Cited by 504Statistical Methods and Bayesian InferenceFunctional Brain Connectivity StudiesAdvanced Statistical Methods and Models

The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.

Robust group analysis using outlier inference

Woolrich, Mark·2008·NeuroImage

DOI 10.1016/j.neuroimage.2008.02.042

Open this paper in CoChat — free account

Abstract

Neuroimaging group studies are typically performed with the assumption that subjects used are randomly drawn from a population of subjects. The population of subjects is assumed to have a distribution of effect sizes associated with it that are Gaussian distributed. However, in practice, group studies can include "outlier" subjects whose effect sizes are completely at odds with the general population for reasons that are not of experimental interest. If ignored, these outliers can dramatically affect the inference results. To solve this problem, we propose a group inference approach which includes inference of outliers using a robust general linear model (GLM) approach. This approach models the errors as being a mixture of two Gaussian distributions, one for the normal population and one for the outliers. Crucially the robust GLM is part of a traditional hierarchical group model which uses GLMs at each level of the hierarchy. This combines the benefits of outlier inference with the benefits of using variance information from lower levels in the hierarchy. A Bayesian inference framework is used to infer on the robust GLM, while using the lower level variance information. The performance of the method is demonstrated on simulated and fMRI data and is compared with iterative reweighted least squares and permutation testing.

Integrity signals

Registry

Crossref · 10.1016/j.neuroimage.2008.02.042

Cites

20 of 20 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Neural networks for pattern recognition
Bishop, C. (1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,537
A global optimisation method for robust affine registration of brain images
Jenkinson, M., & Smith, S. M. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(01)00036-6 · Cited by 7,018
Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data
Woolrich, M. W., Ripley, B. D., Brady, M., & Smith, S. M. (2001) · NeuroImage · doi:10.1006/nimg.2001.0931 · Cited by 2,859
Bayesian Density Estimation and Inference Using Mixtures
Escobar, M. D., & West, M. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476550 · Cited by 2,236
Learning the value of information in an uncertain world
Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007) · Nature Neuroscience · doi:10.1038/nn1954 · Cited by 2,053
Self-citation
Multilevel linear modelling for FMRI group analysis using Bayesian inference
Woolrich, M. W., Behrens, T. E. J., Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.12.023 · Cited by 1,719
General multilevel linear modeling for group analysis in FMRI
Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00435-x · Cited by 1,464
Statistical methods of estimation and inference for functional MR image analysis
Bullmore, E. T., Brammer, M. J., Williams, S., Rabe‐Hesketh, S., Janot, N., David, A. S., Mellers, J. D. C., Howard, R. J., & Sham, P. C. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910350219 · Cited by 683
Analysis of a large fMRI cohort: statistical and methodological issues for group analyses
Thirion, B., Pinel, P., Mériaux, S., Roche, A., Dehaene, S., & Poline, J.-B. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.054 · Cited by 563

