Altered Intramodular Functional Connectivity in Drug Naïve Obsessive-Compulsive Disorder Patients

Ye, Qianqian; Zhang, Zongfeng; Fan, Qing; Li, Yao

doi:10.1109/embc44109.2020.9175845

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Göttlich et al found decreased inter-modular connectivity between the limbic network and several other networks (i.e., the basal ganglia network, DMN, and frontoparietal network [FPN]) (Göttlich, Krämer, Kordon, Hohagen, & Zurowski, 2014). Increased connectivity was also found within the sensorimotor network (SMN), and decreased connectivity was found within the SN (Ye, Zhang, Fan, & Li, 2020). However, these studies investigated the altered modular structure of brain networks from only a single scale (i.e., modularity or intramodular and intermodular connectivity), which may result in information loss, and did not explore the effect of medication status in OCD patients.…”

Section: Introductionmentioning

confidence: 99%

Characterizing multiscale modular structures in medication‐free obsessive–compulsive disorder patients with no comorbidity

Jiang

et al. 2022

Human Brain Mapping

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Brain networks exhibit signatures of modular structure, which maintains a fine tradeoff between wiring cost and efficiency of information transmission. Alterations in modular structure have been found in patients with obsessive-compulsive disorder (OCD). However, previous studies were focused on a single scale (i.e., modularity or intra/intermodular connectivity) for investigation. Here, we recruited 92 OCD patients and 90 healthy controls. A comprehensive analysis was performed on modular architecture alterations in the voxelwise functional connectome at the "global" (modularity), "meso" (modular segregation and within-and between-module connections), and "local" (participation coefficients, PC) scales. We also examined the correlation between modular structure metrics and clinical symptoms. The findings revealed that (1) there was no significant group difference in global modularity;(2) both primary modules (visual network, sensorimotor network) and high-order modules (dorsal attention network, frontoparietal network) exhibited lower modular segregation in OCD patients, which was mainly driven by increased numbers of between-module connections; and (3) OCD patients showed higher PC in several connectors including the bilateral middle occipital gyri, left medial orbital frontal gyrus, left superior frontal gyrus, left posterior cingulate gyrus, right superior temporal gyrus and right middle frontal gyrus, and lower PC in the right lingual gyrus. Moreover, these alterations in modular structure were associated with clinical symptoms in patients. Our findings provide further insights into the involvement of different modules in functional network dysfunction in OCD from a connectomic perspective and suggest a synergetic mechanism of module interactions that may be related to the pathophysiology of OCD.

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Section: Introductionmentioning

confidence: 99%

Characterizing multiscale modular structures in medication‐free obsessive–compulsive disorder patients with no comorbidity

Jiang

et al. 2022

Human Brain Mapping

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“…A considerable number of resting-state functional magnetic resonance imaging (RS-fMRI) studies have reported that OCD may result from altered large-scale brain networks rather than from a single brain region [ 2 – 4 ]. Functional connectivity (FC) is an index for characterizing temporal and spatial functional communication among different brain regions, and has been widely used in OCD [ 5 – 7 ]. Previous studies used region-of-interest (ROI) seed-based FC and independent component analysis (ICA) approaches to evaluate FC changes in specific regions/networks [ 8 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

Anatomical distance affects functional connectivity at rest in medicine-free obsessive–compulsive disorder

Chen

et al. 2022

BMC Psychiatry

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Background Brain functional abnormalities at rest have been observed in obsessive–compulsive disorder (OCD). However, whether and how anatomical distance influences functional connectivity (FC) at rest is ambiguous in OCD. Methods Using resting-state functional magnetic resonance imaging data, we calculated the FC of each voxel in the whole-brain and divided FC into short- and long-range FCs in 40 medicine-free patients with OCD and 40 healthy controls (HCs). A support vector machine (SVM) was used to determine whether the altered short- and long-range FCs could be utilized to distinguish OCD from HCs. Results Patients had lower short-range positive FC (spFC) and long-range positive FC (lpFC) in the left precentral/postcentral gyrus (t = -5.57 and -5.43; P < 0.05, GRF corrected) and higher lpFC in the right thalamus/caudate, left thalamus, left inferior parietal lobule (IPL) and left cerebellum CrusI/VI (t = 4.59, 4.61, 4.41, and 5.93; P < 0.05, GRF corrected). Furthermore, lower spFC in the left precentral/postcentral gyrus might be used to distinguish OCD from HCs with an accuracy of 80.77%, a specificity of 81.58%, and a sensitivity of 80.00%. Conclusion These findings highlight that anatomical distance has an effect on the whole-brain FC patterns at rest in OCD. Meanwhile, lower spFC in the left precentral/postcentral gyrus might be applied in distinguishing OCD from HCs.

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Towards a Scalable Parallel Infomap Algorithm for Community Detection

Santos,

Lakhotia,

De Rose

2024

2024 32nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

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Altered Intramodular Functional Connectivity in Drug Naïve Obsessive-Compulsive Disorder Patients

Cited by 3 publications

References 15 publications

Characterizing multiscale modular structures in medication‐free obsessive–compulsive disorder patients with no comorbidity

Characterizing multiscale modular structures in medication‐free obsessive–compulsive disorder patients with no comorbidity

Anatomical distance affects functional connectivity at rest in medicine-free obsessive–compulsive disorder

Towards a Scalable Parallel Infomap Algorithm for Community Detection

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