Functional gradients in the human lateral prefrontal cortex revealed by a comprehensive coordinate-based meta-analysis

  1. Majd Abdallah
  2. Gaston E Zanitti
  3. Valentin Iovene
  4. Demian Wassermann

  • Curated by eLife

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    Evaluation Summary:

    A meta-analysis of over 14,000 fMRI studies revealed a principle rostral-caudal gradient in the lateral prefrontal cortex. This gradient reflected an internal/external axis, which helps to organize the LPFC's involvement in widespread processes from affect, to memory, to control, and action. This is an important contribution to the literature, particularly as a meta-analytic approach has not been applied to this axis of organization and can complement the limitations of single studies. The evidence for the conclusions could be strengthened by ruling out bias in the analysis and drawing a clearer relationship to functional networks.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

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Abstract

The lateral prefrontal cortex (LPFC) of humans enables flexible goal-directed behavior. However, its functional organization remains actively debated after decades of research. Moreover, recent efforts aiming to map the LPFC through meta-analysis are limited, either in scope or in the inferred specificity of structure-function associations. These limitations are in part due to the limited expressiveness of commonly-used data analysis tools, which restricts the breadth and complexity of questions that can be expressed in a meta-analysis. Here, we adopt NeuroLang, a novel approach to more expressive meta-analysis based on probabilistic first-order logic programming, to infer the organizing principles of the LPFC from 14,371 neuroimaging studies. Our findings reveal a rostrocaudal and a dorsoventral gradient, respectively explaining the most and second most variance in meta-analytic connectivity across the LPFC. Moreover, we identify a unimodal-to-transmodal spectrum of coactivation patterns along with a concrete-to-abstract axis of structure-function associations extending from caudal to rostral regions of the LPFC. Finally, we infer inter-hemispheric asymmetries along the principal rostrocaudal gradient, identifying hemisphere-specific associations with topics of language, memory, response inhibition, and sensory processing. Overall, this study provides a comprehensive meta-analytic mapping of the LPFC, grounding future hypothesis generation on a quantitative overview of past findings.

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  1. Version published to 10.7554/elife.76926 on eLife
  2. eLife

    Evaluation Summary:

    A meta-analysis of over 14,000 fMRI studies revealed a principle rostral-caudal gradient in the lateral prefrontal cortex. This gradient reflected an internal/external axis, which helps to organize the LPFC's involvement in widespread processes from affect, to memory, to control, and action. This is an important contribution to the literature, particularly as a meta-analytic approach has not been applied to this axis of organization and can complement the limitations of single studies. The evidence for the conclusions could be strengthened by ruling out bias in the analysis and drawing a clearer relationship to functional networks.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

  3. eLife

    Reviewer #1 (Public Review):

    The present study used an innovative meta-analytic approach to elucidate the functional organization of the lateral prefrontal cortex (LPFC). Co-activation profiles based upon over 14,000 fMRI studies revealed a principle rostral-caudal gradient in the LPFC, as well as a secondary dorsal-ventral gradient. Rostral-ventral zones in this gradient tended to contain areas in cognitive control (Control B) and salience networks and were associated with terms involving memory and affect. Caudal-dorsal zones in the gradient tended to contain areas in cognitive control (Control A) and spatial attention networks and were associated with terms involving perception and action. Areas in-between overlapped prominently with a variety of networks including Control A and were associated with various cognitive terms associated with language, working memory, and cognitive control. Moreover, the authors found hemispheric asymmetries with the left hemisphere associated with language-related topics and the right hemisphere with response inhibition and error processing. Hemispheric differences did not show an obvious rostral-caudal topography. Collectively, the data provide quantification of the general organization of the LPFC along rostral-caudal, dorsal-ventral, and hemispheric axes. From the associations of networks and terms, the authors conclude that the rostral-caudal axis reflects an internal/external axis, with areas in the middle supporting integrative processing.

    Detailing the functional organization of the LPFC has remained a challenge given the diversity of its functions and widespread involvement across various tasks. Due to the limitations of single studies in terms of what can be measured (i.e. number of tasks used), construct validity of what is measured (e.g. purity of contrasts), and the reliability and reproducibility with which things can be measured, a meta-analysis of this scale can provide a welcome synthesis.

    A major challenge with meta-analyses of fMRI data is obtaining appropriate specificity. Most meta-analytic methods that have been applied to fMRI data are both spatially and functionally coarse, which hinders efforts to properly synthesize the literature. Here, the authors employ innovative techniques to maximize specificity insofar as possible. As a result, the present data can be considered our best summary to date of the functional organization of the LPFC as detailed by fMRI.

