Christine Tipper, BA, MA, PhD
Investigator, BC Children’s Hospital
Assistant Professor (Partner), Department of Psychiatry, University of British Columbia
Scientific Director, Brain Dynamics Lab, University of British Columbia
Research and Interests
Cognitive and social neuroscience in health and illness. Quantifying organization patterns in complex brain networks for attention and social awareness. Non-pharmacological neurotherapeutics discovery. EEG, fMRI, DTI, tDCS.
My work utilizes multimodal brain imaging along with cutting edge analytical techniques to quantify complex brain network organization and dynamics.
My primary goal is to map complex clinical phenotypes to complex anatomical, functional, and biochemical organization in brain systems that support attention and social awareness. I am building research partnerships with clinicians to better understand developmental, neuropsychiatric, and neurological disorders such as autism, attention deficit/hyperactivity disorder, anxiety, chronic pain, and traumatic brain injury. A key emphasis is to pursue avenues holding promise for the development of non-pharmacological interventions that can improve quality of life for children and families affected by these conditions.
Functional brain networks of the frontoparietal attention system
Paradoxically, despite being one of the most intensively studied topics in cognitive neuroscience, “attention” remains a nebulous concept. This is because attention is not a unitary phenomenon with a single underlying brain mechanism, but rather is comprised of multiple component processes mediated cortically by a distributed network of frontal and parietal brain regions. The goal of this project is to distinguish component processes of attention and determine their neural substrates. This study utilizes fMRI and a novel application of a variance-based brain analytical technique known as constrained principal component analysis (CPCA) to identify functional brain networks that mediate the various component process of attention.
Dynamic brain networks for the integration of attention functions: A combined fMRI-EEG study
This study collected fMRI and dense-array EEG data on the same participants while they perfromed a challenging, attention-demaning task. Using a variance-based brain imaging analysis technique known as constrained principal component analysis (CPCA), we aim to identify distinct brain networks that mediate the various component processes of attention, and quantify the functional dynamics underlying their inegration. A key component of this study is the development of analytical techniques to combine the precise spatial information provided by fMRI with the precise temporal information provided by EEG.
The role of neural simulation in action understanding: A simultaneous fMRI-EEG study
This study investigates the role of agent-independent brain substrates in coding various properties of action. Previous research has revealed a functionally stratefied, hierarchiacally organized action observation brain network (AON). This network is involved in representing both executed and observed actions. There has been much debate over whether this functional overlap reflects a human “mirror neuron system”. This study utilized a novel virtual reality task and an fMRI design known as repetition suppression (RS) to directly test for brain substrates that responded identically regardless of whether an action was seen or performed. EEG data collected simultaneously also enables us to chart the functional dynamics linked to the integration of distinct brain processes underlying action representation.
Brain network architechtures for multidimensional attentional control: A graph-theoretic framework
Individuals vary in how they control attention based on multiple dynamic sources of information. We tested if there are characteristic organizational features of the attention network associated with successful cognitive control over volitional orienting to changing spatial cues and the ability to adapt detection criteria given varying event probabilities. Novel and robust community detection algorithms identified groups of subjects with attention networks having similar functional modularity, defined by functional magnetic resonance imaging (fMRI). This investigation relies on the application of emerging computational techniques for quantifying human functional connectomes.
For the latest publications, please visit Dr. Tipper’s ORCID profile (http://orcid.org/0000-0003-1666-9203) .
1. Kaplan, J., Gimbel, S., Dehghani, M. Immordino-Yang, M. H., Sagae, K., Wong, J., Tipper, C. M., Damasio, H., Gordon, A., & Damasio, A. (in press). Processing narratives concerning protected values: A cross-cultural investigation of neural correlates. Cerebral Cortex. http://cercor.oxfordjournals.org/content/early/2016/01/06/cercor.bhv325.abstract (http://cercor.oxfordjournals.org/content/early/2016/01/06/cercor.bhv325.abstract)
2. Woodward, T. S., Leong, K. W., Sanford, N., Tipper, C. M., & Lavigne, K. M. (2016). Altered Balance of Functional Brain Networks in Schizophrenia. Psychiatry Research: Neuroimaging, 248, 94-104. PMID: 26786152 (http://www.ncbi.nlm.nih.gov/pubmed/26786152)
3. Tipper, C. M., Signorini, G., & Grafton, S. T. (2015). Body language in the brain: constructing meaning from expressive movement. Frontiers in Human Neuroscience, 9:450. PMID: 26347635 (http://www.ncbi.nlm.nih.gov/pubmed/26347635)
4. Woodward, T. S., Tipper, C. M., Leung, A., Lavigne, K. M., & Metzak, P. (2015). Reduced functional connectivity during controlled semantic integration in schizophrenia: A multivariate approach. Human Brain Mapping, 36. 2948-2964. CA (IF 6.924). PMID: 26014890 (http://www.ncbi.nlm.nih.gov/pubmed/26014890)
5. Hermundstad, A. M., Brown, K. S., Bassett, D. S., Aminoff, E. M., Frithsen, A., Johnson, A., Tipper, C. M., Miller, M. B., Grafton, S. T., & Carlson, J. M. (2014). Structurally-constrained relationships between cognitive states in the human brain. Public Library of Science Computational Biology, 10(5), e1003591. CA. PMID: 24830758 (http://www.ncbi.nlm.nih.gov/pubmed/24830758)
6. Hermundstad, A. M., Bassett, D. S., Brown, K. S., Aminoff, E. M., Clewitt, D., Freeman, S., Frithsen, A., Johnson, A., Tipper, C. M., Miller, M. B., Grafton, S. T., & Carlson, J. M. (2013). Structural foundations of resting-state and task-based functional connectivity in the human brain. Proceedings of the National Academy of Sciences, 110(15), 6169-6174. CA (IF 9.809). PMID: 23530246 (http://www.ncbi.nlm.nih.gov/pubmed/23530246)
7. Smallwood, J., Tipper, C. M., Brown, K., Baird, B., Engen, H., Michaels, J. R., Grafton, S. T., & Schooler, J. W. (2013). Escaping the here and now: Evidence for a role of the default mode network in perceptually decoupled thought. Neuroimage, 69, 120-125. CA (IF 6.956). PMID: 23261640 (http://www.ncbi.nlm.nih.gov/pubmed/23261640)
8. Grafton, S. T. & Tipper, C. M. (2012). Decoding intention: A neuroergonomic perspective. Neuroimage, 59(1), 14-24. CA (IF 6.956). PMID: 21651985 (http://www.ncbi.nlm.nih.gov/pubmed/21651985)
9. McCall, C., Tipper, C. M., Blascovich, J. B., & Grafton, S. T. (2012). Attitudes trigger motor behavior through conditioned associations: neural and behavioral evidence. Social, Cognitive, and Affective Neuroscience, 7(7), 841-849. CA (IF 5.884). PMID: 21948955 (http://www.ncbi.nlm.nih.gov/pubmed/21948955)
10. Tipper, C. M., Handy, T. C., Giesbrecht, B., & Kingstone, A. (2008). Brain responses to biological relevance. Journal of Cognitive Neuroscience, 20(5), 879-891. FA (IF 4.687). PMID: 18201123 (http://www.ncbi.nlm.nih.gov/pubmed/18201123)