COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Machine Learning @ CUED > Population Inference for Functional Brain Connectivity

## Population Inference for Functional Brain ConnectivityAdd to your list(s) Download to your calendar using vCal - Genevera I. Allen (Rice University)
- Wednesday 04 March 2015, 11:00-12:00
- Engineering Department, CBL Room BE-438.
If you have a question about this talk, please contact Dr R.E. Turner. Functional brain connections are the set of statistical relationships between neural activity in different parts of the brain; these are typically estimated from neuroimaging data such as functional MRI . In multi-subject studies, many are interested in identifying the individual functional brain connections or patterns of connections that are different between two groups of subjects or across a clinical population. Popular approaches to this problem include estimating a network for each subject, and then assuming the subject networks are fixed, conducting inference over network metrics. These approaches, however, fail to account for the variability and network estimation error associated with estimating each subject’s brain network, thus resulting in incorrect inferences. In this talk, we study this problem using Markov Networks as the model for brain connectivity. Statistically, our problem can be described as conducting large scale inference over network edges or groups of edges post graphical model selection, part of a novel statistical paradigm we call Population Post Selection Inference (popPSI). We show that for this popPSI problem, current approaches in neuroimaging have both low statistical power and highly inflated false positive rates. We then develop a new procedure which we term R^3, standing for resampling, random effects, and random penalization. Our approach uses the correct two-level random effects model to account for network variability within a subject (due to network estimation) as well as between subjects. Through simulation studies we show that our method solves many of the problems associated with existing techniques, yielding substantial improvements in terms of both error control and statistical power. We conclude our talk by applying our methods in two case studies – a color-sequence synesthesia study and a neurofibromatosis one study. Joint work with Manjari Narayan and Steffie Tomson. This talk is part of the Machine Learning @ CUED series. ## This talk is included in these lists:- Seminar
- All Talks (aka the CURE list)
- Biology
- CBL important
- Cambridge Centre for Data-Driven Discovery (C2D3)
- Cambridge Forum of Science and Humanities
- Cambridge Language Sciences
- Cambridge Neuroscience Seminars
- Cambridge University Engineering Department Talks
- Centre for Smart Infrastructure & Construction
- Chris Davis' list
- Creating transparent intact animal organs for high-resolution 3D deep-tissue imaging
- Engineering Department, CBL Room BE-438
- Featured lists
- Guy Emerson's list
- Inference Group Summary
- Information Engineering Division seminar list
- Interested Talks
- Joint Machine Learning Seminars
- Life Science
- Life Sciences
- ML
- Machine Learning @ CUED
- Machine Learning Summary
- Neuroscience
- Neuroscience Seminars
- Neuroscience Seminars
- Required lists for MLG
- School of Technology
- Simon Baker's List
- Stem Cells & Regenerative Medicine
- Trust & Technology Initiative - interesting events
- bld31
- dh539
- ndk22's list
- rp587
- yk373's list
Note that ex-directory lists are not shown. |
## Other listsCentre of Governance and Human Rights Events ‘Diglossia, Bidialectalism, or Bilingualism? Portuguese as a Foreign Language in the Classroom’ Logic & Semantics for Dummies## Other talksBiomolecular Thermodynamics and Calorimetry (ITC) Building intuition about coherence My VM is Lighter (and Safer) than your Container Laser Printed Organic Electronics, Metal-Organic Framework - Polymer Nanofiber Composites for Gas Separation Auxin and cytokinin regulation of root architecture - antagonism or synergy Yikes! Why did past-me say he'd give a talk on future discounting? |