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Neural Oscillation Features of ADHD Symptoms in Children: EEG Evidence from Resting State and Oddball Task

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Title: Neural Oscillation Features of ADHD Symptoms in Children: EEG Evidence from Resting State and Oddball Task
Language: English
Authors: Siyuan Zhang, Shuting Yu, Xiaobing Cui, Lixia Liang, Xuebing Li (ORCID 0000-0002-4713-9208)
Source: Journal of Attention Disorders. 2026 30(4):552-565.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 14
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Children, Attention Deficit Hyperactivity Disorder, Brain, Biofeedback, Severity (of Disability), Symptoms (Individual Disorders), Attention Span, Measures (Individuals), Performance Tests, Intelligence Tests, Child Behavior, Check Lists, Rating Scales
Assessment and Survey Identifiers: Continuous Performance Test, Raven Progressive Matrices, Child Behavior Checklist, Conners Rating Scales
DOI: 10.1177/10870547251405008
ISSN: 1087-0547
1557-1246
Abstract: Objective: The aim of this study was to explore neural oscillation features (resting-state+oddball-EROs) of ADHD symptoms in children in a dimensional approach and to construct a multi-metric model combining objective EEG measures and subjective parental ratings to predict children's behavioral performance. Method: Seventy-seven children (age range: 6-12 years) participated in laboratory assessment. ADHD symptoms were first evaluated using the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), followed by EEG recordings during both resting-state and oddball task conditions. Three parent rating scales were also used to evaluate children's behavioral performance: the ADHD Rating Scale-IV (ADHD RS-IV): Home Version, the Child Behavior Checklist (CBCL), and the Conners' Parent Rating Scales (CPRS). Results: Seventy-one children with valid IVA-CPT results were included in data analysis. The main results revealed a relationship between poorer attention performance and decreased eye-open alpha1 power in the resting state, reduced N2 delta power in the oddball condition, and elevated non-delta band power in the standard condition of the oddball task. Poorer response control performance was associated with increased eye-closed alpha1 power, as well as increased eyeopen alpha2 and beta2 power. Stepwise regression analysis showed that the inattention subscale from parental assessments on the RS-IV, combined with P3 alpha power in the standard condition of the oddball task, was the strongest predictor of children's attention performance. Conclusion: The current study identified important neural oscillation features of ADHD symptoms in both the resting state and during an oddball task and offers new insights into multi-metric prediction for ADHD assessment and diagnosis.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1499931
Database: ERIC
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  Value: <anid>AN0192008570;gs001apr.26;2026Mar05.05:44;v2.2.500</anid> <title id="AN0192008570-1">Neural Oscillation Features of ADHD Symptoms in Children: EEG Evidence From Resting State and Oddball Task </title> <p>Objective: The aim of this study was to explore neural oscillation features (resting-state + oddball-EROs) of ADHD symptoms in children in a dimensional approach and to construct a multi-metric model combining objective EEG measures and subjective parental ratings to predict children's behavioral performance. Method: Seventy-seven children (age range: 6–12 years) participated in laboratory assessment. ADHD symptoms were first evaluated using the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), followed by EEG recordings during both resting-state and oddball task conditions. Three parent rating scales were also used to evaluate children's behavioral performance: the ADHD Rating Scale-IV (ADHD RS-IV): Home Version, the Child Behavior Checklist (CBCL), and the Conners' Parent Rating Scales (CPRS). Results: Seventy-one children with valid IVA-CPT results were included in data analysis. The main results revealed a relationship between poorer attention performance and decreased eye-open alpha1 power in the resting state, reduced N2 delta power in the oddball condition, and elevated non-delta band power in the standard condition of the oddball task. Poorer response control performance was associated with increased eye-closed alpha1 power, as well as increased eye-open alpha2 and beta2 power. Stepwise regression analysis showed that the inattention subscale from parental assessments on the RS-IV, combined with P3 alpha power in the standard condition of the oddball task, was the strongest predictor of children's attention performance. Conclusion: The current study identified important neural oscillation features of ADHD symptoms in both the resting state and during an oddball task and offers new insights into multi-metric prediction for ADHD assessment and diagnosis.</p> <p>Keywords: ADHD; EEG; ERO; oddball</p> <hd id="AN0192008570-2">Introduction</hd> <p>ADHD is a common neurodevelopmental disorder characterized by problems with attention, hyperactivity, and impulsivity. It typically emerges in early childhood and has a global prevalence of approximately 5% ([<reflink idref="bib4" id="ref1">4</reflink>]). These symptoms lead to academic and social difficulties in children and contribute to increased parenting stress ([<reflink idref="bib5" id="ref2">5</reflink>]; [<reflink idref="bib34" id="ref3">34</reflink>]; [<reflink idref="bib39" id="ref4">39</reflink>]; [<reflink idref="bib63" id="ref5">63</reflink>]). Notably, an additional 5% of children experience significant challenges with core ADHD symptoms despite not meeting full diagnostic criteria ([<reflink idref="bib75" id="ref6">75</reflink>]). These children often demonstrate behavioral problems and functional impairments yet face barriers to accessing evidence-based treatments ([<reflink idref="bib24" id="ref7">24</reflink>]; [<reflink idref="bib43" id="ref8">43</reflink>]; [<reflink idref="bib75" id="ref9">75</reflink>]; [<reflink idref="bib84" id="ref10">84</reflink>]). Therefore, some researchers have proposed viewing ADHD as a continuum ([<reflink idref="bib24" id="ref11">24</reflink>]; [<reflink idref="bib57" id="ref12">57</reflink>]; [<reflink idref="bib62" id="ref13">62</reflink>]). From this broader classification approach to ADHD, it is important to focus on the neural mechanisms underlying abnormal behavioral performance in current research and in guiding clinical practice.</p> <p>In investigating these neural mechanisms, resting-state EEG and event-related potentials (ERPs) have emerged as primary research tools. For resting state conditions, EEG signal power from various scalp locations is quantified to extract meaningful information from oscillatory activity over time intervals of several minutes. The most robust resting-state EEG findings in ADHD include elevated slow-wave (theta, 4–8 Hz) power and/or reduced fast-wave (beta, 13–30 Hz) power, typically observed over frontocentral electrode sites ([<reflink idref="bib2" id="ref14">2</reflink>]; [<reflink idref="bib38" id="ref15">38</reflink>]; [<reflink idref="bib40" id="ref16">40</reflink>]; [<reflink idref="bib41" id="ref17">41</reflink>]). Additionally, studies examining arousal mechanisms in ADHD have identified abnormal alpha activity associated with hypoarousal ([<reflink idref="bib52" id="ref18">52</reflink>]; [<reflink idref="bib59" id="ref19">59</reflink>]). Numerous studies have investigated resting-state EEG applications for both diagnosis and treatment ([<reflink idref="bib7" id="ref20">7</reflink>]; [<reflink idref="bib58" id="ref21">58</reflink>]; [<reflink idref="bib71" id="ref22">71</reflink>]). However, the field has yet to establish generalizable electrophysiological biomarkers for ADHD, largely due to the condition's substantial neurobiological heterogeneity ([<reflink idref="bib27" id="ref23">27</reflink>]; [<reflink idref="bib78" id="ref24">78</reflink>]).</p> <p>Unlike resting-state EEG, which measures brain state, ERPs offer insight into transient neural dynamics. ERPs are thought to accurately reflect specific stages of attentional processing and provide information related to behavioral performance. The P3 component has been most extensively studied in individuals with ADHD, as it is closely associated with abnormalities in attention and response inhibition ([<reflink idref="bib47" id="ref25">47</reflink>]; [<reflink idref="bib64" id="ref26">64</reflink>]; [<reflink idref="bib76" id="ref27">76</reflink>]). Early sensory-related components such as P2 and N2 have also been investigated, although findings remain inconsistent ([<reflink idref="bib46" id="ref28">46</reflink>]; [<reflink idref="bib48" id="ref29">48</reflink>]). While ERP components capture both early (attentional detection/classification) and late (resource allocation) processing stages, inconsistent results restrict their clinical diagnostic application.