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Atypical Developmental Trajectories of Early Perception among School-Age Children with Attention Deficit Hyperactivity Disorder during a Visual Search Task

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Title: Atypical Developmental Trajectories of Early Perception among School-Age Children with Attention Deficit Hyperactivity Disorder during a Visual Search Task
Language: English
Authors: Luo, Xiangsheng, Guo, Jialiang, Li, Dongwei, Liu, Lu, Chen, Yanbo, Zhu, Yu, Johnstone, Stuart J., Wang, Yufeng, Song, Yan, Sun, Li (ORCID 0000-0002-2330-6622)
Source: Child Development. e1186-e1197 Nov-Dec 2021 92(6):e1186-e1197.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 12
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Descriptors: Attention Deficit Hyperactivity Disorder, Cognitive Ability, Children, Foreign Countries, Visual Perception, Cognitive Development, Intervention, Brain
Geographic Terms: China
DOI: 10.1111/cdev.13604
ISSN: 0009-3920
Abstract: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by cognitive deficits associated with attention. Prior studies have revealed the potential impact of ADHD on basic perception and cognitive ability in patients with ADHD. In this study, bilateral posterior P1 and N1 were measured in 122 Chinese children aged 7-12 years (64 with ADHD) to investigate the developmental characteristics of early perception during visual processing in school-age children with ADHD. For children with ADHD, a larger P1 activity with an atypical developmental pattern was evoked and observed for the visual search performance. These findings offer new insights into the mechanisms of cognitive developmental deficits and intervention techniques in children with ADHD.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1319848
Database: ERIC
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  Value: <anid>AN0153631283;cdv01nov.21;2021Nov19.04:27;v2.2.500</anid> <title id="AN0153631283-1">Atypical Developmental Trajectories of Early Perception Among School‐Age Children With Attention Deficit Hyperactivity Disorder During a Visual Search Task </title> <p>Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by cognitive deficits associated with attention. Prior studies have revealed the potential impact of ADHD on basic perception and cognitive ability in patients with ADHD. In this study, bilateral posterior P1 and N1 were measured in 122 Chinese children aged 7–12 years (64 with ADHD) to investigate the developmental characteristics of early perception during visual processing in school‐age children with ADHD. For children with ADHD, a larger P1 activity with an atypical developmental pattern was evoked and observed for the visual search performance. These findings offer new insights into the mechanisms of cognitive developmental deficits and intervention techniques in children with ADHD.</p> <p>Attention deficit hyperactivity disorder (ADHD) is a childhood‐onset neurodevelopmental disorder characterized by cognitive deficits associated with attention (Barkley, 1997; Sonuga‐Barke, 2003). Recent studies have reported that basic cognitive functions, such as sensory and perceptual function, might play an important role in explaining cognitive and behavioral abnormalities in ADHD (Bijlenga, Tjon‐Ka‐Jie, Schuijers, & Kooij, 2017; Fuermaier et al., 2018; Little, Dean, Tomchek, & Dunn, 2018). Visual perception can be measured by visual cortex activity, such as the BOLD signal and event‐related potential (ERP). Functional MRI studies have observed ADHD‐related hyperactivation in the visual system which was identified as a compensation for the impaired task performance (Cortese et al., 2012). Kim, Banaschewski, and Tannock (2015) also reported on the perceptual performance compensation for children with ADHD due to enlarged visual perception related to the P1 component. With the high temporal resolution of electrophysiological signals, we can also investigate visual perception in visual search processes.</p> <p>Generally, the symptoms and executive functions of children with ADHD improve with age (Coghill, Hayward, Rhodes, Grimmer, & Matthews, 2014; Doehnert, Brandeis, Schneider, Drechsler, & Steinhausen, 2013; Faraone, Biederman, & Mick, 2006; Murray, Robinson, & Tripp, 2017). However, the development of brain structures and cognitive functions in children with ADHD are different from that in children with typical development (Cao et al., 2013; Downes, Bathelt, & De Haan, 2017; Gupta & Kar, 2009; Janssen et al., 2017; Valera, Faraone, Murray, & Seidman, 2007). For the occipital cortex dominated by visual function, structural MRI has shown that the abnormal maturation trajectory in children with ADHD (Castellanos et al., 2002; Shaw et al., 2009), combined with the overactivation of visual cortex function in ADHD during cognitive tasks noted above, there may be abnormalities in the developmental pattern of visual perception. Visual sensory perception, as the basic process of cognitive function, will gradually change as the brain matures (Braddick & Atkinson, 2011), and visual evoked P1 and N1 seem to be suitable developmental indicators of visual function, which evolve earlier and smaller with typical maturity (Overbye, Huster, Walhovd, Fjell, & Tamnes, 2018; Shaw & Cant, 1981).