Improved correspondence of fMRI visual field localizer data after cortex-based macroanatomical alignment
Authors:   , Qubad Mishal 
Published: May 2023

The visual system encompasses about 20% of the cerebral cortex1 and plays a pivotal role in higher-order cognitive processes such as attention and working memory. Cognitive impairments constitute a central role in neuropsychiatric disorders such as schizophrenia (SZ). Impairments are described in visual perceptual processes including contrast, and emotion discrimination as well as in the ability to identify visual irregularities and in higher-order cognition like visual attention and working memory. Furthermore, perceptual and higher-order cognitive processes are part of the Research Domain Criteria (RDoC) project that aims to develop dimensional and transdiagnostic constructs with defined links to specific brain circuits.Therefore, the detailed study of the visual system using functional magnetic resonance imaging (fMRI) is essential to understand the processes in healthy individuals but also in populations with neuropsychiatric disorders. Visual mapping techniques include functional localizer tasks to map functionally defined regions like the fusiform face area (FFA), retinotopic mapping to map specific brain regions that are retinotopically organized in full, and visual-field localizer paradigms to define circumscribed areas within retinotopically organized areas.Thus, the latter allow studying local information processing in early visual areas. Despite advances in neuroimaging techniques, analyses of fMRI data at the group-level are impeded by interindividual macroanatomical variability. This reduces the reliability to accurately define visual areas particularly at the group-level and decreases statistical power. Single-subject based solutions for this problem are not appropriate. Analyses after volume-based alignment (VBA) and primary surface-based analyses without macroanatomical alignment do not increase macroanatomical correspondence sufficiently. Cortex-based alignment (CBA) approaches are recommended as an alternative technique to address this obstacle. However, CBA has not been evaluated for visual-field localizer paradigms. Therefore, we aimed to evaluate potential benefits of CBA for an attention-enhanced visual field localizer paradigm that maps circumscribed regions in retinotopically organized visual areas. Since previous studies solely compared surface-based data before and after CBA, we aimed to compare all three techniques: (1) a volume-based alignment (VBA), (2) a surface-based data set without (SBAV) and (3) a surface- based data set with macroanatomical alignment (CBA). Furthermore, we sought to define regions of interest (ROI) that subsequently can be used for the study of higher-order cognitive processes. Also, we aimed to investigate whether CBA facilitates the study of functional asymmetries in early visual areas as these were described in previous studies. Healthy volunteers (n=50) underwent fMRI in a 3- Tesla Siemens Trio scanner while performing an attention-enhanced visual field localizer paradigm. Our task consisted of a series of flickering, black-and white colored checkerboard stimuli that randomly appeared at one of four locations comprising the participants’ visual quadrants. In 25% of the trials the centrally located squares briefly changed their color to yellow (target trial). Participants had to indicate detection of a target by button press. Data analysis was conducted using Brain Voyager 20.6. Our approach for macroanatomical alignment included a high-resolution, multiscale curvature driven alignment procedure minimizing interindividual macroanatomical variability. Here, each folding pattern was aligned to a dynamically updated group average. Thus, we counteracted a possible confounding effect of a suboptimal selection of an individual target brain with a folding pattern deviating considerably from the cohort average. Group ROIs after CBA showed increased spatial consistency, vertical symmetry, and an increase of size. This was corroborated by an increase in the probability of activation overlap of up to 86%. CBA increased macroanatomical correspondence and thus ameliorated results of multi-subject ROI analyses. Functional differences in the form of a downward bias in visual hemifields were measured with increased reliability. In summary, our findings provide clear evidence for the superiority of CBA for the study of local information processing in early visual cortex at the group-level. This approach is of relevance for the study of visual dysfunction in neuropsychiatric disorders including schizophrenia as they show impaired visual processing that in turn impacts higher-order cognitive processes and in consequence functional outcome. In addition, our attention-enhanced visual field localizer paradigm will be useful for machine learning approaches such as multivariate pattern analysis decoding local information processes and connectivity patterns.