Flexing the principal gradient of the cerebral cortex to suit changing semantic task demands
Abstract
Understanding how thought emerges from the topographical structure of the cerebral cortex is a primary goal of cognitive neuroscience. Recent work has revealed a principal gradient of intrinsic connectivity capturing the separation of sensory-motor cortex from transmodal regions of the default mode network (DMN); this is thought to facilitate memory-guided cognition. However, studies have not explored how this dimension of connectivity changes when conceptual retrieval is controlled to suit the context. We used gradient decomposition of informational connectivity in a semantic association task to establish how the similarity in connectivity across brain regions changes during familiar and more original patterns of retrieval. Multivoxel activation patterns at opposite ends of the principal gradient were more divergent when participants retrieved stronger associations; therefore, when long-term semantic information is sufficient for ongoing cognition, regions supporting heteromodal memory are functionally separated from sensory-motor experience. In contrast, when less related concepts were linked, this dimension of connectivity was reduced in strength as semantic control regions separated from the DMN to generate more flexible and original responses. We also observed fewer dimensions within the neural response towards the apex of the principal gradient when strong associations were retrieved, reflecting less complex or varied neural coding across trials and participants. In this way, the principal gradient explains how semantic cognition is organised in the human cerebral cortex: the separation of DMN from sensory-motor systems is a hallmark of the retrieval of strong conceptual links that are culturally shared.
Data availability
Experiment materials, behavioral data, source data for producing the figures, brain parcellation template, and group-level neuroimaging data (gradient-relevant analysis) are accessible in the Open Science Framework at https://osf.io/mkgcy/.The Neurovault collection provides the processed version of the data set for the other analyses, including neural dimensionality and second-order representational analysis: https://neurovault.org/collections/12539/.I have uploaded all analysis codes and software being used in this study onto osf: https://osf.io/mkgcy/, which include but are not limited to the gradient analysis (Matlab), dimensionality analysis (python), and second-RSA analysis (Matlab).The conditions of our ethical approval do not permit public archiving of the data because participants did not provide sufficient consent for the release of their biomedical data.Researchers who wish to access the data should contact the Research Ethics and Governance Committee of the York Neuroimaging Centre, University of York, or the corresponding authors. Data will be released to researchers when this is possible under the terms of the GDPR (General Data Protection Regulation). The decision as to whether the data can be reused and how access can be provided will be taken by the Research Ethics and Governance Committee of the York Neuroimaging Centre; data access arrangements are likely to exclude commercial use of the data.
Article and author information
Author details
Funding
European Research Council (771863 - FLEXSEM)
- Elizabeth Jefferies
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The study was approved by the Research Ethics Committee of the York Neuroimaging Centre (Project number: P1391). All volunteers provided informed written consent and received monetary compensation or course credit for their participation.
Copyright
© 2022, Gao et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,853
- views
-
- 211
- downloads
-
- 13
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
Childhood adversity is a strong predictor of developing psychopathological conditions. Multiple theories on the mechanisms underlying this association have been suggested which, however, differ in the operationalization of ‘exposure.’ Altered (threat) learning mechanisms represent central mechanisms by which environmental inputs shape emotional and cognitive processes and ultimately behavior. 1402 healthy participants underwent a fear conditioning paradigm (acquisition training, generalization), while acquiring skin conductance responses (SCRs) and ratings (arousal, valence, and contingency). Childhood adversity was operationalized as (1) dichotomization, and following (2) the specificity model, (3) the cumulative risk model, and (4) the dimensional model. Individuals exposed to childhood adversity showed blunted physiological reactivity in SCRs, but not ratings, and reduced CS+/CS- discrimination during both phases, mainly driven by attenuated CS+ responding. The latter was evident across different operationalizations of ‘exposure’ following the different theories. None of the theories tested showed clear explanatory superiority. Notably, a remarkably different pattern of increased responding to the CS- is reported in the literature for anxiety patients, suggesting that individuals exposed to childhood adversity may represent a specific sub-sample. We highlight that theories linking childhood adversity to (vulnerability to) psychopathology need refinement.
-
- Genetics and Genomics
- Neuroscience
Spinal muscular atrophy (SMA) is caused by mutations in the Survival Motor Neuron 1 (SMN1) gene. While traditionally viewed as a motor neuron disorder, there is involvement of various peripheral organs in SMA. Notably, fatty liver has been observed in SMA mouse models and SMA patients. Nevertheless, it remains unclear whether intrinsic depletion of SMN protein in the liver contributes to pathology in the peripheral or central nervous systems. To address this, we developed a mouse model with a liver-specific depletion of SMN by utilizing an Alb-Cre transgene together with one Smn2B allele and one Smn1 exon 7 allele flanked by loxP sites. Initially, we evaluated phenotypic changes in these mice at postnatal day 19 (P19), when the severe model of SMA, the Smn2B/- mice, exhibit many symptoms of the disease. The liver-specific SMN depletion does not induce motor neuron death, neuromuscular pathology or muscle atrophy, characteristics typically observed in the Smn2B/- mouse at P19. However, mild liver steatosis was observed, although no changes in liver function were detected. Notably, pancreatic alterations resembled that of Smn2B/-mice, with a decrease in insulin-producing β-cells and an increase in glucagon-producingα-cells, accompanied by a reduction in blood glucose and an increase in plasma glucagon and glucagon-like peptide (GLP-1). These changes were transient, as mice at P60 exhibited recovery of liver and pancreatic function. While the mosaic pattern of the Cre-mediated excision precludes definitive conclusions regarding the contribution of liver-specific SMN depletion to overall tissue pathology, our findings highlight an intricate connection between liver function and pancreatic abnormalities in SMA.