The brain is organized into networks that reorganize dynamically in response to cognitive demands and exogenous stimuli. In recent years, repetitive transcranial magnetic stimulation (rTMS) has gained increasing use as a noninvasive means to modulate cortical physiology, with effects both proximal to the stimulation site and in distal areas that are intrinsically connected to the proximal target. In light of these network-level neuromodulatory effects, there has been a rapid growth in studies attempting to leverage information about network connectivity to improve neuromodulatory control and intervention outcomes. However, the mechanisms-of-action of rTMS on network-level effects remain poorly understood and is based primarily on heuristics from proximal stimulation findings. To help bridge this gap, the current paper presents a systematic review of 33 rTMS studies with baseline and post-rTMS measures of fMRI resting-state functional connectivity (RSFC). Literature synthesis revealed variability across studies in stimulation parameters, studied populations, and connectivity analysis methodology. Despite this variability, it is observed that active rTMS induces significant changes on RSFC, but the prevalent low-frequency-inhibition/high-frequency-facilitation heuristic endorsed for proximal rTMS effects does not fully describe distal connectivity findings. This review also points towards other important considerations, including that the majority of rTMS-induced changes were found outside the stimulated functional network, suggesting that rTMS effects tend to spread across networks. Future studies may therefore wish to adopt conventions and systematic frameworks, such as the Yeo functional connectivity parcellation atlas adopted here, to better characterize network-level effect that contribute to the efficacy of these rapidly developing noninvasive interventions.

Working memory is the ability to perform mental operations on information that is stored in a flexible, limited capacity buffer. The ability to manipulate information in working memory is central to many aspects of human cognition, but also declines with healthy aging. Given the profound importance of such working memory manipulation abilities, there is a concerted effort towards developing approaches to improve them. The current study tested the capacity to enhance working memory manipulation with online repetitive transcranial magnetic stimulation in healthy young and older adults. Online high frequency (5Hz) repetitive transcranial magnetic stimulation was applied over the left dorsolateral prefrontal cortex to test the hypothesis that active repetitive transcranial magnetic stimulation would lead to significant improvements in memory recall accuracy compared to sham stimulation, and that these effects would be most pronounced in working memory manipulation conditions with the highest cognitive demand in both young and older adults. Repetitive transcranial magnetic stimulation was applied while participants were performing a delayed response alphabetization task with three individually-titrated levels of difficulty. The left dorsolateral prefrontal cortex was identified by combining electric field modeling to individualized functional magnetic resonance imaging activation maps and was targeted during the experiment using stereotactic neuronavigation with real-time robotic guidance, allowing optimal coil placement during the stimulation. As no accuracy differences were found between young and older adults, the results from both groups were collapsed. Subsequent analyses revealed that active stimulation significantly increased accuracy relative to sham stimulation, but only for the hardest condition. These results point towards further investigation of repetitive transcranial magnetic stimulation for memory enhancement focusing on high difficulty conditions as those most likely to exhibit benefits.

Objective. Transcranial magnetic stimulation (TMS) can modulate brain function via an electric field (E-field) induced in a brain region of interest (ROI). The ROI E-field can be computationally maximized and set to match a specific reference using individualized head models to find the optimal coil placement and stimulus intensity. However, the available software lacks many practical features for prospective planning of TMS interventions and retrospective evaluation of the experimental targeting accuracy. Approach. The TMS targeting and analysis pipeline (TAP) software uses an MRI/fMRI-derived brain target to optimize coil placement considering experimental parameters such as the subject’s hair thickness and coil placement restrictions. The coil placement optimization is implemented in SimNIBS 3.2, for which an additional graphical user interface (TargetingNavigator) is provided to visualize/adjust procedural parameters. The coil optimization process also computes the E-field at the target, allowing the selection of the TMS device intensity setting to achieve specific E-field strengths. The optimized coil placement information is prepared for neuronavigation software, which supports targeting during the TMS procedure. The neuronavigation system can record the coil placement during the experiment, and these data can be processed in TAP to quantify the accuracy of the experimental TMS coil placement and induced E-field. Main results. TAP was demonstrated in a study consisting of three repetitive TMS sessions in five subjects. TMS was delivered by an experienced operator under neuronavigation with the computationally optimized coil placement. Analysis of the experimental accuracy from the recorded neuronavigation data indicated coil location and orientation deviations up to about 2 mm and 2°, respectively, resulting in an 8% median decrease in the target E-field magnitude compared to the optimal placement. Significance. TAP supports navigated TMS with a variety of features for rigorous and reproducible stimulation delivery, including planning and evaluation of coil placement and intensity selection for E-field-based dosing.

The process of manipulating information within working memory is central to many cognitive functions, but also declines rapidly in old age. Improving this process could markedly enhance the health-span in older adults. The current pre-registered, randomized and placebo-controlled study tested the potential of online repetitive transcranial magnetic stimulation (rTMS) applied at 5 Hz over the left lateral parietal cortex to enhance working memory manipulation in healthy elderly adults. rTMS was applied, while participants performed a delayed-response alphabetization task with two individually titrated levels of difficulty. Coil placement and stimulation amplitude were calculated from fMRI activation maps combined with electric field modeling on an individual-subject basis in order to standardize dosing at the targeted cortical location. Contrary to the a priori hypothesis, active rTMS significantly decreased accuracy relative to sham, and only in the hardest difficulty level. When compared to the results from our previous study, in which rTMS was applied over the left prefrontal cortex, we found equivalent effect sizes but opposite directionality suggesting a site-specific effect of rTMS. These results demonstrate engagement of cortical working memory processing using a novel TMS targeting approach, while also providing prescriptions for future studies seeking to enhance memory through rTMS.

The brain is an inherently dynamic system, and much work has focused on the ability to modify neural activity through both local perturbations and changes in the function of global network ensembles. Network controllability is a recent concept in network neuroscience that purports to predict the influence of individual cortical sites on global network states and state changes, thereby creating a unifying account of local influences on global brain dynamics. While this notion is accepted in engineering science, it is subject to ongoing debates in neuroscience as empirical evidence linking network controllability to brain activity and human behavior remains scarce. Here, we present an integrated set of multimodal brain–behavior relationships derived from fMRI, diffusion tensor imaging, and online repetitive transcranial magnetic stimulation (rTMS) applied during an individually calibrated working memory task performed by individuals of both sexes. The modes describing the structural network system dynamics showed direct relationships to brain activity associated with task difficulty, with difficult-to-reach modes contributing to functional brain states in the hard task condition. Modal controllability (a measure quantifying the contribution of difficult-to-reach modes) at the stimulated site predicted both fMRI activations associated with increasing task difficulty and rTMS benefits on task performance. Furthermore, fMRI explained 64% of the variance between modal controllability and the working memory benefit associated with 5 Hz online rTMS. These results therefore provide evidence toward the functional validity of network control theory, and outline a clear technique for integrating structural network topology and functional activity to predict the influence of stimulation on subsequent behavior. SIGNIFICANCE STATEMENT The network controllability concept proposes that specific cortical nodes are able to steer the brain into certain physiological states. By applying external perturbation to these control nodes, it is theorized that brain stimulation is able to selectively target difficult-to-reach states, potentially aiding processing and improving performance on cognitive tasks. The current study used rTMS and fMRI during a working memory task to test this hypothesis. We demonstrate that network controllability correlates with fMRI modulation because of working memory load and with the behavioral improvements that result from a multivisit intervention using 5 Hz rTMS. This study demonstrates the validity of network controllability and offers a new targeting approach to improve efficacy.