Neuromodulation of Deep Cerebellar Nuclei: principles for Focused Ultrasound stimulation and EEG monitoring

Neuromodulation of Deep Cerebellar Nuclei: principles for Focused Ultrasound stimulation and EEG monitoring | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Neuromodulation of Deep Cerebellar Nuclei: principles for Focused Ultrasound stimulation and EEG monitoring Xavier Corominas Teruel, Björn Sigurðsson, Samuel Pichardo, Axel Thielscher, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9650438/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Non-invasive modulation of deep cerebellar nuclei offers a promising approach for basic neurophysiological research and potential therapeutic applications in movement disorders and beyond. In the present report, we define key principles for implementing low-intensity transcranial focused ultrasound (tFUS) targeting of cerebellar output nuclei, with the aim of supporting both mechanistic and clinical research. We propose a structured framework covering targeting strategies, stimulation delivery, experimental design and concurrent monitoring, with particular emphasis on individualized anatomical planning and reproducible workflows. We also evaluate current approaches and limitations in assessing target engagement using electroencephalography. Together, this work provides a practical foundation for ultrasound-based neuromodulation of deep cerebellar structures in humans, positioning, for the first time, cerebellar tFUS as a feasible non-invasive methodological alternative to invasive techniques. Computational Neuroscience focused ultrasound neuromodulation cerebellum brain stimulation electroencephalography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Highlights ● We present a methodological framework for individualized targeting of cerebellar output nuclei. ● Targeting optimization integrates spatial, temporal, and state-dependent parameters. ● EEG provides a scalable approach to assess deep-target engagement. Introduction Brain stimulation has been employed for more than three decades to causally probe and modulate cerebellar function in humans (Grimaldi et al., 2014 ; Miterko et al., 2019 ). Non-invasive stimulation approaches targeting the cerebellar cortex have contributed greatly to the understanding of cerebello-cortical interactions (Caligiore et al., 2017 ). However, the limited depth penetration of conventional transcranial magnetic and electrical stimulation impedes the possibility of modulating deep cerebellar targets directly. Consequently, causal evidence regarding the functional contributions of the deep cerebellar nuclei (DCN), along with the therapeutic potential of non-invasive DCN neuromodulation, remains limited. In this manuscript, we position Low-Intensity Transcranial Focused Ultrasound Stimulation as a way to directly target cerebellar nuclei for research and therapeutic purposes. We aim to provide a theoretically grounded, practical framework for successfully stimulating deep cerebellar targets with tFUS. For an in-depth introduction of tFUS mechanisms of action and safety considerations, the reader is referred to the extensive body of existing literature (Aubry et al., 2023 ; Blackmore et al., 2019 ; Caffaratti et al., 2025 ; Darmani et al., 2022 ; Jerusalem et al., 2019 ; Martin et al., 2024 ; Murphy et al., 2025 ; Pasquinelli et al., 2019 ; Plaksin et al., 2016 ; Rabut et al., 2020 ; Sarica et al., 2022 ; Sorum et al., 2021 ; Yoo et al., 2022 ). Current neuromodulatory strategies fall short of scalable, precise targeting of DCN Non-invasive causal interrogation of human cerebellar function has been limited to transcranial magnetic and electrical stimulation of the cerebellar cortex (Miterko et al., 2019 ). In parallel, deep brain stimulation (DBS) delivered via implanted electrodes has provided insights into deep cerebellar function (especially the dentate nucleus, DN) and alluded to the clinical potential of deep cerebellar stimulation in the management of movement disorders in a limited number of clinical cases (see Supplementary Table 1 for a comprehensive list of all cerebellar-DBS reported studies). Collectively, experimental and clinical results have stimulated increasing interest within the clinical neurology and neuroscience communities, positioning deep cerebellar neuromodulation not only as a promising therapeutic strategy for movement disorders (Baker et al., 2023 ; Tai & Tseng, 2022 ; Wessel & Hummel, 2018 ), but also as an opportunity to advance the functional and anatomical understanding of cerebello-cortical and cerebello-pontine networks. However, current neuromodulatory approaches targeting the DCN suffer from a lack of depth penetrance and precision, which constrains their widespread use. The rapid decay of the magnetic field limits Transcranial Magnetic Stimulation (TMS) to the superficial layers of the cerebellar cortex (Siebner et al., 2022 ), and the heavily convoluted cerebellar cortex prevents lobule-specific targeting (Çan et al., 2019 ; Deng et al., 2013 ; Rohira et al., 2025 ). Similarly, tES also faces a fundamental depth-focality trade-off: targeting deeper brain structures calls for higher intensities, which in turn produce greater current spread (Guiomar et al., 2025 ; Liu et al., 2018 ; Opitz et al., 2015 ; Saturnino et al., 2021 ; Saturnino, Siebner, et al., 2019 ; Van Hoornweder et al., 2024 ). Accordingly, these methods cannot provide selective lobular or direct cerebellar nuclear stimulation. Direct nuclear stimulation via deep brain stimulation (DBS) is currently restricted to clinical populations and remains a relatively novel neurosurgical intervention, with procedure-related risks and secondary effects that may not yet be fully documented (see Supplementary Table 1). Consequently, the functional roles of the deep cerebellar nuclei and their therapeutic potential as neuromodulation targets remain insufficiently characterized, underscoring the urgent need to develop novel, effective, non-invasive approaches for deep cerebellar stimulation. Low-Intensity Transcranial Focused Ultrasound Stimulation can target deep structures Low-intensity Transcranial Focused Ultrasound Stimulation (tFUS) has emerged as a promising non-invasive neuromodulation modality. By concentrating acoustic energy at focal points located centimeters away from the transducer radiating surface, tFUS permits focal modulation of neural activity in deep brain regions. tFUS generate mechanical forces that impact neuronal signalling via mechanical, with sign and magnitude defined by the temporal structure of the sonication regimes (Caffaratti et al., 2025 ; Murphy et al., 2024 ; Plaksin et al., 2016 ). Despite the rapidly growing interest in tFUS for experimental and clinical neuroscience (Loh et al., 2026 ; Pellow et al., 2024 ; Sarica et al., 2022 ), substantial methodological challenges have thus far limited its application to deep cerebellar nuclei, leaving these structures largely unexplored. Here, we outline and address these challenges and present a comprehensive experimental and methodological framework for implementing personalized tFUS. The presented planning and experimental framework will enable the user to accurately target deep cerebellar nuclei while monitoring neural responses using electroencephalography (EEG). Beyond neuromodulation (Rabut et al., 2020 ), the proposed framework provides a scalable, individualized foundational methodological resource for cerebellar ultrasound-based interventions (e.g., blood-brain barrier opening) while addressing key sources of uncertainty inherent in out-of-MRI ultrasound applications. General methodological considerations Similar to other neuromodulation techniques, precise modulation of deep neural targets with tFUS requires control over spatial (target location), temporal (stimulation pattern), and contextual (underlying network state) domains (Bergmann et al., 2016 ; Siddiqi et al., 2024 ; Siebner et al., 2009 ). Moreover, like any other neuromodulation technique, the sequential or concurrent combination of tFUS with neuroimaging and/or electrophysiology enables precise spatiotemporal targeting, verification of target engagement, and enables causal coupling of brain and behavior. However, cerebellar applications introduce specific anatomical, physiological, and functional constraints that may require deviations from the general neuromodulatory principles and motivate tailored methodological strategies. In the following sections, we therefore explore specific considerations for cerebellar tFUS, highlighting both general considerations and domain-specific challenges. Spatial optimization of transducer placement The spatial localization and orientation of the ultrasound transducer radiating surface relative to the human scalp critically determine the propagation path of the acoustic beam and the location of the resulting focal spot within the brain (Aubry et al., 2003 ). To target deep brain structures, and in particular deep cerebellar nuclei, it is therefore pertinent to accommodate inter-individual variability in brain and skull morphology via pre-experimental planning based on individual anatomical imaging. Imaging for cerebellar nuclei segmentation and pre-experimental planning To effectively plan ultrasound neuromodulation interventions in out-of-MR settings a minimum but necessary set of subject-specific neuroanatomical information must be acquired. High-resolution structural MRI is essential, where T1- and T2-weighted imaging (T1w and T2w) provide the anatomical detail required for accurate localization of deep cerebellar nuclei and to generate subject-specific computational models of the participant’s head and brain (Nielsen et al., 2018 ; Puonti et al., 2020 ) for geometrically based heuristic trajectory planning (Atkinson-Clement & Kaiser, 2025 ; Lueckel et al., 2025 ). In addition, given the high iron content of cerebellar nuclei, susceptibility-weighted imaging (SWI) or quantitative susceptibility mapping (QSM) offer enhanced contrast that improves delineation of deep nuclear boundaries at the individual level (Maderwald et al., 2012 ). Optimization of acoustic beam trajectories for deep cerebellar neuromodulation Trajectory planning for ultrasound neuromodulation requires explicit specification of the acoustic beam orientation, focal depth, and expected energy losses arising from variations in tissue density, composition, and geometry. While geometrically based heuristic approaches(Atkinson-Clement & Kaiser, 2025 ; Lueckel et al., 2025 ) can be used to identify anatomically feasible scalp locations within the transducer range and approximate beam trajectories intersecting with the target area, rigorous biophysical modeling(Pichardo, 2023 ; Treeby & Cox, 2010 ) is essential to accurately characterize acoustic wave propagation, focal pressure, attenuation, refraction, phase aberration, and thermal dose across the heterogeneous cranial occipital bone and brain tissues (Aubry et al., 2022 ). While computed tomography (CT) provides gold-standard estimates of skull and tissue density (Aubry et al., 2003 ; Montanaro et al., 2021 ), its use in healthy participants is often constrained by concerns related to ionizing radiation exposure. To address this limitation, MRI-based alternatives have been developed to approximate CT-derived density information. In particular, pointwise encoding time reduction with radial acquisition (PETRA), or Zero Echo TIME (ZTE, or similar ultra-short time echo imaging) MRI sequences can be used to generate synthetic pseudo-CT images or to predict tissue density and acoustic properties (Aubry et al., 2003 ; Leung et al., 2022 ; Miller et al., 2015 ; Miscouridou et al., 2022 ; Montanaro et al., 2021 ; Yaakub et al., 2023 ). Importantly, while refraction and attenuation are unavoidable, especially when accessing cerebellar nuclei through posterior or occipital acoustic windows with complex curvature, these effects can be predicted with simulations. Therefore, they do not pose a fundamental limitation to neuromodulation if they are appropriately compensated for during planning. Likewise, the direction of the trajectory intersecting the skull surface with a normal incidence can be identified and projected back to scalp entry points with reduced bone intersection angulation and consequent skull refraction. While this approach may improve acoustic transmission through the skull, it may increase transducer angulation at the scalp surface, which challenges transducer coupling and stability. Finally, trajectory planning should also account for additional anatomical constraints within the posterior fossa and occipital regions. Major dural venous sinuses, particularly the transverse and sigmoid sinuses, should be avoided or carefully considered during acoustic planning due to their vascular nature and potential effects on ultrasound propagation. In addition, acoustic transmission is influenced by regional variations in bone thickness and density. Specifically, the mastoid and posterolateral temporal bone regions are substantially thicker and denser than adjacent occipital bone (Zhang et al., 2023), leading to increased attenuation of the ultrasound beam when transducers are positioned closer to the temporal regions. Consequently, optimal trajectory planning for targeting the deep cerebellar nuclei involves balancing acoustic efficiency, geometric feasibility, and coupling constraints (Fig. 1 ). Subject- and site-specific acoustic lenses, holographic phase plates or phased-array systems provide another way of steering tFUS to the deep cerebellar nuclei by compensating skull-induced losses, which render a broader range of scalp locations viable (Attali et al., 2025 ; Maimbourg et al., 2018 ). This approach offers a substantial advantage over purely geometric trajectory planning, as it enables maximal coupling and phase alignment of the ultrasound wavefront at the target and maximizes focal pressure deposition despite cranial heterogeneity. Moreover, because acoustic lenses can impose precise phase delays on the propagating wave, they enable advanced pressure field shaping, including adjustment of focal size, multiple simultaneous focal spots, or delivery of spatiotemporally structured traveling pressure patterns (Cengiz et al., 2025 ; Sayed Ahmed & Shahab, 2025 ). Such ultrasound pressure patterns can be achieved only through acoustic lenses or multi-element phased array systems, thereby opening a broad and largely unexplored design space for cerebellar neuromodulation that extends beyond conventional single-focus stimulation paradigms. Transducer constraints A variety of commercially available and custom-built ultrasound transducer systems are currently in