東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Data-Driven Approaches to Fast Volta...

Movassaghi, Cameron Shane.

Linked to FindBook

Google Book

Amazon

博客來

Data-Driven Approaches to Fast Voltammetry for Neurochemical Detection.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-Driven Approaches to Fast Voltammetry for Neurochemical Detection./
Author:	Movassaghi, Cameron Shane.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	357 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Analytical chemistry. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31300681
ISBN:	9798382790046

Data-Driven Approaches to Fast Voltammetry for Neurochemical Detection.
Movassaghi, Cameron Shane.

Data-Driven Approaches to Fast Voltammetry for Neurochemical Detection. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 357 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

My research develops tools to enable understanding of how information is encoded in chemical messengers (neurotransmitters; e.g., serotonin, dopamine) in the brain by measuring them in real-time, in awake, behaving mice models. A molecular scale understanding of complex behavior and brain (dys)function would enable better treatments for neuropsychiatric disorders such as anxiety and depression (which affect 50% of families worldwide). A major challenge is the lack of analytical techniques that can detect multiple neurotransmitters simultaneously, across physiologically and behaviorally relevant time scales (ms, min, h). To date, the most suitable technique is voltammetry, which applies a specific voltage waveform to a micron-sized brain-implantable sensor. However, voltammetry is limited by the number of analytes and timescales that can be achieved for in vivo monitoring, impeding an understanding of how our brains encode information across neurotransmitters in a coordinated manner.My graduate work involves developing a new voltammetry technique that combines novel voltammetry waveforms with machine learning. The technique is called rapid pulse voltammetry with partial least squares regression (RPV-PLSR), which utilizes a new type of pulse waveform and data analysis approach. Using RPV-PLSR, we published a proof-of-concept study to monitor serotonin and dopamine at both basal and stimulated levels, simultaneously, and compared these results to conventional approaches. We showed the utility of faradaic and non-faradaic current responses to build robust statistical models. The unique electrochemical signals present in pulse, rather than sweep, waveforms provide valuable chemical information in the background current that other methods do not generate.{A0}However, the design of RPV waveforms (and voltammetry waveforms in general) is an arduous and inefficient process. To address this challenge, I developed a machine learning-guided approach for systematic design of novel-yet-optimal voltammetry waveforms using Bayesian optimization. Here, I identified optimal serotonin detection RPV waveforms under various physiologically relevant conditions.Lastly, the custom waveforms and data analysis procedures used for RPV required the development of several open-source software solutions for user-friendly acquisition, analysis, storage, and sharing of voltammetry data at scale, which are reported herein. I outline how future extensions of fast voltammetry and machine learning can address domain generalizability (i.e., the 'beaker-to-brain' issue), and how the techniques developed herein will broaden the scope of multi-analyte electroanalytical method development.{A0}

ISBN: 9798382790046Subjects--Topical Terms:

3168300
Analytical chemistry.
Subjects--Index Terms:

Dopamine

Data-Driven Approaches to Fast Voltammetry for Neurochemical Detection.
LDR:03931nmm a2200421 4500 001 2398259
005 20240812064417.5
006 m o d
007 cr#unu||||||||
008 251215s2024 ||||||||||||||||| ||eng d
020 $a 9798382790046
035 $a (MiAaPQ)AAI31300681
035 $a AAI31300681
040 $a MiAaPQ $c MiAaPQ
100 1 $a Movassaghi, Cameron Shane. $3 3768175
245 1 0 $a Data-Driven Approaches to Fast Voltammetry for Neurochemical Detection.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 357 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
500 $a Advisor: Andrews, Anne M.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2024.
520 $a My research develops tools to enable understanding of how information is encoded in chemical messengers (neurotransmitters; e.g., serotonin, dopamine) in the brain by measuring them in real-time, in awake, behaving mice models. A molecular scale understanding of complex behavior and brain (dys)function would enable better treatments for neuropsychiatric disorders such as anxiety and depression (which affect 50% of families worldwide). A major challenge is the lack of analytical techniques that can detect multiple neurotransmitters simultaneously, across physiologically and behaviorally relevant time scales (ms, min, h). To date, the most suitable technique is voltammetry, which applies a specific voltage waveform to a micron-sized brain-implantable sensor. However, voltammetry is limited by the number of analytes and timescales that can be achieved for in vivo monitoring, impeding an understanding of how our brains encode information across neurotransmitters in a coordinated manner.My graduate work involves developing a new voltammetry technique that combines novel voltammetry waveforms with machine learning. The technique is called rapid pulse voltammetry with partial least squares regression (RPV-PLSR), which utilizes a new type of pulse waveform and data analysis approach. Using RPV-PLSR, we published a proof-of-concept study to monitor serotonin and dopamine at both basal and stimulated levels, simultaneously, and compared these results to conventional approaches. We showed the utility of faradaic and non-faradaic current responses to build robust statistical models. The unique electrochemical signals present in pulse, rather than sweep, waveforms provide valuable chemical information in the background current that other methods do not generate.{A0}However, the design of RPV waveforms (and voltammetry waveforms in general) is an arduous and inefficient process. To address this challenge, I developed a machine learning-guided approach for systematic design of novel-yet-optimal voltammetry waveforms using Bayesian optimization. Here, I identified optimal serotonin detection RPV waveforms under various physiologically relevant conditions.Lastly, the custom waveforms and data analysis procedures used for RPV required the development of several open-source software solutions for user-friendly acquisition, analysis, storage, and sharing of voltammetry data at scale, which are reported herein. I outline how future extensions of fast voltammetry and machine learning can address domain generalizability (i.e., the 'beaker-to-brain' issue), and how the techniques developed herein will broaden the scope of multi-analyte electroanalytical method development.{A0}
590 $a School code: 0031.
650 4 $a Analytical chemistry. $3 3168300
650 4 $a Neurosciences. $3 588700
650 4 $a Physical chemistry. $3 1981412
650 4 $a Bioinformatics. $3 553671
653 $a Dopamine
653 $a Electroanalytical method
653 $a Electrochemistry
653 $a Machine learning
653 $a Neurotransmitter
653 $a Serotonin
690 $a 0486
690 $a 0317
690 $a 0800
690 $a 0715
690 $a 0494
710 2 $a University of California, Los Angeles. $b Chemistry 0153. $3 2096181
773 0 $t Dissertations Abstracts International $g 85-12B.
790 $a 0031
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31300681