Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects
Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Principal component analysis: A natural approach to data exploration
Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …
areas. This work reports, in an accessible and integrated manner, several theoretical and …
A review of feature reduction techniques in neuroimaging
B Mwangi, TS Tian, JC Soares - Neuroinformatics, 2014 - Springer
Abstract Machine learning techniques are increasingly being used in making relevant
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …
Regional homogeneity approach to fMRI data analysis
Kendall's coefficient concordance (KCC) can measure the similarity of a number of time
series. It has been used for purifying a given cluster in functional MRI (fMRI). In the present …
series. It has been used for purifying a given cluster in functional MRI (fMRI). In the present …
Probabilistic independent component analysis for functional magnetic resonance imaging
CF Beckmann, SM Smith - IEEE transactions on medical …, 2004 - ieeexplore.ieee.org
We present an integrated approach to probabilistic independent component analysis (ICA)
for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian …
for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian …
Machine learning classifiers and fMRI: a tutorial overview
Interpreting brain image experiments requires analysis of complex, multivariate data. In
recent years, one analysis approach that has grown in popularity is the use of machine …
recent years, one analysis approach that has grown in popularity is the use of machine …
Introduction to machine learning for brain imaging
Machine learning and pattern recognition algorithms have in the past years developed to
become a working horse in brain imaging and the computational neurosciences, as they are …
become a working horse in brain imaging and the computational neurosciences, as they are …
Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition
N Koutsouleris, EM Meisenzahl… - Archives of general …, 2009 - jamanetwork.com
Context Identification of individuals at high risk of developing psychosis has relied on
prodromal symptomatology. Recently, machine learning algorithms have been successfully …
prodromal symptomatology. Recently, machine learning algorithms have been successfully …
Independent component analysis of functional MRI: what is signal and what is noise?
Many sources of fluctuation contribute to the functional magnetic resonance imaging (fMRI)
signal, complicating attempts to infer those changes that are truly related to brain activation …
signal, complicating attempts to infer those changes that are truly related to brain activation …
Multivariate statistical analyses for neuroimaging data
AR McIntosh, B Mišić - Annual review of psychology, 2013 - annualreviews.org
As the focus of neuroscience shifts from studying individual brain regions to entire networks
of regions, methods for statistical inference have also become geared toward network …
of regions, methods for statistical inference have also become geared toward network …