Thalamic contributions to psychosis susceptibility: Evidence from co-activation patterns accounting for intra-seed spatial variability (µCAPs).

The temporal variability of the thalamus in functional networks may provide valuable insights into the pathophysiology of schizophrenia. To address the complexity of the role of the thalamic nuclei in psychosis, we introduced micro-co-activation patterns (µCAPs) and employed this method on the human genetic model of schizophrenia 22q11.2 deletion syndrome (22q11.2DS). Participants underwent resting-state functional MRI and a data-driven iterative process resulting in the identification of six whole-brain µCAPs with specific activity patterns within the thalamus. Unlike conventional methods, µCAPs extract dynamic spatial patterns that reveal partially overlapping and non-mutually exclusive functional subparts. Thus, the µCAPs method detects finer foci of activity within the initial seed region, retaining valuable and clinically relevant temporal and spatial information. We found that a µCAP showing co-activation of the mediodorsal thalamus with brain-wide cortical regions was expressed significantly less frequently in patients with 22q11.2DS, and its occurrence negatively correlated with the severity of positive psychotic symptoms. Additionally, activity within the auditory-visual cortex and their respective geniculate nuclei was expressed in two different µCAPs. One of these auditory-visual µCAPs co-activated with salience areas, while the other co-activated with the default mode network (DMN). A significant shift of occurrence from the salience+visuo-auditory-thalamus to the DMN + visuo-auditory-thalamus µCAP was observed in patients with 22q11.2DS. Thus, our findings support existing research on the gatekeeping role of the thalamus for sensory information in the pathophysiology of psychosis and revisit the evidence of geniculate nuclei hyperconnectivity with the audio-visual cortex in 22q11.2DS in the context of dynamic functional connectivity, seen here as the specific hyper-occurrence of these circuits with the task-negative brain networks.

Functional MRI Neurofeedback Outperforms Cognitive Behavioral Therapy for Reducing Tinnitus Distress: A Prospective Randomized Clinical Trial.

Background Cognitive behavioral therapy (CBT) is the current standard treatment for chronic severe tinnitus; however, preliminary evidence suggests that real-time functional MRI (fMRI) neurofeedback therapy may be more effective. Purpose To compare the efficacy of real-time fMRI neurofeedback against CBT for reducing chronic tinnitus distress. Materials and Methods In this prospective controlled trial, participants with chronic severe tinnitus were randomized from December 2017 to December 2021 to receive either CBT (CBT group) for 10 weekly group sessions or real-time fMRI neurofeedback (fMRI group) individually during 15 weekly sessions. Change in the Tinnitus Handicap Inventory (THI) score (range, 0-100) from baseline to 6 or 12 months was assessed. Secondary outcomes included four quality-of-life questionnaires (Beck Depression Inventory, Pittsburgh Sleep Quality Index, State-Trait Anxiety Inventory, and World Health Organization Disability Assessment Schedule). Questionnaire scores between treatment groups and between time points were assessed using repeated measures analysis of variance and the nonparametric Wilcoxon signed rank test. Results The fMRI group included 21 participants (mean age, 49 years ± 11.4 [SD]; 16 male participants) and the CBT group included 22 participants (mean age, 53.6 years ± 8.8; 16 male participants). The fMRI group showed a greater reduction in THI scores compared with the CBT group at both 6 months (mean score change, -28.21 points ± 18.66 vs -12.09 points ± 18.86; P = .005) and 12 months (mean score change, -30 points ± 25.44 vs -4 points ± 17.2; P = .01). Compared with baseline, the fMRI group showed improved sleep (mean score, 8.62 points ± 4.59 vs 7.25 points ± 3.61; P = .006) and trait anxiety (mean score, 44 points ± 11.5 vs 39.84 points ± 10.5; P = .02) at 1 month and improved depression (mean score, 13.71 points ± 9.27 vs 6.53 points ± 5.17; P = .01) and general functioning (mean score, 24.91 points ± 17.05 vs 13.06 points ± 10.1; P = .01) at 6 months. No difference in these metrics over time was observed for the CBT group (P value range, .14 to >.99). Conclusion Real-time fMRI neurofeedback therapy led to a greater reduction in tinnitus distress than the current standard treatment of CBT. ClinicalTrials.gov registration no.: NCT05737888; Swiss Ethics registration no.: BASEC2017-00813 © RSNA, 2024 Supplemental material is available for this article.

