Evidence map›Paper›PMID 36936612›Full record

ReviewFrontiers in human neuroscience2023

Neuromodulation for treatment-resistant depression: Functional network targets contributing to antidepressive outcomes.

Shaquia L Idlett-Ali, Claudia A Salazar, Marcus S Bell, E Baron Short, Nathan C Rowland

Open access · goldFull text readReview
In one paragraph

Review in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.8field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 23 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. From Stress to Synapse: The Neuronal Atrophy Pathway to Mood Dysregulation.International journal of molecular sciences · 2025
    Review
  8. Review
  9. Article
  10. Closed-Loop Brain Stimulation.Biological psychiatry · 2024
    Review
  11. Review
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Shaquia L Idlett-AliDepartment of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Claudia A SalazarDepartment of Neurosurgery, Medical University of South Carolina, Charleston, SC, United States.
Marcus S BellDepartment of Neurosurgery, Medical University of South Carolina, Charleston, SC, United States.
E Baron ShortDepartment of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, United States.
Nathan C RowlandDepartment of Neurosurgery, Medical University of South Carolina, Charleston, SC, United States.
Medical University of South Carolina · USUniversity of Colorado Anschutz Medical Campus · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-invasive brain stimulation is designed to target accessible brain regions that underlie many psychiatric disorders. One such method, transcranial magnetic stimulation (TMS), is commonly used in patients with treatment-resistant depression (TRD). However, for non-responders, the choice of an alternative therapy is unclear and often decided empirically without detailed knowledge of precise circuit dysfunction. This is also true of invasive therapies, such as deep brain stimulation (DBS), in which responses in TRD patients are linked to circuit activity that varies in each individual. If the functional networks affected by these approaches were better understood, a theoretical basis for selection of interventions could be developed to guide psychiatric treatment pathways. The mechanistic understanding of TMS is that it promotes long-term potentiation of cortical targets, such as dorsolateral prefrontal cortex (DLPFC), which are attenuated in depression. DLPFC is highly interconnected with other networks related to mood and cognition, thus TMS likely alters activity remote from DLPFC, such as in the central executive, salience and default mode networks. When deeper structures such as subcallosal cingulate cortex (SCC) are targeted using DBS for TRD, response efficacy has depended on proximity to white matter pathways that similarly engage emotion regulation and reward. Many have begun to question whether these networks, targeted by different modalities, overlap or are, in fact, the same. A major goal of current functional and structural imaging in patients with TRD is to elucidate neuromodulatory effects on the aforementioned networks so that treatment of intractable psychiatric conditions may become more predictable and targeted using the optimal technique with fewer iterations. Here, we describe several therapeutic approaches to TRD and review clinical studies of functional imaging and tractography that identify the diverse loci of modulation. We discuss differentiating factors associated with responders and non-responders to these stimulation modalities, with a focus on mechanisms of action for non-invasive and intracranial stimulation modalities. We advance the hypothesis that non-invasive and invasive neuromodulation approaches for TRD are likely impacting shared networks and critical nodes important for alleviating symptoms associated with this disorder. We close by describing a therapeutic framework that leverages personalized connectome-guided target identification for a stepwise neuromodulation paradigm.

Indexed as

deep brain stimulationelectroconvulsive therapyepidural cortical stimulationneuromodulationsalience networktranscranial magnetic stimulation (repetitive)treatment-resistant depression

Identifiers

PMID36936612
PMCPMC10018031
OpenAlexW4322773873

What OpenQuestion holds

Textfull text, public
LicenceCC BY
reference markers read6
measurements read26
Read underepoch 390

Registered trials

None linked

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.