Innovative approaches for managing patients with chronic vestibular disorders: follow-up indicators and predictive markers for studying the vestibular error signal

Abstract

Introduction: Despite significant advancements in understanding the biochemical, anatomical, and functional impacts of vestibular lesions, developing standardized and effective rehabilitation strategies for patients unresponsive to conventional therapies remains a challenge. Chronic vestibular disorders, characterized by permanent or recurrent imbalances and blurred vision or oscillopsia, present significant complexity in non-pharmacological management. The complex interaction between peripheral vestibular damage and its impact on the central nervous system (CNS) raises questions about neuroplasticity and vestibular compensation capacity. Although fundamental research has examined the consequences of lesions on the vestibular system, the effect of a chronic peripheral vestibular error signal (VES) on the CNS remains underexplored. The VES refers to the discrepancy between sensory expectations and perceptions of the vestibular system has been clarified through recent engineering studies. This deeper understanding of VES is crucial not only for vestibular physiology and pathology but also for designing effective measures and methods of vestibular rehabilitation, shedding light on the importance of compensation mechanisms and sensory integration.

Methods: This retrospective study, targeting patients with chronic unilateral peripheral vestibulopathy unresponsive to standard treatments, sought to exclude any interference from pre-existing conditions. Participants were evaluated before and after a integrative vestibular exploratory and rehabilitation program through questionnaires, posturographic tests, and videonystagmography.

Results: The results indicate significant improvements in postural stability and quality of life, demonstrating positive modulation of the CNS and an improvement of vestibular compensation.

Discussion: Successful vestibular rehabilitation likely requires a multifaceted approach that incorporates the latest insights into neuroplasticity and sensory integration, tailored to the specific needs and clinical progression of each patient. Focusing on compensating for the VES and enhancing sensory-perceptual-motor integration, this approach aims not just to tailor interventions but also to reinforce coherence among the vestibular, visual, and neurological systems, thereby improving the quality of life for individuals with chronic vestibular disorders.

Keywords: integrative vestibular rehabilitation; monitoring indicators; predictive markers; sensori-perceptual-motor system; vestibular error signal; visual fusion.

Figure 1

Decisional tree (Two parts). This figure illustrates the various symptoms reported by chronic vestibular patients. A comprehensive initial assessment is conducted at the beginning of treatment, and the main areas of focus are determined based on the most debilitating symptoms for the patient. At the start of each week, a screening of complaints (symptoms) is conducted. For each complaint, an evaluation is performed, and treatment is adjusted based on the results. VNGk: kinetic videonystagmography, DVA: dynamic visual acuity, VHIT: video head impulse test, VOMS: Vestibular Oculomotor Motor Screening.

Figure 2

(A) In the presence of an uncompensated VES resulting from a subcortical compensation defect: The observed phenomenon will cause a shift in the intersection of the reflectivity lines along baseline 1 towards the pathological side and a shift in the intersection along baseline 2 upwards, which may indicate either an incomplete state of compensation of the vestibular nuclei during warm stimulation on the healthy side or a defect in reflectivity during cold stimulation on the pathological side. A revealed nystagmus beating towards the pathological side will be present (shift towards the upper left quadrant of the intersection point of reflectivity lines). (B) In the presence of a compensated deficient VES: The observed phenomenon will cause a shift in the intersection of the reflectivity lines towards the pathological side along baseline 1 without a parallel shift along baseline 2. The intersection of the reflectivity lines remains on the horizontal axis. A revealed nystagmus beating towards the healthy side will be present. VES, vestibular error signal; baseline 1, axis of directional preponderance; baseline 2, axis of reflectivities; red reflectivity line, results of warm stimulations of the right and left ears; blue reflectivity line, results of cold stimulations of the right and left ears; RE, right ear; LE, left ear; RN, right nystagmus; LN, left nystagmus.

Figure 3

Description of the geometric angle and the bisector of the angle modeled on SVV measurements. We proceed with a random selection of the initial tilt. For example, for a right-sided selection: we perform a series of 4 measurements with the patient seated in darkness, starting from the right side [red line figure (A)], followed by 4 measurements from the left side [blue line figure (A)]. Each starting point is randomly positioned within an interval of [18°; 22°] on the right side and [−18°; −22°] on the left side relative to the vertical axis. Under dynamic conditions, optokinetic stimulation is initiated at 20°/s clockwise (green arrow) for measurements starting on the right [red line figure (B)], and counterclockwise (orange arrow) for measurements starting on the left [blue line figure (B)]. The same principles are applied except that we perform 6 measurements on each side. By averaging each series, we obtain 2 angles: one in static condition [figure (A)] and one in dynamic condition [figure (B)]. The bisector of each angle (yellow line) is then plotted. We evaluate the tracking of the geometric angle (closure = increased precision; opening = increased imprecision) and the variation of the bisector angle relative to the vertical (increased angle = decreased accuracy; decreased angle = increased accuracy).

Figure 4

Flow chart.

Figure 5

Distribution of scores across the three components of the dizziness handicap inventory (DHI). Red: scores at A1; Blue: scores at A2. Higher scores indicate a poorer state of the evaluated component.

