Lifespan Development and Aging
Degradation of neural dynamics with age
Early theory on aging, and in particular, the age-based degradation of cognition, is historically ascribed to some form of heightened “neural noise” that interferes with efficient processing (e.g., Crossman & Szafran, 1956; Cremer and Zeef, 1987; Welford, 1965, 1981). However, these early theories had rarely been tested by examining within-subject brain signals directly. Perhaps counterintuitively, our extant in vivo work consistently shows that older, poorer performing brains may generally have more stable brain signals than younger, higher performing adults (e.g., Garrett et al., 2010, 2011, JNeurosci; 2013 Cerebral Cortex; 2013 NBR). Overall, our group seeks to understand why healthy brains are highly and robustly dynamic across moments (adaptability, dynamic range, multi-stability?), and how this changes with development and aging.
Behavioral relevance of neural dynamics
Our interests in the relations between cognition and brain signal variability have been broad to date, spanning simple reaction time, perceptual matching, working memory, and face processing using a variety of stimuli types and performance metrics (e.g., accuracy, mean RT, RT variability). Broadly, we find that better performers have higher brain signal variability, and that within-person signal variability can parametrically tune with changes in task difficulty (Garrett et al., 2013a,b, Cerebral Cortex; 2013, NBR). We continue to examine whether variability may be an expression of the flexible modulation of brain states that allows a probabilistic search for an optimal state, given either unexpected or anticipated changes in the environment (see Garrett et al., 2013, NBR).
A basis for understanding local dynamics
It is well known that functional dynamics occur around a static anatomical (white matter) skeleton (Honey et al., 2009). Computational models suggest that realistic network dynamics, and the influence of noise within them, cannot exist without a healthy, biologically plausible WM structure (Deco et al., 2009). However, it remains unknown to what extent the WM skeleton constrains functional dynamics/variability in humans, and across the lifespan.
Conversely, although structural connections form the “core skeleton” of brain function, functional connections also exist broadly in absence of direct structural connections (Deco et al., 2009; Ghosh et al., 2008; Honey et al., 2007). Notably, a theoretically and computationally informed contributing factor to brain signal variability effects is that our neural system can explore a broad repertoire of brain states/networks from moment to moment when variability is present (Deco et al., 2009; 2011; Garrett et al., 2013, NBR). Accordingly, a healthy, developed brain (i.e., in young adults) may be more flexible in the face of changing systemic or environmental demands, as it can reconfigure efficiently as required. Assuming that node-based signal variability reflects a dynamic system that is constantly reconfiguring in strength and direction, it is clear that explicitly measuring network-level dynamics is required to determine the bounds of node-based dynamics.
Neurochemical bases for neural dynamics
We are interested in linking theories of dopamine (DA)-based neural “precision” (e.g., Ballo et al., 2012; Coull et al., 2012; Friston, 2010) to brain signal variability effects. It is an interesting paradox that signal variability often appears functional for healthy, functional neural systems, yet optimal DA function is presumed to generally invoke tighter bounds on temporal coding. We are currently collaborating with groups in Berlin, Stockholm, and Montreal to investigate how DA agonists/antagonists may relate to working memory-based signal variability in young and older adults, as well as how tyrosine depletion may impact resting state dynamics.
boosting deficient dynamics?
Our initial studies in this domain focus on directly invoking stochastic resonance, which is classically observed when the presence of additive noise allows input signals to be better detected in the brain. Historically, “noise” has been experimentally applied to the nervous system in a number of ways via noisy peripheral visual/tactile/auditory stimuli (e.g., Collins et al., 1996; Wells et al., 2005). However, good evidence for whether/how these peripherally applied “noise” sources reach the brain has been lacking. Alternatively, noise stimulation methods (tRNS) provide a direct route to the cortex of the brain, thus piquing our interest in simultaneous tRNS-neuroimaging to examine links between stimulation and brain signal dynamics.