use, each with distinct characteristics and architectures. However, not all systems may be suitable for targeting deep cerebellar nuclei, and a set of minimal common requirements is likely necessary to enable effective and reliable stimulation of deep structures. A first consideration for cerebellar nuclei targeting relies on the effective penetration range (i.e., focal depth or length) of the ultrasound transducer, which is, at the technical level, primarily determined by the aperture diameter and the radius of curvature of the radiating surface (Murphy et al., 2025 ). As the cerebellar nuclei are typically located approximately 45–65 mm beneath the skin (Schmahmann et al., 1999 ), theoretical focal depths of at least ~ 55 mm may be required to reach these targets under ideal propagation free-water conditions. However, in practice, ultrasound propagation towards the cerebellum is substantially impeded by pronounced skull inhomogeneities, spatial variations in skull thickness and density along with angular mismatch between the curved occipital bone and the overlying scalp. The resulting large, highly non-perpendicular ultrasound incidence angles can exacerbate refraction and phase aberration at the skull, leading to focal distortion and reduced acoustic energy transmission toward the cerebellar nuclei. Consequently, focal depths exceeding the nominal anatomical target depth may be necessary in most cases to compensate for skull-induced propagation losses and to ensure effective energy delivery to the cerebellar nuclei. Closely related to penetration range, the focal volume determines the volume of tissue exposed to effective acoustic energy. The focal volume is commonly defined by the full width at half maximum (FWHM) and typically characterized by the − 6 dB and/or -3 dB intensity contours (Martin et al., 2024 ). Depending on the experimental objectives, the focal volume may be optimized to be either larger or more spatially confined. For instance, when the goal is to engage the entire dentate nucleus, transducers producing larger focal volumes at the target depth may be advantageous. Conversely, when selectively targeting smaller cerebellar nuclei or specific subregions within the dentate nucleus, substantially smaller focal volumes may be preferable to enhance spatial precision and minimize off-target effects. Beyond transducer geometry, the fundamental frequency must also be considered in targeting the cerebellar nuclei, as it is tightly linked to the volume of tissue exposed. At higher frequencies (> 700 kHz), skull-induced attenuation and phase aberrations may increase substantially, leading to focal distortion and reduced intracranial pressure (Attali et al., 2023 ). Conversely, low frequencies (< 200 kHz) can improve transmission through the skull, but increase the ultrasound wavelength, producing enlarged focal volumes. Along the same lines, transducer architecture (e.g., single-element, multielement, phased-array designs) must be considered. While annular array and multielement phased-array transducers increase the complexity of driving electronics and cost, they enable electronic dynamic focusing and beam steering of focal depth within a single system, accommodating interindividual variability in human neuroanatomy. Conversely, single-element transducers with fixed focal depths may reach the cerebellar nuclei in some individuals but cannot reliably be steered, requiring, in most cases, custom-fabricated holographic acoustic lenses for precise individualized targeting. Table 1 Technical specifications of the evaluated transducers Transducer Focal length Operating Frequency Device architecture Diameter aperture CTX250 Steerable, up to 60 mm 250 Khz 2-elements annular array transducer 64 mm CTX500 Steerable, up to 63 mm 500 Khz 4-elements annular array 64 mm Pulse Fixed focus, 80 mm 650 Khz single-element transducer 61 mm H317 Steerable, up to 133 mm 250 Khz 128-elements phased array 135 mm DPX500 Steerable, up to 150 mm 500 Khz 4-elements annular array 64 mm To further guide transducer selection for deep cerebellar neuromodulation, we conducted an in-silico comparison (see Supplementary Materials. 1) of several commercially available transducers across different system configurations. Specifically, our objective was to identify transducer designs capable of accurately and precisely targeting the anterior-inferior-lateral region of the dentate ( MNI coordinates: x = -16, y = -54, z = -37 ), while minimizing off-target exposure to surrounding structures (see Supplementary Materials 1 for methodological details). Five commonly used commercially ultrasound transducers were evaluated (see Table 1 for details), including the following: (1) CTX250 (SonicConcepts, USA); (2) CTX500 (SonicConcepts, USA); (3) Pulse 80mm (BrainSonix, USA); (4) H317 (SonicConcepts, USA); and (5) DPX500 (SonicConcepts, USA). All tested transducers except the CTX250 exhibited comparable − 1dB target overlap with the DN region (Fig. 2 ). Despite this general ability to reach the DN, differences were observed across the remaining transducers in the distance between the spatial peak location and the intended MNI target coordinates. Specifically, the multielement design and electronic steering capabilities of the DPX500 and H315 allowed for focus steering that minimized the distance between the spatial peak and the target coordinates. In contrast, although the CTX500 was able to reach the DN, its limited depth range (up to 63.2mm) restricted its ability to effectively target the anterior portion of the nuclei. Similarly, the single-element Pulse80mm transducer could reach the mid-anterior region of the DN with maximal spatial precision; however, in practical setting, its lack of electronic steering may impose experimental limitations in certain participants. Regarding focal volume characteristics, the DPX500, CTX500, and Pulse80mm transducers exhibited smaller − 3 dB volumes compared to the H315 and CTX250. Thus, our in-silico analyses suggest that DPX500-like and H315-like transducers may offer the most balanced performance for deep cerebellar neuromodulation, combining high focality with steering capability. Parametric selection of ultrasound pulsing regimes and state-dependency tFUS capacity to perturb neural activity or induce sustained neuromodulatory effects is highly influenced by the stimulation regime. tFUS delivers high-frequency mechanical energy as continuous acoustic wave, repeated sequentially forming pulse trains with a specific pulse repetition interval or frequency (PRF). This pulsed delivery is essential for limiting thermal accumulation within neural tissue and enabling partial recovery or hyperpolarization of membrane potentials between successive pulses. For neuromodulatory applications, pulse trains are further organized into repeated stimulation regimes defined by the pulse train repetition interval or frequency (Martin et al., 2024 ; Murphy et al., 2025 ). This hierarchical organization, spanning from individual pulses to pulse trains and repeated pulse-train regimes, enables precise control over stimulation timing and supports a wide range of neuromodulatory patterns. The modulatory direction (i.e., excitatory, inhibitory) of both acute and longer lasting neural effects has been proposed to depend on an interaction between the acoustic pressure and duty cycle (defined as the proportion of active sonication within each pulse repetition interval) (Caffaratti et al., 2025 ; Cox et al., 2025 ; Murphy et al., 2024 ; Plaksin et al., 2016 ). Current evidence suggests that low duty cycles (< 10% DC) associated with low PRFs ( 40% DC) preferentially cause neuronal excitation. The exact mechanisms linking acoustic pressure and DC to neural effects remain poorly understood and are likely not uniform across neural circuits. A growing body of work indicates that ultrasound stimulation can produce cell-type dependent responses (Lemaire et al., 2024 ; Murphy et al., 2024 ), suggesting to reflect differences in neuronal morphology, membrane mechanics, ion-channel composition, myelin concentration, and microcircuit cytoarchitecture. Despite substantial progress in characterizing ultrasound neuromodulation across cortical and subcortical targets, the extent to which the existing pressure-frequency dyad principles generalize to the cerebellar cortex and deep cerebellar nuclei in remains unexplored. In fact, to date, no human studies have directly targeted deep cerebellar nuclei with neuromodulatory ultrasound, which underscores a gap in the existing literature and highlights the need for further systematic parametric investigations. Importantly, the availability of internationally defined safety and practice guidelines, such as those proposed by the International Transcranial Ultrasonic Stimulation Safety and Standards (ITRUSST) (Aubry et al., 2023 ), provides a robust framework for the controlled, systematic, and safe exploration of these parameters in human cerebellar research. Moreover, like for other neuromodulation techniques, temporal timing and brain-state may be critical, shaping the physiological effects of tFUS (Bradley et al., 2022 ; Ly et al., 2016 ; Salehinejad et al., 2021 ; Silvanto & Pascual-Leone, 2008 ; Zrenner et al., 2018 ). Brief pulses and pulse repetition may interact with neurotransmitter gating, spike timing and ongoing oscillatory processes, modulating neuronal excitability in a phase-dependent manner (Herrmann et al., 2016 ; Krause et al., 2019 ; Lakatos et al., 2019 ; S. Y. Lee et al., 2024 ; Thut et al., 2017 ; Vosskuhl et al., 2018 ). Beyond the immediate neural excitability state, longer temporal scales must also be considered (Fig. 3 ). Repeated exposure to stimulation can interact with ongoing metaplasticity and homeostatic regulation process (Ding et al., 2024 ; Turrigiano, 2008 ; Turrigiano & Nelson, 2004 ). At these timescales, baseline brain-states are no longer static, and prior stimulation history may alter excitability, sensitivity to mechanical perturbation, or the directionality of neuromodulatory effects. Consequently, identical ultrasound quantitative parameters (e.g., repetition frequency, pressure) delivered on different days may produce qualitatively different outcomes altering dose-response relationship. In addition, pharmacological agents or physiological states (arousal, vigilance, sleep-wake phase, circadian rhythms, or metabolic status) affecting neurotransmitter balance, synaptic history, or ion concentrations can modify baseline excitability and alter, either increasing or decreasing, ultrasound-induced immediate and lasting effects. As illustrated in Fig. 3 , online monitoring of acute neural and/or behavioral effects provides a means to address this variability via state-dependent or even closed-loop stimulation. Neuronavigation co-registration and coupling considerations Despite extensive and careful pre-experimental planning, tFUS may still fail to engage deep cerebellar targets. While concurrent neuronavigation enables precise control of transducer positioning relative to the individual subject’s head, three primary sources of imprecision can contribute to response variability: (I) brain positional deformation, (II) inconsistencies in transducer-scalp coupling, and (III) co-registration errors for neuronavigation and small imprecision by the human operator. MR Imaging induce shifts in brain positioning: implications for spatial targeting While out-of-MR tFUS application favours scalability and clinical translation, it introduces challenges related to targeting accuracy. Structural MRI is typically acquired with participants in supine position, whereas experimental ultrasound stimulation may be performed under different postural conditions. The brain is not rigidly fixed within the cranial vault; gravitational forces can induce subtle but measurable posture-dependent displacements of brain tissue, including posterior shifts in the supine position (Schmid & Crone, 2025 ; Schnaudigel et al., 2010 ; Yokoyama et al., 2021 ; Zappalá et al., 2021 ). For cerebellar regions, gravitational displacement of > 1mm along the anterior-posterior axis have been reported when comparing MRI images acquired in prone versus supine positions (Zappalá et al., 2021 ). Furthermore, MRI hardware, such as head coils and participant support systems, which can exert mechanical pressure on the neck during MRI acquisition. Such compression may deform occipital soft tissues or alter cervical alignment, influencing computational anatomical reconstructions (i.e., brain segmentations and FEM), affecting in silico transducer placement and acoustic modeling. To mitigate posture- and hardware-related biases, consistent participant positioning across MRI scanning and subsequent stimulation sessions may mitigate targeting inaccuracies. Coupling introduces a source of variability that cannot readily be quantified During ultrasound interventions, the transducer(s) needs to be coupled to the scalp. This is done by placing acoustic coupling media between the radiating surface of the transducer and the scalp to establish an effective physical transmission pathway. Common coupling solutions include hydrogel pads, water-filled balloons, and/or liquid ultrasound gels. Unfortunately, the presence of air bubbles within the coupling can result in substantial acoustic pressure/intensity attenuation and phase aberration, which is aggravated by the presence of hair. The extent of air-induced attenuation and aberration are unknown, and no standardized methodology currently exists to quantitatively assess coupling quality in practical experimental setups. Several strategies have been proposed to minimize trapped air, including centrifugation of ultrasound gel, meticulous hair preparation (or removal) combined with palpation-based inspection of the gelled surface (Murphy et al., 2025 ). However, no current method ensures nor quantify the coupling quality between interspersed gel pads and the skin. Study designs, consequently, need to account for some expectancy variability, with the accumulated literature guiding the calculation of adequate sample sizes. Neuronavigation co-registration as an additional source of error Co-registration procedures typically depend on anatomical landmarks to align physical-world and image MRI spaces, and even small registration inaccuracies can propagate into meaningful deviations in tFUS accuracy. Given the small focal volume and high spatial specificity of tFUS (transducer- and depth-dependent), minor translational errors or slight angular misalignments at the transducer surface can result in off-target acoustic trajectories, particularly when stimulating deep brain structures. This is further aggregated by small