Dynamic functional changes upon thalamotomy in essential tremor depend on baseline brain morphometry

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Bolton, Dimitri Van De Ville, Jean Régis, Tatiana Witjas, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2702374/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jan, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Patients with drug-resistant essential tremor (ET) may undergo Gamma Knife stereotactic radiosurgical thalamotomy (SRS-T), where the ventro-intermediate nucleus of the thalamus (Vim) is lesioned by focused beams of gamma radiations to induce clinical improvement. Here, we studied SRS-T impacts on left Vim dynamic functional connectivity (dFC, n = 23 ET patients scanned before and 1 year after intervention), and on surface-based morphometric brain features ( n = 34 patients, including those from dFC analysis). In matched healthy controls (HCs), three dFC states were extracted from resting-state functional MRI data. In ET patients, state 1 spatial stability increased upon SRS-T ( p = 0.0041). Lower pre-intervention spatial variability in state 2 expression, and more frequent expression of state 3 over state 1, correlated with greater clinical recovery ( p = 0.015 and p = 0.008, respectively). ET morphometric profiles showed significantly lower similarity to HCs in 13 regions upon SRS-T ( p ≤ 0.02), and a joint analysis revealed that before thalamotomy, morphometric similarity and states 2/3 mean spatial similarity to HCs were anticorrelated, a relationship that disappeared upon SRS-T ( p < 0.001). Our results show that left Vim functional dynamics directly relates to upper limb tremor lowering upon intervention, while morphometry instead has a supporting role in reshaping such dynamics. Biological sciences/Neuroscience Biological sciences/Neuroscience/Diseases of the nervous system probabilistic modelling cortical thickness surface area mean curvature temporal occurrences cross-modality analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Essential tremor (ET) is a prominent, extremely disabling movement disorder affecting approximately 3% of individuals older than 80 years.1 It is primarily characterized by the presence of upper limb action tremor for at least 3 years,2 and can induce other symptoms such as impairments in executive function and memory, mood disorders and dementia.3–4 While primary pathological and subsequent compensatory molecular and morphological brain changes are believed to center around the cerebellum and eventually recruit deeper structures,5 tremor generation and maintenance crucially involve cerebellar functional interplays within the cerebello-dentato-rubro-olivary-cerebellar6-7 and cortico-ponto-cerebello-dentato- thalamo-cortical2,8 networks. The ventro-intermediate nucleus of the thalamus (Vim) is centrally placed within the latter network and is a well-established surgical target for standard deep brain stimulation (DBS) or stereotactic ablation for tremor,9 which are indicated for the ET patients who cannot tolerate or do not properly respond to commonly prescribed medications.10 Standard DBS applies high-frequency electrical pulses through deeply implanted electrodes and is considered safe and efficient to treat ET. It enables stimulation interruption or fine-tuning if needed, but comes with inherent risks ( e.g. , infection) given the required intervention and the reliance on implanted hardware.11–12 More recently, magnetic resonance-guided focused ultrasound thalamotomy (MRgFUS) generates a lesion by controlled thermocoagulation, and does not necessitate an invasive operation, while providing a risk/benefit balance akin to more established standard surgical approaches.10,13 Gamma Knife stereotactic radiosurgical thalamotomy is a minimally invasive alternative that uses stereotactic coordinates to target the Vim, avoiding the need for open surgery, while focusing multiple beams of gamma radiations. It is considered a valuable alternative, particularly for patients with medical comorbidities or uneasy with the prospect of an operation, but it has two main limitations: the target site cannot be confirmed intraoperatively, and clinical benefits only appear after a median of three months, and sometimes up to one year after the intervention.14 Past works have validated radiosurgical thalamotomy as a safe and effective noninvasive surgical strategy in ET.15–18 How the brain of ET patients changes following radiosurgical thalamotomy, and which features may serve as pre-interventional predictors of clinical improvement, are important questions that have been explored in several structural and functional studies. In recent work quantifying cortical thickness (CT), surface area (SA) and mean curvature (MC) as morphometric descriptors of cortical brain regions ( i.e. , surface-based morphometry [SBM] analysis), the statistical dependences across them (inferred through Pearson’s correlation coefficient) were compared before and after the thalamotomy. At the whole-brain level, CT and MC became anticorrelated following thalamotomy, an effect mostly driven by the left fusiform and paracentral gyri, left posterior cingulate cortex, right banks superior temporal sulcus and right inferior temporal cortex. On the other hand, SA and MC became correlated, mostly so in the bilateral fusiform gyrus and right inferior temporal cortex.19 Graph theoretical investigations provided further insight on regional changes, as CT in the right lingual and bilateral rostral middle frontal gyri exhibited lower dependence to MC of the rest of the brain after thalamotomy, while conversely, an increase was seen for the left precentral gyrus.20 In reports characterizing the functional interplays of the targeted left Vim by computing its resting-state functional connectivity (RS FC) to the rest of the brain, FC to the right insular and orbitofrontal cortices, to the right posterior parietal, supramarginal and inferior frontal gyri, and to the bilateral frontal eye fields decreased to non-significant values upon thalamotomy, while conversely, FC to the right supplementary motor area increased to around zero from an initial anticorrelation.21 In addition, more negative left Vim FC to the ipsilateral fusiform gyrus pre-intervention correlated with larger clinical improvement.22 Despite their valuable insight, the above previous studies suffer from some shortcomings. Given their reliance on Pearson’s correlation coefficient, the conducted SBM investigations could not individually address the effects of cross-property covariance (which would contribute to the numerator of the coefficient) and variance of individual properties (which would instead impact its denominator). In addition, only pairs of morphometric properties were considered in each assessment, falling short of a fully multivariate treatment. As for functional analyses, they operated under the assumption of static cross-regional interplays, even though FC exhibits prominent temporal dynamics with cognitive and clinical relevance.23–24 In ET, only one study to date has explicitly examined functional brain dynamics and its changes upon thalamotomy,25 but it focused on co-(de)activations with the right extrastriate cortex. The dynamic interplays of the targeted Vim, and their evolution upon thalamotomy, thus remain unknown. In the present work, we seek to overcome these limitations in a joint analysis of SBM and RS functional MRI data acquired on a partly overlapping set of patients with ET, scanned before and 1 year after radiosurgical thalamotomy. We address the following questions: (1) whether thalamotomy induces a renormalization of morphometric and dynamic functional brain properties in ET , (2) which pre-thalamotomy features better correlate with clinical recovery , and (3) how potential structure/function couplings are impacted by the intervention . To answer these questions, we quantify similarity of the pre- and post-thalamotomy ET data points (respectively termed ETpre and ETpost from there onwards) to a set of matched healthy controls (HCs). For SBM analysis, we leverage a recently introduced analytical pipeline26 to model the HC morphometric data by a multivariate Gaussian, and subsequently quantify the likelihood of ET data points to be issued from this distribution. By this mean, we derive a measure of similarity to HCs. For dynamic FC (dFC) analysis, we employ a sliding-window approach to extract recurring dFC states across time and subjects27 from our HC population. Then, ETpre and ETpost dFC estimates are matched to these states to quantify spatial similarity and temporal occurrences. Results The dynamic evolution of FC in HCs could be disentangled into K = 3 separate dFC states ( Supplementary Fig. 1 ), as seen from a clear PAC global minimum and upon inspection of the consensus matrices. State 1 occurred in 419 (36.88%) frames and was expressed 36.38 ± 36.95% of the time in individual subjects. State 2 only occurred in 182 (16%) frames (16.47 ± 35.78% of the time per subject). State 3 occurred the most (535 or 47.1% of frames, 47.15 ± 37.8% of the time subject-wise). In terms of spatial properties (Fig. 1 ; see also Supplementary Fig. 2 ), all states showcased their largest positive-valued dFC ( i.e. , correlated activity with the left Vim) with other subcortical areas, as well as negative-valued dFC (anticorrelated activity with the left Vim) with cerebellar regions. A more restricted set of subcortical areas, and stronger cerebellar anti-correlations, were observed for state 2. Regarding interplays with the cortex, states 1 and 3 primarily featured positive-valued dFC, while the pattern was more mixed for state 2. Only state 1 showed correlations with the full visual network, while state 2 displayed (anti)correlations with specific peripheral regions, and state 3 mostly included positive-valued dFC with the periphery. Primary somatomotor areas were only largely correlated with the left Vim in state 2, as opposed to higher-level somatomotor areas for the two others. Dorsal attention, salience and control networks exhibited a gradient of positive-valued dFC across states, strongest in state 3 and weakest in state 2. For the default mode network, state 3 exhibited the most widespread correlations, but the strongest connections were found in state 1. Finally, state 1 also showcased strong temporo- parietal dFC. Matching of the ETpre and ETpost windowed dFC estimates to these states (Fig. 2 A) revealed that they were all also expressed in the ET patient population, in a way that differed along time and across subjects. Furthermore, the spatial similarity of dFC estimates to their assigned state also fluctuated along the same dimensions. We thus examined temporal occurrences and spatial similarity features to assess whether there was an impact of thalamotomy, including a random subject effect to account for multiple measurements in only a subset of patients (Fig. 2 B). The analysis revealed no group difference for temporal occurrences (all p -values > 0.1). For mean spatial similarity, there was a significant random subject effect for state 1 ( F 1,22 = 7.89, p = 0.0025), indicating strong differences between individual patients with this measure. The standard deviation of spatial similarity showed a significant decrease upon thalamotomy in state 1 ( F 1,22 = 12.87, p = 0.015). The coefficient of variation, which jointly accounts for mean and standard deviation impacts, yielded both significant effects for state 1 ( F 1,22 = 19.32, p = 0.0041 and F 1,22 = 4.01, p = 0.0495 for the fixed and random effects, respectively), with a decrease observed upon thalamotomy. Thus, significant spatial renormalization was observed in terms of state 1 expression. Investigation of clinical predictive potential revealed a significant relationship between clinical improvement (quantified as the drop in TSTH) and the standard deviation ( t 12=-2.38, p = 0.041) and coefficient of variation ( t 12=-2.86, p = 0.019) of state 2 expression: hence, better recovery goes with a reduced spatial variability in state 2 expression. In addition, there was an MR signature-by-metric interaction for state 3 ( t 12 = 3.12, p = 0.009) and state 1 ( t 12=-2.66, p = 0.021) temporal occurrences. As the interactions had opposite signs, we probed the difference between state 1 and 3 counts (Counts3 – Counts1) in a follow-up analysis: the interaction remained significant ( t 12 = 3.11, p = 0.009), indicating that the more state 3 is expressed over state 1, the better the recovery, in a way that also depends on the MR signature. On morphometric data, we quantified the log-likelihood for a regional ET estimate to be issued from the HC distribution as a measure of similarity (Fig. 3 A). The average log-likelihood across subjects was consistently larger before than after thalamotomy, a difference significant in 11 cortical regions: the bilateral fusiform (ETpost-ETpre group difference: z =-4.017, p = 0.0051 and z =-4.16, p = 0.0027, respectively left and right sides) and parahippocampal ( z =-4.84, p = 0.0001 and z =- 5.75, p < 10 − 5) gyri, left cuneus ( z =-4.017, p = 0.0051), lateral orbitofrontal cortex ( z =- 4.12, p = 0.0034), precentral gyrus ( z =-3.81, p = 0.0122) and insula ( z =-3.66, p = 0.0218), right entorhinal cortex ( z =-3.78, p = 0.0134), lingual cortex ( z =-3.66, p = 0.0218) and superior temporal cortex ( z =-3.67, p = 0.0208). There were also two significant subcortical areas: the bilateral hippocampus ( z =-4.48, p = 0.0006 and z =-4.03, p = 0.0049). To further characterize ETpre versus ETpost differences, we compared both groups at the level of individual mean and (co)variance coefficients. There were no differences in mean values across groups, but CT variance increased upon thalamotomy in the bilateral fusiform gyrus ( p = 0.0021 and p = 0.0031, respectively left and right sides), left lateral orbitofrontal cortex ( p = 0.0021), precentral gyrus ( p = 0.0031), and right lingual cortex ( p = 0). MC variance also increased in the bilateral fusiform gyrus ( p = 0.0082 and p = 0) and left insula ( p = 0). Covariance between CT and MC decreased in the left fusiform gyrus ( p = 0.0031), lateral orbitofrontal cortex ( p = 0.0124) and right superior temporal cortex ( p = 0.0103). Covariance between CT and SA decreased as well in the left precentral gyrus ( p = 0.0062). However, covariance between SA and MC increased in the bilateral fusiform gyrus ( p = 0.0093 and p = 0.0134). Variance in subcortical volume increased in the bilateral hippocampus ( p = 0 and p = 0.0093). Supplementary Fig. 3 visually illustrates all these changes. There was no link between pre-intervention log-likelihood in any of the regions that showed significance and clinical recovery (all p -values > 0.1). In sum, there was thus no evidence for morphometric renormalization upon thalamotomy, or for a clinical predictive potential of such features. Finally, we quantified the correlation between the mean spatial similarity in dFC state expression and the morphometric similarity to HCs (Fig. 3 B; see also Supplementary Fig. 4 ) in a cross-modality analysis. For state 1, the distributions of correlation values were similarly centered around 0 both before and after thalamotomy (rank-sum test for ETpost – ETpre group difference: z = 1.89, p = 0.058). However, for states 2 and 3, we observed a broad pattern of anticorrelation before thalamotomy, which disappeared afterwards (respectively z = 4.39, p = 1.16 10 − 5 and z = 5.83, p = 5.54 10 − 9). In other words, before thalamotomy, a patient who showed greater average spatial conformity to HC dFC states 2 and 3 also tended to show less similarity to the regional HC morphometric profiles, but this relationship disappeared after thalamotomy. Materials And Methods Subjects We studied 34 right-handed patients (17 males) with drug-resistant ET. They were 70.06 ± 9.12 years old when initially assessed. Neurological assessment was performed by T.W., a neurologist specialized in movement disorders. All patients had a clear diagnosis of ET based on consensus clinical criteria28 and showed no structural abnormalities upon 3T MRI. They were clinically assessed and scanned before and 1 year after