We continue to both develop and apply brain signal variability methods, moving beyond general variance measures to ask more explicit questions about the nature of that variance (e.g., spectral content and structure, time delay embedding). We are also inherently interested in discrete patterns in brain signals (e.g., entropy), and how they relate to overall variance.
We have a keen interest in multivariate models (e.g., singular value decomposition (SVD)-based techniques), which best enable various correlates of signal dynamics to be simultaneously examined. We are currently exploring how SVD models can be optimized within a mixed-modeling framework in an attempt to link latent-level parametric cognitive performance to parametric task-based brain dynamics. We are also developing models linking ASL and BOLD data to investigate the impact of vascular responses (on task and during CO2 hypercapnia) on BOLD signal variability using whole-to-whole brain decompositions, a statistical technique we recently applied when linking white matter to functional data (*Burzynska, *Garrett, et al., 2013, JNeurosci).
An important goal of our proposed work is to support the future examination of brain signal variability in neuroimaging through open source software. At present, no analysis package provides for a comprehensive brain signal variability analysis. As such, we continue to develop an event-related and block design fMRI-based Variability Toolbox (VarTbx) for SPM. Mimicking a Level-1 analysis, our toolbox allows SPM users (or more generally, Matlab users) to import any preprocessed fMRI data, choose from a number of different variability measures, and to output resulting individual maps for further analysis within SPM’s standard Level-2 module (or elsewhere). The first version of VarTbx is now available; see our software page for details.
If you are interested in becoming a tester of VarTbx, contact us at: email@example.com
The LNDG brain trust
Douglas D. Garrett, Ph.D.
Principal Investigator/Senior Researcher
Prior to heading the Lifespan Neural Dynamics Group (LNDG) within the Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Doug completed degrees at the University of Victoria (BA) and University of Toronto (MA/Ph.D., 2011). He was also a Fellow with the MPS-UCL Initiative for Computational Psychiatry and Ageing Research (2011-2013), and a Research Scientist/Project Leader for the Intra-Person Neural Dynamics Project at the Center for Lifespan Psychology, Max Planck Institute for Human Development in Berlin, Germany. Outside of the lab, you’ll find Doug on a mountain, playing guitar or hockey, running with his dog Baley, or running in-house twin studies with his twin 1 year-old boys :-) CV
Niels joined the lab in September 2015, after completing his PhD in cognitive neuroscience and a postdoc position with Prof. Tobias Donner at the University of Amsterdam. He is interested in the brain processes that underlie our perceptual experience and that enable us to select sensible courses of action. Within the lab, he focuses on the role of neural variability in perception and decision making, as well as how and why this variability changes with age. Outside of the lab, Niels likes to hang out with his family, listen to electronic music, and play football and tennis. His personal site is here.
Iris received her Ph.D (2013) from the Graduate School of Systemic Neuroscience at Ludwig-Maximilians-Universität München (LMU). From 2014 to 2016, she received independent research funding for her postdoctoral work ("Determinants of Mental Capacity) from the Danish Council for Independent Research. Earlier this year, Iris was awarded a 3-year Marie Curie Fellowship by the European Research Council, during which she will be affiliated with Jeremy Wolfe’s visual attention laboratory at Harvard University, and with the LNDG at the Max Planck UCL Centre. Her current work in the LNDG focuses on brain dynamics in relation to attentional cueing and decision-making processes. Outside of the lab, Iris loves music and dancing, independent movies, and whenever there is a sea to jump in, you can find her there.
Since 2008, Steffen has been a research assistant at the MPI for Human Development, and is now the LNDG Manager. He works on fMRI acquisition, data processing/analysis, programming, and grid computing. Steffen recently achieved his diplom in psychology at Humboldt University in Berlin, and his research interests are connected to lifespan fMRI-based resting state data, as well as working memory and mood influences (at MPIB, Freie Universität Berlin). Besides work and study, he likes to travel, eat, cook, and hit the mountains.