variation in transducer to head position whether it is handheld, mechanically stabilized or controlled by a robotized arm. Summed, a 2mm co-registration error and 1mm imprecision in transducer placement result in a detrimental shift of focal accuracy (Fig. 4 ). These considerations highlight the need for rigorous co-registration procedures and continuous monitoring of transducer position and orientation throughout stimulation sessions. While concurrent tFUS with MRI enables assessment of spatial target engagement and acoustic attenuation, for example, through acoustic radiation force imaging (MR-ARFI) or thermography, uncertainties in both spatial localization and delivered intensity arising from co-registration errors and transducer-scalp coupling remain difficult to resolve in out-of-scanner tFUS applications. Within this context, electrophysiological readouts, such as electroencephalography, offer a unique means to verify target engagement. Informing tFUS with EEG The concurrent/consecutive combination of tFUS with EEG is emerging as a novel method for monitoring the effects of ultrasound stimulation, both online and offline. Concurrent EEG serves a dual purpose; it can inform on the state of the targeted network and provide a read-out of network effects. While this opens the possibility of state-triggered open-loop or even closed-loop tFUS, this application remains in the horizon for now. Instead, EEG may provide low-cost and scalable markers of target engagement. Across brain targets, tFUS-EEG has been employed in a limited number of studies (see Supplementary Table 2 for a comprehensive list of all tFUS-EEG reported studies to date), most of which have focused on the modulatory effects on somatosensory evoked potentials or visual evoked potentials. Both somatosensory and visually evoked potentials have been demonstrated to be sensitive to tFUS modulation of thalamic and cortical areas (Butler et al., 2022 ; Dunsford et al., 2026 ; Kim et al., 2023 ; Kosnoff et al., 2024 , 2026 ; W. Lee et al., 2015 , 2016 ; Legon et al., 2014 , 2018 ; Mueller et al., 2014 ; Nandi et al., 2023 ; Tang et al., 2025 ). Here, tFUS is thought to interrupt extrinsically triggered neural synchronization that mediates highly coordinated firing located at the site of stimulation or downstream in the network. Whether sensory evoked potentials or other EEG markers are best suited to detect acute and/or neural after-effects DN targeted tFUS is yet to be investigated. Low-intensity focused ultrasound is generally considered a subthreshold technique (Caffaratti et al., 2025 ) that do not elicit action potentials on a pulse-by-pulse basis. This separate tFUS from TMS, where the simultaneous suprathreshold excitation of large population of pyramidal neurons generating a strong enough signal for scalp EEG measure time- and phase-locked evoked responses (Hernandez-Pavon et al., 2023 ; Ilmoniemi & Kičić, 2010 ; Thut et al., 2011 ; Tremblay et al., 2019 ). Within the safety limits for neuromodulatory tFUS, it is unlikely that TEP-like phase locked responses can be evoked. Rather, tFUS neuromodulatory effects are theoretically consistent with sub-threshold modulation of ongoing neural dynamics, comparable to those proposed for other subthreshold stimulation modalities (e.g., tDCS, tACS). Therefore, alongside subtle changes in evoked potentials, modulation of resting or task-based periodic or aperiodic cortical activity may present markers that are sensitive to tFUS modulation of deep cerebellar nuclei (Fig. 5 ). Assessing Dentate Nucleus Modulation by tFUS Through EEG Biomarkers The dentate nucleus (DN) is the largest of the deep cerebellar nuclei and the most accessible to tFUS stimulation due to its spatial location (Schmahmann et al., 1999 ). It receives extensive white-matter projections from a broad range of cerebellar cortical regions (Nettekoven et al., 2024 ; Stoodley et al., 2022 ) and is subdivided based on afferent and efferent connectivity profile (Strick et al., 2009 ). Due to the dentato-thalamo-cortical pathway, distant cortical electrophysiological signatures can, in principle, inform on changes DN activity. The DN is composed of local inhibitory interneurons alongside large glutamatergic projection neurons that constitute its principal output (Benarroch, 2024 ). These neurons are the main target of inhibitory Purkinje cells projecting from cerebellar cortex. Within motor networks, these excitatory neurons project predominantly to the ventrolateral motor thalamus and the red nucleus (Sakai et al., 2002 ). At the thalamic level, motor related inputs from DN are integrated with signals from basal ganglia circuits before being relayed onwards to pre- and primary motor cortices. An increased output from activity in the motor division of DN can therefore be expected to drive motor related thalamic dynamics (Halassa & Sherman, 2019 ; Sherman & Guillery, 1996 ; Shine, 2021 ), and at the cortical level, induce characteristic movement related desynchronization (i.e., readiness potentials, contingent negative variation) together with pericentral alpha (8–12 Hz) and beta (13–30 Hz) oscillatory desynchronization (Kilavik et al., 2013 ; Tan et al., 2016 ). Reversely, acute or longer lasting inhibition of DN output should increase pericentral synchronization (Schurger et al., 2021 ) although this effect is likely to depend on the motor state. The most consistent acute tFUS effects in humans appear to be inhibitory (Caffaratti et al., 2025 , Supplementary Table 2), which renders online tFUS better suited to interfere with an ongoing process than to directly augment or entrain network activity. Given the baseline tonic excitation from DN to thalamus and onwards, the dentato-thalamo-cortical pathway present an ideal neuromodulatory target to change movement related cortical activity assessable with scalp EEG (Fig. 6 ). An interference with DN signaling may also affect motor cortical responsiveness to TMS or intracranial stimulation (Xin Li et al., 2024 ). While motor evoked electromyographic (EMG) potentials provide an experimentally simple approach to indirectly investigate such modulation in motor cortical excitability, it is confounded by other inputs onto spinal motoneurons and restricted to the recorded muscle(s). Thus, MEPs both reflect the cortical impact of DN stimulation as well as potential effects mediated via the red nucleus. In contrary TMS-EEG offers the possible of assessing muscle-independent effects on motor cortical and cortico-subcortical networks, indexed as changes in immediate (Beck et al., 2024 ; Nuyts et al., 2025 ) and early TMS-evoked EEG potentials (Beck et al., 2025 ). Paired-pulse TMS-EEG might even provide the possibility of studying how DN modulation affects inhibitory and excitatory networks within M1 (Christiansen, Song et al., 2026 ). Concurrent tFUS-EEG: general limitations and considerations To ensure efficient ultrasound transmission, a coupling medium must be placed between the transducer and the scalp. This requirement prevents the placement of EEG electrodes directly beneath the ultrasound transducer, thereby reducing EEG sensitivity to activity originating from areas under the transducer brain region and limiting the feasibility of direct local monitoring, especially in more superficial or cortical stimulation targets. Consequently, EEG is better suited for capturing distributed, network-level effects. Moreover, tFUS-EEG can also be sensitive to physiological and non-physiologically artifacts (Braun et al., 2020 ). Despite growing interest in the method, standardized protocols for data acquisition and signal preprocessing recommendations are still lacking. The frequency, spectral or topographical characteristics and origins of potential artifacts remain poorly understood and a detailed characterization is required to prevent the inaccurate interpretations of artifact-related effects as genuine neural responses. tFUS-EEG setups can lead to electrical coupling between the EEG and ultrasonic driver system, generating spurious activity at the pulse repetition frequency that may be mistakenly interpreted as genuine neural entrainment (see Supplementary Materials 2 for details). On the same line, tFUS can induce peripheral somatosensory stimulation (Kop et al., 2025 ) and/or auditory (air and bone conducted) stimulation (Braun et al., 2020 ; Kop et al., 2024 ). Although these artifacts have not yet been thoroughly characterized in tFUS-EEG human studies, it has been demonstrated that peripheral multisensory evoked responses to single-pulse TMS can give rise to precentral and frontocentral EEG potentials at relatively late latencies (typically > 60 ms post-stimulation) (Conde et al., 2019 ; Rocchi et al., 2021 ). In ultrasound, the high carrier frequencies used make it unlikely that artifacts originate from the ultrasound frequency itself. Instead, the pulse repetition structure is the most probable source of somatosensory and auditory responses. Furthermore, mechanical vibrations of skin or brain tissue may introduce motion artifacts mimicking neural responses (Nguyen et al., 2025 ), particularly in EEG electrodes located near the transducer. Consequently, careful inspection, control, and mitigation of these artifacts, whenever present, should be a priority in tFUS-EEG recordings. Recommended strategies may include implementation of appropriate control stimulation conditions (i.e., defocusing protocols, active controls), auditory and, when feasible, somatosensory masking procedures (e.g., topical anesthetics or somatosensory saturation), and/or post hoc signal processing approaches capable of identifying and attenuating artifact-related components. Perspectives Converging evidence indicates that the cerebellum supports rapid and efficient information processing, achieved through feedback-informed forward models, across sensorimotor and cognitive domains. Despite causal insights into the function of the deep cerebellar nuclei, as well as lobule-specific function, remain largely unexplored due to the limitations of current non-invasive neurostimulation techniques. The development of novel neuromodulation approaches, such as focused ultrasound, enables selective targeting of deep cerebellar nuclei in both experimental and clinical populations. In this work, we have presented a practical methodological framework and outlined the considerations for successful deep cerebellar targeting. This framework facilitates future investigations into the mechanisms of action of ultrasound within cerebellar regions, including electrophysiological target engagement monitoring, adaptive network-level neuromodulation, or brain-state dependent modulation. Focused ultrasound combined with EEG monitoring enables causal mapping of cerebello-cortical spectral networks, linking network-level electrophysiological signatures to the precise anatomy of deep nuclei. In parallel, symptom-specific responses to ultrasound (e.g., tremor modulation) can be localized within anatomical space, supporting the development of brain atlases that link spectro-spatial and temporal network dynamics to functionally relevant “sweet spots” for personalized therapeutic ultrasound targeting. The presented framework is not limited to the motor system. Beyond its established role in motor control, the cerebellum is increasingly recognized as contributing to a wide range of non-motor functions (Nettekoven et al., 2024 ; Strick et al., 2009 ). For example, stimulation of non-motor subregions of the dentate nucleus (DN) is expected to elicit cortical responses distinguishable from pure-motor pathways consistent with their preferential connectivity with higher-order cerebellar regions (e.g., Crus I and Crus II) (Nettekoven et al., 2024 ). Likewise, other deep cerebellar nuclei, such as the fastigial nucleus, exhibit markedly different connectivity profiles, projecting to brainstem autonomic centers, vestibular and reticular nuclei, as well as medial and intralaminar thalamic nuclei, thereby influencing widespread cortical and subcortical systems involved in arousal, vigilance, autonomic regulation, and oculomotor and postural control (Carey, 2024 ). Thus, tFUS poses for the first time a novel tool to characterize unexplored deep cerebellar functions and their spectro-spatial maps. Collectively, this work establishes, for the first time, the potential and consideration of focused ultrasound as a non-invasive approach for stimulating deep cerebellar nuclei and highlights the opportunities and challenges of concurrent/consecutive EEG to monitor tFUS-induced effects. Ultimately, it lays the groundwork for broader experimental and clinical adoption of cerebellar focused ultrasound in movement disorders and beyond. Declarations Declaration of competing interests Hartwig R. Siebner has received honoraria as speaker from Lundbeck AS, Denmark, as ad-hoc consultant from Lundbeck AS, Denmark, and as editor (Neuroimage Clinical) from Elsevier Publishers (Amsterdam, The Netherlands). He has received royalties as book editor from Springer Publishers (Stuttgart, Germany,), Oxford University Press (Oxford, UK), and from Gyldendal Publishers (Copenhagen, Denmark). The other authors declare no conflicts of interest. Credit authorship contribution statement Xavier Corominas Teruel: Writing - review & editing, Writing - original draft, Visualization, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Axel Thielscher: Writing - review & editing, Methodology, Conceptualization. Hartwig Roman Siebner: Writing - review & editing, Resources, Conceptualization. Lasse Christiansen: Writing - review & editing, Writing - original draft, Methodology, Resources, Funding acquisition, Investigation, Conceptualization. Acknowledgements This work was supported by a ‘Experiment grant’ (grant no. R436-2023-1137) from The Lundbeck Foundation awarded to Lasse Christiansen, a Grand Solutions grant “Precision Brain-Circuit Therapy - Precision-BCT” from Innovation Fund Denmark to Hartwig R. Siebner (grant no. 9068-00025B) and a Collaborative Project grant “ADAptive and Precise Targeting of cortex-basal ganglia circuits in Parkinson’s Disease - ADAPT-PD” from The Lundbeck Foundation to Hartwig R. Siebner (grant no. R336-2020-1035). Axel Thielscher was supported by The Lundbeck Foundation (grant no. R313-2019-622). References Atkinson-Clement C, Kaiser M (2025) Optimizing Transcranial Focused Ultrasound Stimulation: An Open-source Tool for Precise Targeting. 