thalamotomy of the left Vim, to account for the progressive and delayed clinical effect. The Tremor Score on Treated Hand (TSTH) from the Fahn-Tolosa- Marín rating scale29 was used to quantify tremor severity in the patients and its evolution upon intervention. For details on data acquisition, see Supplementary information . For SBM analysis (based on T1-weighted structural images), the data from all 34 patients was considered. For dFC analysis, following the thorough exclusion of poor- quality recordings (see below for details), a total of 23 patients contributed at least one RS fMRI scan to the analyses, and the recordings of both time points were retained for 13 of them. There were 18 remaining scans both in the ETpre and ETpost groups. For SBM analysis, ET patients were compared to 29 age-matched HCs (69.93 ± 7.14 years old). Following quality control, the RS fMRI data from 14 was also retained (70.21 ± 6.8 years old, 5 males). All demographic and clinical data are summarized in Table 1 . Table 1 Demographic and clinical details of the subjects. For healthy controls (HCs) and ET patients before (ET pre ) and after (ET post ) intervention, values are reported as mean ± standard deviation, with minimum, median and maximum into squared brackets. Significant statistical comparisons are highlighted in bold. M: male, F: female, TSTH: tremor score on treated hand. Morphometry analyses Dynamic functional connectivity analyses Variable HC ET pre ET post p (HC – ET) p (ET post – ET pre ) HC ET pre ET post p (HC – ET) p (ET post – ET pre ) N 29 34 34 14 18 18 Age at baseline evaluation [years] 69.93±7.14 [59,69,83] 70.06±9.12 [49,72,83] t 61 =-0.06 p = 0.95 All 1-year increase 70.21±6.8 [61,69,81] 69.3±10.1 [49,71.5,82] 71±9.68 [51,72.5,83] t 30 = 0.45 p = 0.66 t 34 = 0.51 p = 0.62 Gender [M:F] 12:17 17:17 5:9 7:11 8:10 TSTH score [points] 20.41±5.53 [8,20.5,30] 6.26±7.71 [0,3,27] t 66 =-8.69 p = 1.52 10 − 12 19.94±6.21 [8,20.5,30] 5.83±8.79 [0,2,27] t 34 =-5.57 p = 3.17 10 − 6 Symptoms’ duration [months] 35.53±18.28 [ 5 , 33 , 61 ] Same subjects 30.94±16.52 [5,27.5,61] 29.89±16.38 [ 5 , 26 , 55 ] t 34 =-0.19 p = 0.85 Family history [Y:N] 11:23 8:10 8:10 MR signature [ml] 0.12±0.13 [0.002,0.076,0.6] Same subjects 0.12±0.16 [0.002,0.069,0.6] 0.15±0.16 [0.014,0.093,0.6] t 34 = 0.53 p = 0.6 The Timone University Hospital Ethical Committee (ID-RCB: 2017-A01249–44) granted formal approval for this study (including by the Ethics Committee at national level, CNIL-MR-03). All methods were performed in accordance with the relevant guidelines and regulations. Individual informed consent was obtained from all subjects. Intervention and one-year MR signature volume assessment Thalamotomy was performed using Leksell Gamma Knife (LGK, Elekta Instruments, AB, Sweden) between September 2014 and April 2016, at the Centre Hospitalier Universitaire de la Timone (Marseille, France), always by the same neurosurgeon (J.R.). In each case, the Leksell coordinate G frame (Elekta Instruments, AB, Sweden) was applied under local anesthesia. After its positioning, stereotactic computed tomography and MRI were both performed on the patient. Landmarks of interest, such as the anterior and posterior commissures, were individually identified on an MR scan (T2 CISS/FIESTA sequence, Siemens). Targeting was achieved individually with the Guiot diagram30, placed 2.5 mm above the anterior-posterior commissure line and 11 mm lateral to the third ventricle wall. A single 4-mm isocenter was used with a maximum prescription dose of 130 Gy. There is a specific MR signature of radiosurgical thalamotomy, known to differ across subjects both in terms of aspect (with or without contrast enhancement) and corresponding volume.14 It was contoured on a T1-weighted Gadolinium-injected scan acquired at one-year follow-up, and usually corresponds to the 90 Gy isodose line. The individual patient’s Gadolinium-injected MR image was imported in the Leksell GammaPlan software (Elekta Instruments, AB, Sweden), and co-registered with the stereotactic imaging. A manual drawing was made for each individual case, on each slice. Data processing Surface-based morphometry Freesurfer 31 was used to extract CT, SA and MC from native structural MR images for a set of P cort = 68 cortical regions (see Supplementary information for details). In addition, we also extracted regional volume for P noncort = 19 non-cortical areas, including the cerebellum and subcortex, using Freesurfer ’s automatic subcortical segmentation approach.32 Supplementary Table 1 summarizes all brain regions considered in our morphometric analyses. To account for the confounding impacts of age, gender and total grey matter volume in our analyses while handling the fact that two scans were available for ET patients but not for HCs, a mixed-effects model strategy was employed (see Supplementary information for details). Dynamic functional connectivity A similar preprocessing scheme was applied to each of the functional scans, using SPM12 ( https://www.fil.ion.ucl.ac.uk/spm/software/spm12/ ) for initial steps. Functional volumes were realigned and subsequently co-registered to the T1 structural image. Segmentation was performed to derive the deformation field that was then used to warp fMRI volumes into the common MNI space. Subsequent steps were performed using custom scripts and MATLAB version 2020b (MathWorks, Natick, MA). The first 3 volumes were discarded to enable magnetization equilibration. Then, the voxel-wise data was converted into a restricted set of parcels, combining three different atlases: the Schaefer atlas33 (400 regions, 17 networks) for cortical areas, the Tian atlas34 (scale 3, 50 regions) for subcortical regions, and the decomposition into 26 subregions from the AAL atlas35 for the cerebellum. In total, there were thus 476 areas available, of which 13 were excluded because they were not included in at least one scan owing to a trimmed field of view. Thus, the present work considers dFC of the left Vim with 462 other brain regions, summarized in Supplementary Table 2 . To clean the regional time courses from artefactual signal sources, conservative white matter and cerebrospinal fluid masks from the DPARSF toolbox36 were used to compute associated regressors. Together with the 6 head motion parameters obtained at the realignment step and a Discrete Cosine Transform basis for drifts (cut-off: 0.01 Hz), they were regressed out from the data. Finally, framewise displacement (FD)37 was computed, and the scans for which more than 30% of frames were corrupted (defined as the frames with FD > 0.5 mm, the frames beforehand and the two frames afterwards) were discarded. From this preprocessed data, sliding-window analysis was conducted. We used a rectangular window with size W = 30 TRs (99.9 s)! i.e. , the inverse of the minimal frequency remaining in the data (0.01 Hz),38 and step size "=2 TRs (6.6 s). FC within each temporal sub-window was quantified with Pearson’s correlation coefficient, discarding the frames corrupted by head movement. To further guarantee the quality of the analyzed data, only windows for which more than 20 samples remained were retained for analysis, and ET scans for which less than 85 windows remained were discarded. Following all these steps, dFC data remained for 14 HCs, 18 ETpre scans, and 18 ETpost scans. Data analysis Surface-based morphometry We computed the log-likelihood of ETpre and ETpost data to be issued from the HC distribution as a similarity measure (see Supplementary information for details). It was compared across groups with a rank-sum test, run separately for each region. The obtained p -values were Bonferroni-corrected for the number of performed tests (87). For the regions that reached significance in the above assessment, individual mean and (co)variance coefficients were subsequently contrasted across groups through a non-parametric permutation-based significance assessment of each ETpost – ETpre difference (100’000 folds, two-tailed testing). The obtained p -values were Bonferroni- corrected for the number of performed tests (103). Dynamic functional connectivity To extract dFC states expressed in our HC population, clean dFC estimates ( i.e. , windows with at least 20 non-corrupted samples) were concatenated across all 14 subjects. This resulted in 1136 samples, each of dimension 462 (the number of connections to the left Vim). To define an optimal number of states, we used consensus clustering39: over 200 folds, 80% of samples were randomly selected, and K -means clustering was performed (cosine distance) for K ranging from 2 to 20. The percentage of ambiguously clustered pairs (PAC)40 was computed as a measure of clustering quality, and clearly revealed K = 3 as an optimum ( Supplementary Fig. 1 ). A final K -means clustering step was then performed (300 folds, 200 iterations each), yielding K = 3 dFC states characteristic of the HC population. The first 85 dFC estimates from each available ET scan were then matched to these dFC states through Pearson’s correlation coefficient. The most similar state to a given dFC estimate was assumed to be expressed at that time point ( i.e. , winner-takes-all approach), enabling the computation of temporal occurrences (number of times a given state is expressed) for each scan. In addition, the mean and standard deviation of spatial similarity to the expressed state were computed, and the coefficient of variation was derived as a measure of spatial stability of state expression. Temporal occurrences ( K values per scan), and each spatial stability measure ( K values per scan as well), were considered as metrics of interest in our analyses. Quantification of the impact of thalamotomy To determine whether a given metric differed between the pre- and post-thalamotomy stages, a two-way ANOVA was conducted, using group label (ETpre and ETpost) as a fixed effect and subject label as a random effect. The model takes the form: $${M}_{g,s}=\mu +{\alpha }_{g}+{\beta }_{s}+{ϵ}_{g,s}$$ . In the above equation, \({M}_{g,s}\) is the value of the metric at hand for subject s in group g , \(\mu\) is the global mean across both groups, \({\alpha }_{g}\) is the fixed group effect, \({\beta }_{s}\) is the random subject effect, and \({ϵ}_{g,s}\) is the error term. The random effect is assumed to follow a normal distribution with mean 0 and variance \({\sigma }_{\beta }^{2}\) , and the error follows a normal distribution with mean 0 and variance \({\sigma }_{ϵ}^{2}\) . A significant fixed group effect means that the null hypothesis of equal means across groups can be rejected (that is, there is an impact of thalamotomy at the group level). A significant random subject effect means that the null hypothesis \({\sigma }_{\beta }^{2}\) =0 can be rejected (in other words, different subjects show different extents of expression of the metric at hand). We evaluated temporal occurrences and spatial stability of the expressed dFC states for potential group differences (4 K features). Obtained p -values were Bonferroni- corrected for the number of states ( K ). Extraction of predictive pre-thalamotomy features To establish whether a given metric before thalamotomy correlates with clinical improvement, we considered the difference in TSTH score ( \({\Delta }\) TSTH = TSTHpre - TSTHpost) as outcome measure, so that a more positive value indicates greater recovery, and the following general linear model: $${{\Delta }}_{TSTH}={\beta }_{0}+{\beta }_{1}{A}_{s}+{\beta }_{2}{G}_{s}+{\beta }_{3}{M}_{s}+{\beta }_{4}{R}_{s}+{\beta }_{5}\left({M}_{s}\times {R}_{s}\right)+{ϵ}_{s}$$ . Importantly, in addition to modelling the impacts of age A and gender G for subject s , we also consider that of the radiological signature R , as well as its interaction with the metric M . This is because previous work has shown that the MR signature correlates with pre-interventional functional brain features.41 Note that for the assessments performed on morphometric features, since age and gender were already regressed out during processing, these two covariates were omitted from the model. Similarity across modalities To compare SBM and dFC data, we considered the log-likelihood to be issued from the HC distribution (for SBM analysis, P values per subject), and the mean similarity to the dFC states (for dFC analysis, K values per subject). Pearson’s correlation was computed to yield a P x K matrix of similarity values for the ETpre group, and another for the ETpost group. For each state, the distribution of correlation values with the whole brain was contrasted between before and after thalamotomy through a rank-sum test. The resulting p -values were Bonferroni-corrected for K = 3 states. Previous data uses and implementation details The morphometric data analyzed therein was already examined in two previous structural covariance analysis studies,19–20 which performed pair-wise assessments of cross-property dependences and did not discriminate between property variance and cross-property covariance. The analytical framework leveraged in the present work was applied on ETpre morphometric profiles to assess their differences to HCs,26 but never to the ETpost data. A subpart of the functional data considered here was already analyzed in a series of studies addressing the specificities of ET and the impacts of thalamotomy.21–22,25,41–43 However, none of these previous reports examined dFC of the left Vim, and only one focused on temporal dynamics in ET.25 Colormaps for plotting were generated with the cbrewer toolbox. Surface visualizations of brain patterns were created using BrainNet .44 All the scripts used in this work are freely available at https://github.com/TiBiUan/DYNET_Analysis.git . Discussion The left Vim: a dynamic cornerstone of the tremor network In this study, we observed that the left Vim reconfigures its functional interactions with the rest of the brain in HCs over the course of an RS fMRI scanning session, showcasing the recurring expression of three different dFC states (Fig. 1 , Supplementary Fig. 2 ). We also showed that patients with ET express these states with subject-specific temporal occurrences and stability in spatial similarity, in a way that is partly modulated by thalamotomy (Fig. 2 ). Correlated activity with other subcortical areas, anticorrelations with cerebellar regions, and broad connectivity patterns with cortical networks, were hallmarks of all three states. This is consistent with known anatomical connections, as the Vim is linked to the cerebellum through the dentate nucleus, and to the motor cortex.1–2 Furthermore, there are known projections from the thalamus (including its ventral posterior subpart, studied here) to the basal ganglia.45 There were also several notable differences between states. State 2 exhibited a sparser set of strong functional connections to the rest of the subcortex. Further inspection revealed that the main differences involved basal ganglia substructures, as the bilateral putamen, anterior caudate and anterior globus pallidus all featured characteristically weak dFC with the left Vim in state 2. The basal ganglia are involved in the modulation of somatomotor cortical information processing,46 and contribute to tremor generation.47 Although ET has been primarily considered related to the cerebellum,2,5–6 DBS of the posterior subthalamic area, an input source to the basal ganglia,46 has shown effectiveness to treat ET,48 and abnormal putamen activity49-50 and FC51 have been reported in ET. Thus, differential patterns of FC to the basal ganglia could relate to tremor symptomatology. State 2 was also the one displaying the strongest anticorrelations between the left Vim and most of the cerebellum. This particularly involved the bilateral cerebellum lobules III and IV/V, as well as vermis III and vermis IV/V, which jointly contribute to the cerebellar sensorimotor component.52–54 In addition, there was specifically more positive dFC to left cerebellum crus II and vermis VIII. The former is functionally related to the default mode network,52 and a recent meta-analysis showed its specific roles in social mentalizing and emotional self-experiences.55 As for Vermis VIII, its FC to the default mode, executive control