</p> <p>Despite decades of EEG research, robust and reliable biomarkers for ADHD remain elusive. To address this challenge, the present study introduces a focus on the oscillatory dynamics underlying attentional processes—a perspective that may have been relatively overlooked in the quest for diagnostic biomarkers. [<reflink idref="bib11" id="ref30">11</reflink>] proposed the susceptibility rules theoretical framework, which systematically explains how the brain generates responses to external and internal stimuli and its dynamic response mechanisms. According to this theory, the brain responds at specific frequencies depending on the types of stimuli and cognitive processes. These generated brain oscillations (i.e., event-related oscillations [EROs]) are considered the "functional building blocks" of sensory and cognitive processes ([<reflink idref="bib82" id="ref31">82</reflink>]). In contrast to averaged ERPs, which offer only a rough estimate of the EEG response, EROs capture the dynamic changes in the brain's sensory and cognitive processing activities. Whereas resting-state oscillations also reflect ongoing brain states, EROs represent the synchronization of neural populations into an ordered state in response to specific events ([<reflink idref="bib10" id="ref32">10</reflink>]; [<reflink idref="bib12" id="ref33">12</reflink>]). Therefore, investigating EROs across different frequency bands and processing stages during attentional tasks may contribute to a more mechanistic neurophysiological understanding of stimulus-related brain functioning.</p> <p>Furthermore, compared to extracting ERP amplitudes and latencies, extracting neural oscillatory features during attentional processing holds greater clinical application value. Research has demonstrated that transcranial alternating current stimulation (tACS), which targets abnormal neural oscillations, can effectively modulate aberrant oscillatory activity in various psychiatric disorders ([<reflink idref="bib31" id="ref34">31</reflink>]; [<reflink idref="bib67" id="ref35">67</reflink>]). This modulatory effect is particularly enhanced when stimulation is applied synchronously with cognitive tasks (i.e., online tACS; [<reflink idref="bib20" id="ref36">20</reflink>]; [<reflink idref="bib60" id="ref37">60</reflink>]). Therefore, the identification of abnormal ERO features during attentional processes can be considered potential targets for neuromodulation therapies. In summary, focusing on ERO features not only contributes to a deeper understanding of the neurophysiological mechanisms of attentional processing but also informs the selection of targets for clinical intervention.</p> <p>Guided by the ADHD continuum perspective, this study aims to investigate neural oscillatory features underlying ADHD symptoms in children and examine how these features relate to symptom severity. For resting-state EEG, we will extract spectral features from all brain regions during both eye-open and eye-closed conditions. For EROs, we employ the classic oddball paradigm to capture fundamental attentional processing. In addition to these objective measures, we include parent-rated scales given their clinical significance in pediatric ADHD children diagnosis. Furthermore, we propose evaluating a multi-metric prediction approach that combines both objective and subjective measures. We hypothesize the following: (<reflink idref="bib1" id="ref38">1</reflink>) During the resting state, increased theta activity and decreased beta activity in anterior regions, along with enhanced alpha activity in posterior regions, will correlate with the severity of ADHD symptoms; (<reflink idref="bib2" id="ref39">2</reflink>) During the oddball task, neural oscillations associated with the P2, N2, and P3 components (which reflect distinct stages of attentional processing) will show significant associations with ADHD symptoms; and (<reflink idref="bib3" id="ref40">3</reflink>) While all three measures (EEG, EROs, and parent ratings) will predict symptom severity, with EROs demonstrating superior predictive validity, the model combining these measures yields optimal explanatory power.</p> <hd id="AN0192008570-3">Methods</hd> <p></p> <hd id="AN0192008570-4">Participants</hd> <p>We recruited 77 children aged 6 to 12 years (30 girls, <emph>M</emph><subs>age</subs> = 8.09, <emph>SD</emph> = 1.82) through advertisements in local primary schools and online platforms. The advertisements invited participation in an EEG study on attention functions, offering individualized attention and intelligence assessment reports as compensation.</p> <p>The sole exclusion criterion was age beyond the target range. Among the participants, one child with ADHD (medicated with methylphenidate hydrochloride) was included after discontinuing medication 24 hr prior to testing. All participants provided assent, while their parents provided written informed consent for the study. The study was approved by the Ethics Committee of the Institute of Psychology, Chinese Academy of Sciences.</p> <hd id="AN0192008570-5">Procedure</hd> <p>All participants were accompanied by their parents to the laboratory. After providing written assent (with parental consent obtained), each child completed the assessments for ADHD symptoms and intelligence individually in a quiet room. ADHD symptoms were assessed using the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), while intelligence was measured by the Raven's Standard Progressive Matrices (RSPM). Meanwhile, parents provided basic demographic information about their child and completed the following behavioral rating scales: the ADHD Rating Scale-IV (ADHD RS-IV): Home Version, the Child Behavior Checklist (CBCL), and the Conners' Parent Rating Scales (CPRS). Subsequently, parents assisted their children with hair washing in a dedicated preparation room to facilitate the subsequent EEG recording. Following the preparation, each child underwent the EEG recording in a separate room. They first completed the resting-state EEG acquisition under eyes-open and eyes-closed conditions (3 min per condition). Then, they underwent EEG recording during a visual oddball task. Throughout the EEG recording, the child remained alone in the room, while both the parent and the experimenter observed the procedure in real-time via a monitoring system from outside. Upon completion of the study, all children received attention-training workbooks, and parents were provided with individualized interpretations of their child's IVA-CPT and RSPM results as compensation.</p> <hd id="AN0192008570-6">Materials and Measurements</hd> <p></p> <hd id="AN0192008570-7">ADHD Symptoms and Intelligence Assessments</hd> <p>ADHD symptoms were assessed using the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT; [<reflink idref="bib73" id="ref41">73</reflink>]), a standardized measure of attention and response control for individuals aged 6 years and older. This test is widely used as an assisted diagnostic tool for ADHD in clinical and research settings ([<reflink idref="bib68" id="ref42">68</reflink>]; [<reflink idref="bib85" id="ref43">85</reflink>]). The present study employed the IVA+Plus software (BrainTrain Inc.), which administers 500 trials of pseudo-randomized visual and auditory stimuli (1.5-s interstimulus interval), requiring rapid set-shifting between modalities. Participants were instructed to click the mouse when the stimulus was an auditory or visual "1" and to refrain from clicking when the stimulus was an auditory or visual "2." Upon completion, key performance indices (e.g., commission/omission errors, reaction time, and response bias) were evaluated, with the software generating standardized attention and response control quotient scores based on age- and gender-matched norms. These standardized scores serve as objective behavioral markers, reflecting children's attention and response control functions, with lower scores indicating greater impairment consistent with ADHD symptom severity ([<reflink idref="bib65" id="ref44">65</reflink>]; [<reflink idref="bib86" id="ref45">86</reflink>]).</p> <p>Raven's Standard Progressive Matrices (RSPM; [<reflink idref="bib69" id="ref46">69</reflink>]) were used to assess children's general cognitive ability (fluid intelligence). This test is widely used as a measure of reasoning ability and regarded as a valid indicator of general intelligence (g factor) worldwide ([<reflink idref="bib70" id="ref47">70</reflink>]). As an easy-to-administer non-verbal test, the results are often served as background variables in developmental studies ([<reflink idref="bib55" id="ref48">55</reflink>]). Specifically, the RSPM contains 60 items. Each item presents a geometric pattern with a missing piece, requiring participants to select the correct answer option that completes the pattern. The test consists of five sets of 12 items each, with progressively increasing difficulty. The entire test takes approximately 45 min to complete. Final scores were converted to percentile ranks by comparing with gender-, age-, and region-specific normative data, providing standardized estimates of participants' intellectual functioning.