Network-level macroscale structural connectivity predicts propagation of transcranial magnetic stimulation

Abstract

Information processing in the brain is mediated by structural white matter pathways and is highly dependent on topological brain properties. Here we combined transcranial magnetic stimulation (TMS) with high-density electroencephalography (EEG) and Diffusion Weighted Imaging (DWI), specifically looking at macroscale connectivity to understand whether regional, network-level or whole-brain structural properties are more responsible for stimulus propagation. Neuronavigated TMS pulses were delivered over two individually defined nodes of the default mode (DMN) and dorsal attention (DAN) networks in a group of healthy subjects, with test-retest reliability assessed 1-month apart. TMS-evoked activity was predicted by the modularity and structural integrity of the stimulated network rather than the targeted region(s) or the whole-brain connectivity, suggesting network-level structural connectivity as more relevant than local and global brain properties in shaping TMS signal propagation. The importance of network structural connectome was unveiled only by evoked activity, but not resting-state data. Future clinicals interventions might enhance target engagement by adopting DWI-guided, network-focused TMS.

Keywords: Default mode network; Dorsal attention network; Network; Structural connectivity; Transcranial magnetic stimulation.

Fig. 1.

Study design and conceptual framework. (A) TMS targets were individualized based on resting-state fMRI data. A high degree of variability in functional connectivity of the TMS targets was present. (B) fMRI-guided TMS was applied to two neighboring parietal nodes corresponding to the DMN and DAN. Anatomical MRI were used for the neuronavigation of the TMS spots while hd-EEG with 64 channels was simultaneously recorded. (C) TMS-induced electric field was modelled with SimNIBS (Thielscher et al., 2015). The final map was overlapped with the 7 Network parcellation (Schaefer et al., 2018) in order to assure network engagement specificity. (D) The EEG signal was projected at source level using dynamic statistical parametric mapping (dSPM) and constraining source dipoles to the cortical surface. The RSNs time series were extracted for both DMN and DAN. The raw time series were first rectified (Cheng et al., 2013 ) and then a baseline bootstrapping procedure (Lv et al., 2007 ) was applied. Then, 1000 permutation t -test were performed in which the surrogated post-TMS vs pre-TMS difference was computed after each iteration and statistically compared with the real difference ( Pernet et al., 2015 ). Finally, the cluster threshold was determined as the 95th percentile of the cluster’s surrogate distribution and the area under the curve (AUC) of the significant clusters was extracted. (E) The individual whole brain structural connectome was computed. A five-layers hierarchical model was created where several connectivity metrics were extracted, ranging from local to whole-brain measures. Note: V/m: Volt per meter; DAN: Dorsal attention network; DMN: Default mode network. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2.

Specificity of network engagement and reproducibility of TMS-EEG measures. (A) Individualized DMN and DAN targets mapped to MNI space are provided to show variability of TMS sites across individuals. (B) Average TMS-induced electric field as modelled with simNIBS ( Thielscher et al., 2015 ). The normalized electric field (Efield) distribution was thresholded considering only the 83% of the maximal E-field. For each site/session, the thresholded cluster was overlapped with the RSNs parcellation by Yeo et al. (2011) ( Schaefer et al., 2018 ) in order to quantify network engagement. (C) Quantitative spatial overlap analysis ( Dice, 1945 ) between thesholded E-field maps and stimulated networks. High overlap was found between the stimulated network and the E-field maps both for DMN (top, 91.3%) and DAN (bottom, 86.9%). (D) The percentage of network engagement as measured via EEG source analysis for each network is shown, demonstrating high propagation specificity after TMS of DMN and DAN. (E) Test-retest reliability of source-level network engagement showing high reproducibility across visits for both DMN (top, red lines) and DAN (bottom, green lines). (F) Subject’s topographical maps for visit 1 (top, magenta) and visit 2 (bottom, cyan) show the reproducibility of TMS-evoked measures. (G) Evoked activity map (left) and EEG time series for electrodes F6, C6 and P6 (right) for visit 1 (magenta line) and visit 2 (cyan line). Note: V/m: Volt per meter; AUC: Area under the curve; DAN: Dorsal attention network, SM: Sensorimotor network; VIS: Visual network; DMN: Default mode network; FPN: Fronto-parietal network; LIM: Limbic network; AS: Anterior salience network. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig 3.

