Big Question 2

What are the characteristics and consequences of internal brain organization for language?

The human brain provides a neurobiological infrastructure that allows us to acquire and process language, and that co-determines the characteristics of spoken (and sign) and written language. The internal organization of the brain and its cognitive architecture both determine and constrain the space of possibilities for human language. This internal organization can be called the Kantian brain for language. It has resulted in a language-readiness of the human brain that is found nowhere else in the animal kingdom. The big question is to characterize the Kantian brain for language.

Currently BQ2 is in the process of building links between the various sub-projects. Each sub-project has had the opportunity to present their most recent work/ideas/questions of interest, and BQ2 is now in a phase of bridging the sub-projects to try to define new research questions based on collaborations between sub-themes. To foster such collaborations, meetings are planned where pairings of sub-projects will present ideas that culminate from joint brainstorm sessions about potential links between one another’s work and expertise. In the long run the hope is that such combinations of expertise and perspectives will lead to innovative and cutting-edge projects that address the overarching goal of how the human brain supports language processing.

People involved

Steering group

Prof. dr. Peter Hagoort
Programme Director
PI / Coordinator BQ2
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Dr. Ashley Lewis
Coordinating Postdoc BQ2
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Team members

Prof. dr. Christian Beckmann
PI
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Talking Headlines: Simon Fisher |

Prof. dr. Simon Fisher
PI
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Prof. dr. Elia Formisano
PI
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Dr. Clyde Francks
PI
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Loop | Annet H. De Lange

Prof. dr. Roy Kessels
PI
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Floris de Lange

Prof. dr. Floris de Lange
PI
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4 "Rogier Mars" profiles | LinkedIn

Dr. Rogier Mars
PI
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Dr. Vitória Piai
Tenure track researcher
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Prof. dr. Nick Ramsey
PI
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Ardi Roelofs - Hoogleraar - Radboud Universiteit Nijmegen | LinkedIn

Prof. dr. Ardi Roelofs
PI
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Dr. Joanna Sierpowska
Postdoc
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Loop | Jan Mathijs Schoffelen

Dr. Jan-Mathijs Schoffelen
PI
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Prof. dr. Ivan Toni
PI / Coordinator BQ3
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PhD Candidates

Ileana Camerino

Ileana Camerino
PhD Candidate
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João Ferreira

João Ferreira
PhD Candidate
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Guilherme Blasquez-Freches
PhD Candidate
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Collaborators

Dr. Xiangzhen Kong
Dr. Zhiqiang Sha
Dr. Tineke Snijders
Dr. Maggie Wong

Alumni

Nikki Janssen – PhD
Daniel Sharoh – PhD

Research Highlights (2020)

Highlight 1

How the speed of word finding depends on ventral tract integrity in primary progressive aphasia

Team members: Nikki Janssen, Ardi Roelofs, Margot Mangnus, Joanna Sierpowska, Roy Kessels, and Vitória Piai

This study explored the extent to which word finding difficulty in Primary Progressive Aphasia (PPA) may be linked to altered integrity of white matter tracts ventral to the sylvian fissure in the human brain.  It used picture-word interference (PWI) to emulate contextual noise, and computer simulations based on the WEAVER++ model of word finding to relate the neural results to (disrupted) behaviour. Mixed-effects modelling was performed on naming accuracy and reaction time (RT) data, and fixel-based tractography analyses were conducted to assess the relation between ventral white-matter integrity and naming performance (see Figure 1). As expected, naming RTs were longer for individuals with PPA compared to controls and, critically, individuals with PPA showed a larger noise effect. Moreover, the noise effect in control participants did not depend on tract integrity, whereas in individuals with PPA a decreased tract integrity was related to a reduced noise effect. Computer simulations supported an explanation of this paradoxical finding in terms of reduced propagation of noise when tract integrity is low.

Figure 1. (A) Fibre density and cross-section (FDC) and reaction time per group per condition for the inferior longitudinal fasciculus (ILF). Each dot corresponds to a participant. Lines depict the best-fitting linear regression line to the data and shaded areas indicate 95% CI. RT = response time. (B) Whole-brain tractogram of a PPA patient. (C) Whole-brain tractogram of a cognitively unimpaired control participant.