.Julian joined the LNDG in October 2016 as a doctoral student in the IMPRS COMP2PSYCH program. He previously did research assistantships with the ConMem group and obtained degrees from Freie Universität Berlin (BSc Psychology) and Humboldt University of Berlin (MSc Mind & Brain), interspersed with visits to the National University of Singapore and University College London. His interests lie in age-related signal variability in different neuroimaging modalities as well as in spectral analysis techniques. Outside of the institute, he enjoys (among others) appreciating local and global scenery, collecting Donald Duck comics, and learning Chinese
visiting Ph.D. student
Lanfang joined the LNDG in 2017 as a visiting student from the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University. She is interested in graph theoretical analysis and neural variability. She has worked on dyslexia and bilingualism during her masters program. Now she works on the aging-based effect of speech comprehension in noise, with a focus on the speaker-listener neural coupling. In her spare time, she enjoys swimming, traveling, cooking and watching Sci-fi movies.
Masters student/research assistant
Samira became a research assistant in the LNDG in 2014. She works on data processing and analysis and has research interests in cognition, lifespan development and brain networks. Her particular focus lies in brain dynamics and functional connectivity, specifically in the temporal dynamics of brain modules. Samira recently finished her Master Degree in psychology (cognitive neuropsychology) at Humboldt University in Berlin. In her spare time she enjoys climbing, traveling, mountaineering, cooking and reading.
Masters student/research assistant
I am a Master’s student in the Social, Cognitive and Affective Neuroscience program at Free University Berlin. My research interests are learning, decision making and lifespan development. In my thesis project, I focus on reinforcement learning in young and old adults in relation to fMRI-based brain dynamics. In my spare time, I enjoy running, marbling, watching movies, reading, and trying different cuisines.
José Yordán Ramirez
Masters student/research assistant
José joined the LNDG as a research assistant/master’s student in 2016. He handles fMRI and EEG data processing/analysis and is currently doing his Masters Degree at the Freie Universität¹s Social, Cognitive and Affective Neuroscience program. He is interested in brain-signal variability as an important measure of psychological processes, lifespan psychology and, in the future, would like to explore its role within comparative neuroscience. In his personal time, he enjoys reading about history, politics, language and philosophy, as well as discussing cinema.
Lars Bäckman (Aging Research Center, Karolinska Institute)
Cheryl Grady (Rotman Research Institute, Baycrest; University of Toronto)
Marc Guitart-Masip (Aging Research Center, Karolinska Institute)
Hauke Heekeren (Free University Berlin)
Ulman Lindenberger (Center for Lifespan Psychology, MPI for Human Development)
Stu MacDonald (University of Victoria)
Randy McIntosh (Rotman Research Institute, Baycrest; University of Toronto)
Rafael Polania (University of Zurich)
Christian Ruff (University of Zurich)
Alireza Salami (Aging Research Center, Karolinska Institute)
Greg Samanez-Larkin (Duke)
Elisabeth Höhne (intern)
Matthias Kerkemeyer (intern)
Janina Marchner (research assistant)
Patrick Werner (research assistant)
Peer reviewed articles
Garrett, D.D., Lindenberger, U., Hoge, R., and Gauthier, C.J. (in press). Age differences in brain signal variability are robust to multiple vascular controls. Scientific Reports.
