Kathleen E. Cullen’s research while affiliated with Johns Hopkins University and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (229)


Complexes of vertebrate TMC1/2 and CIB2/3 proteins form hair-cell mechanotransduction cation channels
  • Article
  • Full-text available

January 2025

·

13 Reads

eLife

Arnaud PJ Giese

·

·

·

[...]

·

Calcium and integrin-binding protein 2 (CIB2) and CIB3 bind to transmembrane channel-like 1 (TMC1) and TMC2, the pore-forming subunits of the inner-ear mechano-electrical transduction (MET) apparatus. These interactions have been proposed to be functionally relevant across mechanosensory organs and vertebrate species. Here, we show that both CIB2 and CIB3 can form heteromeric complexes with TMC1 and TMC2 and are integral for MET function in mouse cochlea and vestibular end organs as well as in zebrafish inner ear and lateral line. Our AlphaFold 2 models suggest that vertebrate CIB proteins can simultaneously interact with at least two cytoplasmic domains of TMC1 and TMC2 as validated using nuclear magnetic resonance spectroscopy of TMC1 fragments interacting with CIB2 and CIB3. Molecular dynamics simulations of TMC1/2 complexes with CIB2/3 predict that TMCs are structurally stabilized by CIB proteins to form cation channels. Overall, our work demonstrates that intact CIB2/3 and TMC1/2 complexes are integral to hair-cell MET function in vertebrate mechanosensory epithelia.

Download

Schematics of gaze-stabilization exercises in the yaw direction. Yaw and pitch are the directions of interest and only head movements in the yaw direction are shown for a clear demonstration. (a) Three continuous gaze-stabilization exercises, in which subjects were asked to continuously move the head from left to right at a comfortable range of motion, which was self-determined. Subjects were asked to fixate on an “X” shaped object either hand-held at ~ 1 m away from their eyes (‘VOR × 1 near’ exercise, left panel) or taped on a vertical wall 1 m (‘VOR × 1 near’, left panel) or 2 m away at eye level (‘VOR × 1 far’ exercises, middle panel). An example head motion consisting of two continuous head-rotation repetitions is shown in the right panel. (b) Three transient gaze-stabilization exercises, in which subjects were instructed to make self-initiated and self-determined impulsive head movement from the center to an eccentric location, followed by a slower resetting movement back to the center. Subjects were asked to either imagine or fixate on an ‘X’ shaped object placed 1 m from them on the wall at eye level (‘Imaginary target’ exercise and ‘Visual target’ exercise, left panel), or to alternate gaze between two targets ‘X’ fixed on the wall 60 cm apart (‘Gaze shift’ exercise, middle panel). An example head motion consisting of two transient head-rotation repetitions is shown in the right panel.
Example continuous head rotations from (a) a healthy control, (b) preoperative, and (c) postoperative testing from the same patient in the gaze-stabilization exercise with head rotation in the horizontal plane (yaw direction). The target was hand-held approximately 1 m away (‘VOR × 1 near’ exercise). The left panel shows the individual head-velocity traces throughout the first half-session of each trial. The middle panel shows the head-velocity traces from individual head-rotation repetitions superimposed with the mean and standard deviation of the range of motion. The three insets on the right include histograms in the upper panel, which characterize the distribution of the cycle frequency and peak velocity parameterized from individual head-movement cycles of each example subject. The scatterplots in the lower panel visualize the cycle-frequency and peak-velocity changes from trial to trial. (d) The summary histograms superimpose the group average cycle-frequency and peak-velocity distributions. Asterisks indicate differences at three significance levels (* for p < 0.05, ** for p < 0.01, *** for p < 0.001, ns for insignificant difference).
Example transient head rotations from (a) a healthy control, (b) preoperative, and (c) postoperative testing from the same patient in the gaze-stabilization exercise in the horizontal plane (yaw direction). The healthy control and the patient are the same as in the continuous head-movement examples shown in Fig. 2. Subjects were asked to move their heads in impulses while fixating on an imaginary target 1 m away (‘Imaginary target’ exercise), which required them to turn quickly from midline to one self-determined eccentric location (left and right sides of midline, interleaved), followed by a slow resetting head movement back to the midline, repeated. Compared with the continuous gaze-stabilization exercises, subjects needed to pause at eccentric locations and only the impulses were analyzed. The left panel shows the individual head-velocity traces throughout the first half-session of each trial. The middle panel shows head-velocity traces from individual head-rotation repetitions superimposed with the mean and standard deviation of the range of motion. The three insets on the right include histograms in the upper panel, which characterize the distribution of the cycle frequency and peak velocity parameterized from individual head-movement cycles of each example subject. The scatterplots in the lower panel visualize the cycle-frequency and peak-velocity changes from trial to trial. (d) The summary histograms superimpose the group average cycle-frequency and peak-velocity distributions. Asterisks indicate differences at three significance levels (* for p < 0.05, ** for p < 0.01, *** for p < 0.001, ns for insignificant difference).
Trial to trial variability of peak velocity parameterized from chronologically ordered continuous head-movement cycles. (a) Group-averaged correlation coefficients of peak velocity between the current cycle and subsequent cycles (up to the twentieth) from six continuous gaze-stabilization exercises. The six exercises comprised three conditions (as shown in three columns) and two head-movement directions (pitch in the upper panel and yaw in the lower panel). Error bars represent the standard error of the mean (SEM). (b) The group-averaged intertrial correlation difference (ICD) for each exercise (x-axis). Error bars are standard error of the mean (SEM). ICDs represent the absolute difference of coefficients between each trial and the subsequent trial in the autocorrelation function.
Sample entropy results in two continuous exercises. Comparisons between (a) preoperative patients and healthy controls, (b) postoperative patients and healthy controls, and (c) preoperative patients and postoperative patients are shown. The sample-entropy measurements are generated by parameters m = 2 and r = 0.133 and are shown in histograms overlayed with fitted Gaussian curves. The y axis shows the number of values. Asterisks indicate differences at three significance levels (* for p < 0.05, ** for p < 0.01, *** for p < 0.001, ns for insignificant difference).
Vestibular patients generate more regular head movements than healthy individuals during gaze-stabilization exercises

