-
Categories
-
Pharmaceutical Intermediates
-
Active Pharmaceutical Ingredients
-
Food Additives
- Industrial Coatings
- Agrochemicals
- Dyes and Pigments
- Surfactant
- Flavors and Fragrances
- Chemical Reagents
- Catalyst and Auxiliary
- Natural Products
- Inorganic Chemistry
-
Organic Chemistry
-
Biochemical Engineering
- Analytical Chemistry
-
Cosmetic Ingredient
- Water Treatment Chemical
-
Pharmaceutical Intermediates
Promotion
ECHEMI Mall
Wholesale
Weekly Price
Exhibition
News
-
Trade Service
Written by Li Dongwei, edited by Song Yan -- Wang Sizhen, Fang Yiyi: The "neural foundation" school was changed to "neural foundation" Editor—Summer Leaf
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized
by symptoms of inattention, hyperactivity, and impulsivity.
Approximately 5% of school-age children[1] suffer from attention impairment, which significantly affects many higher cognitive functions and academic performance [2].
However, the underlying pathophysiology of ADHD is unclear, and clinical biomarkers are still lacking to aid in the early diagnosis of ADHD and as potential targets for
intervention.
Visuospatial abnormalities in children with ADHD, such as increased variability in reaction time, have been reported in many behavioral studies [3].
Previous functional magnetic resonance studies have found that the blood oxygen level of the parietal occipital lobe in children with ADHD is weaker than that of normal development (TD) children during the attention selection process [4].
Previous studies in the researchers' group have used the visual search paradigm to explore impaired attention in children with ADHD [5-7].
Through univariate event-related potential (ERP) analysis, the researchers examined the processing time course of visuospatial attention and found that the amplitude of the parietal occipital lobe N2pc component associated with attention selection in children with ADHD was significantly smaller than that of the attentional selection process In children with TD, the underlying neural basis of visuospatial attention impairment in children with ADHD has been identified for the first time [5].
However, visual search does not rely solely on a specific brain region, and traditional univariate ERP analysis loses a lot of information in high-density electroencephalogram (HD-EEG).
Existing research still lacks bridges
linking abnormal brain-wide neuroprocessing patterns in children with ADHD to impaired visual attention selection.
2 On October 15, 021, Song Yan's team from Beijing Normal University and Sun Li's team from Peking University Sixth Hospital held Human Brain Mapping (HBM) published a title “Information-based multivariate decoding reveals impreciseneural encoding in children with attention deficit hyperactivitydisorder during visual selective attention” In the article, doctoral students Li Dongwei and Dr.
Luo Xiangsheng (currently clinicians of Anding Hospital) are the joint first authors of the paper, and Professor Song Yan and researcher Sun Li are the joint corresponding authors
of the paper.
This work collected EEG and behavioral data from 70 ADHD children and 65 TD children for visual search tasks, using individualized multivariate machine learning methods.
It was revealed that the neural representation of inaccurate and delayed target location can predict the behavioral performance of impaired visual search in children with ADHD, which provides a potential research direction
for early diagnosis and optimization of personalized intervention treatment in children with ADHD.
Implicit visuospatial attention plays a very important role
in daily life learning.
What is implicit attention? Think of a common scenario, as a proficient native Chinese speaker, you must not read the article
word by word when reading.
In order to complete the reading task more fluently, the brain will preprocess the information that the future eye will see before moving to the next word, thereby improving the reading speed [8, 9].
For another example, when the driver drives the car on the highway, although the eyes are always on the road ahead, the driver still needs to pay attention to the situation
of the vehicle behind in the rearview mirror.
Therefore, researchers call this kind of attention that does not move the eye but focuses on the peripheral visual field implicit attention
.
While deficits in implicit visuospatial attention in children with ADHD are well known, whether the underlying core disorder is target selection is highly controversial
.
Based on previous research, the team proposed the neurocoding hypothesis of chaos in children with neurodevelopmental disorders (Figure 1), arguing that attention is like a searchlight looking for task-related targets in space, ADHD Disorganized, inefficient neural activity in children leads to searchlights not being focused, which can lead to poor
concentration.
1 Neurocoding hypothesis of chaos in children with neurodevelopmental disorders and multivariate machine learning methods based on individual information
(Source: Li, D.
et al.
, HBM, 2022).
Therefore, the team advanced the understanding of cognitive deficits in neurodevelopmental disorders through non-invasive EEG signaling, a multivariate machine learning approach based on individual information, and the specific results are as follows:
1.