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Brain Stimulation: Basic Translational Clin Res Neuromodulation 16(1):75–78. https://doi.org/10.1016/j.brs.2023.01.838 Yokoyama Y, Yamada Y, Kosugi K, Yamada M, Narita K, Nakahara T, Fujiwara H, Toda M, Jinzaki M (2021) Effect of gravity on brain structure as indicated on upright computed tomography. Sci Rep 11(1):392. https://doi.org/10.1038/s41598-020-79695-z Yoo S, Mittelstein DR, Hurt RC, Lacroix J, Shapiro MG (2022) Focused ultrasound excites cortical neurons via mechanosensitive calcium accumulation and ion channel amplification. Nat Commun 13(1):493. https://doi.org/10.1038/s41467-022-28040-1 Zappalá S, Bennion NJ, Potts MR, Wu J, Kusmia S, Jones DK, Evans SL, Marshall D (2021) Full-field MRI measurements of in-vivo positional brain shift reveal the significance of intra-cranial geometry and head orientation for stereotactic surgery. Sci Rep 11(1):17684. https://doi.org/10.1038/s41598-021-97150-5 Zrenner C, Desideri D, Belardinelli P, Ziemann U (2018) Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex. Brain Stimulation: Basic Translational Clin Res Neuromodulation 11(2):374–389. https://doi.org/10.1016/j.brs.2017.11.016 Additional Declarations The authors declare potential competing interests as follows: Hartwig R. Siebner has received honoraria as speaker from Lundbeck AS, Denmark, as ad-hoc consultant from Lundbeck AS, Denmark, and as editor (Neuroimage Clinical) from Elsevier Publishers (Amsterdam, The Netherlands). He has received royalties as book editor from Springer Publishers (Stuttgart, Germany,), Oxford University Press (Oxford, UK), and from Gyldendal Publishers (Copenhagen, Denmark). The other authors declare no conflicts of interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9650438","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":636706960,"identity":"7c630554-02cc-4811-8e00-8aa4c495b6c6","order_by":0,"name":"Xavier Corominas Teruel","email":"","orcid":"https://orcid.org/0000-0003-2469-2753","institution":"Danish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine, Copenhagen University Hospital - Amager and Hvidovre, Kettegård Allé 30, 2650 Hvidovre, Denmark","correspondingAuthor":false,"prefix":"","firstName":"Xavier","middleName":"Corominas","lastName":"Teruel","suffix":""},{"id":636755700,"identity":"d8bd2ea9-9d56-401a-81ad-44856e77160a","order_by":1,"name":"Björn Sigurðsson","email":"","orcid":"https://orcid.org/0000-0002-7484-7779","institution":"Danish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine, Copenhagen University Hospital - Amager and Hvidovre, Kettegård Allé 30, 2650 Hvidovre, Denmark","correspondingAuthor":false,"prefix":"","firstName":"Björn","middleName":"","lastName":"Sigurðsson","suffix":""},{"id":636755701,"identity":"6627df3c-2760-4fd8-a604-61bf50b99dea","order_by":2,"name":"Samuel Pichardo","email":"","orcid":"https://orcid.org/0000-0002-7919-8587","institution":"Department of Radiology, University of Calgary, Calgary, Alberta, Canada","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Pichardo","suffix":""},{"id":636755702,"identity":"85d8b7a9-1c6c-46c2-b9b1-d3f3527b4bca","order_by":3,"name":"Axel Thielscher","email":"","orcid":"https://orcid.org/0000-0002-4752-5854","institution":"Danish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine, Copenhagen University Hospital - Amager and Hvidovre, Kettegård Allé 30, 2650 Hvidovre, Denmark","correspondingAuthor":false,"prefix":"","firstName":"Axel","middleName":"","lastName":"Thielscher","suffix":""},{"id":636755703,"identity":"f97a9f74-433c-4bca-845c-6a42503449ec","order_by":4,"name":"Hartwig Roman Siebner","email":"","orcid":"https://orcid.org/0000-0002-3756-9431","institution":"Danish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine, Copenhagen University Hospital - Amager and Hvidovre, Kettegård Allé 30, 2650 Hvidovre, Denmark","correspondingAuthor":false,"prefix":"","firstName":"Hartwig","middleName":"Roman","lastName":"Siebner","suffix":""},{"id":636755704,"identity":"4450bdc3-334f-4091-95bd-0b152f5fcc94","order_by":5,"name":"Lasse Christiansen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYBACPghlw9gAptkSCGthg1BppGs5TIoW9uOPP3yoOS+7vf102geGsjQitPDkmEnOOHbbeM6Z3M0zGM7lEOOwHDZm3obbiTMkeDczMLZVEKGF//njz38bzpGiRSLBQJqx4QBMCzEOk3hjJtlzLNl4Bk/uZoaEc0R4n58//fGHHzV2sjPYz25m+FCWTFgLKkggVcMoGAWjYBSMAuwAAPRSNXi89tbKAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9316-1529","institution":"Danish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine, Copenhagen University Hospital - Amager and Hvidovre, Kettegård Allé 30, 2650 Hvidovre, Denmark","correspondingAuthor":true,"prefix":"","firstName":"Lasse","middleName":"","lastName":"Christiansen","suffix":""}],"badges":[],"createdAt":"2026-05-08 07:33:34","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9650438/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9650438/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108948582,"identity":"b26fa9d8-690d-4462-9285-dafa5f4ce6f4","added_by":"auto","created_at":"2026-05-11 06:44:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":914716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial transducer optimization workflow for cerebellar tFUS human delivery. \u003c/strong\u003eAnatomical, and tissue-density related prior information, together with complementary functional or structural imaging, is required as an initial step to inform stimulation planning and to evaluate safety constraints. Individual CT or MR-PETRA/ZTE acquisitions are required to estimate subject-specific skull density and derive acoustic tissue properties. In parallel, high-resolution structural anatomical imaging (e.g., T1- and T2-weighted, SWI, DWI and fMRI) is necessary to identify target structures in subject-native space and neuronavigation. Structural imaging also supports the reconstruction of finite element models (FEM), required for geometry-based numerical simulations and optimization procedures. Ideal acoustic scalp “sweet spots” of transducer position and trajectory can be identified through geometric exploration of brain structures, favoring regions with maximal beam convergence and minimal phase aberration. Quantification of angular difference between skin and skull surface, transducer angular tilt, transducer range, skin elements normally intersecting target nuclei, and skull elements normally intersecting target nuclei together with its skin origins (i.e., backward skull-skin intersection) can be employed to identify geometrical sweet-spots for cerebellar targeting. Identified trajectories must be evaluated simulating the forward propagation of the beam. Finally, mechanical adjustments, steering or phase correction can be employed to further optimize transducer positioning when necessary. T1w T1-weighted imaging; T2w: T2-weighted imaging; SWI: susceptibility-weighted imaging; PETRA: pointwise encoding time reduction with radial acquisition; CT: computed tomography; DWI: diffusion-weighted imaging; fMRI: functional magnetic resonance imaging; MRI: magnetic resonance imaging; FDTD: Finite-Difference Time-Domain.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9650438/v1/2c5e10f066f653e0cd38aa6a.png"},{"id":108948583,"identity":"a663a295-a026-4e56-855d-6f69130820e4","added_by":"auto","created_at":"2026-05-11 06:44:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":738882,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTransducer comparison for cerebellar dentate nucleus targeting. \u003c/strong\u003eMultiple transducer models are currently in use and commercially available, with different ranges, fundamental frequencies and focal volumes. Here, we systematically evaluated the suitability of five commonly used transducers for targeting the anterior-inferior-lateral region of the DN, using an MNI152 template brain model (see Supplementary Material 1 for methodological details).\u003cstrong\u003e (A)\u003c/strong\u003e Simulated acoustic intensity distributions for the five evaluated transducers (CTX250, CTX500, Pulse80 single-element, H317, and DPX500). For visualization consistency, displayed intensities are restricted to the -3 dB focal region (corresponding to a 0.5 fractional threshold of the normalized 0-10 W/cm² intensity range). Individual transducer fields and their spatial overlap are shown relative to the segmented dentate nucleus mask (blue-green color coded), with the intended target location. \u003cstrong\u003e(B) \u003c/strong\u003eExample functional connectivity map of the targeted dentate nucleus subregion obtained from the -3dB to whole brain connectivity map overlap across tested transducers (positive clusters are displayed in red, while negative clusters in blue). Functional connectivity profile further exemplifies the potential of tFUS for the exploration cerebellar networks beyond motor functions and networks, according to the example targeted anterior-inferior-lateral DN subregion on this simulation (functional connectivity maps were obtained from NeuroSynth Database). \u003cstrong\u003e(C) Top panel:\u003c/strong\u003eradar plot summarizing the relative performance of each transducer across four normalized metrics: maximum effective range, -3dB focal volume, -1dB target overlap, and focus-target distance. Metrics were normalized using min-max scaling, with directions adjusted such that higher values (i.e., more peripheral positions on the radar plot) consistently indicate more favorable performance; accordingly, greater range and -1dB overlap, as well as smaller -3dB volume and focus-target distance, were treated as optimal. Each radar axis spans from 0 (worst, plot center) to 1 (best, outer ring). Individual transducer profiles are shown as colored polygons. The dashed black polygon represents an idealized performance pattern, defined as the mean normalized performance of the \u003cem\u003ePareto-optimal\u003c/em\u003e (i.e., optimized pattern) transducers identified within the small, tested set. Larger enclosed areas indicate a more favorable overall trade-off between focality, targeting accuracy, and effective operating range. \u003cstrong\u003eBottom panel:\u003c/strong\u003e bar plots showing the absolute (non-normalized) values of the four evaluated metrics in each transducer.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9650438/v1/e145e20364b67687be698bd5.png"},{"id":108948584,"identity":"9120843b-b2fd-4e54-a094-6e8df7281a1f","added_by":"auto","created_at":"2026-05-11 06:44:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130535,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParametric selection of tFUS and state dependency. \u003c/strong\u003eStimulation parameters, such as pulse length, amplitude, waveform, pulse repetition intervals or pulse train repetition intervals must be defined pre-experimentally according to safety constrains and the desired biological effect. However, internal and external contextual factors can condition neuromodulation response and direction, even in the presence of identical stimulation parameters. Thus, ongoing brain states, pharmacological influences, and temporal alignment of stimulation with task demands or behavioral states needs to be controlled for precise and predictable stimulation effects. Finally, monitoring of ultrasound-induced effects, in particular circuit-level oscillatory dynamics and behavioral outcomes, provides critical feedback for assessing target engagement and enable iterative, subject-specific closed-loop parameter refinement.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9650438/v1/2e33bfc830988d1a0c2e17e8.png"},{"id":108948588,"identity":"9ff59607-ee0a-4d13-9342-84d623ace676","added_by":"auto","created_at":"2026-05-11 06:45:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":506011,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of transducer misalignment on targeting accuracy. \u003c/strong\u003eConsecutive registration of tFUS to MRI enables off-scanner ultrasound stimulation but may compromise targeting precision. Even minor discrepancies between imaging space and physical space can result in suboptimal transducer placement, including small positional offsets, angular misalignment, or inadequate acoustic coupling. To investigate the impact of brain displacement and transducer misalignment, we simulated ultrasound propagation under conditions of transducer displacement. Using the target defined in Figure 2 (anterior-inferior-lateral region of the dentate nucleus; MNI coordinates: x = -16, y = -54, z = -37), we evaluated how misalignment affects stimulation accuracy. Simulations were conducted using the DPX500 transducer and the same MNI template model described previously. Stimulation inaccuracy was assessed by introducing spatial shifts of the transducer along the X and Y axes. The resulting -3 dB volume of the normalized in situ derated pressure is shown to illustrate the effects. \u003cstrong\u003eA.\u003c/strong\u003eThe \u003cstrong\u003etop panel\u003c/strong\u003e shows the optimized beam trajectory, with the transducer centered in the X, Y, and Z planes. \u003cstrong\u003eB.\u003c/strong\u003e The \u003cstrong\u003ecenter-left panel \u003c/strong\u003eillustrates beam propagation with small positional deviations (+1 mm), including shifts along the Y axis, X axis, and combined X+Y displacement relative to the optimized position. The \u003cstrong\u003ecenter-right panel\u003c/strong\u003e shows larger misalignments (+3 mm), again along the Y axis, X axis, and combined X+Y displacement. Note that, according to \u003cem\u003eoffset = height × tan(θ)\u003c/em\u003e, the focal displacement resulting from angular tilt of the transducer can be estimated (assuming free-water conditions). Accordingly, even when the transducer is properly centered, a 1° tilt toward the +X plane will shift the focal spot along the positive X axis as a function of propagation depth. At an approximate depth of 60 mm (i.e., average skin to dentate distance), this results in a lateral displacement of ~1 mm along the +X axis, similar to the simulated pressure field produced by a +1 mm lateral transducer shift. Similarly, a physical displacement of the transducer by +3 mm along the X axis produces an effect equivalent to a ~3° tilt at the centered position, resulting in an approximate 3 mm lateral shift of the focal beam at ~60 mm depth. \u003cstrong\u003eC.\u003c/strong\u003e The \u003cstrong\u003ebottom-left panel\u003c/strong\u003e shows the fractional percentage overlap (ranging from 0 to 1) between the -1 dB focal volume and the dentate nuclei across all simulated conditions. The \u003cstrong\u003ebottom-right panel\u003c/strong\u003e presents the Euclidean distance (mm) between the peak intensity location and the intended MNI target coordinates for each condition. Notably, a 3 mm misalignment critically impairs targeting accuracy, resulting in increased focal displacement and minimal overlap with the target region. Furthermore, skull heterogeneity may introduce subject-specific phase aberrations and refraction effects that are not consistent across individuals. This variability underscores the importance of individualized biophysical simulations and real-time monitoring of target engagement.