Show all 20 references

Sorted by citations

Items 401–500 of 502

A low-cost telescope for enhanced stimulus visual field coverage in functional MRI
Jolly, J. K., Sheldon, A. A., Alvarez, I., Gallagher, C., MacLaren, R. E., & Bridge, H. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.109023 · Cited by 4
“Due to the small number of participants, a fixed effects model was used by forcing the random effects variance to zero in FLAME (FMRIB's Local Analysis of Mixed Effects)(Beckmann et al., 2003; Woolrich, 2008; Woolrich et al., 2004).”
Method
Cognitive interference processing in adult survivors of childhood acute lymphoblastic leukemia using functional magnetic resonance imaging
Svärd, D., Erfurth, E. M., Hellerstedt, R., Mannfolk, P., Mårtensson, J., Sundgren, P. C., & Follin, C. (2021) · Acta Oncologica · doi:10.1080/0284186x.2021.1987514 · Cited by 4
Effects of Repetitive Peripheral Sensory Stimulation in the Subacute and Chronic Phases After Stroke: Study Protocol for a Pilot Randomized Trial
Kroth, J. B., Handfas, B. W., Rodrigues, G., Zepeda, F., Oliveira, M. A., Wang, D. J. J., Neto, R. M. D. A., Silva, G. S., Amaro, E., Sorinola, I. O., et al. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.779128 · Cited by 4
Effect of spinal anesthesia-induced deafferentation on pain processing in healthy male volunteers: A task-related fMRI study
Sitsen, E., Khalili‐Mahani, N., de Rover, M., Dahan, A., & Niesters, M. (2022) · Frontiers in Pain Research · doi:10.3389/fpain.2022.1001148 · Cited by 4
Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space
Hallquist, M. N., Hwang, K., Luna, B., & Dombrovski, A. Y. (2024) · Science Advances · doi:10.1126/sciadv.adj2219 · Cited by 4
Dynamic integration of conceptual information during learning
Inhoff, M. C., Libby, L. A., Noguchi, T., Love, B. C., & Ranganath, C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207357 · Cited by 6
Turtles all the way down? Psychometric approaches to the reduction problem
Kievit, R. (2014) · UvA-DARE (University of Amsterdam) · Cited by 3
The Evaluative Role of Rostrolateral Prefrontal Cortex in Rule-Based Category LearningPreprint
Paniukov, D., & Davis, T. H. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/107110 · Cited by 4
Comparing fMRI inter-subject correlations between groups using permutation testsPreprint
Tohka, J., Pollick, F. E., Pajula, J., & Kauppi, J.-P. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/370023 · Cited by 4
“Higher-level analysis was carried out using FLAME (FMRIB’s Local Analysis of Mixed Effects) stage 1 and stage 2 (Beckmann et al., 2003; Woolrich et al., 2004; Woolrich, 2008).”
Method
It’s all connected! Multivariate pattern analysis of inter-network connectivity distinguishes between reappraisal and passive viewing of emotional scenes
Horner, S., Rawliuk, T., Ferstl, R. M., Lyons, A. L., Martin, J., Gorbet, D. J., Stevens, W. D., & Greening, S. G. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf161 · Cited by 3
“We then performed a standard univariate activation analysis using FLAME stage 1 (Beckmann et al. 2003; Woolrich et al. 2004; Woolrich 2008).”
Interpersonal early life trauma is associated with increased cerebral perfusion and poorer memory performance in post-9/11 veterans
Sullivan, D. R., Salat, D. H., Wolf, E. J., Logue, M. W., Fortier, C. B., Fonda, J. R., Degutis, J. M., Esterman, M. S., Milberg, W., McGlinchey, R. E., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102365 · Cited by 4
“To determine voxel-wise perfusion correlates with variables of interest, group-level analyses were conducted using mixed effects model FLAME stage 1 (Beckmann et al., 2003; Woolrich, 2008; Woolrich et al., 2004).”
Method
Mindfulness training and exercise differentially impact fear extinction neurocircuitry
Leibovitz, S. E., Sevinc, G., Greenberg, J., Hölzel, B. K., Gard, T., Calahan, T., Vangel, M. G., Orr, S. P., Milad, M. R., & Lazar, S. W. (2023) · Psychological Medicine · doi:10.1017/s0033291723002593 · Cited by 4
Cognitive interference processing in adults with childhood craniopharyngioma using functional magnetic resonance imaging
Svärd, D., Follin, C., Fjalldal, S. B., Hellerstedt, R., Mannfolk, P., Mårtensson, J., Sundgren, P. C., & Erfurth, E. M. (2021) · Endocrine · doi:10.1007/s12020-021-02824-9 · Cited by 3