Figure 6

Distribution of scores across the eight dimensions of the SF36 questionnaire. Red: scores at A1; Blue: scores at A2. Higher scores indicate a better state of the evaluated component.

Figure 7

Distribution of scores across the five dimensions of the EPN-31 questionnaire. Red: scores at A1; Blue: scores at A2. Higher scores indicate that the evaluated emotional component is experienced more frequently, and vice versa.

Figure 8

Distribution of scores across the five dimensions of the BFI questionnaire. Red: scores at A1; Blue: scores at A2. The higher the score, the more pronounced the corresponding personality trait (Extraversion, Agreeableness, Conscientiousness, Neuroticism, Openness to Experience), and vice versa.

Figure 9

Distribution of scores across the ten dimensions of the vestiQ-VS questionnaire. Red: scores at A1; Blue: scores at A2. The higher the score, the more deteriorated the state of the evaluated component.

Figure 10

Ordinary least squares (OLS) regression analysis EStEOML. This graph shows the beta coefficients of the variables used in the OLS regression analysis for the dynamic change in the EStEOML. The beta coefficients indicate the strength and direction of the association between each variable and the dynamic EStEOML. Figure Components: • Black Dots: Each black dot represents a beta coefficient for a given variable. • Error Bars: The horizontal bars around the dots indicate the 95% confidence intervals for each beta coefficient. They show the range within which the true beta coefficient is likely to lie with a 95% probability. • Red Dots: Red dots indicate variables whose beta coefficients are statistically significant (p < 0.05). Significant variables are annotated with the text “Significant”. • Horizontal Dashed Line at Zero: The dashed line indicates the zero value of the beta coefficient. A beta coefficient of zero means there is no association between the variable and the dynamic EStEOML How to Read the Figure: • Identify the Variables: The variables are listed on the x-axis. They include “Constant”, “dSE”, “dSF36SG”, “dEPN31P”, “dBIG5A”, “I”, “P”, and “T”. • Understand the Coefficients:The position of the black dots on the y-axis represents the beta coefficients for each variable. A positive coefficient indicates a positive association with EStEOML, while a negative coefficient indicates a negative association. Evaluate Significance: • Look at the red dots to identify significant variables. These variables have a statistically significant association with EStEOML. • Error bars that do not cross the horizontal dashed line at zero also indicate significance. Variable Definitions: dSE: Emotional dimension of the VestiQ-VS questionnaire, dSF36SG: General health dimension of the SF36 questionnaire, dEPN31P: Fear dimension of the EPN31 questionnaire, dBIG5A: Agreeableness, Altruism, Affection dimension. I: Inhibition without Deficit Profile, P: Partial Contralateral Inhibition Profile, T: Total Contralateral Inhibition Profile.

Figure 11

Ordinary least squares (OLS) regression analysis EStVCML. This graph shows the beta coefficients of the variables used in the OLS regression analysis for the dynamic change in EStVCML. The beta coefficients indicate the strength and direction of the association between each variable and the dynamic EStVCML. Figure Components: • Black Dots: Each black dot represents a beta coefficient for a given variable. • Error Bars: The horizontal bars around the dots indicate the 95% confidence intervals for each beta coefficient, showing the range within which the true beta coefficient is likely to lie with 95% probability. • Red Dots: Red dots indicate variables whose beta coefficients are statistically significant (p < 0.05). Significant variables are annotated with the text “Significant”. • Horizontal Dashed Line at Zero: The dashed line indicates the zero value of the beta coefficient. A beta coefficient of zero means there is no association between the variable and EStEOML. How to Read the Figure: • Identify the Variables: The variables are listed on the x-axis. These include “Constant,” “dSM,” “dSE,” “dEPN31TS,” “dSF36BE,” “I,” “P,” and “T”. • Understand the Coefficients: The position of the black dots on the y-axis represents the beta coefficients for each variable. A positive coefficient indicates a positive association with dynamic EStVCML, while a negative coefficient indicates a negative association. Evaluate Significance: • Look at the red dots to identify significant variables. These variables have a statistically significant association with dynamic EStVCML. • Error bars that do not cross the horizontal dashed line at zero also indicate significance. Variable Definitions: dSM: Memory dimension of the VestiQ-VS questionnaire, dSE: Emotional dimension of the VestiQ-VS questionnaire, dEPN31TS: Surprise dimension of the EPN31 questionnaire, dSF36BE: Emotional well-being dimension of the SF36 questionnaire.

