How is information integrated into the brain's memory networks?

I study the human brain's memory systems, by researching  learning, consolidation, and existing knowledge using fMRI and behavioral studies.

In the course of my work, I develop and use new ways to extract information from fMRI data.



Creating concepts from converging features in human cortex

Coutanche, M. N., & Thompson-Schill (In press). Creating concepts from converging features in human cortex. Cerebral Cortex. •  Download PDF

In this new study, we decode the fruit or vegetable participants are thinking about from activity in the ATL, and its shape and color from areas of visual cortex. We discovered these codes are linked: decoding 'tangerine' is more likely when both 'spherical' and 'green' codes are present in shape and color regions.

Distinguishing multi-voxel patterns and mean activation: Why, how, and what does it tell us?

Coutanche, M. N. (2013). Distinguishing multi-voxel patterns and mean activation: Why, how, and what does it tell us? Cognitive, affective & behavioral neuroscience, 13(3), 667–673.  •  Download PDF

In a CABN special issue on fMRI, I discuss how the overall BOLD response and multi-voxel pattern discrimination are interpreted and measured.

The role of sleep in forming a memory representation of a two-dimensional space

Coutanche, M.N., Gianessi, C.A., Chanales, A.J.H., Willison, K.W., & Thompson-Schill, S.L. (2013). The role of sleep in forming a memory representation of a two-dimensional space. Hippocampus, 23(12), 1189-1197. •  Download PDF

In Hippocampus, my colleagues and I report that learned associations become more fully integrated into memory representations after sleep, giving people access to untaught (inferred) relational information.

Informational Connectivity: Identifying synchronized discriminability of multi-voxel patterns across the brain

Coutanche, M.N., & Thompson-Schill, S.L. (2013). Informational Connectivity: Identifying synchronized discriminability of multi-voxel patterns across the brain. Frontiers in Human Neuroscience, 7:15, 1-14. •  Download PDF













What do you get if you cross multi-voxel pattern analysis with functional connectivity? Informational Connectivity! In Frontiers in Human Neuroscience, I describe this new method and find that semantic memory regions are connected through shared fluctuations in multi-voxel information.

Reversal without remapping: What we can (and cannot) conclude about learned associations from training-induced behavior changes

Coutanche, M.N., & Thompson-Schill, S.L. (2012). Reversal without remapping: What we can (and cannot) conclude about learned associations from training-induced behavior changes. Perspectives on Psychological Science, 7(2), 118–134. •  Download PDF

After just a few hours in the lab, behavioral training studies can appear to completely reverse associations that developed over a life-time. Does a reversal in behavior mean the original association has been reversed? In Perspectives on Psychological Science, I argue 'no' and describe how associative learning theory can shed new light on modern cognitive psychology studies.

The advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs

Coutanche, M.N., & Thompson-Schill, S.L. (2012). The advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs. NeuroImage, 61(4), 1113–1119. •  Download PDF

The fMRI signal differences found across scanner runs can interfere with extracting multi-voxel information from the brain. In this methodological work in NeuroImage, I show that using many short runs (instead of several long runs) can improve multi-voxel pattern detection.

Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity

Coutanche, M.N., Thompson-Schill, S.L., & Schultz, R.T. (2011). Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity. NeuroImage, 57(1), 113–123. •  Download PDF

It has been established that multi-voxel patterns contain can more information than overall mean response. In NeuroImage, my colleagues and I report that multi-voxel pattern information can also predict individual differences with greater sensitivity: in this case, symptom severity in autism spectrum disorder.