</p> <hd id="AN0192008570-8">Parental Assessments</hd> <p>Three parent rating scales were used to assess children's behavioral performance: the ADHD Rating Scale-IV (ADHD RS-IV): Home Version, the Child Behavior Checklist (CBCL), and the Conners' Parent Rating Scales (CPRS).</p> <p>The ADHD RS-IV: Home Version is a widely used DSM-based assessment tool for school-age children, comprising 18 items directly aligned with DSM-IV diagnostic criteria ([<reflink idref="bib28" id="ref49">28</reflink>]; [<reflink idref="bib29" id="ref50">29</reflink>]). It evaluates two core ADHD symptom domains (inattention and hyperactivity-impulsivity) and demonstrates strong discriminant validity in differentiating both ADHD subtypes and children with ADHD from typically developing peers. The Chinese version employed in this study has established reliability and validity in Chinese populations ([<reflink idref="bib80" id="ref51">80</reflink>]).</p> <p>The CBCL is a valid tool to evaluate children's social skills and behavioral problems ([<reflink idref="bib1" id="ref52">1</reflink>]). It is a 118-item scale used internationally to assess children's behavior and emotional problems and includes subscales of Attention Problems to evaluate children's attention problems. It is available in three versions: for parents, for teachers, and as a self-report scale for older children, and the parent-report version is widely used in China ([<reflink idref="bib88" id="ref53">88</reflink>]).</p> <p>The Conners' Parent Rating Scales (CPRS) is a widely used tool for assessing children's behavioral problems, particularly in ADHD screening ([<reflink idref="bib22" id="ref54">22</reflink>]; [<reflink idref="bib37" id="ref55">37</reflink>]). The 48-item Chinese version ([<reflink idref="bib88" id="ref56">88</reflink>]) evaluates six dimensions: Conduct Problems (Factor I), Learning Problems (Factor II), Psychosomatic Symptoms (Factor III), Impulsivity-Hyperactivity (Factor IV), Anxiety (Factor V), and the ADHD Index. In this study, we analyzed the total scores along with the Impulsivity-Hyperactivity and ADHD Index subscales, as these measures are clinically validated for ADHD symptom assessment.</p> <hd id="AN0192008570-9">Behavioral and EEG Tasks</hd> <p>Resting-state EEG data were recorded after the completion of the IVA-CPT and the RSPM. Participants were instructed by an experimenter to either fixate on a central cross or close their eyes while remaining relaxed. Both eye-open and eye-closed conditions were recorded for 3 min each.</p> <p>Next, a visual oddball task was administered with simultaneous EEG recording to measure attention-related brain activity. The task used two black-and-white cartoon images: a frequently presented standard stimulus (pear, 80% probability) and a rare target/oddball stimulus (apple, 20%). Participants were instructed to press the "F" key as quickly and accurately as possible when the target (apple) appeared at the screen center, and the "J" key for the standard stimulus (pear). To ensure sustained attention, responses were required for every stimulus. The task comprised three runs, each consisting of 80 standard and 20 target trials, with approximately 30-s breaks between runs (Figure 1). All participants completed practice trials before the formal experiment.</p> <p>Graph: Figure 1. Flowchart of the oddball task. As shown, each stimulus was presented on a white background for 350 ms followed by a blank randomly from 800 ms to 1,200 ms. The first button pressed within the blank was counted as the response for each trial. Instruction: "If you see an apple, press 'F', otherwise press 'J'. The pictures are presented for a very short time, please pay attention and response as fast and accurate as possible."</p> <hd id="AN0192008570-10">EEG Data Acquisition and Processing</hd> <p>EEG data were acquired using a 64-channel NeuroScan system (1 kHz sampling rate), referenced to the left mastoid with a ground electrode between Fpz and Fz. Electrooculography (EOG) was recorded via bipolar electrodes: horizontal (1 cm lateral to each outer canthus) and vertical (above/below the left eye). All impedances were maintained below 10 kΩ.</p> <p>All data were preprocessed in EEGLAB (v2021.1, MATLAB R2020a). Raw signals were band-pass filtered (1–30 Hz). Resting-state data were segmented into 2-s epochs; oddball task data were epoched (−200 to 800 ms relative to stimulus onset, baseline-corrected using −200 to 0 ms). Artifacts were removed via visual inspection and bad channels interpolated. For resting-state data, the middle 2-min without artifacts were retained. Synchronous or partially synchronous artifactual activity (mostly blinks) were detected on the basis of the topographical and spectral distribution and on the time series of the independent component analysis (ICA) on continuous data. Finally, data were re-referenced to the average reference.</p> <p>Power spectrum analysis was conducted by Fast Fourier Transform (FFT) via MATLAB R2020a (downsampled to 250 Hz). To balance electrode coverage uniformity and research validity, we grouped electrodes into six anatomically defined regions (left frontal, LF: FP1, AF3, F1, F3, F5, and F7; right frontal, RF: FP2, AF4, F8, F2, F4, and F6; left central, LC: FC1, FC3, FC5, C1, C3, C5, CP1, CP3, and CP5; right central, RC: FC2, FC4, FC6, C2, C4, C6, CP2, CP4, and CP6; left parietal-occipital, LPO: P1, P3, P5, P7, PO3, PO5, PO7, and O1; right parietal-occipital, RPO: P2, P4, P6, P8, PO4, PO6, PO8, and O2). Absolute power was extracted for delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), alpha1 (8–10 Hz), alpha2 (10–13 Hz), beta (13–30 Hz), beta1 (13–20 Hz) and beta2 (20–30 Hz) bands for each region, then normalized to total power (Formula 1: <emph>P<subs>rel</subs></emph>, relative power; <emph>P<subs>abs</subs></emph>, absolute power):</p> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>P</mi><mrow><mi>r</mi><mi>e</mi><mi>l</mi></mrow></msub><mrow><mo>(</mo><mi>δ</mi><mo>)</mo></mrow><mo>=</mo><mfrac><mrow><msub><mi>P</mi><mrow><mi>a</mi><mi>b</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mi>δ</mi><mo>)</mo></mrow></mrow><mrow><msub><mi>P</mi><mrow><mi>a</mi><mi>b</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mi>δ</mi><mo>)</mo></mrow><mo>+</mo><msub><mi>P</mi><mrow><mi>a</mi><mi>b</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mi>θ</mi><mo>)</mo></mrow><mo>+</mo><msub><mi>P</mi><mrow><mi>a</mi><mi>b</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mi>α</mi><mo>)</mo></mrow><mo>+</mo><msub><mi>P</mi><mrow><mi>a</mi><mi>b</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mi>β</mi><mo>)</mo></mrow></mrow></mfrac></mrow></math> </ephtml> </p> <p>Graph</p> <p>Time frequency analysis was conducted by Short Time Fourier Transform (STFT) with baseline correction (subtraction method; Formula 2: <emph>P<subs>BC</subs></emph>, baseline-corrected power; <emph>P(t,f)</emph>, power value at time-frequency point of <emph>(t,f)</emph>; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mover accent="true"><mi>P</mi><mo>¯</mo></mover><mrow><mo>(</mo><mi>f</mi><mo>)</mo></mrow></mrow></math> </ephtml> , mean of baseline power values):</p> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>P</mi><mrow><mi>B</mi><mi>C</mi></mrow></msub><mo>=</mo><mi>P</mi><mrow><mo>(</mo><mrow><mi>t</mi><mo>,</mo><mspace width="0.25em" /><mi>f</mi></mrow><mo>)</mo></mrow><mo>−</mo><mover accent="true"><mi>P</mi><mo>¯</mo></mover><mrow><mo>(</mo><mi>f</mi><mo>)</mo></mrow></mrow></math> </ephtml> </p> <p>Graph</p> <p>Regions of interest (ROIs) were defined based on point-by-point amplitude <emph>t</emph>-tests (standard vs. target conditions) and difference topographies (target minus standard; Figure 2 and Supplemental Figure S1). Significant time windows and electrode sites showing condition differences were selected. Finally, three ROIs corresponding to the P2, N2 and P3 components were identified (P2: 150–220 ms, Fz; N2: 250–320 ms, Cz; P3: 300–600 ms, Oz).</p> <p>Graph: Figure 2. Difference topographies (target minus standard) of three ROIs.</p> <hd id="AN0192008570-11">Statistical Analyses</hd> <p>Statistical analyses were performed using SPSS 22. First, Spearman's correlation analysis screened the collected questionnaire and EEG measures, with each variable separately correlated with the IVA-CPT attention and response control scores. False discovery rate (FDR) correction was applied for multiple comparisons. Significantly correlated variables were then entered into general linear models (GLMs) using stepwise regression, incorporating objective EEG, ERO data, and subjective parent-rated scores as predictors of children's ADHD symptoms.