Targeting Lower Limb, Upper Limb, and Face Representation in the Primary Motor Cortex for the Practice of Neuronavigated Transcranial Magnetic Stimulation

OBJECTIVE

The primary motor cortex (M1) is a usual target for therapeutic application of repetitive transcranial magnetic stimulation (rTMS), especially the region of hand motor representation. However, other M1 regions can be considered as potential rTMS targets, such as the region of lower limb or face representation. In this study, we assessed the localization of all these regions on magnetic resonance imaging (MRI) with the aim of defining three standardized M1 targets for the practice of neuronavigated rTMS.

MATERIALS AND METHODS

A pointing task of these targets was performed by three rTMS experts on 44 healthy brain MRI data to assess interrater reliability (including the calculation of intraclass correlation coefficients [ICCs] and coefficients of variation [CoVs] and the construction of Bland-Altman plots). In addition, two "standard" brain MRI data were randomly interspersed with the other MRI data to assess intrarater reliability. A barycenter was calculated for each target (with x-y-z coordinates provided in normalized brain coordinate systems), in addition to the geodesic distance between the scalp projection of the barycenters of these different targets.

RESULTS

Intrarater and interrater agreement was good, according to ICCs, CoVs, or Bland-Altman plots, although interrater variability was greater for anteroposterior (y) and craniocaudal (z) coordinates, especially for the face target. The scalp projection of the barycenters between the different cortical targets ranged from 32.4 to 35.5 mm for either the lower-limb-to-upper-limb target distance or the upper-limb-to-face target distance.

CONCLUSIONS

This work clearly delineates three different targets for the application of motor cortex rTMS that correspond to lower limb, upper limb, and face motor representations. These three targets are sufficiently spaced to consider that their stimulation can act on distinct neural networks.

Reference-free Bayesian model for pointing errors of typein neurosurgical planning

PURPOSE

Many neurosurgical planning tasks rely on identifying points of interest in volumetric images. Often, these points require significant expertise to identify correctly as, in some cases, they are not visible but instead inferred by the clinician. This leads to a high degree of variability between annotators selecting these points. In particular, errors of type are when the experts fundamentally select different points rather than the same point with some inaccuracy. This complicates research as their mean may not reflect any of the experts' intentions nor the ground truth.

METHODS

We present a regularised Bayesian model for measuring errors of type in pointing tasks. This model is reference-free; in that it does not require a priori knowledge of the ground truth point but instead works on the basis of the level of consensus between multiple annotators. We apply this model to simulated data and clinical data from transcranial magnetic stimulation for chronic pain.

RESULTS

Our model estimates the probabilities of selecting the correct point in the range of 82.6[Formula: see text]88.6% with uncertainties in the range of 2.8[Formula: see text]4.0%. This agrees with the literature where ground truth points are known. The uncertainty has not previously been explored in the literature and gives an indication of the dataset's strength.

CONCLUSIONS

Our reference-free Bayesian framework easily models errors of type in pointing tasks. It allows for clinical studies to be performed with a limited number of annotators where the ground truth is not immediately known, which can be applied widely for better understanding human errors in neurosurgical planning.

Automatic cortical target point localisation in MRI for transcranial magnetic stimulation via a multi-resolution convolutional neural network

PURPOSE

Transcranial magnetic stimulation (TMS) is a growing therapy for a variety of psychiatric and neurological disorders that arise from or are modulated by cortical regions of the brain represented by singular 3D target points. These target points are often determined manually with assistance from a pre-operative T1-weighted MRI, although there is growing interest in automatic target point localisation using an atlas. However, both approaches can be time-consuming which has an effect on the clinical workflow, and the latter does not take into account patient variability such as the varying number of cortical gyri where these targets are located.

METHODS

This paper proposes a multi-resolution convolutional neural network for point localisation in MR images for a priori defined points in increasingly finely resolved versions of the input image. This approach is both fast and highly memory efficient, allowing it to run in high-throughput centres, and has the capability of distinguishing between patients with high levels of anatomical variability.

RESULTS

Preliminary experiments have found the accuracy of this network to be [Formula: see text] mm, compared to [Formula: see text] mm for deformable registration and [Formula: see text] mm for a human expert. For most treatment points, the human expert and proposed CNN statistically significantly outperform registration, but neither statistically significantly outperforms the other, suggesting that the proposed network has human-level performance.

CONCLUSIONS

The human-level performance of this network indicates that it can improve TMS planning by automatically localising target points in seconds, avoiding more time-consuming registration or manual point localisation processes. This is particularly beneficial for out-of-hospital centres with limited computational resources where TMS is increasingly being administered.