Investigation of Brain Functional Networks in Children Suffering from Attention Deficit Hyperactivity Disorder

Dini, Hossein; Sendi, Mohammad S.E.

doi:10.1007/s10548-020-00794-1

Cited by 23 publications

(19 citation statements)

References 50 publications

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“…20,21 The decreased characteristic path length and increased global efficiency also found in many cross-frequency PAC networks in the large-scale cost of children with ADHD, which is consistent with Cao et al 32 but inconsistent with previous studies. 21,22 Based on event-related phase coherence of face emotional task EEG, Dini et al 37 observed that children suffering from ADHD showed higher clustering coefficient and short characteristic path length in undirected weighted network, and this is completely consistent with the results of our study. ADHD is an extremely heterogeneous disease, and there are few common findings, such as mean clustering coefficient and characteristic path length aspects, across research in the existing structural and functional network studies.…”

Section: Discussionsupporting

confidence: 91%

Phase-Amplitude Coupling Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder

et al. 2022

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In cognitive neuroscience, there is an increasing interest in identifying and understanding the synchronization of distinct neural oscillations with different frequencies that might support dynamic communication within the brain. This study explored the cross-frequency phase-amplitude coupling brain network characteristics of resting-state electroencephalograms between 30 children with attention-deficit/hyperactivity disorder (ADHD) and 30 age-matched typically developing children. Compared with control group, children with ADHD show increased coupling intensity and altered distribution patterns of dominant paired channels, especially in the δ-γH, θ-γH, α-γH, βL-γH, and βH-γH coupling networks. Regarding graph theory properties, the characteristic path length, the mean clustering coefficient, the global efficiency, and the mean local efficiency significant difference in many cross-frequency coupling networks, especially in the δ-γH, θ-γH, α-γH, βL-γH, and βH-γH coupling networks. The area under the receiver operating characteristic curve (AUC) in low-frequency coupling with a high-gamma frequency was larger than that in coupling with low-gamma frequency (AUC values of δ-γL, θ-γL, α-γL, βL-γL, βH-γL, δ-γH, θ-γH, α-γH, βL-γH, and βH-γH were 0.794, 0.722, 0.666, 0.570, 0.881, 0.992, 0.998, 0.998, 0.989, and 0.974, respectively). These findings demonstrate altered coupling intensity and disrupted topological organization of coupling networks, support the altered brain network theory in children with ADHD. The coupling intensity and graph theory properties of low-frequency coupling with high-gamma frequency were promising resting-state electroencephalogram biomarkers of ADHD in children.

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Section: Discussionsupporting

confidence: 91%

Phase-Amplitude Coupling Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder

et al. 2022

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“…To reduce the volume conduction effect, we used the current source density (CSD) method (Mitzdorf, 1985). This method obtains the spatial properties of each de-noised channel while neglecting the effect of other channels by utilizing the second spatial derivative of the EEG recorded in that channel (Dini et al, 2020). The CSD toolbox (Sijmen Duineveld, 2022) was used to apply the CSD method to the de-noised data with the medium spline flexibility of m = 4, as delineated by Dini et al (2020) and Fitzgibbon et al (2015).…”

Section: Neuro Partmentioning

confidence: 99%

“…This method obtains the spatial properties of each de-noised channel while neglecting the effect of other channels by utilizing the second spatial derivative of the EEG recorded in that channel (Dini et al, 2020). The CSD toolbox (Sijmen Duineveld, 2022) was used to apply the CSD method to the de-noised data with the medium spline flexibility of m = 4, as delineated by Dini et al (2020) and Fitzgibbon et al (2015). Finally, the data obtained after the implementation of CSD were z-normalized across time and down-sampled to 200 Hz to reduce the calculation load.…”