</p> <p>Therefore, a cross‐sectional study was designed to examine the early ERP characteristics of school‐age children with and without ADHD during a visual search task (a) to analyze the development of visual search ability and early ERP components, and (b) the visual perceptual characteristics of children with ADHD. The analysis of visual perception in children with ADHD is confirmatory, it is hoped to verify their overactivation visual perceptual characteristics, and the investigation of potential developmental characteristics of visual perception is exploratory, by using the electrophysiological indicators of school‐age children. Considering the compensatory characteristics of perception of visual processing, an atypical perceptual developmental trajectory for children with ADHD is expected, which may have an impact on their attention deficit behavior.</p> <hd id="AN0153631283-2">Method</hd> <p></p> <hd id="AN0153631283-3">Participants</hd> <p>The study used a sample of school‐age children from Beijing, including 122 Chinese children (64 children with ADHD and 58 control children) aged 7–12 years. Children with ADHD were outpatients recruited from clinics of Peking University Sixth Hospital/Institute of Mental Health, and controls were enrolled from local primary schools (April 2012–January 2020). The <emph>Diagnostic and Statistical Manual of Mental Disorders</emph>, 4th ed. diagnoses for all children were obtained through a semistructured diagnostic interview with parents and children using the Kiddie Schedule for Affective Disorders and Schizophrenia for School‐Age Children (K‐SADS). The interviews were conducted by a qualified psychiatrist. Parents' scores on the ADHD Rating Scale‐IV (ADHD‐RS) were used to measure the severity of symptoms. All participants met the following criteria: (a) right‐handed, (b) no history of neurological illness or other severe diseases, (c) no current diagnosis of schizophrenia, mood disorders, or autism, and (d) a full‐scale IQ above 80 as measured by the Wechsler Intelligence Scale for Children (WISC). Because of the update of the intelligence test version during the process of data collection, both the WISC‐III and WISC‐IV versions were used, but there was no difference in the proportion of intelligence test version between the two groups (χ<sups>2</sups>(<reflink idref="bib1" id="ref1">1</reflink>, _I_N_i_ = 122) = .25, <emph>p</emph> = .616). The specific proportions of intelligence tests are provided in Appendix S1. Each participant was assigned to one of three age groups: 7–8 years, 9–10 years, or 11–12 years. The participants' demographic information is shown in Table 1.</p> <p>1 TableSummary of the Demographic Information</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left" /><th align="left">ADHD (<italic>N</italic> = 64)</th><th align="left">Control (<italic>N</italic> = 58)</th></tr><tr><th align="left">M:F</th><th align="left">Age ± <italic>SD</italic></th><th align="left">M:F</th><th align="left">Age ± <italic>SD</italic></th></tr></thead><tbody><tr><td align="left">7–8 years</td><td align="left">16:5</td><td align="char" char=".">8.0 ± 0.6</td><td align="left">14:4</td><td align="char" char=".">8.0 ± 0.6</td></tr><tr><td align="left">9–10 years</td><td align="left">15:7</td><td align="char" char=".">10.0 ± 0.6</td><td align="left">11:10</td><td align="char" char=".">10.2 ± 0.5</td></tr><tr><td align="left">11–12 years</td><td align="left">17:4</td><td align="char" char=".">11.8 ± 0.5</td><td align="left">16:3</td><td align="char" char=".">11.9 ± 0.6</td></tr><tr><td align="left">Subtypes</td><td align="left">ADHD‐I (45), ADHD‐C (19)</td><td align="char" char=".">—</td></tr></tbody></table> </ephtml> </p> <p>1 Note</p> <p>2 M = male; F = female; ADHD = attention deficit hyperactivity disorder; ADHD‐I = ADHD inattention type; ADHD‐C = ADHD combined type.</p> <p>There was no significant difference in age (<emph>t</emph>(<reflink idref="bib120" id="ref2">120</reflink>) = 0.51, <emph>p</emph> = .608) or sex (χ<sups>2</sups>(<reflink idref="bib1" id="ref3">1</reflink>, _I_N_i_ = 122) = .29, <emph>p</emph> = .592) between the two groups. The IQ of the children with ADHD was significantly lower than that of the control children (<emph>t</emph>(<reflink idref="bib120" id="ref4">120</reflink>) = 5.65 <emph>p</emph> < .001). IQ and sex were included as covariates in the subsequent statistical analyses. Among children with ADHD enrolled in the study, 12 had a comorbid oppositional defiant disorder, one had a comorbid conduct disorder, two had comorbid tic disorders and one had Tourette's syndrome.</p> <p>Written consent was obtained from all children and their parents according to the Declaration of Helsinki. The study was approved by the Ethics Committee of Peking University Institute of Mental Health.