</p> <hd id="AN0192008570-12">Results</hd> <p></p> <hd id="AN0192008570-13">Basic Information</hd> <p>Seventy-seven children were invited to the laboratory and completed all the experimental tasks. Six children were excluded from further analysis due to invalid IVA-CPT scores. Descriptive statistics for the IVA-CPT scores and parental assessments of the remaining 71 participants (27 girls, <emph>M</emph><subs>age</subs> = 8.20, <emph>SD</emph> = 1.83, age range: 6–12) are presented in Table 1. Thirty participants exhibited attention scores ≤90 (the clinical cut-off), and 19 children demonstrated response control scores at or below the established threshold. In addition, all participants completed the RSPM and none had intellectual problems.</p> <p>Table 1. Descriptive Statistics for the IVA-CPT Scores and Parental Assessments.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Variables</th><th align="center"><italic>M</italic> ± <italic>SD</italic></th><th align="center">Min-max</th><th align="center">Skewness</th><th align="center">Kurtosis</th></tr></thead><tbody><tr><td colspan="5">IVA-CPT</td></tr><tr><td> Attention</td><td>93.87 ± 17.92</td><td>40–126</td><td>−.37</td><td>−.12</td></tr><tr><td> Response control</td><td>101.08 ± 17.03</td><td>42–132</td><td>−.74</td><td>.74</td></tr><tr><td colspan="5">ADHD RS-IV</td></tr><tr><td> Total scores</td><td>16.97 ± 11.07</td><td>0–54</td><td>.97</td><td>1.54</td></tr><tr><td> Inattention</td><td>9.42 ± 5.82</td><td>0–27</td><td>.75</td><td>.90</td></tr><tr><td> Hyperactivity-impulsivity</td><td>7.55 ± 6.06</td><td>0–27</td><td>1.10</td><td>.98</td></tr><tr><td>CBCL</td><td>35.56 ± 23.87</td><td>9–136</td><td>2.28</td><td>7.09</td></tr><tr><td colspan="5">CPRS</td></tr><tr><td> Total scores</td><td>22.44 ± 20.16</td><td>0–132</td><td>2.64</td><td>11.59</td></tr><tr><td> Factor IV</td><td>2.66 ± 2.54</td><td>0–12</td><td>1.18</td><td>1.45</td></tr><tr><td> ADHD index</td><td>6.68 ± 5.49</td><td>0–30</td><td>1.53</td><td>3.83</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>. IVA-CPT = the integrated visual and auditory continuous performance test; ADHD RS-IV = the ADHD Rating Scale-IV home version; CBCL = the child behavior checklist; CPRS = the Conners' Parent Rating Scales.</p> <p>For EEG data analysis, eye-closed resting-state EEG data from three participants were excluded due to excessive artifacts, and oddball task data from thirty participants were excluded because of missing behavioral responses (details see Figure 3). To validate the representativeness of the remaining sample (<emph>n</emph> = 41), we performed two checks. First, the univariate normality of the IVA scores for this sample was supported by skewness and kurtosis statistics (Attention: Skewness = −0.39, Kurtosis = 0.07; Response Control: Skewness = −1.11, Kurtosis = 1.54), which were all within the acceptable range for a normal distribution (i.e., |skewness| <2 and |kurtosis| <2; [<reflink idref="bib35" id="ref57">35</reflink>]). Second, independent-samples <emph>t</emph>-tests revealed no significant differences in these scores between the included and excluded participants (Attention: <emph>t<subs>(<reflink idref="bib65" id="ref58">65</reflink>)</subs></emph> = 1.34, <emph>p</emph> =.184; Response Control: <emph>t<subs>(<reflink idref="bib65" id="ref59">65</reflink>)</subs></emph> = 0.92, <emph>p</emph> =.360). This suggests that the missing data are random and supports the use of this subset for further analysis.</p> <p>Graph: Figure 3. Data acquisition and analysis flow chart.</p> <p>Subsequent analyses of the oddball task were therefore conducted on this final sample of 41 participants. For this analysis, only correct-response trials were included in the analysis. The descriptive statistics of the reaction time and accuracy of both standard and oddball condition are shown in Table 2.</p> <p>Table 2. Reaction Time and Accuracy for the Oddball Task.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Variables</th><th align="center"><italic>M</italic></th><th align="center"><italic>SD</italic></th></tr></thead><tbody><tr><td colspan="3">Standard condition</td></tr><tr><td> RT</td><td>455.37</td><td>88.79</td></tr><tr><td> ACC</td><td>.88</td><td>.09</td></tr><tr><td colspan="3">Oddball condition</td></tr><tr><td> RT</td><td>593.22</td><td>115.72</td></tr><tr><td> ACC</td><td>.63</td><td>.19</td></tr></tbody></table> </ephtml> </p> <p>2 <emph>Note</emph>. RT = reaction time; ACC = accuracy.</p> <hd id="AN0192008570-14">Variables Associated With ADHD Symptoms</hd> <p>Spearman's correlation analysis was used to examine associations between parent-rated scores, resting-state EEG spectral power, and EROs with attention and response control performance. For parental assessments, we analyzed questionnaire scores that are commonly used as ADHD behavioral indicators, specifically the ADHD RS-IV total score, Inattention and Hyperactivity-impulsivity subscale scores, CPRS total score, Factor IV (Impulsivity-Hyperactivity), ADHD Index scores, and CBCL total score. The results showed significant negative correlations only between the IVA-Attention score and both the ADHD RS-IV total score (ρ = −.247, <emph>p</emph> =.038) and Inattention subscale score (ρ = −.292, <emph>p</emph> =.014), suggesting that poorer attention performance in children was associated with higher parental ratings on these scales. All other correlations were non-significant (Table 3).</p> <p>Table 3. Correlation Analysis of Parent Rating Scales and Subscales With ADHD Symptoms.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="center" colspan="2"><italic>rho</italic></th></tr><tr><th align="left">Scale</th><th align="center">IVA_Response Control</th><th align="center">IVA_Attention</th></tr></thead><tbody><tr><td>RS-IV_total</td><td>−.084</td><td>−.247<xref ref-type="table-fn" rid="tfn3">*</xref></td></tr><tr><td>RS-IV_Hyperactivity-Impulsivity</td><td>.002</td><td>−.141</td></tr><tr><td>RS-IV_Inattention</td><td>−.089</td><td>−.292<xref ref-type="table-fn" rid="tfn3">*</xref></td></tr><tr><td>Conners_total</td><td>−.041</td><td>−.128</td></tr><tr><td>Conners_Factor IV</td><td>−.025</td><td>−.165</td></tr><tr><td>Conners_ADHD Index</td><td>−.065</td><td>−.180</td></tr><tr><td>CBCL_total</td><td>.037</td><td>−.138</td></tr></tbody></table> </ephtml> </p> <p>3 <emph>Note</emph>. *.01 ≤ p <.05.</p> <p>Resting-state EEG band power values (relative power of δ, θ, α, and β bands across all six defined regions in both eye-open and eye-closed conditions) were correlated with children's attention and response control performance.</p> <p>The results demonstrated negative associations between children's response control performance and eye-closed alpha power in the LPO region (ρ = −.263, <emph>p</emph> =.030, FDR-corrected <emph>p</emph> =.120), and eye-open beta power in the RPO region (ρ = −.247, <emph>p</emph> =.038, FDR-corrected <emph>p</emph> =.152). Despite not reaching strict statistical significance, the direction and effect sizes of these findings suggest distinct patterns of neural activity related to response control depending on both frequency band and vigilance state (for the full correlation matrix, see Supplemental Table S1).</p> <p>Given the functional heterogeneity within broad frequency bands, we subdivided alpha and beta bands into discrete sub-bands (i.e., α1, α2, β1, and β2) and analyzed their correlations with children's performance in parieto-occipital regions. The results showed that, during eye-open resting-state, alpha1 power in the LPO region was positively correlated with attention (ρ =.253, <emph>p</emph> =.034, FDR-corrected <emph>p</emph> =.068), while alpha2 in both LPO (ρ = −.234, <emph>p</emph> =.049, FDR-corrected <emph>p</emph> =.049) and RPO (ρ = −.249, <emph>p</emph> =.036, FDR-corrected <emph>p</emph> =.049) regions, along with beta2 in RPO region (ρ = −.243, <emph>p</emph> =.042, FDR-corrected <emph>p</emph> =.084), negatively correlated with response control. These results further distinguish the distinct neural oscillatory components underlying children's behavioral performance. Alpha1 enhancement predicts better attention, while alpha2/beta2 suppression is associated with improved response control.</p> <p>For EROs, the power of three ROIs corresponding to the P2, N2, and P3 components was considered. Surprisingly, no variables were found to be associated with children's performance on response control. For children's attention, the P3 component provided the most significant results. The results showed that power in all frequency bands or conditions was significantly negatively correlated with children's attention, except for delta power in the standard condition. Furthermore, delta power corresponding to the N2 component in the oddball condition was found to be positively correlated with children's attention performance (details see Supplemental Table S2).