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

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 (F1,22 = 19.13, p = 0.004). More frequent expression of state 3 over state 1 before SRS-T correlated with greater clinical recovery in a way that depended on the MR signature volume (t6 = 4.6, p = 0.004). Lower pre-intervention spatial variability in state 3 expression also did (t6 = - 4.24, p = 0.005) and interacted with the presence of familial ET so that these patients improved less (t6 = 4.14, p = 0.006). ET morphometric profiles showed significantly lower similarity to HCs in 13 regions upon SRS-T (z ≤ - 3.66, p ≤ 0.022), 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 (z ≥ 4.39, 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.

The effect of mindfulness-based intervention on neurobehavioural functioning and its association with white-matter microstructural changes in preterm young adolescents.

Very preterm (VPT) young adolescents are at high risk of executive, behavioural and socio-emotional difficulties. Previous research has shown significant evidence of the benefits of mindfulness-based intervention (MBI) on these abilities. This study aims to assess the association between the effects of MBI on neurobehavioral functioning and changes in white-matter microstructure in VPT young adolescents who completed an 8-week MBI program. Neurobehavioural assessments (i.e., neuropsychological testing, parents- and self-reported questionnaires) and multi-shell diffusion MRI were performed before and after MBI in 32 VPT young adolescents. Combined diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) measures were extracted on well-defined white matter tracts (TractSeg). A multivariate data-driven approach (partial least squares correlation) was used to explore associations between MBI-related changes on neurobehavioural measures and microstructural changes. The results showed an enhancement of global executive functioning using parent-reported questionnaire after MBI that was associated with a general pattern of increase in fractional anisotropy (FA) and decrease in axonal dispersion (ODI) in white-matter tracts involved in executive processes. Young VPT adolescents with lower gestational age at birth showed the greatest gain in white-matter microstructural changes after MBI.

Real-time and Recursive Estimators for Functional MRI Quality Assessment.

Real-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well as time-series of target brain areas and resting-state networks. We estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR), and real-time head motion parameters by a framewise rigid-body transformation (translations and rotations) using the conventional current to template volume registration. In addition, we derived real-time framewise (FD) and micro (MD) displacements based on head motion parameters and evaluated the temporal derivative of root mean squared variance over voxels (DVARS). For monitoring time-series of target regions and networks, we estimated the number of spikes and amount of filtered noise by means of a modified Kalman filter. Finally, we applied the incremental general linear modeling (GLM) to evaluate real-time contributions of nuisance regressors (linear trend and head motion). Proposed rtQA was demonstrated in real-time fMRI neurofeedback runs without and with excessive head motion and real-time simulations of neurofeedback and resting-state fMRI data. The rtQA was implemented as an extension of the open-source OpenNFT software written in Python, MATLAB and C++ for neurofeedback, task-based, and resting-state paradigms. We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications. Flexible estimation and visualization of rtQA facilitates efficient rtQA of fMRI data and helps the robustness of fMRI acquisitions by means of substantiating decisions about the necessity of the interruption and re-start of the experiment and increasing the confidence in neural estimates.

Musical memories in newborns: A resting-state functional connectivity study.

