Quantitative Analysis of Factors of Attrition in a Double-blind rTMS Study for Alzheimer Treatment.

Attrition is a particular concern in studies examining the efficacy of a treatment for Alzheimer disease. Analyzing reasons for withdrawal in Alzheimer studies is crucial to ruling out attrition bias, which can undermine a study's validity. In contrast, attrition in studies using repetitive transcranial magnetic stimulation (rTMS) has received much less attention. Our goal was to identify any commonalities between participants who withdrew for the same reasons. Three independent coders rated each response concerning the reasons for withdrawal, and frequency tables were generated to characterize the participants within each category. This study was conducted on the 28 withdrawn cases from a 7-month study investigating the short-term and long-term therapeutic effects of rTMS for Alzheimer disease among 156 participants across 3 sites of the study. Seven reasons for withdrawal were identified, with health and medical changes being the most commonly reported reason (7 participants). Personal issues involving family or caregivers were the next most common (5 participants), and the remaining 5 categories consisted of 3 participants each. Although the limited sample size prevented the use of inferential statistics, our findings highlight the need for more transparent reporting of attrition rates and withdrawal reasons by rTMS researchers.

Evaluating the Diagnostic Value of Electrovestibulography (EVestG) in Alzheimer's Patients with Mixed Pathology: A Pilot Study.

Background and Objectives: Diagnosis of dementia subtypes caused by different brain pathophysiologies, particularly Alzheimer's disease (AD) from AD mixed with levels of cerebrovascular disease (CVD) symptomology (AD-CVD), is challenging due to overlapping symptoms. In this pilot study, the potential of Electrovestibulography (EVestG) for identifying AD, AD-CVD, and healthy control populations was investigated. Materials and Methods: A novel hierarchical multiclass diagnostic algorithm based on the outcomes of its lower levels of binary classifications was developed using data of 16 patients with AD, 13 with AD-CVD, and 24 healthy age-matched controls, and then evaluated on a blind testing dataset made up of a new population of 12 patients diagnosed with AD, 9 with AD-CVD, and 8 healthy controls. Multivariate analysis was run to test the between population differences while controlling for sex and age covariates. Results: The accuracies of the multiclass diagnostic algorithm were found to be 85.7% and 79.6% for the training and blind testing datasets, respectively. While a statistically significant difference was found between the populations after accounting for sex and age, no significant effect was found for sex or age covariates. The best characteristic EVestG features were extracted from the upright sitting and supine up/down stimulus responses. Conclusions: Two EVestG movements (stimuli) and their most informative features that are best selective of the above-populations' separations were identified, and a hierarchy diagnostic algorithm was developed for three-way classification. Given that the two stimuli predominantly stimulate the otholithic organs, physiological and experimental evidence supportive of the results are presented. Disruptions of inhibition associated with GABAergic activity might be responsible for the changes in the EVestG features.

Correlation between Ocular and Vestibular Abnormalities and Convergence Insufficiency in Post-Concussion Syndrome.

The vestibular and oculomotor/visual systems are commonly affected in post-concussion syndrome (PCS). Convergence insufficiency (CI) is the most common ocular abnormality after concussion. Electrovestibulography (EVestG) is a relatively new non-invasive method that measures the peripheral vestibular responses; it has shown abnormal vestibular responses in a PCS. Here, we report the results of investigating the correlation between the vestibular and oculomotor systems in PCS population using EVestG and CI measures. Forty-eight PCS patients were tested using EVestG, out of which 20 also completed the Rivermead post-concussion questionnaire (RPQ). An EVestG feature (Field Potential (FP)-area) was extracted from the stationary part of the EVestG signals. A neuro-ophthalmologist (author BM) measured participants' CI at near vision using cross-cover examination and a prism-bar. Results indicate: (1) vestibular abnormality (i.e. FP-area) and CI values are significantly correlated in PCS (R = 0.68, p < .01), and (2) there are significant correlations between severity of concussion (i.e. RPQ3) and CI (R = 0.70, p < .01) and between RPQ3 and FP-area (R = -0.56, p < .02). To the best of our knowledge, this is the first study that objectively demonstrates a significant positive correlation between the CI and vestibular systems' abnormality. These findings are scientifically important as they help localise the pathology of PCS, and are clinically valuable as they help physicians in their decision-making about PCS diagnosis and rehabilitation strategies.

