{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/119273"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/119273","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Brain Signal Quantification and Functional Unit Analysis in Fluorescent Imaging Data by Unsupervised Learning","abstract":"Optical recording of various brain signals is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. A major challenge in leveraging the technique is to identify and quantify the rich patterns embedded in the data. However, existing methods often struggle, either due to their limited signal analysis capabilities or poor performance. Here we present Activity Quantification and Analysis (AQuA2), an innovative analysis platform built upon machine learning theory. AQuA2 features a novel event detection pipeline for precise quantification of intricate brain signals and incorporates a Consensus Functional Unit (CFU) module to explore interactions among potential functional units driving repetitive signals. To enhance efficiency, we developed BIdirectional pushing with Linear Component Operations (BILCO) algorithm to handle propagation analysis, a time-consuming step using traditional algorithms. Furthermore, considering user-friendliness, AQuA2 is implemented as both a MATLAB package and a Fiji plugin, complete with a graphical interface for enhanced usability. AQuA2's validation through both simulation and real-world applications demonstrates its superior performance compared to its peers. Applied across various sensors (Calcium, NE, and ATP), cell types (astrocytes, oligodendrocytes, and neurons), animal models (zebrafish and mouse), and imaging modalities (two-photon, light sheet, and confocal), AQuA2 consistently delivers promising results and novel insights, showcasing its versatility in fluorescent imaging data analysis.","abstract_html":"Optical recording of various brain signals is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. A major challenge in leveraging the technique is to identify and quantify the rich patterns embedded in the data. However, existing methods often struggle, either due to their limited signal analysis capabilities or poor performance. Here we present Activity Quantification and Analysis (AQuA2), an innovative analysis platform built upon machine learning theory. AQuA2 features a novel event detection pipeline for precise quantification of intricate brain signals and incorporates a Consensus Functional Unit (CFU) module to explore interactions among potential functional units driving repetitive signals. To enhance efficiency, we developed BIdirectional pushing with Linear Component Operations (BILCO) algorithm to handle propagation analysis, a time-consuming step using traditional algorithms. Furthermore, considering user-friendliness, AQuA2 is implemented as both a MATLAB package and a Fiji plugin, complete with a graphical interface for enhanced usability. AQuA2's validation through both simulation and real-world applications demonstrates its superior performance compared to its peers. Applied across various sensors (Calcium, NE, and ATP), cell types (astrocytes, oligodendrocytes, and neurons), animal models (zebrafish and mouse), and imaging modalities (two-photon, light sheet, and confocal), AQuA2 consistently delivers promising results and novel insights, showcasing its versatility in fluorescent imaging data analysis.","abstract_has_math":false,"creators":["Mi, Xuelong"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Electrical Engineering","degree_department":"Electrical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Yu, Guoqiang"],"committee_members":["Wang, Yue J.","Wong, Kenneth H.","Chantem, Thidapat","Dimarino, Christina Marie"],"year":2024,"date_issued":"2024-06-04","date_published":"2024-06-04","updated_at":"2026-07-22T22:18:46Z","subjects":["AQuA2","brain activity","interaction analysis","functional units","time-lapse imaging","machine learning"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:40391"],"render_values":[{"text":"vt_gsexam:40391","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/119273","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Yu, Guoqiang"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Wang, Yue J.","Wong, Kenneth H.","Chantem, Thidapat","Dimarino, Christina Marie"]},{"key":"dc:contributor.department","label":"Department","values":["Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Mi, Xuelong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-06-05T08:01:03Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-06-05T08:01:03Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-06-04"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["AQuA2","brain activity","interaction analysis","functional units","time-lapse imaging","machine learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:40391"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/119273"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Optical recording of various brain signals is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. 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AQuA2's validation through both simulation and real-world applications demonstrates its superior performance compared to its peers. Applied across various sensors (Calcium, NE, and ATP), cell types (astrocytes, oligodendrocytes, and neurons), animal models (zebrafish and mouse), and imaging modalities (two-photon, light sheet, and confocal), AQuA2 consistently delivers promising results and novel insights, showcasing its versatility in fluorescent imaging data analysis."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Understanding and effectively treating brain diseases requires a deep insight into how the brain operates. A crucial aspect of this exploration involves directly visualizing different signals within the brain, allowing researchers to delve into the functions of brain cells and their interactions. However, as data collection expands rapidly, analyzing this wealth of information presents a significant challenge. Existing methods often fall short due to their limited capacity to analyze signals or their subpar performance, failing to keep pace with current demands. In this work, we introduce Activity Quantification and Analysis (AQuA2), an innovative platform rooted in machine learning principles. AQuA2 features a novel event detection pipeline for accurately quantifying intricate brain signals. Additionally, it incorporates a Consensus Functional Unit (CFU) module, which facilitates the exploration of interactions among potential functional units associated with repetitive signals. To enhance efficiency and usability, we have developed acceleration algorithms and released AQuA2 in two versions: a MATLAB package and a Fiji plugin, each designed to address unique user requirements. AQuA2 has demonstrated its efficacy through real-world applications, effectively quantifying and analyzing signals across various platforms such as biosensors, cell types, animal models, and imaging modalities, with promising outcomes. Furthermore, the utilization of AQuA2 has facilitated the discovery of new insights, thereby augmenting its value. 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AQuA2's validation through both simulation and real-world applications demonstrates its superior performance compared to its peers. Applied across various sensors (Calcium, NE, and ATP), cell types (astrocytes, oligodendrocytes, and neurons), animal models (zebrafish and mouse), and imaging modalities (two-photon, light sheet, and confocal), AQuA2 consistently delivers promising results and novel insights, showcasing its versatility in fluorescent imaging data analysis."],"dc:description.abstractgeneral":["Understanding and effectively treating brain diseases requires a deep insight into how the brain operates. A crucial aspect of this exploration involves directly visualizing different signals within the brain, allowing researchers to delve into the functions of brain cells and their interactions. However, as data collection expands rapidly, analyzing this wealth of information presents a significant challenge. Existing methods often fall short due to their limited capacity to analyze signals or their subpar performance, failing to keep pace with current demands. In this work, we introduce Activity Quantification and Analysis (AQuA2), an innovative platform rooted in machine learning principles. AQuA2 features a novel event detection pipeline for accurately quantifying intricate brain signals. Additionally, it incorporates a Consensus Functional Unit (CFU) module, which facilitates the exploration of interactions among potential functional units associated with repetitive signals. To enhance efficiency and usability, we have developed acceleration algorithms and released AQuA2 in two versions: a MATLAB package and a Fiji plugin, each designed to address unique user requirements. AQuA2 has demonstrated its efficacy through real-world applications, effectively quantifying and analyzing signals across various platforms such as biosensors, cell types, animal models, and imaging modalities, with promising outcomes. Furthermore, the utilization of AQuA2 has facilitated the discovery of new insights, thereby augmenting its value. 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