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September 2023

4 participants
12 discussions

FW: Seminars in Hearing Research. September 14. Edward Bartlett. Nelson Hall - Room 1215. 12:00 - 1:00 pm
by Abrol, Sangeeta Saddul 08 Sep '23

08 Sep '23

Seminars in Hearing Research. September 14. Edward Bartlett. Nelson Hall - Room 1215. 12:00 - 1:00 pm [cid:7322f8b5-d221-4f6d-97a5-0b2291b76d9c] Seminars in Hearing Research Date: September 14, 2023 Place: Nelson Hall Room 1215 (located on University St right across from Lyles-Porter). Time: 12:00 - 1:00 pm Speaker: Dr. Edward Bartlett Title: Spatially-specific, closed-loop, infrared thalamocortical deep brain stimulation Brandon S Coventry1,2*, Georgia L Lawlor1,2, Christina B Bagnatti1, Claudia Krogmeier4, Edward L Bartlett1,2,4* Abstract: Deep brain stimulation (DBS) is a powerful clinical tool for the treatment of circuit-based neurological disorders such as Parkinson's disease and obsessive-compulsive disorder. Electrical DBS is, however, limited by the spread of stimulus currents into tissue unrelated to treatment, potentially causing abhorrent patient side effects. In this work, we utilize infrared neural stimulation (INS), an optical neuromodulation technique which uses near to mid infrared light, to drive graded excitatory and inhibitory responses in nerves and neurons, to facilitate an optical and spatially constrained DBS paradigm. INS has been shown to provide spatially constrained responses in the cochlea and in cortical neurons. Unlike other optical techniques, INS does not require genetic modification of neural targets. In this study, we show that INS produces graded, biophysically relevant single-unit responses with robust information transfer in thalamocortical circuits. Importantly, we show that cortical spread of activation from thalamic INS produces more spatially constrained response profiles than conventional electrical stimulation. Owing to observed spatial precision, we used deep reinforcement learning to close the loop on thalamocortical INS, creating real time representations of stimulus-response dynamics while driving cortical neurons to precise firing patterns. Our data suggest that INS can serve as a spatially precise and for both open and closed-loop DBS. This year's SHRP schedule is available here: https://purdue.edu/TPAN/hearing/shrp_schedule<https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fogww.mj.am…> Titles and abstracts of all SHRP talks are here: https://purdue.edu/TPAN/hearing/shrp_abstracts<https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fogww.mj.am…> There will not be a hybrid option this year. Talks will either be held in-person only or via Zoom only for long-distance speakers. Christine Reidy Senior Administrative Assistant, Bindley Bioscience Center BIND, 1203 W. State Str., West Lafayette, IN 47907 [A close up of a logo Description automatically generated]<http://www.purdue.edu/>

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FW: Seminars in Hearing Research. September 7. Dalton J. Aaker. Nelson Hall - Room 1215. 12:00 - 1:00 pm
by Abrol, Sangeeta Saddul 01 Sep '23

01 Sep '23

[cid:844384ce-32d1-4268-b1cc-61f8978e58ad] Seminars in Hearing Research Date: September 7, 2023 Place: Nelson Hall Room 1215 (located on University St right across from Lyles-Porter). Time: 12:00 - 1:00 pm Speaker: Dalton Aaker, undergraduate student, BME Title: Decoding fNIRS Neural Responses: A Machine Learning Approach Abstract: The aim of this project is to explore a machine learning model that accurately identifies positive auditory-evoked neural responses while controlling for factors that introduce noise to the neural signal and observe the effects of decoding these interferences. Human neuroimaging data collected via functional near-infrared spectroscopy (fNIRS) from a single subject twice daily for five consecutive days was analyzed. The data followed a block-design paradigm with two conditions: meaningful auditory speech and silence serving as a baseline control. Hemoglobin concentration data was collected using a continuous-wave fNIRS system (NIRx NIRSport2) with specific source-detector pairs optimized for brain regions associated with sound perception and language comprehension. Standard fNIRS data cleaning and preprocessing practices were applied, and Python's Sci-kit learn library was utilized for decoding and prediction on the extracted datasets. Estimators were trained on hemoglobin concentrations and applied stimuli, with cross-validation using Stratified K Folding. Some estimators required training on both systemic physiological and fNIRS datasets, using a feature union technique to join the relevant features. Preliminary analysis revealed that the model achieved the strongest predictive ability using only the oxygenated hemoglobin signal. At low subject counts, the best decoding accuracies were achieved using a combination of Galvanic Skin Response (GSR) and oxygenated hemoglobin signals. In general, physiological data did not consistently improve decoding accuracy, except for GSR data. This study provides insights applicable to machine learning, neuroscience, and optical engineering and the ability to combine cofactors for maximum prediction capabilities in machine learning models is a key area of ongoing research. This year's SHRP schedule is available here: https://purdue.edu/TPAN/hearing/shrp_schedule<https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fogww.mj.am…> Titles and abstracts of all SHRP talks are here: https://purdue.edu/TPAN/hearing/shrp_abstracts<https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fogww.mj.am…> There will not be a hybrid option this year. Talks will either be held in-person only or via Zoom only for long-distance speakers. Christine Reidy Senior Administrative Assistant, Bindley Bioscience Center BIND, 1203 W. State Str., West Lafayette, IN 47907 [A close up of a logo Description automatically generated]<http://www.purdue.edu/>

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