Figure 12

Ordinary least squares (OLS) regression analysis EDECML. This graph shows the beta coefficients of the variables used in the OLS regression analysis for EDECML. The beta coefficients indicate the strength and direction of the association between each variable and EDECML. Figure Components: • Black Dots: Each black dot represents a beta coefficient for a given variable. • Error Bars: The horizontal bars around the dots indicate the 95% confidence intervals for each beta coefficient. They show the range within which the true beta coefficient is likely to lie with a 95% probability. • Red Dots: Red dots indicate variables whose beta coefficients are statistically significant (p < 0.05). Significant variables are annotated with the text “Significant”. • Horizontal Dashed Line at Zero: The dashed line indicates the zero value of the beta coefficient. A beta coefficient of zero means there is no association between the variable and EDECML. How to Read the Figure: • Identify the Variables: The variables are listed on the x-axis. They include “Constant”, “dSM”, “dEPN31J”, “dSF36FP”, and “dBIG5E”. • Understand the Coefficients: The position of the black dots on the y-axis represents the beta coefficients for each variable. A positive coefficient indicates a positive association with EDECML, while a negative coefficient indicates a negative association. Evaluate Significance: • Look at the red dots to identify significant variables. These variables have a statistically significant association with EDECML. • Error bars that do not cross the horizontal dashed line at zero also indicate significance. Variable Definitions: dSM: Memory dimension of the VestiQ-VS questionnaire, dBIG5E: Extraversion, Energy, Enthusiasm dimension of the BFI questionnaire, dEPN31J: Joy dimension of the EPN31 questionnaire, dSF36FP: Physical Functioning dimension of the SF36 questionnaire.

Figure 13

Ordinary least squares (OLS) regression analysis EDVCML. This graph shows the beta coefficients of the variables used in the OLS regression analysis for EDVCML. The beta coefficients indicate the strength and direction of the association between each variable and the outcome measure (EDVCML). Figure Components: • Black Dots: Each black dot represents a beta coefficient for a given variable. • Error Bars: The horizontal bars around the dots indicate the 95% confidence intervals for each beta coefficient. They show the range within which the true beta coefficient is likely to lie with a 95% probability. • Red Dots: Red dots indicate variables whose beta coefficients are statistically significant (p < 0.05). Significant variables are annotated with the text “Significant”. • Horizontal Dashed Line at Zero: The dashed line indicates the zero value of the beta coefficient. A beta coefficient of zero means there is no association between the variable and the outcome measure (EDVCML). How to Read the Figure: • Identify the Variables: The variables are listed on the x-axis. They include “Constant”, “dSF36FP”, “dEPN31”, “dSC”, “dBIG5A”, and “dSE”. • Understand the Coefficients: The position of the black dots on the y-axis represents the beta coefficients for each variable. A positive coefficient indicates a positive association with EDVCML, while a negative coefficient indicates a negative association. Evaluate Significance: • Look at the red dots to identify significant variables. These variables have a statistically significant association with EDVCML. • Error bars that do not cross the horizontal dashed line at zero also indicate significance. Variable Definitions: dSC: Cognition dimension from the VestiQ-VS questionnaire, dSE: Emotional dimension from the VestiQ-VS questionnaire, dEPN31J: Joy dimension from the EPN31 questionnaire, dBIG5A: Agreeableness, Altruism, Affection dimension from the BFI questionnaire, dSF36FP: Physical functioning dimension from the SF36 questionnaire.

Figure 14

Ordinary least squares (OLS) regression analysis for dynamic SVV bisector angle change (AbSVVd). This graph shows the beta coefficients of the variables used in OLS regression analysis for the dynamic variation of the bisector angle of SVV (AbSVVd). The beta coefficients indicate the strength and direction of the association between each variable and the dynamic change in the SVV bisector angle. Figure Components: • Black Dots: Each black dot represents a beta coefficient for a given variable. • Error Bars: The horizontal bars around the dots indicate the 95% confidence intervals for each beta coefficient. They show the range within which the true beta coefficient is likely to lie with a 95% probability. • Red Dots: Red dots indicate variables whose beta coefficients are statistically significant (p < 0.05). Significant variables are annotated with the text “Significant”. • Horizontal Dashed Line at Zero: The dashed line indicates the zero value of the beta coefficient. A beta coefficient of zero means there is no association between the variable and the dynamic bisector angle change of SVV. How to Read the Figure: • Identify the Variables: The variables are listed on the x-axis. They include measures such as “Constant”, “VVORprep”, “VORprep”, “IFOg”, “CORg”, and categories of “Presence of Abnormal Absolute Preponderance (PA)”. • Understand the Coefficients: The position of the black dots on the y-axis represents the beta coefficients for each variable. A positive coefficient indicates a positive association with the dynamic SVV bisector angle change, while a negative coefficient indicates a negative association. • Evaluate Significance: Look at the red dots to identify significant variables. These variables have a statistically significant association with the dynamic SVV bisector angle change. Error bars that do not cross the horizontal dashed line at zero also indicate significance. Variable Definitions: • VVORprep: Preponderance observed during the sensitized burst test for the visuo-vestibulo-ocular reflex study, • VORprep: Preponderance observed during the sensitized burst test for the vestibulo-ocular reflex study, • IFOg: Gain obtained during the sensitized burst test for the study of the ocular fixation index, • CORg: Gain obtained during the sensitized burst test for the study of the cervico-ocular reflex index, • PA (Yes): Abnormal absolute preponderance (≥2°/s) in the bithermal test, • PA (No): Normal absolute preponderance (≤2°/s) in the bithermal test.