bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/788216

Thickness of deep layers in the fusiform face area predicts face recognition

McGugin, R. W.·Newton, A. T.·Tamber-Rosenau, B.·Tomarken, A.·Gauthier, I.
View original source ↗Export metadata ↓

Abstract

People with superior face recognition have relatively thin cortex in face-selective brain areas, while those with superior vehicle recognition have relatively thick cortex in the same areas. We suggest that these opposite correlations reflect distinct mechanisms influencing cortical thickness (CT) for abilities acquired at different points in development. We explore a new prediction regarding the specificity of these effects through the depth of the cortex: that face recognition selectively and negatively correlates with thickness of the deepest laminar subdivision in face-selective areas. With ultra-high resolution MRI at 7T, we estimated the thickness of three laminar subdivisions, which we term MR layers, in the right fusiform face area (rFFA) in 14 adult male humans. Face recognition was negatively associated with the thickness of deep MR layers, while vehicle recognition was positively related to the thickness of all layers. Regression model comparisons provided overwhelming support for a model specifying that the magnitude of the association between face recognition and CT differs across MR layers (deep vs. superficial/middle) while the magnitude of the association between vehicle recognition and CT is invariant across layers. The total CT of rFFA accounted for 69% of the variance in face recognition, and thickness of the deep layer alone accounted for 84% of this variance. Our findings demonstrate the functional validity of MR laminar estimates in FFA. Studying the structural basis of individual differences for multiple abilities in the same cortical area can reveal effects of distinct mechanisms that are not apparent when studying average variation or development.\n\nSignificance StatementFace and object recognition vary in the normal population and are only modestly related to each other. The recognition of faces and vehicles are both positively related to neural responses in the fusiform face area (FFA), but show different relations to the cortical thickness of FFA. Here, we use very high-resolution MRI, and find that face recognition ability (a skill acquired early in life) is negatively correlated with thickness of FFAs deepest MR-defined layers, whereas recognition of vehicles (a skill acquired later in life) is positively related to thickness at of all cortical layers. Our methods can be used in the future to characterize sources of variability in human abilities and relate them to distinct mechanisms of neural plasticity.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

McGugin, R. W., Newton, A. T., Tamber-Rosenau, B., Tomarken, A., Gauthier, I.. 2019-10-02. Thickness of deep layers in the fusiform face area predicts face recognition. https://doi.org/10.1101/788216

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Same Movements, Different Skills: How Context Shapes Learning in Humans and Neural Networks

What determines whether experiences are integrated into shared representations or split? Motor skill learning offers a revealing test: when two tasks require an identical sequence of movements, both intuition and theory suggest that practice in one should generalize to the other. Yet we find that changing context can make identical movements different skills. In simple recurrent networks trained on the same sequence across contexts, splitting was greatest when strong contextual inputs coincided with a high initial weight scale; neither alone sufficed. Greater representational separation coincided with separate context-specific learning curves. Humans practicing the same finger sequence while alternating between two performance-irrelevant contexts likewise exhibited context-specific curves, rather than the single continuous trajectory observed when practicing in a single context. State-space modeling favored split learning states in humans and very weakly coupled states in the most context-dependent networks. These findings have broader implications for understanding how learning generalizes across contexts.

neuroscience↗
bioRxiv2026

Postpartum resource scarcity reshapes maternal care, offspring prioritization, and the steroid-sensitive medial preoptic area

Parental care requires balancing offspring needs with environmental constraints, yet how resource scarcity alters the organization of maternal responses remains poorly understood. Here, we examined maternal behavior, offspring prioritization, and steroid receptor-associated extracellular matrix organization in postpartum rats exposed to limited bedding and nesting (LBN) or control conditions with ample nesting materials. LBN increased pup-directed and reduced self-directed behaviors but also increased behavioral entropy, a measure of behavioral fragmentation, indicating less predictable caregiving sequences. LBN dams preferentially retrieved male pups first, whereas control dams preferentially retrieved females first. LBN also increased the proportion of pup-investigation time directed toward males compared with controls. In offspring on postnatal day 10, corticosterone was elevated in female but not male LBN pups, a pattern that paralleled the maternal male bias. To identify associated neurobiological changes, we assessed steroid receptors and extracellular matrix organization in the medial preoptic area (mPOA), a key region for maternal behavior. LBN increased numbers of estrogen receptor (ER)- and androgen receptor (AR)-immunoreactive cells, reduced perineuronal net (PNN) abundance, and decreased receptor-positive cell enwrapment. Because PNNs constrain structural plasticity, these findings suggest that the postpartum environment alters the organization of steroid receptor-associated extracellular matrix, potentially modulating experience-dependent plasticity in neurosteroid-sensitive circuits. In sum, LBN increases the levels of maternal engagement while reshaping its temporal organization and allocation, accompanied by selective remodeling of steroid-sensitive preoptic architecture. Therefore, maternal adaptation to adversity may be better understood as changes in caregiving allocation strategies rather than a simple gain or loss of maternal care.

neuroscience↗
bioRxiv2026

The Computational Grammar of Human Behaviour in a Biological Environment

Human behaviour unfolds as an extended stream of spontaneous actions shaped by environmental demands and the agent's goals. The organisation of these actions, situated between setting abstract goals and concrete movement execution, remains poorly characterised. Here, we introduce DynaClade, an unsupervised temporal action segmentation algorithm for human full-body motion capture data, and apply it to freely moving participants navigating ecologically realistic risky foraging environments in virtual reality. DynaClade identifies discrete actions whose kinematic characteristics and spatial distributions correspond to environmental affordances, supporting the validity of the approach. Among these actions, we discovered a hitherto undescribed movement pattern defined by increased body sway without locomotion. Action sequences were highly non-random and showed clear history dependence beyond first-order Markov structure, most of which was explained by a second-order prediction. This higher-order structure was well captured in a hidden Markov model, in which some of the observed actions were associated with more than one latent state. Each of these latent states corresponded to the same action in a distinct action context. These findings suggest that spontaneous human actions are predominantly selected based on the current action and context, rather than following pre-planned or habitually assembled sequences.

neuroscience↗