Survey methodology
This review followed the SANRA checklist ( Baethge, Goldbeck-Wood & Mertens, 2019 ). The review is intended for clinicians and researchers with an interest in MS neurophysiology.

The search was planned by the entire study team and performed by a single author (NJS).

A single author (NJS) searched the PubMED, Embase, Web of Science, and Scopus electronic databases for studies published between January 1, 1985 (the first year of TMS publication) ( Barker, Jalinous & Freeston, 1985 ) and February 28, 2022. The following search terms were adapted for each database:

(“multiple sclerosis” (all fields) OR “clinically isolated syndrome” (all fields)) AND (“transcranial magnetic stimulation” (all fields)) AND (sensitiv* (all fields) OR specific* (all fields) OR “predictive value” (all fields) OR “likelihood ratio” (all fields) OR “odds ratio” (all fields) OR “risk ratio” (all fields) OR “hazard ratio” (all fields)).

A single author (NJS) also scanned reference lists of relevant review articles and full-text articles.

English, peer-reviewed journal articles of original studies were screened by a single author (NJS). Screening criteria were planned by the entire study team using the PICOS format ( Schardt et al., 2007 ).

Population . Adults with MS; diagnosed using validated criteria; and sample size n > 40, to enhance sensitivity and specificity estimates ( Langer-Gould et al., 2006 ; Schaffler et al., 2011 ).

Intervention . Observational research that reported sensitivity and/or specificity.

Control . Healthy controls, free of neurologic or other disease; persons with alternative diagnoses.

Outcome . Sensitivity and/or specificity of any upper and/or lower extremity TMS technique.

Study . Cross-sectional or case-control studies comparing MS to control participants; or cohort studies following participants from symptom onset to diagnosis.

The approach to data extraction was planned by the entire study team and performed by a single author (NJS). The study team verified all transcribed data.

From study methods, a single author (NJS) transcribed study and participant characteristics, TMS methods, and criteria to determine abnormal TMS results relative to controls ( Chipchase et al., 2012 ; Langer-Gould et al., 2006 ; Schaffler et al., 2011 ; Snow et al., 2019 ).

From study results, a single author (NJS) transcribed 2 × 2 contingency findings ( Adeniyi et al., 2016 ; Glas et al., 2003 ; McInnes et al., 2018 ) ( Table 2 ). Sensitivity was considered the percentage of participants with a diagnosis of MS, who exhibited abnormal TMS results (Sensitivity = True Positives ÷ (True Positives + False Negatives)). Specificity was considered the percentage of control participants ( i.e ., without a diagnosis of MS), who exhibited normal TMS results (Specificity = True Negatives ÷ (False Positives + True Negatives)). All studies provided sufficient information to determine sensitivity. In cases where insufficient information was available to determine specificity, only sensitivity was reported. Sensitivity or specificity estimates below 50% indicated that the TMS outcome performed worse than chance at ruling out or in MS, respectively.

Note:

MS, multiple sclerosis; TMS, transcranial magnetic stimulation.

When both sensitivity and specificity outcomes were available, the diagnostic odds ratio (DOR) was estimated ( Glas et al., 2003 ). DOR was calculated as DOR = (True Positives ÷ False Negatives) ÷ (False Positives ÷ True Negatives) ( Glas et al., 2003 ). Any DOR above 1.0 was associated with increased diagnostic accuracy ( i.e ., an increased odds that an abnormal TMS result was associated with diagnosis of MS) ( Glas et al., 2003 ). DOR values were interpreted as trivial if < 1.68, small if 1.68–3.46, medium if 3.47–6.71, and large if > 6.71 ( Chen, Cohen & Chen, 2010 ). When possible, 95% confidence intervals for sensitivity, specificity, and DOR were estimated using the methods outlined by Glas et al. (2003) .

The approach to critical appraisal was planned by the entire study team and completed by a single author (NJS). The study team verified all critical appraisal findings.

Risk of bias. Risk of bias was assessed using the QUADAS-2 tool ( Whiting et al., 2011 ), which evaluates studies’ reporting in domains of participant selection, the index test (TMS), the reference standard (MS diagnostic criteria), and participant flow and timing.

Participant selection questions evaluated reporting of the participant selection process and the level of detail used to describe participant samples: Was a consecutive or random sample of patients enrolled? Was a case-control design avoided? Did the study avoid inappropriate exclusions?

Was a consecutive or random sample of patients enrolled?

Was a case-control design avoided?

Did the study avoid inappropriate exclusions?

Index test questions assessed reporting of TMS data collection, analysis, interpretation, and summarization: Were the index test results interpreted without knowledge of the results of the reference standard ( i.e ., was blinding employed)? If a threshold was used, was it pre-specified?

Were the index test results interpreted without knowledge of the results of the reference standard ( i.e ., was blinding employed)?

If a threshold was used, was it pre-specified?

Reference standard questions examined reporting of MS diagnostic criteria: Is the reference standard likely to correctly classify the target condition? Were the reference standard results interpreted without knowledge of the results of the index test ( i.e ., was blinding employed)?

Is the reference standard likely to correctly classify the target condition?

Were the reference standard results interpreted without knowledge of the results of the index test ( i.e ., was blinding employed)?

