ArticleJournal of clinical medicine2023
Anatomical and Functional Connectivity of Critical Deep Brain Structures and Their Potential Clinical Application in Brain Stimulation.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Meta-analytic connectivity perturbation analysis (MACPA): a new method for enhanced precision in fMRI connectivity analysis.Brain structure & function · 2024Pooled it
- Brain stimulation targets for chronic pain: Insights from meta-analysis, functional connectivity and literature review.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2024Pooled it
- Lifetime Cannabis Use Is Associated With Brain Volume and Cognitive Function in Middle-Aged and Older Adults.Journal of studies on alcohol and drugs · 2026Article
- A comparative study assessing neural recording quality and inflammatory tissue response between stiff and flexible microelectrode arrays.Biomaterials · 2026Article
- Methodological Evolution of Diffusion MRI in Chronic Pain: A Narrative Review.Journal of pain research · 2026Review
- Potential scalp acupuncture and brain stimulation targets for common neurological disorders: evidence from neuroimaging studies.Chinese medicine · 2025Article
- Intravascular delivery of an ultraflexible neural electrode array for recordings of cortical spiking activity.Nature communications · 2024Article
- Investigation of a chondroitin sulfate-based bioactive coating for neural interface applications.Journal of materials chemistry. B · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Subcortical structures, such as the hippocampus, amygdala, and nucleus accumbens (NAcc), play crucial roles in human cognitive, memory, and emotional processing, chronic pain pathophysiology, and are implicated in various psychiatric and neurological diseases. Interventions modulating the activities of these deep brain structures hold promise for improving clinical outcomes. Recently, non-invasive brain stimulation (NIBS) has been applied to modulate brain activity and has demonstrated its potential for treating psychiatric and neurological disorders. However, modulating the above deep brain structures using NIBS may be challenging due to the nature of these stimulations. This study attempts to identify brain surface regions as source targets for NIBS to reach these deep brain structures by integrating functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). We used resting-state functional connectivity (rsFC) and probabilistic tractography (PTG) analysis to identify brain surface stimulation targets that are functionally and structurally connected to the hippocampus, amygdala, and NAcc in 119 healthy participants. Our results showed that the medial prefrontal cortex (mPFC) is functionally and anatomically connected to all three subcortical regions, while the precuneus is connected to the hippocampus and amygdala. The mPFC and precuneus, two key hubs of the default mode network (DMN), as well as other cortical areas distributed at the prefrontal cortex and the parietal, temporal, and occipital lobes, were identified as potential locations for NIBS to modulate the function of these deep structures. The findings may provide new insights into the NIBS target selections for treating psychiatric and neurological disorders and chronic pain.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.