structural connectivity predictors of TMS-EEG propagation. (A) At visit 1 (left panel) a significant positive correlation was found between the structural connectivity of the stimulated network and the TMS-EEG response for both DMN (R ² = 23%, p = 0.02) and DAN (R ² = 32%, p = 0.006). The same pattern was observed at visit 2 (right panel) for both DMN (R ² = 23%, p = 0.02) and DAN (R ² = 25%, p = 0.01). (B) With DMN stimulation (top), a significant positive correlation was found between brain modularity and DMN response (red dots) for both visit 1 (R ² = 18%, p = 0.04) and visit 2 (R ² = 27%, p = 0.01), while no significant correlation was found for DAN (green dots) for both visit 1 (R ² = 0.002%, p = 0.83) and visit 2 (R ² = 0.005%, p = 0.73). With DAN stimulation (bottom), a significant positive correlation was found between the brain modularity and the DAN response (green dots) for both visit 1 (R ² = 21%, p = 0.03) and visit 2 (R ² = 23%, p < 0.02), while no significant correlation was found for DMN (red dots) for both visit 1 (R ² = 0.02%, p = 0.45) and visit 2 (R ² = 0.01%, p = 0.60). Note: AUC: Area under the curve; DAN: Dorsal attention network; DMN: Default mode network. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig 4.

Control analyses. (A) No significant correlation was found between the TMS-EEG response and Bra in (Visit 1: DMN: R² = 0.009%, p < 0.65; DAN: R² = 0.08%, p < 0.18; Visit 2: DMN: R² = 0.005%, p < 0.73; DAN: R² = 0.11%, p < 0.14), Stim2Network (Visit 1: DMN: R² = 0.001%, p < 0.85; DAN: R² = 0.01%, p < 0.49; Visit 2: DMN: R² = 0.08%, p < 0.21; DAN: R² = 0.04%, p < 0.26) or Stim2Brain (Visit 1: DMN: R² = 0.03%, p < 0.39; DAN: R² = 0.008%, p < 0.69; Visit 2: DMN: R² = 0.03%, p < 0.38; DAN: R² = 0.0001%, p < 0.99) connectivity. (B) FPN (top) and VIS (bottom) intrinsic connectivity do not correlate with visit 1 and visit 2 of TMS-EEG response both for DMN (left panel: visit 1 FPN: R² = 0.006%, p = 0.72; VIS: R² = 0.004%, p = 0.75; visit 2: FPN: R² = 0.02%, p = 0.44; VIS: R ² = 0.00001%, p = 0.98) and DAN (right panel: visit 1: FPN: R² = 0.06%, p = 0.25; VIS: R² = 0.04%, p = 0.34; visit 2: FPN: R² = 0.03%, p = 0.37; VIS: R² = 0.05%, p = 0.28). Note: AUC: Area under the curve; DAN: Dorsal attention network; DMN: Default mode network.

Fig. 5.

Spontaneous vs Evoked EEG activity. (A) While TMS-evoked activity display correlations with network-level structural connectivity (red, green), resting-state EEG is not correlated with structural connectivity of the stimulated network, for both DMN (left panel: visit 1: R² = 0.002%, p = 0.83; visit 2: R² = 0.01%, p = 0.54) and DAN (right panel: visit 1: R² = 0.09%, p = 0.16; visit 2: R² = 0.01%, p = 0.64). (B) TMS-evoked activity was correlated with brain structural modularity (red, green) whereas no relationship was found considering resting-state EEG baseline for both DMN (left panel: visit 1: R² = 0.005%, p = 0.74; visit 2: R² = 0.0003%, p = 0.91) and DAN (right panel: visit 1: R² = 0.002%, p = 0.82; visit 2: R² = 0.03%, p = 0.39). Note: AUC: Area under the curve; DAN: Dorsal attention network; DMN: Default mode network. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)