Bipolar disorder in the balance.

Bipolar disorder (BD) is a severe mood disorder that lacks established electrophysiological, neuroimaging or biological markers to assist with both diagnosis and monitoring disease severity. This study's aim is to describe the potential of new neurophysiological features assistive in BD diagnosis and severity measurement utilizing the recording of electrical activity from the outer ear canal called Electrovestibulography (EVestG). From EVestG data sensory vestibulo-acoustic features were extracted from a single supine-vertical translation stimulus to distinguish 50 depressed and partly remitted/remitted bipolar disorder patients [18 symptomatic (BD-S, MADRS > 19), 32 reduced symptomatic (BD-R, MADRS ≤ 19)] and 31 age and gender matched healthy individuals (controls). Six features were extracted from the measured firing pattern interval histogram and the extracted shape of the average field potential response. Five of the six features had low but significant correlations (p < 0.05) with the MADRS assessment. Using leave-one-out-cross-validation, unbiased parametric and non-parametric classification routines resulted in 75-79%, 84-86%, 76-85% and 79-82% accuracy for separation of control from BD, BD-S and BD-R as well as BD-S from BD-R groups, respectively. The main limitation of this study was the inability to fully disentangle the impact of prescribed medication from the responses recorded. A mix of stationary and movement evoked EVestG features produced good discrimination between control and BD patients whether BD-S or BD-R. Moreover, BD-S and BD-R appear to have measurably different pathophysiological manifestations. The firing pattern features used were dissimilar to those observed in a prior major depressive disorder study.

Physiological Differences in the Follicular, Luteal, and Menstrual Phases in Healthy Women Determined by Electrovestibulography: Depression, Anxiety, or Other Associations?

Electrovestibulography (EVestG) recordings have been previously applied toward classifying and/or measuring the severity of several neurological disorders including depression with and without anxiety. This study's objectives were to: (1) extract EVestG features representing physiological differences of healthy women during their menses, and follicular and luteal phases of their menstrual cycle, and (2) compare these features to those observed in previous studies for depression with and without anxiety. Three EVestG recordings were made on 15 young healthy menstruating females during menses, and follicular and luteal phases. Three features were extracted, using the shape and timing of the detected spontaneously evoked vestibulo-acoustic field potentials. Using these features, a 3-way separation of the 3 phases was achieved, with a leave-one-out cross-validation, resulting in accuracy of > 72%. Using an EVestG shape feature, separation of the follicular and luteal phases was achieved with a leave-one-out cross-validation accuracy of > 93%. The mechanism of separation was not like that in previous depression analyses, and is postulated to be more akin to a form of anxiety and/or progesterone sensitivity.

Development of an ultra low noise, miniature signal conditioning device for vestibular evoked response recordings.

