Transcranial magnetic stimulation (TMS) for geriatric depression

Abstract

Background: The prevalence of treatment-resistant geriatric depression (GD) highlights the need for treatments that preserve cognitive functions and recognize polypharmacy in elderly, yet effectively reduce symptom burden. Transcranial magnetic stimulation (TMS) is a proven intervention for treatment-resistant depression in younger adults but the efficacy of TMS to treat depressed older adults is still unclear. This review provides an updated view on the efficacy of TMS treatment for GD, discusses methodological differences between trials in TMS application, and explores avenues for optimization of TMS treatment in the context of the ageing brain.

Methods: A systematic review was conducted to identify published literature on the antidepressant efficacy of TMS for GD. Databases PubMed, Embase, and PsycINFO were searched for English language articles in peer-reviewed journals in March 2021.

Results: Seven randomized controlled trials (RCTs) (total n = 260, active n = 148, control n = 112) and seven uncontrolled trials (total n = 160) were included. Overall, we found substantial variability in the clinical response, ranging from 6.7% to 54.3%.

Conclusions: The reviewed literature highlights large heterogeneity among studies both in terms of the employed TMS dosage and the observed clinical efficacy. This highlights the need for optimizing TMS dosage by recognizing the unique clinical features of GD. We showcase a set of novel approaches for the optimization of the TMS protocol for depression and discuss the possibility for a standardized TMS protocol tailored for the treatment of GD.

Keywords: Geriatric depression (GD); Non-invasive brain stimulation; Nonpharmacological intervention; Polypharmacy; Transcranial magnetic stimulation (TMS).

Figure. 1:. Transcranial Magnetic Stimulation (TMS) and Brain Networks in Geriatric Depression.

An illustration of a TMS coil inducing a magnetic field to target the L-DLPFC as guided by a neuronavigation device, depicted on the left. Brain regions associated with depressive symptomatology are highlighted (red indicates hyperactivation and blue indicates hypoactivation). Brain networks involved in GD are described in the table alongside their psychological phenotype as well as whether they are hyper- or hypo-activated.

Fig. 2.

Parameters space for randmoized controlled trials (RCTs) TMS studies for geriatric depression. On the left the stimulation intensity and the total number of sessions are plotted for each RCT alongside the FDA approved protocol. On the right the total pulse counts for each RCT and the FDA protocol are plotted. It is striking that the vast majority of RCTs used parameters that effectively under dosed rTMS relative to the corresponding FDA-cleared protocol. Most protocols (including the FDA’s) incorporated conventional rTMS and a figure-8 coil, except for Leblhuber et al. (2019) and Kaster et al. (2018), signified by the asterisks. Leblhuber et al. (2019) varied stimulation intensity per participant and is thus not included in the parameter space graph on the left. Trevizol et al. (2019) employed two different TMS protocols, one with a total pulse count of 18,225, and another with a total pulse count of 31,500 (represented on the right with red and yellow, respectively).

Figure 3:

Remission, response, and non-response following TMS treatment in geriatric depression (GD) for randomized controlled trials (RCTs) and uncontrolled trials.

Fig. 4.

Recent studies advancing the optimization of TMS protocols for depression. (A) Anticorrelation between DLPFC and Subgenual Anterior Cingulate Cortex (SGC). i. Map of cortical functional connectivity with the SGC (Fz(r)), masked to highlight the DLPFC. ii. The anticorrelation of the TMS stimulation site with the SGC predicted the degree of treatment efficacy. iii. Example functional anti-correlation between TMS target in the DLPFC and the SGC (Weigand et al., 2018). (B) Personalized TMS targeting based on the individual functional map of anticorrelation between the DLPFC and the SGC. i. Map of averaged functional connectivity with the SGC based on averaged group data, with optimal TMS stimulation site. ii. Map of functional connectivity with the SGC for a single subject, with optimal TMS stimulation site. iii. The TMS target based on data from the single subject is more strongly anti-correlated with the SGC than the target generated from the group data (Fox et al., 2013). (C) TMS targeting based on subset of depressive symptomology. Two optimal TMS targeting maps were identified for dysphoric symptoms (i.) and anxiosomatic symptoms (ii.). (Siddiqi et al., 2020a). (D) Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT). i. iTBS allows for shorter stimulation durations (Cole et al., 2020). ii. iTBS is non-inferior to FDA-approved protocol (Blumberger et al., 2018). iii. SAINT administered a significantly shorter treatment duration and yet a similar total pulse count (total of 5 days, 10 session per day, a 50-min intersession interval (ISI), 1800 pulses per session) to the FDA-approved treatment course. iv. Significant decreases in HAM-D 6-item score were observed for 21 participants (Blue line indicates remission (HAM-D score ≤ 5)) (Cole et al., 2020). v. Functional anti-correlation between the DLPFC and SGC increased for participants after iTBS. (E) Using heart rate to determine the optimal TMS stimulation site. i. Iseger et al. (2020) propose a crucial depression network between the DLPFC, SGC, and the Vagus Nerve. They argue that, since the DLPFC is functionally connected to the Vagus Nerve and the heart through the SGC, the degree to which rTMS modulates heart rate should predict how successfully the DLPFC stimulation is affecting the network. ii. They found that stimulating the left frontal area at given L-DLPFC locations successfully decreases heart rate after rTMS trains (Iseger et al., 2017).