Cognitive, Systems and Computational Neuroscience

New preprint! Model connectivity: leveraging the power of encoding models to overcome the limitations of functional connectivity (Meschke et al., in review). Functional connectivity (FC) is the most popular method for recovering functional networks of brain areas with fMRI. However, because FC is defined as temporal correlations in brain activity, FC networks are inevitably confounded by noise and their function cannot be determined directly from FC. To overcome these limitations, we have developed model connectivity (MC). MC is defined as similarities in encoding model weights, which quantify reliable functional activity in terms of interpretable stimulus- or task-related features. In this paper we compare these two methods directly in a language comprehension dataset. We confirm the confounds of FC, and we show that MC does not suffer from these confounds. MC recovers more spatially localized networks and it reveals their functional assignment. MC is powerful tool for recovering the functional networks that support complex cognitive processes.

New paper! Phonemic segmentation of narrative speech in human cerebral cortex (Gong et al., Nature Communications, 2023). Phonemes are a critical intermediate element of speech. This fMRI study identifies the brain representation of single phonemes, and of diphones and triphones. We find that many regions in and around the auditory cortex represent phonemes. These regions include classical areas in the dorsal superior temporal gyrus and a larger region in the lateral temporal cortex (where diphone features appear to be represented). Furthermore, we identify regions where phonemic processing and lexical retrieval are intertwined. (Note: this is work done in collaboration with the Theunissen lab here at UCB.)

Our (former) senior postdoc, Dr. Fatma Deniz, has accepted a tenured full Professor position at the Technical University of Berlin. She began her new position as of April 1, 2023. Congratulations Professor Deniz! We expect great things from you!

New paper! Semantic representations during language production are affected by context (Deniz et al., J. Neuroscience, 2023). Context is important for understanding the meaning of natural language, but most neuroimaging language studies use isolated words and sentences with little context. This study investigates whether the results of studies that use out-of-context stimuli generalize to natural language. We find that increasing context improves the quality of neuroimaging data, and that it changes the representation of semantic information in the brain. These results suggest that findings from studies using out-of-context stimuli may not generalize to natural language used in daily life.

New paper! Feature-space selection with banded ridge regression (Dupre la Tour et al., Neuroimage, 2022). Encoding models identify the information represented in brain recordings, but fitting multiple models simultaneously presents several challenges. This paper describes how banded ridge regression can be used to solve these problems. Furthermore, several methods are proposed to address the computational challenge of fitting banded ridge regressions on large numbers of voxels and feature spaces. All implementations are released in an open-source Python package called Himalaya.

Christine Tseng has received their PhD! Christine has recently been working on functional mapping of the self, others, and social relationships. They will be taking up a postdoctoral position in the lab while the studies are prepared for publication. Congratulations Christine!

New paper! Visual and linguistic semantic representations are aligned at the border of human visual cortex (Popham et al., Nature Neuroscience, 2021). The human brain contains functionally and anatomically distinct networks for representing semantic information in each sensory modality, and a separate, distributed amodal conceptual network. In this study we examined the spatial organization of visual and amodal semantic functional maps. The pattern of semantic selectivity in these two distinct networks corresponds along the boundary of visual cortex: for visual categories represented posterior to the boundary, the same categories are represented linguistically on the anterior side. These results suggest that these two networks are smoothly joined to form one contiguous map.