</p> <hd id="AN0153631283-4">Paradigm</hd> <p>The task implemented is a classical visual‐spatial search paradigm (Wang et al., 2016). A schematic of the stimuli and the trial design is shown in Figure 1. Each trial included an interstimulus interval of 900–1,100 ms, a stimulus presentation period of 200 ms, and a response period of up to 2,800 ms. Twelve stimuli (one circle as the target and 11 diamonds as distractors) were distributed around a central fixation point in a clockwise manner, with a visual angle of 5° from the central fixation cross. Participants were instructed to maintain their gaze at the fixation point and report the position of the circle (i.e., if it appeared in the upper or lower half of the screen) but to ignore the diamonds. There were 30 trials for each block, with a task consisting of eight blocks. The total task duration was approximately 15 min, with a short break after each block. Each participant performed at least one practice block (20 trials) to ensure that they understood the task and maintained proper fixation. The formal experiment began if the accuracy of the practice block was higher than 70%. Behavioral indicators included accuracy (correct response with an RT between 200 and 2,800 ms), false alarm rate (incorrect response), omission rate (no response or RT < 200 ms), RT (only for the correct responses), and reaction time standard deviation (RTSD). The accuracy level of all 122 participants was higher than 60%.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01nov21/cdev13604-fig-0001.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev13604-fig-0001.jpg" title="1 Task paradigm. Task consists of three processes, 900–1,100 ms trial interval with a "+" in the center of the screen, 200 ms search array and up to 2,800 ms response interval. Participants need to determine whether the circle in the search array is in the upper the lower half of the screen, and response interval will end immediately after the button press or persistent up to 2,800 ms." /> </p> <p></p> <hd id="AN0153631283-6">ERP Recording and Analysis</hd> <p>EEG data were acquired from 128 channels (HydroCel Geodesic Sensor Net; Electrical Geodesics, Eugene, OR) with Net Station EEG Software. The impedance of all electrodes was maintained below 50 kΩ during the data acquisition. All electrodes were physically referenced to Cz (fixed by the EGI system). The EEG recordings were amplified with a bandpass filter of 0.01–400 Hz (half‐power cutoff) and digitized online at 1000 Hz.</p> <p>Offline EEG processing and analyses were performed using custom scripts from the EEGLAB toolbox in the MATLAB environment. Thirty‐eight lateral electrodes were not included in the following offline EEG analysis because of their susceptibility to movement interference (see Appendix S2). The resampling frequency was 250 Hz, the bandpass filter frequency band was 1–30 Hz, and an average reference was used. Electrodes containing excessive artifacts (exceeding 10% of the total recording time) were interpolated using the EEGLAB function. Artifact rejection was applied after an independent component analysis (ICA) decomposition, discarding the average voltages that exceeded ± 100 µV in the recordings. ICA components associated with vertical and horizontal eye movements were visually identified and removed. The data were then segmented relatively to the stimulus onset (–400 to 800 ms), and the baseline preceding the stimulus (–200 to 0 ms) was subtracted. To eliminate the influence of experimental trials, 80 trials were randomly selected from each participant in the follow‐up analysis.</p> <p>Difference topographic maps between different age groups were calculated to determine electrodes which represented the most prominent developmental characteristic, and the bilateral posterior electrodes were selected (shown as Figure 2). Therefore, we focused on bilateral posterior electrodes for P1 (80–140 ms) and N1 (120–240 ms) components. Electrodes around PO7 (<reflink idref="bib58" id="ref5">58</reflink>, 59, 65) and PO8 (<reflink idref="bib90" id="ref6">90</reflink>, 91, 96) were used to quantify the P1 and N1 components. For each child, the peak of the averaged wave from the selected electrodes was extracted to determine the amplitude (the mean amplitude of 20 ms around the maximum value) and latency (the time of the maximum value) of each component. An additional analysis for P1 on the posterior 13 electrodes (<reflink idref="bib58" id="ref7">58</reflink>, 59, 65, 66, 70, 71, 75, 76, 83, 84, 90, 91, 96) according to the distribution of P1 was presented in Appendix S3.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01nov21/cdev13604-fig-0004.