Seeing in my way or your way: impact of intelligence, attention, and empathy on brain reactivity
Montandon, M., Rodriguez, C. K., Herrmann, F. R., Eytan, A., Pegna, A. J., Haller, S., & Giannakopoulos, P. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1071676 · Cited by 3
“Higher-level analysis was carried out using a mixed effects model, by forcing the random effects variance to zero in FLAME (FMRIB’s Local Analysis of Mixed Effects) (Beckmann et al., 2003; Woolrich et al., 2004; Woolrich, 2008).”
Method
DNA methylation of the oxytocin receptor interacts with age to impact neural response to social stimuli
Puglia, M. H., Lynch, M. E., Nance, M. G., Connelly, J. J., & Morris, J. P. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1252478 · Cited by 3
“…were conducted at the whole-brain level using FSL’s local analysis of mixed effects (FLAME) stage 1 (Beckmann et al., 2003; Woolrich et al., 2004; Woolrich, 2008) with FSL’s automatic outlier de-weighting algorithm applied, which identifies and de-weights outliers within the fMRI data…”
MethodInfluential
Differences in temporal profile of brain responses by pleasantness of somatosensory stimulation in autistic individuals
Zoltowski, A. R., Failla, M. D., Quinde-Zlibut, J. M., Dunham-Carr, K., Moana-Filho, E. J., Essick, G. K., Baranek, G. T., Rogers, B. P., & Cascio, C. J. (2023) · Somatosensory & Motor Research · doi:10.1080/08990220.2023.2294715 · Cited by 3
“Second-level analyses combined runs for each subject using a fixed-effect model in FSL (FLAME; Beckmann et al. 2003; Woolrich et al. 2004; Woolrich 2008).”
Neurobehavioral Correlates of Familism and Adolescent Risk Taking
Telzer, E. H. (2012) · eScholarship (California Digital Library) · Cited by 3
Fractionating the neural substrates of incidental recognition memoryPreprint
Greene, C. M., Vidaki, K., & Soto, D. (2014) · Learning & Memory · doi:10.1101/lm.036327.114 · Cited by 3
“Higher-level analysis was carried out using FLAME (FMRIB’s Local Analysis of Mixed Effects) stage 1 + 2 (Beckmann et al. 2003; Woolrich et al. 2004; Woolrich 2008).”
Method
Individual differences in reading comprehension and off-task thought relate to perceptually-coupled and decoupled cognitionPreprint
Zhang, M., Savill, N. J., Margulies, D. S., Smallwood, J., & Jefferies, E. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/387035 · Cited by 3
“In the higher-level analysis at the group level, z-transformed behavioural data for comprehension, off-task frequency and NYC-Q were added as explanatory variables, using FMRIB’s Local Analysis of Mixed Effects (FLAME1), with automatic outlier de-weighting (Woolrich, 2008).”
Method
Pupil-linked phasic arousal relates to the reduction of response inhibition: inferences from a simultaneous pupillometry and EEG-fMRI studyPreprint
Hong, L., He, H., & Sajda, P. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.22.504728 · Cited by 4
Newly trained navigation and verbal memory skills in humans elicit changes in task-related networks but not brain structurePreprint
Zheng, L. J., Boogaart, Z., McAvan, A. S., Garren, J. D., Doner, S., Wilkes, B. J., Groves, W., Yuksel, E., Cherep, L. A., Ekstrom, A. D., et al. (2025) · eLife · doi:10.1101/2025.04.14.648747 · Cited by 3
Statistical Analysis and Software
Rocca, M. L. (2016) · Current clinical pathology · doi:10.1007/978-1-4939-3469-0_3 · Cited by 2
Aportes de los estudios de neuroimagen funcional a la comprensión del trauma infantil: estudio preliminar
Delgado, L. A. C., Valencia, M., Noriega, I., Al‐Khalil, K., Trejos‐Castillo, E., & ́Boyle, M. O. (2021) · CES Psicología · doi:10.21615/cesp.5437 · Cited by 2
Neural responses to gender‐based microaggressions in academic medicine
Balters, S., Foland-Ross, L. C., Bruno, J., Periyakoil, V., Valantine, H., & Reiss, A. (2023) · Journal of Neuroscience Research · doi:10.1002/jnr.25240
Incidental visual processing of spatiotemporal cues in communicative interactions: An fMRI investigation
Atkinson, A. P., & Vuong, Q. C. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00048 · Cited by 2
“…estimates from the first level are averaged across runs within each participant), analysis was carried out using a fixed-effects model, by forcing the random effects variance to zero in FLAME (FMRIB’s Local Analysis of Mixed Effects) ( Beckmann et al., 2003; Woolrich, 2008; Woolrich et al., 2004).”
