An agile, data‐driven approach for target selection in rTMS therapy for anxiety symptoms: Proof of concept and preliminary data for two novel targets

Abstract

Introduction: Data-driven approaches to transcranial magnetic stimulation (TMS) might yield more consistent and symptom-specific results based on individualized functional connectivity analyses compared to previous traditional approaches due to more precise targeting. We provide a proof of concept for an agile target selection paradigm based on using connectomic methods that can be used to detect patientspecific abnormal functional connectivity, guide treatment aimed at the most abnormal regions, and optimize the rapid development of new hypotheses for future study. Methods:We used the resting-state functional MRI data of 28 patients with medically refractory generalized anxiety disorder to perform agile target selection based on abnormal functional connectivity patterns between the Default Mode Network (DMN) and Central Executive Network (CEN). The most abnormal areas of connectivity within these regions were selected for subsequent targeted TMS treatment by a machine learning based on an anomalous functional connectivity detection matrix. Areas with mostly hyperconnectivity were stimulated with continuous theta burst stimulation and the converse with intermittent theta burst stimulation. An image-guided accelerated theta burst stimulation paradigm was used for treatment.Results: Areas 8Av and PGs demonstrated consistent abnormalities, particularly in the left hemisphere. Significant improvements were demonstrated in anxiety symptoms, and few, minor complications were reported (fatigue (n = 2) and headache (n = 1)). Conclusions:Our study suggests that a left-lateralized DMN is likely the primary functional network disturbed in anxiety-related disorders, which can be improvedThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Personalized, parcel‐guided rTMS for the treatment of major depressive disorder: Safety and proof of concept

“…Most of our patients had rTMS targets outside of the dlPFC. In previous studies, we have shown that the use of connectome‐guided rTMS can induce neuroplasticity and alter functional connectivity (Yeung et al., 2021 ; Young, Taylor, et al., 2023 ). Therefore, it is likely that the effects of rTMS will remain with the patient for the long term, but longer follow‐up is warranted to determine if this holds true.…”

“…Nevertheless, L8Av (within the dlPFC) and LPGs (outside of the dlPFC) were the most common targets shared by these patients. These two areas of the DMN were also common targets in the treatment of anxiety (Young, Taylor, et al., 2023 ). These results highlight the importance of personalized rTMS, which will account for individualized connectivity differences in treatment regiments.…”

“…Finally, there were no adverse effects associated with the rTMS treatment. We have previously reported the use of this personalized rTMS method in numerous studies (Dadario et al., 2022 ; Young, Taylor, et al., 2023 ; Poologaindran et al., 2022 ; Yeung et al., 2021 ), and it has been shown to be safe and able to effectively target multiple ROIs. In the original clinical trials for rTMS, scalp discomfort was identified as the largest contributor to dropping out of the study (O'Reardon et al., 2007 ).…”

“…The Infinitome software was used as previously described (Poologaindran et al., 2022 ; Yeung et al., 2021 ; Young, Taylor, et al., 2023 ). Below are the brief technical summaries involved in image processing.…”

“…To determine the normal range of correlations for each functional connectivity pair in the matrix, outlier detection was performed using a tangent space connectivity matrix; the results obtained were compared against the tangent space connectivity transformation of 200 normal subject rsfMRI samples collected from healthy controls from the OpenNeuro ( https://openneuro.org/ ) and SchizConnect ( http://schizconnect.org ) datasets. To reduce the false discovery rate, abnormal connectivity from rsfMRI was determined as a three‐sigma outlier for that correlation after excluding the highest variance 1/3 of pairs (Poologaindran et al., 2022 ; Tang et al., 2022 ; Young, Taylor, et al., 2023 ).…”