A scalp-measurement based parameter space: Towards locating TMS coils in a clinically-friendly way

Jiang, Yihan; Du, Bin; Chen, Yuanyuan; Wei, Lijiang; Zong, Zhi‐Min; Cao, Zhengcao; Xie, Cong; Li, Quanqun; Cai, Zhongxuan; Li, Zheng; Zhu, Chaozhe

doi:10.1016/j.brs.2022.06.001

Cited by 12 publications

(13 citation statements)

References 16 publications

(12 reference statements)

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“…We utilized a scalp geometry-based parameter space that describes any possible TMS coil placement with two key parameters (position s and orientation θ) on the individual scalp surface ( Jiang et al, 2022 ). The description of position s is a pair of coordinates ( p NZ , p AL ) on a continuous proportional coordinate system (CPC), where p NZ indicates the position along nasion to inion direction, p AL indicates the position along with left preauricular point (AL) to right preauricular point (AR) direction, and ( p NZ and p AL ) ∈[0 1] × [0 1] ( Xiao et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

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Targeting the pathological network: Feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders

et al. 2023

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It has been recognized that the efficacy of TMS-based modulation may depend on the network profile of the stimulated regions throughout the brain. However, what profile of this stimulation network optimally benefits treatment outcomes is yet to be addressed. The answer to the question is crucial for informing network-based optimization of stimulation parameters, such as coil placement, in TMS treatments. In this study, we aimed to investigate the feasibility of taking a disease-specific network as the target of stimulation network for guiding individualized coil placement in TMS treatments. We present here a novel network-based model for TMS targeting of the pathological network. First, combining E-field modeling and resting-state functional connectivity, stimulation networks were modeled from locations and orientations of the TMS coil. Second, the spatial anti-correlation between the stimulation network and the pathological network of a given disease was hypothesized to predict the treatment outcome. The proposed model was validated to predict treatment efficacy from the position and orientation of TMS coils in two depression cohorts and one schizophrenia cohort with auditory verbal hallucinations. We further demonstrate the utility of the proposed model in guiding individualized TMS treatment for psychiatric disorders. In this proof-of-concept study, we demonstrated the feasibility of the novel network-based targeting strategy that uses the whole-brain, system-level abnormity of a specific psychiatric disease as a target. Results based on empirical data suggest that the strategy may potentially be utilized to identify individualized coil parameters for maximal therapeutic effects.

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Section: Methodsmentioning

confidence: 99%

“…For anti-clockwise rotation, θ∈(0° to 180°]. In practice, both parameters of s and θ can be implemented with manual measurement ( Jiang et al, 2022 ) and computer-assistant navigation ( Xiao et al, 2018 ; Jiang et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Targeting the pathological network: Feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders

et al. 2023

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“…The copyright holder for this preprint this version posted June 30, 2023. ; https://doi.org/10.1101/2023.06.28.545400 doi: bioRxiv preprint T1 images of 134 participants and generate individual parameter spaces using their head surface nodes (Jiang et al, 2022). For MDD, a total of 462 coil placements (462 positions × 1 orientation) were used for calculation for each individual.…”

Section: Tms Coil Placementsmentioning

confidence: 99%

“…The coil orientation was fixed at 45° from the midline, and the coil handle pointed backward (Fitzgerald et al, 2003;Thomson et al, 2013)(Supplementary Figure 2Aii). For schizophrenia with auditory verbal hallucinations (AVH), the handle direction was perpendicular to the TP3 line, which was around 23° from the midline measured within the Scalp Geometry-based Parameter (SGP) coordinate system (Jiang et al, 2022). The handle pointed backward (Paillère- Martinot et al, 2017)(Supplementary Figure 2Bii).…”

Section: Tms Coil Placementsmentioning

confidence: 99%

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

Cao

Xiao

Xie

et al. 2023

Preprint

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Consider the limited clinical efficacy of transcranial magnetic stimulation (TMS) due to heterogeneity in treatment outcomes, the utilization of individual functional connectivity (FC) can enhance the prediction accuracy in the network targeting model. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC (rsFC). To overcome this challenge, proposed solutions include increasing the scan duration and employing clustering approaches to enhance the stability of FC. In this study, we aimed to evaluate the stability of a personalized functional-based network targeting model in individuals with major depressive disorder (MDD) and schizophrenia with auditory verbal hallucinations (AVH). Using resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP), we assessed the model's stability and employed longer scan durations (7 minutes, 14 minutes, 21 minutes, 28 minutes) and clustering methodologies to improve the precision of identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the clustering approach lead to stable identification of individual sites, as evidenced by the intraindividual distance falling below the ~1cm spatial resolution of TMS. These findings contribute to the understanding of individualized TMS targeting and have implications for improving treatment outcomes in psychiatric disorders.

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“…The error of the iterative closest point (ICP) registration method is around 2-3 mm, but dozens of position points need to be collected on the subject's head [13][14]. Due to their high application cost and cumbersome operation, neuro-navigation technology is not widely used in the clinical practice of TMS [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

3D-Printed helmet-type Neuro-navigation approach (I-Helmet) for transcranial magnetic stimulation

Wang,

Cui,

Jin

et al. 2023

Preprint

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Neuro-navigation is a key technology to ensure the clinical efficacy of TMS. However, the neuro-navigation system based on positioning sensor is currently unable to be promoted and applied in clinical practice due to its time-consuming and high-cost. In the present study, we designed I-Helmet system to promote an individualized and clinically friendly neuro-navigation approach to TMS clinical application. I-Helmet system is based on C + + with a graphical user interface that allows users to design a 3D-printed helmet model for coil navigation. Besides, a dedicated coil positioning accuracy detection method was promoted based on three-dimensional (3D) printing and 3D laser scanning for evaluation. T1 images were collected from 24 subjects, and based on each image, phantom were created to simulate skin and hair. Six 3D-printed helmets with the head positioning hole enlarged by 0–5% tolerance in 1% increments were designed to evaluate the influences of skin, hair, and helmet-tolerance on the positioning accuracy and contact force of I-Helmet. Finally, I-Helmet system was evaluated by comparing its positioning accuracy with three skin hardnesses, three hair styles, three operators, and with or without landmarks. The accuracy of the proposed coil positioning accuracy detection method was about 0.30 mm in position and 0.22° in orientation. Skin and hair had significant influences on positioning accuracy (p < 0.0001), whereas different skin hardnesses, hair styles, and operators did not (p > 0.05). The tolerance of the helmet presented significant influences on positioning accuracy (p < 0.0001) and contact force (p < 0.0001). The positioning accuracy significantly increased (p < 0.0001) with landmark guided I-Helmet. 3D-printed helmet-type Neuro-navigation approach (I-Helmet) with 3% tolerance and landmarks met the positioning requirements for TMS in clinical practice with less than 5 N mean contact force, 3–5 mm positioning accuracy, 65.7 s mean operation time, and 50-yuan material cost.

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A scalp-measurement based parameter space: Towards locating TMS coils in a clinically-friendly way

Cited by 12 publications

References 16 publications

Targeting the pathological network: Feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders

Targeting the pathological network: Feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders

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

3D-Printed helmet-type Neuro-navigation approach (I-Helmet) for transcranial magnetic stimulation

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