Non-invasive Brain Stimulation for Cognition and Aging

In modern psychiatry, dysfunctional brain networks are considered the neural substrates of mental disorders. These networks can be precisely targeted with TMS, a non-invasive, FDA-cleared treatment with minimal side effects. In one meta-analysis (Pabst et al., 2022) and one review paper (Gogulski et al., 2022), we assess the state of the field with regard to using repetitive TMS protocols to modulate brain networks and cognition.

Another application of TMS is to probe the brain to identify dysfunctional brain networks, for example in the case of post-surgery delirium. This work is based on a conceptual model of delirium as the result of a stressor in an individual with pre-existing deficits in connectivity and/or atypical mechanisms of plasticity. The identification of pre-operative predictors of post-operative delirium can help stratify individual risk and identify novel therapeutic targets for interventions to prevent delirium or mitigate its impact in predisposed individuals. We demonstrates the feasibility of gathering EEG and TMS in individuals scheduled to undergo elective surgery to identify neurophysiologic signatures of vulnerability to post-operative delirium and present preliminary experimental evidence that EEG and TMS-EEG of cerebral oscillatory activity and cortical plasticity, which are non-invasive and scalable neurophysiological measures, identify individuals at risk of post-operative delirium (Ross et al., 2022, JAGS).

Figure 1. Non-invasive and scalable neurophysiologic measures can identify individuals at risk of post-operative delirium.

Delirium is a common complication after surgery, particularly in older adults where it affects up to one in three individuals and has substantial morbidity including lengthier hospitalizations, deleterious effects on long-term wellbeing and cognitive health, loss of functional independence, and greater mortality. Some risk factors for delirium have been identified, but the underlying cerebral pathophysiology that leads to delirium is unknown. In 2017 Shafi et al. we proposed a framework for the identification of novel quantitative diagnostic and prognostic neurophysiologic markers of post-operative delirium, motivated by the hypothesis that an individual’s risk of delirium after elective surgery is mediated through pathophysiology in individual brain reactivity, connectivity and plasticity. We further hypothesized these features could be systematically characterized using electroencephalography (EEG) and transcranial magnetic stimulation (TMS). Here we present data collected from 23 patients prior to elective surgery demonstrating the feasibility of this approach and we offer preliminary experimental support for the proposed framework. Resting-state EEG spectral power ratio (SPR) served as a measure of integrity of neural circuits. TMS-EEG metrics of plasticity (TMS-plasticity) were used as indicators of brain capacity to respond to stressors. Specifically, abnormalities in resting-state EEG spectral power or TMS-plasticity may indicate sub-clinical risk for post-surgery delirium. Extension and confirmation of these findings in a larger sample is needed to assess the clinical utility of the proposed neurophysiologic markers, and to identify specific connectivity and plasticity targets for therapeutic interventions that might minimize the risk of delirium. Figure from Ross et al., 2022, JAGS.

Figure 2. Meta-analysis shows that standardization of iTBS is urgent and necessary.

Standardization of iTBS is urgent and necessary to determine if neuroenhancement of particular cognitive faculties are reliable and robust, and measurable through observable behavior. Intermittent theta-burst stimulation (iTBS) has been used to focally regulate excitability of neural cortex over the past decade – however there is little consensus on the generalizability of effects reported in individual studies. Many studies use small sample sizes (N < 30), and there is a considerable amount of methodological heterogeneity in application of the stimulation itself. This systematic meta-analysis aims to consolidate the extant literature and determine if up-regulatory theta-burst stimulation reliably enhances cognition through measurable behavior. Results show that iTBS – when compared to suitable control conditions — may enhance cognition when outlier studies are removed, but also that there is a significant amount of heterogeneity across studies. Significant contributors to between-study heterogeneity include location of stimulation and method of navigation to the stimulation site. Surprisingly, the type of cognitive domain investigated was not a significant contributor of heterogeneity. Figure from Pabst et al., 2022, Neurosci. Biobehav. Rev.

Figure 3. The precision medicine approach can be applied to repetitive transcranial magnetic stimulation (rTMS) and a better understanding of the wide and modifiable parameter space of rTMS will greatly improve clinical outcome.

rTMS is well tolerated and clinically effective for treatment-resistant depression (TRD) and other neuropsychiatric disorders. However, despite its wide stimulation parameter space (location, angle, pattern, frequency, and intensity can be adjusted), rTMS is currently applied in a one-size-fits-all manner, potentially contributing to its suboptimal clinical response (~50%). In this review, we examine components of rTMS that can be optimized to account for inter-individual variability in neural function and anatomy. We discuss current treatment options for TRD, the neural mechanisms thought to underlie treatment, differences in FDA-cleared devices, targeting strategies, stimulation parameter selection, and adaptive closed-loop rTMS to improve treatment outcomes. (A) Closed-loop TMS involves real-time biomarker monitoring and feedback to adapt TMS parameters in order to maximize the desired biomarker change. Bayesian optimization could be used to discover the optimal TMS parameter combination with a limited number of trials. (B) Within-session and between-session parameters can potentially be personalized and adjusted in real-time using adaptive TMS, whereas hardware parameters are more difficult to modify in real time.