</p> <hd id="AN0192008570-15">Regression Analysis of Screened Variables</hd> <p>Stepwise regression was employed to identify EEG features that predict children's attention and response control. First, resting-state EEG features were considered. For the response control performance, the results showed that the best predictor was alpha power in the LPO region during the eye-closed condition (<emph>R</emph><sups>2</sups> =.100, <emph>R</emph><sups>2</sups><subs>adj</subs> =.086, <emph>F</emph><subs>(<reflink idref="bib1" id="ref60">1</reflink>,<reflink idref="bib65" id="ref61">65</reflink>)</subs> = 7.24, <emph>p</emph> =.009; see Table 4, Model 1). For the attention performance, the alpha1 power in the LPO region during the eye-open condition could not predict children's attention (<emph>R</emph><sups>2</sups> =.024, <emph>R</emph><sups>2</sups><subs>adj</subs> =.010, <emph>F</emph><subs>(<reflink idref="bib1" id="ref62">1</reflink>,<reflink idref="bib69" id="ref63">69</reflink>)</subs> = 1.71, <emph>p</emph> =.196; see Table 4, Model 2).</p> <p>Table 4. Comparison of Regression Models.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Model</th><th /><th align="center">Indicators</th><th align="center"><italic>B</italic></th><th align="center"><italic>SE</italic></th><th align="center"><italic>Beta</italic></th><th align="center"><italic>t</italic></th><th align="center"><italic>p</italic></th><th align="center"><italic>VIF</italic></th></tr></thead><tbody><tr><td rowspan="2">Model 1 (<italic>n</italic> = 67)</td><td /><td>constant</td><td>116.19</td><td>5.89</td><td /><td>19.74</td><td>.000</td><td /></tr><tr><td /><td>EC_alpha_LPO</td><td>−44.78</td><td>16.65</td><td>−.32</td><td>−2.69</td><td>.009</td><td>1.000</td></tr><tr><td rowspan="2">Model 2 (<italic>n</italic> = 71)</td><td /><td>constant</td><td>88.60</td><td>4.56</td><td /><td>19.44</td><td>.000</td><td /></tr><tr><td /><td>EO_alpha1_LPO</td><td>12.29</td><td>9.41</td><td>.16</td><td>1.31</td><td>.196</td><td>1.000</td></tr><tr><td rowspan="5">Model 3 (<italic>n</italic> = 41)</td><td>Step1</td><td>constant</td><td>84.00</td><td>3.98</td><td /><td>21.13</td><td>.000</td><td /></tr><tr><td /><td>P3_standard_alpha</td><td>−1.90</td><td>.70</td><td>−.40</td><td>−2.71</td><td>.010</td><td>1.000</td></tr><tr><td>Step2</td><td>constant</td><td>76.83</td><td>4.96</td><td /><td>15.50</td><td>.000</td><td /></tr><tr><td /><td>P3_standard_alpha</td><td>−1.86</td><td>.67</td><td>−.39</td><td>−2.77</td><td>.009</td><td>1.001</td></tr><tr><td /><td>N2_oddball_delta</td><td>3.65</td><td>1.63</td><td>.31</td><td>2.24</td><td>.031</td><td>1.001</td></tr><tr><td rowspan="4">Model 4 (<italic>n</italic> = 41)</td><td /><td>constant</td><td>77.88</td><td>6.41</td><td /><td>12.15</td><td>.000</td><td /></tr><tr><td /><td>P3_standard_alpha</td><td>−1.95</td><td>.76</td><td>−.41</td><td>−2.56</td><td>.015</td><td>1.262</td></tr><tr><td /><td>N2_oddball_delta</td><td>3.68</td><td>1.65</td><td>.32</td><td>2.23</td><td>.032</td><td>1.005</td></tr><tr><td /><td>EO_alpha1_LPO</td><td>−3.40</td><td>12.86</td><td>−.04</td><td>−.26</td><td>.793</td><td>1.267</td></tr><tr><td rowspan="2">Model 5 (<italic>n</italic> = 71)</td><td /><td>constant</td><td>104.56</td><td>3.81</td><td /><td>27.43</td><td>.000</td><td /></tr><tr><td /><td>RS-IV_Inattention</td><td>−1.13</td><td>.35</td><td>−.37</td><td>−3.29</td><td>.002</td><td>1.000</td></tr><tr><td rowspan="5">Model 6 (<italic>n</italic> = 41)</td><td>Step1</td><td>constant</td><td>108.91</td><td>5.78</td><td /><td>18.83</td><td>.000</td><td /></tr><tr><td /><td>RS-IV_Inattention</td><td>−1.73</td><td>.52</td><td>−.47</td><td>−3.31</td><td>.002</td><td>1.000</td></tr><tr><td>Step2</td><td>constant</td><td>100.53</td><td>6.32</td><td /><td>15.90</td><td>.000</td><td /></tr><tr><td /><td>RS-IV_Inattention</td><td>−1.57</td><td>.49</td><td>−.42</td><td>−3.17</td><td>.003</td><td>1.017</td></tr><tr><td /><td>P3_standard_alpha</td><td>−1.64</td><td>.64</td><td>−.34</td><td>−2.56</td><td>.014</td><td>1.017</td></tr><tr><td rowspan="4">Model 7 (<italic>n</italic> = 41)</td><td /><td>constant</td><td>102.26</td><td>7.77</td><td /><td>13.17</td><td>.000</td><td /></tr><tr><td /><td>RS-IV_Inattention</td><td>−1.58</td><td>.50</td><td>−.43</td><td>−3.16</td><td>.003</td><td>1.024</td></tr><tr><td /><td>P3_standard_alpha</td><td>−1.77</td><td>.72</td><td>−.37</td><td>−2.44</td><td>.019</td><td>1.270</td></tr><tr><td /><td>EO_alpha1_LPO</td><td>−4.76</td><td>12.17</td><td>−.06</td><td>−.39</td><td>.698</td><td>1.271</td></tr></tbody></table> </ephtml> </p> <p>4 <emph>Note</emph>. EC = eye-closed; EO = eye-open; LPO = left parietal-occipital; Standard = standard condition in the oddball task; Oddball = oddball condition in the oddball task; RS-IV = the ADHD RS-IV Home Version; Inattention = inattention subscale scores in the RS-IV; VIF = variance inflation factor.</p> <p>Considering some ERO features were associated with children's attention performance, we further included the ERO features in the stepwise regression. In Step 1, the regression equation was constructed using P3 alpha power in the standard condition (<emph>R</emph><sups>2</sups> =.158, <emph>R</emph><sups>2</sups><subs>adj</subs> =.136, <emph>F</emph><subs>(<reflink idref="bib1" id="ref64">1</reflink>,<reflink idref="bib39" id="ref65">39</reflink>)</subs> = 7.32, <emph>p</emph> =.010; see Table 4, Model 3, Step1). In Step 2, N2 delta power in the oddball condition was added to the model. The final model demonstrated greater explanatory power (<emph>R</emph><sups>2</sups> =.256, <emph>R</emph><sups>2</sups><subs>adj</subs> =.217, <emph>F</emph><subs>(<reflink idref="bib2" id="ref66">2</reflink>,<reflink idref="bib38" id="ref67">38</reflink>)</subs> = 6.55, <emph>p</emph> =.004; see Table 4, Model 3, Step2), with the additional predictor contributing 9.8% incremental variance. To further validate the predictive robustness, we constructed a general linear model that included both the resting-state EEG feature and the ERO features (i.e., the P3 alpha power in the standard condition and the N2 delta power in the oddball condition). When we added the alpha1 power in the LPO region during the eyes-open condition as a third predictor, there was negligible improvement in the model's explanatory power (<emph>R</emph><sups>2</sups> =.258, <emph>R</emph><sups>2</sups><subs>adj</subs> =.198, <emph>F</emph><subs>(<reflink idref="bib3" id="ref68">3</reflink>,<reflink idref="bib37" id="ref69">37</reflink>)</subs> = 4.28, <emph>p</emph> =.011; see Table 4, Model 4). This confirms that the two-predictor model (Model 3) is substantially more parsimonious and predictive than models that include resting-state EEG features. This suggests that task-evoked neural markers may be more reliable predictors of children's attention performance than resting-state measures.</p> <p>Since the results of the correlation analysis showed that the total RS-IV score and the inattention subscale score were significantly correlated with children's attention performance, we finally included parental ratings in the regression model to test the predictive effect of multiple dimensions (objective and subjective indicators). First, we examined the predictive effect of parental ratings alone. The results showed that the inattention subscale score of the RS-IV significantly predicted children's attention performance (<emph>R</emph><sups>2</sups> =.368, <emph>R</emph><sups>2</sups><subs>adj</subs> =.1136, <emph>F<subs>(<reflink idref="bib1" id="ref70">1</reflink>,<reflink idref="bib69" id="ref71">69</reflink>)</subs></emph> = 10.82, <emph>p</emph> =.002; see Table 4, Model 5). Next, we constructed a stepwise regression model incorporating EEG and ERO features, as well as parental ratings, to predict children's attention. In Step 1, we included the inattention subscale score of the RS-IV was included in the regression equation (<emph>R</emph><sups>2</sups> =.219, <emph>R</emph><sups>2</sups><subs>adj</subs> =.119, <emph>F</emph><subs>(<reflink idref="bib1" id="ref72">1</reflink>,<reflink idref="bib39" id="ref73">39</reflink>)</subs> = 10.95, <emph>p</emph> =.002; see Table 3, Model 6, Step1). In Step 2, P3 alpha power in the standard condition was added to the model. The final model demonstrated greater explanatory power (<emph>R</emph><sups>2</sups> =.334, <emph>R</emph><sups>2</sups><subs>adj</subs> =.299, <emph>F</emph><subs>(<reflink idref="bib2" id="ref74">2</reflink>,<reflink idref="bib38" id="ref75">38</reflink>)</subs> = 9.54, <emph>p</emph> <.001; see Table 3, Model 6, Step2), with the additional predictor contributing an incremental 11.5% variance. Finally, resting-state EEG features (i.e., the alpha1 power in the LPO region during the eyes-open condition) were added as a third predictor. However, the results showed that there was negligible improvement in the model's explanatory power (<emph>R</emph><sups>2</sups> =.337, <emph>R</emph><sups>2</sups><subs>adj</subs> =.283, <emph>F</emph><subs>(<reflink idref="bib3" id="ref76">3</reflink>,<reflink idref="bib37" id="ref77">37</reflink>)</subs> = 6.27, <emph>p</emph> =.002; see Table 4, Model 7). Overall, the alpha power corresponding to the P3 component in the standard condition of the oddball task, together with the inattention subscale of the RS-IV parental ratings, best predicted children's attention performance.</p> <hd id="AN0192008570-16">Discussion</hd> <p>This study explored neural oscillation features of ADHD symptoms using resting-state EEG and an oddball task under a dimensional approach. Parental assessments were also included and used as subjective indicators for predicting children's behavioral performance. The main findings revealed that children's attention performance were primarily associated with questionnaire scores and ERO features, whereas response control was more closely linked to resting-state EEG characteristics. Furthermore, regression analyses revealed that eye-closed alpha power in the LPO region significantly predicted response control, while eye-open alpha1 power in the LPO region, P3 alpha power under standard condition, and N2 delta power under oddball condition were key predictors of attention performance. The final results of stepwise regression analysis identified that the inattention subscale of the RS-IV from parental assessments combined with P3 alpha power during the standard condition in EROs was the strongest predictor of children's attention scores.</p> <hd id="AN0192008570-17">Neural Oscillation Features of ADHD Symptoms</hd> <p>Both resting-state EEG and EROs showed distinct neural oscillation patterns related to response control and attention, respectively. For resting-state EEG features, increased eye-closed alpha power in the RPO region, eye-open alpha2 power in the bilateral parietal-occipital region, and eye-open beta2 power in the RPO region correlated with poorer response control. However, only decreased eye-open alpha1 power in the LPO region was linked to attention deficits. Regression analysis further identified eye-closed alpha power and eye-open alpha1 power as predictors of children's attention and response control performance. These findings indicate that abnormal alpha activity in the posterior brain under resting state is closely related to ADHD symptoms in children.</p> <p>The alpha band is a key frequency band in studies of arousal in individuals with ADHD. Alpha activity is thought to be generated in the thalamo-cortical network. It dominates during the eye-closed resting state and reflects inhibited cortical excitability ([<reflink idref="bib13" id="ref78">13</reflink>]; [<reflink idref="bib44" id="ref79">44</reflink>]). We found that alpha power in the RPO region during the eye-closed state was negatively correlated with response control performance. This suggests that symptoms such as impulsivity and hyperactivity may be associated with lower baseline cortical excitability. These findings align with the hypoarousal model of ADHD, which posits that individuals with ADHD exhibit reduced central nervous system arousal compared to neurotypical individuals, consequently leading to impaired performance in cognitive tasks ([<reflink idref="bib52" id="ref80">52</reflink>]; [<reflink idref="bib74" id="ref81">74</reflink>]; [<reflink idref="bib89" id="ref82">89</reflink>]).</p> <p>Eye-open alpha activity is also important and plays a key role in cognition and behavioral control ([<reflink idref="bib26" id="ref83">26</reflink>]; [<reflink idref="bib33" id="ref84">33</reflink>]; [<reflink idref="bib50" id="ref85">50</reflink>]; [<reflink idref="bib72" id="ref86">72</reflink>]; [<reflink idref="bib83" id="ref87">83</reflink>]). Our results revealed a functional dissociation within two narrow alpha bands: alpha1 showed a positive correlation with attention, whereas alpha2 exhibited a negative association with response control. The narrow alpha bands were proposed because broad-band alpha analyses might obscure possible frequency-specific effects ([<reflink idref="bib51" id="ref88">51</reflink>]). Previous research has suggested that, in cognitive tasks, alpha1 presumably reflects general task demands, such as attentional processes, while alpha2 is more likely to reflect specific task requirements ([<reflink idref="bib32" id="ref89">32</reflink>]). In the present study, similar patterns were observed during resting conditions. The decreased alpha1 activity in the resting state may impair the gating control of irrelevant sensory inputs, contributing to inattention. Conversely, the increased alpha2 activity may reflect compensatory over-engagement of attentional networks during rest, which could deplete cognitive resources and subsequently impair inhibitory control during task performance.</p> <p>In addition, we found that beta2 activity during the eye-open condition was also associated with response control. Beta oscillations are predominantly observed in the eye-open state. Previous research has generally suggested that decreased beta activity is linked to hyperactivity, while a small subset of children with ADHD exhibit excess beta activity, reflecting developmental deviations ([<reflink idref="bib16" id="ref90">16</reflink>]). Specifically, [<reflink idref="bib18" id="ref91">18</reflink>], [<reflink idref="bib19" id="ref92">19</reflink>], [<reflink idref="bib16" id="ref93">16</reflink>]) found that this subtype of children did not exhibit hyperarousal despite increased beta activity. Their manifestations were consistent with those of other ADHD subtypes, all presenting in a hypoaroused state. However, they tended to display greater mood instability and more frequent temper tantrums, which may be related to the involvement of beta activity in self-regulation and inhibitory control. Our research findings are consistent with these earlier discoveries, indicating that greater beta activity is associated with lower response control scores. Moreover, by extending prior reports on beta-behavior correlations, we precisely localized this effect to the beta2 sub-band, suggesting that conventional broad-band beta measures may obscure frequency-specific relationships.</p> <p>For ERO features, the results revealed distinct stage- and frequency-specific associations with attention. Regression analyses showed that delta activity during oddball stimuli in early processing (N2 stage) and alpha activity under standard conditions in late processing (P3 stage) played a significant role in children's attention performance. In ERP studies, N2 component is thought to reflect early attentional detection and classification, while P3 component is important in late neural resource allocation ([<reflink idref="bib9" id="ref94">9</reflink>]; [<reflink idref="bib53" id="ref95">53</reflink>]; [<reflink idref="bib56" id="ref96">56</reflink>]). Some research has suggested that EROs represent a potential mechanism responsible for generating the ERPs ([<reflink idref="bib45" id="ref97">45</reflink>]). According to this view, each basic frequency band (i.e., δ, θ, α, and β) is considered responsible for one or more cognitive functions in specific brain region and processing stage ([<reflink idref="bib42" id="ref98">42</reflink>]). Previous studies have found that neural oscillations in low frequency range (0–8 Hz) represent possible mechanisms for the generation of the P3 component ([<reflink idref="bib87" id="ref99">87</reflink>]). Although existing research has identified delta and theta oscillations in the P3 stage of oddball paradigms, little attention has been paid to their specific manifestations in individuals with attention deficits ([<reflink idref="bib23" id="ref100">23</reflink>]; [<reflink idref="bib54" id="ref101">54</reflink>]). Our findings complement this literature by demonstrating that enhanced alpha activity during standard stimulus processing in later attention stages may reflect insufficient neural activation, potentially impairing attention performance. Furthermore, while the N2 component has been primarily associated with theta oscillations ([<reflink idref="bib8" id="ref102">8</reflink>]), our study revealed a positive correlation between delta activity under oddball conditions and attention performance. This suggests that enhanced delta oscillations during early processing stages may play a crucial role in information monitoring and stimulus classification. Collectively, compared to conventional ERP research focusing on discrete components, our findings provide novel insights into ongoing brain dynamics across distinct attentional processing stages.</p> <p>Overall, this study focused on brain states and neural oscillation features in resting state and oddball task processing. For resting-state features, we explored sub-band spectral features, providing more precise brain activity features associated with attention and response control. For oddball task-state features, we identified distinct frequency- and condition-specific patterns across different attentional processing stages. These findings offer a more complete framework for understanding mechanisms of impaired attentional performance.