and somatomotor networks was stronger in Parkinson’s disease patients suffering from visuospatial disorder on top of motor impairments.56 Interestingly, we note that state 2 also stood out by its anticorrelations with parahippocampal and extrastriate visual areas, as well as with hippocampal subparts, which all contribute to visuospatial processing, and locomotor monitoring towards the local and distant environment,57 relevant in ET patients. Another intriguing feature of left Vim-to-cortex FC in the context of sensorimotor activity was the fact that states 1 and 3 showed only little correlated activity with primary sensorimotor areas, while the opposite was seen in state 2. In a recent study, Sharifi et al .58 studied cortico-muscular coherence (CMC) through coupled EEG and EMG recordings, in patients with ET and HCs asked to voluntarily mimic tremor. They showed that in both groups, CMC could be reliably detected and was modulated by performing a cognitive task in parallel; thus, non-tremor networks were posited to exert an interfering influence. Additionally, when tracking CMC changes with a sliding-window approach (30 s windows), there were dynamic fluctuations that could not be attributed solely to changes in tremor signal-to-noise ratio (SNR) or to task settings. The present work shows that motor cortical activity does reconfigure with time, given its evolving functional interplay with the left Vim, both in HCs and in patients with ET. Transitions between the expression of the more similar states 1 and 3, and state 2, may thus be hypothesized to capture fluctuating interfering influences from non-tremor networks, which would occur at rest and manifest in task settings too. Interactions with specific networks hinder or benefit clinical recovery Interestingly, the dichotomy between states 1/3 and state 2 also extended to their respective impacts on tremor. On the one hand, we found that a more robust spatial similarity ( i.e. , lower coefficient of variation) in state 2 expression to the HC template before thalamotomy correlated with greater TSTH drop, while there was no clinical relevance in how frequently this state was expressed. On the other hand, for states 1 and 3, we found that temporal occurrences, but not spatial similarity, correlated with clinical recovery: more frequently expressing state 3 before the intervention associated with greater tremor improvement, while the opposite was true for state 1. When assessing the clinical predictive potential of the balance between these two states in a follow-up analysis, we confirmed that greater clinical recovery was in fact tied to a balance more geared towards state 3 expression. Given this antagonistic relationship, a natural question pertains to the differences in left Vim dFC between states 1 and 3. Accordingly, we inspected the state 3 – state 1 dFC differences (Fig. 4 ; see also Supplementary Fig. 5 ) and observed that more pronounced correlations between the left Vim and extrastriate central visual areas, temporal regions (from the temporal pole or occipito-temporal cortex) and the parahippocampal gyrus were particularly detrimental to clinical recovery (positive- valued in state 1 while weak in state 3). This set of regions (which, fittingly, were also partly pinpointed in state 2 as anti-correlated to left Vim activity) constitute what has been referred by some as the parieto-medial temporal pathway for visuospatial processing,57 which has been resolved in humans through RS FC analysis.59 The relevance of the visuospatial circuitry in ET has become increasingly appreciated from the voxel-based morphometry,60 SBM,20 task-based fMRI61 and RS fMRI25 forefronts. Our results further contribute to this line of research and identify a more specific pathway whose intermittent functional coupling with the left Vim may be detrimental to tremor recovery following thalamotomy. On the other hand, greater dFC to the insula, precuneus/posterior cingulate cortex and frontal eye fields was beneficial (positive-valued in state 3 while weak in state 1). These regions are all part of higher-order functional networks. When one recalls that state 2 (whose spatially accurate expression was also beneficial to clinical recovery) featured less potent left Vim connectivity to the basal ganglia and anti-correlations with the sensorimotor cerebellum (but correlations with subparts involved in higher- order cognitive functions), a possibility could be that spontaneous “decoupling” of the tremor network at rest, through interactions of the left Vim with other interfering high-order networks, is somehow beneficial to recovery following thalamotomy. While the clinical relevance of state 2 was captured in a direct effect, that of the states 3 versus 1 balance was seen as an interaction with MR signature one year after thalamotomy. This demonstrates that to extract potentially predictive markers of clinical recovery, it is important to consider as many involved sources of variance as possible. In the case of thalamotomy performed by radiosurgery, the outcome itself may be related to factors such as individual sensitivity to radiation14 or specificity in the location of the relevant brain substructures,62 thus yielding different individual radiobiological effects. This latter point has been the subject of intense investigations: in a prospective DBS study, direct targeting of the dentato-rubro-thalamic tract yielded tremor improvement,63 evidencing the benefits of targeting more mechanistically relevant structures. On a set of HCs, Middlebrooks et al .64 showed that the voxels targeted by stereotactic coordinates were mixed between the primary motor cortex and supplementary motor area/premotor cortex, in a way that largely varied across subjects. Through diffusion MRI, several DBS studies also put forward candidate areas to which the volume of tissue activated (VTA) should be structurally connected to optimize interventional efficacy, including the connected area to the contralateral dentate nucleus,9 the supplementary motor area and premotor cortex,65 or the primary motor cortex.66 Purrer et al.67 showed that the MRgFUS lesion induced through Vim targeting only had 4% overlap with the Vim on average, but 43% with the cerebello-thalamic tract. In another study, the larger the fraction of lesioned voxels structurally connected to the precentral gyrus, the better the clinical recovery.68 Collectively, these reports highlight the limitations of relying on stereotactic coordinates and suggest that to better extract potential pre-intervention markers of tremor arrest, the targeting of a specific tract or thalamic subregion, via dedicated pre- thalamotomy imaging, may be a more suitable option. Dynamic functional renormalization, but morphometric heterogenization upon thalamotomy In terms of left Vim dFC, renormalization upon thalamotomy was observed in two ways (Fig. 2 B). First, stability in the expression of all three states tended to increase upon intervention, significantly so for state 1. Second, the balance in expression of the three states following the intervention also evolved back towards that seen in HCs, and even overshot it, potentially reflecting a compensatory mechanism. Somehow puzzlingly, however, we did not observe any renormalization when quantifying the evolution of whole-brain SBM properties upon intervention. In fact, the similarity of ET morphometric profiles to the HC distribution decreased even further upon thalamotomy in all regions (Fig. 3 A). Further inspection revealed that mean values were unchanged upon thalamotomy. However, there were significant increases in CT and MC variance, and a global decrease of cross-property covariance, thus denoting morphometric heterogenization. To explain this apparent discrepancy, we quantified the correlation between the log- likelihood of a morphometric profile to be issued from the HC distribution ( i.e. , morphometric similarity to HCs) and mean spatial similarity in state expression ( i.e. , dynamic functional similarity to HCs), before and after thalamotomy. We observed marked anti-correlations, at baseline, between states 2 and 3 (but not state 1) and the whole brain, which disappeared following thalamotomy (Fig. 3 B, Supplementary Figure 4 ). Out personal view, as derived from the present work, is that brain function in dedicated areas induces tremor. As such, “healthy” dFC states can be seen as the target towards which an individual patient should tend to minimize tremor symptomatology. Brain structure, partly characterized by morphometric properties, is the underlying scaffold over which functional signals propagate. Essential tremor patients who resemble HCs in their whole-brain morphometric attributes, but suffer from local impairments within the tremor network, may not be able to express HC- like dFC states owing to these local alterations. To achieve such expression, further reconfigurations of the whole-brain structural circuitry are required. Thus, the patients who undergo such reconfigurations the most (by this mean becoming least like HCs morphometry-wise) are also the ones who can express the dFC states most similar to HCs, resulting in the observed anti-correlations. These anti-correlations are only present for states 2 and 3, the “beneficial” ones, as these are the desired endpoints to limit tremor. After thalamotomy, these relationships disappear because further extensive changes are induced in all subjects, in a way that is also heterogeneous. Figure 5 summarizes our results in the context of this hypothesis. Technical outlook, limitations, and future perspectives In this work, we analyzed the data from ET patients in terms of similarity to a pool of matched HCs. This strategy is particularly suitable to the study of ET for two reasons: first, it enables to investigate renormalization upon thalamotomy more directly as opposed to a three-group comparison ( i.e. , HC versus ETpre versus ETpost). Second, it yields distinct, potentially more relevant features in the context of a clinical disorder. We were inspired by recent work that successfully leveraged a conceptually related approach: in a DBS study of ET, Al-Fatly and colleagues69 quantified structural and functional connectivity to the VTA, on a population of ET patients, using normative maps. To create a so-called “R-map”, for each voxel, they correlated the obtained values ( i.e. , connectivity to the VTA) with clinical improvement, so that the resulting pattern has large positive weights for the voxels that, when connected to the VTA, promote clinical recovery. Out-of-sample data could then be compared to this R-map to estimate clinical improvement, and prediction was significant when using both structural and functional connectivity information. In the present study, we showed that a similarity-based approach also has potential in the context of morphometric and dynamic functional connectivity data. In future work, it will be necessary to validate the unraveled dynamic functional markers on a larger cohort of subjects. Our seed-based dFC analysis was conducted using the Tian scale 3 subcortical atlas.34 Despite an exhaustive state-of-the-art segmentation of the subcortex, only the ventral posterior lateral thalamus was available as a seed, not the Vim per se , which covers its ventral portion only.70 When interpreting our findings, one should thus keep in mind that they could also be influenced by changes in activity of the dorsal half of the ventral posterior lateral thalamus. Future work should determine to what extent the different states that we extracted colocalize in terms of their exact seed focus of activity, and whether spatial dynamics may be at play.71 Finally, if one wishes to further understand the links between brain structure and function, and how they may be altered upon thalamotomy, morphometric data (indicative of local structure within specific regions) should be complemented by diffusion MRI. By this mean, the physical wiring between brain regions could refine cross-modality assessments. Declarations Acknowledgments The authors wish to thank all the subjects who accepted to participate to this study. Author contributions J. R., T. W., N. G. and C. T. contributed to the acquisition and preprocessing of the analyzed imaging and clinical data. D. V. D. V., M. L. and C. T. surveyed the work and devised the main axes of the conducted analyses. T. A. W. B. performed the analyses, interpreted the findings and wrote the manuscript. All authors reread and approved the manuscript. Data availability statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Additional information The authors have no competing interests to declare. Constantin Tuleasca gratefully acknowledges the receipt of a grant “Jeune Chercheur en Recherche Clinique” from the University of Lausanne, Faculty of Biology and Medicine. 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Supplementary Files SciRepSupplementarymaterial.pdf SciRepSupplementaryFigures.pdf SupplementaryTable1.xlsx SupplementaryTable2.xlsx Cite Share Download PDF Status: Published Journal Publication published 31 Jan, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 20 Sep, 2023 Reviews received at journal 07 Sep, 2023 Reviewers agreed at journal 29 Aug, 2023 Reviewers agreed at journal 28 Jul, 2023 Reviews received at journal 29 May, 2023 Reviewers agreed at journal 05 May, 2023 Reviewers invited by journal 30 Apr, 2023 Editor assigned by journal 30 Apr, 2023 Editor invited by journal 05 Apr, 2023 Submission checks completed at journal 05 Apr, 2023 First submitted to journal 16 Mar, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Bolton","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACCQiVwMAOoioYGNjbGRiY8enggWthBqk7AxQ5TJIWxjYitNhLNx/d8OMPQx4/M//Bx4XzbPJ4mBkYPxfgs0XmWNrN3jaGYslmZmbjmdvSioFamKVn4HVYjtkN3gaGxA2HmdmkebcdTtzPzMDGzENAy80/f2Ba5vxP7CFGy20eNpiWhgNEaLmRlnZbtk0C5BdjY55jyUC/MDZL49PCPiP52M03f2zy+NkbHz7mqbHL42FvPvgZnxYokICzEoCx00BYAzJIIE35KBgFo2AUjAQAAPU+P1lPVS7XAAAAAElFTkSuQmCC","orcid":"","institution":"Centre Hospitalier Universitaire Vaudois","correspondingAuthor":true,"prefix":"","firstName":"Thomas","middleName":"A.W.","lastName":"Bolton","suffix":""},{"id":189412751,"identity":"3e928b08-5cbb-4818-a52e-9f3ffed94cb7","order_by":1,"name":"Dimitri Van De Ville","email":"","orcid":"","institution":"Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Dimitri","middleName":"Van","lastName":"De Ville","suffix":""},{"id":189412752,"identity":"e041b08c-63fa-4a06-be48-aa7b047f20ee","order_by":2,"name":"Jean Régis","email":"","orcid":"","institution":"Assistance Publique-Hopitaux de Marseille, Centre Hospitalier Universitaire La Timone","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"","lastName":"Régis","suffix":""},{"id":189412753,"identity":"01ec1c26-066b-4e6f-bf6d-a35a5ac22c63","order_by":3,"name":"Tatiana Witjas","email":"","orcid":"","institution":"Assistance Publique-Hopitaux de Marseille, Centre Hospitalier Universitaire de la Timone","correspondingAuthor":false,"prefix":"","firstName":"Tatiana","middleName":"","lastName":"Witjas","suffix":""},{"id":189412754,"identity":"d4103311-4ed5-49f4-8560-8795abaa264d","order_by":4,"name":"Nadine Girard","email":"","orcid":"","institution":"Assistance Publique-Hopitaux de Marseille, Centre Hospitalier Universitaire de la Timone","correspondingAuthor":false,"prefix":"","firstName":"Nadine","middleName":"","lastName":"Girard","suffix":""},{"id":189412755,"identity":"30638dd4-7e90-44bd-9bc3-6781c0eb48f9","order_by":5,"name":"Marc Levivier","email":"","orcid":"","institution":"Centre Hospitalier Universitaire Vaudois","correspondingAuthor":false,"prefix":"","firstName":"Marc","middleName":"","lastName":"Levivier","suffix":""},{"id":189412756,"identity":"801cd8f3-7d32-400e-9849-8636a5b12a38","order_by":6,"name":"Constantin Tuleasca","email":"","orcid":"","institution":"Centre Hospitalier Universitaire Vaudois","correspondingAuthor":false,"prefix":"","firstName":"Constantin","middleName":"","lastName":"Tuleasca","suffix":""}],"badges":[],"createdAt":"2023-03-16 23:14:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2702374/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2702374/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-52410-y","type":"published","date":"2024-01-31T15:00:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":35459099,"identity":"48da9d98-44dc-4bb9-adc9-db1b0214e1c8","added_by":"auto","created_at":"2023-04-07 18:16:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":519999,"visible":true,"origin":"","legend":"\u003cp\u003eThree dynamic functional connectivity states expressed by healthy controls. Surface representation (A) and circular plot (B) of all three states in terms of cortical dFC with the left Vim. For surface images, color denotes dFC strength. For circular plots, red/blue edges denote positive/negative-valued dFC, and edge width is proportional to dFC strength. Brain regions are arranged clockwise, starting from the far right of the representation, and dashed lines separate left and right cortical regions, as well as subcortical/cerebellar areas. Node color depicts network assignment,75 and squares/diamonds symbolize left/right hemispheric regions (stars depict vermis cerebellar areas). (C) For all investigated networks (columns) and states (rows), mean and standard deviation across regions. L: left, R: right, VIS: visual network, SM: somatomotor network, DAN: dorsal attention network, SAL: salience network, LIM; limbic network, CON: control network, DMN: default mode network, TP: temporo- parietal network, SC: subcortical network, CB: cerebellar network, Cen: central, Peri: peripheral, std: standard deviation.