January 2025

·

40 Reads

The vestibular system is vital for maintaining stable vision during daily activities. When peripheral vestibular input is lost, patients initially experience impaired gaze stability due to reduced effectiveness of the vestibular-ocular-reflex pathway. To aid rehabilitation, patients are often prescribed gaze-stabilization exercises during which they make self-initiated active head movements. Analyzing statistical pattern of sequences of stereotyped behaviors to characterize degrees of randomness or repeatability has proven to be a powerful approach for diagnosing disease states, yet this approach has not been applied to patients with vestibular loss. Accordingly, here we investigated whether the patterning of head movements is altered in vestibular-loss patients by using trial-based analysis and sample-entropy measurement. The subjects completed gaze-stability exercises in both the yaw and pitch directions. In trial-based analysis, we calculated the trial-to-trial variability of head movement duration and peak velocity for each individual head movement. Our results showed that healthy individuals exhibited a temporally repetitive (correlated) structure in peak velocity, especially for head movements in the pitch direction, which was absent in most patients. In the sample entropy analysis, which measures the irregularity or randomness of a time series, our results revealed that head-movement generation was more regular in vestibular-loss patients compared to healthy controls. Together, these analyses suggest that vestibular-loss patients display less flexibility in the patterning of their head motions. Our results provide the first experimental evidence that temporal head stability is a valuable metric for distinguishing individuals with impaired vestibular function from healthy ones.



Restoring vestibular function during natural self-motion: Progress and challenges

eLife

The vestibular system is integral to behavior; the loss of peripheral vestibular function leads to disabling consequences, such as blurred vision, dizziness, and unstable posture, severely limiting activities of daily living. Fortunately, the vestibular system’s well-defined peripheral structure and well-understood encoding strategies offer unique opportunities for developing sensory prostheses to restore vestibular function. While these devices show promising results in both animal models and implanted patients, substantial room for improvement remains. Research from an engineering perspective has largely focused on optimizing stimulation protocol to improve outcomes. However, this approach has often been pursued in isolation from research in neuroscience that has enriched our understanding of neural responses at the synaptic, cellular, and circuit levels. Accordingly, this review bridges the domains of neuroscience and engineering to consider recent progress and challenges in vestibular prosthesis development. We advocate for interdisciplinary approaches that leverage studies of neural circuits at the population level, especially in light of recent advancement in large-scale recording technology, to identify impediments still to overcome and to develop more naturalistic stimulation strategies. Fully integrating neuroscience and engineering in the context of prosthesis development will help advance the field forward and ultimately improve patient outcomes.


