Schizophrenia is a neurological disorder known to influence the motor region. Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique being investigated as a treatment for schizophrenia. Resting motor threshold (RMT) is the dosage parameter for TMS treatment protocols and is known to vary between participants with limited understanding of the drivers of this variance. Previous investigations have used functional magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and individual level neuroanatomy to explain RMT variability. Our previous investigation showed neuroanatomy influences RMT in both schizophrenia and healthy populations and this relationship was weakened by the presence of schizophrenia. In this study, 54 participants with schizophrenia-diagnosed, who were antipsychotic naive and 43 non-impaired controls underwent single pulse TMS, structural magnetic resonance imaging, and fMRI. An independent component analysis (ICA) was used to process fMRI data into 25 distinct channels where correlations were derived between channels. Linear and multiple regression models were used to evaluate first, the influence of these channel interactions on RMT followed by their influence when individual level neuroanatomy was also considered. We found that between-channel functional connectivity was altered in individuals with schizophrenia and that fMRI can contribute to prediction of RMT, but differently in both cohorts and to a lesser degree than individual level neuroanatomical measures. This suggests that functional connectivity influences TMS response and fMRI might help in dosage calculations of clinical TMS protocols.

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation technique used in the treatment of several neurological conditions. The dosage parameter for TMS protocols is the resting motor threshold (RMT) which has been shown to vary between participants with limited understanding. The goal of this study was to investigate how white matter-derived fiber tracts integrated into finite element analysis simulations influence TMS response in the form of RMT. Ten healthy participants were included in this study who underwent TMS, diffusion tensor imaging, and structural magnetic resonance imaging (MRI). Anatomically accurate head models were created and fiber tracts were extracted from Diffusion tensor imaging and integrated into these head models before finite element analysis simulations were performed to model the effects of empirical TMS. Linear mixed effects models were used to evaluate how the induced electric field strength on the fiber tracts (EFSTract) influenced RMT. We found the induced electric field strength along fiber tracts did influence RMT, however the effect of this relationship on RMT is not clinically relevant due to its small magnitude. This suggests finite element analysis of the fiber tracts is not meaningful when tracts are considered a homogenous material and thus lacking physiology. However, tractography provides a valuable framework within which to organize physiological models of signal transmission, and it is likely a combination of this approach with more physiologically detailed modeling would provide more accurate RMT prediction.

Transcranial magnetic stimulation (TMS) is a non-invasive method for treating neurological and psychiatric disorders. It is being tested as an experimental treatment for patients with mild to moderate traumatic brain injuries (mTBI). Due to the complex, heterogeneous composition of the brain, it is difficult to determine if targeted brain regions receive the correct amount of electric field (E-field) induced by the TMS coil. E-field distributions can be calculated by running time-consuming finite element analysis (FEA) simulations of TMS on patient head models. Using machine learning, the E-field can be predicted in real-time. Our prior work used a Deep Convolutional Neural Network (DCNN) to predict the E-field in healthy patients. This study applies the same DCNN to mTBI patients and investigates how model depth and color space of E-field images affect model performance. Nine  DCNNs were created using combinations of 3, 4, or 5 encoder and decoder blocks with the color spaces RGB, LAB, and YCbCr. As depth increased, training and testing peak signal-to-noise ratios (PSNR) increased and mean squared errors (MSE) decreased. The depth 5 YCbCr model had the highest training and testing PSNRs of 34.77 dB and 29.08 dB and lowest training and testing MSEs of 3.335 * 10-4 and 1.237 * 10-3 respectively. Compared to the model in our prior work, models of depth 5 have higher testing PSNRs and lower MSEs and, except for RGB. Thus, DCNNs with depth 5 and alternative color spaces, despite losing information through color space conversions, resulted in higher PSNRs and lower MSEs.

Transcranial magnetic stimulation (TMS) is utilized as a treatment method for a variety of neurological and psychiatric disorders. The standard dose parameter for administration of TMS is known as the resting motor threshold (RMT). Between individuals, the RMT has traditionally been thought to reflect differences in neuroanatomy; however, the functional state of the brain has also been shown to have an influence on this value. In this study, 19 mild traumatic brain injury participants (mTBI) resting state electroencephalography (EEG) was obtained before undergoing TMS treatment. Various time frequency and power spectra values were derived from this EEG data to examine the relationship between RMT and neurophysiology. These EEG connectivity metrics were then used to establish relationships with RMT alone and accounting for differences in individual neuroanatomy. We found that the incorporation of EEG functional connectivity improved multiple regression model predictions of inter-participant RMT variability than neuroanatomy alone. Future investigations into the evaluation of EEG in predicting RMT variability should include a larger participant population with a healthy control.

Transcranial magnetic stimulation is a noninvasive neuromodulation technique utilized to treat several neuropsychiatric disorders. Schizophrenia patients typically experience positive and negative symptoms, which are commonly treated with antipsychotic medications. Repetitive (r)TMS is explored to alleviate these symptoms by modulating cortical excitability. TMS stimulation strength is determined by the resting motor threshold (RMT), which is determined from single pulse stimulation to the precentral gyrus region of the brain. In this study, 62 schizophrenia and 41 healthy participants’ magnetic resonance imaging (MRI) were used to create anatomically accurate head models. Head models were then used to calculate the coil-to-cortex distance (CCD), gray matter volume (GMV), and finite element analysis was performed to calculate the induced electric field strength (EFS) and depolarized gray matter volume (DGMV). These MRI-derived parameters were then evaluated to establish their relationship with clinically collected RMT in both healthy and schizophrenia populations. In both populations, RMT correlated with CCD, EFS, and DGMV. Our hypothesis that the presence of schizophrenia would have a negative impact on the influence of MRI model-driven parameters was largely supported. Future investigations into RMT variability should incorporate structural connectivity using white matter-based fiber tractography and functional connectivity.