In this paper we use the modified and integrated version of the balloon model in the analysis of fMRI data. We propose a new state space model realization for this balloon model and represent it with the standard A,B,C and D matrices widely used in system theory. A second order Padé approximation with equal numerator and denominator degree is used for the time delay approximation in the modeling of the cerebral blood flow. The results obtained through numerical solutions showed that the new state space model realization is in close agreement to the actual modified and integrated version of the balloon model. This new system theoretic formulation is likely to open doors to a novel way of analyzing fMRI data with real time robust estimators. With further development and validation, the new model has the potential to devise a generalized measure to make a significant contribution to improve the diagnosis and treatment of clinical scenarios where the brain functioning get altered. Concepts from system theory can readily be used in the analysis of fMRI data and the subsequent synthesis of filters and estimators.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/IEMBS.2010.5627400 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
ds-FCRN: three-dimensional dual-stream fully convolutional residual networks and transformer-based global-local feature learning for brain age prediction.
Brain Struct Funct
January 2025
Department of Biomedical Engineering, College of Chemistry and Life Sciences, Beijing University of Technology, Beijing, 100124, China.
The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks.
View Article and Find Full Text PDFDisentangling the neural underpinnings of response inhibition in disruptive behavior and co-occurring ADHD.
Eur Child Adolesc Psychiatry
January 2025
Department of Child and Adolescent Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
While impaired response inhibition has been reported in attention-deficit/hyperactivity disorder (ADHD), findings in disruptive behavior disorders (DBDs) have been inconsistent, probably due to unaccounted effects of co-occurring ADHD in DBD. This study investigated the associations of behavioral and neural correlates of response inhibition with DBD and ADHD symptom severity, covarying for each other in a dimensional approach. Functional magnetic resonance imaging data were available for 35 children and adolescents with DBDs (8-18 years old, 19 males), and 31 age-matched unaffected controls (18 males) while performing a performance-adjusted stop-signal task.
View Article and Find Full Text PDFPhotorealistic rendering of fetal faces from raw magnetic resonance imaging data.
Ultrasound Obstet Gynecol
January 2025
Decision and Bayesian Computation, Neuroscience & Computational Biology Departments, CNRS UMR 3571, Institut Pasteur, Paris, France.
Symptomatic Accessory Navicular Treated With Endoscopic Accessory Navicular and Partial Navicular Resection.
Foot Ankle Int
January 2025
Center for Foot and Ankle Surgery, Department of Orthopedic Surgery, Yashio Central General Hospital, Saitama, Japan.
Background: This study aims to report the results of the patients with symptomatic accessory navicular (AN) who underwent endoscopic AN and partial navicular resection.
Methods: The medical records of patients with type 2 symptomatic AN who underwent the aforementioned surgery at our hospital from November 2019 to May 2022 with a follow-up of >2 years were reviewed. Data on clinical, radiographic, and patient-reported outcomes were obtained.
VGX: VGG19-Based Gradient Explainer Interpretable Architecture for Brain Tumor Detection in Microscopy Magnetic Resonance Imaging (MMRI).
Microsc Res Tech
January 2025
AIDA Lab. College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia.
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!