Music is known to induce emotions and activate associated memories, including musical memories. In adults, it is well known that music activates both working memory and limbic networks. We have recently discovered that as early as during the newborn period, familiar music is processed differently from unfamiliar music. The present study evaluates music listening effects at the brain level in newborns, by exploring the impact of familiar or first-time music listening on the subsequent resting-state functional connectivity in the brain. Using a connectome-based framework, we describe resting-state functional connectivity (RS-FC) modulation after music listening in three groups of newborn infants, in preterm infants exposed to music during their neonatal-intensive-care-unit (NICU) stay, in control preterm, and full-term infants. We observed modulation of the RS-FC between brain regions known to be implicated in music and emotions processing, immediately following music listening in all newborn infants. In the music exposed group, we found increased RS-FC between brain regions known to be implicated in familiar and emotionally arousing music and multisensory processing, and therefore implying memory retrieval and associative memory. We demonstrate a positive correlation between the occurrence of the prior music exposure and increased RS-FC in brain regions implicated in multisensory and emotional processing, indicating strong engagement of musical memories; and a negative correlation with the Default Mode Network, indicating disengagement due to the aforementioned cognitive processing. Our results describe the modulatory effect of music listening on brain RS-FC that can be linked to brain correlates of musical memory engrams in preterm infants.

Real-time fMRI and EEG neurofeedback: A perspective on applications for the rehabilitation of spatial neglect.

Spatial neglect is a neuropsychological syndrome characterized by a failure to orient, perceive, and act toward the contralesional side of the space after brain injury. Neglect is one of the most frequent and disabling neuropsychological syndromes following right-hemisphere damage, often persisting in the chronic phase and responsible for a poor functional outcome at hospital discharge. Different rehabilitation approaches have been proposed over the past 60 years, with a variable degree of effectiveness. In this point-of-view article, we describe a new rehabilitation technique for spatial neglect that directly targets brain activity and pathological physiological processes: namely, neurofeedback (NFB) with real-time brain imaging methodologies. In recent proof-of-principle studies, we have demonstrated the potential of this rehabilitation technique. Using real-time functional MRI (rt-fMRI) NFB in chronic neglect, we demonstrated that patients are able to upregulate their right visual cortex activity, a response that is otherwise reduced due to losses in top-down attentional signals. Using real-time electroencephalography NFB in patients with acute or chronic condition, we showed successful regulation with partial restoration of brain rhythm dynamics over the damaged hemisphere. Both approaches were followed by mild, but encouraging, improvement in neglect symptoms. NFB techniques, by training endogenous top-down modulation of attentional control on sensory processing, might induce sustained changes at both the neural and behavioral levels, while being non-invasive and safe. However, more properly powered clinical studies with control groups and longer follow-up are needed to fully establish the effectiveness of the techniques, identify the most suitable candidates, and determine how the techniques can be optimized or combined in the context of rehabilitation.

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist).

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

Music in premature infants enhances high-level cognitive brain networks.

Neonatal intensive care units are willing to apply environmental enrichment via music for preterm newborns. However, no evidence of an effect of music on preterm brain development has been reported to date. Using resting-state fMRI, we characterized a circuitry of interest consisting of three network modules interconnected by the salience network that displays reduced network coupling in preterm compared with full-term newborns. Interestingly, preterm infants exposed to music in the neonatal intensive care units have significantly increased coupling between brain networks previously shown to be decreased in premature infants: the salience network with the superior frontal, auditory, and sensorimotor networks, and the salience network with the thalamus and precuneus networks. Therefore, music exposure leads to functional brain architectures that are more similar to those of full-term newborns, providing evidence for a beneficial effect of music on the preterm brain.

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms.

Task-based functional magnetic resonance imaging bears great potential to understand how our brain reacts to various types of stimulation; however, this is often achieved without considering the dynamic facet of functional processing, and analytical outputs typically account for merged influences of task-driven effects and underlying spontaneous fluctuations of brain activity. Here, we introduce a novel methodological pipeline that can go beyond these limitations: the use of a sliding-window analytical scheme permits tracking of functional changes over time, and through cross-subject correlational measurements, the approach can isolate purely stimulus-related effects. Thanks to a rigorous thresholding process, significant changes in inter-subject functional correlation can be extracted and analyzed. On a set of healthy subjects who underwent naturalistic audio-visual stimulation, we demonstrate the usefulness of the approach by tying the unraveled functional reconfigurations to particular cues of the movie. We show how, through our method, one can capture either a temporal profile of brain activity (the evolution of a given connection), or focus on a spatial snapshot at a key time point. We provide a publicly available version of the whole pipeline, and describe its use and the influence of its key parameters step by step.