Flow and timing questions appraised reporting of participant exclusions and the timing between MS diagnosis and TMS testing: Was there an appropriate interval between index test and reference standard? Did all patients receive a reference standard? Did all patients receive the same reference standard? Were all patients included in the analysis?

Was there an appropriate interval between index test and reference standard?

Did all patients receive a reference standard?

Did all patients receive the same reference standard?

Were all patients included in the analysis?

A single author (NJS) answered signaling questions as Yes/No/Unclear, to derive High/Low/Unclear risk of bias for each domain. Based on the risk of bias from each domain, an overall risk of bias rating was assigned to each study ( Sterne et al., 2019 ).

Biomarker validity. To explore whether studies provided sufficient evidence to justify TMS techniques as biomarkers for MS diagnosis, Bielekova & Martin’s (2004) MS biomarker criteria were used. The Bielekova & Martin (2004) criteria classify biomarker studies according to MS-related pathophysiologic process, grade studies’ methodologic quality, evaluate studies’ clinical utility, and assess studies’ clinical usefulness.

MS-specific pathophysiologic processes were classified as: Biomarkers reflecting alteration of the immune system, Biomarkers of blood-brain barrier (BBB) disruption, Biomarkers of demyelination, Biomarkers of oxidative stress and excitotoxicity, Biomarkers of axonal/neuronal damage, Biomarkers of gliosis, and/or Biomarkers of remyelination and repair.

Biomarkers reflecting alteration of the immune system,

Biomarkers of blood-brain barrier (BBB) disruption,

Biomarkers of demyelination,

Biomarkers of oxidative stress and excitotoxicity,

Biomarkers of axonal/neuronal damage,

Biomarkers of gliosis, and/or

Biomarkers of remyelination and repair.

Methodologic quality of studies was based on the following questions: Are complete (raw) data provided? Was there an independent comparison to a reference standard or age- and sex-matched reference group? Was an appropriate spectrum of patients included ( e.g ., clinical subtypes, sample size)? Were the methods used valid ( e.g ., data collection, processing, and analysis)? Was there a processing and/or work-up bias ( e.g ., blinded processing and analysis)?

Are complete (raw) data provided?

Was there an independent comparison to a reference standard or age- and sex-matched reference group?

Was an appropriate spectrum of patients included ( e.g ., clinical subtypes, sample size)?

Were the methods used valid ( e.g ., data collection, processing, and analysis)?

Was there a processing and/or work-up bias ( e.g ., blinded processing and analysis)?

Clinical utility was evaluated according to the following criteria: Biological rationale ( i.e ., rational association with a pathogenic aspect of MS). Clinical relevance ( i.e ., positioned in the causal chain of pathological events leading to a meaningful clinical endpoint). Practicality ( i.e ., invasiveness of collection, need for serial analyses, reproducibility, ease, cost). Correlation with disease activity ( i.e ., relationship with clinical [relapses, progression, disability scale] or neuroimaging [lesion numbers, atrophy] end points). Correlation with disability/prognosis ( i.e ., relationship with disability accumulation over time). Correlation with treatment effect (omitted).

Biological rationale ( i.e ., rational association with a pathogenic aspect of MS).

Clinical relevance ( i.e ., positioned in the causal chain of pathological events leading to a meaningful clinical endpoint).

Practicality ( i.e ., invasiveness of collection, need for serial analyses, reproducibility, ease, cost).

Correlation with disease activity ( i.e ., relationship with clinical [relapses, progression, disability scale] or neuroimaging [lesion numbers, atrophy] end points).

Correlation with disability/prognosis ( i.e ., relationship with disability accumulation over time).

Correlation with treatment effect (omitted).

Clinical usefulness was assessed against the following criteria: Sensitivity/specificity ( i.e ., sensitivity/specificity relative to reference standard). Reliability ( i.e ., consistency of a measurement across time or raters, probability of false-positive or false-negative results). Evaluation of a biomarker in epidemiological studies or natural history cohorts ( i.e ., establishing a statistical relationship between the biomarker and clinical endpoint in cross-sectional or longitudinal studies). Evaluation of a biomarker in proof-of-principle clinical trials (omitted).

Sensitivity/specificity ( i.e ., sensitivity/specificity relative to reference standard).

Reliability ( i.e ., consistency of a measurement across time or raters, probability of false-positive or false-negative results).

Evaluation of a biomarker in epidemiological studies or natural history cohorts ( i.e ., establishing a statistical relationship between the biomarker and clinical endpoint in cross-sectional or longitudinal studies).

Evaluation of a biomarker in proof-of-principle clinical trials (omitted).

A single author (NJS) applied ratings of Yes/No/Unclear to each criterion, to derive Yes/No/Unclear ratings for each domain. Based on ratings in each domain, an overall validity rating (Yes/No/Unclear) was assigned to each study ( Sterne et al., 2019 ).

To visually compare the diagnostic accuracy of TMS techniques across studies, a single author (NJS) prepared a Forest plot of DOR point estimates and their 95% confidence intervals, organized by TMS outcome and study. DOR values were coded according to risk of bias rating. DOR values were interpreted as above ( Chen, Cohen & Chen, 2010 ; Glas et al., 2003 ).