Controversy in statistical analysis of functional magnetic resonance imaging data

Brown, Emery N.; Behrmann, Marlene

doi:10.1073/pnas.1705513114

Cited by 32 publications

(22 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2016) revealed that parametric statistical methods implemented in many common neuroimage analysis software packages are invalid for cluster-wise inference, calling into question findings from a number of fMRI experiments 9 . This last report has generated a great deal of controversy 10 – 14 (see also http://www.ohbmbrainmappingblog.com/blog/keep-calm-and-scan-on ) and has drawn attention to efforts within the neuroimaging community to identify best practices for fMRI research, from study design to data collection, to data analysis ( http://www.humanbrainmapping.org/cobidas ). Most recently, Nature Neuroscience published a Focus issue on Human Brain Mapping that presents several articles discussing these issues in more detail, including how to define what constitutes reproducibility in fMRI research 15 , 16 .…”

Section: Introductionmentioning

confidence: 99%

Replication of Resting State-Task Network Correspondence and Novel Findings on Brain Network Activation During Task fMRI in the Human Connectome Project Study

Nickerson

2018

Sci Rep

View full text Add to dashboard Cite

There have been many recent reports highlighting a crisis in replication and reliability of research in psychology, neuroscience, and neuroimaging. After a series of reports uncovered various methodological problems with functional magnetic resonance imaging (fMRI) research, considerable attention has been given to principles and practices to improve reproducibility of neuroimaging findings, including promotion of openness, transparency, and data sharing. However, much less attention has been given to use of open access neuroimaging datasets to conduct replication studies. A major barrier to reproducing neuroimaging studies is their high cost, in money and labor, and utilizing such datasets is an obvious solution for breaking down this barrier. The Human Connectome Project (HCP) is an open access dataset consisting of extensive neurological, behavioral, and genetics assessments and neuroimaging data from over 1,100 individuals. In the present study, findings supporting the replication of a highly cited neuroimaging study that showed correspondence between resting state and task brain networks, and novel findings on activation of brain networks during task performance that arose with this exercise are presented as a demonstration of use of the HCP for replication studies.

show abstract

Section: Introductionmentioning

confidence: 99%

Replication of Resting State-Task Network Correspondence and Novel Findings on Brain Network Activation During Task fMRI in the Human Connectome Project Study

Nickerson

2018

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Moreover, an ideal fMRI statistical analysis should incorporate a proper characterisation of the spatial and temporal properties of physiological activities. 9 In this review, our primary aim was to investigate what data analytic approaches are used to analyse retinal imaging data: in both association (epidemiological) and diagnostic (prognostic, predictive, screening) studies. Specifically, we aimed to see if the spatial correlation is estimated, reported and incorporated (adjusted) in the analysis.…”

Section: Introductionmentioning

confidence: 99%

Spatial and spatio-temporal statistical analyses of retinal images: a review of methods and applications

et al. 2020

View full text Add to dashboard Cite

BackgroundClinical research and management of retinal diseases greatly depend on the interpretation of retinal images and often longitudinally collected images. Retinal images provide context for spatial data, namely the location of specific pathologies within the retina. Longitudinally collected images can show how clinical events at one point can affect the retina over time. In this review, we aimed to assess statistical approaches to spatial and spatio-temporal data in retinal images. We also review the spatio-temporal modelling approaches used in other medical image types.MethodsWe conducted a comprehensive literature review of both spatial or spatio-temporal approaches and non-spatial approaches to the statistical analysis of retinal images. The key methodological and clinical characteristics of published papers were extracted. We also investigated whether clinical variables and spatial correlation were accounted for in the analysis.ResultsThirty-four papers that included retinal imaging data were identified for full-text information extraction. Only 11 (32.4%) papers used spatial or spatio-temporal statistical methods to analyse images, others (23 papers, 67.6%) used non-spatial methods. Twenty-eight (82.4%) papers reported images collected cross-sectionally, while 6 (17.6%) papers reported analyses on images collected longitudinally. In imaging areas outside of ophthalmology, 19 papers were identified with spatio-temporal analysis, and multiple statistical methods were recorded.ConclusionsIn future statistical analyses of retinal images, it will be beneficial to clearly define and report the spatial distributions studied, report the spatial correlations, combine imaging data with clinical variables into analysis if available, and clearly state the software or packages used.

show abstract

“…see [12]. Those badly in need of (whole paper) reviews include: Granger causality, see [10] and the critique in [11]; multi-modal methods, see [15]; software, a starting point is the recent controversy over major fMRI software ‘bugs’ as discussed in [14].…”

Section: Introductionmentioning

confidence: 99%

Connectivity in fMRI: Blind Spots and Breakthroughs

Solo

Poline²,

Lindquist

et al. 2018

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.

show abstract

Controversy in statistical analysis of functional magnetic resonance imaging data

Cited by 32 publications

References 13 publications

Replication of Resting State-Task Network Correspondence and Novel Findings on Brain Network Activation During Task fMRI in the Human Connectome Project Study

Replication of Resting State-Task Network Correspondence and Novel Findings on Brain Network Activation During Task fMRI in the Human Connectome Project Study

Spatial and spatio-temporal statistical analyses of retinal images: a review of methods and applications

Connectivity in fMRI: Blind Spots and Breakthroughs

Contact Info

Product

Resources

About