Using real-time fMRI neurofeedback to restore right occipital cortex activity in patients with left visuo-spatial neglect: proof-of-principle and preliminary results.

Hemineglect is common after right parietal stroke, characterised by impaired awareness for stimuli in left visual space, with suppressed neural activity in the right visual cortex due to losses in top-down attention signals. Here we sought to assess whether hemineglect patients are able to up-regulate their right visual cortex activity using auditory real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback. We also examined any effect of this training procedure on neglect severity. Two different neurofeedback methods were used. A first group of six patients was trained to up-regulate their right visual cortex activity and a second group of three patients was trained to control interhemispheric balance between their right and left visual cortices. Over three sessions, we found that the first group successfully learned to control visual cortex activity and showed mild reduction in neglect severity, whereas the second group failed to control the feedback and showed no benefit. Whole brain analysis further indicated that successful up-regulation was associated with a recruitment of bilateral fronto-parietal areas. These findings provide a proof of concept that rt-fMRI neurofeedback may offer a new approach to the rehabilitation of hemineglect symptoms, but further studies are needed to identify effective regulation protocols and determine any reliable impact on clinical symptoms.

Music processing in preterm and full-term newborns: A psychophysiological interaction (PPI) approach in neonatal fMRI.

Neonatal Intensive Care Units (NICU) provide special equipment designed to give life support for the increasing number of prematurely born infants and assure their survival. More recently NICU's strive to include developmentally oriented care and modulate sensory input for preterm infants. Music, among other sensory stimuli, has been introduced into NICUs, but without knowledge on the basic music processing in the brain of preterm infants. In this study, we explored the cortico-subcortical music processing of different types of conditions (Original music, Tempo modification, Key transposition) in newborns shortly after birth to assess the effective connectivity of the primary auditory cortex with the entire newborn brain. Additionally, we investigated if early exposure during NICU stay modulates brain processing of music in preterm infants at term equivalent age. We approached these two questions using Psychophysiological Interaction (PPI) analyses. A group of preterm infants listened to music (Original music) starting from 33 weeks postconceptional age until term equivalent age and were compared to two additional groups without music intervention; preterm infants and full-term newborns. Auditory cortex functional connectivity with cerebral regions known to be implicated in tempo and familiarity processing were identified only for preterm infants with music training in the NICU. Increased connectivity between auditory cortices and thalamus and dorsal striatum may not only reflect their sensitivity to the known music and the processing of its tempo as familiar, but these results are also compatible with the hypothesis that the previously listened music induces a more arousing and pleasant state. Our results suggest that music exposure in NICU's environment can induce brain functional connectivity changes that are associated with music processing.

Data-driven tensor independent component analysis for model-based connectivity neurofeedback.

Neurofeedback based on real-time functional MRI is an emerging technique to train voluntary control over brain activity in healthy and disease states. Recent developments even allow for training of brain networks using connectivity feedback based on dynamic causal modeling (DCM). DCM is an influential hypothesis-driven approach that requires prior knowledge about the target brain network dynamics and the modulatory influences. Data-driven approaches, such as tensor independent component analysis (ICA), can reveal spatiotemporal patterns of brain activity without prior assumptions. Tensor ICA allows flexible data decomposition and extraction of components consisting of spatial maps, time-series, and session/subject-specific weights, which can be used to characterize individual neurofeedback regulation per regulation trial, run, or session. In this study, we aimed to better understand the spatiotemporal brain patterns involved and affected by model-based feedback regulation using data-driven tensor ICA. We found that task-specific spatiotemporal brain patterns obtained using tensor ICA were highly consistent with model-based feedback estimates. However, we found that the DCM approach captured specific network interdependencies that went beyond what could be detected with either general linear model (GLM) or ICA approaches. We also found that neurofeedback-guided regulation resulted in activity changes that were characteristic of the mental strategies used to control the feedback signal, and that these activity changes were not limited to periods of active self-regulation, but were also evident in distinct gradual recovery processes during subsequent rest periods. Complementary data-driven and model-based approaches could aid in interpretation of the neurofeedback data when applied post-hoc, and in the definition of the target brain area/pattern/network/model prior to the neurofeedback training study when applied to the pilot data. Systematically investigating the triad of mental effort, spatiotemporal brain network changes, and activity and recovery processes might lead to a better understanding of how learning with neurofeedback is accomplished, and how such learning can cause plastic brain changes along with specific behavioral effects.