Section: Neuro Partmentioning

confidence: 99%

Exploring the Neural Processes behind Narrative Engagement: An EEG Study

Dini

Simonetti

Bruni

2022

Preprint

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Past cognitive neuroscience studies using naturalistic stimuli have considered narratives holistically and focused on cognitive processes. In this study, we incorporated the narrative structure the dramatic arc as an object of investigation, to examine how engagement levels fluctuate across a narrative-aligned dramatic arc. We explored the possibility of predicting self-reported engagement ratings from neural activity and investigated the idiosyncratic effects of each phase of the dramatic arc on brain responses as well as the relationship between engagement and brain responses. We presented a movie excerpt following the six-phase narrative arc structure to female and male participants while collecting EEG signals. We then asked this group of participants to recall the excerpt, another group to segment the video based on the dramatic arc model, and a third to rate their engagement levels while watching the movie. The results showed that the self-reported engagement ratings followed the pattern of the narrative dramatic arc. Moreover, whilst EEG amplitude could not predict group-averaged engagement ratings, other features comprising dynamic inter-subject correlation, dynamic functional connectivity patterns and graph features were able to achieve this. Furthermore, neural activity in the last two phases of the dramatic arc significantly predicted engagement patterns. This study is the first to explore the cognitive processes behind the dramatic arc and its phases. By demonstrating how neural activity predicts self-reported engagement, which itself aligns with the narrative structure, this study provides insights on the interrelationships between narrative structure, neural responses, and viewer engagement.

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“…Recently, functional network connectivity (FNC), which is obtained evaluating the temporal dependence over the entire resting-state fMRI (rs-fMRI) time series (called static functional network connectivity analysis), has been demonstrated to be highly informative to reveal underlying brain connectivity patterns in mental disorders (Mulders et al, 2015; Yan et al, 2019; Dini et al, 2020; Liu et al, 2020; Luo et al, 2021). Previous studies used various features including spatial functional networks, and FNC based on independent component analysis (ICA) to discriminate BPP and SZ from the HC group (Arribas et al, 2010; Dini et al, 2020),(Jafri et al, 2008). Xia et al, in a study investigating functional network features (e.g., clustering coefficient) of BP, SZ, and major depressive disorder, reported that their network features go toward randomized configurations with different degrees.…”

Section: Introductionmentioning

confidence: 99%

“…Identifying biomarkers from brain functional connectivity derived from functional magnetic resonance imaging (fMRI) data is a potential supportive/alternative measure for symptom-based clinical features to diagnose/modify treatment for psychiatric disorders (Sendi et al, 2021c) (Stephan et al, 2017;Du et al, 2018bDu et al, , 2020b. Recently, functional network connectivity (FNC), which is obtained evaluating the temporal dependence over the entire resting-state fMRI (rs-fMRI) time series (called static functional network connectivity analysis), has been demonstrated to be highly informative to reveal underlying brain connectivity patterns in mental disorders (Mulders et al, 2015;Yan et al, 2019;Dini et al, 2020;Liu et al, 2020;Luo et al, 2021). Previous studies used various features including spatial functional networks , and FNC based on independent component analysis (ICA) to discriminate BPP and SZ from the HC group (Arribas et al, 2010;Dini et al, 2020), (Jafri et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Overlap across psychotic disorders: A functional network connectivity analysis

Dini

Bruni

Ramsøy

et al. 2022

Preprint

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Functional network connectivity has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not clear yet. This study focuses on finding overlap across these three psychotic disorders using dynamic FNC (dFNC) and compares it with static FNC (sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar Schizophrenia Network on Intermediate Phenotypes cohort. The data includes three groups of patients with schizophrenia (SZP, N=102), bipolar (BPP, N=102), and schizoaffective (SADP, N=102), their relatives SZR (N=102), BPR (N=102), SADR (N=102), and HC (N=118) groups. After estimating each individual's dFNC, we put them into three identical states. We estimated five different features, including occupancy rate (OCR), number of transitions, the total number of transitions, and the total distance traveled. Finally, the extracted features are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We found the OCR difference between SZP and SZR in state2, between BPP and HC in state1, and between SADP and HC in state2. Also, state2 OCR separates SZP from BPP, state3 OCR separates BPP from SZP and SADP. Moreover, the OCR and traveled distance feature extracted from SZ and BP could significantly predict PANSS Total and PANSS General scores. Finally, combined distance features of all disorders showed a significant relationship to PANSS Total and PANSS General scores.

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Investigation of Brain Functional Networks in Children Suffering from Attention Deficit Hyperactivity Disorder

Cited by 23 publications

References 50 publications

Phase-Amplitude Coupling Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder

Phase-Amplitude Coupling Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder

Exploring the Neural Processes behind Narrative Engagement: An EEG Study

Overlap across psychotic disorders: A functional network connectivity analysis

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