Instrumented swim test for quantifying motor impairment in rodents

November 2024

·

17 Reads

Swim tests are highly effective for identifying vestibular deficits in rodents by offering significant vestibular motor challenges with reduced proprioceptive input, unlike rotarod and balance beam tests. Traditional swim tests rely on subjective assessments, limiting objective quantification and reproducibility. We present a novel instrumented swim test using a miniature motion sensor with a 3D accelerometer and 3D gyroscope affixed to the rodent’s head. This setup robustly quantifies six-dimensional motion—three translational and three rotational axes—during swimming with high temporal resolution. We demonstrate the test’s capabilities by comparing head movements of Gpr156-/- mutant mice, which have impaired otolith organ development, to their heterozygous littermates. Our results show axis-specific differences in head movement probability distribution functions and dynamics that identify mice with the Gpr156 mutation. Axis-specific power spectrum analyses reveal selective movement alterations within distinct frequency ranges. Additionally, our spherical visualization and 3D analysis quantifies swimming performance based on head vector distance from upright. We use this analysis to generate a single classifier metric—a weighted average of an animal’s head deviation from upright during swimming. This metric effectively distinguishes animals with vestibular dysfunction from those with normal vestibular function. Overall, this instrumented swim test provides quantitative metrics for assessing performance and identifying subtle, axis- and frequency-specific deficits not captured by existing systems. This novel quantitative approach can enhance understanding of rodent sensorimotor function including enabling more selective and reproducible studies of vestibular-motor deficits.


Contributions of mirror-image hair cell orientation to mouse otolith organ and zebrafish neuromast function

November 2024

·

16 Reads

eLife

Otolith organs in the inner ear and neuromasts in the fish lateral-line harbor two populations of hair cells oriented to detect stimuli in opposing directions. The underlying mechanism is highly conserved: the transcription factor EMX2 is regionally expressed in just one hair cell population and acts through the receptor GPR156 to reverse cell orientation relative to the other population. In mouse and zebrafish, loss of Emx2 results in sensory organs that harbor only one hair cell orientation and are not innervated properly. In zebrafish, Emx2 also confers hair cells with reduced mechanosensory properties. Here, we leverage mouse and zebrafish models lacking GPR156 to determine how detecting stimuli of opposing directions serves vestibular function, and whether GPR156 has other roles besides orienting hair cells. We find that otolith organs in Gpr156 mouse mutants have normal zonal organization and normal type I-II hair cell distribution and mechano-electrical transduction properties. In contrast, gpr156 zebrafish mutants lack the smaller mechanically evoked signals that characterize Emx2-positive hair cells. Loss of GPR156 does not affect orientation-selectivity of afferents in mouse utricle or zebrafish neuromasts. Consistent with normal otolith organ anatomy and afferent selectivity, Gpr156 mutant mice do not show overt vestibular dysfunction. Instead, performance on two tests that engage otolith organs is significantly altered – swimming and off-vertical-axis rotation. We conclude that GPR156 relays hair cell orientation and transduction information downstream of EMX2, but not selectivity for direction-specific afferents. These results clarify how molecular mechanisms that confer bi-directionality to sensory organs contribute to function, from single hair cell physiology to animal behavior.


Contributions of mirror-image hair cell orientation to mouse otolith organ and zebrafish neuromast function

October 2024

·

9 Reads

Otolith organs in the inner ear and neuromasts in the fish lateral-line harbor two populations of hair cells oriented to detect stimuli in opposing directions. The underlying mechanism is highly conserved: the transcription factor EMX2 is regionally expressed in just one hair cell population and acts through the receptor GPR156 to reverse cell orientation relative to the other population. In mouse and zebrafish, loss of Emx2 results in sensory organs that harbor only one hair cell orientation and are not innervated properly. In zebrafish, Emx2 also confers hair cells with reduced mechanosensory properties. Here, we leverage mouse and zebrafish models lacking GPR156 to determine how detecting stimuli of opposing directions serves vestibular function, and whether GPR156 has other roles besides orienting hair cells. We find that otolith organs in Gpr156 mouse mutants have normal zonal organization and normal type I-II hair cell distribution and mechano-electrical transduction properties. In contrast, gpr156 zebrafish mutants lack the smaller mechanically-evoked signals that characterize Emx2-positive hair cells. Loss of GPR156 does not affect orientation-selectivity of afferents in mouse utricle or zebrafish neuromasts. Consistent with normal otolith organ anatomy and afferent selectivity, Gpr156 mutant mice do not show overt vestibular dysfunction. Instead, performance on two tests that engage otolith organs is significantly altered – swimming and off-vertical-axis rotation. We conclude that GPR156 relays hair cell orientation and transduction information downstream of EMX2, but not selectivity for direction-specific afferents. These results clarify how molecular mechanisms that confer bi-directionality to sensory organs contribute to function, from single hair cell physiology to animal behavior.


