Impact of methodological variability on EEG responses evoked by transcranial magnetic stimulation: A meta-analysis.

OBJECTIVE: This meta-analysis aimed to synthesize the existing literature on how different parameters of transcranial magnetic stimulation (TMS) and electroencephalogram (EEG) modulate the amplitudes of TMS-evoked potentials (TEPs). METHODS: A comprehensive search was run in PubMed and completed by Google Scholar to find articles studying healthy participants who underwent single pulse TMS-EEG sessions over their left primary motor cortex (M1) or dorsolateral prefrontal cortex (DLPFC). The amplitudes of the most commonly investigated TEP peaks for DLPFC stimulation (positives: 25, 60, 185 ms, negatives: 40, 100 ms) and M1 stimulation (positives: 30, 55,180 ms and negatives: 15, 45, 100, 280 ms) were extracted from studies. RESULTS: Cohen's d effect sizes were obtained in five independent categories that were stratified based on the stimulation, recording, and analyzing parameters. The overall effect sizes and equivalent means and standard deviations were computed within every category. CONCLUSIONS: This meta-analysis spotlights the need to rigorously and systematically control for the critical parameters in recording and analyzing TMS-EEG data to make the outcomes of further studies more comparable to the current body of literature. SIGNIFICANCE: The study demonstrates the possibility of reliably measuring TEPs by offering approximate ranges for every TEP peak in the most commonly targeted areas of DLPFC and M1.

Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data.

Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman's general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and restingstate fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.