Greater Pattern Similarity between Mother Tongue and Second Language in the Right ATL Facilitates Understanding of Written Language
Dong, J., Yan, H. J., Mei, L., Wang, G., Qu, J., Liu, X., Xu, S., Jiang, W., Zheng, A., & Feng, G. (2024) · Neuroscience · doi:10.1016/j.neuroscience.2024.02.030 · Cited by 2
Maximum likelihood estimation for second level fMRI data analysis with expectation trust region algorithm
Li, X., Coyle, D., Maguire, L., & McGinnity, T. M. (2013) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2013.10.007 · Cited by 2
Striking a chord with healthy aging: memory system cooperation is related to preserved configural response learning in older adults
Voss, M. W., Clark, R., Freedberg, M., Weng, T. B., & Hazeltine, E. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.11.001 · Cited by 2
“Further, a block design is robust for optimizing the detection of regional activation (Amaro and Barker, 2006), and the mixed-effects analysis approach conjointly accounts for individual differences in variance in between-subjects analysis and deweights voxels for individuals that are detected as outliers compared to…”
Incongruent pitch cues are associated with increased activation and functional connectivity in the frontal areas
Lin, J.-F. L., Imada, T., Kuhl, P. K., & Lin, F. (2018) · Scientific Reports · doi:10.1038/s41598-018-23287-5 · Cited by 2
Cognitive assessment of deviant sexual interest using the emotional stroop and fMRI
Price, S. A. (2011) · University of Birmingham Institutional Research Archive (University of Birmingham) · Cited by 2
Sjögren,'s syndrome and benign hyperglobulinemic purpura of Waldenstrom.
Lu, L. W., Carbone, A. O., & Katzman, B. (1981) · PubMed · Cited by 2
Neural correlates of the effects of tDCS stimulation over the LIFG for phonological processing in dyslexiaPreprint
de Almeida, L. R., & Hansen, P. (2019) · bioRxiv · doi:10.1101/522847 · Cited by 3
“Group analyses were carried out with random effect models using FLAME stage 1 (Beckmann et al., 2003; Woolrich et al., 2004; Woolrich, 2008).”
Method
Concordance among eating behaviors defined by the three factor eating questionnaire and brain regions of interest as measured by FMRI
Boyd, L. A. (2016) · ThinkTech (Texas Tech University) · Cited by 2
The Neural Correlates of the Core Number Systems Contribute to Mathematical Ability in AdolescencePreprint
Abreu‐Mendoza, R. A., Zarabozo‐Hurtado, D., Chamorro, Y., Vazquez, P., Matute, E., & Fandakova, Y. (2020) · doi:10.31234/osf.io/96tuy · Cited by 2
Decision value signals in the ventromedial prefrontal cortex and motivational and hedonic symptoms across mood and psychotic disordersPreprint
Kang, M. S., Wolf, D. H., Kazinka, R., Lee, S., Ruparel, K., Elliott, M. A., Xu, A., Cieslak, M., Prettyman, G., Satterthwaite, T. D., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.01.407197 · Cited by 2
Progressive brain function changes in patients with neuromyelitis optica: insights from resting-state fMRI
Wang, J., Huang, J., Kang, X., Dong, H., & Lu, J. (2025) · Brain Imaging and Behavior · doi:10.1007/s11682-025-01012-x · Cited by 1
“Analysis of ALFF differences (i.e. HC v.s. Baseline, HC v.s. Follow-up, Baseline v.s. Follow-up) was conducted using a mixed-effects model strategy (FLAME, Functional MRI of the Brain’s Local Analysis of Mixed Effects) (Woolrich, 2008), with Z statistic maps being subjected to thresholding based on clusters identified…”
Influential
The Neural Processes Underpinning Flexible Semantic Retrieval in Visual and Auditory Modalities
Shao, X., Zhang, M., Wang, X., Gouws, A. D., Jackson, R. L., Smallwood, J., Krieger‐Redwood, K., & Jefferies, E. (2026) · Human Brain Mapping · doi:10.1002/hbm.70536 · Cited by 1
“In the higher-level analysis at the group level, the combined contrasts were analysed using FMRIB's Local Analysis of Mixed Effects (FLAME1), with automatic outlier de-weighting (Woolrich 2008).”
Developmental differences in social information use under uncertainty: A neurocomputational approach
Hofmans, L., & van den Bos, W. (2025) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2025.101604 · Cited by 1
“The resulting contrast images per run were averaged at the participant level, which was carried out using a fixed effects model, by forcing the random effects variance to zero in FLAME (FMRIB ’ s Local Analysis of Mixed Effects) (Beckmann et al., 2003; Woolrich, 2008; Woolrich et al., 2004).”
