Image-guided TMS is safe in a predominately pediatric clinical population

Braden, Anneliesse; Weatherspoon, Sarah; Boardman, Talitha; Williard, Theresa; Adkins, Abigail; Gibbs, Savannah K.; Wheless, James W.; Narayana, Shalini

doi:10.1016/j.clinph.2022.01.133

Cited by 7 publications

(3 citation statements)

References 59 publications

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“…Although two such studies did report on the "complexity" of TMS evoked potentials in children (Määttä et al, 2019(Määttä et al, , 2017, the authors apparently defined complexity informally, e.g., based on the number of peaks in the TMS evoked potential, rather than using formal measures such as entropy or LZc. Furthermore, while one large sample, single site study concluded that MRI-guided TMS is safe overall in clinical pediatric populations (Braden et al, 2022), administering TMS to children who do not directly benefit from such stimulation may still be ethically ill-advised, as adverse events do occasionally occur during TMS sessions with children (Braden et al, 2022), a risk which is unlikely to be acceptable when TMS has no direct benefit for the child. Furthermore, while the above study was conducted as a retrospective chart review, a longitudinal study is likely needed to rule out long-term developmental effects of TMS in young children.…”

Section: Validating Spcimentioning

confidence: 99%

Not with a “zap” but with a “beep”: Measuring the origins of perinatal experience

et al. 2023

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Section: Validating Spcimentioning

confidence: 99%

Not with a “zap” but with a “beep”: Measuring the origins of perinatal experience

et al. 2023

View full text Add to dashboard Cite

“…Although two such studies did report on the complexity of TMS evoked potentials in children (Määttä et al, 2019(Määttä et al, , 2017, the authors appeared to define complexity informally, e.g., based on the number of peaks in the TMS evoked potential, rather than using formal measures such as entropy or LZc. Furthermore, while MRI-guided TMS is safe overall in pediatric populations (Braden et al, 2022), administering TMS to children who do not directly benefit from such stimulation may be ethically ill-advised and, as such, validation efforts in children should only use sPCI without a TMS-based comparison. Consequently, cortical signals could be recorded using either EEG or MEG at this stage of validation.…”

Section: Ongoing and Future Studiesmentioning

confidence: 99%

Not with a “zap” but with a “beep”: measuring the origins of perinatal experience

Frohlich¹,

Bayne²,

Dallavecchia³

et al. 2022

Preprint

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When does the mind begin? Infant psychology is mysterious in part because we cannot remember our first months of life, nor can we directly communicate with infants. Even more speculative is the possibility of mental life prior to birth. The question of when consciousness, or subjective experience, begins in human development thus remains incompletely answered, though boundaries can be set using current knowledge from developmental neurobiology and recent investigations of the perinatal brain. Here, we offer our perspective on how the development of a sensory perturbation complexity index (sPCI) based on auditory (“beep-and-zip”) or visual (“flash-and-zip”) cortical perturbations in place of electromagnetic perturbations (“zap-and-zip”) might be used to address this question. First, we discuss recent studies of perinatal cognition and consciousness using techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and, in particular, magnetoencephalography (MEG). While newborn infants are the archetypal subjects for studying early human development, researchers may also benefit from fetal studies, as the womb is, in many respects, a more controlled environment than the cradle. The earliest possible timepoint when subjective experience might begin is likely the establishment of thalamocortical connectivity at 26 weeks gestation, as the thalamocortical system is necessary for consciousness according to most theoretical frameworks. To infer at what age and in which behavioral states consciousness might emerge following the initiation of thalamocortical pathways, we advocate for the development of the sPCI to estimate the perinatal brain’s state of consciousness.

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“…Compared to fMRI, TMS motor mapping is in closer agreement with direct cortical stimulation (DCS) mapping, which is regarded as the current gold standard for delineating the motor cortex ( Krieg et al, 2012 ; Coburger et al, 2013 ; Mangraviti et al, 2013 ). Moreover, TMS requires less patient cooperation such as performing motor tasks, which is difficult for patients with paresis or plegia or children with autism or developmental delay ( Narayana et al, 2015 , 2021 ; Braden et al, 2022 ). Such advantages have made TMS motor cortex mapping promising in clinical applications, such as pre-surgical planning ( Takahashi et al, 2013 ; Lefaucheur and Picht, 2016 ), risk stratification ( Rosenstock et al, 2017 ), motor rehabilitation ( Lüdemann-Podubecká and Nowak, 2016 ) and basic research such as developmental plasticity ( Narayana et al, 2015 ; Grab et al, 2018 ; Babwani et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms

Chen,

Jiang,

Zhang

et al. 2023

Front. Neurosci.

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BackgroundThere are currently five different kinds of transcranial magnetic stimulation (TMS) motor mapping algorithms available, from ordinary point-based algorithms to advanced field-based algorithms. However, there have been only a limited number of comparison studies conducted, and they have not yet examined all of the currently available algorithms. This deficiency impedes the judicious selection of algorithms for application in both clinical and basic neuroscience, and hinders the potential promotion of a potential superior algorithm. Considering the influence of algorithm complexity, further investigation is needed to examine the differences between fMRI peaks and TMS cortical hotspots that were identified previously.MethodsTwelve healthy participants underwent TMS motor mapping and a finger-tapping task during fMRI. The motor cortex TMS mapping results were estimated by five algorithms, and fMRI activation results were obtained. For each algorithm, the prediction error was defined as the distance between the measured scalp hotspot and optimized coil position, which was determined by the maximum electric field strength in the estimated motor cortex. Additionally, the study identified the minimum number of stimuli required for stable mapping. Finally, the location difference between the TMS mapping cortical hotspot and the fMRI activation peak was analyzed.ResultsThe projection yielded the lowest prediction error (5.27 ± 4.24 mm) among the point-based algorithms and the association algorithm yielded the lowest (6.66 ± 3.48 mm) among field-based estimation algorithms. The projection algorithm required fewer stimuli, possibly resulting from its suitability for the grid-based mapping data collection method. The TMS cortical hotspots from all algorithms consistently deviated from the fMRI activation peak (20.52 ± 8.46 mm for five algorithms).ConclusionThe association algorithm might be a superior choice for clinical applications and basic neuroscience research, due to its lower prediction error and higher estimation sensitivity in the deep cortical structure, especially for the sulcus. It also has potential applicability in various other TMS domains, including language area mapping and more. Otherwise, our results provide further evidence that TMS motor mapping intrinsically differs from fMRI motor mapping.

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Image-guided TMS is safe in a predominately pediatric clinical population

Cited by 7 publications

References 59 publications

Not with a “zap” but with a “beep”: Measuring the origins of perinatal experience

Not with a “zap” but with a “beep”: Measuring the origins of perinatal experience

Not with a “zap” but with a “beep”: measuring the origins of perinatal experience

Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms

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