{"title":"通过脑电图复杂性识别注意力缺陷/多动症","authors":"","doi":"10.1016/j.physa.2024.130093","DOIUrl":null,"url":null,"abstract":"<div><p>There are reasons to suggest that a number of mental disorders may be related to alteration in the neural complexity (NC). Thus, quantitative analysis of NC could be helpful in classifying mental and understanding conditions. Here, focusing on a methodological procedure, we have worked with young individuals, typical and with attention-deficit/hyperactivity disorder (ADHD) whose NC was assessed using q-statistics applied to the electroencephalogram (EEG). The EEG was recorded while subjects performed the visual Attention Network Test (ANT) and during a short pretask period of resting state. Time intervals of the EEG amplitudes that passed a threshold were collected from task and pretask signals from each subject. The data were satisfactorily fitted with a stretched <span><math><mi>q</mi></math></span>-exponential including a power-law prefactor(characterized by the exponent c), thus determining the best <span><math><mrow><mo>(</mo><mi>c</mi><mo>,</mo><mi>q</mi><mo>)</mo></mrow></math></span> for each subject, indicative of their individual complexity. We found larger values of <span><math><mi>q</mi></math></span> and <span><math><mi>c</mi></math></span> in ADHD subjects as compared with the typical subjects both at task and pretask periods, the task values for both groups being larger than at rest. The <span><math><mi>c</mi></math></span> parameter was highly specific in relation to DSM diagnosis for inattention, where well-defined clusters were observed. The parameter values were organized in four well-defined clusters in <span><math><mrow><mo>(</mo><mi>c</mi><mo>,</mo><mi>q</mi><mo>)</mo></mrow></math></span>-space. As expected, the tasks apparently induced greater complexity in neural functional states with likely greater amount of internal information processing. The results suggest that complexity is higher in ADHD subjects than in typical pairs. The distribution of values in the <span><math><mrow><mo>(</mo><mi>c</mi><mo>,</mo><mi>q</mi><mo>)</mo></mrow></math></span>-space derived from <span><math><mi>q</mi></math></span>-statistics seems to be a promising biomarker for ADHD diagnosis.</p></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identifying attention-deficit/hyperactivity disorder through the electroencephalogram complexity\",\"authors\":\"\",\"doi\":\"10.1016/j.physa.2024.130093\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>There are reasons to suggest that a number of mental disorders may be related to alteration in the neural complexity (NC). Thus, quantitative analysis of NC could be helpful in classifying mental and understanding conditions. Here, focusing on a methodological procedure, we have worked with young individuals, typical and with attention-deficit/hyperactivity disorder (ADHD) whose NC was assessed using q-statistics applied to the electroencephalogram (EEG). The EEG was recorded while subjects performed the visual Attention Network Test (ANT) and during a short pretask period of resting state. Time intervals of the EEG amplitudes that passed a threshold were collected from task and pretask signals from each subject. The data were satisfactorily fitted with a stretched <span><math><mi>q</mi></math></span>-exponential including a power-law prefactor(characterized by the exponent c), thus determining the best <span><math><mrow><mo>(</mo><mi>c</mi><mo>,</mo><mi>q</mi><mo>)</mo></mrow></math></span> for each subject, indicative of their individual complexity. We found larger values of <span><math><mi>q</mi></math></span> and <span><math><mi>c</mi></math></span> in ADHD subjects as compared with the typical subjects both at task and pretask periods, the task values for both groups being larger than at rest. The <span><math><mi>c</mi></math></span> parameter was highly specific in relation to DSM diagnosis for inattention, where well-defined clusters were observed. The parameter values were organized in four well-defined clusters in <span><math><mrow><mo>(</mo><mi>c</mi><mo>,</mo><mi>q</mi><mo>)</mo></mrow></math></span>-space. As expected, the tasks apparently induced greater complexity in neural functional states with likely greater amount of internal information processing. The results suggest that complexity is higher in ADHD subjects than in typical pairs. The distribution of values in the <span><math><mrow><mo>(</mo><mi>c</mi><mo>,</mo><mi>q</mi><mo>)</mo></mrow></math></span>-space derived from <span><math><mi>q</mi></math></span>-statistics seems to be a promising biomarker for ADHD diagnosis.</p></div>\",\"PeriodicalId\":20152,\"journal\":{\"name\":\"Physica A: Statistical Mechanics and its Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physica A: Statistical Mechanics and its Applications\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378437124006022\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica A: Statistical Mechanics and its Applications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378437124006022","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
Identifying attention-deficit/hyperactivity disorder through the electroencephalogram complexity
There are reasons to suggest that a number of mental disorders may be related to alteration in the neural complexity (NC). Thus, quantitative analysis of NC could be helpful in classifying mental and understanding conditions. Here, focusing on a methodological procedure, we have worked with young individuals, typical and with attention-deficit/hyperactivity disorder (ADHD) whose NC was assessed using q-statistics applied to the electroencephalogram (EEG). The EEG was recorded while subjects performed the visual Attention Network Test (ANT) and during a short pretask period of resting state. Time intervals of the EEG amplitudes that passed a threshold were collected from task and pretask signals from each subject. The data were satisfactorily fitted with a stretched -exponential including a power-law prefactor(characterized by the exponent c), thus determining the best for each subject, indicative of their individual complexity. We found larger values of and in ADHD subjects as compared with the typical subjects both at task and pretask periods, the task values for both groups being larger than at rest. The parameter was highly specific in relation to DSM diagnosis for inattention, where well-defined clusters were observed. The parameter values were organized in four well-defined clusters in -space. As expected, the tasks apparently induced greater complexity in neural functional states with likely greater amount of internal information processing. The results suggest that complexity is higher in ADHD subjects than in typical pairs. The distribution of values in the -space derived from -statistics seems to be a promising biomarker for ADHD diagnosis.
期刊介绍:
Physica A: Statistical Mechanics and its Applications
Recognized by the European Physical Society
Physica A publishes research in the field of statistical mechanics and its applications.
Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents.
Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.