Neural correlates of Bayesian social belief updating in the medial prefrontal cortex
Hofmans, L., & van den Bos, W. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf251 · Cited by 1
“The resulting contrast images per run were averaged at the participant level, which was carried out using a fixed effects model, by forcing the random effects variance to zero in FLAME (FMRIB’s Local Analysis of Mixed Effects) (Beckmann et al. 2003; Woolrich et al. 2004; Woolrich 2008).”
Prefrontal Default Mode Network Interactions with Posterior Hippocampus during Exploration
Papale, A. E., Brown, V. M., Ianni, A. M., Hallquist, M. N., Luna, B., & Dombrovski, A. Y. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0399-25.2025 · Cited by 2
Sender–receiver subdivisions of the default mode network in perceptual and memory-guided cognition
Zhang, M., Paquola, C., Krieger‐Redwood, K., Mckeown, B., Murphy, C., Liu, C., Margulies, D. S., Leech, R., Smallwood, J., & Jefferies, E. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2528851123 · Cited by 1
Frontopolar Cortex Response to Positive Feedback Relates to Nonincentivized Task Persistence
Tashjian, S. M., & Galván, A. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab317 · Cited by 1
Evidence for vibration coding of sliding tactile textures in auditory cortex
Roberts, R. D., Loomes, A. R., Kwok, H. F., Wing, A. M., & Allen, H. A. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1282566 · Cited by 1
“Higher-level analysis (mixed effects) was carried out using FLAME (FMRIB’s Local Analysis of Mixed Effects) stage 1 (Beckmann et al., 2003; Woolrich et al., 2004; Woolrich, 2008).”
Method
Effects of short-term second language learning on the development of individual semantic networks in written and spoken language
Dong, J., Zhang, W., Li, A., Lee, Y., Cai, H., Jiang, W., & Yan, H. J. (2023) · Neuroscience Letters · doi:10.1016/j.neulet.2023.137558 · Cited by 1
Correlates of asymmetric venous drainage in resting state functional magnetic resonance imaging data
Nastaran, S., Sundermann, B., & Mathys, C. (2025) · Brain Research · doi:10.1016/j.brainres.2025.149792 · Cited by 1
Functional connectivity alterations in women with subjective cognitive decline
Parker, A., Szoeke, C., Kwan, H., Henri-Bhargava, A., & Gawryluk, J. R. (2025) · Journal of Alzheimer's disease reports · doi:10.1177/25424823251328340 · Cited by 1
Second-Level fMRI Data Analysis Using Mixed Model
Li, X. (2013) · Springer eBooks · doi:10.1007/978-94-007-7302-8_3 · Cited by 1
Robust Group-Level Inference in Neuroimaging Genetic Studies
Fritsch, V., Mota, B. D., Varoquaux, G. P., Frouin, V., Loth, E., Poline, J., & Thirion, B. (2013) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2013.15 · Cited by 1
“Penny [3] and Woolrich [4] separate regular data from outliers with Bayesian mixture models.”
Background
Right Inferior Frontal Gyrus and Motor Response Inhibition A Real-Time FMRI Neurofeedback Study
Nilsen, A. S. (2014) · NORA - Norwegian Open Research Archives · Cited by 1
Untangling the Temporal Dynamics of Bilateral Neural Activation in the Bilingual Brain
Jasińska, K. K. (2014) · TSpace (University of Toronto) · Cited by 1
Effects of action observation on brain activity, function and strength
Salama, I. M. M. (2011) · University of Birmingham Institutional Research Archive (University of Birmingham) · Cited by 1
Differential reinforcement encoding along the hippocampal long axis helps resolve the explore/exploit dilemmaPreprint
Dombrovski, A. Y., Luna, B., & Hallquist, M. N. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.02.893255 · Cited by 1
Children’s visual attention, neural function, and memory for distressing information
Noriega, I. (2019) · ThinkTech (Texas Tech University) · Cited by 1
Early excitatory and inhibitory modifications in the motor cortex following skill learning support motor memory consolidation and cortical plasticity overnightPreprint
Eisenstein, T., Furman‐Haran, E., & Tal, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.27.521981 · Cited by 1
Imaging the effects of tumour necrosis factor inhibition on pain processing in severe rheumatoid arthritisPreprint
