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• We assessed the number of pulses required for prefrontal TMS-EEG testing. • The 80-pulse was found to be equivalent to 160/240-pulse in TEP waveform similarity. • The 40 pulses makes it difficult to obtain a stable TEP early component. • For reliable TMS-evoked EEG, around 80 pulses are considered reasonable. Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG), TMS-EEG, is a useful neuroscientific tool for the assessment of neurophysiology in the human cerebral cortex. Theoretically, TMS-EEG data is expected to have a better data quality as the number of stimulation pulses increases. However, since TMS-EEG testing is a modality that is examined on human subjects, the burden on the subject and tolerability of the test must also be carefully considered. In this study, we aimed to determine the number of stimulation pulses that satisfy the reliability and validity of data quality in single-pulse TMS (spTMS) for the dorsolateral prefrontal cortex (DLPFC). TMS-EEG data for (1) 40-pulse, (2) 80-pulse, (3) 160-pulse, and (4) 240-pulse conditions were extracted from spTMS experimental data for the left DLPFC of 20 healthy subjects, and the similarities between TMS-evoked potentials (TEP) and oscillations across the conditions were evaluated. As a result, (2) 80-pulse and (3) 160-pulse conditions showed highly equivalent to the benchmark condition of (4) 240-pulse condition. However, (1) 40-pulse condition showed only weak to moderate equivalence to the (4) 240-pulse condition. Thus, in the DLPFC TMS-EEG experiment, 80 pulses of stimulations was found to be a reasonable enough number of pulses to extract reliable TEPs, compared to 160 or 240 pulses. This is the first substantial study to examine the appropriate number of stimulus pulses that are reasonable and feasible for TMS-EEG testing of the DLPFC. [ABSTRACT FROM AUTHOR]

Depression is the disorder with the greatest socioeconomic burdens. Its diagnosis is still based on an operational diagnosis derived from symptoms, and no objective diagnostic indicators exist. Thus, the present study aimed to develop an artificial intelligence (AI) model to aid in the diagnosis of depression from electroencephalography (EEG) data by applying machine learning to resting-state EEG and transcranial magnetic stimulation (TMS)-evoked EEG acquired from patients with depression and healthy controls. Resting-state EEG and single-pulse TMS-EEG were acquired from 60 patients and 60 healthy controls. Power spectrum analysis, phase synchronization analysis, and phase-amplitude coupling analysis were conducted on EEG data to extract feature candidates to apply different types of machine learning algorithms. Furthermore, to address the limitation of the sample size, dimensionality reduction was performed in a manner to increase the quality of information by featuring robust neurophysiological metrics that showed significant differences between the two groups. Then, nine different machine learning models were applied to the data. For the EEG data, we created models combining four modalities, including (1) resting-state EEG, (2) pre-stimulus TMS-EEG, (3) post-stimulus TMS-EEG, and (4) differences between pre- and post-stimulus TMS-EEG, and evaluated their performance. We found that the best estimation performance (a mean area under the curve of 0.922) was obtained using receiver operating characteristic curve analysis when linear discriminant analysis (LDA) was applied to the combination of the four feature sets. This study showed that by using TMS-EEG neurophysiological indices as features, it is possible to develop a depression decision-support AI algorithm that exhibits high discrimination accuracy. [ABSTRACT FROM AUTHOR]

TMS combined with EEG (TMS-EEG) is a tool to characterize the neurophysiological dynamics of the cortex. Among the TMS paradigms, short-latency afferent inhibition (SAI) allows the investigation of inhibitory effects mediated by the cholinergic system. The aim of this study was to compare cholinergic function in the DLPFC between individuals with mild cognitive impairment (MCI) and healthy controls (HC) using TMS-EEG with the SAI paradigm. In this study, 30 MCI and 30 HC subjects were included. The SAI paradigm consisted of 80 single pulse TMS and 80 SAI stimulations applied to the left DLPFC. N100 components, global mean field power (GMFP) and total power were calculated. As a result, individuals with MCI showed reduced inhibitory effects on N100 components and GMFP at approximately 100 ms post-stimulation and on β-band activity at 200 ms post-stimulation compared to HC. Individuals with MCI showed reduced SAI, suggesting impaired cholinergic function in the DLPFC compared to the HC group. We conclude that these findings underscore the clinical applicability of the TMS-EEG method as a powerful tool for assessing cholinergic function in individuals with MCI. [Display omitted] • TMS-EEG combined with SAI paradigm was applied to individuals with MCI. • Decreased inhibitory effects by SAI paradigm were found in individuals with MCI. • Decreased inhibition in the β-band by SAI was observed in individuals with MCI. • Inhibitory effect by SAI and memory function were correlated in individuals with MCI. [ABSTRACT FROM AUTHOR]