Univariate ERP results
.
A reliable N2pc component was induced within 200-300 milliseconds of the start of visual search in the ADHD and TD groups
。 Previous work by the research team has shown that the N2pc component is smaller in children with ADHD than in children with TD [5].
In this study, the research team replicated the previous results with a larger sample size (Figure 2D), further supporting impaired attention choices in
children with ADHD.
Fig.
2 The amplitude of the Parietal occipital lobe ERP component N2pc associated with attention selection in children with ADHD is significantly smaller than that of normal children
(Source: Li, D.
et al.
, HBM, 2022).
2.
Multivariate decoding results
at about 200 milliseconds after the visual search begins.
What's more, decoding accuracy showed significant differences between ADHD and TD children over a period of 240-340 ms after the start of the visual search (Figure 3A).
。 Specifically, children with ADHD showed a more inaccurate peak delay for target localization and decoding accuracy (ADHD: 420±70) than children with TD Millisecond; TD: 391 ±70 ms).
Fig.
3 The decoding accuracy of target spatial position in children with ADHD is significantly lower than that of normal children
(Source: Li, D.
et al.
, HBM, 2022).
3.
Other relevant results
This result suggests that children with ADHD have a unique characterization of the coding of the target location, which is not contributed to by the
N2pc component.
In addition, to investigate whether imprecise targeting compromised behavioral performance, the research team calculated a correlation
between decoding accuracy and behavioral outcomes.
The results showed that the decoding accuracy of children with ADHD was associated with time-to-response (Figure 4C) and reaction-time variability (Figure 4D).
Significant negative associations, which were not found
in children with TD.
The research team also found a clear correlation between decoding accuracy and age in the ADHD group, suggesting developmental delays
in spatial localization in children with ADHD.
Fig.
4 The decoding accuracy of target spatial position in children with ADHD is significantly lower than that of normal children
(Source: Li, D.
et al.
, Hum Brain Mapp, 2022).
This study uses the classical visual search paradigm combined with EEG and multivariate neural decoding methods to explore neural patterns
with abnormal target spatial location coding in school-age children with ADHD.
Behaviorally, children with ADHD all showed a decrease in accuracy and a delay in reaction time, indicating slow and imprecise
targeting.
In terms of brain activity, children with ADHD have poor neural decoding ability to target locations, indicating that their neural activity is noisy and their spatial localization ability is impaired
.
The negative correlation between neural activity and behavior further suggests that imprecision in neural activity will adversely affect
attention performance in children with ADHD.
Therefore, the decoding of neural signals can better help us understand the dense information contained in neurophysiological signals and provide new insights
into the underlying neural pattern abnormalities of selective attention in neurodevelopmental disorders.
The shortcomings of this study are: first, because the visual search paradigm is very easy to cause eye movements in children with ADHD, future research needs to address the influence of
eye movement artifacts on EEG signal interference.
In addition, in order to study the IQ of the two groups of children in the largest possible sample, the IQ of the two groups did not match exactly, and follow-up studies need to further demonstrate the accuracy of the decoding model in this study in a larger sample
.
In conclusion, this study suggests that increased neural noise and chaotic neural responses may be fundamental deficits
in neurodevelopmental disorders in children with ADHD.
Our findings provide new neurophysiological evidence for understanding cognitive dysfunction in children with ADHD and potential directions
for early diagnosis and individualized intervention of neurodevelopmental disorders in children with ADHD.
Original link: https://onlinelibrary.
wiley.
com/doi/10.
1002/hbm.
26115
The first author of the paper is Li Dongwei (Twitter: dwLi_Neuro), a doctoral student in Song Yan's research group of Beijing Normal University, Dr.
Luo Xiangsheng (currently a clinician in Anding Hospital) of the Sixth Hospital of Peking University, and the corresponding authors are Professor Song Yan of Beijing Normal University and researcher Sun Li of Peking University Sixth Hospital.
Professor Wang Yufeng of Peking University Sixth Hospital also made important contributions
to this work.
The research work was supported by the National Natural Science Foundation of China (31871099, 81971284, 81771479), Beijing Municipal Science and Technology Commission (Z171100001017089) and other projects.
