Evoked Component Analysis (ECA): Decomposing the Functional Ultrasound Signal With GLM-Regularization

• Published in: IEEE transactions on biomedical engineering Vol. 71; no. 10; pp. 2823 - 2832
• Main Authors: Erol, Aybuke, Generowicz, Bastian, Kruizinga, Pieter, Hunyadi, Borbala
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Animals; Biomedical engineering; Brain; Brain - diagnostic imaging; Brain - physiology; Brain slice preparation; Correlation; Correlation analysis; Data analysis; Decomposition; Design analysis; Design factors; Functional ultrasound; Functionals; general linear model; Hemodynamic responses; Image acquisition; Image processing; Image Processing, Computer-Assisted - methods; Linear Models; Matrix decomposition; Medical imaging; Mice; Neuroimaging; Nonlinear response; Probes; Regularization; regularized low-rank factorization; Response functions; Signal Processing, Computer-Assisted; Statistical analysis; Three-dimensional displays; Ultrasonic imaging; Ultrasonic testing; Ultrasonography - methods; Ultrasound; Visual tasks

Analysis of functional neuroimaging data aims to unveil spatial and temporal patterns of interest. Existing analysis methods fall into two categories: fully data-driven approaches and those reliant on prior information, e.g. the stimulus time course. While using the stimulus signal directly can help...