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The concurrent use of transcranial magnetic stimulation with electroencephalography (TMS-EEG) is growing in popularity as a method for assessing various cortical properties such as excitability, oscillations and connectivity. However, this combination of methods is technically challenging, resulting in artifacts both during recording and following typical EEG analysis methods, which can distort the underlying neural signal. In this article, we review the causes of artifacts in EEG recordings resulting from TMS, as well as artifacts introduced during analysis (e.g. as the result of filtering over high-frequency, large amplitude artifacts). We then discuss methods for removing artifacts, and ways of designing pipelines to minimise analysis-related artifacts. Finally, we introduce the TMS-EEG signal analyser (TESA), an open-source extension for EEGLAB, which includes functions that are specific for TMS-EEG analysis, such as removing and interpolating the TMS pulse artifact, removing and minimising TMS-evoked muscle activity, and analysing TMS-evoked potentials. The aims of TESA are to provide users with easy access to current TMS-EEG analysis methods and to encourage direct comparisons of these methods and pipelines. It is hoped that providing open-source functions will aid in both improving and standardising analysis across the field of TMS-EEG research.

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This study examined changes in corticomotor excitability and plasticity after a thumb abduction training task in young and old adults. Electromyographic (EMG) recordings were obtained from right abductor pollicis brevis (APB, target muscle) and abductor digiti minimi (ADM, control muscle) in 14 young (18-24 yr) and 14 old (61-82 yr) adults. The training task consisted of 300 ballistic abductions of the right thumb to maximize peak thumb abduction acceleration (TAAcc). Transcranial magnetic stimulation (TMS) of the left primary motor cortex was used to assess changes in APB and ADM motor evoked potentials (MEPs) and short-interval intracortical inhibition (SICI) before, immediately after, and 30 min after training. No differences in corticomotor excitability (resting and active TMS thresholds, MEP input-output curves) or SICI were observed in young and old adults before training. Motor training resulted in improvements in peak TAAcc in young (177% improvement, P < 0.001) and old (124%, P = 0.005) subjects, with greater improvements in young subjects (P = 0.002). Different thumb kinematics were observed during task performance, with increases in APB EMG related to improvements in peak TAAcc in young (r(2) = 0.46, P = 0.008) but not old (r(2) = 0.09, P = 0.3) adults. After training, APB MEPs were 50% larger (P < 0.001 compared with before) in young subjects, with no change after training in old subjects (P = 0.49), suggesting reduced use-dependent corticomotor plasticity with advancing age. These changes were specific to APB, because no training-related change in MEP amplitude was observed in ADM. No significant association was observed between change in APB MEP and improvement in TAAcc with training in individual young and old subjects. SICI remained unchanged after training in both groups, suggesting that it was not responsible for the diminished use-dependent corticomotor plasticity for this task in older adults.

The past decade has seen significant developments in the concurrent use of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to directly assess cortical network properties such as excitability and connectivity in humans. New hardware solutions, improved EEG amplifier technology, and advanced data processing techniques have allowed substantial reduction of the TMS-induced artifact, which had previously rendered concurrent TMS-EEG impossible. Various physiological artifacts resulting from TMS have also been identified, and methods are being developed to either minimize or remove these sources of artifact. With these developments, TMS-EEG has unlocked regions of the cortex to researchers that were previously inaccessible to TMS. By recording the TMS-evoked response directly from the cortex, TMS-EEG provides information on the excitability, effective connectivity, and oscillatory tuning of a given cortical area, removing the need to infer such measurements from indirect measures. In the following review, we investigate the different online and offline methods for reducing artifacts in TMS-EEG recordings and the physiological information contained within the TMS-evoked cortical response. We then address the use of TMS-EEG to assess different cortical mechanisms such as cortical inhibition and neural plasticity, before briefly reviewing studies that have utilized TMS-EEG to explore cortical network properties at rest and during different functional brain states.

Noninvasive brain stimulation is increasingly being investigated for the enhancement of cognition, yet current approaches appear to be limited in their degree and duration of effects. The majority of studies to date have delivered stimulation in "standard" ways (i.e., anodal transcranial direct current stimulation or high-frequency transcranial magnetic stimulation). Specialized forms of stimulation, such as theta burst stimulation (TBS), which more closely mimic the brains natural firing patterns may have greater effects on cognitive performance. We report here the findings from the first-ever investigation into the persistent cognitive and electrophysiological effects of intermittent TBS (iTBS) delivered to the left dorsolateral prefrontal cortex. In 19 healthy controls, active iTBS significantly improved performance on an assessment of working memory when compared with sham stimulation across a period of 40 min post stimulation. The behavioral findings were accompanied by increases in task-related fronto-parietal theta sychronization and parietal gamma band power. These results have implications for the role of more specialized stimulation approaches in neuromodulation.

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