Brain imaging of locomotion in neurological conditions.

Impaired locomotion is a frequent and major source of disability in patients with neurological conditions. Different neuroimaging methods have been used to understand the brain substrates of locomotion in various neurological diseases (mainly in Parkinson's disease) during actual walking, and while resting (using mental imagery of gait, or brain-behavior correlation analyses). These studies, using structural (i.e., MRI) or functional (i.e., functional MRI or functional near infra-red spectroscopy) brain imaging, electrophysiology (i.e., EEG), non-invasive brain stimulation (i.e., transcranial magnetic stimulation, or transcranial direct current stimulation) or molecular imaging methods (i.e., PET, or SPECT) reveal extended brain networks involving both grey and white matters in key cortical (i.e., prefrontal cortex) and subcortical (basal ganglia and cerebellum) regions associated with locomotion. However, the specific roles of the various pathophysiological mechanisms encountered in each neurological condition on the phenotype of gait disorders still remains unclear. After reviewing the results of individual brain imaging techniques across the common neurological conditions, such as Parkinson's disease, dementia, stroke, or multiple sclerosis, we will discuss how the development of new imaging techniques and computational analyses that integrate multivariate correlations in "large enough datasets" might help to understand how individual pathophysiological mechanisms express clinically as an abnormal gait. Finally, we will explore how these new analytic methods could drive our rehabilitative strategies.

Ventrolateral Motor Thalamus Abnormal Connectivity in Essential Tremor Before and After Thalamotomy: A Resting-State Functional Magnetic Resonance Imaging Study.

OBJECTIVE: To evaluate functional connectivity (FC) of the ventrolateral thalamus, a common target for drug-resistant essential tremor (ET), resting-state data were analyzed before and 1 year after stereotactic radiosurgical thalamotomy and compared against healthy controls (HCs). METHODS: In total, 17 consecutive patients with ET and 10 HCs were enrolled. Tremor network was investigated using the ventrolateral ventral (VLV) thalamic nucleus as the region of interest, extracted with automated segmentation from pretherapeutic diffusion magnetic resonance imaging. Temporal correlations of VLV at whole brain level were evaluated by comparing drug-naïve patients with ET with HCs, and longitudinally, 1 year after stereotactic radiosurgical thalamotomy. 1 year thalamotomy MR signature was always located inside VLV and did not correlate with any of FC measures (P > 0.05). This suggested presence of longitudinal changes in VLV FC independently of the MR signature volume. RESULTS: Pretherapeutic ET displayed altered VLV FC with left primary sensory-motor cortex, pedunculopontine nucleus, dorsal anterior cingulate, left visual association, and left superior parietal areas. Pretherapeutic negative FC with primary somatosensory cortex and pedunculopontine nucleus correlated with poorer baseline tremor scores (Spearman = 0.04 and 0.01). Longitudinal study displayed changes within right dorsal attention (frontal eye-fields and posterior parietal) and salience (anterior insula) networks, as well as areas involved in hand movement planning or language production. CONCLUSIONS: Our results demonstrated that patients with ET and HCs differ in their left VLV FC to primary somatosensory and supplementary motor, visual association, or brainstem areas (pedunculopontine nucleus). Longitudinal changes display reorganization of dorsal attention and salience networks after thalamotomy. Beside attentional gateway, they are also known for their major role in facilitating a rapid access to the motor system.