BACKGROUND: Inner ear evoked potentials are small amplitude (<1 µVpk) signals that require a low noise signal acquisition protocol for successful extraction; an existing such technique is Electrocochleography (ECOG). A novel variant of ECOG called Electrovestibulography (EVestG) is currently investigated by our group, which captures vestibular responses to a whole body tilt. The objective is to design and implement a bio-signal amplifier optimized for ECOG and EVestG, which will be superior in noise performance compared to low noise, general purpose devices available commercially. METHOD: A high gain configuration is required (>85 dB) for such small signal recordings; thus, background power line interference (PLI) can have adverse effects. Active electrode shielding and driven-right-leg circuitry optimized for EVestG/ECOG recordings were investigated for PLI suppression. A parallel pre-amplifier design approach was investigated to realize low voltage, and current noise figures for the bio-signal amplifier. RESULTS: In comparison to the currently used device, PLI is significantly suppressed by the designed prototype (by >20 dB in specific test scenarios), and the prototype amplifier generated noise was measured to be 4.8 nV/Hz @ 1 kHz (0.45 µVRMS with bandwidth 10 Hz-10 kHz), which is lower than the currently used device generated noise of 7.8 nV/Hz @ 1 kHz (0.76 µVRMS). A low noise (<1 nV/Hz) radio frequency interference filter was realized to minimize noise contribution from the pre-amplifier, while maintaining the required bandwidth in high impedance measurements. Validation of the prototype device was conducted for actual ECOG recordings on humans that showed an increase (p < 0.05) of ~5 dB in Signal-to-Noise ratio (SNR), and for EVestG recordings using a synthetic ear model that showed a ~4% improvement (p < 0.01) over the currently used amplifier. CONCLUSION: This paper presents the design and evaluation of an ultra-low noise and miniaturized bio-signal amplifier tailored for EVestG and ECOG. The increase in SNR for the implemented amplifier will reduce variability associated with bio-features extracted from such recordings; hence sensitivity and specificity measures associated with disease classification are expected to increase. Furthermore, immunity to PLI has enabled EVestG and ECOG recordings to be carried out in a non-shielded clinical environment.

Gray and White Matter Voxel-Based Morphometry of Alzheimer's Disease With and Without Significant Cerebrovascular Pathologies.

Alzheimer's disease (AD) is the most common type of dementia, and AD individuals often present significant cerebrovascular disease (CVD) symptomology. AD with significant levels of CVD is frequently labeled mixed dementia (or sometimes AD-CVD), and the differentiation of these two neuropathologies (AD, AD-CVD) from each other is challenging, especially at early stages. In this study, we compared the gray matter (GM) and white matter (WM) volumes in AD (n = 83) and AD-CVD (n = 37) individuals compared with those of cognitively healthy controls (n = 85) using voxel-based morphometry (VBM) of their MRI scans. The control individuals, matched for age and sex with our two dementia groups, were taken from the ADNI. The VBM analysis showed widespread patterns of significantly lower GM and WM volume in both dementia groups compared to the control group (P < .05, family-wise error corrected). While comparing with AD-CVD, the AD group mainly demonstrated a trend of lower volumes in the GM of the left putamen and right hippocampus and WM of the right thalamus (uncorrected P < .005 with cluster threshold, K = 10). The AD-CVD group relative to AD tended to present lower GM and WM volumes, mainly in the cerebellar lobules and right brainstem regions, respectively (uncorrected P < .005 with cluster threshold, K = 10). Although finding a discriminatory feature in structural MRI data between AD and AD-CVD neuropathologies is challenging, these results provide preliminary evidence that demands further investigation in a larger sample size.

Measuring anxiety disorder in bipolar disorder using EVestG: broad impact of medication groups.

Objectives: Anxiety disorder is present in approximately half of all bipolar disorder (BD) patients. There are neurologic bases for the comorbidity of balance (vestibular) disorders and anxiety. Our objective is to use electrovestibulography (EVestG), which is predominantly a measure of vestibular neural activity to not only quantitatively detect and measure comorbid anxiety disorder but also to quantitatively measure the impacts of anti-depressant, anti-psychotic, and mood stabilizer medication groups on anxiety measures in BD patients. Methods: In a population of 50 (24 with anxiety disorder) depressive phase BD patients, EVestG signals were measured. Participants were labeled depression-wise as anxious or non-anxious using standard questionnaires. Analyses were conducted on the whole dataset as well as on matched (age/gender/MADRS) and "modeled medication-free" subsets. Modulations of the low-frequency EVestG firing pattern data were measured. Findings: For BD, the main anxious minus non-anxious difference was the presence of an increase in spectral power proximal to 8-9 Hz, which was best attenuated by mood stabilizers. Novelty: This is the first study to use an oto-acoustic physiological measure to quantify anxiety disorder in BD wherein it appears to manifest as a peak proximal to 8-9 Hz which we hypothesize as likely linked to hippocampal theta.

