Personalizing Repetitive Transcranial Magnetic Stimulation Parameters for Depression Treatment Using Multimodal Neuroimaging

Effects of repetitive transcranial magnetic stimulation in children and young people with psychiatric disorders: a systematic review

Repetitive transcranial magnetic stimulation (rTMS) has demonstrated benefits in adults with psychiatric disorders, but its clinical utility in children and young people (CYP) is unclear. This PRISMA systematic review used published and ongoing studies to examine the effects of rTMS on disorder-specific symptoms, mood and neurocognition in CYP with psychiatric disorders. We searched Medline via PubMed, Embase, PsychINFO via OVID, and Clinicaltrials.gov up to July 2023. Eligible studies involved multiple-session (i.e., treatment) rTMS in CYP (≤ 25 years-old) with psychiatric disorders. Two independent raters assessed the eligibility of studies and extracted data using a custom-built form. Out of 78 eligible studies (participant N = 1389), the majority (k = 54; 69%) reported an improvement in at least one outcome measure of disorder-specific core symptoms. Some studies (k = 21) examined rTMS effects on mood or neurocognition,: findings were largely positive. Overall, rTMS was well-tolerated with minimal side-effects. Of 17 ongoing or recently completed studies, many are sham-controlled RCTs with better blinding techniques and a larger estimated participant enrolment. Findings provide encouraging evidence for rTMS-related improvements in disorder-specific symptoms in CYP with different psychiatric disorders. However, in terms of both mood (for conditions other than depression) and neurocognitive outcomes, evidence is limited. Importantly, rTMS is well-tolerated and safe. Ongoing studies appear to be of improved methodological quality; however, future studies should broaden outcome measures to more comprehensively assess the effects of rTMS and develop guidance on dosage (i.e., treatment regimens).

Monitoring Changes in TMS-Evoked EEG and EMG Activity During 1 Hz rTMS of the Healthy Motor Cortex

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique capable of inducing neuroplasticity as measured by changes in peripheral muscle electromyography (EMG) or electroencephalography (EEG) from pre-to-post stimulation. However, temporal courses of neuromodulation during ongoing rTMS are unclear. Monitoring cortical dynamics via TMS-evoked responses using EMG (motor-evoked potentials; MEPs) and EEG (transcranial-evoked potentials; TEPs) during rTMS might provide further essential insights into its mode of action - temporal course of potential modulations. The objective of this study was to first evaluate the validity of online rTMS-EEG and rTMS-EMG analyses, and second to scrutinize the temporal changes of TEPs and MEPs during rTMS. As rTMS is subject to high inter-individual effect variability, we aimed for single-subject analyses of EEG changes during rTMS. Ten healthy human participants were stimulated with 1,000 pulses of 1 Hz rTMS over the motor cortex, while EEG and EMG were recorded continuously. Validity of MEPs and TEPs measured during rTMS was assessed in sensor and source space. Electrophysiological changes during rTMS were evaluated with model fitting approaches on a group- and single-subject level. TEPs and MEPs appearance during rTMS was consistent with past findings of single pulse experiments. Heterogeneous temporal progressions, fluctuations or saturation effects of brain activity were observed during rTMS depending on the TEP component. Overall, global brain activity increased over the course of stimulation. Single-subject analysis revealed inter-individual temporal courses of global brain activity. The present findings are in favor of dose-response considerations and attempts in personalization of rTMS protocols.

Group-common and individual-specific effects of structure-function coupling in human brain networks with graph neural networks

The human cerebral cortex is organized into functionally segregated but synchronized regions bridged by the structural connectivity of white matter pathways. While structure-function coupling has been implicated in cognitive development and neuropsychiatric disorders, studies yield inconsistent findings. The extent to which the structure-function coupling reflects reliable individual differences or primarily group-common characteristics remains unclear, at both the global and regional brain levels. By leveraging two independent, high-quality datasets, we found that the graph neural network accurately predicted unseen individuals' functional connectivity from structural connectivity, reflecting a strong structure-function coupling. This coupling was primarily driven by network topology and was substantially stronger than that of the linear models. Moreover, we observed that structure-function coupling was dominated by group-common effects, with subtle yet significant individual-specific effects. The regional group and individual effects of coupling were hierarchically organized across the cortex along a sensorimotor-association axis, with lower group and higher individual effects in association cortices. These findings emphasize the importance of considering both group and individual effects in understanding cortical structure-function coupling, suggesting insights into interpreting individual differences of the coupling and informing connectivity-guided therapeutics.

