Browsing by Author "Acevedo, Nicola"

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    Anatomical targeting for electrode localization in subthalamic nucleus deep brain stimulation: A comparative study.
    (Journal of NeuroImaging, 2023-06-08) Tonroe, Thomas; McDermott, Hugh; Pearce, Patrick; Acevedo, Nicola; Thevathasan, Wesley; Xu, San San; Bulluss, Kristian; Perera, Thushara
    In deep brain stimulation (DBS), accurate electrode placement is essential for optimizing patient outcomes. Localizing electrodes enables insight into therapeutic outcomes and development of metrics for use in clinical trials. Methods of defining anatomical targets have been described with varying accuracy and objectivity. To assess variability in anatomical targeting, we compare four methods of defining an appropriate target for DBS of the subthalamic nucleus for Parkinson's disease.
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    Objective measurement of tinnitus using functional near-infrared spectroscopy and machine learning
    (Plos One, 2020-11) Shoushtarian, Mehrnaz; Alizadehsani, Roohallah; Khosravi, Abbas; Acevedo, Nicola; McKay, Colette; Nahavandi, Saeid; Fallon, James
    Chronic tinnitus is a debilitating condition which affects 10-20% of adults and can severely impact their quality of life. Currently there is no objective measure of tinnitus that can be used clinically. Clinical assessment of the condition uses subjective feedback from individuals which is not always reliable. We investigated the sensitivity of functional near-infrared spectroscopy (fNIRS) to differentiate individuals with and without tinnitus and to identify fNIRS features associated with subjective ratings of tinnitus severity. We recorded fNIRS signals in the resting state and in response to auditory or visual stimuli from 25 individuals with chronic tinnitus and 21 controls matched for age and hearing loss. Severity of tinnitus was rated using the Tinnitus Handicap Inventory and subjective ratings of tinnitus loudness and annoyance were measured on a visual analogue scale. Following statistical group comparisons, machine learning methods including feature extraction and classification were applied to the fNIRS features to classify patients with tinnitus and controls and differentiate tinnitus at different severity levels. Resting state measures of connectivity between temporal regions and frontal and occipital regions were significantly higher in patients with tinnitus compared to controls. In the tinnitus group, temporal-occipital connectivity showed a significant increase with subject ratings of loudness. Also in this group, both visual and auditory evoked responses were significantly reduced in the visual and auditory regions of interest respectively. Naïve Bayes classifiers were able to classify patients with tinnitus from controls with an accuracy of 78.3%. An accuracy of 87.32% was achieved using Neural Networks to differentiate patients with slight/ mild versus moderate/ severe tinnitus. Our findings show the feasibility of using fNIRS and machine learning to develop an objective measure of tinnitus. Such a measure would greatly benefit clinicians and patients by providing a tool to objectively assess new treatments and patients' treatment progress.
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    Objective Tinnitus Measurement via fNIRS and Machine Learning
    (Wolters Kluwer Health, 2021-02) Shoushtarian, Mehrnaz; Alizadehsani, Roohallah; Khosravi, Abbas; Acevedo, Nicola; McKay, Colette; Nahavandi, Saeid; Fallon, James