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9650438/v1/71dfbc131b32b62fd6a3e4e3.png"},{"id":108948586,"identity":"49b4cdd3-ba74-4c27-9892-b060c708a09e","added_by":"auto","created_at":"2026-05-11 06:45:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":354350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElectroencephalography target engagement monitoring during tFUS interventions: General EEG assessment opportunities. \u003c/strong\u003eConcurrent and/or consecutive EEG recordings can be used to monitor the neuromodulatory effects of tFUS in deep cerebellar nuclei. Although direct electrophysiological signatures arising from the deep cerebellar nuclei are difficult to capture with scalp EEG, stimulation-induced network-level responses can provide an indirect yet specific readout of tFUS effects. EEG offers a wide range of unexplored analytical approaches for target engagement monitoring, including phase-based measures (e.g., inter-trial phase coherence or connectivity metrics), frequency-domain oscillatory analyses (e.g., time-frequency power domains, cross-frequency-phase schemes, etc.), or characterization of aperiodic signal components. Finally, although time-, phase-, frequency- and aperiodic features can be analyzed independently, neural communication is inherently non-linear, and interactions across these domains may provide further critical insight into the modulatory effects of ultrasound stimulation. For instance, phase-to-phase coupling within or across different frequency ranges, phase-to-amplitude coupling, or amplitude-to-amplitude coupling (AAC), offer further opportunities for the characterization of tFUS effects, providing a multidimensional framework for characterizing complex neural communication dynamics.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9650438/v1/a408eba62dbde93ba35e1288.png"},{"id":108948587,"identity":"86b33200-8573-49c8-982e-450cfa6fda17","added_by":"auto","created_at":"2026-05-11 06:45:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":456506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEEG target engagement monitoring opportunities during cerebellar tFUS. \u003c/strong\u003eEstablished models of cerebellar motor function and connectivity indicate that the dentate nucleus is functionally linked to the motor cortex via disynaptic pathways through the ventrolateral thalamic nuclei. During voluntary movement and visuomotor tasks, readiness potentials and/or contingent negative variation, beta and mu rhythms constitute well-characterized electrophysiological signatures of local network activity. Modulation of motor dentate pathways can therefore be assessed indirectly at the cortical level, serving as candidate markers of dentate nucleus target engagement. Beyond motor networks, this networked framework further enables monitoring and spectro-spatial mapping of tFUS-induced effects across non-motor distributed cerebellar-cortical systems.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9650438/v1/00737f5a1b9107f84934eb3f.png"},{"id":108948630,"identity":"9fd79d38-5e2d-48e0-8a70-52775bd6439f","added_by":"auto","created_at":"2026-05-11 06:45:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3396796,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9650438/v1/77618391-0a15-4e00-acb0-658b9c05cd0f.pdf"},{"id":108948613,"identity":"8a3b37c2-ff27-45b3-aa08-753c06de0154","added_by":"auto","created_at":"2026-05-11 06:45:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1473927,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9650438/v1/32e073862f2393f997faeb33.docx"}],"financialInterests":"The authors declare potential competing interests as follows: Hartwig R. Siebner has received honoraria as speaker from Lundbeck AS, Denmark, as ad-hoc consultant from Lundbeck AS, Denmark, and as editor (Neuroimage Clinical) from Elsevier Publishers (Amsterdam, The Netherlands). He has received royalties as book editor from Springer Publishers (Stuttgart, Germany,), Oxford University Press (Oxford, UK), and from Gyldendal Publishers (Copenhagen, Denmark). The other authors declare no conflicts of interest.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eNeuromodulation of Deep Cerebellar Nuclei: principles for Focused Ultrasound stimulation and EEG monitoring\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cp\u003e● We present a methodological framework for individualized targeting of cerebellar output nuclei.\u003c/p\u003e\u003cp\u003e● Targeting optimization integrates spatial, temporal, and state-dependent parameters.\u003c/p\u003e\u003cp\u003e● EEG provides a scalable approach to assess deep-target engagement.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eBrain stimulation has been employed for more than three decades to causally probe and modulate cerebellar function in humans (Grimaldi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Miterko et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Non-invasive stimulation approaches targeting the cerebellar cortex have contributed greatly to the understanding of cerebello-cortical interactions (Caligiore et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, the limited depth penetration of conventional transcranial magnetic and electrical stimulation impedes the possibility of modulating deep cerebellar targets directly. Consequently, causal evidence regarding the functional contributions of the deep cerebellar nuclei (DCN), along with the therapeutic potential of non-invasive DCN neuromodulation, remains limited. In this manuscript, we position Low-Intensity Transcranial Focused Ultrasound Stimulation as a way to directly target cerebellar nuclei for research and therapeutic purposes. We aim to provide a theoretically grounded, practical framework for successfully stimulating deep cerebellar targets with tFUS. For an in-depth introduction of tFUS mechanisms of action and safety considerations, the reader is referred to the extensive body of existing literature (Aubry et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Blackmore et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Caffaratti et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Darmani et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jerusalem et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Martin et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Murphy et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pasquinelli et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Plaksin et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rabut et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sarica et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sorum et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yoo et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrent neuromodulatory strategies fall short of scalable, precise targeting of DCN\u003c/p\u003e \u003cp\u003eNon-invasive causal interrogation of human cerebellar function has been limited to transcranial magnetic and electrical stimulation of the cerebellar cortex (Miterko et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In parallel, deep brain stimulation (DBS) delivered via implanted electrodes has provided insights into deep cerebellar function (especially the dentate nucleus, DN) and alluded to the clinical potential of deep cerebellar stimulation in the management of movement disorders in a limited number of clinical cases (see Supplementary Table\u0026nbsp;1 for a comprehensive list of all cerebellar-DBS reported studies). Collectively, experimental and clinical results have stimulated increasing interest within the clinical neurology and neuroscience communities, positioning deep cerebellar neuromodulation not only as a promising therapeutic strategy for movement disorders (Baker et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tai \u0026amp; Tseng, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wessel \u0026amp; Hummel, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), but also as an opportunity to advance the functional and anatomical understanding of cerebello-cortical and cerebello-pontine networks. However, current neuromodulatory approaches targeting the DCN suffer from a lack of depth penetrance and precision, which constrains their widespread use. The rapid decay of the magnetic field limits Transcranial Magnetic Stimulation (TMS) to the superficial layers of the cerebellar cortex (Siebner et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and the heavily convoluted cerebellar cortex prevents lobule-specific targeting (\u0026Ccedil;an et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rohira et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, tES also faces a fundamental depth-focality trade-off: targeting deeper brain structures calls for higher intensities, which in turn produce greater current spread (Guiomar et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Opitz et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Saturnino et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Saturnino, Siebner, et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Van Hoornweder et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Accordingly, these methods cannot provide selective lobular or direct cerebellar nuclear stimulation. Direct nuclear stimulation via deep brain stimulation (DBS) is currently restricted to clinical populations and remains a relatively novel neurosurgical intervention, with procedure-related risks and secondary effects that may not yet be fully documented (see Supplementary Table\u0026nbsp;1). Consequently, the functional roles of the deep cerebellar nuclei and their therapeutic potential as neuromodulation targets remain insufficiently characterized, underscoring the urgent need to develop novel, effective, non-invasive approaches for deep cerebellar stimulation.\u003c/p\u003e \u003cp\u003eLow-Intensity Transcranial Focused Ultrasound Stimulation can target deep structures\u003c/p\u003e \u003cp\u003eLow-intensity Transcranial Focused Ultrasound Stimulation (tFUS) has emerged as a promising non-invasive neuromodulation modality. By concentrating acoustic energy at focal points located centimeters away from the transducer radiating surface, tFUS permits focal modulation of neural activity in deep brain regions. tFUS generate mechanical forces that impact neuronal signalling via mechanical, with sign and magnitude defined by the temporal structure of the sonication regimes (Caffaratti et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Murphy et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Plaksin et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the rapidly growing interest in tFUS for experimental and clinical neuroscience (Loh et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Pellow et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sarica et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), substantial methodological challenges have thus far limited its application to deep cerebellar nuclei, leaving these structures largely unexplored. Here, we outline and address these challenges and present a comprehensive experimental and methodological framework for implementing personalized tFUS. The presented planning and experimental framework will enable the user to accurately target deep cerebellar nuclei while monitoring neural responses using electroencephalography (EEG). Beyond neuromodulation (Rabut et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the proposed framework provides a scalable, individualized foundational methodological resource for cerebellar ultrasound-based interventions (e.g., blood-brain barrier opening) while addressing key sources of uncertainty inherent in out-of-MRI ultrasound applications.\u003c/p\u003e \u003cp\u003eGeneral methodological considerations\u003c/p\u003e \u003cp\u003eSimilar to other neuromodulation techniques, precise modulation of deep neural targets with tFUS requires control over spatial (target location), temporal (stimulation pattern), and contextual (underlying network state) domains (Bergmann et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Siddiqi et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Siebner et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Moreover, like any other neuromodulation technique, the sequential or concurrent combination of tFUS with neuroimaging and/or electrophysiology enables precise spatiotemporal targeting, verification of target engagement, and enables causal coupling of brain and behavior. However, cerebellar applications introduce specific anatomical, physiological, and functional constraints that may require deviations from the general neuromodulatory principles and motivate tailored methodological strategies. In the following sections, we therefore explore specific considerations for cerebellar tFUS, highlighting both general considerations and domain-specific challenges.\u003c/p\u003e"},{"header":"Spatial optimization of transducer placement","content":"\u003cp\u003eThe spatial localization and orientation of the ultrasound transducer radiating surface relative to the human scalp critically determine the propagation path of the acoustic beam and the location of the resulting focal spot within the brain (Aubry et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). To target deep brain structures, and in particular deep cerebellar nuclei, it is therefore pertinent to accommodate inter-individual variability in brain and skull morphology via pre-experimental planning based on individual anatomical imaging.