\u003c/p\u003e","description":"","filename":"SJFigure1Final.png","url":"https://assets-eu.researchsquare.com/files/rs-2702374/v1/c83c8b93986daccb23897ac1.png"},{"id":35457775,"identity":"caeb2926-1eb6-4111-baf7-a4944a0b2754","added_by":"auto","created_at":"2023-04-07 17:52:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":294042,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of dynamic functional connectivity metrics in essential tremor patients upon thalamotomy. (A) (Top) State assignment for ET frames (before/after thalamotomy: left/right) across subjects (columns) and over time (rows). (Bottom) In a similar representation, evolution of spatial similarity of the dynamic functional connectivity estimate to the assigned state. (B) For all three states (rows) and four investigated metrics (columns), values across subjects before (ETpre) and after (ETpost) thalamotomy. Bars denote the average, error bars depict standard error of the mean, and colored data points stand for individual subject values. Dashed lines connect the data points that belong to the same patient, and the horizontal dashed blue lines for occurrences bar plots denote the average in the healthy controls. Mean sim.: mean similarity, Std sim.: standard deviation of similarity, CV sim.: coefficient of variation of similarity.\u003c/p\u003e","description":"","filename":"SJFigure2Final.png","url":"https://assets-eu.researchsquare.com/files/rs-2702374/v1/e3723cdb390b44d5a24ef9f5.png"},{"id":35458539,"identity":"fd6535d3-3d0b-440b-8274-8301f062fb3f","added_by":"auto","created_at":"2023-04-07 18:00:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122924,"visible":true,"origin":"","legend":"\u003cp\u003eMorphometric heterogenization upon intervention, and state-specific disappearance of cross-modality anti-correlations. (A) For cortical (left) and non- cortical (right) regions, log-likelihood to be issued from the HC distribution for the data points of the HC group (green), ETpre group (blue) and ETpost group (red). Regions for which the ETpre \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;versus ETpost \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;log-likelihood difference reached significance are highlighted in light grey. (B) For each state, histogram of similarity values (log-likelihood to be issued from the morphometric HC distribution correlated to average spatial similarity of dFC state expression) across regions before (blue) or after (red) thalamotomy.\u003c/p\u003e","description":"","filename":"SJFigure3Final.png","url":"https://assets-eu.researchsquare.com/files/rs-2702374/v1/ed94ad085324ea9f65f7ba2a.png"},{"id":35459629,"identity":"bb1af09f-2629-4cd4-862a-fa137c1ac68a","added_by":"auto","created_at":"2023-04-07 18:24:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":207980,"visible":true,"origin":"","legend":"\u003cp\u003eState 3 – state 1 dynamic functional connectivity differences. Differences in dFC of the left Vim with cortical areas are visualized as a surface plot, where dark/light tones highlight stronger dFC in state 1/3.\u003c/p\u003e","description":"","filename":"SJFigure4Final.png","url":"https://assets-eu.researchsquare.com/files/rs-2702374/v1/de0b7bc5c46b8a31f05b1baa.png"},{"id":50674712,"identity":"652a6945-4991-4a69-925f-79468a029a66","added_by":"auto","created_at":"2024-02-05 15:11:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1695042,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2702374/v1/b5885fe3-aed4-437e-918c-1cd2907488f5.pdf"},{"id":35459101,"identity":"4727d741-11fd-40c4-adfb-ad848b3eaba9","added_by":"auto","created_at":"2023-04-07 18:16:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":169049,"visible":true,"origin":"","legend":"","description":"","filename":"SciRepSupplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2702374/v1/433cc24ed7d2a3dbe0140bfb.pdf"},{"id":35458745,"identity":"bf37728c-9454-4df1-ad01-f2f1b0ef2a81","added_by":"auto","created_at":"2023-04-07 18:08:24","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1867577,"visible":true,"origin":"","legend":"","description":"","filename":"SciRepSupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2702374/v1/34d41f78714116c84669eba0.pdf"},{"id":35457780,"identity":"09b47485-0898-40db-afe4-7dfc103953ac","added_by":"auto","created_at":"2023-04-07 17:52:24","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11333,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2702374/v1/950024f050e350ccc7b8bea0.xlsx"},{"id":35458544,"identity":"a0b989c0-d300-4e3b-98ec-0ad25e052aa9","added_by":"auto","created_at":"2023-04-07 18:00:24","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":26476,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2702374/v1/9c02ade26df3c322ec138c89.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic functional changes upon thalamotomy in essential tremor depend on baseline brain morphometry","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEssential tremor (ET) is a prominent, extremely disabling movement disorder\u003c/p\u003e \u003cp\u003eaffecting approximately 3% of individuals older than 80 years.1 It is primarily\u003c/p\u003e \u003cp\u003echaracterized by the presence of upper limb action tremor for at least 3 years,2 and\u003c/p\u003e \u003cp\u003ecan induce other symptoms such as impairments in executive function and memory,\u003c/p\u003e \u003cp\u003emood disorders and dementia.3\u0026ndash;4 While primary pathological and subsequent\u003c/p\u003e \u003cp\u003ecompensatory molecular and morphological brain changes are believed to center\u003c/p\u003e \u003cp\u003earound the cerebellum and eventually recruit deeper structures,5 tremor generation\u003c/p\u003e \u003cp\u003eand maintenance crucially involve cerebellar functional interplays within the\u003c/p\u003e \u003cp\u003ecerebello-dentato-rubro-olivary-cerebellar6-7 and cortico-ponto-cerebello-dentato-\u003c/p\u003e \u003cp\u003ethalamo-cortical2,8 networks.\u003c/p\u003e \u003cp\u003eThe ventro-intermediate nucleus of the thalamus (Vim) is centrally placed within the\u003c/p\u003e \u003cp\u003elatter network and is a well-established surgical target for standard deep brain\u003c/p\u003e \u003cp\u003estimulation (DBS) or stereotactic ablation for tremor,9 which are indicated for the ET\u003c/p\u003e \u003cp\u003epatients who cannot tolerate or do not properly respond to commonly prescribed\u003c/p\u003e \u003cp\u003emedications.10 Standard DBS applies high-frequency electrical pulses through deeply\u003c/p\u003e \u003cp\u003eimplanted electrodes and is considered safe and efficient to treat ET. It enables\u003c/p\u003e \u003cp\u003estimulation interruption or fine-tuning if needed, but comes with inherent risks (\u003cem\u003ee.g.\u003c/em\u003e,\u003c/p\u003e \u003cp\u003einfection) given the required intervention and the reliance on implanted hardware.11\u0026ndash;12\u003c/p\u003e \u003cp\u003eMore recently, magnetic resonance-guided focused ultrasound thalamotomy\u003c/p\u003e \u003cp\u003e(MRgFUS) generates a lesion by controlled thermocoagulation, and does not\u003c/p\u003e \u003cp\u003enecessitate an invasive operation, while providing a risk/benefit balance akin to more\u003c/p\u003e \u003cp\u003eestablished standard surgical approaches.10,13 Gamma Knife stereotactic radiosurgical\u003c/p\u003e \u003cp\u003ethalamotomy is a minimally invasive alternative that uses stereotactic coordinates to\u003c/p\u003e \u003cp\u003etarget the Vim, avoiding the need for open surgery, while focusing multiple beams of\u003c/p\u003e \u003cp\u003egamma radiations. It is considered a valuable alternative, particularly for patients with\u003c/p\u003e \u003cp\u003emedical comorbidities or uneasy with the prospect of an operation, but it has two\u003c/p\u003e \u003cp\u003emain limitations: the target site cannot be confirmed intraoperatively, and clinical\u003c/p\u003e \u003cp\u003ebenefits only appear after a median of three months, and sometimes up to one year\u003c/p\u003e \u003cp\u003eafter the intervention.14 Past works have validated radiosurgical thalamotomy as a\u003c/p\u003e \u003cp\u003esafe and effective noninvasive surgical strategy in ET.15\u0026ndash;18\u003c/p\u003e \u003cp\u003eHow the brain of ET patients changes following radiosurgical thalamotomy, and\u003c/p\u003e \u003cp\u003ewhich features may serve as pre-interventional predictors of clinical improvement, are\u003c/p\u003e \u003cp\u003eimportant questions that have been explored in several structural and functional\u003c/p\u003e \u003cp\u003estudies. In recent work quantifying cortical thickness (CT), surface area (SA) and\u003c/p\u003e \u003cp\u003emean curvature (MC) as morphometric descriptors of cortical brain regions (\u003cem\u003ei.e.\u003c/em\u003e,\u003c/p\u003e \u003cp\u003esurface-based morphometry [SBM] analysis), the statistical dependences across them\u003c/p\u003e \u003cp\u003e(inferred through Pearson\u0026rsquo;s correlation coefficient) were compared before and after\u003c/p\u003e \u003cp\u003ethe thalamotomy. At the whole-brain level, CT and MC became anticorrelated\u003c/p\u003e \u003cp\u003efollowing thalamotomy, an effect mostly driven by the left fusiform and paracentral\u003c/p\u003e \u003cp\u003egyri, left posterior cingulate cortex, right banks superior temporal sulcus and right\u003c/p\u003e \u003cp\u003einferior temporal cortex. On the other hand, SA and MC became correlated, mostly so\u003c/p\u003e \u003cp\u003ein the bilateral fusiform gyrus and right inferior temporal cortex.19 Graph theoretical\u003c/p\u003e \u003cp\u003e investigations provided further insight on regional changes, as CT in the right lingual\u003c/p\u003e \u003cp\u003eand bilateral rostral middle frontal gyri exhibited lower dependence to MC of the rest\u003c/p\u003e \u003cp\u003eof the brain after thalamotomy, while conversely, an increase was seen for the left\u003c/p\u003e \u003cp\u003eprecentral gyrus.20\u003c/p\u003e \u003cp\u003eIn reports characterizing the functional interplays of the targeted left Vim by\u003c/p\u003e \u003cp\u003ecomputing its resting-state functional connectivity (RS FC) to the rest of the brain, FC\u003c/p\u003e \u003cp\u003eto the right insular and orbitofrontal cortices, to the right posterior parietal,\u003c/p\u003e \u003cp\u003esupramarginal and inferior frontal gyri, and to the bilateral frontal eye fields\u003c/p\u003e \u003cp\u003edecreased to non-significant values upon thalamotomy, while conversely, FC to the\u003c/p\u003e \u003cp\u003eright supplementary motor area increased to around zero from an initial\u003c/p\u003e \u003cp\u003eanticorrelation.21 In addition, more negative left Vim FC to the ipsilateral fusiform\u003c/p\u003e \u003cp\u003egyrus pre-intervention correlated with larger clinical improvement.22\u003c/p\u003e \u003cp\u003eDespite their valuable insight, the above previous studies suffer from some\u003c/p\u003e \u003cp\u003eshortcomings. Given their reliance on Pearson\u0026rsquo;s correlation coefficient, the conducted\u003c/p\u003e \u003cp\u003eSBM investigations could not individually address the effects of cross-property\u003c/p\u003e \u003cp\u003ecovariance (which would contribute to the numerator of the coefficient) and variance\u003c/p\u003e \u003cp\u003eof individual properties (which would instead impact its denominator). In addition,\u003c/p\u003e \u003cp\u003eonly pairs of morphometric properties were considered in each assessment, falling\u003c/p\u003e \u003cp\u003eshort of a fully multivariate treatment. As for functional analyses, they operated under\u003c/p\u003e \u003cp\u003ethe assumption of static cross-regional interplays, even though FC exhibits prominent\u003c/p\u003e \u003cp\u003etemporal dynamics with cognitive and clinical relevance.23\u0026ndash;24 In ET, only one study to\u003c/p\u003e \u003cp\u003edate has explicitly examined functional brain dynamics and its changes upon\u003c/p\u003e \u003cp\u003ethalamotomy,25 but it focused on co-(de)activations with the right extrastriate cortex.\u003c/p\u003e \u003cp\u003eThe dynamic interplays of the targeted Vim, and their evolution upon thalamotomy,\u003c/p\u003e \u003cp\u003ethus remain unknown.\u003c/p\u003e \u003cp\u003eIn the present work, we seek to overcome these limitations in a joint analysis of SBM\u003c/p\u003e \u003cp\u003eand RS functional MRI data acquired on a partly overlapping set of patients with ET,\u003c/p\u003e \u003cp\u003escanned before and 1 year after radiosurgical thalamotomy. We address the following\u003c/p\u003e \u003cp\u003equestions: (1) \u003cem\u003ewhether thalamotomy induces a renormalization of morphometric and\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003edynamic functional brain properties in ET\u003c/em\u003e, (2) \u003cem\u003ewhich pre-thalamotomy features better\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003ecorrelate with clinical recovery\u003c/em\u003e, and (3) \u003cem\u003ehow potential structure/function couplings\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eare impacted by the intervention\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTo answer these questions, we quantify similarity of the pre- and post-thalamotomy\u003c/p\u003e \u003cp\u003eET data points (respectively termed ETpre and ETpost from there onwards) to a set of\u003c/p\u003e \u003cp\u003ematched healthy controls (HCs). For SBM analysis, we leverage a recently introduced\u003c/p\u003e \u003cp\u003eanalytical pipeline26 to model the HC morphometric data by a multivariate Gaussian,\u003c/p\u003e \u003cp\u003eand subsequently quantify the likelihood of ET data points to be issued from this\u003c/p\u003e \u003cp\u003edistribution. By this mean, we derive a measure of similarity to HCs. For dynamic FC\u003c/p\u003e \u003cp\u003e(dFC) analysis, we employ a sliding-window approach to extract recurring dFC states\u003c/p\u003e \u003cp\u003eacross time and subjects27 from our HC population. Then, ETpre and ETpost dFC\u003c/p\u003e \u003cp\u003eestimates are matched to these states to quantify spatial similarity and temporal\u003c/p\u003e \u003cp\u003eoccurrences.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe dynamic evolution of FC in HCs could be disentangled into \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 separate dFC\u003c/p\u003e \u003cp\u003estates (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e), as seen from a clear PAC global minimum and\u003c/p\u003e \u003cp\u003eupon inspection of the consensus matrices. State 1 occurred in 419 (36.88%) frames\u003c/p\u003e \u003cp\u003eand was expressed 36.38\u0026thinsp;\u0026plusmn;\u0026thinsp;36.95% of the time in individual subjects. State 2 only\u003c/p\u003e \u003cp\u003eoccurred in 182 (16%) frames (16.47\u0026thinsp;\u0026plusmn;\u0026thinsp;35.78% of the time per subject). State 3\u003c/p\u003e \u003cp\u003eoccurred the most (535 or 47.1% of frames, 47.15\u0026thinsp;\u0026plusmn;\u0026thinsp;37.8% of the time subject-wise).