Comparison of resting-state functional and effective connectivity between default mode network and memory encoding related areas

Saetia, Supat; Rosas, Fernando; Ogata, Yuji; Yoshimura, Natsue; Koike, Yasuharu

doi:10.29328/journal.jnnd.1001031

Cited by 1 publication

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“…Several researchers who used and compared both analyses approaches confirmed this study’s assumption. Among them, Saetia et al (2020) studied the interpretability of the effective connectivity model compared with the functional connectivity model. Park et al (2018) also evaluated the ensuing dynamic effective connectivity in terms of the consistency of baseline connectivity within DMN using the rs-fMRI.…”

Section: Discussionmentioning

confidence: 99%

Functional and effective connectivity analysis of drug-resistant epilepsy: a resting-state fMRI analysis

Bacon

Jin

et al. 2023

Front. Neurosci.

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ObjectiveEpilepsy is considered as a neural network disorder. Seizure activity in epilepsy may disturb brain networks and damage brain functions. We propose using resting-state functional magnetic resonance imaging (rs-fMRI) data to characterize connectivity patterns in drug-resistant epilepsy.MethodsThis study enrolled 47 participants, including 28 with drug-resistant epilepsy and 19 healthy controls. Functional and effective connectivity was employed to assess drug-resistant epilepsy patients within resting state networks. The resting state functional connectivity (FC) analysis was performed to assess connectivity between each patient and healthy controls within the default mode network (DMN) and the dorsal attention network (DAN). In addition, dynamic causal modeling was used to compute effective connectivity (EC). Finally, a statistical analysis was performed to evaluate our findings.ResultsThe FC analysis revealed significant connectivity changes in patients giving 64.3% (18/28) and 78.6% (22/28) for DMN and DAN, respectively. Statistical analysis of FC was significant between the medial prefrontal cortex, posterior cingulate cortex, and bilateral inferior parietal cortex for DMN. For DAN, it was significant between the left and the right intraparietal sulcus and the frontal eye field. For the DMN, the patient group showed significant EC connectivity in the right inferior parietal cortex and the medial prefrontal cortex for the DMN. There was also bilateral connectivity between the medial prefrontal cortex and the posterior cingulate cortex, as well as between the left and right inferior parietal cortex. For DAN, patients showed significant connectivity in the right frontal eye field and the right intraparietal sulcus. Bilateral connectivity was also found between the left frontal eye field and the left intraparietal sulcus, as well as between the right frontal eye field and the right intraparietal sulcus. The statistical analysis of the EC revealed a significant result in the medial prefrontal cortex and the right intraparietal cortex for the DMN. The DAN was found significant in the left frontal eye field, as well as the left and right intraparietal sulcus.ConclusionOur results provide preliminary evidence to support that the combination of functional and effective connectivity analysis of rs-fMRI can aid in diagnosing epilepsy in the DMN and DAN networks.

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Section: Discussionmentioning

confidence: 99%

Functional and effective connectivity analysis of drug-resistant epilepsy: a resting-state fMRI analysis

Bacon

Jin

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

Comparison of resting-state functional and effective connectivity between default mode network and memory encoding related areas

Abstract: How to cite this article: Saetia S, Rosas F, Ogata Y, Yoshimura N, Koike Y. Comparison of resting-state functional and eff ective connectivity between default mode network and memory encoding related areas.

Cited by 1 publication

References 42 publications

Functional and effective connectivity analysis of drug-resistant epilepsy: a resting-state fMRI analysis

Functional and effective connectivity analysis of drug-resistant epilepsy: a resting-state fMRI analysis

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