Nyberg, L., Karaljia, N., Salami, A., Andersson, M., Wåhlin, A., Kaboovand, N., Axelsson, J., Rieckmann, A., Papenberg, G., Garrett, D.D., Ricklund, K., Lövden, M., Lindeberger, U., Köhncke, Y., and Bäckman, L. (2016). Dopamine D2 receptor availability is linked to striato-hippocampal functional connectivity and episodic memory. Proceedings of the National Academy of Sciences of the United States of America, 113, 7918-7923. Link
*Grandy, T., *Garrett, D.D., Schmiedek, F., and Werkle-Bergner, M. (2016). On the estimation of brain signal entropy from sparse neuroimaging data. Scientific Reports, 6, 23073. Link
Guitart-Masip, M., Salemi, A., Garrett, D.D., Rieckmann, A., Lindenberger, U., & Bäckman, L. (2016). BOLD variability is related to dopaminergic transmission and cognitive aging. Cerebral Cortex, 26, 2074–2083 . Link
Garrett, D.D., Nagel, I.E., Preuschhof, C., Burzynska, A.Z., Marchner, J., Wiegert, S., Jungehülsing, G., Nyberg, L., Villringer, A., Li, S-C., Heekeren, H.E., Bäckman, L., & Lindenberger, U. (2015). Amphetamine modulates brain signal variability and working memory in younger and older adults. Proceedings of the National Academy of Sciences of the United States of America, 112, 7593-7598. Link
Garrett, D.D., McIntosh, A.R., & Grady, C.L. (2014). Brain signal variability is parametrically modifiable. Cerebral Cortex, 24, 2931-2940. Link
Grady, C.L. & Garrett, D.D. (2013). Understanding variability in the BOLD signal and why it matters for aging. Brain Imaging and Behavior, doi: 10.1007/s11682-013-9253-0. Link
*Burzynska, A.Z., *Garrett, D.D., Preuschhof, C., Nagel, I.E., Li, S-C., Bäckman, L., Heekeren, H.E., & Lindenberger, U. (2013). A scaffold for efficiency in the human brain. Journal of Neuroscience, 33, 17150-17159. Link
Garrett, D.D., Samanez-Larkin, G.L., MacDonald, S.W.S., Lindenberger, U., McIntosh, A.R., & Grady, C.L. (2013). Moment-to-moment brain signal variability: A next frontier in brain mapping? Neuroscience & Biobehavioral Reviews, 37, 610-624. Link
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. Link
Garrett, D.D., MacDonald, S.W.S., & Craik, F.I.M. (2012). Intraindividual reaction time variability is malleable: Feedback- and cognitive reserve-related reductions in variability with age. Frontiers in Human Neuroscience, 6, 101. doi: 10.3389/fnhum.2012.00101. Link
Garrett, D.D., Kovacevic, N., McIntosh, A.R., & Grady, C.L. (2011). The importance of being variable. Journal of Neuroscience, 31, 4496-4503. Link
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. Link
Garrett, D.D., Grady, C.L., & Hasher, L. (2010). Everyday memory compensation: The impact of cognitive reserve, subjective memory, and stress. Psychology and Aging, 25, 74-83. Link
McIntosh, A.R., Kovacevic, N., Lippe, S, Garrett, D.D., Grady, C.L., & Jirsa, V. (2010). The development of a noisy brain. Archives of Italian Biology, 148, 323-337. Link
Lindstrom-Forneri, W., Tuokko, H., Garrett, D.D., & Molnar, F. (2010). Everyday competence: Theories, measurement strategies, and its relations to driving. Clinical Gerontologist, 33, 283-297. Link
Garrett, D.D., Tuokko, H., & Stajduhar, K. (2008). Planning for end-of-life care: Findings from the Canadian Study of Health and Aging. Canadian Journal on Aging, 27, 11-21. Link
Stepaniuk, J.A., Tuokko, H.A., McGee, P., Garrett, D.D. & Benner, E.L. (2008). Impact of transit training and free bus pass on public transportation use by older drivers. Preventative Medicine, 47, 335-337. Link
Dixon, R.A., Garrett, D.D., Lentz, T.L., Strauss, E., & Hultsch, D.F. (2007). Neurocognitive markers of mild cognitive impairment: Exploring the roles of speed and inconsistency. Neuropsychology, 21, 381-399. Link
Tuokko, H., Garrett, D.D., McDowell, I., Silverberg, N., & Kristjansson, B. (2003). Cognitive decline in high-functioning older adults: Reserve or ascertainment bias? Aging and Mental Health, 7, 259-270. Link
Chapters and Commentaries
Dixon, R.A., Garrett, D.D., & Backman, L. (2008). Compensation in cognitive neurorehabilitation. In D.T. Stuss, G. Winocur and I.H. Robertson (Eds.), Cognitive Neurorehabilitation, 2nd Edition. Cambridge: Cambridge University Press. Link
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. Link
Garrett, D.D., Kovacevic, N., McIntosh, A.R. & Grady, C.L., (2010). All within-subject BOLD standard deviations are not created equal [response to Mohr, P.N. & Nagel, I.E.]. Journal of Neuroscience, 30, 7755-7757. [doi:10.1523/JNEUROSCI.1560-10.2010]. Link
Garrett, D.D. & Grady, C.L. (2010). Review of Stephens et al. (2010). Faculty of 1000, Dec, 2010.
