Metacognitive ability correlates with hippocampal and prefrontal microstructure

Abstract

The ability to introspectively evaluate our experiences to form accurate metacognitive beliefs, or insight, is an essential component of decision-making. Previous research suggests individuals vary substantially in their level of insight, and that this variation is related to brain volume and function, particularly in the anterior prefrontal cortex (aPFC). However, the neurobiological mechanisms underlying these effects are unclear, as qualitative, macroscopic measures such as brain volume can be related to a variety of microstructural features. Here we leverage a high-resolution (800µm isotropic) multi-parameter mapping technique in 48 healthy individuals to delineate quantitative markers of in vivo histological features underlying metacognitive ability. Specifically, we examined how neuroimaging markers of local grey matter myelination and iron content relate to insight as measured by a signal-theoretic model of subjective confidence. Our results revealed a pattern of microstructural correlates of perceptual metacognition in the aPFC, precuneus, hippocampus, and visual cortices. In particular, we extend previous volumetric findings to show that right aPFC myeloarchitecture positively relates to metacognitive insight. In contrast, decreased myelination in the left hippocampus correlated with better metacognitive insight. These results highlight the ability of quantitative neuroimaging to reveal novel brain-behaviour correlates and may motivate future research on their environmental and developmental underpinnings.

Keywords: Hippocampus; Iron; Metacognition; Microstructure; Myelination; Quantitative MRI.

Fig. 1

Behavioural Paradigm and Metacognitive Accuracy. Schematic of global dot-motion metacognition task (A) and plot of metacognitive vs motion detection accuracy (B). Participants were required to judge the global or average motion of a brief dot display, and then rate their confidence in this judgement from 0 (guessing) to 100 (certain). Performance was held constant using an adaptive threshold adjusting either signal mean or variance on each trial (see Methods for more details). Right hand plot demonstrates substantial individual differences in metacognitive accuracy, estimated as the type-II area under the curve (AROC), independently of motion discrimination performance. Inter-individual differences in AROC were then used in a multiple regression analysis to explain variation in microstructural brain features (see VBQ Analysis).

Fig. 2

aPFC VBQ findings. Figure shows correlation of metacognitive ability (AROC) and anterior prefrontal (aPFC) microstructural measures of white-matter concentration (R₁ and MT) across 48 participants. Orientation of crosshairs given below the top left brain in MNI XYZ coordinates. Right side, scatter plots showing peak voxel vs AROC, with least-squares line for illustration purposes. Bottom left, zoomed in view shows overlap of AROC correlation in both MT and R₁ maps. Colour bars indicate t-values, blobs displayed on average MT map from our 48 participants. Volume of interest analysis, FWE-peak corrected p<0.05 within mask generated from previously reported coordinates (Fleming et al., 2010a, McCurdy et al., 2013). See VBQ Analysis for more details.

Fig. 3

Hippocampal markers of myelination and iron correlate with metacognitive ability. Voxel-based quantification results in the left hippocampus. Hippocampus MT negatively relates to metacognitive ability (AROC) (top left, A), whereas iron levels in the same region positively predict metacognition, albeit at exploratory thresholds only (top right, B). Orientation of crosshairs given below each brain in MNI XYZ coordinates. Blobs depict results of whole-brain multiple regression analyses vs each map type, while controlling for age, gender, ICV, and a variety of performance-related variables (see Methods for more information). Results shown on average MT map in MNI space. Scatterplots are for illustration only and depict the peak voxel from each SPM versus raw AROC. Colorbars indicate t-values at each voxel. SPMs are FWE-cluster corrected whole brain analysis, pFWE < 0.05.