Background The hippocampus and amygdala are densely interconnected structures that work together in multiple affective and cognitive processes that are important to the etiology of major depressive disorder(MDD).Each ...Background The hippocampus and amygdala are densely interconnected structures that work together in multiple affective and cognitive processes that are important to the etiology of major depressive disorder(MDD).Each of these structures consists of several heterogeneous subfields.We aim to explore the topologic properties of the volume-based intrinsic network within the hippocampus–amygdala complex in medication-nale patients with first-episode MDD.Methods High-resolution T1-weighted magnetic resonance imaging scans were acquired from 123 first-episode,medication-nale,and noncomorbid MDD patients and 81 age-,sex-,and education level-matched healthy control participants(HCs).The structural covariance network(SCN)was constructed for each group using the volumes of the hippocampal subfields and amygdala subregions;the weights of the edges were defined by the partial correlation coefficients between each pair of subfields/subregions,controlled for age,sex,education level,and intracranial volume.The global and nodal graphmetrics were calculated and compared between groups.Results Compared with HCs,the SCN within the hippocampus–amygdala complex in patients with MDD showed a shortened mean characteristic path length,reduced modularity,and reduced small-worldness index.At the nodal level,the left hippocampal tail showed increased measures of centrality,segregation,and integration,while nodes in the left amygdala showed decreased measures of centrality,segregation,and integration in patients with MDD compared with HCs.Conclusion Our results provide the first evidence of atypical topologic characteristics within the hippocampus–amygdala complex in patients with MDD using structure network analysis.It provides more delineate mechanism of those two structures that underlying neuropathologic process in MDD.展开更多
Background Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental disorder with behavioural symptoms and grey matter volume(GMV)changes.However,previous studies have not fully elucidated the prog...Background Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental disorder with behavioural symptoms and grey matter volume(GMV)changes.However,previous studies have not fully elucidated the progressive and causal GMV changes associated with behavioural symptoms in ADHD.Aims This study aimed to explore the causal relationship between GMV alterations and behavioural symptoms in children and adolescents with ADHD using behaviourcausal structural covariance network(BCaSCN)analysis.Methods Structural magnetic resonance imaging(sMRI)data from 135 children and adolescents with ADHD and182 neurotypical controls(NCs)were analysed.ADHD subtypes were identified based on GMV using a clustering algorithm to address the neuroanatomical heterogeneity.To investigate the causal relationships of GMV changes related to behavioural symptoms,sMRI data were sequentially ordered by ADHD index,inattentive index and hyperactive/impulsive index values to generate pseudotime series data.These data were then analysed using region-of-interest-based BCaSCN analysis to explore potential progressive patterns of GMV change.Results Neuroanatomical subtyping revealed two ADHD subtypes with distinct GMV patterns compared with NCs.BCaSCN analysis showed that ADHD subtype 1 was closely associated with inattentiveness,involving prominent nodes in the frontal regions and cerebellum.In contrast,ADHD subtype 2 was more strongly linked to overall disease severity,with the cerebellum and hippocampus as primary hubs.Conclusions ADHD is associated with heterogeneous changes in GMV corresponding to distinct behavioural domains,highlighting the need for subtype-specific diagnostic and therapeutic strategies.展开更多
基金This study is supported by grants from 1.3.5 Project for Disciplines of Excellence,West China Hospital,Sichuan University(ZYJC21041)The Research Project of Shanghai Science and Technology Commission(20dz2260300)The Fundamental Research Funds for the Central Universities.
文摘Background The hippocampus and amygdala are densely interconnected structures that work together in multiple affective and cognitive processes that are important to the etiology of major depressive disorder(MDD).Each of these structures consists of several heterogeneous subfields.We aim to explore the topologic properties of the volume-based intrinsic network within the hippocampus–amygdala complex in medication-nale patients with first-episode MDD.Methods High-resolution T1-weighted magnetic resonance imaging scans were acquired from 123 first-episode,medication-nale,and noncomorbid MDD patients and 81 age-,sex-,and education level-matched healthy control participants(HCs).The structural covariance network(SCN)was constructed for each group using the volumes of the hippocampal subfields and amygdala subregions;the weights of the edges were defined by the partial correlation coefficients between each pair of subfields/subregions,controlled for age,sex,education level,and intracranial volume.The global and nodal graphmetrics were calculated and compared between groups.Results Compared with HCs,the SCN within the hippocampus–amygdala complex in patients with MDD showed a shortened mean characteristic path length,reduced modularity,and reduced small-worldness index.At the nodal level,the left hippocampal tail showed increased measures of centrality,segregation,and integration,while nodes in the left amygdala showed decreased measures of centrality,segregation,and integration in patients with MDD compared with HCs.Conclusion Our results provide the first evidence of atypical topologic characteristics within the hippocampus–amygdala complex in patients with MDD using structure network analysis.It provides more delineate mechanism of those two structures that underlying neuropathologic process in MDD.
基金funded by the Department of Science and Technology of Shandong ProvinceNatural Science Foundation of Shandong Province(ZR2023QH109)Taishan Scholars Program of Shandong Province(TS201712065)。
文摘Background Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental disorder with behavioural symptoms and grey matter volume(GMV)changes.However,previous studies have not fully elucidated the progressive and causal GMV changes associated with behavioural symptoms in ADHD.Aims This study aimed to explore the causal relationship between GMV alterations and behavioural symptoms in children and adolescents with ADHD using behaviourcausal structural covariance network(BCaSCN)analysis.Methods Structural magnetic resonance imaging(sMRI)data from 135 children and adolescents with ADHD and182 neurotypical controls(NCs)were analysed.ADHD subtypes were identified based on GMV using a clustering algorithm to address the neuroanatomical heterogeneity.To investigate the causal relationships of GMV changes related to behavioural symptoms,sMRI data were sequentially ordered by ADHD index,inattentive index and hyperactive/impulsive index values to generate pseudotime series data.These data were then analysed using region-of-interest-based BCaSCN analysis to explore potential progressive patterns of GMV change.Results Neuroanatomical subtyping revealed two ADHD subtypes with distinct GMV patterns compared with NCs.BCaSCN analysis showed that ADHD subtype 1 was closely associated with inattentiveness,involving prominent nodes in the frontal regions and cerebellum.In contrast,ADHD subtype 2 was more strongly linked to overall disease severity,with the cerebellum and hippocampus as primary hubs.Conclusions ADHD is associated with heterogeneous changes in GMV corresponding to distinct behavioural domains,highlighting the need for subtype-specific diagnostic and therapeutic strategies.
