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Sacchetti, D.

Publications and source records attributed to Sacchetti, D..

2 recordsLinked to original sources
bioRxiv2025

Predicting Neuroplasticity Effects of Continuous Theta Burst Stimulation with Biomarkers from the Motor Evoked Potential TMS Input-Output Curve

The field of neuromodulation lacks predictors of individual differences in plasticity that influence responses to repetitive transcranial magnetic stimulation (rTMS). Continuous theta burst stimulation (cTBS), a form of rTMS known for its inhibitory effects, shows variable responses between individuals, potentially due to differences in neuroplasticity. Predicting individual cTBS effects could vastly enhance its clinical and experimental utility. This study explores whether motor evoked potential (MEP) input-output (IO) parameters measured prior to neuromodulation can predict motor cortex responses to cTBS. IO curves were sampled from healthy adults by recording MEPs over a range of single pulse TMS intensities to obtain parameters including MEPmax and S50 (midpoint intensity). Subjects later received cTBS over the same location of motor cortex and their MEPs before and after stimulation were compared. Both MEPmax and S50 predicted responses, significantly correlating (p<0.05, R2>0.25) with individuals MEP changes at 10, 20, and 30 minutes after cTBS. Further, we introduced and validated an easily implementable biomarker that does not require the time-consuming sampling of full IO curve: MEP130RMT (median of 10 MEPs at 130% RMT). MEP130RMT was also a strong predictor of cTBS response (p<0.005, R2>0.3). Head-to-head comparison against a previously studied genetic biomarker of rTMS responses (BDNF polymorphism) showed that IO based predictors had a superior performance in explaining more response variability. Thus, IO curves derived prior to cTBS administration can reliably predict cTBS-induced changes in cortical excitability. This work points toward an accessible strategy for tailoring stimulation procedures in both diagnostic and therapeutic applications of rTMS, and potentially boosting response rate to other brain stimulation approaches. HIGHLIGHTSO_LIBaseline TMS-MEP Input-Output (IO) Curve parameters significantly predict MEP responses to M1 cTBS. C_LIO_LIHigher MEPmax at baseline predicts more robust inhibitory response to cTBS, while higher midpoint intensity (S50) is associated with less response. C_LIO_LID We developed and validated a new biomarker MEP130RMT, which predicts cTBS response using just 10 baseline MEPs from single TMS pulses of 130% RMT intensity. C_LIO_LIHead to head comparison against BDNF genotyping shows superior performance of IO biomarkers. C_LI

neuroscience↗
bioRxiv2022

Individual-level Functional ConnectivityPredicts Cognitive Control Efficiency

Cognitive control (CC) is a vital component of cognition associated with problem-solving in everyday life. Many neurological and neuropsychiatric conditions have deficits associated with CC. CC is composed of multiple behaviors including switching, inhibiting, and updating. The fronto-parietal control network B (FPCN-B), the dorsal attention network (DAN), the cingulo-opercular network (CON) and the dorsal default-mode network (dorsal-DMN) have been associated with switching and inhibiting behaviors. However, our understanding of how these brain regions interact to bring about CC behaviors is still unclear. In the current study, participants performed two in-scanner tasks that required switching and inhibiting. We then used a series of support vector regression (SVR) models containing individually-estimated functional connectivity between the networks of interest derived during tasks and at rest to predict inhibition and switching behaviors in individual subjects. We observed that the combination of between-network connectivity from these individually estimated functional networks predicted accurate and timely inhibition and switching behaviors in individuals. We also observed that the relationships between canonical task-positive and task-negative networks predicted inhibiting and switching behaviors. Finally, we observed a functional dissociation between the FPCN-A and FPCNB during rest, and task performance predicted inhibiting and switching behaviors. These results suggest that individually estimated networks can predict individual CC behaviors, that between-network functional connectivity estimated within individuals is vital to understanding how CC arises, and that the fractionation of the FPCN and the DMN may be associated with different behaviors than their canonically accepted behaviors.

neuroscience↗