Wartolowska, K. A., Jenkinson, M., Andersson, J., Wordsworth, B. P., & Tracey, I. (2023) · medRxiv · doi:10.1101/2023.02.27.23286245 · Cited by 1
“3 Higher-level analysis The higher-level analysis was performed using FLAME2 with automatic outlier detection [28,29].”
Method
The Dark Side of Mentalizing: Learning Signals in the Default Network During Social Exchanges Support Cooperation and ExploitationPreprint
Allen, T. A., Hallquist, M. N., & Dombrovski, A. Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.03.538867 · Cited by 1
“Group-level analyses were carried out using FSL FLAME 1+2 with automatic outlier deweighting (Woolrich, 2008), which implements Bayesian mixed effects estimation of the group parameter estimates including full Markov Chain Monte Carlo-based estimation for near-494 threshold voxels (Woolrich et al.,…”
Reinforcement-based option competition in human dorsal stream during exploration/exploitation of a continuous spacePreprint
Hallquist, M. N., Hwang, K., Luna, B., & Dombrovski, A. Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.22.541828 · Cited by 1
“…PPC sends map-based outputs to the frontal dorsal and ventral premotor (PMd and PMv ) cortex and the frontal eye fields (FEF) that guide action 9–11 . forebrain and striatum, which integrates reinforcement slowly and retains long-term values 12 Moreover, PPC maps contain rich visuomotor data…”
Background
Newly trained navigation and verbal memory skills elicit changes in task-related networks but not brain structurePreprint
Zheng, L. J., Boogaart, Z., McAvan, A. S., Garren, J. D., Doner, S., Wilkes, B. J., Groves, W., Yuksel, E., Cherep, L. A., Ekstrom, A. D., et al. (2025) · eLife · doi:10.7554/elife.106873.1 · Cited by 1
Zmiany czynności kory ruchowej mózgu po leczeniu botuliną u pacjentów ze stwardnieniem rozsianym i spazmem kończyn dolnych
Hok, P., Hluštı́k, P., Tüdös, Z., Frantis, P., Klosova, J., Sládková, V., Mareš, J., Otruba, P., & Kaňovský, P. (2011) · DOAJ (DOAJ: Directory of Open Access Journals)
Cognitive and negative symptoms in schizophrenia : studies of patients and healthy controls using magnetic resonance imaging
Rahm, C. (2013) · Figshare
An examination of blood oxygen level dependent functional magnetic resonance imaging methods in analysing chronic non-malignant pain patients' responses to non -painful pain stimuli
Taylor, A. (2012) · ORCA Online Research @Cardiff (Cardiff University)
Selection of a Model of Cerebral Activity for fMRI Group Data AnalysisPreprint
Keller, M., Roche, A., & Lavielle, M. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1005.3225
“In [Woolrich, 2008], a mixture model consisting of two Gaussian distributions is used to describe population heterogeneity, one class corresponding to possible outlier subjects.”
Method
Brain Responses (fMRI) to Month Words in Time-Space Synesthetes and ControlsPreprint
Teuscher, U., & Ruhl, D. A. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2333961
fMRI activation patterns in an analytic reasoning task: consistency with EEG source localization
Li, B., Vasanta, K. C., O’Boyle, M. W., Baker, M. C., Nutter, B. S., & Mitra, S. D. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.845637
Tools of the Trade Independence in ROI analysis: where is the voodoo?
Poldrack, R. A., & Mumford, J. A. (2016)
The Neural and Behavioral Processes Underlying Limb Selection
Elenzil, R. H. S. (2013) · eScholarship (California Digital Library)
Neurocognitive networks in higher-level visual perception
Postans, M. (2015) · ORCA Online Research @Cardiff (Cardiff University)
Surgical Strategy based on Review of Prospective, Randomized Controlled Trial of Standard versus Extended Lymphadenectomy for Cancer of the Pancreatic Head
Hiraoka, T., & Inoue, K. (2010) · 日本消化器外科学会雑誌 · doi:10.5833/jjgs.43.696
Serotonin Transporter (5-HTTLPR) Variation and Anterior Cingulate Cortex in Relation to Cognitive Load: An fMRI Study
Krogsrud, S. K. (2011) · NORA - Norwegian Open Research Archives
Inter- and intra-individual differences in fluid reasoning show
Kievit, R., Scholte, H. S., Waldorp, L., & Borsboom, D. (2016)
RIGHT INFERIOR FRONTAL GYRUS AND MOTOR RESPONSE INHIBITION
Nilsen, A. S. (2014)
Quantitative Functional Magnetic Resonance Imaging of Multiple Sclerosis
Bird, J. (2016) · Libraries and Cultural Resources (University of Calgary) · doi:10.11575/prism/27506