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev13604-fig-0004.jpg" title="2 The P1 and N1 amplitude change between age groups. The figure shows the change in topographic maps between age groups, and the selected electrodes represented the changes of P1 and N1 components. ADHD = attention deficit hyperactivity disorder." /> </p> <p></p> <p>Statistical analyses consisted of analysis of covariance (ANCOVA) and were independently performed for task performance measures and each ERP component, with Group (control, ADHD) and Age (7–8 years, 9–10 years, 11–12 years) as between‐subject factors. An additional within‐subject factor, Hemisphere (left, right), was included in the ERP analyses. If the interaction effect was significant, a simple effect analysis was performed and adjusted using the Bonferroni method. The alpha‐level was.05 in this study. To control for the impact of sex and IQ on the results, we used IQ and sex as covariables. IQ was used as a covariable in the statistical analysis to eliminate any potential impact on the results. Sex was first added as a factor in the model and the main effect and interaction effect between group and age were computed. If a significant interaction effect was present, the sex and the significant interaction factor were added into the model, whereas if there was no significant interaction effect, only sex was included in the model as a dummy regressor. To further describe the effect of continuous age distribution on the indicators, we re‐analyzed the results using the regression method, in which age was added as a continuous variable. The regression results are presented in Appendix S4.</p> <hd id="AN0153631283-8">Results</hd> <p></p> <hd id="AN0153631283-9">Behavioral Performance</hd> <p>The ANCOVA for accuracy did not show a significant main effect of Group (<emph>F</emph>(<reflink idref="bib1" id="ref8">1</reflink>,<reflink idref="bib114" id="ref9">114</reflink>) = 1.65, <emph>p</emph> = .20, η<sups>2</sups> =.014), with both groups showing high accuracy (ADHD: 91.2%, control: 92.9%), but there was a significant main effect of Age (<emph>F</emph>(<reflink idref="bib2" id="ref10">2</reflink>,<reflink idref="bib114" id="ref11">114</reflink>) = 4.42, <emph>p</emph> = .014, η<sups>2</sups> = .072) with post hoc analysis indicating that accuracy was lower in the 7‐ to 8‐year‐old group compared to the 11‐ to 12‐year‐old children (90.1% vs. 94.3%, <emph>p</emph> = .012). There was no significant interaction between Age and Group (<emph>F</emph>(<reflink idref="bib2" id="ref12">2</reflink>,<reflink idref="bib114" id="ref13">114</reflink>) = 0.18, <emph>p</emph> = .835, η<sups>2</sups> = .003); thus, the children's ability to correctly respond to targets improved with age in both groups.</p> <p>For the false alarm rate, we did not find a significant main effect of Group (<emph>F</emph>(<reflink idref="bib1" id="ref14">1</reflink>,<reflink idref="bib114" id="ref15">114</reflink>) = 0.79, <emph>p</emph> = .377, η<sups>2</sups> = .007), Age (<emph>F</emph>(<reflink idref="bib2" id="ref16">2</reflink>,<reflink idref="bib114" id="ref17">114</reflink>) = 2.92, <emph>p</emph> = .058, η<sups>2</sups> = .049), as well as Age × Group interaction (<emph>F</emph>(<reflink idref="bib2" id="ref18">2</reflink>,<reflink idref="bib114" id="ref19">114</reflink>) = 0.36, <emph>p</emph> = .699, η<sups>2</sups> = .006).</p> <p>The omission error rate for children with ADHD was significantly higher than that for control children (Group main effect: <emph>F</emph>(<reflink idref="bib1" id="ref20">1</reflink>,<reflink idref="bib114" id="ref21">114</reflink>) = 5.44, <emph>p</emph> = .021, η<sups>2</sups> = .046). The significant Age main effect (<emph>F</emph>(<reflink idref="bib2" id="ref22">2</reflink>,<reflink idref="bib114" id="ref23">114</reflink>) = 7.39, <emph>p</emph> = .001, η<sups>2</sups> = .115) indicated that children showed less missed responses with increasing age. The Age × Group interaction was not significant (<emph>F</emph>(<reflink idref="bib2" id="ref24">2</reflink>,<reflink idref="bib114" id="ref25">114</reflink>) = 0.39, <emph>p</emph> = .677, η<sups>2</sups> = .007).</p> <p>Children with ADHD showed slower RT (Group main effect: <emph>F</emph>(<reflink idref="bib1" id="ref26">1</reflink>,<reflink idref="bib114" id="ref27">114</reflink>) = 5.70, <emph>p</emph> = .019, η<sups>2</sups> = .048) and larger RTSD (Group main effect: <emph>F</emph>(<reflink idref="bib1" id="ref28">1</reflink>,<reflink idref="bib114" id="ref29">114</reflink>) = 4.27, <emph>p</emph> = .041, η<sups>2</sups> = .036) than control children. The main effect of Age (<emph>F</emph>(<reflink idref="bib2" id="ref30">2</reflink>,<reflink idref="bib114" id="ref31">114</reflink>) = 27.01, <emph>p</emph> < .001, η<sups>2</sups> = .332) and nonsignificant Age × Group interaction (<emph>F</emph>(<reflink idref="bib2" id="ref32">2</reflink>,<reflink idref="bib114" id="ref33">114</reflink>) = 0.37, <emph>p</emph> = .693, η<sups>2</sups> = .006) indicated that RT decreased with increasing age, regardless of the group. Similarly, the Age main effect (<emph>F</emph>(<reflink idref="bib1" id="ref34">1</reflink>,<reflink idref="bib114" id="ref35">114</reflink>) = 13.69, <emph>p</emph> < .001, η<sups>2</sups> = .194) and nonsignificant Age × Group interaction (<emph>F</emph>(<reflink idref="bib2" id="ref36">2</reflink>,<reflink idref="bib114" id="ref37">114</reflink>) = 0.55, <emph>p</emph> = .577, η<sups>2</sups> = .010) indicated that RTSD decreased with increasing age (Figure 3).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01nov21/cdev13604-fig-0002.