\u003c/p\u003e \u003cp\u003eImaging for cerebellar nuclei segmentation and pre-experimental planning\u003c/p\u003e \u003cp\u003eTo effectively plan ultrasound neuromodulation interventions in out-of-MR settings a minimum but necessary set of subject-specific neuroanatomical information must be acquired. High-resolution structural MRI is essential, where T1- and T2-weighted imaging (T1w and T2w) provide the anatomical detail required for accurate localization of deep cerebellar nuclei and to generate subject-specific computational models of the participant\u0026rsquo;s head and brain (Nielsen et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Puonti et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for geometrically based heuristic trajectory planning (Atkinson-Clement \u0026amp; Kaiser, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lueckel et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition, given the high iron content of cerebellar nuclei, susceptibility-weighted imaging (SWI) or quantitative susceptibility mapping (QSM) offer enhanced contrast that improves delineation of deep nuclear boundaries at the individual level (Maderwald et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOptimization of acoustic beam trajectories for deep cerebellar neuromodulation\u003c/p\u003e \u003cp\u003eTrajectory planning for ultrasound neuromodulation requires explicit specification of the acoustic beam orientation, focal depth, and expected energy losses arising from variations in tissue density, composition, and geometry. While geometrically based heuristic approaches(Atkinson-Clement \u0026amp; Kaiser, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lueckel et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) can be used to identify anatomically feasible scalp locations within the transducer range and approximate beam trajectories intersecting with the target area, rigorous biophysical modeling(Pichardo, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Treeby \u0026amp; Cox, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) is essential to accurately characterize acoustic wave propagation, focal pressure, attenuation, refraction, phase aberration, and thermal dose across the heterogeneous cranial occipital bone and brain tissues (Aubry et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While computed tomography (CT) provides gold-standard estimates of skull and tissue density (Aubry et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Montanaro et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), its use in healthy participants is often constrained by concerns related to ionizing radiation exposure. To address this limitation, MRI-based alternatives have been developed to approximate CT-derived density information. In particular, pointwise encoding time reduction with radial acquisition (PETRA), or Zero Echo TIME (ZTE, or similar ultra-short time echo imaging) MRI sequences can be used to generate synthetic pseudo-CT images or to predict tissue density and acoustic properties (Aubry et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Leung et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Miller et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Miscouridou et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Montanaro et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yaakub et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Importantly, while refraction and attenuation are unavoidable, especially when accessing cerebellar nuclei through posterior or occipital acoustic windows with complex curvature, these effects can be predicted with simulations. Therefore, they do not pose a fundamental limitation to neuromodulation if they are appropriately compensated for during planning. Likewise, the direction of the trajectory intersecting the skull surface with a normal incidence can be identified and projected back to scalp entry points with reduced bone intersection angulation and consequent skull refraction. While this approach may improve acoustic transmission through the skull, it may increase transducer angulation at the scalp surface, which challenges transducer coupling and stability. Finally, trajectory planning should also account for additional anatomical constraints within the posterior fossa and occipital regions. Major dural venous sinuses, particularly the transverse and sigmoid sinuses, should be avoided or carefully considered during acoustic planning due to their vascular nature and potential effects on ultrasound propagation. In addition, acoustic transmission is influenced by regional variations in bone thickness and density. Specifically, the mastoid and posterolateral temporal bone regions are substantially thicker and denser than adjacent occipital bone (Zhang et al., 2023), leading to increased attenuation of the ultrasound beam when transducers are positioned closer to the temporal regions. Consequently, optimal trajectory planning for targeting the deep cerebellar nuclei involves balancing acoustic efficiency, geometric feasibility, and coupling constraints (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubject- and site-specific acoustic lenses, holographic phase plates or phased-array systems provide another way of steering tFUS to the deep cerebellar nuclei by compensating skull-induced losses, which render a broader range of scalp locations viable (Attali et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Maimbourg et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This approach offers a substantial advantage over purely geometric trajectory planning, as it enables maximal coupling and phase alignment of the ultrasound wavefront at the target and maximizes focal pressure deposition despite cranial heterogeneity. Moreover, because acoustic lenses can impose precise phase delays on the propagating wave, they enable advanced pressure field shaping, including adjustment of focal size, multiple simultaneous focal spots, or delivery of spatiotemporally structured traveling pressure patterns (Cengiz et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sayed Ahmed \u0026amp; Shahab, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such ultrasound pressure patterns can be achieved only through acoustic lenses or multi-element phased array systems, thereby opening a broad and largely unexplored design space for cerebellar neuromodulation that extends beyond conventional single-focus stimulation paradigms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTransducer constraints\u003c/p\u003e \u003cp\u003eA variety of commercially available and custom-built ultrasound transducer systems are currently in use, each with distinct characteristics and architectures. However, not all systems may be suitable for targeting deep cerebellar nuclei, and a set of minimal common requirements is likely necessary to enable effective and reliable stimulation of deep structures.\u003c/p\u003e \u003cp\u003eA first consideration for cerebellar nuclei targeting relies on the effective \u003cem\u003epenetration range\u003c/em\u003e (i.e., focal depth or length) of the ultrasound transducer, which is, at the technical level, primarily determined by the aperture diameter and the radius of curvature of the radiating surface (Murphy et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As the cerebellar nuclei are typically located approximately 45\u0026ndash;65 mm beneath the skin (Schmahmann et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), theoretical focal depths of at least\u0026thinsp;~\u0026thinsp;55 mm may be required to reach these targets under ideal propagation free-water conditions. However, in practice, ultrasound propagation towards the cerebellum is substantially impeded by pronounced skull inhomogeneities, spatial variations in skull thickness and density along with angular mismatch between the curved occipital bone and the overlying scalp. The resulting large, highly non-perpendicular ultrasound incidence angles can exacerbate refraction and phase aberration at the skull, leading to focal distortion and reduced acoustic energy transmission toward the cerebellar nuclei. Consequently, focal depths exceeding the nominal anatomical target depth may be necessary in most cases to compensate for skull-induced propagation losses and to ensure effective energy delivery to the cerebellar nuclei.\u003c/p\u003e \u003cp\u003eClosely related to penetration range, the focal volume determines the volume of tissue exposed to effective acoustic energy. The focal volume is commonly defined by the full width at half maximum (FWHM) and typically characterized by the \u0026minus;\u0026thinsp;6 dB and/or -3 dB intensity contours (Martin et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Depending on the experimental objectives, the focal volume may be optimized to be either larger or more spatially confined. For instance, when the goal is to engage the entire dentate nucleus, transducers producing larger focal volumes at the target depth may be advantageous. Conversely, when selectively targeting smaller cerebellar nuclei or specific subregions within the dentate nucleus, substantially smaller focal volumes may be preferable to enhance spatial precision and minimize off-target effects. Beyond transducer geometry, the fundamental frequency must also be considered in targeting the cerebellar nuclei, as it is tightly linked to the volume of tissue exposed. At higher frequencies (\u0026gt;\u0026thinsp;700 kHz), skull-induced attenuation and phase aberrations may increase substantially, leading to focal distortion and reduced intracranial pressure (Attali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, low frequencies (\u0026lt;\u0026thinsp;200 kHz) can improve transmission through the skull, but increase the ultrasound wavelength, producing enlarged focal volumes.\u003c/p\u003e \u003cp\u003eAlong the same lines, \u003cem\u003etransducer architecture\u003c/em\u003e (e.g., single-element, multielement, phased-array designs) must be considered. While annular array and multielement phased-array transducers increase the complexity of driving electronics and cost, they enable electronic dynamic focusing and beam steering of focal depth within a single system, accommodating interindividual variability in human neuroanatomy. Conversely, single-element transducers with fixed focal depths may reach the cerebellar nuclei in some individuals but cannot reliably be steered, requiring, in most cases, custom-fabricated holographic acoustic lenses for precise individualized targeting.\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\u003eTechnical specifications of the evaluated transducers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransducer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFocal length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOperating Frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDevice architecture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDiameter aperture\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCTX250\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteerable, up to 60 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250 Khz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2-elements annular array transducer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCTX500\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteerable, up to 63 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500 Khz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4-elements annular array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePulse\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed focus, 80 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e650 Khz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003esingle-element transducer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH317\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteerable, up to 133 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250 Khz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128-elements phased array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDPX500\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteerable, up to 150 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500 Khz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4-elements annular array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 mm\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\u003eTo further guide transducer selection for deep cerebellar neuromodulation, we conducted an in-silico comparison (see Supplementary Materials. 1) of several commercially available transducers across different system configurations. Specifically, our objective was to identify transducer designs capable of accurately and precisely targeting the anterior-inferior-lateral region of the dentate (\u003cem\u003eMNI coordinates: x = -16, y = -54, z = -37\u003c/em\u003e), while minimizing off-target exposure to surrounding structures (see Supplementary Materials 1 for methodological details). Five commonly used commercially ultrasound transducers were evaluated (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details), including the following: (1) CTX250 (SonicConcepts, USA); (2) CTX500 (SonicConcepts, USA); (3) Pulse 80mm (BrainSonix, USA); (4) H317 (SonicConcepts, USA); and (5) DPX500 (SonicConcepts, USA). All tested transducers except the CTX250 exhibited comparable \u0026minus;\u0026thinsp;1dB target overlap with the DN region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Despite this general ability to reach the DN, differences were observed across the remaining transducers in the distance between the spatial peak location and the intended MNI target coordinates. Specifically, the multielement design and electronic steering capabilities of the DPX500 and H315 allowed for focus steering that minimized the distance between the spatial peak and the target coordinates. In contrast, although the CTX500 was able to reach the DN, its limited depth range (up to 63.2mm) restricted its ability to effectively target the anterior portion of the nuclei. Similarly, the single-element Pulse80mm transducer could reach the mid-anterior region of the DN with maximal spatial precision; however, in practical setting, its lack of electronic steering may impose experimental limitations in certain participants. Regarding focal volume characteristics, the DPX500, CTX500, and Pulse80mm transducers exhibited smaller\u0026thinsp;\u0026minus;\u0026thinsp;3 dB volumes compared to the H315 and CTX250. Thus, our in-silico analyses suggest that DPX500-like and H315-like transducers may offer the most balanced performance for deep cerebellar neuromodulation, combining high focality with steering capability.\u003c/p\u003e "},{"header":"Parametric selection of ultrasound pulsing regimes and state-dependency","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cp\u003etFUS capacity to perturb neural activity or induce sustained neuromodulatory effects is highly influenced by the stimulation regime. tFUS delivers high-frequency mechanical energy as continuous acoustic wave, repeated sequentially forming pulse trains with a specific pulse repetition interval or frequency (PRF). This pulsed delivery is essential for limiting thermal accumulation within neural tissue and enabling partial recovery or hyperpolarization of membrane potentials between successive pulses. For neuromodulatory applications, pulse trains are further organized into repeated stimulation regimes defined by the pulse train repetition interval or frequency (Martin et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Murphy et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This hierarchical organization, spanning from individual pulses to pulse trains and repeated pulse-train regimes, enables precise control over stimulation timing and supports a wide range of neuromodulatory patterns.