\u003c/p\u003e \u003cp\u003eIn terms of spatial properties (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; see also \u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e), all states\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eshowcased their largest positive-valued dFC (\u003cem\u003ei.e.\u003c/em\u003e, correlated activity with the left\u003c/p\u003e \u003cp\u003eVim) with other subcortical areas, as well as negative-valued dFC (anticorrelated\u003c/p\u003e \u003cp\u003eactivity with the left Vim) with cerebellar regions. A more restricted set of subcortical\u003c/p\u003e \u003cp\u003eareas, and stronger cerebellar anti-correlations, were observed for state 2. Regarding\u003c/p\u003e \u003cp\u003einterplays with the cortex, states 1 and 3 primarily featured positive-valued dFC,\u003c/p\u003e \u003cp\u003ewhile the pattern was more mixed for state 2. Only state 1 showed correlations with\u003c/p\u003e \u003cp\u003ethe full visual network, while state 2 displayed (anti)correlations with specific\u003c/p\u003e \u003cp\u003eperipheral regions, and state 3 mostly included positive-valued dFC with the\u003c/p\u003e \u003cp\u003eperiphery. Primary somatomotor areas were only largely correlated with the left Vim\u003c/p\u003e \u003cp\u003ein state 2, as opposed to higher-level somatomotor areas for the two others. Dorsal\u003c/p\u003e \u003cp\u003eattention, salience and control networks exhibited a gradient of positive-valued dFC\u003c/p\u003e \u003cp\u003eacross states, strongest in state 3 and weakest in state 2. For the default mode\u003c/p\u003e \u003cp\u003enetwork, state 3 exhibited the most widespread correlations, but the strongest\u003c/p\u003e \u003cp\u003econnections were found in state 1. Finally, state 1 also showcased strong temporo-\u003c/p\u003e \u003cp\u003eparietal dFC.\u003c/p\u003e \u003cp\u003eMatching of the ETpre and ETpost windowed dFC estimates to these states (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003erevealed that they were all also expressed in the ET patient population, in a way that\u003c/p\u003e \u003cp\u003ediffered along time and across subjects. Furthermore, the spatial similarity of dFC\u003c/p\u003e \u003cp\u003eestimates to their assigned state also fluctuated along the same dimensions. We thus\u003c/p\u003e \u003cp\u003eexamined temporal occurrences and spatial similarity features to assess whether there\u003c/p\u003e \u003cp\u003ewas an impact of thalamotomy, including a random subject effect to account for\u003c/p\u003e \u003cp\u003emultiple measurements in only a subset of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The analysis revealed\u003c/p\u003e \u003cp\u003eno group difference for temporal occurrences (all \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026gt;\u0026thinsp;0.1). For mean spatial\u003c/p\u003e \u003cp\u003esimilarity, there was a significant random subject effect for state 1 (\u003cem\u003eF\u003c/em\u003e1,22\u0026thinsp;=\u0026thinsp;7.89,\u003c/p\u003e \u003cp\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0025), indicating strong differences between individual patients with this\u003c/p\u003e \u003cp\u003emeasure. The standard deviation of spatial similarity showed a significant decrease\u003c/p\u003e \u003cp\u003eupon thalamotomy in state 1 (\u003cem\u003eF\u003c/em\u003e1,22\u0026thinsp;=\u0026thinsp;12.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015). The coefficient of variation,\u003c/p\u003e \u003cp\u003ewhich jointly accounts for mean and standard deviation impacts, yielded both\u003c/p\u003e \u003cp\u003esignificant effects for state 1 (\u003cem\u003eF\u003c/em\u003e1,22\u0026thinsp;=\u0026thinsp;19.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0041 and \u003cem\u003eF\u003c/em\u003e1,22\u0026thinsp;=\u0026thinsp;4.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0495 for the\u003c/p\u003e \u003cp\u003efixed and random effects, respectively), with a decrease observed upon thalamotomy.\u003c/p\u003e \u003cp\u003eThus, significant spatial renormalization was observed in terms of state 1 expression.\u003c/p\u003e \u003cp\u003eInvestigation of clinical predictive potential revealed a significant relationship\u003c/p\u003e \u003cp\u003ebetween clinical improvement (quantified as the drop in TSTH) and the standard\u003c/p\u003e \u003cp\u003edeviation (\u003cem\u003et\u003c/em\u003e12=-2.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041) and coefficient of variation (\u003cem\u003et\u003c/em\u003e12=-2.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) of\u003c/p\u003e \u003cp\u003estate 2 expression: hence, better recovery goes with a reduced spatial variability in\u003c/p\u003e \u003cp\u003estate 2 expression. In addition, there was an MR signature-by-metric interaction for\u003c/p\u003e \u003cp\u003estate 3 (\u003cem\u003et\u003c/em\u003e12\u0026thinsp;=\u0026thinsp;3.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) and state 1 (\u003cem\u003et\u003c/em\u003e12=-2.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) temporal occurrences. As\u003c/p\u003e \u003cp\u003ethe interactions had opposite signs, we probed the difference between state 1 and 3\u003c/p\u003e \u003cp\u003ecounts (Counts3 \u0026ndash; Counts1) in a follow-up analysis: the interaction remained\u003c/p\u003e \u003cp\u003esignificant (\u003cem\u003et\u003c/em\u003e12\u0026thinsp;=\u0026thinsp;3.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), indicating that the more state 3 is expressed over state\u003c/p\u003e \u003cp\u003e1, the better the recovery, in a way that also depends on the MR signature.\u003c/p\u003e \u003cp\u003eOn morphometric data, we quantified the log-likelihood for a regional ET estimate to\u003c/p\u003e \u003cp\u003ebe issued from the HC distribution as a measure of similarity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eaverage log-likelihood across subjects was consistently larger before than after\u003c/p\u003e \u003cp\u003ethalamotomy, a difference significant in 11 cortical regions: the bilateral fusiform\u003c/p\u003e \u003cp\u003e(ETpost-ETpre group difference: \u003cem\u003ez\u003c/em\u003e=-4.017, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0051 and \u003cem\u003ez\u003c/em\u003e=-4.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0027,\u003c/p\u003e \u003cp\u003erespectively left and right sides) and parahippocampal (\u003cem\u003ez\u003c/em\u003e=-4.84, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001 and \u003cem\u003ez\u003c/em\u003e=-\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e5.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;10\u0026thinsp;\u0026minus;\u0026thinsp;5) gyri, left cuneus (\u003cem\u003ez\u003c/em\u003e=-4.017, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0051), lateral orbitofrontal cortex (\u003cem\u003ez\u003c/em\u003e=-\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e4.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0034), precentral gyrus (\u003cem\u003ez\u003c/em\u003e=-3.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0122) and insula (\u003cem\u003ez\u003c/em\u003e=-3.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0218),\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eright entorhinal cortex (\u003cem\u003ez\u003c/em\u003e=-3.78, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0134), lingual cortex (\u003cem\u003ez\u003c/em\u003e=-3.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0218) and\u003c/p\u003e \u003cp\u003esuperior temporal cortex (\u003cem\u003ez\u003c/em\u003e=-3.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0208). There were also two significant\u003c/p\u003e \u003cp\u003esubcortical areas: the bilateral hippocampus (\u003cem\u003ez\u003c/em\u003e=-4.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0006 and \u003cem\u003ez\u003c/em\u003e=-4.03,\u003c/p\u003e \u003cp\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0049).\u003c/p\u003e \u003cp\u003eTo further characterize ETpre \u003cem\u003eversus\u003c/em\u003e ETpost differences, we compared both groups at\u003c/p\u003e \u003cp\u003ethe level of individual mean and (co)variance coefficients. There were no differences\u003c/p\u003e \u003cp\u003ein mean values across groups, but CT variance increased upon thalamotomy in the\u003c/p\u003e \u003cp\u003ebilateral fusiform gyrus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0021 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0031, respectively left and right sides),\u003c/p\u003e \u003cp\u003eleft lateral orbitofrontal cortex (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0021), precentral gyrus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0031), and right\u003c/p\u003e \u003cp\u003elingual cortex (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0). MC variance also increased in the bilateral fusiform gyrus\u003c/p\u003e \u003cp\u003e(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0082 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0) and left insula (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0). Covariance between CT and MC decreased\u003c/p\u003e \u003cp\u003ein the left fusiform gyrus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0031), lateral orbitofrontal cortex (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0124) and right\u003c/p\u003e \u003cp\u003esuperior temporal cortex (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0103). Covariance between CT and SA decreased as\u003c/p\u003e \u003cp\u003ewell in the left precentral gyrus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0062). However, covariance between SA and\u003c/p\u003e \u003cp\u003eMC increased in the bilateral fusiform gyrus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0093 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0134). Variance in\u003c/p\u003e \u003cp\u003esubcortical volume increased in the bilateral hippocampus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0093).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e visually illustrates all these changes.\u003c/p\u003e \u003cp\u003eThere was no link between pre-intervention log-likelihood in any of the regions that\u003c/p\u003e \u003cp\u003eshowed significance and clinical recovery (all \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026gt;\u0026thinsp;0.1). In sum, there was thus\u003c/p\u003e \u003cp\u003eno evidence for morphometric renormalization upon thalamotomy, or for a clinical\u003c/p\u003e \u003cp\u003epredictive potential of such features.\u003c/p\u003e \u003cp\u003eFinally, we quantified the correlation between the mean spatial similarity in dFC state\u003c/p\u003e \u003cp\u003eexpression and the morphometric similarity to HCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; see also\u003c/p\u003e \u003cp\u003e \u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e) in a cross-modality analysis. For state 1, the distributions\u003c/p\u003e \u003cp\u003eof correlation values were similarly centered around 0 both before and after\u003c/p\u003e \u003cp\u003ethalamotomy (rank-sum test for ETpost \u0026ndash; ETpre group difference: \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.058).\u003c/p\u003e \u003cp\u003eHowever, for states 2 and 3, we observed a broad pattern of anticorrelation before\u003c/p\u003e \u003cp\u003ethalamotomy, which disappeared afterwards (respectively \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.16 10\u0026thinsp;\u0026minus;\u0026thinsp;5 and\u003c/p\u003e \u003cp\u003e \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.83, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.54 10\u0026thinsp;\u0026minus;\u0026thinsp;9). In other words, before thalamotomy, a patient who showed\u003c/p\u003e \u003cp\u003egreater average spatial conformity to HC dFC states 2 and 3 also tended to show less\u003c/p\u003e \u003cp\u003e similarity to the regional HC morphometric profiles, but this relationship disappeared\u003c/p\u003e \u003cp\u003eafter thalamotomy.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eWe studied 34 right-handed patients (17 males) with drug-resistant ET. They were\u003c/p\u003e \u003cp\u003e70.06\u0026thinsp;\u0026plusmn;\u0026thinsp;9.12 years old when initially assessed. Neurological assessment was performed\u003c/p\u003e \u003cp\u003eby T.W., a neurologist specialized in movement disorders. All patients had a clear\u003c/p\u003e \u003cp\u003ediagnosis of ET based on consensus clinical criteria28 and showed no structural\u003c/p\u003e \u003cp\u003eabnormalities upon 3T MRI. They were clinically assessed and scanned before and 1\u003c/p\u003e \u003cp\u003eyear after thalamotomy of the left Vim, to account for the progressive and delayed\u003c/p\u003e \u003cp\u003eclinical effect. The Tremor Score on Treated Hand (TSTH) from the Fahn-Tolosa-\u003c/p\u003e \u003cp\u003eMar\u0026iacute;n rating scale29 was used to quantify tremor severity in the patients and its\u003c/p\u003e \u003cp\u003eevolution upon intervention. For details on data acquisition, see \u003cb\u003eSupplementary\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003einformation\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eFor SBM analysis (based on T1-weighted structural images), the data from all 34\u003c/p\u003e \u003cp\u003epatients was considered. For dFC analysis, following the thorough exclusion of poor-\u003c/p\u003e \u003cp\u003equality recordings (see below for details), a total of 23 patients contributed at least\u003c/p\u003e \u003cp\u003eone RS fMRI scan to the analyses, and the recordings of both time points were\u003c/p\u003e \u003cp\u003eretained for 13 of them. There were 18 remaining scans both in the ETpre and ETpost\u003c/p\u003e \u003cp\u003egroups.\u003c/p\u003e \u003cp\u003eFor SBM analysis, ET patients were compared to 29 age-matched HCs (69.93\u0026thinsp;\u0026plusmn;\u0026thinsp;7.14\u003c/p\u003e \u003cp\u003eyears old). Following quality control, the RS fMRI data from 14 was also retained\u003c/p\u003e \u003cp\u003e(70.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 years old, 5 males). All demographic and clinical data are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDemographic and clinical details of the subjects.\u003c/b\u003e For healthy controls (HCs) and ET patients before (ET\u003csub\u003epre\u003c/sub\u003e) and after (ET\u003csub\u003epost\u003c/sub\u003e) intervention, values are reported as mean \u0026plusmn; standard deviation, with minimum, median and maximum into squared brackets. Significant statistical comparisons are highlighted in bold. M: male, F: female, TSTH: tremor score on treated hand.