Complexes of vertebrate TMC1/2 and CIB2/3 proteins form hair-cell mechanotransduction cation channels

September 2024

·

23 Reads

Calcium and integrin-binding protein 2 (CIB2) and CIB3 bind to transmembrane channel-like 1 (TMC1) and TMC2, the pore-forming subunits of the inner-ear mechano-electrical transduction (MET) apparatus. These interactions have been proposed to be functionally relevant across mechanosensory organs and vertebrate species. Here we show that both CIB2 and CIB3 can form heteromeric complexes with TMC1 and TMC2 and are integral for MET function in mouse cochlea and vestibular end organs as well as in zebrafish inner ear and lateral line. Our AlphaFold 2 models suggest that vertebrate CIB proteins can simultaneously interact with at least two cytoplasmic domains of TMC1 and TMC2 as validated using nuclear magnetic resonance spectroscopy of TMC1 fragments interacting with CIB2 and CIB3. Molecular dynamics simulations of TMC1/2 complexes with CIB2/3 predict that TMCs are structurally stabilized by CIB proteins to form cation channels. Overall, our work demonstrates that intact CIB2/3 and TMC1/2 complexes are integral to hair-cell MET function in vertebrate mechanosensory epithelia.


Data collection conditions and analysis pipeline. We collected videos of five wild-type and five (Cib2−/−;Cib3−/−) dual knockout mice exploring a 30 cm-diameter cylindrical arena. Each of 6 combinations of light and distance conditions was repeated 4 times for each mouse, resulting in a total of 236 videos as 4 became corrupted. After behavior videos were recorded, all videos of one mutant mouse and one wild-type mouse were set aside for human behavioral labeling as a test set. For each of these held-out videos, three observers independently marked occurrences of circling behavior. These behavioral labels were compared to produce a set of consensus labels on which all observers agreed. A separate training set of human behavior labels was constructed by randomly selecting 24 mutant and 24 wild-type videos from among the remaining 188 videos. Additionally, positions of the snout and tailbase were manually labeled in 20 random frames from each of these 188 videos. Manually-labeled bodypart locations were used to train a computer vision model using DeepLabCut. This trained model was then used to track animals in the human-scored videos, and the resulting paths were analyzed by three candidate circling detection algorithms. After the parameters of these algorithms were optimized for F1 score on the training set, they were applied to the test set for evaluation.
Human F1 scores. (A) Treating one independent observer as the gold standard for another reveals that humans show substantial variability in labeling circling behavior. In particular, although average F1 scores for each pair (AB, BC, CA) are similar (0.53, 0.52, 0.49), the distributions of scores across videos differ significantly between one pair and the other two (pair CA, p = 3.5E−2 and 1.4E−4 versus pairs AB, BC respectively) while the other pair did not differ significantly (AB versus BC, p = 0.28). (B) Scoring of independent observers' labels against another observer (left columns) or against consensus labels (agreement among 3 observers, right columns) produce similar results (p = 0.2), as does comparing between our two human data subsets (train versus test subset, p = 0.65 and 0.75). Pooled pairwise F1 scores averaged 0.51 (95% CI 0.47–0.55) in the training set and 0.53 (0.41–0.62) in the testing set. Scoring against consensus occurrences, in which all observers mark a complete circle within 0.1 s of one another, produced similar scores of 0.51 (0.44–0.57) in the training set and 0.53 (0.38–0.65). Each point in a column represents a single video. Labeler-video combinations for which F1 score is undefined (i.e., both scorer and ground truth marked no circling instances), are not displayed for either paired or consensus scoring but were included in bootstrapping for purposes of calculating confidence intervals.
Method parameters and performance levels. (A) Timelapse of keypoint-labeled frames of a mouse engaged in circling behavior. (B) Parameter distributions and associated exponential and Gaussian fits from two sample videos. To accommodate the substantial variability observed across videos, we relied on a two-step process of Gaussian kernel estimation followed by fitting to a weighted sum of an exponential and normal distribution. This allowed the same technique to account for differences in e.g. average duration (left column, compare blue Gaussian fits) or greater numbers of small collisions likely to be false positives (right column, compare pink exponential fits). (C) Illustration of circle detection using each of the described methods. Duration-Only considers only time taken to complete the putative circle, Time-Angle additionally calculates the angle of the tail-to-snout vector for each frame and considers its total net change, and Box-Angle removes duration requirements and instead constraints the geometry of the circle based on the axes of a rectangle bounding the candidate circling instance. (D) Examples of false-positive detections using each method. There are clear features which indicate an instance should be filtered out for the Duration-Only (minimal head movement relative to the tail) and Time-Angle (oblong or missized snout path geometry) methods.