An unbiased algorithm for objective separation of Alzheimer's, Alzheimer's mixed with cerebrovascular symptomology, and healthy controls from one another using electrovestibulography (EVestG).

Diagnosis of Alzheimer's disease (AD) from AD with cerebrovascular disease pathology (AD-CVD) is a rising challenge. Using electrovestibulography (EVestG) measured signals, we develop an automated feature extraction and selection algorithm for an unbiased identification of AD and AD-CVD from healthy controls as well as their separation from each other. EVestG signals of 24 healthy controls, 16 individuals with AD, and 13 with AD-CVD were analyzed within two separate groupings: One-versus-One and One-versus-All. A multistage feature selection process was conducted over the training dataset using linear support vector machine (SVM) classification with 10-fold cross-validation, k nearest neighbors/averaging imputation, and exhaustive search. The most frequently selected features that achieved highest classification performance were selected. 10-fold cross-validation was applied via a linear SVM classification on the entire dataset. Multivariate analysis was run to test the between population differences while controlling for the covariates. Classification accuracies of ≥ 80% and 78% were achieved for the One-versus-All classification approach and AD versus AD-CVD separation, respectively. The results also held true after controlling for the effect of covariates. AD/AD-CVD participants showed smaller/larger EVestG averaged field potential signals compared to healthy controls and AD-CVD/AD participants. These characteristics are in line with our previous study results.

Baseline Prediction of rTMS efficacy in Alzheimer patients.

Repetitive transcranial magnetic stimulation (rTMS) with extensive 2-6-week protocols are applied to improve cognition and/or slow the cognitive decline seen in Alzheimer's Disease (AD). To date, there are no means to predict the response of a patient to rTMS treatment at baseline. Electrovestibulography (EVestG) biomarkers can be used to predict, at baseline, the efficacy of rTMS when applied to AD individuals. In a population of 27 AD patients (8 with significant cerebrovascular symptomatology, labelled ADcvd) EVestG signals were measured before and after rTMS treatment, and then compared with 16 age-matched healthy controls. MoCA was measured at baseline, whilst ADAS-Cog was the primary outcome measure. AD severity and comorbid cerebrovascular disease were treated as covariates. Using ADAS-Cog total score change, 13/27 AD/ADcvd patients improved with rTMS and 14/27 showed no-improvement. Leave-one-out-cross-validated linear-discriminant-analysis using two EVestG features yielded a blind accuracy of 75% for separating the improved and non-improved populations. Three-way separation of improved/non-improved/control accuracy was 91.9% using MoCA (67% alone) and one EVestG feature (66% alone). AD severity affects the rTMS treatment efficacy. The effect of existing significant cerebrovascular symptomatology on the efficacy of rTMS treatment remains unresolved. Baseline EVestG features can be predictive of the efficacy of rTMS treatment.

Physiological separation of Alzheimer's disease and Alzheimer's disease with significant levels of cerebrovascular symptomology and healthy controls.

Most dementia patients with a mixed dementia (MxD) diagnosis have a mix of Alzheimer's disease (AD) and vascular dementia. Electrovestibulography (EVestG) records vestibuloacoustic afferent activity. We hypothesize EVestG recordings of AD and MxD patients are different. All patients were assessed with the Montreal cognitive assessment (MoCA) and Hachinski ischemic scale (HIS) (> 4 HIS score < 7 is representative of MxD cerebrovascular symptomology). EVestG recordings were made from 26 AD, 21 MxD and 44 healthy (control) participants. Features were derived from the EVestG recordings of the average field potential and field potential interval histogram to classify the AD, MxD and control groups. Multivariate analysis was used to test the features' significance. Using a leave-one-out cross-validated linear discriminant analysis with 3 EVestG features yielded accuracies > 80% for separating pairs of AD/MxD/control. Using the MoCA assessment and 2 EVestG features, a best accuracy of 81 to 91% depending on the classifier was obtained for the 3-way identification of AD, MxD and controls. EVestG measures provide a physiological basis for identifying AD from MxD. EVestG measures are hypothesized to be partly related to channelopathies and changes in the descending input to the vestibular periphery. Four of the five AD or MxD versus control features used had significant correlations with the MoCA. This supports assertions that the pathologic changes associated with AD impact the vestibular system and further are suggestive that the postulated physiological changes behind these features have an association with cognitive decline severity.

