Juliana Wall, Gillian N. Miller, Joseph J. Taylor, Jacob L. Stubbs, Simon K. Warfield, Alexander L. Cohen
{"title":"ADHD局灶脑容量差异的坐标网络映射揭示了缺乏特异性的共同模式:一项系统综述","authors":"Juliana Wall, Gillian N. Miller, Joseph J. Taylor, Jacob L. Stubbs, Simon K. Warfield, Alexander L. Cohen","doi":"10.1002/cns3.20108","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>Attention-deficit/hyperactivity disorder (ADHD) has been associated with decreased regional brain volume, yet no consistent localization has emerged across studies. This discrepancy has been attributed to ADHD's diagnostic heterogeneity; however, one alternative is that ADHD is associated with alterations of brain networks, not individual regions. To test this hypothesis, we compared a traditional anatomic likelihood estimate (ALE) with a “coordinate network mapping” (CNM) approach using data from 38 studies comparing regional brain volumes in ADHD versus healthy controls.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We performed an ALE analysis, determining above-chance convergence between experiments. Next, we calculated the overlap with the putamen and default mode network, defined a priori. We then applied CNM, generating connectivity maps for each study and statistically comparing these maps to identify common areas of connectivity across studies. Finally, we compared the network map of ADHD with several control groups of neuropsychiatric disorders and with randomly generated coordinates.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>ALE identified no significant spatial convergence between experiments. We also found only limited spatial overlap with the default mode network and weak functional connectivity with the putamen. Conversely, CNM revealed that the heterogenous coordinates fell within a consistent brain network characterized by connectivity with the reward and cingulo-opercular “action mode” networks. However, we could not differentiate this network from the CNM-derived networks in control groups.</p>\n </section>\n \n <section>\n \n <h3> Interpretation</h3>\n \n <p>Although this network is biologically plausible and consistent with ADHD symptoms, the findings suggest that this network is not specific to ADHD and may reflect large-scale brain networks. Although this meta-analysis adds to the literature on the neurobiology of ADHD, the nonspecific findings convey the importance of studying ADHD at the symptom level.</p>\n </section>\n </div>","PeriodicalId":72232,"journal":{"name":"Annals of the Child Neurology Society","volume":"3 2","pages":"91-104"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cns3.20108","citationCount":"0","resultStr":"{\"title\":\"Coordinate Network Mapping of Focal Brain Volume Differences in ADHD Reveals Common Patterns That Lack Specificity: A Systematic Review\",\"authors\":\"Juliana Wall, Gillian N. Miller, Joseph J. Taylor, Jacob L. Stubbs, Simon K. Warfield, Alexander L. Cohen\",\"doi\":\"10.1002/cns3.20108\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>Attention-deficit/hyperactivity disorder (ADHD) has been associated with decreased regional brain volume, yet no consistent localization has emerged across studies. This discrepancy has been attributed to ADHD's diagnostic heterogeneity; however, one alternative is that ADHD is associated with alterations of brain networks, not individual regions. To test this hypothesis, we compared a traditional anatomic likelihood estimate (ALE) with a “coordinate network mapping” (CNM) approach using data from 38 studies comparing regional brain volumes in ADHD versus healthy controls.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We performed an ALE analysis, determining above-chance convergence between experiments. Next, we calculated the overlap with the putamen and default mode network, defined a priori. We then applied CNM, generating connectivity maps for each study and statistically comparing these maps to identify common areas of connectivity across studies. Finally, we compared the network map of ADHD with several control groups of neuropsychiatric disorders and with randomly generated coordinates.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>ALE identified no significant spatial convergence between experiments. We also found only limited spatial overlap with the default mode network and weak functional connectivity with the putamen. Conversely, CNM revealed that the heterogenous coordinates fell within a consistent brain network characterized by connectivity with the reward and cingulo-opercular “action mode” networks. However, we could not differentiate this network from the CNM-derived networks in control groups.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Interpretation</h3>\\n \\n <p>Although this network is biologically plausible and consistent with ADHD symptoms, the findings suggest that this network is not specific to ADHD and may reflect large-scale brain networks. Although this meta-analysis adds to the literature on the neurobiology of ADHD, the nonspecific findings convey the importance of studying ADHD at the symptom level.</p>\\n </section>\\n </div>\",\"PeriodicalId\":72232,\"journal\":{\"name\":\"Annals of the Child Neurology Society\",\"volume\":\"3 2\",\"pages\":\"91-104\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cns3.20108\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of the Child Neurology Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cns3.20108\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of the Child Neurology Society","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cns3.20108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Coordinate Network Mapping of Focal Brain Volume Differences in ADHD Reveals Common Patterns That Lack Specificity: A Systematic Review
Objective
Attention-deficit/hyperactivity disorder (ADHD) has been associated with decreased regional brain volume, yet no consistent localization has emerged across studies. This discrepancy has been attributed to ADHD's diagnostic heterogeneity; however, one alternative is that ADHD is associated with alterations of brain networks, not individual regions. To test this hypothesis, we compared a traditional anatomic likelihood estimate (ALE) with a “coordinate network mapping” (CNM) approach using data from 38 studies comparing regional brain volumes in ADHD versus healthy controls.
Methods
We performed an ALE analysis, determining above-chance convergence between experiments. Next, we calculated the overlap with the putamen and default mode network, defined a priori. We then applied CNM, generating connectivity maps for each study and statistically comparing these maps to identify common areas of connectivity across studies. Finally, we compared the network map of ADHD with several control groups of neuropsychiatric disorders and with randomly generated coordinates.
Results
ALE identified no significant spatial convergence between experiments. We also found only limited spatial overlap with the default mode network and weak functional connectivity with the putamen. Conversely, CNM revealed that the heterogenous coordinates fell within a consistent brain network characterized by connectivity with the reward and cingulo-opercular “action mode” networks. However, we could not differentiate this network from the CNM-derived networks in control groups.
Interpretation
Although this network is biologically plausible and consistent with ADHD symptoms, the findings suggest that this network is not specific to ADHD and may reflect large-scale brain networks. Although this meta-analysis adds to the literature on the neurobiology of ADHD, the nonspecific findings convey the importance of studying ADHD at the symptom level.