{"doi":"10.1016/j.brs.2020.12.002","title":"Metabolic state and gustatory perception shapes dynamic interplay between cortical excitability and motor response","abstract":"The measurement of corticospinal excitability is a cornerstone for the field of transcranial magnetic stimulation (TMS) research. Though, how an individual’s metabolism and physiology modulate motor cortex (M1) excitability is a matter of some debate. In healthy individuals, consuming a high-sugar drink prior to TMS has been shown to increase M1 excitability [[1]Specterman M. Bhuiya A. Kuppuswamy A. Strutton P.H. Catley M. Davey N.J. The effect of an energy drink containing glucose and caffeine on human corticospinal excitability.Physiol Behav. 2005; 83: 723-728Crossref PubMed Scopus (29) Google Scholar] or have no effect [[2]Toepp S.L. Turco C.V. Locke M.B. Nicolini C. Ravi R. Nelson A.J. The impact of glucose on corticospinal and intracortical excitability.Brain Sci. 2019; 9Crossref PubMed Scopus (3) Google Scholar]. The interaction between metabolism and corticospinal excitability can also depend on disease state. For example, patients with generalized epilepsy show greater inhibition of MEPs by paired-pulse TMS after consuming a meal [[3]Badawy R.A. Vogrin S.J. Lai A. Cook M.J. Cortical excitability changes correlate with fluctuations in glucose levels in patients with epilepsy.Epilepsy Behav. 2013; 27: 455-460Abstract Full Text Full Text PDF PubMed Scopus (20) Google Scholar]. Given the high-sugar content of Western diets and recent trends in ketogenic diets and intermittent fasting, we evaluated the effect of glucose consumption on corticospinal excitability in an experimenter-blinded crossover design study of glucose consumption on both the motor evoked potential (MEP) and the TMS evoked potential (TEP) measured by electroencephalography (EEG). We sought to clarify the role of glucose in modulating measurements of excitability with TMS in an effort to provide guidance for reducing variability and increasing the signal-to-noise in future TMS studies. We recruited 7 healthy participants with body mass indices less than 30 kg/m2 and a fasting blood glucose level (BGL) less than 95 mg/dL at initial screening. All participants provided informed consent in accordance with the Institutional Review Board at the University of North Carolina at Chapel Hill. Participants fasted for 12 hours and abstained from alcohol for 24 hours prior to two study visits where participants consumed either a 75 g glucose drink (298 mL; Azer Scientific Inc., Morgantown, PA, USA)—equivalent to two 12-ounce cans of a typical soft drink—or a volume-matched control drink (300 mL water). At each visit, we measured BGLs and combined TMS-EEG-EMG (electromyography) before and 0, 30, 60, and 120 minutes after the drink (Fig. 1A). Capillary BGLs were measured with a Hemocue Glucose 201 Analyzer (HemoCue America Inc., Brea, CA, USA) (Fig. 1B). EEG/EMG data were recorded using a 128-channel system at 1000 Hz sampling rate (Geodesic EEG system 410, Electrical Geodesics, Inc., OR, USA). Data from all time points were concatenated and the stimulation artifact from −10 to 20 ms peri-TMS was interpolated with Gaussian noise (parameters sampled from −50 to −11 ms). We removed high-amplitude artifacts using automatic subspace reconstruction before filtering from 2 to 50 Hz. Residual ocular, muscle, cardiac, and TMS artifacts were identified and removed with independent component analysis using the TESA toolbox [[4]Rogasch N.C. Sullivan C. Thomson R.H. Rose N.S. Bailey N.W. Fitzgerald P.B. et al.Analysing concurrent transcranial magnetic stimulation and electroencephalographic data : a review and introduction to the open-source TESA software.Neuroimage. 2017; 147 (June 2016): 934-951Crossref PubMed Scopus (84) Google Scholar]. TEPs were source localized in Brainstorm [[5]Tadel F. Baillet S. Mosher J.C. Pantazis D. Leahy R.M. Brainstorm: a user-friendly application for MEG/EEG analysis.Comput Intell Neurosci. 2011; 2011: 879716Crossref PubMed Scopus (1338) Google Scholar] with individual magnetic resonance images (Fig. 1C). Source activations were extracted from three ","journal":"Brain stimulation","year":2020,"id":113826,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.964,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":536555,"name":"Amber L. McFerren","orcid":"0000-0003-4832-3274","position":1,"is_corresponding":false},{"id":536556,"name":"Davindra R. Rammani","orcid":"0000-0001-7401-4024","position":2,"is_corresponding":false},{"id":34495,"name":"John B. Buse","orcid":"0000-0002-9723-3876","position":3,"is_corresponding":false},{"id":395860,"name":"Flavio Frӧhlich","orcid":"0000-0002-3724-5621","position":4,"is_corresponding":false},{"id":510081,"name":"Christopher Walker","orcid":"0000-0002-1042-075X","position":0,"is_corresponding":true}],"reference_count":11,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:13:25.964732Z","pmid":"33309841","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}