Pretherapeutic Functional Imaging Allows Prediction of Head Tremor Arrest After Thalamotomy for Essential Tremor: The Role of Altered Interconnectivity Between Thalamolimbic and Supplementary Motor Circuits.

OBJECTIVE: To correlate pretherapeutic resting-state functional magnetic resonance imaging (rs-fMRI) measures with pretherapeutic head tremor presence and/or further improvement 1 year after stereotactic radiosurgical thalamotomy (SRS-T) for essential tremor (ET). METHODS: We prospectively collected head tremor scores (range, 0-3) and rs-fMRI data for a cohort of 17 consecutive ET patients in pretherapeutic and 1 year after SRS-T states. We additionally acquired rs-fMRI data for a healthy control (HC) group (n = 12). Group-level independent component analysis (n = 17 for pretherapeutic rs-fMRI) was applied to decompose neuroimaging data into 20 large-scale brain networks using a standard approach. Through spatial regression, we projected 1 year after SRS-T and HC rs-fMRI time points, on the same 20 brain networks. RESULTS: Pretherapeutic interconnectivity (IC) strength between the network including bilateral thalamus and limbic system with left supplementary motor area predicted head tremor improvement at 1 year after SRS-T (family-wise corrected P < 0.001, cluster size Kc = 146). For the statistically significant cluster, IC strength was strongest in HCs (mean, 4.6; median, 3.8) compared with pre- (mean, 0.1; median, 0.2) or posttherapeutic (mean, -0.2; median, 0.09) states. CONCLUSIONS: Baseline measures of IC between bilateral thalamus and limbic system with left supplementary motor area may predict head tremor arrest after thalamotomy. However, procedures such as SRS-T, for this particular clinical feature, do not align patients to HCs in terms of functional brain connectivity. We postulate that supplementary motor area is modulating head tremor appearance, by abnormal connectivity with the thalamolimbic system.

Clinical response to Vim's thalamic stereotactic radiosurgery for essential tremor is associated with distinctive functional connectivity patterns.

INTRODUCTION: Essential tremor (ET) is the most common movement disorder. Drug-resistant ET can benefit from standard surgical stereotactic procedures (deep brain stimulation, thalamotomy) or minimally invasive high-intensity focused ultrasound (HIFU) or stereotactic radiosurgical thalamotomy (SRS-T). Resting-state fMRI (rs-fMRI) is a non-invasive imaging method acquired in absence of a task. We examined whether rs-fMRI correlates with tremor score on the treated hand (TSTH) improvement 1 year after SRS-T. METHODS: We included 17 consecutive patients treated with left unilateral SRS-T in Marseille, France. Tremor score evaluation and rs-fMRI were acquired at baseline and 1 year after SRS-T. Resting-state data (34 scans) were analyzed without a priori hypothesis, in Lausanne, Switzerland. Based on degree of improvement in TSTH, to consider SRS-T at least as effective as medication, we separated two groups: 1, ≤ 50% (n = 6, 35.3%); 2, > 50% (n = 11, 64.7%). They did not differ statistically by age (p = 0.86), duration of symptoms (p = 0.41), or lesion volume at 1 year (p = 0.06). RESULTS: We report TSTH improvement correlated with interconnectivity strength between salience network with the left claustrum and putamen, as well as between bilateral motor cortices, frontal eye fields and left cerebellum lobule VI with right visual association area (the former also with lesion volume). Longitudinal changes showed additional associations in interconnectivity strength between right dorsal attention network with ventro-lateral prefrontal cortex and a reminiscent salience network with fusiform gyrus. CONCLUSIONS: Brain connectivity measured by resting-state fMRI relates to clinical response after SRS-T. Relevant networks are visual, motor, and attention. Interconnectivity between visual and motor areas is a novel finding, revealing implication in movement sensory guidance.