\u003c/p\u003e \u003cp\u003eThe modulatory direction (i.e., excitatory, inhibitory) of both acute and longer lasting neural effects has been proposed to depend on an interaction between the acoustic pressure and duty cycle (defined as the proportion of active sonication within each pulse repetition interval) (Caffaratti et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Cox et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Murphy et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Plaksin et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Current evidence suggests that low duty cycles (\u0026lt;\u0026thinsp;10% DC) associated with low PRFs (\u0026lt;\u0026thinsp;20 Hz) predominantly inhibit neural activity when delivered at relatively low acoustic pressures (e.g., ISPPA\u0026thinsp;~\u0026thinsp;10 W/cm\u0026sup2;). On the other hand, higher duty cycles (\u0026gt;\u0026thinsp;40% DC) preferentially cause neuronal excitation. The exact mechanisms linking acoustic pressure and DC to neural effects remain poorly understood and are likely not uniform across neural circuits. A growing body of work indicates that ultrasound stimulation can produce cell-type dependent responses (Lemaire et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Murphy et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), suggesting to reflect differences in neuronal morphology, membrane mechanics, ion-channel composition, myelin concentration, and microcircuit cytoarchitecture. Despite substantial progress in characterizing ultrasound neuromodulation across cortical and subcortical targets, the extent to which the existing pressure-frequency dyad principles generalize to the cerebellar cortex and deep cerebellar nuclei in remains unexplored. In fact, to date, no human studies have directly targeted deep cerebellar nuclei with neuromodulatory ultrasound, which underscores a gap in the existing literature and highlights the need for further systematic parametric investigations. Importantly, the availability of internationally defined safety and practice guidelines, such as those proposed by the International Transcranial Ultrasonic Stimulation Safety and Standards (ITRUSST) (Aubry et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), provides a robust framework for the controlled, systematic, and safe exploration of these parameters in human cerebellar research.\u003c/p\u003e \u003cp\u003eMoreover, like for other neuromodulation techniques, temporal timing and brain-state may be critical, shaping the physiological effects of tFUS (Bradley et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ly et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Salehinejad et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Silvanto \u0026amp; Pascual-Leone, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zrenner et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Brief pulses and pulse repetition may interact with neurotransmitter gating, spike timing and ongoing oscillatory processes, modulating neuronal excitability in a phase-dependent manner (Herrmann et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Krause et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lakatos et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; S. Y. Lee et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Thut et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vosskuhl et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Beyond the immediate neural excitability state, longer temporal scales must also be considered (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Repeated exposure to stimulation can interact with ongoing metaplasticity and homeostatic regulation process (Ding et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Turrigiano, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Turrigiano \u0026amp; Nelson, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). At these timescales, baseline brain-states are no longer static, and prior stimulation history may alter excitability, sensitivity to mechanical perturbation, or the directionality of neuromodulatory effects. Consequently, identical ultrasound quantitative parameters (e.g., repetition frequency, pressure) delivered on different days may produce qualitatively different outcomes altering dose-response relationship. In addition, pharmacological agents or physiological states (arousal, vigilance, sleep-wake phase, circadian rhythms, or metabolic status) affecting neurotransmitter balance, synaptic history, or ion concentrations can modify baseline excitability and alter, either increasing or decreasing, ultrasound-induced immediate and lasting effects. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, online monitoring of acute neural and/or behavioral effects provides a means to address this variability via state-dependent or even closed-loop stimulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Neuronavigation co-registration and coupling considerations","content":"\u003cp\u003eDespite extensive and careful pre-experimental planning, tFUS may still fail to engage deep cerebellar targets. While concurrent neuronavigation enables precise control of transducer positioning relative to the individual subject\u0026rsquo;s head, three primary sources of imprecision can contribute to response variability: (I) brain positional deformation, (II) inconsistencies in transducer-scalp coupling, and (III) co-registration errors for neuronavigation and small imprecision by the human operator.\u003c/p\u003e \u003cp\u003eMR Imaging induce shifts in brain positioning: implications for spatial targeting\u003c/p\u003e \u003cp\u003eWhile out-of-MR tFUS application favours scalability and clinical translation, it introduces challenges related to targeting accuracy. Structural MRI is typically acquired with participants in supine position, whereas experimental ultrasound stimulation may be performed under different postural conditions. The brain is not rigidly fixed within the cranial vault; gravitational forces can induce subtle but measurable posture-dependent displacements of brain tissue, including posterior shifts in the supine position (Schmid \u0026amp; Crone, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Schnaudigel et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yokoyama et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zappal\u0026aacute; et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For cerebellar regions, gravitational displacement of \u0026gt;\u0026thinsp;1mm along the anterior-posterior axis have been reported when comparing MRI images acquired in prone versus supine positions (Zappal\u0026aacute; et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, MRI hardware, such as head coils and participant support systems, which can exert mechanical pressure on the neck during MRI acquisition. Such compression may deform occipital soft tissues or alter cervical alignment, influencing computational anatomical reconstructions (i.e., brain segmentations and FEM), affecting in silico transducer placement and acoustic modeling. To mitigate posture- and hardware-related biases, consistent participant positioning across MRI scanning and subsequent stimulation sessions may mitigate targeting inaccuracies.\u003c/p\u003e \u003cp\u003eCoupling introduces a source of variability that cannot readily be quantified\u003c/p\u003e \u003cp\u003eDuring ultrasound interventions, the transducer(s) needs to be coupled to the scalp. This is done by placing acoustic coupling media between the radiating surface of the transducer and the scalp to establish an effective physical transmission pathway. Common coupling solutions include hydrogel pads, water-filled balloons, and/or liquid ultrasound gels. Unfortunately, the presence of air bubbles within the coupling can result in substantial acoustic pressure/intensity attenuation and phase aberration, which is aggravated by the presence of hair. The extent of air-induced attenuation and aberration are unknown, and no standardized methodology currently exists to quantitatively assess coupling quality in practical experimental setups. Several strategies have been proposed to minimize trapped air, including centrifugation of ultrasound gel, meticulous hair preparation (or removal) combined with palpation-based inspection of the gelled surface (Murphy et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, no current method ensures nor quantify the coupling quality between interspersed gel pads and the skin. Study designs, consequently, need to account for some expectancy variability, with the accumulated literature guiding the calculation of adequate sample sizes.\u003c/p\u003e \u003cp\u003eNeuronavigation co-registration as an additional source of error\u003c/p\u003e \u003cp\u003eCo-registration procedures typically depend on anatomical landmarks to align physical-world and image MRI spaces, and even small registration inaccuracies can propagate into meaningful deviations in tFUS accuracy. Given the small focal volume and high spatial specificity of tFUS (transducer- and depth-dependent), minor translational errors or slight angular misalignments at the transducer surface can result in off-target acoustic trajectories, particularly when stimulating deep brain structures. This is further aggregated by small variation in transducer to head position whether it is handheld, mechanically stabilized or controlled by a robotized arm. Summed, a 2mm co-registration error and 1mm imprecision in transducer placement result in a detrimental shift of focal accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These considerations highlight the need for rigorous co-registration procedures and continuous monitoring of transducer position and orientation throughout stimulation sessions.\u003c/p\u003e \u003cp\u003eWhile concurrent tFUS with MRI enables assessment of spatial target engagement and acoustic attenuation, for example, through acoustic radiation force imaging (MR-ARFI) or thermography, uncertainties in both spatial localization and delivered intensity arising from co-registration errors and transducer-scalp coupling remain difficult to resolve in out-of-scanner tFUS applications. Within this context, electrophysiological readouts, such as electroencephalography, offer a unique means to verify target engagement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Informing tFUS with EEG","content":"\u003cp\u003eThe concurrent/consecutive combination of tFUS with EEG is emerging as a novel method for monitoring the effects of ultrasound stimulation, both online and offline. Concurrent EEG serves a dual purpose; it can inform on the state of the targeted network and provide a read-out of network effects. While this opens the possibility of state-triggered open-loop or even closed-loop tFUS, this application remains in the horizon for now. Instead, EEG may provide low-cost and scalable markers of target engagement.\u003c/p\u003e \u003cp\u003eAcross brain targets, tFUS-EEG has been employed in a limited number of studies (see Supplementary Table\u0026nbsp;2 for a comprehensive list of all tFUS-EEG reported studies to date), most of which have focused on the modulatory effects on somatosensory evoked potentials or visual evoked potentials. Both somatosensory and visually evoked potentials have been demonstrated to be sensitive to tFUS modulation of thalamic and cortical areas (Butler et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dunsford et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kosnoff et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2026\u003c/span\u003e; W. Lee et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Legon et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mueller et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Nandi et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Here, tFUS is thought to interrupt extrinsically triggered neural synchronization that mediates highly coordinated firing located at the site of stimulation or downstream in the network. Whether sensory evoked potentials or other EEG markers are best suited to detect acute and/or neural after-effects DN targeted tFUS is yet to be investigated.\u003c/p\u003e \u003cp\u003eLow-intensity focused ultrasound is generally considered a subthreshold technique (Caffaratti et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) that do not elicit action potentials on a pulse-by-pulse basis. This separate tFUS from TMS, where the simultaneous suprathreshold excitation of large population of pyramidal neurons generating a strong enough signal for scalp EEG measure time- and phase-locked evoked responses (Hernandez-Pavon et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ilmoniemi \u0026amp; Kičić, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Thut et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tremblay et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Within the safety limits for neuromodulatory tFUS, it is unlikely that TEP-like phase locked responses can be evoked. Rather, tFUS neuromodulatory effects are theoretically consistent with sub-threshold modulation of ongoing neural dynamics, comparable to those proposed for other subthreshold stimulation modalities (e.g., tDCS, tACS). Therefore, alongside subtle changes in evoked potentials, modulation of resting or task-based periodic or aperiodic cortical activity may present markers that are sensitive to tFUS modulation of deep cerebellar nuclei (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAssessing Dentate Nucleus Modulation by tFUS Through EEG Biomarkers\u003c/p\u003e \u003cp\u003eThe dentate nucleus (DN) is the largest of the deep cerebellar nuclei and the most accessible to tFUS stimulation due to its spatial location (Schmahmann et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). It receives extensive white-matter projections from a broad range of cerebellar cortical regions (Nettekoven et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Stoodley et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and is subdivided based on afferent and efferent connectivity profile (Strick et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Due to the dentato-thalamo-cortical pathway, distant cortical electrophysiological signatures can, in principle, inform on changes DN activity.