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev13604-fig-0002.jpg" title="3 Behavioral performance. The red line represented the behavior performance of children with ADHD, and the blue line for the control children. IQ and sex were used as covariates. The bars represented SE. *Group main effect, p < .05. RT = reaction time; RTSD = reaction time standard deviation; ADHD = attention deficit hyperactivity disorder." /> </p> <p></p> <hd id="AN0153631283-11">ERP Analysis</hd> <p></p> <hd id="AN0153631283-12">P1</hd> <p>A significantly larger P1 was shown in the right than in the left posterior region of both TD and ADHD groups (Hemisphere main effect: <emph>F</emph>(<reflink idref="bib1" id="ref38">1</reflink>,<reflink idref="bib114" id="ref39">114</reflink>) = 11.33, <emph>p</emph> = .001, η<sups>2</sups> = .090, Figure 4). Children with ADHD showed significantly larger P1 amplitude than control children over the left posterior region (Group main effect: <emph>F</emph>(<reflink idref="bib1" id="ref40">1</reflink>,<reflink idref="bib114" id="ref41">114</reflink>) = 5.76, <emph>p</emph> = .018, η<sups>2</sups> = .048; Group × Hemisphere: <emph>F</emph>(<reflink idref="bib1" id="ref42">1</reflink>,<reflink idref="bib114" id="ref43">114</reflink>) = 5.38, <emph>p</emph> = .022, η<sups>2</sups> = .045). Simple effect analysis confirmed that the P1 amplitude of children with ADHD was significantly higher than that of control children over the left posterior region (5.5 vs. 8.1 μV, <emph>p</emph> < .001), but not in the right posterior region (9.0 vs. 9.4 μV, <emph>p</emph> = .660). Moreover, the Age × Group interaction was also significant (<emph>F</emph>(<reflink idref="bib2" id="ref44">2</reflink>,<reflink idref="bib114" id="ref45">114</reflink>) = 3.64, <emph>p</emph> = .029, η<sups>2</sups> = .060) with simple effect analysis showing that the P1 amplitude of control children decreased with increasing age, with the amplitude of 11‐ to 12‐year‐old children being significantly smaller than 7–8 year‐olds (5.3 vs. 8.7 μV, <emph>p</emph> = .002) and 9–10 year‐olds (5.3 vs. 7.9 μV, <emph>p</emph> = .022). Meanwhile, for children with ADHD the amplitude of 9‐ to 10‐year‐old children was significantly larger than 7–8 year‐olds (10.9 vs. 8.2 μV, <emph>p</emph> = .010) and 11–12 year‐olds (10.9 vs. 7.2 μV, <emph>p</emph> < .001), and there was no significant difference between 7–8 year‐olds and 11–12 year‐olds (8.2 vs. 7.2 μV, <emph>p</emph> = .825), indicating an atypical developmental trajectory of P1 amplitude in children with ADHD. The simple effect analysis in the age group showed that the P1 amplitude of children with ADHD was significantly higher than that of normal children in 9‐ to 10‐year‐old group (10.9 vs. 7.9 μV, <emph>p</emph> = .002), whereas not significant in 7–8 year‐olds (8.2 vs. 8.7 μV, <emph>p</emph> = .623) and 11‐ to 12‐year‐old group (7.2 vs. 5.3 μV, <emph>p</emph> = .052). But the Age × Group × Hemisphere interaction was not significant (<emph>F</emph>(<reflink idref="bib2" id="ref46">2</reflink>,<reflink idref="bib114" id="ref47">114</reflink>) = 0.47, <emph>p</emph> = .624, η<sups>2</sups> = .008). For further regression analysis, the bilateral P1 for children with ADHD showed inverted U‐shaped developmental characteristics in the continuous age distribution, which was better fitted by the quadratic curve. However, in neurotypical children, bilateral P1 decreased linearly with age (see Appendix S5). There were no significant effects on P1 latency.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01nov21/cdev13604-fig-0003.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev13604-fig-0003.jpg" title="4 ERP wave and topo‐map. (A) ERP wave on the PO7 and PO8 for children with ADHD and control children. The warm color used for the children with ADHD, and the cold color for control children. The colors from light to dark represented the 7–8, 9–10, and 11–12 years old group, respectively. (B) Topo‐map for P1 (100–120 ms), and N1 (160–200 ms) components. ERP = event‐related potential; ADHD = attention deficit hyperactivity disorder." /> </p> <p></p> <hd id="AN0153631283-14">N1</hd> <p>The posterior N1 amplitude significantly decreased with increasing age in both groups (Age main effect: <emph>F</emph>(<reflink idref="bib2" id="ref48">2</reflink>,<reflink idref="bib114" id="ref49">114</reflink>) = 10.67, <emph>p</emph> < .001, η<sups>2</sups> = .158; Age × Group: <emph>F</emph>(<reflink idref="bib2" id="ref50">2</reflink>,<reflink idref="bib114" id="ref51">114</reflink>) = 1.25, <emph>p</emph> = .290, η<sups>2</sups> = .022), with the N1 amplitude of 7‐ to 8‐year‐old children being significantly larger than 9–10 year‐olds (–8.3 vs. –5.4 μV, <emph>p</emph> = .021) and 11–12 year‐olds (–8.3 vs. –3.5 μV, <emph>p</emph> < .001). The latency of posterior N1 also decreased with increasing age in both groups (Age main effect: <emph>F</emph>(<reflink idref="bib2" id="ref52">2</reflink>,<reflink idref="bib114" id="ref53">114</reflink>) = 8.0, <emph>p</emph> = .001, η<sups>2</sups> = .123; Age × Group: <emph>F</emph>(<reflink idref="bib2" id="ref54">2</reflink>,<reflink idref="bib114" id="ref55">114</reflink>) = 1.23, <emph>p</emph> = .296, η<sups>2</sups> = .021), with N1 latency of 11‐ to 12‐year‐old children being significantly shorter than 7–8 year‐olds (169 vs. 186 ms, <emph>p</emph> < .001) and 9–10 year‐olds (169 vs. 180 ms, <emph>p</emph> = .036). We did not find any significant differences between the groups for either N1 amplitude or N1 latency (Figure 3).