Method performance comparison. After optimizing behavior detection algorithms on the human-labeled training set, each was scored on the human consensus circling labels of the test set. Each column represents one algorithm, with one dot for each test set video with a defined score. Videos for which F1 score is undefined (i.e., the automated method and human consensus both marked no circling instances) were included in confidence interval calculations but not displayed as individual datapoints. The Duration-Only and Time-Angle methods significantly underperformed independent human observers (mean and 95% CI 0.1 (0.02–0.17) and 0.22 (0.03–0.47), p = 1.1E−11 and 4.7E−6, respectively). Only the Box-Angle method reaches statistical parity (mean F1 0.43 (0.21–0.57), p = 0.51).
Dataset size performance comparison. (A) Labeling performance (error, in pixels) for each of 10 trained networks on datasets of progressively smaller sizes. All dataset sizes resulted in greater labeling error than the Full Dataset model (dashed horizontal line), particularly for frames not seen during training (test frames). Notably, this trend was not monotonic—the set of quarter-dataset models performed better on test frames, on average, than the set of half-dataset models. Root-mean-squared errors on training set frames were (mean and 95% CI) 9.29 (8.13–10.73), 9.84 (8.53–11.7), and 11.02 (9.11–12.91) pixels respectively. For unseen frames, these errors increased to 19.37 (16.92–22.28), 12.3 (10.51–14.4), and 14.34 (12.66–15.98). Dashed horizontal line represents Full Dataset model training frame error (7.82 pixels). (B) To determine whether these changes in labeling quality impacted, we applied the optimized Box-Angle method to the keypoint tracking produced by each network at each dataset size. Within a dataset, the true-positive, false-positive, and false-negative scores for each video were summed to calculate a representative F1 score, plotted here as individual dots in the half-, quarter-, and eighth-sized datasets. The resulting distributions are compared to scores from the Full Dataset network (left column) and to independent human scores (right). As elsewhere, video-net combinations for which F1 score are undefined are included in confidence interval calculations but not displayed as individual datapoints. These smaller datasets underperformed the Full Dataset network (p = 0.03, 0.03, 0.02) as well as human labels (p = 1.7E−4, 1.4E−4, 3.9E−5), indicating that even small reductions in keypoint tracking quality can impact behavioral detection. *p < 0.05, ***p < 0.001.
An open-source tool for automated human-level circling behavior detection

September 2024

·

20 Reads

Quantitatively relating behavior to underlying biology is crucial in life science. Although progress in keypoint tracking tools has reduced barriers to recording postural data, identifying specific behaviors from this data remains challenging. Manual behavior coding is labor-intensive and inconsistent, while automatic methods struggle to explicitly define complex behaviors, even when they seem obvious to the human eye. Here, we demonstrate an effective technique for detecting circling in mice, a form of locomotion characterized by stereotyped spinning. Despite circling's extensive history as a behavioral marker, there currently exists no standard automated detection method. We developed a circling detection technique using simple postprocessing of keypoint data obtained from videos of freely-exploring (Cib2−/−;Cib3−/−) mutant mice, a strain previously found to exhibit circling behavior. Our technique achieves statistical parity with independent human observers in matching occurrence times based on human consensus, and it accurately distinguishes between videos of wild type mice and mutants. Our pipeline provides a convenient, noninvasive, quantitative tool for analyzing circling mouse models without the need for software engineering experience. Additionally, as the concepts underlying our approach are agnostic to the behavior being analyzed, and indeed to the modality of the recorded data, our results support the feasibility of algorithmically detecting specific research-relevant behaviors using readily-interpretable parameters tuned on the basis of human consensus.