Quantitative separation of the depressive phase of bipolar disorder and major depressive disorder using electrovestibulography.

Objectives: No electrophysiological, neuroimaging or genetic markers have been established that strongly relate to the diagnostic separation of bipolar disorder (BD) and major depressive disorder (MDD). This paper's objective is to describe the potential of features, extracted from the recording of electrical activity from the outer ear canal, in a process called electrovestibulography (EVestG), for identifying depressed and partly remitted/remitted MDD and BD patients from each other.Methods: From EVestG data four sensory vestibulo-acoustic features were extracted from both background (no movement) and using a single supine-vertical translation stimulus to distinguish 27 controls, 39 MDD and 43 BD patients.Results: Using leave-one-out-cross-validation, unbiased parametric and non-parametric classification routines resulted in 78-83% (2-3 features), 80-81% (1-2 features) and 66-68% (3 features) accuracies for separation of MDD from BD, controls from depressed (BD & MDD) and the 3-way separation of BD from MDD from control groups, respectively. The main limitations of this study were the inability to fully disentangle the impact of prescribed medication from the responses and also the limited sample size.Conclusions: EVestG features can reliably identify depressed and partly remitted/remitted MDD and BD patients from each other.

Parkinson's disease: disturbed vestibular function and levodopa.

Evidence indicates Levodopa effects central postural control. As electrophysiological postural control biomarkers, sensory oto-acoustic features were extracted from Electrovestibulography (EVestG) data to identify 20 healthy age and gender matched individuals as Controls from 20 PD subjects before (PDlowmed) and 18 after (PDmed) morning doses of Levodopa. EVestG data was collected using a single tilt stimulus applied in the pitch plane. The extracted features were based on the measured firing pattern, interval histogram and the shape of the average field potential response. An unbiased cross validated classification accuracy of 88%, 88% and 79% was achieved using combinations of 2 features for separating PDlowmed from control, control from PD (combined PDlowmed and PDmed), and PDlowmed from PDmed groups respectively. One feature showed significant correlations (p<0.05) with the Modified Hoehn and Yahr PD staging scale. The results indicate disturbed vestibular function is observed in both the PDmed and PDlowmed conditions, and these are separable. The implication is that Levodopa may also affect peripheral as well as central postural control.

Major depression and electrovestibulography.

OBJECTIVES: No electrophysiological neuroimaging or genetic markers have been established that strongly relate to a diagnosis of major depression or its severity. The objective of this paper is to describe the preliminary evaluation of a potential new biomarker for depression utilizing the recording of electrical activity from the outer ear canal referred to as electrovestibulography (EVestG). METHODS: Sensory oto-acoustic features were extracted from EVestG data to compare 31 healthy age- and gender-matched individuals as controls to 43 major depressive disorder (MDD) subjects (22 symptomatic (MDD-S), 21 reduced symptomatic (MDD-R)). The stimulus was a single supine-vertical translation. The six features examined were based on the measured firing pattern interval histogram and the shape of the average field potential response. RESULTS: An unbiased classification accuracy of 85, 87 and 77% was achieved for separating Control from MDD-S, Control from MDD, and MDD-S from MDD-R groups respectively. Features used showed low but significant correlations (P < 0.05) with MADRS and CORE assessments. CONCLUSIONS: The results support the use of separate features for measuring MDD symptomatology versus diagnosing MDD, representing plausible different mechanisms of brain function in MDD-S and MDD-R. The first evidence of the successful application of sensory oto-acoustic features toward diagnosing and measuring the symptomatology of MDD is presented.