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eMorphometry analyses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eDynamic functional connectivity analyses\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eET\u003csub\u003epre\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eET\u003csub\u003epost\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e(HC \u0026ndash; ET)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e(ET\u003csub\u003epost\u003c/sub\u003e \u0026ndash; ET\u003csub\u003epre\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eET\u003csub\u003epre\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eET\u003csub\u003epost\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e(HC \u0026ndash; ET)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e(ET\u003csub\u003epost\u003c/sub\u003e \u0026ndash; ET\u003csub\u003epre\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at baseline evaluation [years]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.93\u0026plusmn;7.14 [59,69,83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e70.06\u0026plusmn;9.12\u003c/p\u003e \u003cp\u003e[49,72,83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003csub\u003e61\u003c/sub\u003e=-0.06\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAll 1-year increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.21\u0026plusmn;6.8\u003c/p\u003e \u003cp\u003e[61,69,81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69.3\u0026plusmn;10.1\u003c/p\u003e \u003cp\u003e[49,71.5,82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e71\u0026plusmn;9.68\u003c/p\u003e \u003cp\u003e[51,72.5,83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003csub\u003e30\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.45\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003csub\u003e34\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.51\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender [M:F]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12:17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e17:17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5:9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7:11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8:10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSTH score [points]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.41\u0026plusmn;5.53 [8,20.5,30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.26\u0026plusmn;7.71\u003c/p\u003e \u003cp\u003e[0,3,27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003et\u003c/span\u003e\u003csub\u003e\u003cb\u003e66\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e=-8.69\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;1.52 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;12\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.94\u0026plusmn;6.21\u003c/p\u003e \u003cp\u003e[8,20.5,30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.83\u0026plusmn;8.79\u003c/p\u003e \u003cp\u003e[0,2,27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003et\u003c/span\u003e\u003csub\u003e\u003cb\u003e34\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e=-5.57\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;3.17 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptoms\u0026rsquo; duration [months]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e35.53\u0026plusmn;18.28\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSame subjects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.94\u0026plusmn;16.52\u003c/p\u003e \u003cp\u003e[5,27.5,61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.89\u0026plusmn;16.38\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003csub\u003e34\u003c/sub\u003e=-0.19\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history [Y:N]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e11:23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8:10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8:10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR signature [ml]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.12\u0026plusmn;0.13\u003c/p\u003e \u003cp\u003e[0.002,0.076,0.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSame subjects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u0026plusmn;0.16\u003c/p\u003e \u003cp\u003e[0.002,0.069,0.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u0026plusmn;0.16\u003c/p\u003e \u003cp\u003e[0.014,0.093,0.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003csub\u003e34\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.53\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e The Timone University Hospital Ethical Committee (ID-RCB: 2017-A01249\u0026ndash;44)\u003c/p\u003e \u003cp\u003e granted formal approval for this study (including by the Ethics Committee at national\u003c/p\u003e \u003cp\u003elevel, CNIL-MR-03). All methods were performed in accordance with the relevant\u003c/p\u003e \u003cp\u003e guidelines and regulations. Individual informed consent was obtained from all\u003c/p\u003e \u003cp\u003esubjects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eIntervention and one-year MR signature volume assessment\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThalamotomy was performed using Leksell Gamma Knife (LGK, Elekta Instruments,\u003c/p\u003e \u003cp\u003eAB, Sweden) between September 2014 and April 2016, at the Centre Hospitalier\u003c/p\u003e \u003cp\u003eUniversitaire de la Timone (Marseille, France), always by the same neurosurgeon\u003c/p\u003e \u003cp\u003e(J.R.). In each case, the Leksell coordinate G frame (Elekta Instruments, AB, Sweden)\u003c/p\u003e \u003cp\u003ewas applied under local anesthesia. After its positioning, stereotactic computed\u003c/p\u003e \u003cp\u003etomography and MRI were both performed on the patient.\u003c/p\u003e \u003cp\u003eLandmarks of interest, such as the anterior and posterior commissures, were\u003c/p\u003e \u003cp\u003eindividually identified on an MR scan (T2 CISS/FIESTA sequence, Siemens).\u003c/p\u003e \u003cp\u003eTargeting was achieved individually with the Guiot diagram30, placed 2.5 mm above\u003c/p\u003e \u003cp\u003ethe anterior-posterior commissure line and 11 mm lateral to the third ventricle wall. A\u003c/p\u003e \u003cp\u003esingle 4-mm isocenter was used with a maximum prescription dose of 130 Gy.\u003c/p\u003e \u003cp\u003eThere is a specific MR signature of radiosurgical thalamotomy, known to differ across\u003c/p\u003e \u003cp\u003esubjects both in terms of aspect (with or without contrast enhancement) and\u003c/p\u003e \u003cp\u003ecorresponding volume.14 It was contoured on a T1-weighted Gadolinium-injected\u003c/p\u003e \u003cp\u003escan acquired at one-year follow-up, and usually corresponds to the 90 Gy isodose\u003c/p\u003e \u003cp\u003eline. The individual patient\u0026rsquo;s Gadolinium-injected MR image was imported in the\u003c/p\u003e \u003cp\u003eLeksell GammaPlan software (Elekta Instruments, AB, Sweden), and co-registered\u003c/p\u003e \u003cp\u003ewith the stereotactic imaging. A manual drawing was made for each individual case,\u003c/p\u003e \u003cp\u003eon each slice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eData processing\u003c/b\u003e\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003eSurface-based morphometry\u003c/b\u003e\u003c/h2\u003e \u003cp\u003e \u003cem\u003eFreesurfer\u003c/em\u003e31 was used to extract CT, SA and MC from native structural MR images\u003c/p\u003e \u003cp\u003efor a set of \u003cem\u003eP\u003c/em\u003ecort\u0026thinsp;=\u0026thinsp;68 cortical regions (see \u003cb\u003eSupplementary information\u003c/b\u003e for details). In\u003c/p\u003e \u003cp\u003eaddition, we also extracted regional volume for \u003cem\u003eP\u003c/em\u003enoncort\u0026thinsp;=\u0026thinsp;19 non-cortical areas,\u003c/p\u003e \u003cp\u003eincluding the cerebellum and subcortex, using \u003cem\u003eFreesurfer\u003c/em\u003e\u0026rsquo;s automatic subcortical\u003c/p\u003e \u003cp\u003esegmentation approach.32 \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e summarizes all brain regions\u003c/p\u003e \u003cp\u003econsidered in our morphometric analyses.\u003c/p\u003e \u003cp\u003eTo account for the confounding impacts of age, gender and total grey matter volume\u003c/p\u003e \u003cp\u003ein our analyses while handling the fact that two scans were available for ET patients\u003c/p\u003e \u003cp\u003ebut not for HCs, a mixed-effects model strategy was employed (see \u003cb\u003eSupplementary\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003einformation\u003c/b\u003e for details).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003eDynamic functional connectivity\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eA similar preprocessing scheme was applied to each of the functional scans, using\u003c/p\u003e \u003cp\u003eSPM12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/spm12/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/software/spm12/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e for initial steps.\u003c/p\u003e \u003cp\u003eFunctional volumes were realigned and subsequently co-registered to the T1\u003c/p\u003e \u003cp\u003estructural image. Segmentation was performed to derive the deformation field that\u003c/p\u003e \u003cp\u003ewas then used to warp fMRI volumes into the common MNI space.\u003c/p\u003e \u003cp\u003eSubsequent steps were performed using custom scripts and MATLAB version 2020b\u003c/p\u003e \u003cp\u003e(MathWorks, Natick, MA). The first 3 volumes were discarded to enable\u003c/p\u003e \u003cp\u003emagnetization equilibration. Then, the voxel-wise data was converted into a restricted\u003c/p\u003e \u003cp\u003eset of parcels, combining three different atlases: the Schaefer atlas33 (400 regions, 17\u003c/p\u003e \u003cp\u003enetworks) for cortical areas, the Tian atlas34 (scale 3, 50 regions) for subcortical\u003c/p\u003e \u003cp\u003eregions, and the decomposition into 26 subregions from the AAL atlas35 for the\u003c/p\u003e \u003cp\u003ecerebellum. In total, there were thus 476 areas available, of which 13 were excluded\u003c/p\u003e \u003cp\u003ebecause they were not included in at least one scan owing to a trimmed field of view.\u003c/p\u003e \u003cp\u003eThus, the present work considers dFC of the left Vim with 462 other brain regions,\u003c/p\u003e \u003cp\u003esummarized in \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eTo clean the regional time courses from artefactual signal sources, conservative white\u003c/p\u003e \u003cp\u003ematter and cerebrospinal fluid masks from the DPARSF toolbox36 were used to\u003c/p\u003e \u003cp\u003ecompute associated regressors. Together with the 6 head motion parameters obtained\u003c/p\u003e \u003cp\u003eat the realignment step and a Discrete Cosine Transform basis for drifts (cut-off: 0.01\u003c/p\u003e \u003cp\u003eHz), they were regressed out from the data. Finally, framewise displacement (FD)37\u003c/p\u003e \u003cp\u003ewas computed, and the scans for which more than 30% of frames were corrupted\u003c/p\u003e \u003cp\u003e(defined as the frames with FD\u0026thinsp;\u0026gt;\u0026thinsp;0.5 mm, the frames beforehand and the two frames\u003c/p\u003e \u003cp\u003eafterwards) were discarded.\u003c/p\u003e \u003cp\u003eFrom this preprocessed data, sliding-window analysis was conducted. We used a\u003c/p\u003e \u003cp\u003erectangular window with size W\u0026thinsp;=\u0026thinsp;30 TRs (99.9 s)!\u003cem\u003ei.e.\u003c/em\u003e, the inverse of the minimal\u003c/p\u003e \u003cp\u003efrequency remaining in the data (0.01 Hz),38 and step size \"=2 TRs (6.6 s). FC within\u003c/p\u003e \u003cp\u003eeach temporal sub-window was quantified with Pearson\u0026rsquo;s correlation coefficient,\u003c/p\u003e \u003cp\u003ediscarding the frames corrupted by head movement. To further guarantee the quality\u003c/p\u003e \u003cp\u003eof the analyzed data, only windows for which more than 20 samples remained were\u003c/p\u003e \u003cp\u003eretained for analysis, and ET scans for which less than 85 windows remained were\u003c/p\u003e \u003cp\u003ediscarded. Following all these steps, dFC data remained for 14 HCs, 18 ETpre scans,\u003c/p\u003e \u003cp\u003eand 18 ETpost scans.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eData analysis\u003c/b\u003e\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003eSurface-based morphometry\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWe computed the log-likelihood of ETpre and ETpost data to be issued from the HC\u003c/p\u003e \u003cp\u003edistribution as a similarity measure (see \u003cb\u003eSupplementary information\u003c/b\u003e for details). It\u003c/p\u003e \u003cp\u003ewas compared across groups with a rank-sum test, run separately for each region. The\u003c/p\u003e \u003cp\u003eobtained \u003cem\u003ep\u003c/em\u003e-values were Bonferroni-corrected for the number of performed tests (87).\u003c/p\u003e \u003cp\u003eFor the regions that reached significance in the above assessment, individual mean\u003c/p\u003e \u003cp\u003eand (co)variance coefficients were subsequently contrasted across groups through a\u003c/p\u003e \u003cp\u003enon-parametric permutation-based significance assessment of each ETpost \u0026ndash; ETpre\u003c/p\u003e \u003cp\u003edifference (100\u0026rsquo;000 folds, two-tailed testing). The obtained \u003cem\u003ep\u003c/em\u003e-values were Bonferroni-\u003c/p\u003e \u003cp\u003ecorrected for the number of performed tests (103).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003eDynamic functional connectivity\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo extract dFC states expressed in our HC population, clean dFC estimates (\u003cem\u003ei.e.\u003c/em\u003e,\u003c/p\u003e \u003cp\u003ewindows with at least 20 non-corrupted samples) were concatenated across all 14\u003c/p\u003e \u003cp\u003esubjects. This resulted in 1136 samples, each of dimension 462 (the number of\u003c/p\u003e \u003cp\u003econnections to the left Vim).\u003c/p\u003e \u003cp\u003eTo define an optimal number of states, we used consensus clustering39: over 200\u003c/p\u003e \u003cp\u003efolds, 80% of samples were randomly selected, and \u003cem\u003eK\u003c/em\u003e-means clustering was\u003c/p\u003e \u003cp\u003eperformed (cosine distance) for \u003cem\u003eK\u003c/em\u003e ranging from 2 to 20. The percentage of\u003c/p\u003e \u003cp\u003eambiguously clustered pairs (PAC)40 was computed as a measure of clustering\u003c/p\u003e \u003cp\u003equality, and clearly revealed \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 as an optimum (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). A final\u003c/p\u003e \u003cp\u003e \u003cem\u003eK\u003c/em\u003e-means clustering step was then performed (300 folds, 200 iterations each), yielding\u003c/p\u003e \u003cp\u003e \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 dFC states characteristic of the HC population.\u003c/p\u003e \u003cp\u003eThe first 85 dFC estimates from each available ET scan were then matched to these\u003c/p\u003e \u003cp\u003edFC states through Pearson\u0026rsquo;s correlation coefficient. The most similar state to a given\u003c/p\u003e \u003cp\u003edFC estimate was assumed to be expressed at that time point (\u003cem\u003ei.e.\u003c/em\u003e, winner-takes-all\u003c/p\u003e \u003cp\u003eapproach), enabling the computation of temporal occurrences (number of times a\u003c/p\u003e \u003cp\u003egiven state is expressed) for each scan. In addition, the mean and standard deviation\u003c/p\u003e \u003cp\u003eof spatial similarity to the expressed state were computed, and the coefficient of\u003c/p\u003e \u003cp\u003evariation was derived as a measure of spatial stability of state expression. Temporal\u003c/p\u003e \u003cp\u003eoccurrences (\u003cem\u003eK\u003c/em\u003e values per scan), and each spatial stability measure (\u003cem\u003eK\u003c/em\u003e values per scan\u003c/p\u003e \u003cp\u003eas well), were considered as metrics of interest in our analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003eQuantification of the impact of thalamotomy\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo determine whether a given metric differed between the pre- and post-thalamotomy\u003c/p\u003e \u003cp\u003estages, a two-way ANOVA was conducted, using group label (ETpre and ETpost) as a\u003c/p\u003e \u003cp\u003efixed effect and subject label as a random effect. The model takes the form:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${M}_{g,s}=\\mu +{\\alpha }_{g}+{\\beta }_{s}+{ϵ}_{g,s}$$\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e \u003cp\u003eIn the above equation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M}_{g,s}\\)\u003c/span\u003e\u003c/span\u003e is the value of the metric at hand for subject \u003cem\u003es\u003c/em\u003e in group \u003cem\u003eg\u003c/em\u003e,\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mu\\)\u003c/span\u003e \u003c/span\u003e is the global mean across both groups, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{g}\\)\u003c/span\u003e\u003c/span\u003e is the fixed group effect, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{s}\\)\u003c/span\u003e\u003c/span\u003e is the random\u003c/p\u003e \u003cp\u003esubject effect, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ϵ}_{g,s}\\)\u003c/span\u003e\u003c/span\u003e is the error term. The random effect is assumed to follow a\u003c/p\u003e \u003cp\u003enormal distribution with mean 0 and variance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{\\beta }^{2}\\)\u003c/span\u003e\u003c/span\u003e, and the error follows a normal\u003c/p\u003e \u003cp\u003edistribution with mean 0 and variance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{ϵ}^{2}\\)\u003c/span\u003e\u003c/span\u003e. A significant fixed group effect means that\u003c/p\u003e \u003cp\u003ethe null hypothesis of equal means across groups can be rejected (that is, there is an\u003c/p\u003e \u003cp\u003eimpact of thalamotomy at the group level). A significant random subject effect means\u003c/p\u003e \u003cp\u003ethat the null hypothesis \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{\\beta }^{2}\\)\u003c/span\u003e\u003c/span\u003e=0 can be rejected (in other words, different subjects show\u003c/p\u003e \u003cp\u003edifferent extents of expression of the metric at hand).