Treino de estratégia de memória para associação de faces e nomes no comprometimento cognitivo leve amnéstico: desfecho em medidas cognitivas e ressonância magnética funcional
Simon, S. S. (2017) · doi:10.11606/t.5.2017.tde-11092017-095736
Integração visuomotora em ações interceptativas:mecanismos cerebrais e influência da memória de curto prazo e da expectativa
Neto, R. M. D. A. (2017) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.5.2017.tde-25102017-153544
Dynamic integration of conceptual information during learningPreprint
Inhoff, M. C., Libby, L. A., Noguchi, T., Love, B. C., & Charan Ranganath, A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/280362
Convergência multimodal em neurorradiologia: aplicações na epilepsia generalizada idiopática
Garcia, L. Á. L. (2019) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.5.2019.tde-14112019-103455
Neural Correlates of Interobserver Visual Congruency in Free-Viewing Condition
Liu, H., Hu, X., Ren, Y., Wang, L., Guo, L., Guo, C. C., & Han, J. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3002765
Funktionelle Konnektivität der Substantia nigra in einem generellen Aufmerksamkeitstest bei idiopathischem Stottern – eine klinische Studie mittels funktioneller Magnetresonanztomografie
Metzger, F. L. (2020) · doi:10.53846/goediss-8289
Regional Cerebral Blood Flow and Blood-oxygen-level-dependent Smoking-cue Reactivity Responses to Abstinence, Satiety, and Nicotine Replacement Therapy in Treatment-seeking Smokers: A Functional Magnetic Resonance Imaging Study
Wannas, P. A. (2020) · TSpace (University of Toronto)
The neural architecture of semantic retrieval with and without cues: evidence from neuropsychology and neuroimaging
Lanzoni, L. (2020) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · doi:10.13140/rg.2.2.32176.48645
Reprint of ‘A Comprehensive review of group level model performance in the presence of heteroscedasticity: Can a single model control Type I errors in the presence of outliers?’
Mumford, J. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.032
“The Flame 1 and Flame 1 with outlier de-weighting algorithms were implemented using the flameo function, which is part of FSL. Permutation tests were coded in R.”
Influential
Mental Imagery in Fear Extinction: A Multi-Component Examination Based on Behavioral, Physiological, and Neurological Measures
Jiang, X. (2021) · Louisiana State University · doi:10.31390/gradschool_dissertations.5532
Epigenetic and neural correlates of selective social attention across adulthoodPreprint
Puglia, M. H., Lynch, M. E., Nance, M. G., Connelly, J. J., & Morris, J. P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.06.547939
“…were conducted at the whole-brain level using FSL’s local analysis of mixed effects (FLAME) stage 1 (Beckmann et al., 2003; Woolrich et al., 2004; Woolrich, 2008) with FSL’s automatic outlier de-weighting algorithm applied, which identifies and de-weights outliers within the fMRI data…”
MethodInfluential
Human frontopolar cortex plays a causal role in digesting complex information during decision makingPreprint
Law, C.-K., Wong, N., Wong, J. J., Huang, E. Y. H., & Chau, B. K. H. (2023) · Research Square · doi:10.21203/rs.3.rs-3544812/v1
Visual to default network pathways: A double dissociation between semantic and spatial cognitionPreprint
Alam, T. R. G., Krieger‐Redwood, K., Varga, D., Gao, Z., Horner, A. J., Hartley, T., de Schotten, M. T., Sliwinska, M. W., Pitcher, D., Margulies, D. S., et al. (2024) · eLife · doi:10.7554/elife.94902.1
Normative cortical activation patterns in children distinguishing one or two tactile sensory inputs points: An functional MRI study
Donne, J. H., Beare, R. J., Powell, J. A., Fahey, M. C., & Williams, C. (2024) · Journal of Sensory Studies · doi:10.1111/joss.12943
Visual to default network pathways: A double dissociation between semantic and spatial cognitionPreprint
Alam, T. R. G., Krieger‐Redwood, K., Varga, D., Gao, Z., Horner, A. J., Hartley, T., de Schotten, M. T., Sliwinska, M. W., Pitcher, D., Margulies, D. S., et al. (2024) · eLife · doi:10.7554/elife.94902.2
Upregulation of Reward Mesolimbic Activity via fMRI-Neurofeedback Improves Vaccination Efficiency in HumansPreprint
Lubianiker, N., Koren, T., Djerassi, M., Sirotkin, M., Singer, N., Jalon, I., Lerner, A., Sar-El, R., Sharon, H., Azulay‐Debby, H., et al. (2024) · medRxiv · doi:10.1101/2024.09.18.24313899