EVestG: a measure for Meniere's Disease.

Meniere's Disease is commonly diagnosed using Electrocochleography (ECOG). EVestG is a variant of ECOG utilizing one or more patient tilts as stimuli in place of the ECOG's repeated tonal clicks. The dynamic measures averaged 'background-onAA' (onAA=acceleration phase of tilt) and background-onBB (onBB=deceleration phase of tilt) of excitatory (ipsilateral tilt) vestibular responses are compared for a small group of age matched Controls (n=18) and Meniere's Disease patients (n=11). Preliminary data provides for an apparent clearer demarcation between Controls and Meniere's patients. Meniere's patients appear to show not only increased Sp/Ap ratios but also a decreased dynamic range of response as measured by the EVestG response measure averaged 'background-onBB'. Increased sample size is required to validate these findings.

EVestG: a diagnostic measure for schizophrenia.

Schizophrenia is a severe mental illness associated with multiple neuropathological, neurochemical and genetic abnormalities. The benefits of a validated, quantitative diagnosis tool are well established. Electrovestibulography, a new method similar to ECOG, can detect and record neural activity generated by the vestibular system. The normal EVestG response as well as dynamic measures averaged 'background-onAA' (onAA=acceleration phase of tilt) and 'background-onBB' (onBB=deceleration phase of tilt) of excitatory (ipsi-lateral tilt) vestibular responses are compared for a small group of schizophrenia patients (n=4) and age matched healthy controls (n=10). Our preliminary results show an apparent discrimination between control and schizophrenia groups. Schizophrenia patients appear not only to exhibit an overall decreased EVestG signal amplitude but a suppressed dynamic response calculated by the averaged EVestG 'background-onBB' measure. Increased sample size is required to validate these findings.

EVestG: responses in depressed patients.

Depression is a mood disorder characterized by a sustained period of low mood or loss of interest or pleasure in activities. Dysfunction of the limbic system is implicated in depression. Different areas of this system have bidirectional links with the vestibular nucleus. The diagnosis of depression is subjective. Electrovestibulography (EVestG) might be able to assist in the diagnosis of depression in a more objective way. A dynamic measure of the excitatory (ipsilateral) responses during the background, acceleration and deceleration phases of an active tilt are compared with a small group (n=5) of depressive patients and age matched controls (n=10).

A pilot investigation of the effect of extremely low frequency pulsed electromagnetic fields on humans' heart rate variability.

The question whether pulsed electromagnetic field (PEMF) can affect the heart rhythm is still controversial. This study investigates the effects on the cardiocirculatory system of ELF-PEMFs. It is a follow-up to an investigation made of the possible therapeutic effect ELF-PEMFs, using a commercially available magneto therapeutic unit, had on soft tissue injury repair in humans. Modulation of heart rate (HR) or heart rate variability (HRV) can be detected from changes in periodicity of the R-R interval and/or from changes in the numbers of heart-beat/min (bpm), however, R-R interval analysis gives only a quantitative insight into HRV. A qualitative understanding of HRV can be obtained considering the power spectral density (PSD) of the R-R intervals Fourier transform. In this study PSD is the investigative tool used, more specifically the low frequency (LF) PSD and high frequency (HF) PSD ratio (LF/HF) which is an indicator of sympatho-vagal balance. To obtain the PSD value, variations of the R-R time intervals were evaluated from a continuously recorded ECG. The results show a HR variation in all the subjects when they are exposed to the same ELF-PEMF. This variation can be detected by observing the change in the sympatho-vagal equilibrium, which is an indicator of modulation of heart activity. Variation of the LF/HF PSD ratio mainly occurs at transition times from exposure to nonexposure, or vice versa. Also of interest are the results obtained during the exposure of one subject to a range of different ELF-PEMFs. This pilot study suggests that a full investigation into the effect of ELF-PEMFs on the cardiovascular system is justified.