An evaluation of Z-transform algorithms for identifying subject-specific abnormalities in neuroimaging data
Sign Up to like & getrecommendations! Published in 2017 at "Brain Imaging and Behavior"
DOI: 10.1007/s11682-017-9702-2
Abstract: The need for algorithms that capture subject-specific abnormalities (SSA) in neuroimaging data is increasingly recognized across many neuropsychiatric disorders. However, the effects of initial distributional properties (e.g., normal versus non-normally distributed data), sample size, and… read more here.
Keywords: algorithms; transform algorithms; specific abnormalities; neuroimaging data ... See more keywords
Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods
Sign Up to like & getrecommendations! Published in 2017 at "Developmental Cognitive Neuroscience"
DOI: 10.1016/j.dcn.2017.08.009
Abstract: Highlights • The structure of a child brain is significantly different from an adult brain.• Standard software tools for processing brain MRI data might not be appropriate for analyzing pediatric neuroimaging data.• Age-specific and 4D… read more here.
Keywords: data young; structural neuroimaging; processing structural; young children ... See more keywords
Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE/ACM transactions on computational biology and bioinformatics"
DOI: 10.1109/tcbb.2023.3252577
Abstract: Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it is… read more here.
Keywords: factor learning; brain; discrimination; mri ... See more keywords
Effect-Size Estimation Using Semiparametric Hierarchical Mixture Models in Disease-Association Studies with Neuroimaging Data
Sign Up to like & getrecommendations! Published in 2020 at "Computational and Mathematical Methods in Medicine"
DOI: 10.1155/2020/7482403
Abstract: In disease-association studies using neuroimaging data, evaluating the biological or clinical significance of individual associations requires not only detection of disease-associated areas of the brain but also estimation of the magnitudes of the associations or… read more here.
Keywords: estimation; effect size; effect; disease association ... See more keywords
Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data
Sign Up to like & getrecommendations! Published in 2022 at "Network Neuroscience"
DOI: 10.1162/netn_a_00247
Abstract: Abstract Graph-theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting… read more here.
Keywords: analysis method; path; path analysis; method ... See more keywords
The Neuroimaging Data Model Linear Regression Tool (nidm_linreg): PyNIDM Project
Sign Up to like & getrecommendations! Published in 2022 at "F1000Research"
DOI: 10.12688/f1000research.108008.1
Abstract: The Neuroimaging Data Model (NIDM) is a series of specifications for describing all aspects of the neuroimaging data lifecycle from raw data to analyses and provenance. NIDM uses community-driven terminologies along with unambiguous data dictionaries… read more here.
Keywords: linear regression; regression; regression tool; pynidm ... See more keywords
Decoding visual information from high-density diffuse optical tomography neuroimaging data
Sign Up to like & getrecommendations! Published in 2021 at "NeuroImage"
DOI: 10.1364/brain.2018.btu4c.3
Abstract: Using high density diffuse optical tomography neuroimaging data and a template-matching strategy, we were able to decode detailed information about the position of a checkerboard being viewed by a human subject. read more here.
Keywords: high density; diffuse optical; optical tomography; neuroimaging data ... See more keywords
Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples.
Sign Up to like & getrecommendations! Published in 2023 at "EBioMedicine"
DOI: 10.2139/ssrn.4264850
Abstract: BACKGROUND Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications… read more here.
Keywords: learning unprocessed; neuroimaging; standardised neuroimaging; visual deep ... See more keywords
Prediction and Modeling of Neuropsychological Scores in Alzheimer’s Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks
Sign Up to like & getrecommendations! Published in 2021 at "Frontiers in Computational Neuroscience"
DOI: 10.3389/fncom.2021.769982
Abstract: Background: In recent years, predicting and modeling the progression of Alzheimer’s disease (AD) based on neuropsychological tests has become increasingly appealing in AD research. Objective: In this study, we aimed to predict the neuropsychological scores… read more here.
Keywords: neuropsychological scores; artificial neural; neuroimaging data; multimodal neuroimaging ... See more keywords
Editorial: Computational modeling methods for naturalistic neuroimaging data
Sign Up to like & getrecommendations! Published in 2023 at "Frontiers in Computational Neuroscience"
DOI: 10.3389/fncom.2023.1117945
Abstract: COPYRIGHT © 2023 Ren, Liu, Zhang and Lv. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted,… read more here.
Keywords: naturalistic neuroimaging; editorial computational; methods naturalistic; modeling methods ... See more keywords
MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data
Sign Up to like & getrecommendations! Published in 2020 at "Frontiers in Neuroscience"
DOI: 10.3389/fnins.2020.00545
Abstract: With the rapid development of machine learning techniques, multivariate pattern analysis (MVPA) is becoming increasingly popular in the field of neuroimaging data analysis. Several software packages have been developed to facilitate its application in neuroimaging… read more here.
Keywords: graphical user; user interface; analysis; pattern analysis ... See more keywords