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.
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.
Preliminary experiments have found the accuracy of this network to be \(7.26\pm 5.30\) mm, compared to \(9.39\pm 4.63\) mm for deformable registration and \(6.94\pm 5.10\) 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.
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.
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Ahdab R, Ayache S, Brugières P, Goujon C, Lefaucheur JP (2010) Comparison of “standard” and “navigated” procedures of TMS coil positioning over motor, premotor and prefrontal targets in patients with chronic pain and depression. Neurophysiol Clin/Clin Neurophysiol 40(1):27–36
Avants BB, Epstein CL, Grossman M, Gee JC (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12(1):26–41
Baxter JSH, Maguet E, Jannin P (2020) Localisation of the subthalamic nucleus in MRI via convolutional neural networks for deep brain stimulation planning. In: Medical imaging 2020: image-guided procedures, robotic interventions, and modeling. vol. 11315. International Society for Optics and Photonics, p 113150M
Bulteau S, Beynel L, Marendaz C, Dall’Igna G, Peré M, Harquel S, Chauvin A, Guyader N, Sauvaget A, Vanelle JM, Polosan M, Bougerol T, Brunelin J, Szekely D (2019) Twice-daily neuronavigated intermittent theta burst stimulation for bipolar depression: a randomized sham-controlled pilot study. Neurophysiol Clin 49(5):371–375
Freitas C, Mondragón-Llorca H, Pascual-Leone A (2011) Noninvasive brain stimulation in Alzheimer’s disease: systematic review and perspectives for the future. Exp Gerontol 46(8):611–627
Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC (2018) Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans Med Imaging 37(8):1822–1834
Harika-Germaneau G, Rachid F, Chatard A, Lafay-Chebassier C, Solinas M, Thirioux B, Millet B, Langbour N, Jaafari N (2019) Continuous theta burst stimulation over the supplementary motor area in refractory obsessive-compulsive disorder treatment: a randomized sham-controlled trial. Brain Stimul 12(6):1565–1571
Heimann T, Meinzer HP (2009) Statistical shape models for 3d medical image segmentation: a review. Med Image Anal 13(4):543–563
Johnson S, Summers J, Pridmore S (2006) Changes to somatosensory detection and pain thresholds following high frequency repetitive TMS of the motor cortex in individuals suffering from chronic pain. Pain 123(1–2):187–192
Kim WJ, Min YS, Yang EJ, Paik NJ (2014) Neuronavigated vs. conventional repetitive transcranial magnetic stimulation method for virtual Lesioning on the Broca’s area. Neuromodul Technol Neural Interface 17(1):16–21
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp 1097–1105
Lefaucheur JP, Aleman A, Baeken C, Benninger DH, Brunelin J, Di Lazzaro V, Filipović SR, Grefkes C, Hasan A, Hummel FC, Jääskeläinen SK, Kimiskidis VK, Koch G, Langguth B, Nyffeler T, Oliviero A, Padberg F, Poulet E, Rossi S, Rossini PM, Rothwell JC, Schönfeldt-Lecuona C, Siebner HR, Slotema CW, Stagg CJ, Valls-Sole J, Ziemann U, Paulus W, Garcia-Larrea L (2020) Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (RTMS): an update (2014–2018). Clin Neurophysiol 131(2):474–528
Pennisi G, Ferri R, Lanza G, Cantone M, Pennisi M, Puglisi V, Malaguarnera G, Bella R (2011) Transcranial magnetic stimulation in Alzheimer’s disease: a neurophysiological marker of cortical hyperexcitability. J Neural Transm 118(4):587–598
Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. pp 91–99
Ronneberger O, Fischer P, Brox T (2015) U-NET: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241
Rusjan PM, Barr MS, Farzan F, Arenovich T, Maller JJ, Fitzgerald PB, Daskalakis ZJ (2010) Optimal transcranial magnetic stimulation coil placement for targeting the dorsolateral prefrontal cortex using novel magnetic resonance image-guided neuronavigation. Hum Brain Mapp 31(11):1643–1652
Summers JJ, Kagerer FA, Garry MI, Hiraga CY, Loftus A, Cauraugh JH (2007) Bilateral and unilateral movement training on upper limb function in chronic stroke patients: a TMS study. J Neurol Sci 252(1):76–82
Vignaud P, Damasceno C, Poulet E, Brunelin J (2019) Impaired modulation of corticospinal excitability in drug-free patients with major depressive disorder: a theta-burst stimulation study. Front Hum Neurosci 13:72
Weise K, Numssen O, Thielscher A, Hartwigsen G, Knösche TR (2020) A novel approach to localize cortical TMS effects. Neuroimage 209:116486
John S. H. Baxter is supported by the Institut des Neurosciences Cliniques de Rennes (INCR) and the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Post-Doctoral Fellowship (PDF) program. Quoc Anh Bui is funded through a financial support from Région Bretagne. Ehouarn Maguet is also supported INCR. The authors would like to thank X. Morandi, C. Nauczyciel, B. Le Goff, and J.-P. N’Guyen for the annotation of the non-motor treatment points. The authors would also like to thank J.-P. N’Guyen and H. Hodaj for their assistance in annotating the chronic pain treatment points along with J.-P. Lefaucheur.
No funding was received to assist in the preparation of this manuscript.
Conflict of interest
S. Croci, A. Delmas and L. Bredoux are employees of SYNEIKA. The remaining authors have no financial or non-financial conflicts of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
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Baxter, J.S.H., Bui, Q.A., Maguet, E. et al. Automatic cortical target point localisation in MRI for transcranial magnetic stimulation via a multi-resolution convolutional neural network. Int J CARS 16, 1077–1087 (2021). https://doi.org/10.1007/s11548-021-02386-1
- Transcranial magnetic stimulation
- Deep learning
- Convolutional neural networks