\u003c/p\u003e \u003cp\u003eWe evaluated temporal occurrences and spatial stability of the expressed dFC states\u003c/p\u003e \u003cp\u003efor potential group differences (4\u003cem\u003eK\u003c/em\u003e features). Obtained \u003cem\u003ep\u003c/em\u003e-values were Bonferroni-\u003c/p\u003e \u003cp\u003ecorrected for the number of states (\u003cem\u003eK\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003eExtraction of predictive pre-thalamotomy features\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo establish whether a given metric before thalamotomy correlates with clinical\u003c/p\u003e \u003cp\u003eimprovement, we considered the difference in TSTH score (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\Delta }\\)\u003c/span\u003e\u003c/span\u003eTSTH\u0026thinsp;=\u0026thinsp;TSTHpre -\u003c/p\u003e \u003cp\u003eTSTHpost) as outcome measure, so that a more positive value indicates greater\u003c/p\u003e \u003cp\u003erecovery, and the following general linear model:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${{\\Delta }}_{TSTH}={\\beta }_{0}+{\\beta }_{1}{A}_{s}+{\\beta }_{2}{G}_{s}+{\\beta }_{3}{M}_{s}+{\\beta }_{4}{R}_{s}+{\\beta }_{5}\\left({M}_{s}\\times {R}_{s}\\right)+{ϵ}_{s}$$\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e \u003cp\u003eImportantly, in addition to modelling the impacts of age \u003cem\u003eA\u003c/em\u003e and gender \u003cem\u003eG\u003c/em\u003e for subject \u003cem\u003es\u003c/em\u003e,\u003c/p\u003e \u003cp\u003ewe also consider that of the radiological signature \u003cem\u003eR\u003c/em\u003e, as well as its interaction with the\u003c/p\u003e \u003cp\u003emetric \u003cem\u003eM\u003c/em\u003e. This is because previous work has shown that the MR signature correlates\u003c/p\u003e \u003cp\u003ewith pre-interventional functional brain features.41\u003c/p\u003e \u003cp\u003eNote that for the assessments performed on morphometric features, since age and\u003c/p\u003e \u003cp\u003egender were already regressed out during processing, these two covariates were\u003c/p\u003e \u003cp\u003eomitted from the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003eSimilarity across modalities\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo compare SBM and dFC data, we considered the log-likelihood to be issued from\u003c/p\u003e \u003cp\u003ethe HC distribution (for SBM analysis, \u003cem\u003eP\u003c/em\u003e values per subject), and the mean similarity\u003c/p\u003e \u003cp\u003eto the dFC states (for dFC analysis, \u003cem\u003eK\u003c/em\u003e values per subject). Pearson\u0026rsquo;s correlation was\u003c/p\u003e \u003cp\u003ecomputed to yield a \u003cem\u003eP\u003c/em\u003e x \u003cem\u003eK\u003c/em\u003e matrix of similarity values for the ETpre group, and another\u003c/p\u003e \u003cp\u003efor the ETpost group.\u003c/p\u003e \u003cp\u003eFor each state, the distribution of correlation values with the whole brain was\u003c/p\u003e \u003cp\u003econtrasted between before and after thalamotomy through a rank-sum test. The\u003c/p\u003e \u003cp\u003eresulting \u003cem\u003ep\u003c/em\u003e-values were Bonferroni-corrected for \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 states.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003ePrevious data uses and implementation details\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe morphometric data analyzed therein was already examined in two previous\u003c/p\u003e \u003cp\u003estructural covariance analysis studies,19\u0026ndash;20 which performed pair-wise assessments of\u003c/p\u003e \u003cp\u003ecross-property dependences and did not discriminate between property variance and\u003c/p\u003e \u003cp\u003ecross-property covariance. The analytical framework leveraged in the present work\u003c/p\u003e \u003cp\u003ewas applied on ETpre morphometric profiles to assess their differences to HCs,26 but\u003c/p\u003e \u003cp\u003enever to the ETpost data.\u003c/p\u003e \u003cp\u003eA subpart of the functional data considered here was already analyzed in a series of\u003c/p\u003e \u003cp\u003estudies addressing the specificities of ET and the impacts of thalamotomy.21\u0026ndash;22,25,41\u0026ndash;43\u003c/p\u003e \u003cp\u003eHowever, none of these previous reports examined dFC of the left Vim, and only one\u003c/p\u003e \u003cp\u003efocused on temporal dynamics in ET.25\u003c/p\u003e \u003cp\u003eColormaps for plotting were generated with the \u003cem\u003ecbrewer\u003c/em\u003e toolbox. Surface\u003c/p\u003e \u003cp\u003evisualizations of brain patterns were created using \u003cem\u003eBrainNet\u003c/em\u003e.44\u003c/p\u003e \u003cp\u003eAll the scripts used in this work are freely available at\u003c/p\u003e \u003cp\u003e \u003cspan class=\"ExternalRef\"\u003e \u003cspan class=\"RefSource\"\u003ehttps://github.com/TiBiUan/DYNET_Analysis.git\u003c/span\u003e \u003cspan address=\"https://github.com/TiBiUan/DYNET_Analysis.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e \u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eThe left Vim: a dynamic cornerstone of the tremor network\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn this study, we observed that the left Vim reconfigures its functional interactions\u003c/p\u003e \u003cp\u003ewith the rest of the brain in HCs over the course of an RS fMRI scanning session,\u003c/p\u003e \u003cp\u003eshowcasing the recurring expression of three different dFC states (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e,\u003c/p\u003e \u003cp\u003e \u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). We also showed that patients with ET express these states\u003c/p\u003e \u003cp\u003ewith subject-specific temporal occurrences and stability in spatial similarity, in a way\u003c/p\u003e \u003cp\u003ethat is partly modulated by thalamotomy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCorrelated activity with other subcortical areas, anticorrelations with cerebellar\u003c/p\u003e \u003cp\u003eregions, and broad connectivity patterns with cortical networks, were hallmarks of all\u003c/p\u003e \u003cp\u003ethree states. This is consistent with known anatomical connections, as the Vim is\u003c/p\u003e \u003cp\u003elinked to the cerebellum through the dentate nucleus, and to the motor cortex.1\u0026ndash;2\u003c/p\u003e \u003cp\u003eFurthermore, there are known projections from the thalamus (including its ventral\u003c/p\u003e \u003cp\u003eposterior subpart, studied here) to the basal ganglia.45\u003c/p\u003e \u003cp\u003eThere were also several notable differences between states. State 2 exhibited a sparser\u003c/p\u003e \u003cp\u003eset of strong functional connections to the rest of the subcortex. Further inspection\u003c/p\u003e \u003cp\u003erevealed that the main differences involved basal ganglia substructures, as the\u003c/p\u003e \u003cp\u003ebilateral putamen, anterior caudate and anterior globus pallidus all featured\u003c/p\u003e \u003cp\u003echaracteristically weak dFC with the left Vim in state 2. The basal ganglia are\u003c/p\u003e \u003cp\u003einvolved in the modulation of somatomotor cortical information processing,46 and\u003c/p\u003e \u003cp\u003econtribute to tremor generation.47 Although ET has been primarily considered related\u003c/p\u003e \u003cp\u003eto the cerebellum,2,5\u0026ndash;6 DBS of the posterior subthalamic area, an input source to the\u003c/p\u003e \u003cp\u003ebasal ganglia,46 has shown effectiveness to treat ET,48 and abnormal putamen\u003c/p\u003e \u003cp\u003eactivity49-50 and FC51 have been reported in ET. Thus, differential patterns of FC to\u003c/p\u003e \u003cp\u003ethe basal ganglia could relate to tremor symptomatology.\u003c/p\u003e \u003cp\u003eState 2 was also the one displaying the strongest anticorrelations between the left Vim\u003c/p\u003e \u003cp\u003eand most of the cerebellum. This particularly involved the bilateral cerebellum\u003c/p\u003e \u003cp\u003elobules III and IV/V, as well as vermis III and vermis IV/V, which jointly contribute\u003c/p\u003e \u003cp\u003eto the cerebellar sensorimotor component.52\u0026ndash;54 In addition, there was specifically more\u003c/p\u003e \u003cp\u003epositive dFC to left cerebellum crus II and vermis VIII. The former is functionally\u003c/p\u003e \u003cp\u003erelated to the default mode network,52 and a recent meta-analysis showed its specific\u003c/p\u003e \u003cp\u003eroles in social mentalizing and emotional self-experiences.55 As for Vermis VIII, its\u003c/p\u003e \u003cp\u003eFC to the default mode, executive control and somatomotor networks was stronger in\u003c/p\u003e \u003cp\u003eParkinson\u0026rsquo;s disease patients suffering from visuospatial disorder on top of motor\u003c/p\u003e \u003cp\u003eimpairments.56 Interestingly, we note that state 2 also stood out by its anticorrelations\u003c/p\u003e \u003cp\u003ewith parahippocampal and extrastriate visual areas, as well as with hippocampal\u003c/p\u003e \u003cp\u003esubparts, which all contribute to visuospatial processing, and locomotor monitoring\u003c/p\u003e \u003cp\u003etowards the local and distant environment,57 relevant in ET patients.\u003c/p\u003e \u003cp\u003eAnother intriguing feature of left Vim-to-cortex FC in the context of sensorimotor\u003c/p\u003e \u003cp\u003eactivity was the fact that states 1 and 3 showed only little correlated activity with\u003c/p\u003e \u003cp\u003eprimary sensorimotor areas, while the opposite was seen in state 2. In a recent study,\u003c/p\u003e \u003cp\u003eSharifi \u003cem\u003eet al\u003c/em\u003e.58 studied cortico-muscular coherence (CMC) through coupled EEG and\u003c/p\u003e \u003cp\u003eEMG recordings, in patients with ET and HCs asked to voluntarily mimic tremor.\u003c/p\u003e \u003cp\u003eThey showed that in both groups, CMC could be reliably detected and was modulated\u003c/p\u003e \u003cp\u003eby performing a cognitive task in parallel; thus, non-tremor networks were posited to\u003c/p\u003e \u003cp\u003eexert an interfering influence. Additionally, when tracking CMC changes with a\u003c/p\u003e \u003cp\u003esliding-window approach (30 s windows), there were dynamic fluctuations that could\u003c/p\u003e \u003cp\u003enot be attributed solely to changes in tremor signal-to-noise ratio (SNR) or to task\u003c/p\u003e \u003cp\u003esettings. The present work shows that motor cortical activity does reconfigure with\u003c/p\u003e \u003cp\u003etime, given its evolving functional interplay with the left Vim, both in HCs and in\u003c/p\u003e \u003cp\u003epatients with ET. Transitions between the expression of the more similar states 1 and\u003c/p\u003e \u003cp\u003e3, and state 2, may thus be hypothesized to capture fluctuating interfering influences\u003c/p\u003e \u003cp\u003efrom non-tremor networks, which would occur at rest and manifest in task settings\u003c/p\u003e \u003cp\u003etoo.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eInteractions with specific networks hinder or benefit clinical\u003c/b\u003e\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003erecovery\u003c/h2\u003e \u003cp\u003eInterestingly, the dichotomy between states 1/3 and state 2 also extended to their\u003c/p\u003e \u003cp\u003erespective impacts on tremor. On the one hand, we found that a more robust spatial\u003c/p\u003e \u003cp\u003esimilarity (\u003cem\u003ei.e.\u003c/em\u003e, lower coefficient of variation) in state 2 expression to the HC template\u003c/p\u003e \u003cp\u003ebefore thalamotomy correlated with greater TSTH drop, while there was no clinical\u003c/p\u003e \u003cp\u003erelevance in how frequently this state was expressed. On the other hand, for states 1\u003c/p\u003e \u003cp\u003eand 3, we found that temporal occurrences, but not spatial similarity, correlated with\u003c/p\u003e \u003cp\u003eclinical recovery: more frequently expressing state 3 before the intervention\u003c/p\u003e \u003cp\u003eassociated with greater tremor improvement, while the opposite was true for state 1.\u003c/p\u003e \u003cp\u003eWhen assessing the clinical predictive potential of the balance between these two\u003c/p\u003e \u003cp\u003estates in a follow-up analysis, we confirmed that greater clinical recovery was in fact\u003c/p\u003e \u003cp\u003etied to a balance more geared towards state 3 expression.\u003c/p\u003e \u003cp\u003eGiven this antagonistic relationship, a natural question pertains to the differences in\u003c/p\u003e \u003cp\u003eleft Vim dFC between states 1 and 3. Accordingly, we inspected the state 3 \u0026ndash; state 1\u003c/p\u003e \u003cp\u003edFC differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; see also \u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e) and observed that\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003emore pronounced correlations between the left Vim and extrastriate central visual\u003c/p\u003e \u003cp\u003eareas, temporal regions (from the temporal pole or occipito-temporal cortex) and the\u003c/p\u003e \u003cp\u003eparahippocampal gyrus were particularly detrimental to clinical recovery (positive-\u003c/p\u003e \u003cp\u003evalued in state 1 while weak in state 3). This set of regions (which, fittingly, were also\u003c/p\u003e \u003cp\u003epartly pinpointed in state 2 as anti-correlated to left Vim activity) constitute what has\u003c/p\u003e \u003cp\u003ebeen referred by some as the \u003cem\u003eparieto-medial temporal pathway\u003c/em\u003e for visuospatial\u003c/p\u003e \u003cp\u003eprocessing,57 which has been resolved in humans through RS FC analysis.59 The\u003c/p\u003e \u003cp\u003erelevance of the visuospatial circuitry in ET has become increasingly appreciated\u003c/p\u003e \u003cp\u003efrom the voxel-based morphometry,60 SBM,20 task-based fMRI61 and RS fMRI25\u003c/p\u003e \u003cp\u003eforefronts. Our results further contribute to this line of research and identify a more\u003c/p\u003e \u003cp\u003especific pathway whose intermittent functional coupling with the left Vim may be\u003c/p\u003e \u003cp\u003edetrimental to tremor recovery following thalamotomy.\u003c/p\u003e \u003cp\u003eOn the other hand, greater dFC to the insula, precuneus/posterior cingulate cortex and\u003c/p\u003e \u003cp\u003efrontal eye fields was beneficial (positive-valued in state 3 while weak in state 1).\u003c/p\u003e \u003cp\u003eThese regions are all part of higher-order functional networks. When one recalls that\u003c/p\u003e \u003cp\u003estate 2 (whose spatially accurate expression was also beneficial to clinical recovery)\u003c/p\u003e \u003cp\u003efeatured less potent left Vim connectivity to the basal ganglia and anti-correlations\u003c/p\u003e \u003cp\u003ewith the sensorimotor cerebellum (but correlations with subparts involved in higher-\u003c/p\u003e \u003cp\u003eorder cognitive functions), a possibility could be that spontaneous \u0026ldquo;decoupling\u0026rdquo; of the\u003c/p\u003e \u003cp\u003etremor network at rest, through interactions of the left Vim with other interfering\u003c/p\u003e \u003cp\u003ehigh-order networks, is somehow beneficial to recovery following thalamotomy.