Accurate localization and coactivation profiles of the frontal eye field and inferior frontal junction: an ALE and MACM fMRI meta-analysis

Abstract

The frontal eye field (FEF) and the inferior frontal junction (IFJ) are prefrontal structures involved in mediating multiple aspects of goal-driven behavior. Despite being recognized as prominent nodes of the networks underlying spatial attention and oculomotor control, and working memory and cognitive control, respectively, the limited quantitative evidence on their precise localization has considerably impeded the detailed understanding of their structure and connectivity. In this study, we performed an activation likelihood estimation (ALE) fMRI meta-analysis by selecting studies that employed standard paradigms to accurately infer the localization of these regions in stereotaxic space. For the FEF, we found the highest spatial convergence of activations for prosaccade and antisaccade paradigms at the junction of the precentral sulcus and superior frontal sulcus. For the IFJ, we found consistent activations across oddball/attention, working memory, task-switching and Stroop paradigms at the junction of the inferior precentral sulcus and inferior frontal sulcus. We related these clusters to previous meta-analyses, sulcal/gyral neuroanatomy, and a comprehensive brain parcellation, highlighting important differences compared to their results and taxonomy. Finally, we leveraged the ALE peak coordinates as seeds to perform a meta-analytic connectivity modeling (MACM) analysis, which revealed systematic coactivation patterns spanning the frontal, parietal, and temporal cortices. We decoded the behavioral domains associated with these coactivations, suggesting that these may allow FEF and IFJ to support their specialized roles in flexible behavior. Our study provides the meta-analytic groundwork for investigating the relationship between functional specialization and connectivity of two crucial control structures of the prefrontal cortex.

Surface-based probabilistic tractography uncovers segregated white matter pathways underlying spatial and non-spatial control

“…Their overarching role is to provide top-down feedback to bias activity in posterior regions in visual attention and working memory tasks (Baldauf & Desimone, 2014; Gazzaley & Nobre, 2012; Veniero et al, 2021; Zanto et al, 2011; Zhang et al, 2018). Their structural connectivity patterns remained largely elusive to date, partly due to a lack of consensus on the precise localization of these regions in standard space (Bedini et al, 2023) and the some of the intrinsic limitations of dMRI tractography (Jbabdi et al, 2015; Jeurissen et al, 2019; Sotiropoulos & Zalesky, 2017). In this study, we combined an ALE meta-analysis, which allowed us to accurately infer the localization of the FEF and IFJ in MNI152 space, with surface-based probabilistic tractography - to uncover their structural connectivity with the dorsal and ventral visual streams (Goodale & Milner, 1992) using the high-quality dMRI data from the HCP dataset (Van Essen et al, 2013a).…”

“…Each FEF and IFJ sphere therefore consisted of a 6.25mm diameter sphere at this stage. We note that by this method, our spheres included the coordinates corresponding to the ALE peaks reported in Bedini et al (2023). Next, we used the command-line tool surf_proj from FSL to map these spheres to the WM/GM boundary from the FreeSurfer segmentation (i.e., *h_white.gii).…”

“…We performed an ALE fMRI meta-analysis to accurately infer the localization of the FEF and IFJ in the MNI152 space using GingerALE (v. 3.0.2; Eickhoff et al, 2012). This analysis was carried out on part of the experiments that were included in our previous meta-analysis (Bedini et al 2023; see Supplementary Information, Tables S1 and S2 for details), which we later refined and expanded in that study. The ALE parameters were set to p = 0.01 with a cluster-level family-wise error (FWE) = 0.01 and 1000 permutations.…”

“…Therefore, we first performed an activation likelihood estimation (ALE; Eickhoff et al, 2012) fMRI meta-analysis to overcome these issues and accurately infer the localization of FEF and IFJ in standard space. We used two samples of studies using functional localizers and other well-replicated fMRI paradigms that target these regions (as in Bedini et al 2023), which enabled us to investigate their structural connectivity in a large dataset, such as the HCP (Van Essen et al, 2013a).…”

“…Therefore, we first performed an activation likelihood estimation (ALE; Eickhoff et al, 2012) fMRI meta-analysis to overcome these issues and accurately infer the localization of FEF and IFJ in standard space. We used two samples of studies using functional localizers and other well-replicated fMRI paradigms that target these regions (as in Bedini et al 2023), which enabled us to investigate their structural connectivity in a large dataset, such as the HCP (Van Essen et al, 2013a). Although the studies briefly reviewed here suggest that the FEF and the IFJ may communicate with posterior parietal and temporal regions through distinct and segregated pathways (Anderson et al, 2012; Baldauf & Desimone, 2014; Thiebaut de Schotten et al, 2011; Sani et al, 2021; Sczepanszki et al, 2013; Umarova et al, 2010), no study has yet directly contrasted their structural connectivity to the posterior visual regions.…”