Perspective: Hippocampal theta rhythm as a potential vestibuloacoustic biomarker of anxiety.

Anxiety disorders are the most common mental illnesses - afflicting 19% of Americans every year and 31% within their lifetimes - yet diagnoses remain based on symptom checklists because existing technologies have yet to produce biomarkers sufficiently robust for clinical use. Some techniques provide superior spatial resolution of deep brain regions implicated in anxiety but have poor time resolution; while others measure signals in real time but lack spatial resolution. Often, the goal of probing deep brain regions in humans for anxiety research is to measure a putative analogue of a mammalian brain rhythm linked to behaviour that is suggestive of anxiety.

Gold nanostructures in melanoma: Advances in treatment, diagnosis, and theranostic applications.

Melanoma, a lethal form of skin cancer, poses a significant challenge in oncology due to its aggressive nature and high mortality rates. Gold nanostructures, including gold nanoparticles (GNPs), offer myriad opportunities in melanoma therapy and imaging due to their facile synthesis and functionalization, robust stability, tunable physicochemical and optical properties, and biocompatibility. This review explores the emerging role of gold nanostructures and their composites in revolutionizing melanoma treatment paradigms, bridging the gap between nanotechnology and clinical oncology, and offering insights for researchers, clinicians, and stakeholders.

Using principal component analysis to determine which vestibular stimuli provide best biomarkers for separating Alzheimer's from mixed Alzheimer's disease.

Alzheimer's disease (AD) is often mixed with cerebrovascular disease (AD-CVD). Heterogeneity of dementia etiology and the overlapping of neuropathological features of AD and AD-CVD make feature identification of the two challenging. Separation of AD from AD-CVD is important as the optimized treatment for each group may differ.

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.

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

: 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. : 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.

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.

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.

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.

Author Correction: Quantitative measurement of post-concussion syndrome Using Electrovestibulography.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A Pilot Randomised Double-Blind Study of the Tolerability and efficacy of repetitive Transcranial Magnetic Stimulation on Persistent Post-Concussion Syndrome.

This study investigates the effect of Repetitive Transcranial Magnetic Stimulation (rTMS) on persistent post-concussion syndrome (PCS). The study design was a randomized (coin toss), placebo controlled, and double-blind study. Thirty-seven participants with PCS were assessed for eligibility; 22 were randomised and 18 completed the study requirements.

Quantitative measurement of post-concussion syndrome Using Electrovestibulography.

In this study, a noninvasive quantitative measure was used to identify short and long term post-concussion syndrome (PCS) both from each other and from healthy control populations. We used Electrovestibulography (EVestG) for detecting neurophysiological PCS consequent to a mild traumatic brain injury (mTBI) in both short-term (N = 8) and long-term (N = 30) (beyond the normal recovery period) symptomatic individuals. Peripheral, spontaneously evoked vestibuloacoustic signals incorporating - and modulated by - brainstem responses were recorded using EVestG, while individuals were stationary (no movement stimulus).

Application of Vestibular Spontaneous Response as a Diagnostic Aid for Meniere's Disease.

In this paper, we report on a new method for assisting in Meniere's disease diagnosis. An accurate diagnosis of Meniere's is challenging, and requires an expert opinion after observing several clinical assessments and tests over a period of time. Our proposed method is based on the analysis of the spontaneous and driven ear evoked responses recorded using Electrovestibulography (EVestG).

A new diagnostic vestibular evoked response.

Objective: To describe the development of a new clinically applicable method for assessing vestibular function in humans with particular application in Meniere's disease.

Study Design: Sophisticated signal-processing techniques were applied to data from human subject undergoing tilts stimulating the otolith organs and semicircular canals. The most sensitive representatives of vestibular function were extracted as "features".

Preliminary report: neural firing patterns specific for Meniere's disease.

Objective: To describe the application of a new, objective diagnostic test for Meniere's disease.

Introduction: Electrovestibulography (EVestG) is a complex, newly-developed test paradigm that searches for neural firing patterns that may be diagnostic for particular neural disorders. EVestG system was previously "trained" to distinguish Meniere's disease from other patients on a set of training data.

Vestibular spontaneous response as a potential signature for Parkinson's disease.

In this paper, we report on a new method for potential diagnosis of Parkinson's Disease (PD) based on the analysis of the spontaneous response of vestibular system recorded by Electrovestibulography (EVestG). EVestG data of 20 individuals with PD and 28 healthy controls were adopted from a previous study. The field potentials and their firing pattern in response to whole body tilt stimuli from both left and right ears were extracted.

Diagnosis of Parkinson's disease using electrovestibulography.

In this paper, a new method for diagnosis of Parkinson's disease (PD) based on the analysis of electrovestibulography (EVestG) signals is introduced. EVestG signals are in fact the vestibular response modulated by more cortical brain signals; they are recorded from the ear canal. EVestG data of 20 individuals with PD and 28 healthy controls were adopted from a previous study.

Application of fractal dimension on vestibular response signals for diagnosis of Parkinson's disease.

In this paper, a novel method based on analysis of dynamic response of vestibular system for diagnosis of Parkinson's Disease (PD) is introduced. Electrovestibulography (EVestG) signals are recorded from the ear canal in response to a vestibular stimulus. EVestG signals are in fact the vestibular response modulated by more cortical brain signals.

Intelligent arrhythmia detection and classification using ICA.

In this paper a novel approach for cardiac arrhythmias detection is proposed. The proposed method is based on using independent component analysis (ICA) and wavelet transform to extract important features. Using the extracted features different machine learning classification schemas, MLP and RBF neural networks and K-nearest neighbor, are used to classify 274 instance signals from the MIT-BIH database.