{'fraction_non_alphanumeric': 0.0346494762288477, 'fraction_numerical': 0.016116035455278, 'mean_word_length': 5.423027166882277, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Developments in Transcranial Magnetic Stimulation to Study Human Cognition

The study of human cognition through neuroscience has benefitted from a constant development and transformation of associated technology and analysis. Taking working memory (WM) as an example, and also as the area in which Mark Stokes has demonstrated this, it is a field that has often found itself right at the forefront of cognitive neuroscience, with a fascinating progression of theoretical insight and development as a consequence (see D’Esposito and Postle [2014] and Postle [2006] for thorough reviews of the field). Often, this innovation and discovery has produced theoretical upheaval, such as understanding the relationship/dependence/separation between attention and WM (Nobre & Stokes, 2011), challenging the limited capacity of WM (Bays & Husain, 2008), and the role of pFC in WM (Stokes, 2015) to name a few. (At this stage, the reader may have noted the involvement of Mark Stokes in several of these upheavals. It is not likely to be coincidence.) Throughout this journey over these last decades, TMS has been present, providing causal results to complement correlational neuroimaging, and behavioral studies. Despite its theoretical strengths as a class of research tools, the practical challenges and theoretical unknowns regarding physiological effects have meant that TMS has not always lived up to its promise as a “causal silver bullet” that can confirm correlational findings (Parkin, Ekhtiari, & Walsh, 2015; Siebner, Hartwigsen, Kassuba, & Rothwell, 2009). The future of TMS is, however, looking decidedly up (again). Advances in concurrent invasive recordings to visualize neuronal responses to TMS have provided invaluable in vivo evidence on the acute effects of stimulation (Allen, Pasley, Duong, & Freeman, 2007; Moliadze, Zhao, Eysel, & Funke, 2003). In humans, combining TMS with neuroimaging has also given the field a boost, showing effects across the brain (Bergmann et al., 2021), and on electrophysiological markers such as ERPs (Taylor, Walsh, & Eimer, 2008) and oscillations (Thut & Miniussi, 2009). Thus, as this commentary will argue, a crucial way forward for investigating cognition through neuroscience will be through innovative multimodal experimental approaches that can produce convergent evidence in one shot; examples will follow here as to how this approach can drive the field forward, with the focus being on TMS, given its superior spatial and temporal resolution compared with transcranial electrical stimulation. A game-changer for TMS has been in its simultaneous combination with fMRI and EEG. Although these innovations were demonstrated decades ago—for TMS-fMRI: Bohning et al. (1998); TMS-EEG: Ilmoniemi et al. (1997)— their progression from niche technical feats to essential contributors to cognitive neuroscience is coming to fruition. Given that one can now acquire reliable, wellsupported systems for TMS-fMRI and TMS-EEG, the next step is underway in which advanced experimental and analysis approaches will answer questions that would otherwise be difficult to address. The first growth area of cognitive TMS research is in the combination of TMS-fMRI with functional and effective connectivity analysis approaches; TMS can be applied to intervene with brain networks necessary for behavior and causally establish the necessity of associated network nodes and communication pathways. Although there exist many sophisticated methods for establishing connectivity to uncover brain networks through advanced mathematical and statistical algorithms, they all provide indirect “best current estimates” ( Johansen-Berg, 2013); what remains to be established is whether such connections exist beyond the statistical level and TMS is arguably the best placed to accomplish this. Nee and D’Esposito (2017) provided a valuable demonstration of what a TMS-fMRI-effective connectivity—here, dynamic causal modeling—approach can contribute theoretically. The use of TMS to causally test dynamic causal modelings has been suggested previously (Hartwigsen et al., 2015; Bestmann & Feredoes, 2013), and perturbation of a network with TMS has also been proposed in the context of network control theory (Medaglia, Pasqualetti, Hamilton, Thompson-Schill, & Bassett, 2017). A recent study by Sydnor et al. (2022) is a clear indication of the strengths of TMS-fMRI-perturbation, in which single pulses of TMS to ventrolateral pFC produced fMRI signal changes in the amygdala, the magnitude of which were predicted by the density of the white matter fiber pathway between the TMS target and amygdala. One could envisage a similar approach in which multiple lines of evidence including structure and function are combined to reveal patterns of connectivity for different types of behavioral tasks and/or different task stages. In the context of WM, such an approach may contribute to the ongoing debate University of Reading, United Kingdom