</p> <hd id="AN0192008570-18">A Novel Multi-metric Prediction Approach</hd> <p>Due to the complex neural mechanisms and heterogeneous manifestations of ADHD, this study focuses on objective behavioral performance in children under a dimensional approach rather than diagnostic categories. We propose a novel multi-metric method for predicting behavioral performance by integrating objective and subjective measurements. Our results indicate that combining parental assessments with ERO features provides the most effective prediction of children's attention performance.</p> <p>ADHD is characterized by complex neural mechanisms and symptom heterogeneity. Similar variability in attentional function has been observed in the non-ADHD population as well ([<reflink idref="bib6" id="ref103">6</reflink>]). Three dominant models have been proposed to explain such variability: hyper-/hypo-arousal, maturational lag, and developmental deviation ([<reflink idref="bib17" id="ref104">17</reflink>]; [<reflink idref="bib30" id="ref105">30</reflink>]; [<reflink idref="bib74" id="ref106">74</reflink>]; [<reflink idref="bib77" id="ref107">77</reflink>]). These models account for differences among ADHD subtypes and behavioral presentations. While EEG features have shown promise in ADHD diagnosis ([<reflink idref="bib58" id="ref108">58</reflink>]; [<reflink idref="bib78" id="ref109">78</reflink>]), and machine learning has improved diagnostic classification accuracy ([<reflink idref="bib3" id="ref110">3</reflink>]; [<reflink idref="bib14" id="ref111">14</reflink>]; [<reflink idref="bib25" id="ref112">25</reflink>]), the inherent heterogeneity of ADHD still limits the robustness of EEG as a confirmatory diagnostic tool ([<reflink idref="bib58" id="ref113">58</reflink>]). Consistently, growing evidence suggests that multi-pathway models better explain ADHD than single-cause hypotheses, and that single EEG metrics may only capture subtype-specific signatures ([<reflink idref="bib58" id="ref114">58</reflink>]; [<reflink idref="bib79" id="ref115">79</reflink>]). Based on this perspective, we collected a comprehensive set of indicators, including objective resting-state and task-related neural oscillation features as well as subjective parental rating scales. Our findings confirm that multidimensional prediction (combining ERO features and parental assessments) is more effective than any single-measure approach in evaluating children's attention performance.</p> <p>Contrary to our hypothesis, the combination of all three measures did not result in superior predictive power. The inclusion of resting-state EEG features contributed minimally to the model's explanatory variance. Our results indicate that ERO features, particularly those occurring during attentional processing, exhibit enhanced reliability in predicting children's behavioral performance. In contrast, resting-state activity, which reflects fundamental brain state, appears to possess a more limited predictive capacity. These findings further highlight the critical importance of incorporating task-state EEG features in pediatric symptom assessments.</p> <p>Furthermore, although we collected data from three parental rating scales, only the RS-IV scores were significantly correlated with children's performance. The CBCL assesses a broad range of emotional and behavioral problems ([<reflink idref="bib1" id="ref116">1</reflink>]; [<reflink idref="bib15" id="ref117">15</reflink>]; [<reflink idref="bib36" id="ref118">36</reflink>]). However, this broader information may be less effective in evaluating specific behaviors. The CPRS had a hyperactivity/impulsivity subscale and an ADHD index; however, its total score and subscale scores were not correlated with child performance. These results are consistent with those of the RS-IV, which found that parents' ratings of their children's hyperactivity and impulsivity were inconsistent with the children's actual performance and that inattention was more accurately assessed. Our results show that it is easier for parents to assess children's attention issues such as "careless mistakes" or "not listening," but there are difficulties in the accurate assessment of children's problematic behavior, such as "running about" or "leaving the seat." Specifically, parents seem to be more likely to pay attention to and identify problems directly related to children's academic studies, and ignore other behavioral problems ([<reflink idref="bib61" id="ref119">61</reflink>]). These findings suggest that interviews and questionnaires should consider subjective factors, such as educational stress ([<reflink idref="bib21" id="ref120">21</reflink>]; [<reflink idref="bib49" id="ref121">49</reflink>]; [<reflink idref="bib66" id="ref122">66</reflink>]; [<reflink idref="bib81" id="ref123">81</reflink>]). This further underscores the importance of using EEG features to assist in assessment and diagnosis.</p> <hd id="AN0192008570-19">Limitations and Future Direction</hd> <p>First, while our dimensional approach ensured near-normal distributions of attention and response control measures among participants, this design inherently resulted in fewer severe ADHD cases being included. Consequently, the generalizability of our findings to broader ADHD populations may be limited. Future studies should incorporate clinically diagnosed ADHD children to establish a more comprehensive neurodevelopmental spectrum.</p> <p>Second, while ERO features demonstrated greater predictive validity for attention performance, no significant associations were found between EROs and children's response control. This null finding may reflect methodological considerations in both task selection and data processing. To capture attention-modulated neural oscillations within a clinically feasible timeframe of approximately 8 min, we used the classic oddball paradigm. Although the early stages of processing the oddball task involve conflict monitoring and inhibitory control, our data preprocessing strategy, which excluded error trials to ensure that the EEG analyses reflected accurate task engagement, might have reduced the task's sensitivity to response control mechanisms. Future studies could examine paradigms specifically designed to probe response control, such as go/no-go or stop-signal tasks.</p> <hd id="AN0192008570-20">Conclusion</hd> <p>This study provides novel insights into the neural oscillation mechanisms underlying ADHD symptoms in children through a dimensional approach. By employing narrow-band analysis, we identified more precise spectral features associated with attention and response control. Furthermore, we developed an innovative multi-metric prediction framework that integrates objective EEG measures with subjective parental ratings. Our results demonstrate the critical value of incorporating task-state EEG features alongside parental assessments for comprehensive evaluation of children's behavioral and symptomatic profiles.</p> <hd id="AN0192008570-21">Supplemental Material</hd> <p>Graph: Supplemental material, sj-pdf-1-jad-10.1177_10870547251405008 for Neural Oscillation Features of ADHD Symptoms in Children: EEG Evidence From Resting State and Oddball Task by Siyuan Zhang, Shuting Yu, Xiaobing Cui, Lixia Liang and Xuebing Li in Journal of Attention Disorders</p> <hd id="AN0192008570-22">Supplemental Material</hd> <p>Graph: Supplemental material, sj-xlsx-2-jad-10.1177_10870547251405008 for Neural Oscillation Features of ADHD Symptoms in Children: EEG Evidence From Resting State and Oddball Task by Siyuan Zhang, Shuting Yu, Xiaobing Cui, Lixia Liang and Xuebing Li in Journal of Attention Disorders</p> <hd id="AN0192008570-23">Supplemental Material</hd> <p>Graph: Supplemental material, sj-xlsx-3-jad-10.1177_10870547251405008 for Neural Oscillation Features of ADHD Symptoms in Children: EEG Evidence From Resting State and Oddball Task by Siyuan Zhang, Shuting Yu, Xiaobing Cui, Lixia Liang and Xuebing Li in Journal of Attention Disorders</p> <p>We thank Mrs. Jie Song for her invaluable assistance in conducting the experiments.</p> <ref id="AN0192008570-24"> <title> References </title> <blist> <bibl id="bib1" idref="ref38" type="bt">1</bibl> <bibtext> Achenbach T. 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Clinical Neurophysiology, 129(6), 1192–1200. https://doi.org/10.1016/j.clinph.2018.03.009</bibtext> </blist> </ref> <ref id="AN0192008570-25"> <title> Footnotes </title> <blist> <bibtext> Xuebing Li</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0002-4713-9208</bibtext> </blist> <blist> <bibtext> The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Our work is supported by the Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences, No. E2CX3815CX.</bibtext> </blist> <blist> <bibtext> The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> Data will be made available on request.