By using multimodal analyses, this study indicates the significance of the ventral pathway for naming, and the importance of RT measurement in the clinical assessment of PPA. It also used computational modelling to probe, and strengthen some of the counterintuitive relationships observed between white matter integrity and naming latency in PPA individuals. A key insight is that when it comes to the quality of information transmission in the brain, the motto ‘more is better’ is too simplistic to adequately account for the relationship between anatomical connectivity and behavioural performance. This project would not have been possible without a combination of clinical expertise, and expertise in neuroimaging, white matter neuroanatomy, speech production, and computational modelling. The LiI consortium offers unique opportunities for such wide-ranging collaborations.

Highlight 2

Gene expression correlates of the cortical network underlying sentence processing

Team members: Xiang-Zhen Kong, Simon Fisher, and Clyde Francks

A key question in modern neuroscience is which genes regulate brain circuits that underlie cognitive functions. To shed light on the molecular architecture underpinning language circuits, in this project we aim to combine functional brain imaging data from living individuals with gene transcription profiles from post mortem tissue samples from specific brain regions (Figure 2). In our first study of this project (Kong et al. 2020), we revealed reliable gene expression-functional network correlations using three different definition strategies for the sentence processing network, and identified a consensus set of genes related to connectivity within this network. The genes involved showed enrichment for neural development and actin-related functions, as well as association signals with autism, which can involve disrupted language functioning. Our findings help elucidate the molecular basis of the brain’s infrastructure for language, as distinct from functional networks important for other aspects of cognition.

Figure 2. Schematic of the pipeline for computing the correlation between resting-state functional connectivity and transcriptomic similarity, within a network of regions first defined according to task fMRI data.

This project involves the synergy of cognitive neuroscience, brain imaging and genomic data. To our knowledge, we reported the first evidence for a link between gene transcription profiles and language networks. This has contributed to a multi-level understanding of the brain’s infrastructure for language. Due to its interdisciplinary nature, this project requires complementary expertise from cognitive neuroscience, neuroimaging, bioinformatics, genomics, post mortem anatomy and histology. Collaboration and team science are therefore ‘baked in’ to the study concept and execution.

Highlight 3

Mapping the brain’s feedforward and feedback architecture for language with neural oscillations

Team members: Lewis, Hagoort, and de Lange

This project is designed to develop linguistic paradigms in which the relative contributions of feedforward and feedback information streams can be manipulated in order to study the associated neural architecture. The study utilized MEG to probe high and low frequency neural oscillations as indices of feedforward and feedback information, respectively. This initial phase of the experiment employs a lexical decision task with Dutch words and non-words presented visually under varying levels of visual degradedness.

Visual degradedness is achieved through low-pass spatial filtering, somewhat akin to noise-vocoding with speech stimuli. Based on previous literature investigating the visual system, we treated alpha/beta (8-19 Hz) power as an index of feedback and mid gamma (56-76 Hz) power as an index of feedforward signalling in the MEG. In a visual lexical decision task the primary source of feedforward information would be occipital regions responsible for visual processing of the stimulus, while the primary source of feedback information would be left temporal regions responsible for lexical-semantic processing. Preliminary results (N=10 participants) suggest precisely such a dissociation (Figure 3), where in the low degradedness conditions (when participants can read the words) there appears to be a larger alpha/beta desynchronization for words compared to non-words at temporal sensors but not at occipital sensors.


Figure 3. Mean power values in the Alpha/Beta (8-19 Hz) and Mid Gamma Power (56-76 Hz) range for different levels of visual degradedness.Dots represent mean power values for each participant. The data is collapsed across the two highest and two lowest levels of degradedness.

At occipital sensors gamma synchronization is higher for non-words than for words, suggesting greater prediction error for non-words in regions responsible for visual processing. This difference does not appear (or is greatly reduced) over left temporal sensors. These preliminary findings suggest that for word reading feedforward and feedback signalling may indeed proceed via high and low frequency neural oscillations. Future phases of the project will investigate how these signals may be affected when words (both visually degraded and not) are inserted into sentence contexts with differing degrees of semantic constraint (i.e., differing availability of feedback information).

This project pursues a hot topic in language neuroscience and directly addresses a core question of BQ2: the role of low and high frequency neural oscillations in feedback and feedforward signalling in the brain to support language processing. It also addresses the important question of the extent to which links observed in the visual system between high and low frequency neural oscillations on the one hand, and feedforward and feedback signalling on the other, may generalize to higher cognitive functions supported by alternative brain systems. It brings experts from psycholinguistics and the cognitive neuroscience of language into direct contact with experts from prediction and attention in visual processing as well as experts on cutting edge methods for the analysis of neural oscillations.