitted.…”

Section: Resultsmentioning

confidence: 99%

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

Sutoko

Atsumori

Obata

et al. 2020

Sci Rep

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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.

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Section: Resultsmentioning

confidence: 99%

“…The explained variance parameter was quantified by the ratio between total variance minus within-cluster variance and total variance. Furthermore, the elbow method 7 , 8 was empirically applied on the plot of explained variance against cluster number. Psychological scores of patients were then clustered by the optimum cluster number.…”

Section: Methodsmentioning

confidence: 99%

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

Sutoko

Atsumori

Obata

et al. 2020

Sci Rep

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“…2) Dynamic FC: To capture the dynamic FC, we performed the SWC method [22], [34]. Based on previous studies [35], [36], we selected a fixed window size of 30 s to provide a good trade-off between the false fluctuation and over-smoothing.…”

Section: B Proposed Methodsmentioning

confidence: 99%

“…Occurrence Probability: From each subject FC sliding time point, the occurrence probability [34] was computed as the ratio of total occurrence of a particular connectivity state to the total task-relevant sliding time:…”

Section: B Proposed Methodsmentioning

confidence: 99%

Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity

Tang

Chong

Kiguchi

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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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 decisionmaking, 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 taskrelevance, 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.

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“…Therefore, the spline interpolation method was used to amend motion artifacts, which were manifested as an impulse or cliff-type jumps caused by the relative sliding of the scalp and probes. [24][25][26] Subsequently, 0.01-to 0.1-Hz bandpass filtering was performed to remove the noise based on physiological fluctuations such as pulse and respiration. 27 Then the modified Beer-Lambert law was used to transform light intensity data into the relative change of the concentration of oxygenated (HbO) and deoxygenated hemoglobin (HbR) as follows:…”

Section: Data Preprocessingmentioning

confidence: 99%

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

et al. 2021

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Significance: There is an emerging need for convenient and continuous bedside monitoring of fullterm 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.

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

Cited by 19 publications

References 95 publications

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

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

Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity

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

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