</p> <hd id="AN0153631283-15">Correlation Analysis</hd> <p>A correlation analysis was carried out between P1 amplitude and task performance, and P1 amplitude and symptom scores, with age, IQ, and sex as covariates. The results showed that P1 amplitude over the left hemisphere was significantly negatively correlated with RT (<emph>r</emph> = –.29, <emph>p</emph> = .026) in children with ADHD but not in control participants (<emph>r</emph> = −.16, <emph>p</emph> = .253); that is, a larger amplitude P1 component over the left hemisphere was related to faster responses in the visual search task (Figure 5). The correlation between the inattention score and the P1 amplitude over the left hemisphere did not reach a significant level (<emph>r</emph> = –.24, <emph>p</emph> = .062).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01nov21/cdev13604-fig-0005.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev13604-fig-0005.jpg" title="5 ERP components analysis and correlation. (A and B) The mean amplitude of P1 and N1 for ADHD and control children. Red lines used for children with ADHD, and blue for control children. The dash lines used for the left posterior region (PO7), solid lines for right posterior region (PO8). IQ and sex were used as covariates. (C) Scatter plot of RT and P1 activity; (D) attention deficit score and P1 activity on the left posterior region (PO7). IQ, sex and age were used as covariates to calculate the standardized residual (SR) of RT, inattention scores and P1 amplitude. ADHD = attention deficit hyperactivity disorder; RT = reaction time." /> </p> <p></p> <hd id="AN0153631283-17">Discussion</hd> <p>Using a visual search paradigm, we analyzed the developmental characteristics of visual attention task performance and P1 and N1 components in children with ADHD and control children aged 7–12 years. Developmental visual search ability was demonstrated in children with and without ADHD. A compensatory activation of P1 was observed in children with ADHD, especially over the left posterior region, which was negatively correlated with RT in children with ADHD. Moreover, the overactivated P1 amplitude in children with ADHD showed an atypical developmental trajectory with an increase for 9–10 years old compared to 7–8 years old, which was a decline for control children.</p> <p>The visual search task mainly tested the ability to discriminate and search for pop‐out stimuli. Although behavioral impairment was not consistent for children with ADHD in simple pop‐out visual search tasks (Mullane & Klein, 2008), the prolongation of RT and the increase in RTSD in children with ADHD reflected a decrease in cognitive processing speed and increased response instability, which is consistent with previous findings (Kofler et al., 2013; Lin et al., 2017; Wang et al., 2016), slower processing speed is thought to increase the academic difficulty, such as reading and comprehension (Jacobson, Ryan, Denckla, Mostofsky, & Mahone, 2013; Kibby et al., 2015). The higher omission rate of children with ADHD indicated that their attention was more likely to deviate from the current task, which might reflect the effect of attention deficit on their ability to maintain their internal train of thought (Van den Driessche et al., 2017). Regarding accuracy, the two groups of children showed high accuracy under simple task requirement. The behavioral results clearly showed the development of attention ability in children aged 7–12 years, indicating continuous cognitive improvement with increasing age (Brodeur & Pond, 2001; Davidson, Amso, Anderson, & Diamond, 2006; Tau & Peterson, 2010).</p> <p>As shown in Figure 4, a typical P1 component was elicited around 100 ms following the stimuli (Luck & Kappenman, 2011), followed by an N1 component, both distributed in the parietal‐occipital lobe or parietal‐temporal lobe (Marco‐Pallarés, Grau, & Ruffini, 2005; Seeck et al., 2017), which are evoked by visual stimuli and reflect the perception and classification of visual information (VanRullen & Thorpe, 2001; Vogel & Luck, 2000). Compared with the control children, the larger left posterior P1 for children with ADHD might reflect a compensatory activity of early visual perception (Kim et al., 2015), within the visual cortex (Di Russo, Martínez, Sereno, Pitzalis, & Hillyard, 2002). Using a different neurophysiological method, Mulas et al. (2006) found that children with ADHD showed greater magnetoencephalography activity than the control children of in the left inferior parietal lobe and left posterior superior temporal gyrus around 100–400 ms in the Wisconsin Card Sorting Test. The left superior temporal gyrus region is a specific activation region in the immature vigilant attention network of children and