Functional connectivity analysis of the depression connectome provides potential markers and targets for transcranial magnetic stimulation

Hugh Taylor, Peter Nicholas, Kate Hoy, Neil Bailey, Onur Tanglay, Isabella M. Young, Lewis Dobbin, Stephane Doyen, Michael E. Sughrue, Paul B. Fitzgerald

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

Background: Despite efforts to improve targeting accuracy of the dorsolateral prefrontal cortex (DLPFC) as a repetitive transcranial magnetic stimulation (rTMS) target for Major Depressive Disorder (MDD), the heterogeneity in clinical response remains unexplained. Objective: We sought to compare the patterns of functional connectivity from the DLPFC treatment site in patients with MDD who were TMS responders to those who were TMS non-responders. Methods: Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 37 participants before they underwent a course of rTMS to left Brodmann area 46. A novel machine learning method was utilized to identify brain regions associated with each item of the Beck's Depression Inventory II (BDI-II), and for 26 participants who underwent rTMS treatment over the left Brodmann area 46, identify regions differentiating rTMS responders and non-responders. Results: Nine parcels of the Human Connectome Project Multimodal Parcellation Atlas matched to at least three items of the Beck's Depression Inventory II (BDI-II) as predictors of response to rTMS, with many in the temporal, parietal and cingulate cortices. Additionally, pre-treatment mapping for 17 items of the BDI-II demonstrated significant variability in symptom to parcel mapping. When parcels associated with symptom presence and symptom resolution were compared, 15 parcels were uniquely associated with resolution (potential targets), and 12 parcels were associated with both symptom presence and resolution (blockers or biomarkers). Conclusions: Machine learning approaches show promise for the development of pathoanatomical diagnosis and treatment algorithms for MDD. Prospective studies are required to facilitate clinical translation.

Original languageEnglish
Pages (from-to)539-547
Number of pages9
JournalJournal of Affective Disorders
Volume329
DOIs
Publication statusPublished - 15 May 2023

Keywords

  • Connectivity
  • Depression
  • Machine learning
  • Treatment response

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