Personalized connectivity-based network targeting model of transcranial magnetic stimulation for treatment of psychiatric disorders: computational feasibility and reproducibility

Repetitive transcranial magnetic stimulation (rTMS) holds promise for treating psychiatric disorders; however, the variability in treatment efficacy among individuals underscores the need for further improvement. Growing evidence has shown that TMS induces a broad network modulatory effect, and its effectiveness may rely on accurate modulation of the pathological network specific to each disorder. Therefore, determining the optimal TMS coil setting that will engage the functional pathway delivering the stimulation is crucial. Compared to group-averaged functional connectivity (FC), individual FC provides specific information about a person's brain functional architecture, offering the potential for more accurate network targeting for personalized TMS. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC. To overcome this challenge, the proposed solutions include increasing the scan duration and employing the cluster method to enhance the stability of FC. This study aimed to evaluate the stability of a personalized FC-based network targeting model in individuals with major depressive disorder or schizophrenia with auditory verbal hallucinations. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we assessed the model's stability. We employed longer scan durations and cluster methodologies to improve the precision in identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the cluster method achieved stable identification of individual sites, as evidenced by the intraindividual distance falling below the ~1cm spatial resolution of TMS. The current model provides a feasible approach to obtaining stable personalized TMS targets from the scalp, offering a more accurate method of TMS targeting in clinical applications.

Cortical and subcortical microstructure integrity changes after repetitive transcranial magnetic stimulation therapy in cocaine use disorder and relates to clinical outcomes

Cocaine use disorder (CUD) is a worldwide public health condition that is suggested to induce pathological changes in macrostructure and microstructure. Repetitive transcranial magnetic stimulation (rTMS) has gained attention as a potential treatment for CUD symptoms. Here, we sought to elucidate whether rTMS induces changes in white matter (WM) microstructure in frontostriatal circuits after 2 weeks of therapy in patients with CUD and to test whether baseline WM microstructure of the same circuits affects clinical improvement. This study consisted of a 2-week, parallel-group, double-blind, randomized controlled clinical trial (acute phase) (sham [n = 23] and active [n = 27]), in which patients received two daily sessions of rTMS on the left dorsolateral prefrontal cortex (lDLPFC) as an add-on treatment. T1-weighted and high angular resolution diffusion-weighted imaging (DWI-HARDI) at baseline and 2 weeks after served to evaluate WM microstructure. After active rTMS, results showed a significant increase in neurite density compared with sham rTMS in WM tracts connecting lDLPFC with left and right ventromedial prefrontal cortex (vmPFC). Similarly, rTMS showed a reduction in orientation dispersion in WM tracts connecting lDLPFC with the left caudate nucleus, left thalamus, and left vmPFC. Results also showed a greater reduction in craving Visual Analogue Scale (VAS) after rTMS when baseline intra-cellular volume fraction (ICVF) was low in WM tracts connecting left caudate nucleus with substantia nigra and left pallidum, as well as left thalamus with substantia nigra and left pallidum. Our results evidence rTMS-induced WM microstructural changes in fronto-striato-thalamic circuits and support its efficacy as a therapeutic tool in treating CUD. Further, individual clinical improvement may rely on the patient's individual structural connectivity integrity.

In-vivoverified anatomically aware deep learning for real-time electric field simulation

Objective.Transcranial magnetic stimulation (TMS) has emerged as a prominent non-invasive technique for modulating brain function and treating mental disorders. By generating a high-precision magnetically evoked electric field (E-field) using a TMS coil, it enables targeted stimulation of specific brain regions. However, current computational methods employed for E-field simulations necessitate extensive preprocessing and simulation time, limiting their fast applications in the determining the optimal coil placement.Approach.We present an attentional deep learning network to simulate E-fields. This network takes individual magnetic resonance images and coil configurations as inputs, firstly transforming the images into explicit brain tissues and subsequently generating the local E-field distribution near the target brain region. Main results. Relative to the previous deep-learning simulation method, the presented method reduced the mean relative error in simulated E-field strength of gray matter by 21.1%, and increased the correlation between regional E-field strengths and corresponding electrophysiological responses by 35.0% when applied into another dataset.In-vivoTMS experiments further revealed that the optimal coil placements derived from presented method exhibit comparable stimulation performance on motor evoked potentials to those obtained using computational methods. The simplified preprocessing and increased simulation efficiency result in a significant reduction in the overall time cost of traditional TMS coil placement optimization, from several hours to mere minutes.Significance.The precision and efficiency of presented simulation method hold promise for its application in determining individualized coil placements in clinical practice, paving the way for personalized TMS treatments.