Fig. 3. a Correlations between the kinematic and clinical measures during the 6 continuous gaze stabilization exercises (GSE) performed by younger older adults. b Correlations between the kinematic and clinical measures during the 6 continuous gaze stabilization exercises (GSE) performed by older adults. Green and red squares reflect positive and negative correlations, respectively. Brightness and number in the squares indicate the number of exercises (1-6) with a significant correlation (p < 0.05).
List of continuous GSE Exercise number Visual target location Participant-target distance Head movement direction (30") Assigned exercise name
Aging Delays Completion of Head Rotation Cycles in Continuous Gaze Stabilization Exercises Despite Putative Healthy Vestibular Function

August 2024

·

121 Reads

Gerontology

Introduction: Aging is associated with loss of balance, with falls being one of the leading causes of death among the elderly in the USA. Gaze stabilization exercises (GSE) improve balance control in vestibular populations and could be useful to prevent falls in healthy individuals. However, the extent to which aging affects head kinematics in GSE is unknown. Methods: Forty-eight younger (n = 25, 24 ± 6 years, 60% female) and older (n = 23, 66 ± 5 years, 56% female) adults completed six 30-s GSE. Participants were asked to maintain gaze fixation on a stationary target while continuously performing head movements in pitch (e.g., vertical) and yaw (e.g., horizontal) directions. The visual target was placed on the wall 1 m or 2 m away or handheld at arm's length. Head kinematics were recorded with an inertial measurement unit placed on the back of the participants' head. Results: Older adults took significantly more time (e.g., delay) to complete cycles of head rotation in both pitch and yaw compared to younger participants across all GSE. Such delay was further increased during yaw head rotation while fixating gaze of the 1 m target. The average peak velocity (APV) and average angular displacement (AAD), however, were equivalent between groups in all GSE. Conclusion: Aging leads to the maintenance of head rotation APV and AAD at the expense of delayed cycles of head rotation, suggesting an age-dependent prioritization strategy (e.g., adapt duration first, range second) during continuous head movements. The distance of the visual target and head movement direction influenced elderly performance and should be considered when prescribing GSE to older populations.


Citations (57)


... Previous recording [38] and simulation studies [39] indicate pulsatile stimulation produces different encoding patterns than natural inputs in higher-order auditory cortex [40]. However, we hypothesize non-identical inputs could also produce similar deep layer responses [41] and therefore better speech encoding without producing identical cochlear activity to the healthy cochlea which is intractable with current technologies [43]. [44]. ...

Reference:

DeepSpeech models show Human-like Performance and Processing of Cochlear Implant Inputs
Pulsatile electrical stimulation creates predictable, correctable disruptions in neural firing

... To demonstrate the test's capabilities, we recorded and compared the head movements of Gpr156 -/constitutive mutant mice to their heterozygous littermates 18 . These constitutive mutants lack regional hair cell orientation reversal in otolith organs, resulting in a phenotype not easily detected by most tests, except subjectively scored swim tests 19 . We first present the raw IMU data across all axes, focusing specifically on the linear channels for which the recorded signals comprise two main components: translational head acceleration and head orientation relative to gravity. ...

Contributions of mirror-image hair cell orientation to mouse otolith organ and zebrafish neuromast function
  • Citing Preprint
  • June 2024

... Concerning the neurophysiology of vestibular sensory conflict, evidence has emerged in rhesus macaque studies that vestibular only neurons in the brainstem's vestibular nuclei as well as neurons in the rostral fastigial nucleus of the deep cerebellar nuclei fire analogously to sensory conflict terms for rotations and translations (Brooks & Cullen, 2009;Jamali et al., 2009;Roy & Cullen, 2001), encoding responses during passive motions yet demonstrating reduced responses (i.e., reafference cancellation) during active motion, the latter likely due to accurate forward model predications projected by anterior cerebellar vermis Purkinje cells (Laurens & Angelaki, 2017;Zobeiri & Cullen, 2024). While not explored here, vestibular sensory conflict as a causal driver of motion sickness explains the lack of motion sickness during coordinated active motions, where the vestibular consequences of motion are anticipated, as highlighted by Oman and Cullen (2014). ...

