Negin Gholamipourbarogh, Veit Roessner, Annet Bluschke, Christian Beste
{"title":"新的神经活动谱潜在的抑制控制缺陷的ADHD临床相关性-从脑电图张量分解的见解。","authors":"Negin Gholamipourbarogh, Veit Roessner, Annet Bluschke, Christian Beste","doi":"10.1016/j.bpsc.2025.05.001","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Attention-Deficit-Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental disorder that impacts cognitive control processes. While neurophysiological data (e.g., EEG data) have provided valuable insights into its underlying mechanisms, fully understanding the altered cognitive functions in ADHD requires advanced analytical approaches capable of capturing the highly dimensional nature of neurophysiological data more effectively.</p><p><strong>Methods: </strong>We examined N=59 individuals with ADHD and N=63 neurotypical participants using a standard Go/Nogo task to assess response inhibition. We used EEG tensor decomposition to extract spectral, temporal, spatial and trial-level features associated with inhibitory control deficits in ADHD. The trial-level features capture intra-individual variability which is then used in a machine learning analysis to differentiate individuals with ADHD from neurotypical participants. We also applied a feature selection algorithm to identify the most important features for distinguishing between the two groups in the classification process.</p><p><strong>Results: </strong>We observed typical response inhibition deficits in ADHD. Contrary to common assumptions, fronto-central theta band activity did not appear to be the most distinguishing EEG feature between ADHD and neurotypical individuals. Instead, the most important distinguishing features are tensor components reflecting posterior alpha band activity during attentional selection time windows and posterior theta band activity during response selection and control time windows.</p><p><strong>Conclusions: </strong>We identified novel neurophysiological facets of response inhibition in ADHD, enabling the classification of ADHD and neurotypical individuals. Our findings suggest that ADHD-related deficits emerge early during attentional selection and persist through response control stages. The findings underscore the need to refine conceptions about neural peculiarities in ADHD and adapt clinical interventions targeting inhibitory control deficits accordingly.</p>","PeriodicalId":93900,"journal":{"name":"Biological psychiatry. Cognitive neuroscience and neuroimaging","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Novel neural activity profiles underlying inhibitory control deficits of clinical relevance in ADHD - insights from EEG tensor decomposition.\",\"authors\":\"Negin Gholamipourbarogh, Veit Roessner, Annet Bluschke, Christian Beste\",\"doi\":\"10.1016/j.bpsc.2025.05.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Attention-Deficit-Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental disorder that impacts cognitive control processes. While neurophysiological data (e.g., EEG data) have provided valuable insights into its underlying mechanisms, fully understanding the altered cognitive functions in ADHD requires advanced analytical approaches capable of capturing the highly dimensional nature of neurophysiological data more effectively.</p><p><strong>Methods: </strong>We examined N=59 individuals with ADHD and N=63 neurotypical participants using a standard Go/Nogo task to assess response inhibition. We used EEG tensor decomposition to extract spectral, temporal, spatial and trial-level features associated with inhibitory control deficits in ADHD. The trial-level features capture intra-individual variability which is then used in a machine learning analysis to differentiate individuals with ADHD from neurotypical participants. We also applied a feature selection algorithm to identify the most important features for distinguishing between the two groups in the classification process.</p><p><strong>Results: </strong>We observed typical response inhibition deficits in ADHD. Contrary to common assumptions, fronto-central theta band activity did not appear to be the most distinguishing EEG feature between ADHD and neurotypical individuals. Instead, the most important distinguishing features are tensor components reflecting posterior alpha band activity during attentional selection time windows and posterior theta band activity during response selection and control time windows.</p><p><strong>Conclusions: </strong>We identified novel neurophysiological facets of response inhibition in ADHD, enabling the classification of ADHD and neurotypical individuals. Our findings suggest that ADHD-related deficits emerge early during attentional selection and persist through response control stages. The findings underscore the need to refine conceptions about neural peculiarities in ADHD and adapt clinical interventions targeting inhibitory control deficits accordingly.</p>\",\"PeriodicalId\":93900,\"journal\":{\"name\":\"Biological psychiatry. Cognitive neuroscience and neuroimaging\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biological psychiatry. 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Novel neural activity profiles underlying inhibitory control deficits of clinical relevance in ADHD - insights from EEG tensor decomposition.
Background: Attention-Deficit-Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental disorder that impacts cognitive control processes. While neurophysiological data (e.g., EEG data) have provided valuable insights into its underlying mechanisms, fully understanding the altered cognitive functions in ADHD requires advanced analytical approaches capable of capturing the highly dimensional nature of neurophysiological data more effectively.
Methods: We examined N=59 individuals with ADHD and N=63 neurotypical participants using a standard Go/Nogo task to assess response inhibition. We used EEG tensor decomposition to extract spectral, temporal, spatial and trial-level features associated with inhibitory control deficits in ADHD. The trial-level features capture intra-individual variability which is then used in a machine learning analysis to differentiate individuals with ADHD from neurotypical participants. We also applied a feature selection algorithm to identify the most important features for distinguishing between the two groups in the classification process.
Results: We observed typical response inhibition deficits in ADHD. Contrary to common assumptions, fronto-central theta band activity did not appear to be the most distinguishing EEG feature between ADHD and neurotypical individuals. Instead, the most important distinguishing features are tensor components reflecting posterior alpha band activity during attentional selection time windows and posterior theta band activity during response selection and control time windows.
Conclusions: We identified novel neurophysiological facets of response inhibition in ADHD, enabling the classification of ADHD and neurotypical individuals. Our findings suggest that ADHD-related deficits emerge early during attentional selection and persist through response control stages. The findings underscore the need to refine conceptions about neural peculiarities in ADHD and adapt clinical interventions targeting inhibitory control deficits accordingly.