A genuine interindividual variability in number and anatomical localization of face-selective regions in the human brain. (28th January 2022)

• Record Type: Journal Article
• Authors: Gao, Xiaoqing; Wen, Minjie; Sun, Mengdan; Rossion, Bruno
• Is Part Of: Cerebral cortex. Volume 32:Number 21(2022)
• Page Start: 4834
• Page End: 4856
• Publication Date: 2022-01-28
• Subjects: face selectivity -- fMRI -- human brain -- interindividual variability -- ventral occipitotemporal cortex; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhab519
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract: Neuroimaging studies have reported regions with more neural activation to face than nonface stimuli in the human occipitotemporal cortex for three decades. Here we used a highly sensitive and reliable frequency-tagging functional magnetic resonance imaging paradigm measuring high-level face-selective neural activity to assess interindividual variability in the localization and number of face-selective clusters. Although the majority of these clusters are located in the same cortical gyri and sulci across 25 adult brains, a volume-based analysis of unsmoothed data reveals a large amount of interindividual variability in their spatial distribution and number, particularly in the ventral occipitotemporal cortex. In contrast to the widely held assumption, these face-selective clusters cannot be objectively related on a one-to-one basis across individual brains, do not correspond to a single cytoarchitectonic region, and are not clearly demarcated by estimated posteroanterior cytoarchitectonic borders. Interindividual variability in localization and number of cortical face-selective clusters does not appear to be due to the measurement noise but seems to be genuine, casting doubt on definite labeling and interindividual correspondence of face-selective "areas" and questioning their a priori definition based on cytoarchitectony or probabilistic atlases of independent datasets. These observations challenge conventional models of human face recognition based on a fixed number of discrete neurofunctional information processing stages.