contributes to stronger bottom‐up stimulation (Morandini et al., 2020). Overactivation of these components in children with ADHD may reflect their more immature attentional function and dependence on basic stimuli. Furthermore, the overactivation of the left posterior P1 was negatively correlated with RT, which might provide a possible explanation for the behavioral heterogeneity shown by ADHD (Kim et al., 2015). These findings were also consistent with the primary functional compensatory hypothesis for ADHD (Fassbender & Schweitzer, 2006). However, this overactivation may also reflect specific deficits. Singh (2012) found that early sensory overactivation may relate to the dysfunction of the GABAergic system, and it may also be due to the abnormality of the inhibition system in children with ADHD, which leads to a decrease in reaction time. Although the mechanism is not clear, these findings suggest the important role of early perception on behavioral performance and offer new areas of exploration into the mechanisms of cognitive developmental deficits and intervention techniques in children with ADHD.</p> <p>Another prominent finding was the atypical developmental trajectory that overactivates P1 in school‐age children with ADHD. The occipital cortex density gradually decreased with the maturation of school‐age children as a result of increased synaptic pruning (Gogtay et al., 2004). The gradual decrease in P1 in control children may reflect this mature pruning process as well as the growth of skull thickness (Barriga‐Paulino, Rodríguez‐Martínez, Arjona, Morales, & Gómez, 2017; Batty & Taylor, 2002; Segalowitz, Santesso, & Jetha, 2010). Structural MRI has shown that the maturation of cortical thickness of the occipital cortex is delayed in children with ADHD (Castellanos et al., 2002; Shaw et al., 2009), however, the overactivation of visual function was found in the functional related studies (Cortese et al., 2012; Fassbender & Schweitzer, 2006; Kim et al., 2015), some of which have shown compensatory effects. This suggests a different developmental pattern for the visual function in children with ADHD. And in our study, children with ADHD showed an abnormal bilateral activation of P1 activity at 9–10 years and inverted U‐shaped developmental characteristics at 7–12 years of age, which provides evidence for their potential abnormal visual perception development. Furthermore, since P1 amplitude increases with cognitive effort, and this top‐down cognitive processing requires a certain degree of cognitive ability (Hileman, Henderson, Mundy, Newell, & Jaime, 2011). The inverted U‐shaped development of P1 in children with ADHD may reflect a developmental feature of visual perception under the influence of cognitive ability, that is, their top‐down perceptual enhancement gradually emerges at school‐age and meets the requirements of visual search tasks at the age of 9–10 years. This also explains that ADHD has the characteristics of a transition period with age (Pauli‐Pott & Becker, 2015), which is affected by the level of its development cognitively. Combined with the characteristics of brain retardation in children with ADHD (Castellanos et al., 2002; Shaw et al., 2007), the abnormal development of P1 activity may reflect the comprehensive effect of their immature bottom‐up dependence and evolving top‐down effort.</p> <p>The reported results should be considered in light of several limitations. First, as only 7‐ to 12‐year‐old children were included in this study, it is not possible to describe the performance of visual function before the age of 7 and after age 12. The effects of cognitive maturity suggest the necessity of investigating cognitive development during the transition from childhood to adolescence, which is accompanied by major environmental and biological changes (Silk et al., 2016). Second, although we used a sample of 122 participants, the sample size of each age group was still small, and the female sample was insufficient, therefore, the effectiveness of the conclusion was limited. Third, for the P1 analysis, we have only focused on the abnormal development pattern, which is of more significance to explain the functional overactivation of the visual cortex in children with ADHD, and we have mainly selected the typical electrode regions that reflected the characteristics. However, the reason why these regions showed the abnormality still needs more research to explore, in which the synchronous EEG and MRI design may be required to achieve high temporal and spatial resolution. Furthermore, considering the individual variations in cross‐sectional studies, more detailed longitudinal studies are needed to verify the existing results.</p> <hd id="AN0153631283-18">Conclusion</hd> <p>This study explored the developmental trajectory of attention search abilities and early ERP in school‐age children with ADHD through a visual search task paradigm. The overactivation and atypical developmental trajectory of P1 in school‐age children with ADHD might reflect the combination of their immature visual attention function and growing subjective efforts. The results of this study suggest that early perception plays an important role in the understanding and intervention of cognitive dysfunction in children with ADHD, and provide a new perspective for exploring the mechanism and intervention techniques of cognitive developmental impairments in children with ADHD.