Test and re-test reliability of optimal stimulation targets and parameters for personalized neuromodulation

Protocols have been proposed to optimize neuromodulation targets and parameters to increase treatment efficacies for different neuropsychiatric diseases. However, no study has investigated the temporal effects of optimal neuromodulation targets and parameters simultaneously via exploring the test-retest reliability of the optimal neuromodulation protocols. In this study, we employed a publicly available structural and resting-state functional magnetic resonance imaging (fMRI) dataset to investigate the temporal effects of the optimal neuromodulation targets and parameters inferred from our customized neuromodulation protocol and examine the test-retest reliability over scanning time. 57 healthy young subjects were included in this study. Each subject underwent a repeated structural and resting state fMRI scan in two visits with an interval of 6 weeks between two scanning visits. Brain controllability analysis was performed to determine the optimal neuromodulation targets and optimal control analysis was further applied to calculate the optimal neuromodulation parameters for specific brain states transition. Intra-class correlation (ICC) measure was utilized to examine the test-retest reliability. Our results demonstrated that the optimal neuromodulation targets and parameters had excellent test-retest reliability (both ICCs > 0.80). The test-retest reliability of model fitting accuracies between the actual final state and the simulated final state also showed a good test-retest reliability (ICC > 0.65). Our results indicated the validity of our customized neuromodulation protocol to reliably identify the optimal neuromodulation targets and parameters between visits, which may be reliably extended to optimize the neuromodulation protocols to efficiently treat different neuropsychiatric disorders.

Pre-Stimulus Power but Not Phase Predicts Prefrontal Cortical Excitability in TMS-EEG

The cortical response to transcranial magnetic stimulation (TMS) has notable inter-trial variability. One source of this variability can be the influence of the phase and power of pre-stimulus neuronal oscillations on single-trial TMS responses. Here, we investigate the effect of brain oscillatory activity on TMS response in 49 distinct healthy participants (64 datasets) who had received single-pulse TMS over the left dorsolateral prefrontal cortex. Across all frequency bands of theta (4-7 Hz), alpha (8-13 Hz), and beta (14-30 Hz), there was no significant effect of pre-TMS phase on single-trial cortical evoked activity. After high-powered oscillations, whether followed by a TMS pulse or not, the subsequent activity was larger than after low-powered oscillations. We further defined a measure, corrected_effect, to enable us to investigate brain responses to the TMS pulse disentangled from the power of ongoing (spontaneous) oscillations. The corrected_effect was significantly different from zero (meaningful added effect of TMS) only in theta and beta bands. Our results suggest that brain state prior to stimulation might play some role in shaping the subsequent TMS-EEG response. Specifically, our findings indicate that the power of ongoing oscillatory activity, but not phase, can influence brain responses to TMS. Aligning the TMS pulse with specific power thresholds of an EEG signal might therefore reduce variability in neurophysiological measurements and also has the potential to facilitate more robust therapeutic effects of stimulation.

Cerebral blood flow changes induced by high-frequency repetitive transcranial magnetic stimulation combined with cognitive training in Alzheimer's disease

Background and purpose

Hypoperfusion of the posterior cingulate cortex (PCC) and precuneus has consistently been reported in patients with Alzheimer's disease (AD). Repetitive transcranial magnetic stimulation (rTMS) combined with cognitive training (COG) is effective in alleviating the symptoms of patients with mild AD. This study investigated the effects of rTMS-COG therapy on cerebral blood flow (CBF), with a special interest in the PCC/precuneus, and whether observed CBF changes are associated with changes in neuropsychological assessments in AD.

Materials and methods

Twenty-one patients with mild or moderate AD were randomly divided into real rTMS (n = 11) and sham treatment (n = 10) groups, both combined with COG. Neuro-navigated 10 Hz rTMS was used to stimulate the left dorsolateral prefrontal cortex (DLPFC) and then the left lateral temporal lobe (LTL) for 20 min each day for 4 weeks in the real rTMS group. All patients with AD underwent neuropsychological assessment, pseudo-continuous arterial spin labeling, and structural 3D T1-weighted MRI before treatment (T0), immediately after treatment (T1), and 4 weeks after treatment (T2). CBF in the precuneus, PCC, and stimulation targets at the region-of-interest (ROI) level, as well as whole-brain CBF changes at the voxel level, were compared between the two groups at three timepoints.

Results

rTMS-COG therapy revealed significant group × time interactions for the Mini-Mental State Examination (F = 5.339, p = 0.023, η² = 0.433) and activities of daily living (F = 5.409, p = 0.039, η² = 0.436) scores. The regional CBF in the precuneus showed a significant group × time interaction (F = 5.833, p = 0.027, η² = 0.593). For voxel-level analysis, a significant group main effect was found in the left limbic lobe cluster, with the maximal peak in the left parahippocampus (p < 0.001, uncorrected, peak at [-16 -8 -24]). Simple effects analysis indicated that rTMS-COG therapy induced a decrease in CBF in the precuneus at T1 (p = 0.007) and an increase in the left parahippocampus at T2 (p=0.008). CBF decrease in the precuneus was correlated with better cognitive function immediately after treatment (T1) (r =-0.732, p=0.025).

Conclusion

Neuropsychological assessments showed immediate and long-term effects on cognitive function and activities of daily living after rTMS-COG therapy. CBF changes induced by high-frequency rTMS-COG therapy are region-dependent, showing immediate effects in the precuneus and long-term effects in the left parahippocampus. These results provide imaging evidence to understand the underlying neurobiological mechanism for the application of rTMS-COG in AD.