Garrett, D.D. & Grady, C.L. (2010). Review of Gazzaniga (2010). Faculty of 1000, Aug, 2010.
Manuscripts under review
Grady, C.L. & Garrett, D.D. (under review). Brain Signal Variability is Modulated as a Function of Internal and External Demand in Younger and Older Adults.
Garrett, D.D. & Lindenberger, U. (under review). Local variability reflects lower dimensional functional integration in the human brain
Lövdén, M., Karalija, N., Kaboovand, N., Andersson, M., Wåhlin, A., Axelsson, J., Köhncke, Y., Jonasson, L.S., Rieckmann, A., Papenberg, G., Garrett, D.D., Guitart-Masip, M., Salami, A., Riklund, K., Bäckman, L., Nyberg, L., & Lindenberger, U. (under review). Latent-Profile Analysis Reveals Behavioral and Brain Correlates of Dopamine-Cognition Associations.
Salami, A., Rieckmann, A., Karalija, N., Avelar-Pereira, B., Andersson, M., Wåhlin, A., Papenberg, G., Garrett, D.D., Riklund, K., Lövdén, M., Lindenberger, U., Bäckman, L., & Nyberg, L. (under review). Neurocognitive profiles of healthy older adults with working-memory dysfunction
Halliday, D., Mulligan, B., Garrett, D.D., Schmidt, S., Hundza, S., Garcia-Barrera, M., Stawski, R., MacDonald, S.W.S. (under review). Neural Variability in Older Adults During an Executive Function Task using Functional Near Infrared Spectroscopy.
Variability Toolbox (VarTbx) for SPM
VarTbx measures within-voxel time series variability in fMRI data. The VarTbx structure is intended to be similar to a standard SPM first-level analysis. You can then proceed to pass those first-level, variability-based images to a level-2 SPM analysis in order to model group effects of interest. However, the first-level NIFTI output files could also be used within other statistics programs of your choice (e.g., FSL, AFNI, PLS).
VarTbx currently supports modeling block designs with a boxcar model, and computes temporal variability using measures such as: detrended variance (VAR), detrended standard deviation (SD), mean squared successive difference (MSSD), and SQRT(MSSD). We plan to add additional modeling approaches and variability measures in future releases.
Using VarTbx is relatively straightforward. You first specify your first-level SPM model, as usual. You then save the model, which is used as input to specify all sessions, conditions, onsets, durations, and nuisance regressors for VarTbx. Finally, after choosing a variability metric of interest, variability-based NIFTI images are then produced. Click on the video above for a short VarTbx tutorial.
Work within the LNDG
Top row (left to right): Julian Kosciessa, Niels Kloosterman, Steffen Wiegert, Douglas Garrett
Bottom row (left to right): José Yordán Ramirez, Neslihan Sener, Samira Epp, Lanfang Liu
We continue to recruit excellent postdoctoral fellows, with no fixed deadline for applications or expression of interest.
Since 2016, an International Max Planck Research School (Ph.D. level) on computational methods (IMPRS COMP2PSYCH) has been funded, in association with the Max Planck UCL Centre for Computational Psychiatry and Ageing Research. The annual application deadline for positions is typically in late Winter each year.
Student assistant/Master's thesis student positions
Several student assistant positions may be available in LNDG at any one time, depending on the studies we are currently running. Many student assistants also formulate and complete their master's thesis while employed.
Three-month paid internships are also possible within LNDG. There is no specific structure to such an internship, but is typified by students acquiring a specific technique and/or helping with a specific study.
Regarding all available positions, please email us directly at lndg -at- mpib-berlin.mpg.de.