Pinpointing the precise stimulation targets for brain rehabilitation in early-stage Parkinson's disease

Computational simulation of transcranial magnetic stimulation-induced electric fields in the dorsolateral prefrontal cortex of heavy cannabis using individuals

We aimed to investigate the influence of demographic and clinical modulators on the strength of transcranial magnetic stimulation (TMS)-induced electric fields (EFs) in the left dorsolateral prefrontal cortex (lDLPFC) in heavy cannabis using individuals. Structural T1-weighted magnetic resonance imaging scans of 20 heavy cannabis using individuals and 22 non-cannabis users (the controls) in the age range of 18-25 were retrieved. Computational simulations of TMS-induced EFs in the lDLPFC were performed. No significant difference in the strength of TMS-induced EFs was observed between heavy cannabis using individuals and the controls. A negative correlation between the scalp-to-cortex distance demonstrated and the strength of the induced EFs. The severity of cannabis use related problems did not correlate with the induced EFs in the lDLPFC of heavy cannabis using individuals. However, the severity of alcohol use related problems was negatively correlated with the induced EF in the lDLPFC localized by the 5-cm method in the whole sample. Early adulthood seems related to an increase in the induced EFs in the lDLPFC. In conclusion, the dominant factor influencing TMS-induced EFs was the scalp-to-cortex distance. In early adulthood, the interaction between age and comorbid substance use may influence with the magnitude of TMS-induced EFs, thereby complicating the treatment effect of TMS in young people with substance use disorders.

Outcome measures for electric field modeling in tES and TMS: A systematic review and large-scale modeling study

BACKGROUND

Electric field (E-field) modeling is a potent tool to estimate the amount of transcranial magnetic and electrical stimulation (TMS and tES, respectively) that reaches the cortex and to address the variable behavioral effects observed in the field. However, outcome measures used to quantify E-fields vary considerably and a thorough comparison is missing.

OBJECTIVES

This two-part study aimed to examine the different outcome measures used to report on tES and TMS induced E-fields, including volume- and surface-level gray matter, region of interest (ROI), whole brain, geometrical, structural, and percentile-based approaches. The study aimed to guide future research in informed selection of appropriate outcome measures.

METHODS

Three electronic databases were searched for tES and/or TMS studies quantifying E-fields. The identified outcome measures were compared across volume- and surface-level E-field data in ten tES and TMS modalities targeting two common targets in 100 healthy individuals.

RESULTS

In the systematic review, we extracted 308 outcome measures from 202 studies that adopted either a gray matter volume-level (n = 197) or surface-level (n = 111) approach. Volume-level results focused on E-field magnitude, while surface-level data encompassed E-field magnitude (n = 64) and normal/tangential E-field components (n = 47). E-fields were extracted in ROIs, such as brain structures and shapes (spheres, hexahedra and cylinders), or the whole brain. Percentiles or mean values were mostly used to quantify E-fields. Our modeling study, which involved 1,000 E-field models and > 1,000,000 extracted E-field values, revealed that different outcome measures yielded distinct E-field values, analyzed different brain regions, and did not always exhibit strong correlations in the same within-subject E-field model.

CONCLUSIONS

Outcome measure selection significantly impacts the locations and intensities of extracted E-field data in both tES and TMS E-field models. The suitability of different outcome measures depends on the target region, TMS/tES modality, individual anatomy, the analyzed E-field component and the research question. To enhance the quality, rigor, and reproducibility in the E-field modeling domain, we suggest standard reporting practices across studies and provide four recommendations.