Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs), recorded using electroencephalography (EEG), reflect a combination of TMS-induced cortical activity and multi-sensory responses to TMS. The auditory evoked potential (AEP) is a high-amplitude sensory potential—evoked by the “click” sound produced by every TMS pulse—that can dominate the TEP and obscure observation of other neural components. The AEP is peripherally evoked and therefore should not be stimulation site specific. We address the problem of disentangling the peripherally evoked AEP of the TEP from components evoked by cortical stimulation and ask whether removal of AEP enables more accurate isolation of TEP. We hypothesized that isolation of the AEP using Independent Components Analysis (ICA) would reveal features that are stimulation site specific and unique individual features. In order to improve the effectiveness of ICA for removal of AEP from the TEP, and thus more clearly separate the transcranial-evoked and non-specific TMS-modulated potentials, we merged sham and active TMS datasets representing multiple stimulation conditions, removed the resulting AEP component, and evaluated performance across different sham protocols and clinical populations using reduction in Global and Local Mean Field Power (GMFP/LMFP) and cosine similarity analysis. We show that removing AEPs significantly reduced GMFP and LMFP in the post-stimulation TEP (14 to 400 ms), driven by time windows consistent with the N100 and P200 temporal characteristics of AEPs. Cosine similarity analysis supports that removing AEPs reduces TEP similarity between subjects and reduces TEP similarity between stimulation conditions. Similarity is reduced most in a mid-latency window consistent with the N100 time-course, but nevertheless remains high in this time window. Residual TEP in this window has a time-course and topography unique from AEPs, which follow-up exploratory analyses suggest could be a modulation in the alpha band that is not stimulation site specific but is unique to individual subject. We show, using two datasets and two implementations of sham, evidence in cortical topography, TEP time-course, GMFP/LMFP and cosine similarity analyses that this procedure is effective and conservative in removing the AEP from TEP, and may thus better isolate TMS-evoked activity. We show TEP remaining in early, mid and late latencies. The early response is site and subject specific. Later response may be consistent with TMS-modulated alpha activity that is not site specific but is unique to the individual. TEP remaining after removal of AEP is unique and can provide insight into TMS-evoked potentials and other modulated oscillatory dynamics.

Background Post‐surgical delirium is associated with increased morbidity, lasting cognitive decline, and loss of functional independence. Within a conceptual framework that delirium is triggered by stressors when vulnerabilities exist in cerebral connectivity and plasticity, we previously suggested that neurophysiologic measures might identify individuals at risk for post‐surgical delirium. Here we demonstrate the feasibility of the approach and provide preliminary experimental evidence of the predictive value of such neurophysiologic measures for the risk of delirium in older persons undergoing elective surgery. Methods Electroencephalography (EEG) and transcranial magnetic stimulation (TMS) were collected from 23 patients prior to elective surgery. 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. Presence or absence of delirium was assessed using the confusion assessment method (CAM). We included individuals with no baseline clinically relevant cognitive impairment (MoCA scores ≥21) in order to focus on subclinical neurophysiological measures. Results In patients with no baseline cognitive impairment (N = 20, age = 72 ± 6), 3 developed post‐surgical delirium (MoCA = 24 ± 2.6) and 17 did not (controls; MoCA = 25 ± 2.4). Patients who developed delirium had pre‐surgical resting‐state EEG power ratios outside the 95% confidence interval of controls, and 2/3 had TMS‐plasticity measures outside the 95% CI of controls. Conclusions Consistent with our proposed conceptual framework, this pilot study suggests that non‐invasive and scalable neurophysiologic measures can identify individuals at risk of post‐operative delirium. 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.

The majority of patients who undergo nonsedated interventional pain management procedures do not experience severe pain. There is a small but appreciable group of subjects who seem to experience severe pain that cannot be correlated to any particular clinical characteristic in a standard patient evaluation. Even with standard conscious sedation, there is no clear best method to ensure patient comfort for this high-pain level group.