Statistical Parametric Mapping The Analysis Of Functional Brain Images

By A. Bergmann. 2016

3,045 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f6c8de941330197cc120aec69c2dc957b9da69b2

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Citations in the corpus

Informatic parcellation of the network involved in the computation of subjective value. (2014, methodology): “All of these analyses were completed in SPM5 or SPM8 (Friston, 2007).”
Realistic volumetric-approach to simulate transcranial electric stimulation—ROAST—a fully automated open-source pipeline (2017, methodology): “For example, SPM (Statistical Parametric Mapping, Friston (2007)), FSL (FMRIB Software Library, Smith (2002); Smith et al. (2004)) and FreeSurfer (Dale et al., 1999; Fischl et al., 1999) can all generate segmentations for the brain and head, with each one having di ﬀ erent pros and cons (for a…”
Bayesian model reduction and empirical Bayes for group (DCM) studies (2016, methodology): “Typically, when analysing groupDCMs studies, people invert a series of models for each subject separately and then either harvest subjectwise parameter estimates from a selected model (identified as optimal at the group level) or use Bayesian model averaging to summarise each subject in terms of exp”
Survey of Non-Rigid Registration Tools in Medicine (2017, methodology): “It is a collection of MATLAB functions and subroutines implementing the theoretical concepts of Statistical Parametric Mapping [78] in a complete analysis package designed for the analysis of brain imaging data sequences.”
Methods for cleaning the BOLD fMRI signal (2016, unknown): “This bias will reduce the sensitivity and specificity of detecting or characterizing the BOLD response to task-related neuronal activity (Friston et al., 2007).”
Statistical analysis of fNIRS data: A comprehensive review (2014, unknown): “In SPM, Friston et al. (2011) proposed the following Sattherthwaite approximation.”
A guide to group effective connectivity analysis, part 2: Second level analysis with PEB (2019, methodology): “This is the average of the parameters from different models, weighted by the models’ posterior probabilities (Hoeting et al., 1999; Penny et al., 2006).”
Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates (2015, unknown): “We address these limitations in the current work with an evaluation of group inference with the three most common fMRI software packages [SPM (15, 16), FSL (17), and AFNI (18)].”
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, methodology): “To date, a few well maintained neuroimage processing pipelines such as FreeSurfer [11], BrainSuite [12], SPM [13], ANTs [14], or FSL [15] are the only means available to process and evaluate the incoming flow of data.”
SimNIBS 2.1: A Comprehensive Pipeline for Individualized Electric Field Modelling for Transcranial Brain Stimulation (2018, methodology): “Note that mri2mesh is restricted only to the head and does not create models of the neck region. headreco uses the SPM12 [11] toolbox for segmenting the MRI scan, and is now the recommended option in SimNIBS.”
Dixon-VIBE Deep Learning (DIVIDE) Pseudo-CT Synthesis for Pelvis PET/MR Attenuation Correction (2018, methodology): “Then, intrasubject rigid and nonrigid registration of the MR and CT data was performed using SPM8 software (28) and the Elastix package (29).”
FMRIPrep: a robust preprocessing pipeline for functional MRI (2018, unknown): “Alternative workflows such as afni_proc.py (AFNI 12 ), feat (FSL 15 ), C-PAC 24 (configurable pipeline for the analysis of connectomes), Human Connectome Project (HCP 25 ) Pipelines 26 , or the Batch Editor of SPM, are not agnostic because they prescribe particular methodologies to analyze the prepr”
HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation (2018, methodology): “The sequences were aligned by rigid registration using Elastix [57], along with a bias correction performed using SPM8 [58].”