First authors: Li Dongwei (first from left), Luo Xiangsheng (second from left); Corresponding author: Sun Li (second from right), Song Yan (first from right)
(Photo courtesy of: Song Yan Lab)
[1] Nat Neurosci – Breakthrough! Li Bo's research group at Cold Spring Harbor Laboratory revealed the neural mechanism of pan-amygdala structure regulating diet choice and energy metabolism
[2] Nat Commun-Xing Dajun's research group revealed a new mechanism for micro-saccade direction-specific modulation of visual information coding
[3] Sci Adv—Reinterprets the brain's processing of reward information
[4] Brain Stimu—Rong Peijing's research group suggested that percutaneous ear stimulation improved cognitive function in patients with mild cognitive dysfunction
[5] Neurobiol Dis—Li Chen/Li Wei's research team revealed the sensitivity of IC→PVT→BNST neural circuits to regulate the pathogenesis of anxiety disorders
【6】HBM | Highly connected and highly variable: supports the resting core brain network of propofol-induced loss of consciousness
[7] NPP—Xu Yun's research group revealed that microalbumin-positive interneurons in the prefrontal cortex play an important role in the regulation of mood disorders in the early stage of Alzheimer's disease
[8] Nat Neurosci Review—Overview of presynaptic optogenetic tools
[9] Neural Regen Res review—the role and potential of microglial polarization and dialogue with other cells in the treatment of Alzheimer's disease
[10] Nat Commun-Chu Jun's research group has developed a new high-performance genetically encoded cAMP fluorescent probe
Recommended high-quality scientific research training courses [1] The 9th EEG Data Analysis Flight (Training Camp: 2022.11.
23-12.
24) Conference/Forum/Seminar Preview
[1] Immune Zoom Seminar—Screening of B cells in the immune and nervous system (Professor Xu Heping)
[2] Academic Conference - 2022 Symposium on Neural Circuit Tracing Technology and the Second Round of the Second Round of the 6th National Training Course on Neural Circuit Tracing Technology
[3] Roundtable – Xu Fuqiang/Jia Yichang/Han Lanqing/Cai Lei et al.
discussed gene therapy innovation for neurodegenerative diseases and ophthalmic diseases
[1] Willcutt EG.
The prevalence of DSM-IV attention-deficit/hyperactivity disorder: a meta-analytic review.
Neurotherapeutics.
2012; 9: 490-499.
[2] Singer-Harris N, Forbes P, Weiler MD, Bellinger D, Waber DP.
Children with adequate academic achievement scores referred for evaluation of school difficulties: Information processing deficiencies.
Dev Neuropsychol.
2001; 20: 593–603.
[3] Kofler MJ, Rapport MD, Sarver DE, Raiker JS, Orban SA, Friedman LM, et al.
Reaction time variability in ADHD: a meta-analytic review of 319 studies.
Clin Psychol Rev.
2013; 33(6), 795-811.
[4] Schneider MF, Krick CM, Retz W, Hengesch G, Retz-Junginger P, Reith W, et al.
Impairment of fronto-striatal and parietal cerebral networks correlates with attention deficit hyperactivity disorder (ADHD) psychopathology in adults—a functional magnetic resonance imaging (fMRI) study.
Psychiatry Res Neuroimaging.
2010; 183: 75–84.
[5] Wang E, Sun L, Sun M, Huang J, Tao Y, Zhao X, et al.
Attentional selection and suppression in children with attention-deficit/hyperactivity disorder.
Biol Psychiatry Cogn Neurosci Neuroimaging.
2016; 1: 372–380.
[6] Guo, J.
, Luo, X.
, Kong, Y.
, Li, B.
, Si, B.
, Sun, L, et al.
Abnormal reactivity of brain oscillations to visual search target in children with attention-deficit/hyperactivity disorder.
Biol Psychiatry Cogn Neurosci Neuroimaging.
(in the press).
[7] Luo X, Guo, J, Li D, Liu L, Chen Y, Zhu Y, et al.
Atypical developmental trajectories of early perception among school-age children with ADHD during a visual search task.
Child Dev.
2021; 92: e1186-e1197.
[8] Pan, Y.
, Frisson, S.
, & Jensen, O.
(2021).
Neural evidence for lexical parafoveal processing.
Nature Communications, 12(1), 1-9.
[9] Jensen, O.
, Pan, Y.
, Frisson, S.
, & Wang, L.
(2021).
An oscillatory pipelining mechanism supporting previewing during visual exploration and reading.
Trends in Cognitive Sciences, 25(12), 1033-1044.
End of this article