Statistical Analysis of fMRI Data
Woolrich, M. W., Beckmann, C. F., Nichols, T. E., Smith, S. M., Valsasina, P., Rocca, M. A., & Filippi, M. (2025) · Neuromethods · doi:10.1007/978-1-0716-4438-6_7
Irony comprehension in first-degree relatives of patients with bipolar affective disorder – a preliminary fMRI study
Hajnal, A. S., Varga, E., Tényi, T., Pethő, B., Albert, N., Herold, M., Kovács, M. Á., Csulak, T., Hebling, D., & Herold, R. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1606988
Regulating Negative Autobiographical Memories: An fMRI Investigation of Reappraisal and Distraction in Middle-aged and Older Adults
Graner, J. L., Faul, L., Diehl, J. M., Madden, D. J., Smoski, M. J., & LaBar, K. S. (2025) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.a.88
Newly trained navigation and verbal memory skills elicit changes in task-related networks but not brain structurePreprint
Zheng, L. J., Boogaart, Z., McAvan, A. S., Garren, J. D., Doner, S., Wilkes, B. J., Groves, W., Yuksel, E., Cherep, L. A., Ekstrom, A. D., et al. (2025) · eLife · doi:10.7554/elife.106873.2
The perceptual and neural processing of familiar faces is shaped by the statistical regularities of real-world viewing
Quinn, B. P. A., & Andrews, T. J. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf328
“Individual participant data was then entered into a higher-level group analysis using a mixed-effects design in FLAME (FLAME, https://fsl.fmrib.ox.ac. uk; Beckmann et al. 2003; Woolrich et al. 2004; Woolrich 2008).”
Human frontopolar cortex plays a causal role in decomposing high-dimensional information during decision making
Law, C.-K., Wong, N., Wong, J. J., Huang, E. Y. H., Yu, R., & Chau, B. K. H. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121758
Neural representations of perceived engagement during action observation
Arcuri, E., Cerliani, L., Ardizzi, M., Langiulli, N., Ferroni, F., Keysers, C., Gazzola, V., & D, V. G. M. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121780
Improving GNSS-IR sea level retrievals using WinLSP with damping-aware height-rate correction and normalized IRLS
Altuntaş, C., Williams, S. D. P., & Tunalıoğlu, N. (2026) · GPS Solutions · doi:10.1007/s10291-026-02078-w
“685 (Woolrich 2008 ), defining the inlier threshold under the assumption of normally distributed residuals.”
Neural activation by vocal threat requires attention
Witteman, J., Axioti, S., & van Tol, A. (2026) · Cortex · doi:10.1016/j.cortex.2026.04.009
Ventromedial prefrontal cortex activity during extinction recall suggests successful extinction learning via mental imagery
Lyons, A. L., Jiang, X., Rawliuk, T., Burleigh, L. M., Horner, S., Grégoire, L., & Greening, S. G. (2026) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsag031
“2004, Woolrich 2008).”
29513 Neuroimaging in communication science
Wheeler, B. L., & Weber, R. (2026) · De Gruyter eBooks · doi:10.1515/9783111203577-013
Children With Idiopathic Toe Walking Display Different Cortical Activation Patterns When Interpreting Tactile Sensation
Donne, J. H., Beare, R. J., Powell, J. A., Fahey, M. C., & Williams, C. M. (2026) · Developmental Psychobiology · doi:10.1002/dev.70168

Authors: Woolrich, M.
Affiliations: John Radcliffe Hospital (GB)
Corresponding author: Woolrich, Mark
ORCID: Woolrich, Mark
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2008-06
Volume: 41
Issue: 2
Pages: 286-301
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2008.02.042
OpenAlex: W2052515792
Cited by: 504
References: 20
Citations by year: 2026: 13 · 2025: 16 · 2024: 17 · 2023: 27 · 2022: 20 · 2021: 20 · 2020: 39 · 2019: 31 · 2018: 41 · 2017: 43 · 2016: 41 · 2015: 33 · 2014: 32 · 2013: 30 · 2012: 37
FWCI: 19.93
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Funders: Engineering and Physical Sciences Research Council (EP/C539788/1)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
Keywords: Gaussian mixture model, Bayesian inference, effect size distribution, iteratively reweighted least squares, permutation tests
MeSH terms: Algorithms, Brain, Humans, Magnetic Resonance Imaging, Models, Statistical, Normal Distribution

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Robust group analysis using outlier inference

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks