{"id":10058,"date":"2026-01-12T10:50:53","date_gmt":"2026-01-12T15:50:53","guid":{"rendered":"https:\/\/www.bu.edu\/photonics-programs\/?p=10058"},"modified":"2026-02-20T14:48:27","modified_gmt":"2026-02-20T19:48:27","slug":"tian","status":"publish","type":"post","link":"https:\/\/www.bu.edu\/photonics-programs\/2026\/01\/12\/tian\/","title":{"rendered":"Signal Processing and Deep Learning Frameworks for Spatiotemporal Analysis of In-Vivo Neural Activity"},"content":{"rendered":"<h3>Mentors<\/h3>\n\n\t<ul class=\"profile-listing profile-format-advanced\">\n\t\t\t\t\t\n<li class=\"profile-item profile-item-advanced has-title post-4527 profile type-profile status-publish hentry departments-bme departments-ece affiliation-faculty program-year-yr-2017 program-year-yr-2021 program-year-yr-2022\">\n\t<a href=\"https:\/\/www.bu.edu\/photonics-programs\/profile\/lei-tian\/\" class=\"profile-link profile-link-advanced\">\n\t\t\t\t\t<figure class=\"profile-photo profile-photo-advanced\"><img width=\"150\" height=\"150\" src=\"\/photonics-programs\/files\/2025\/04\/lei_tian-300x300.jpg\" alt=\"\" \/><\/figure>\t\t\t\t<h6 class=\"profile-name profile-name-advanced\">Lei Tian<\/h6>\n\t\t<p class=\"profile-title profile-title-advanced\">Associate Professor (ECE, BME)<\/p>\t<\/a>\n\n\t\n<\/li>\n\t\t\t\t\t\n<li class=\"profile-item profile-item-advanced has-title post-10710 profile type-profile status-publish hentry affiliation-graduate-student\">\n\t<a href=\"https:\/\/www.bu.edu\/photonics-programs\/profile\/zhixiong-chen\/\" class=\"profile-link profile-link-advanced\">\n\t\t\t\t\t<figure class=\"profile-photo profile-photo-advanced\"><img width=\"150\" height=\"150\" src=\"\/photonics-programs\/files\/2026\/02\/IMG_7728-636x561-1-300x300.jpg\" alt=\"\" \/><\/figure>\t\t\t\t<h6 class=\"profile-name profile-name-advanced\">Zhixiong Chen<\/h6>\n\t\t<p class=\"profile-title profile-title-advanced\">PhD Candidate<\/p>\t<\/a>\n\n\t\n<\/li>\n\t\t\t<\/ul>\n\t\n<h3><span data-preserver-spaces=\"true\">Project Description<\/span><\/h3>\n<p><span>This project focuses on developing advanced signal processing and machine learning algorithms for a newly developed computational miniature mesoscope that enables cortex-wide neural activity imaging in freely behaving mice. The system captures rich, multi-scale data spanning single-neuron calcium dynamics as well as resting-state vascular and hemodynamic signals. The student will work on algorithmic methods for denoising, motion correction, spatiotemporal demixing, and cross-modal analysis to extract interpretable neural and vascular activity patterns from large-scale in-vivo datasets. The project is conducted in close collaboration with a neuroscience group, with the goal of enabling quantitative studies of functional connectivity, neurovascular coupling, and brain-wide activity organization.<\/span><\/p>\n<p><span data-preserver-spaces=\"true\"><div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Research Goals<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><\/span><\/p>\n<ul>\n<li><span data-preserver-spaces=\"true\">Develop robust signal processing pipelines for motion correction, background suppression, and artifact removal in wide-field mesoscopic neural imaging data.<\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Design and implement machine learning models for neural activity extraction, cell segmentation, and spatiotemporal demixing. <\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Integrate neural and vascular imaging streams to support quantitative analysis of neurovascular coupling and resting-state functional organization. <\/span><\/li>\n<li><span data-preserver-spaces=\"true\">Deliver validated analysis tools that directly support ongoing neuroscience studies with collaborators.<\/div>\n<\/div>\n<\/span><\/li>\n<\/ul>\n<p><span data-preserver-spaces=\"true\"><div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h3 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Learning Goals<\/h3><div class=\"bu_collapsible_section\" style=\"display: none;\"><\/span><\/p>\n<div>\n<ul>\n<li><span>Gain hands-on experience in computational imaging, neural signal processing, and machine learning for biomedical data. <\/span><\/li>\n<li><span>Learn to work with large-scale in-vivo imaging datasets, including data curation, preprocessing, and algorithm evaluation. <\/span><\/li>\n<li><span>Develop skills in Python-based scientific computing, deep learning frameworks, and reproducible research practices. <\/span><\/li>\n<li><span>Experience interdisciplinary research at the interface of optics, computation, and neuroscience through close interaction with experimental collaborators.<\/span> <\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<h3><span data-preserver-spaces=\"true\">Timeline<\/span><\/h3>\n<p><span style=\"color: #003366;\"><strong>Weeks 1-2: <\/strong><\/span><span data-preserver-spaces=\"true\">Onboarding, background reading, and familiarization with the mesoscope system and datasets; introduction to existing analysis pipelines.<br \/>\n<\/span><span style=\"color: #003366;\"><strong>Weeks 3-4:<\/strong><\/span><span data-preserver-spaces=\"true\"> Development of core preprocessing modules (motion correction, denoising, background removal).<br \/>\n<\/span><span style=\"color: #003366;\"><strong>Weeks 5-7:<\/strong><\/span><span data-preserver-spaces=\"true\"> Implementation of machine learning methods for neural activity extraction, segmentation, and spatiotemporal analysis.<br \/>\n<\/span><span style=\"color: #003366;\"><strong>Week 8-9:<\/strong><\/span><span data-preserver-spaces=\"true\"> Integration of neural and vascular data analysis; validation on experimental datasets in collaboration with neuroscience partners.<br \/>\n<\/span><span style=\"color: #003366;\"><strong>Weeks 10:<\/strong><\/span><span data-preserver-spaces=\"true\"> Final analysis, documentation, and presentation of results.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Mentors Project Description This project focuses on developing advanced signal processing and machine learning algorithms for a newly developed computational miniature mesoscope that enables cortex-wide neural activity imaging in freely behaving mice. The system captures rich, multi-scale data spanning single-neuron calcium dynamics as well as resting-state vascular and hemodynamic signals. The student will work on [&hellip;]<\/p>\n","protected":false},"author":19768,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[118],"tags":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/photonics-programs\/wp-json\/wp\/v2\/posts\/10058"}],"collection":[{"href":"https:\/\/www.bu.edu\/photonics-programs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bu.edu\/photonics-programs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/photonics-programs\/wp-json\/wp\/v2\/users\/19768"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/photonics-programs\/wp-json\/wp\/v2\/comments?post=10058"}],"version-history":[{"count":4,"href":"https:\/\/www.bu.edu\/photonics-programs\/wp-json\/wp\/v2\/posts\/10058\/revisions"}],"predecessor-version":[{"id":10727,"href":"https:\/\/www.bu.edu\/photonics-programs\/wp-json\/wp\/v2\/posts\/10058\/revisions\/10727"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/photonics-programs\/wp-json\/wp\/v2\/media?parent=10058"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bu.edu\/photonics-programs\/wp-json\/wp\/v2\/categories?post=10058"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bu.edu\/photonics-programs\/wp-json\/wp\/v2\/tags?post=10058"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}