Cerebellar Purkinje cells combine sensory and motor information to predict the sensory consequences of active self-motion in macaques

... The vestibular system encodes natural motion (Cullen, 2019;Mohammadi et al., 2024) and contributes to the generation of head direction responses (Cullen and Taube, 2017;Taube, 2007). We hypothesize that, in the 'dark' experiments, vestibular cues are a major signal supporting spatial learning. ...

Neural populations within macaque early vestibular pathways are adapted to encode natural self-motion

... Recent developments in inertial sensors-based assessments allow for quantitative evaluations of gait disorders, potentially providing insight into fundamental mechanisms related to stability, symmetry, and smoothness of gait in people with different neurological disorders 15,16 as well as in people with subacute unilateral vestibulopathy (sAUVP). 17 Furthermore, wearable sensor-based assessments enable participants to move freely with minimal spatial constraints, facilitating the evaluation of gait and posture in a more natural and flexible setting. The portability and ease of setup of wearable sensors allow for data collection in environments that better reflect real-world conditions, thereby enhancing the ecological validity of the assessment. ...

Effect of vestibular loss on head-on-trunk stability in individuals with vestibular schwannoma

... Together, these networks orchestrate their activity through direct connections between them and through intermediate networks. For example, all three networks have been found to modulate their activity individually and collectively based on the dynamics of an internal state as dictated by task performance [21]. Studies using resting-state functional connectivity have identified increased connectivity between the DMN and FN in individuals who are greater risk-seekers, highlighting the importance of their relationship during affective decision-making [22]. ...

Internal states as a source of subject-dependent movement variability are represented by large-scale brain networks

... Research has shown that dysfunction of the paracentral lobule can cause abnormal sensorimotor integration (Shen et al. 2022;Teggi et al. 2016;Wei et al. 2020;Zhang et al. 2017). The precuneus, meanwhile, is the parietal region that produces most vestibular responses and is related to the integration between vestibular and visual systems (Fu et al. 2022;Lopez and Cullen 2023). Klingner et al. (2013) studied the functional changes in relevant brain regions as seen by fMRI during temperature stimulation of the vestibular apparatus. ...

Electrical stimulation of the peripheral and central vestibular system
  • Citing Article
  • October 2023

Current Opinion in Neurology

... In particular, they more 266 often rolled sideways enough to necessitate paddling to recover to a stable position, and after bumping into the 267 wall they frequently paused and appeared disoriented. Overall, these results suggest that swimming behaviors of 268 The vestibulocerebellum is involved in balance, posture, compensatory eye movements, and the generation 273 of an internal model of self-motion (Cullen, 2023). Despite their prevalence in vestibular cerebellar regions, the 274 role of UBCs in cerebellar function remains unclear. ...

Internal models of self-motion: neural computations by the vestibular cerebellum
  • Citing Article
  • September 2023

Trends in Neurosciences

... Moreover, in contrast to VOR neurons, the majority of VO neurons do not faithfully encode the time course of head movements but instead optimize coding via temporal whitening-a property thought to contribute to ensuring postural control and perceptual stability during everyday activities (Mackrous et al., 2020). Similar to VOR, prosthesis-evoked vestibulo-spinal reflex (VSR) head movements are measured as the behavioral output of prosthetic stimulation (Mitchell et al., 2013;Wiboonsaksakul et al., 2023). ...

Prosthetic Stimulation of the Vestibular Nerve Can Evoke Robust Eye and Head Movements Despite Prior Labyrinthectomy

Otology & neurotology: official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology

... For example, the high-confidence orthologs clic5a (Clic5), encodes an intracellular chloride channel protein that localizes to the basal region of the hair bundle and loss-of-function is associated with dysmorphic stereocilia, and vestibular and hearing dysfunction (61). The cib3 ortholog, encoding calcium-binding protein, has been shown to be co-expressed with tmc1 in the zebrafish inner ear HCs, with the ortholog Cib3 required for mechanotransduction in cochlear HCs (62)(63)(64). Expression of the gene orthologs anxa5a and Anxa5 is highly conserved among vertebrate HCs, despite unknown functional significance of the protein in auditory HCs (65). ...

Complexes of vertebrate TMC1/2 and CIB2/3 proteins form hair-cell mechanotransduction cation channels
  • Citing Preprint
  • August 2023

eLife