\u003c/p\u003e \u003cp\u003eThe DN is composed of local inhibitory interneurons alongside large glutamatergic projection neurons that constitute its principal output (Benarroch, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These neurons are the main target of inhibitory Purkinje cells projecting from cerebellar cortex. Within motor networks, these excitatory neurons project predominantly to the ventrolateral motor thalamus and the red nucleus (Sakai et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). At the thalamic level, motor related inputs from DN are integrated with signals from basal ganglia circuits before being relayed onwards to pre- and primary motor cortices. An increased output from activity in the motor division of DN can therefore be expected to drive motor related thalamic dynamics (Halassa \u0026amp; Sherman, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sherman \u0026amp; Guillery, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Shine, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and at the cortical level, induce characteristic movement related desynchronization (i.e., readiness potentials, contingent negative variation) together with pericentral alpha (8\u0026ndash;12 Hz) and beta (13\u0026ndash;30 Hz) oscillatory desynchronization (Kilavik et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReversely, acute or longer lasting inhibition of DN output should increase pericentral synchronization (Schurger et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) although this effect is likely to depend on the motor state. The most consistent acute tFUS effects in humans appear to be inhibitory (Caffaratti et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Supplementary Table\u0026nbsp;2), which renders online tFUS better suited to interfere with an ongoing process than to directly augment or entrain network activity. Given the baseline tonic excitation from DN to thalamus and onwards, the dentato-thalamo-cortical pathway present an ideal neuromodulatory target to change movement related cortical activity assessable with scalp EEG (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). An interference with DN signaling may also affect motor cortical responsiveness to TMS or intracranial stimulation (Xin Li et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While motor evoked electromyographic (EMG) potentials provide an experimentally simple approach to indirectly investigate such modulation in motor cortical excitability, it is confounded by other inputs onto spinal motoneurons and restricted to the recorded muscle(s). Thus, MEPs both reflect the cortical impact of DN stimulation as well as potential effects mediated via the red nucleus. In contrary TMS-EEG offers the possible of assessing muscle-independent effects on motor cortical and cortico-subcortical networks, indexed as changes in immediate (Beck et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nuyts et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and early TMS-evoked EEG potentials (Beck et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Paired-pulse TMS-EEG might even provide the possibility of studying how DN modulation affects inhibitory and excitatory networks within M1 (Christiansen, Song et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConcurrent tFUS-EEG: general limitations and considerations\u003c/p\u003e \u003cp\u003eTo ensure efficient ultrasound transmission, a coupling medium must be placed between the transducer and the scalp. This requirement prevents the placement of EEG electrodes directly beneath the ultrasound transducer, thereby reducing EEG sensitivity to activity originating from areas under the transducer brain region and limiting the feasibility of direct local monitoring, especially in more superficial or cortical stimulation targets. Consequently, EEG is better suited for capturing distributed, network-level effects. Moreover, tFUS-EEG can also be sensitive to physiological and non-physiologically artifacts (Braun et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite growing interest in the method, standardized protocols for data acquisition and signal preprocessing recommendations are still lacking. The frequency, spectral or topographical characteristics and origins of potential artifacts remain poorly understood and a detailed characterization is required to prevent the inaccurate interpretations of artifact-related effects as genuine neural responses.\u003c/p\u003e \u003cp\u003etFUS-EEG setups can lead to electrical coupling between the EEG and ultrasonic driver system, generating spurious activity at the pulse repetition frequency that may be mistakenly interpreted as genuine neural entrainment (see Supplementary Materials 2 for details). On the same line, tFUS can induce peripheral somatosensory stimulation (Kop et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and/or auditory (air and bone conducted) stimulation (Braun et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kop et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although these artifacts have not yet been thoroughly characterized in tFUS-EEG human studies, it has been demonstrated that peripheral multisensory evoked responses to single-pulse TMS can give rise to precentral and frontocentral EEG potentials at relatively late latencies (typically\u0026thinsp;\u0026gt;\u0026thinsp;60 ms post-stimulation) (Conde et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rocchi et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In ultrasound, the high carrier frequencies used make it unlikely that artifacts originate from the ultrasound frequency itself. Instead, the pulse repetition structure is the most probable source of somatosensory and auditory responses. Furthermore, mechanical vibrations of skin or brain tissue may introduce motion artifacts mimicking neural responses (Nguyen et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), particularly in EEG electrodes located near the transducer. Consequently, careful inspection, control, and mitigation of these artifacts, whenever present, should be a priority in tFUS-EEG recordings. Recommended strategies may include implementation of appropriate control stimulation conditions (i.e., defocusing protocols, active controls), auditory and, when feasible, somatosensory masking procedures (e.g., topical anesthetics or somatosensory saturation), and/or post hoc signal processing approaches capable of identifying and attenuating artifact-related components.\u003c/p\u003e"},{"header":"Perspectives","content":"\u003cp\u003eConverging evidence indicates that the cerebellum supports rapid and efficient information processing, achieved through feedback-informed forward models, across sensorimotor and cognitive domains. Despite causal insights into the function of the deep cerebellar nuclei, as well as lobule-specific function, remain largely unexplored due to the limitations of current non-invasive neurostimulation techniques. The development of novel neuromodulation approaches, such as focused ultrasound, enables selective targeting of deep cerebellar nuclei in both experimental and clinical populations. In this work, we have presented a practical methodological framework and outlined the considerations for successful deep cerebellar targeting. This framework facilitates future investigations into the mechanisms of action of ultrasound within cerebellar regions, including electrophysiological target engagement monitoring, adaptive network-level neuromodulation, or brain-state dependent modulation. Focused ultrasound combined with EEG monitoring enables causal mapping of cerebello-cortical spectral networks, linking network-level electrophysiological signatures to the precise anatomy of deep nuclei. In parallel, symptom-specific responses to ultrasound (e.g., tremor modulation) can be localized within anatomical space, supporting the development of brain atlases that link spectro-spatial and temporal network dynamics to functionally relevant \u0026ldquo;sweet spots\u0026rdquo; for personalized therapeutic ultrasound targeting.\u003c/p\u003e \u003cp\u003eThe presented framework is not limited to the motor system. Beyond its established role in motor control, the cerebellum is increasingly recognized as contributing to a wide range of non-motor functions (Nettekoven et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Strick et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For example, stimulation of non-motor subregions of the dentate nucleus (DN) is expected to elicit cortical responses distinguishable from pure-motor pathways consistent with their preferential connectivity with higher-order cerebellar regions (e.g., Crus I and Crus II) (Nettekoven et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Likewise, other deep cerebellar nuclei, such as the fastigial nucleus, exhibit markedly different connectivity profiles, projecting to brainstem autonomic centers, vestibular and reticular nuclei, as well as medial and intralaminar thalamic nuclei, thereby influencing widespread cortical and subcortical systems involved in arousal, vigilance, autonomic regulation, and oculomotor and postural control (Carey, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, tFUS poses for the first time a novel tool to characterize unexplored deep cerebellar functions and their spectro-spatial maps. Collectively, this work establishes, for the first time, the potential and consideration of focused ultrasound as a non-invasive approach for stimulating deep cerebellar nuclei and highlights the opportunities and challenges of concurrent/consecutive EEG to monitor tFUS-induced effects. Ultimately, it lays the groundwork for broader experimental and clinical adoption of cerebellar focused ultrasound in movement disorders and beyond.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHartwig R. Siebner has received honoraria as speaker from Lundbeck AS, Denmark, as ad-hoc consultant from Lundbeck AS, Denmark, and as editor (Neuroimage Clinical) from Elsevier Publishers (Amsterdam, The Netherlands). He has received royalties as book editor from Springer Publishers (Stuttgart, Germany,), Oxford University Press (Oxford, UK), and from Gyldendal Publishers (Copenhagen, Denmark). The other authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp id=\"_Toc227447447\"\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXavier Corominas Teruel: Writing - review \u0026amp; editing, Writing - original draft, Visualization, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Axel Thielscher: Writing - review \u0026amp; editing, Methodology, Conceptualization. Hartwig Roman Siebner: Writing - review \u0026amp; editing, Resources, Conceptualization. Lasse Christiansen: Writing - review \u0026amp; editing, Writing - original draft, Methodology, Resources, Funding acquisition, Investigation, Conceptualization.\u003c/p\u003e\n\u003cp id=\"_Toc227447448\"\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a \u0026lsquo;Experiment grant\u0026rsquo; (grant no. R436-2023-1137) from The Lundbeck Foundation awarded to Lasse Christiansen, a Grand Solutions grant \u0026ldquo;Precision Brain-Circuit Therapy - Precision-BCT\u0026rdquo; from Innovation Fund Denmark to Hartwig R. Siebner (grant no. 9068-00025B) and a Collaborative Project grant \u0026ldquo;ADAptive and Precise Targeting of cortex-basal ganglia circuits in Parkinson\u0026rsquo;s Disease - ADAPT-PD\u0026rdquo; from The Lundbeck Foundation to Hartwig R. Siebner (grant no. R336-2020-1035). Axel Thielscher was supported by The Lundbeck Foundation (grant no. R313-2019-622).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAtkinson-Clement C, Kaiser M (2025) Optimizing Transcranial Focused Ultrasound Stimulation: An Open-source Tool for Precise Targeting. Neuromodulation 28(1):185\u0026ndash;187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neurom.2024.06.496\u003c/span\u003e\u003cspan address=\"10.1016/j.neurom.2024.06.496\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAttali D, Tiennot T, Manuel TJ, Daniel M, Houdouin A, Annic P, Dizeux A, Haroche A, Dadi G, Henensal A, Moyal M, Le Berre A, Paolillo C, Charron S, Debacker C, Lui M, Lekcir S, Mancusi R, Gallarda T, Plaze M (2025) Deep transcranial ultrasound stimulation using personalized acoustic metamaterials improves treatment-resistant depression in humans. 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Brain Stimulation: Basic Translational Clin Res Neuromodulation 11(2):374\u0026ndash;389. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.brs.2017.11.016\u003c/span\u003e\u003cspan address=\"10.1016/j.brs.2017.11.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Hvidovre Hospital","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"focused ultrasound, neuromodulation, cerebellum, brain stimulation, electroencephalography","lastPublishedDoi":"10.21203/rs.3.rs-9650438/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9650438/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNon-invasive modulation of deep cerebellar nuclei offers a promising approach for basic neurophysiological research and potential therapeutic applications in movement disorders and beyond. In the present report, we define key principles for implementing low-intensity transcranial focused ultrasound (tFUS) targeting of cerebellar output nuclei, with the aim of supporting both mechanistic and clinical research. We propose a structured framework covering targeting strategies, stimulation delivery, experimental design and concurrent monitoring, with particular emphasis on individualized anatomical planning and reproducible workflows. We also evaluate current approaches and limitations in assessing target engagement using electroencephalography. Together, this work provides a practical foundation for ultrasound-based neuromodulation of deep cerebellar structures in humans, positioning, for the first time, cerebellar tFUS as a feasible non-invasive methodological alternative to invasive techniques.\u003c/p\u003e","manuscriptTitle":"Neuromodulation of Deep Cerebellar Nuclei: principles for Focused Ultrasound stimulation and EEG monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 06:43:16","doi":"10.21203/rs.3.rs-9650438/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f1c27913-6a4d-4620-91da-b246081c2cbd","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67773367,"name":"Computational Neuroscience"}],"tags":[],"updatedAt":"2026-05-11T06:43:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 06:43:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9650438","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9650438","identity":"rs-9650438","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}