</p> <p>GRAPH: Appendix S1. Wechsler Intelligence Scale for Children InformationAppendix S2. Analysis ElectrodesAppendix S3. Additional P1 AnalysisAppendix S4. Regression Analysis ResultsAppendix S5. Fitting Estimation</p> <ref id="AN0153631283-19"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> This work was supported by the National Natural Sciences Foundation of China (Li Sun, 81771479, 81971284; Yan Song, 31871099; Lu Liu, 81873802); the Beijing Municipal Science and Technology Program (Li Sun, Z171100001017089), the Key Scientific Research Projects of Capital Health Development (Li Sun, 2020‐1‐4111), the Beijing Brain Initiative of Beijing Municipal Science and Technology Commission (Yan Song, Z181100001518003), the National Defense Basic Scientific Research Program of China (Yan Song, 2018110B011), and Sanming Project of Medicine in Shenzhen "The AD/HD Research Group from Peking University Sixth Hospital" (SZSM201612036). The authors thank the children and their patients for participating. 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  Label: Title
  Group: Ti
  Data: Atypical Developmental Trajectories of Early Perception among School-Age Children with Attention Deficit Hyperactivity Disorder during a Visual Search Task
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Luo%2C+Xiangsheng%22">Luo, Xiangsheng</searchLink><br /><searchLink fieldCode="AR" term="%22Guo%2C+Jialiang%22">Guo, Jialiang</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Dongwei%22">Li, Dongwei</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Lu%22">Liu, Lu</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Yanbo%22">Chen, Yanbo</searchLink><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Yu%22">Zhu, Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Johnstone%2C+Stuart+J%2E%22">Johnstone, Stuart J.</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yufeng%22">Wang, Yufeng</searchLink><br /><searchLink fieldCode="AR" term="%22Song%2C+Yan%22">Song, Yan</searchLink><br /><searchLink fieldCode="AR" term="%22Sun%2C+Li%22">Sun, Li</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2330-6622">0000-0002-2330-6622</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Child+Development%22"><i>Child Development</i></searchLink>. e1186-e1197 Nov-Dec 2021 92(6):e1186-e1197.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 12
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2021
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Attention+Deficit+Hyperactivity+Disorder%22">Attention Deficit Hyperactivity Disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Ability%22">Cognitive Ability</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+Perception%22">Visual Perception</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Development%22">Cognitive Development</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Brain%22">Brain</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/cdev.13604
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0009-3920
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by cognitive deficits associated with attention. Prior studies have revealed the potential impact of ADHD on basic perception and cognitive ability in patients with ADHD. In this study, bilateral posterior P1 and N1 were measured in 122 Chinese children aged 7-12 years (64 with ADHD) to investigate the developmental characteristics of early perception during visual processing in school-age children with ADHD. For children with ADHD, a larger P1 activity with an atypical developmental pattern was evoked and observed for the visual search performance. These findings offer new insights into the mechanisms of cognitive developmental deficits and intervention techniques in children with ADHD.
– Name: AbstractInfo
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– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2021
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  Label: Accession Number
  Group: ID
  Data: EJ1319848
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1319848
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      – Text: English
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        PageCount: 12
        StartPage: e1186
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      – SubjectFull: Attention Deficit Hyperactivity Disorder
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      – SubjectFull: Cognitive Ability
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      – SubjectFull: Visual Perception
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      – TitleFull: Atypical Developmental Trajectories of Early Perception among School-Age Children with Attention Deficit Hyperactivity Disorder during a Visual Search Task
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