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Intra-Person Neural Dynamics

The Intra-Person Neural Dynamics Project | Projektlogo

Interests within the Intra-Person Neural Dynamics Project center on the examination of EEG/fMRI brain signal variability and dynamics in relation to lifespan development, cognition, neurochemistry, network dynamics, and brain structure. Accordingly, we have an inherent multivariate methods focus that allows the examination of brain signal variability phenomena across multiple levels of analysis.

See also Max Planck UCL Centre for Computational Psychiatry and Ageing Research

Background

It is well known that brain signal variability is ubiquitous in humans and across species, regardless of method of measurement. Historically, brain variability has often been considered "noise." However, the presence of brain variability may be a critical component of healthy brain function. Our recent findings indicate that variability-based brain patterns are highly powerful indicators of age, nearly eliminate any useful age-predictive variance offered by mean brain signals, and are virtually uncorrelated with age-related mean-based patterns (Garrett et al., 2010).

Importantly, we also find that younger, faster, and more consistently responding adults have more variable brains, relative to older (56–85 years), slower, more inconsistent adults (Garrett et al., 2011, 2013). Thus, temporal signal variability may provide an important marker of the cognitive efficiency and complexity of neural systems. We are now expanding this work to examine the role of dopamine in neural dynamics across the lifespan. Further, we continue to explore signal variability in a host of other contexts and samples using a variety of techniques, and ultimately intend to improve diagnostics for various pathological conditions using variability-based neuroimaging methods.

Search for Postdoc

One 2-year postdoctoral position (with possibilities for extension) in cognitive neuroscience/neuroimaging/computational modeling is now available in the Lifespan Neural Dynamics Research Group (PI: Douglas D. Garrett) of the newly established Max Planck UCL Centre for Computational Psychiatry and Ageing Research (Co-Directors: Ray Dolan and Ulman Lindenberger).

For more information, see PDF file below

Team

Douglas Garrett (senior research scientist)
Ulman Lindenberger

Niels Kloosterman
Iris Wiegand
(postdocs)

Steffen Wiegert (research assistant)

Max Planck UCL Centre

MR Lab

In this project we often use data collected in the MR Lab. You can find more information here.

Key References

Garrett, D. D., Nagel, I. E., Preuschhof, C., Burzynska, A. Z., Marchner, J., Wiegert, S., ... Lindenberger, U. (2015). Amphetamine modulates brain signal variability and working memory in younger and older adults. Proceedings of the National Academy of Sciences USA, 112, 7593–7598. doi: 10.1073/pnas.1504090112

Garrett, D. D., McIntosh, A. R., & Grady, C. L. (2014). Brain signal variability is parametrically modifiable. Cerebral Cortex, 24, 2931–2940. doi: 10.1093/cercor/bht150

Garrett, D. D., Samanez-Larkin, G. R., MacDonald, S. W. S., Lindenberger, U., McIntosh, A. R., & Grady, C. L. (2013). Moment-to-moment brain signal variability: A next frontier in human brain mapping? Neuroscience & Biobehavioral Reviews, 37, 610–624. doi: 10.1016/ j.neubiorev.2013.02.015

Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2013). The modulation of BOLD variability between cognitive states varies by age and processing speed. Cerebral Cortex, 23, 684–693. doi: 10.1093/cercor/ bhs055

Garrett, D. D., McIntosh, A.R., & Grady, C. L. (2011). Moment-to-moment brain signal variability can inform models of stochastic facilitation now. Nature Reviews Neuroscience, 12, 612. doi: 10.1038/nrn3061-c1

Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2011). The importance of being variable. Journal of Neuroscience, 31, 4496–4503. doi: 10.1523/ jneurosci.5641-10.2011

Garrett, D. D., Kovacevic, N., McIntosh, A. R. & Grady, C. L. (2010). Blood oxygen level-dependent signal variability is more than just noise. Journal of Neuroscience, 30, 4914–4921. doi: 10.1523/jneurosci.5166-09. 2010