    Even as the study has innovated over previous attempts, limitations of meta-analyses must still be considered. Meta-analysis will never have the spatial resolution of well-performed individual studies. Indeed, the techniques used here may cause spatial blurring given the impression of spatially ordered consistency which may not actually be present. For example, there are data to suggest that there may be multiple rostral-caudal axes along the LPFC, which can potentially be blurred together into a single axis here. So, the spatial organization detailed here may offer a gross overall picture of how the LPFC is organized, but we will naturally get more fine-grained details from carefully conducted individual studies.

    Nevertheless, the approach used here is helpful not only for detailing the functional organization of the LPFC, but as a proof-of-concept that can be applied to future investigations. These techniques may be helpful for detailing the organization of other heteromodal zones of the brain such as the medial frontal wall, and parietal cortices, offering a means of distilling the thousands of fMRI studies that have been conducted into a comprehensive whole.

  4. eLife

    Reviewer #2 (Public Review):

    Abdallah et al. adopted NeuroLang methodology to assess organizing principles of lateral prefrontal cortex (LPFC) organization in humans. The authors identified a rostrocaudal and a dorsoventral gradient. Towards rostral areas, they could furthermore show increasing abstraction as well as increasing functional asymmetry.

    This paper has several strengths:
    - Analysis of the functional organization of the prefrontal cortex as well as the study of organizational gradients in cortical regions is of high interest to the neuroscience community. In the current work, this is coupled with recent innovations to the meta-analysis of functional neuroimaging data.

    - The demonstrated associations between prefrontal gradients and inter-hemispheric asymmetries in functional localization appear meaningful for the understanding of 'more prefrontal' functions.

    - The paper is written in an accessible and clear manner, which will be appreciated by a broad readership. In some instances, the writing could be a bit less wordy, however.

    One however also needs to mention some weaknesses:
    - In the introduction, the authors mention that networks such as SN and FPN sit upon global connectivity gradients, notably the widely recognized principal gradient that runs from sensory to heteromodal regions. One broader question of the current work may be how far the LPFC gradients are mere reflections of global gradients such as the first principal gradient but also the third functional gradient (which differentiates multiple demand networks that subsume FPN) described by Margulies and colleagues, or whether these region-specific gradients are specific cases. In other words, the authors are recommended to also perform a systematic spatial correlation analysis between the LPFC gradients derived from meta-analysis and these previously described rs-fmri gradients in the LPFC mask and to more broadly discuss the interplay of local and more global organizational axes.

    - Meta-analysis is arguably the best technique to synthesize findings across individual studies, but doesn't it also amplify spatial uncertainty in functional localization and thus naturally favour more gradient-like arrangements? Several prior studies have suggested that higher association networks, including some of those that naturally make up LPFC, may often have rather fine-grained (e.g. inter-digitated) spatial patterns, which may be washed out when averaging different datasets. In addition to discussing this issue more extensively, it may make sense to also explore whether subject-specific functional analysis (e.g. based on HCP or other datasets with extensive task fmri per participant) could be a meaningful complement to the current analysis, as it would help to solidify whether the observed functional gradients are also as spatially determined when studying individual subject level functional topographies.

    - Related to the above point is the clear spatial anchoring of the gradients. Are similar gradients also observed when e.g. focusing only on meta-analytical co-activation of longer-range connections? Systematically varying the connectivity distance thresholds could be something quite meaningful here, as it may reduce potential spatial autocorrelation in observed networks.

    - The analysis on page 6 had a slight suboptimal flavour, as network participation was equated with abstraction. In my view, the subsequent definition of abstraction via cognitive terms is better, so I would possibly rephrase the section and avoid reverse inference (e.g. more dmn, more abstraction).

    - The inter-hemispheric asymmetry analysis is interesting and novel. One question that one may want to ask is how far the underlying image processing of the included studies, as well as the meta-analytical synthesis, accounted for potential asymmetries in the brain. In my understanding, a requirement for asymmetry analysis is the mapping of imaging data to a hemisphere unbiased (ie symmetric) template. Is such a step ensured in the included studies and in the meta-analysis inference itself? For example, when carrying out analyses in surface space (results are shown on surfaces) the surface vertices need to ideally be matched across hemispheres to avoid mismatch.

    - Wouldn't an additional bootstrap across the studies that were included be useful in order to further model uncertainty across studies (e.g. with respect to the variance explained and gradient shape for Figure 1, topic associations in Figure 4, and assymetries in Figure 5) .

    - In the introduction, the first paragraph does a rather quick foreshadowing of the paper's purpose. While this is surely fine to also help the reader to see the goals, it felt a bit abrupt in this specific case as the authors did not adequately motivate why meta-analyses are good candidates to reveal functional organization. Moreover, they did not provide any details on the presumed limited specificity of common meta-analysis methods.

  5. Version published to 10.1101/2022.01.21.477198v1 on bioRxiv