\u003c/p\u003e \u003cp\u003eWhile the clinical relevance of state 2 was captured in a direct effect, that of the states\u003c/p\u003e \u003cp\u003e3 \u003cem\u003eversus\u003c/em\u003e 1 balance was seen as an interaction with MR signature one year after\u003c/p\u003e \u003cp\u003ethalamotomy. This demonstrates that to extract potentially predictive markers of\u003c/p\u003e \u003cp\u003eclinical recovery, it is important to consider as many involved sources of variance as\u003c/p\u003e \u003cp\u003epossible. In the case of thalamotomy performed by radiosurgery, the outcome itself\u003c/p\u003e \u003cp\u003emay be related to factors such as individual sensitivity to radiation14 or specificity in\u003c/p\u003e \u003cp\u003ethe location of the relevant brain substructures,62 thus yielding different individual\u003c/p\u003e \u003cp\u003eradiobiological effects. This latter point has been the subject of intense investigations:\u003c/p\u003e \u003cp\u003ein a prospective DBS study, direct targeting of the dentato-rubro-thalamic tract\u003c/p\u003e \u003cp\u003eyielded tremor improvement,63 evidencing the benefits of targeting more\u003c/p\u003e \u003cp\u003emechanistically relevant structures. On a set of HCs, Middlebrooks \u003cem\u003eet al\u003c/em\u003e.64 showed\u003c/p\u003e \u003cp\u003ethat the voxels targeted by stereotactic coordinates were mixed between the primary\u003c/p\u003e \u003cp\u003emotor cortex and supplementary motor area/premotor cortex, in a way that largely\u003c/p\u003e \u003cp\u003evaried across subjects. Through diffusion MRI, several DBS studies also put forward\u003c/p\u003e \u003cp\u003ecandidate areas to which the volume of tissue activated (VTA) should be structurally\u003c/p\u003e \u003cp\u003econnected to optimize interventional efficacy, including the connected area to the\u003c/p\u003e \u003cp\u003econtralateral dentate nucleus,9 the supplementary motor area and premotor cortex,65 or\u003c/p\u003e \u003cp\u003ethe primary motor cortex.66 Purrer et al.67 showed that the MRgFUS lesion induced\u003c/p\u003e \u003cp\u003ethrough Vim targeting only had 4% overlap with the Vim on average, but 43% with\u003c/p\u003e \u003cp\u003ethe cerebello-thalamic tract. In another study, the larger the fraction of lesioned\u003c/p\u003e \u003cp\u003evoxels structurally connected to the precentral gyrus, the better the clinical recovery.68\u003c/p\u003e \u003cp\u003eCollectively, these reports highlight the limitations of relying on stereotactic\u003c/p\u003e \u003cp\u003ecoordinates and suggest that to better extract potential pre-intervention markers of\u003c/p\u003e \u003cp\u003etremor arrest, the targeting of a specific tract or thalamic subregion, \u003cem\u003evia\u003c/em\u003e dedicated pre-\u003c/p\u003e \u003cp\u003ethalamotomy imaging, may be a more suitable option.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eDynamic functional renormalization, but morphometric\u003c/b\u003e\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003eheterogenization upon thalamotomy\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn terms of left Vim dFC, renormalization upon thalamotomy was observed in two\u003c/p\u003e \u003cp\u003eways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). First, stability in the expression of all three states tended to\u003c/p\u003e \u003cp\u003eincrease upon intervention, significantly so for state 1. Second, the balance in\u003c/p\u003e \u003cp\u003eexpression of the three states following the intervention also evolved back towards\u003c/p\u003e \u003cp\u003ethat seen in HCs, and even overshot it, potentially reflecting a compensatory\u003c/p\u003e \u003cp\u003emechanism.\u003c/p\u003e \u003cp\u003eSomehow puzzlingly, however, we did not observe any renormalization when\u003c/p\u003e \u003cp\u003equantifying the evolution of whole-brain SBM properties upon intervention. In fact,\u003c/p\u003e \u003cp\u003ethe similarity of ET morphometric profiles to the HC distribution decreased even\u003c/p\u003e \u003cp\u003efurther upon thalamotomy in all regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Further inspection revealed that\u003c/p\u003e \u003cp\u003emean values were unchanged upon thalamotomy. However, there were significant\u003c/p\u003e \u003cp\u003eincreases in CT and MC variance, and a global decrease of cross-property covariance,\u003c/p\u003e \u003cp\u003ethus denoting morphometric heterogenization.\u003c/p\u003e \u003cp\u003eTo explain this apparent discrepancy, we quantified the correlation between the log-\u003c/p\u003e \u003cp\u003elikelihood of a morphometric profile to be issued from the HC distribution (\u003cem\u003ei.e.\u003c/em\u003e,\u003c/p\u003e \u003cp\u003emorphometric similarity to HCs) and mean spatial similarity in state expression (\u003cem\u003ei.e.\u003c/em\u003e,\u003c/p\u003e \u003cp\u003edynamic functional similarity to HCs), before and after thalamotomy. We observed\u003c/p\u003e \u003cp\u003emarked anti-correlations, at baseline, between states 2 and 3 (but not state 1) and the\u003c/p\u003e \u003cp\u003ewhole brain, which disappeared following thalamotomy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cb\u003eSupplementary\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOut personal view, as derived from the present work, is that brain function in\u003c/p\u003e \u003cp\u003ededicated areas induces tremor. As such, \u0026ldquo;healthy\u0026rdquo; dFC states can be seen as the\u003c/p\u003e \u003cp\u003etarget towards which an individual patient should tend to minimize tremor\u003c/p\u003e \u003cp\u003esymptomatology. Brain structure, partly characterized by morphometric properties, is\u003c/p\u003e \u003cp\u003ethe underlying scaffold over which functional signals propagate. Essential tremor\u003c/p\u003e \u003cp\u003epatients who resemble HCs in their whole-brain morphometric attributes, but suffer\u003c/p\u003e \u003cp\u003efrom local impairments within the tremor network, may not be able to express HC-\u003c/p\u003e \u003cp\u003elike dFC states owing to these local alterations. To achieve such expression, further\u003c/p\u003e \u003cp\u003ereconfigurations of the whole-brain structural circuitry are required. Thus, the patients\u003c/p\u003e \u003cp\u003ewho undergo such reconfigurations the most (by this mean becoming least like HCs\u003c/p\u003e \u003cp\u003emorphometry-wise) are also the ones who can express the dFC states most similar to\u003c/p\u003e \u003cp\u003eHCs, resulting in the observed anti-correlations. These anti-correlations are only\u003c/p\u003e \u003cp\u003epresent for states 2 and 3, the \u0026ldquo;beneficial\u0026rdquo; ones, as these are the desired endpoints to\u003c/p\u003e \u003cp\u003elimit tremor. After thalamotomy, these relationships disappear because further\u003c/p\u003e \u003cp\u003eextensive changes are induced in all subjects, in a way that is also heterogeneous.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes our results in the context of this hypothesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003eTechnical outlook, limitations, and future perspectives\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn this work, we analyzed the data from ET patients in terms of similarity to a pool of\u003c/p\u003e \u003cp\u003ematched HCs. This strategy is particularly suitable to the study of ET for two reasons:\u003c/p\u003e \u003cp\u003efirst, it enables to investigate renormalization upon thalamotomy more directly as\u003c/p\u003e \u003cp\u003eopposed to a three-group comparison (\u003cem\u003ei.e.\u003c/em\u003e, HC \u003cem\u003eversus\u003c/em\u003e ETpre \u003cem\u003eversus\u003c/em\u003e ETpost). Second, it\u003c/p\u003e \u003cp\u003eyields distinct, potentially more relevant features in the context of a clinical disorder.\u003c/p\u003e \u003cp\u003eWe were inspired by recent work that successfully leveraged a conceptually related\u003c/p\u003e \u003cp\u003eapproach: in a DBS study of ET, Al-Fatly and colleagues69 quantified structural and\u003c/p\u003e \u003cp\u003efunctional connectivity to the VTA, on a population of ET patients, using normative\u003c/p\u003e \u003cp\u003emaps. To create a so-called \u0026ldquo;R-map\u0026rdquo;, for each voxel, they correlated the obtained\u003c/p\u003e \u003cp\u003evalues (\u003cem\u003ei.e.\u003c/em\u003e, connectivity to the VTA) with clinical improvement, so that the resulting\u003c/p\u003e \u003cp\u003epattern has large positive weights for the voxels that, when connected to the VTA,\u003c/p\u003e \u003cp\u003epromote clinical recovery. Out-of-sample data could then be compared to this R-map\u003c/p\u003e \u003cp\u003eto estimate clinical improvement, and prediction was significant when using both\u003c/p\u003e \u003cp\u003estructural and functional connectivity information. In the present study, we showed\u003c/p\u003e \u003cp\u003ethat a similarity-based approach also has potential in the context of morphometric and\u003c/p\u003e \u003cp\u003edynamic functional connectivity data. In future work, it will be necessary to validate\u003c/p\u003e \u003cp\u003ethe unraveled dynamic functional markers on a larger cohort of subjects.\u003c/p\u003e \u003cp\u003eOur seed-based dFC analysis was conducted using the Tian scale 3 subcortical atlas.34\u003c/p\u003e \u003cp\u003eDespite an exhaustive state-of-the-art segmentation of the subcortex, only the ventral\u003c/p\u003e \u003cp\u003eposterior lateral thalamus was available as a seed, not the Vim \u003cem\u003eper se\u003c/em\u003e, which covers its\u003c/p\u003e \u003cp\u003eventral portion only.70 When interpreting our findings, one should thus keep in mind\u003c/p\u003e \u003cp\u003ethat they could also be influenced by changes in activity of the dorsal half of the\u003c/p\u003e \u003cp\u003eventral posterior lateral thalamus. Future work should determine to what extent the\u003c/p\u003e \u003cp\u003edifferent states that we extracted colocalize in terms of their exact seed focus of\u003c/p\u003e \u003cp\u003eactivity, and whether spatial dynamics may be at play.71\u003c/p\u003e \u003cp\u003eFinally, if one wishes to further understand the links between brain structure and\u003c/p\u003e \u003cp\u003efunction, and how they may be altered upon thalamotomy, morphometric data\u003c/p\u003e \u003cp\u003e(indicative of local structure within specific regions) should be complemented by\u003c/p\u003e \u003cp\u003ediffusion MRI. By this mean, the physical wiring between brain regions could refine\u003c/p\u003e \u003cp\u003ecross-modality assessments.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank all the subjects who accepted to participate to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e \u003cstrong\u003econtributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ. R., T. W., N. G. and C. T. contributed to the acquisition and preprocessing of the\u003c/p\u003e\n\u003cp\u003eanalyzed imaging and clinical data. D. V. D. V., M. L. and C. T. surveyed the work\u003c/p\u003e\n\u003cp\u003eand devised the main axes of the conducted analyses. T. A. W. B. performed the\u003c/p\u003e\n\u003cp\u003eanalyses, interpreted the findings and wrote the manuscript. All authors reread and\u003c/p\u003e\n\u003cp\u003eapproved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e \u003cstrong\u003eavailability\u003c/strong\u003e \u003cstrong\u003estatement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding\u003c/p\u003e\n\u003cp\u003eauthor upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional\u003c/strong\u003e \u003cstrong\u003einformation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare. Constantin Tuleasca gratefully\u003c/p\u003e\n\u003cp\u003eacknowledges the receipt of a grant \u0026ldquo;Jeune Chercheur en Recherche Clinique\u0026rdquo; from\u003c/p\u003e\n\u003cp\u003ethe University of Lausanne, Faculty of Biology and Medicine.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWelton, T., Cardoso, F., Carr, J. A., Chan, L., Deuschl, G., \u003cem\u003eet al\u003c/em\u003e. Essential tremor. Nature Reviews Disease Primers. 7, 83 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaubenberger, D., Hallett, M. Essential tremor. New England Journal of Medicine. 378, 1802\u0026ndash;1810 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBermejo-Pareja, F. Essential tremor ! a neurodegenerative disorder associated with cognitive defects?. Nature Reviews Neurology. 7, 273\u0026ndash;282 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouis, E. D., Joyce, J. 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Space: a missing piece of the dynamic\u003c/span\u003e \u003cspan\u003epuzzle. \u003cem\u003eTrends in Cognitive Sciences\u003c/em\u003e. 24(2), 135\u0026ndash;149 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"probabilistic modelling, cortical thickness, surface area, mean curvature, temporal occurrences, cross-modality analysis","lastPublishedDoi":"10.21203/rs.3.rs-2702374/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2702374/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePatients with drug-resistant essential tremor (ET) may undergo Gamma Knife stereotactic radiosurgical thalamotomy (SRS-T), where the ventro-intermediate nucleus of the thalamus (Vim) is lesioned by focused beams of gamma radiations to induce clinical improvement. Here, we studied SRS-T impacts on left Vim dynamic functional connectivity (dFC, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23 ET patients scanned before and 1 year after intervention), and on surface-based morphometric brain features (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34 patients, including those from dFC analysis).\u003c/p\u003e \u003cp\u003eIn matched healthy controls (HCs), three dFC states were extracted from resting-state functional MRI data. In ET patients, state 1 spatial stability increased upon SRS-T (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0041). Lower pre-intervention spatial variability in state 2 expression, and more frequent expression of state 3 over state 1, correlated with greater clinical recovery (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, respectively). ET morphometric profiles showed significantly lower similarity to HCs in 13 regions upon SRS-T (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.02), and a joint analysis revealed that before thalamotomy, morphometric similarity and states 2/3 mean spatial similarity to HCs were anticorrelated, a relationship that disappeared upon SRS-T (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eOur results show that left Vim functional dynamics directly relates to upper limb tremor lowering upon intervention, while morphometry instead has a supporting role in reshaping such dynamics.\u003c/p\u003e","manuscriptTitle":"Dynamic functional changes upon thalamotomy in essential tremor depend on baseline brain morphometry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-07 17:52:19","doi":"10.21203/rs.3.rs-2702374/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-09-20T07:34:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-09-07T21:33:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73c17313-d220-4987-a14c-599488f23db8","date":"2023-08-29T07:28:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b9de1284-5e14-46ad-a8ef-b8bc52ef5b9e","date":"2023-07-28T12:35:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-05-29T19:38:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1bebd16a-40bc-4297-8428-38802a1f9f82","date":"2023-05-05T20:11:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-05-01T00:58:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-05-01T00:49:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-04-05T13:13:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-04-05T13:09:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-03-16T23:08:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b59eef7-d63e-4877-bb3c-2aca4f629e77","owner":[],"postedDate":"April 7th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":20482239,"name":"Biological sciences/Neuroscience"},{"id":20482240,"name":"Biological sciences/Neuroscience/Diseases of the nervous system"}],"tags":[],"updatedAt":"2024-02-05T15:11:02+00:00","versionOfRecord":{"articleIdentity":"rs-2702374","link":"https://doi.org/10.1038/s41598-024-52410-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-01-31 15:00:52","publishedOnDateReadable":"January 31st, 2024"},"versionCreatedAt":"2023-04-07 17:52:19","video":"","vorDoi":"10.1038/s41598-024-52410-y","vorDoiUrl":"https://doi.org/10.1038/s41598-024-52410-y","workflowStages":[]},"version":"v1","identity":"rs-2702374","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2702374","identity":"rs-2702374","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}