</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> </ref> <aug> <p>By Siyuan Zhang; Shuting Yu; Xiaobing Cui; Lixia Liang and Xuebing Li</p> <p>Reported by Author; Author; Author; Author; Author</p> <p></p> <p>Siyuan Zhang is a PhD candidate at the State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, and the Department of Psychology, University of Chinese Academy of Sciences.</p> <p>Shuting Yu is a PhD candidate at the State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, and the Department of Psychology, University of Chinese Academy of Sciences.</p> <p>Xiaobing Cui is a PhD candidate at the State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, and the Department of Psychology, University of Chinese Academy of Sciences.</p> <p>Lixia Liang is a masters' student at the State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, and the Department of Psychology, University of Chinese Academy of Sciences.</p> <p>Xuebing Li, PhD, is an Associate Research Fellow at the State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences and the Department of Psychology, University of Chinese Academy of Sciences, with research focusing on the neural mechanisms of emotion and cognition.</p> </aug> <nolink nlid="nl1" bibid="bib34" firstref="ref3"></nolink> <nolink nlid="nl2" bibid="bib39" firstref="ref4"></nolink> <nolink nlid="nl3" bibid="bib63" firstref="ref5"></nolink> <nolink nlid="nl4" bibid="bib75" firstref="ref6"></nolink> <nolink nlid="nl5" bibid="bib24" firstref="ref7"></nolink> <nolink nlid="nl6" bibid="bib43" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib84" firstref="ref10"></nolink> <nolink nlid="nl8" bibid="bib57" firstref="ref12"></nolink> <nolink nlid="nl9" bibid="bib62" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib38" firstref="ref15"></nolink> <nolink nlid="nl11" 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  Group: Ti
  Data: Neural Oscillation Features of ADHD Symptoms in Children: EEG Evidence from Resting State and Oddball Task
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  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Siyuan+Zhang%22">Siyuan Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Shuting+Yu%22">Shuting Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Xiaobing+Cui%22">Xiaobing Cui</searchLink><br /><searchLink fieldCode="AR" term="%22Lixia+Liang%22">Lixia Liang</searchLink><br /><searchLink fieldCode="AR" term="%22Xuebing+Li%22">Xuebing Li</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4713-9208">0000-0002-4713-9208</externalLink>)
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  Label: Source
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Attention+Disorders%22"><i>Journal of Attention Disorders</i></searchLink>. 2026 30(4):552-565.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Label: Peer Reviewed
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 14
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2026
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  Label: Document Type
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  Data: Journal Articles<br />Reports - Research
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  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Deficit+Hyperactivity+Disorder%22">Attention Deficit Hyperactivity Disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Brain%22">Brain</searchLink><br /><searchLink fieldCode="DE" term="%22Biofeedback%22">Biofeedback</searchLink><br /><searchLink fieldCode="DE" term="%22Severity+%28of+Disability%29%22">Severity (of Disability)</searchLink><br /><searchLink fieldCode="DE" term="%22Symptoms+%28Individual+Disorders%29%22">Symptoms (Individual Disorders)</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Span%22">Attention Span</searchLink><br /><searchLink fieldCode="DE" term="%22Measures+%28Individuals%29%22">Measures (Individuals)</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+Tests%22">Performance Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligence+Tests%22">Intelligence Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Child+Behavior%22">Child Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Check+Lists%22">Check Lists</searchLink><br /><searchLink fieldCode="DE" term="%22Rating+Scales%22">Rating Scales</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Continuous+Performance+Test%22">Continuous Performance Test</searchLink><br /><searchLink fieldCode="SU" term="%22Raven+Progressive+Matrices%22">Raven Progressive Matrices</searchLink><br /><searchLink fieldCode="SU" term="%22Child+Behavior+Checklist%22">Child Behavior Checklist</searchLink><br /><searchLink fieldCode="SU" term="%22Conners+Rating+Scales%22">Conners Rating Scales</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/10870547251405008
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1087-0547<br />1557-1246
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: The aim of this study was to explore neural oscillation features (resting-state+oddball-EROs) of ADHD symptoms in children in a dimensional approach and to construct a multi-metric model combining objective EEG measures and subjective parental ratings to predict children's behavioral performance. Method: Seventy-seven children (age range: 6-12 years) participated in laboratory assessment. ADHD symptoms were first evaluated using the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), followed by EEG recordings during both resting-state and oddball task conditions. Three parent rating scales were also used to evaluate children's behavioral performance: the ADHD Rating Scale-IV (ADHD RS-IV): Home Version, the Child Behavior Checklist (CBCL), and the Conners' Parent Rating Scales (CPRS). Results: Seventy-one children with valid IVA-CPT results were included in data analysis. The main results revealed a relationship between poorer attention performance and decreased eye-open alpha1 power in the resting state, reduced N2 delta power in the oddball condition, and elevated non-delta band power in the standard condition of the oddball task. Poorer response control performance was associated with increased eye-closed alpha1 power, as well as increased eyeopen alpha2 and beta2 power. Stepwise regression analysis showed that the inattention subscale from parental assessments on the RS-IV, combined with P3 alpha power in the standard condition of the oddball task, was the strongest predictor of children's attention performance. Conclusion: The current study identified important neural oscillation features of ADHD symptoms in both the resting state and during an oddball task and offers new insights into multi-metric prediction for ADHD assessment and diagnosis.
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  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
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  Data: EJ1499931
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1499931
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/10870547251405008
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 552
    Subjects:
      – SubjectFull: Children
        Type: general
      – SubjectFull: Attention Deficit Hyperactivity Disorder
        Type: general
      – SubjectFull: Brain
        Type: general
      – SubjectFull: Biofeedback
        Type: general
      – SubjectFull: Severity (of Disability)
        Type: general
      – SubjectFull: Symptoms (Individual Disorders)
        Type: general
      – SubjectFull: Attention Span
        Type: general
      – SubjectFull: Measures (Individuals)
        Type: general
      – SubjectFull: Performance Tests
        Type: general
      – SubjectFull: Intelligence Tests
        Type: general
      – SubjectFull: Child Behavior
        Type: general
      – SubjectFull: Check Lists
        Type: general
      – SubjectFull: Rating Scales
        Type: general
      – SubjectFull: Continuous Performance Test
        Type: general
      – SubjectFull: Raven Progressive Matrices
        Type: general
      – SubjectFull: Child Behavior Checklist
        Type: general
      – SubjectFull: Conners Rating Scales
        Type: general
    Titles:
      – TitleFull: Neural Oscillation Features of ADHD Symptoms in Children: EEG Evidence from Resting State and Oddball Task
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Siyuan Zhang
      – PersonEntity:
          Name:
            NameFull: Shuting Yu
      – PersonEntity:
          Name:
            NameFull: Xiaobing Cui
      – PersonEntity:
          Name:
            NameFull: Lixia Liang
      – PersonEntity:
          Name:
            NameFull: Xuebing Li
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 1087-0547
            – Type: issn-electronic
              Value: 1557-1246
          Numbering:
            – Type: volume
              Value: 30
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Journal of Attention Disorders
              Type: main
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