Laminar-specific microstructural gradients reveal differentiated hierarchical organization

Poster No:


Submission Type:

Abstract Submission 


Xindi Wang1, Casey Paquola1, Lindsay Lewis1, Boris Bernhardt1, Alan Evans1


1McGill University, Montreal, Québec

First Author:

Xindi Wang  
McGill University
Montreal, Québec


Casey Paquola  
McGill University
Montreal, Québec
Lindsay Lewis  
McGill University
Montreal, Québec
Boris Bernhardt  
McGill University
Montreal, Québec
Alan Evans  
McGill University
Montreal, Québec


Complex cognitive functions in the human brain are mediated by cortical microstructural organization. Previous studies have found that the gradients of microstructural similarity between cortical areas may represent a gradual transition of laminar differentiation from primary cortex to limbic regions1, suggesting a hierarchy in cortical microstructure. However, the laminar-specific microstructural organization across the whole brain remains largely unknown. We used ultra-high-resolution histological data and large sample myelin-sensitive MRI data to investigate laminar-specific microstructural gradients at both regional and system levels. The results demonstrate remarkable differences in microstructural gradient between supragranular (SG) and infragranular (IG) layers that reflect a differentiated hierarchy between cortical sub-layers.


Two datasets were used: 1) BigBrain2: a high resolution (20μm) 3D histological post mortem human brain from a 65-year-old male. 2) Human Connectome Project (HCP)3: T1w/T2w images of 1035 subjects from the minimally preprocessed S1200 release. After generating equivolumetric surfaces from outer to inner cortical surfaces, microstructural profiles sampled in the vertical direction of cortical columns were extracted. For BigBrain, we extracted 100 SG and IG cytoarchitectural profiles based on layer-1, mid-layer-4 and white surfaces, respectively4. For HCP data, we extracted 16 myeloarchitectural profiles from pial to white surfaces, specifying the first 8 profiles as SG and the rest as IG. A Schaefer-1000 parcellation was used to calculate region-wise profiles and to generate a microstructural profile covariance (MPC) matrix. We then used an extended MSM surface registration pipeline for mapping the parcellation onto the BigBrain histological surface5. Histological and a group-level myeloarchitectural microstructural gradients were estimated, and SG and IG gradients realigned to whole column ones, respectively. For HCP, we also estimated individual-level gradients and realigned them to their corresponding group-level gradients. All gradients were then converted to gradient ranks. For BigBrain and group-level HCP gradients, we estimated the differences in gradient rank between realigned SG and IG layers. For individual-level HCP gradients, Wilcoxon signed rank test was performed to evaluate the statistical different between SG and IG gradient ranks. Finally, for each subject in HCP, system-level gradient differences between SG and IG layers were estimated based on three system parcellations (laminar differentiation6, cytoarchitectural taxonomy7 and functional network8).


Both SG and IG gradients show significant correlations between cyto- and myeloarchitectural profiles (R=0.07 and 0.21, respectively; Fig. 1), indicating their similar ability to reveal microstructural organization. Notably, the spatial patterns of SG and IG gradients are differentiated for both microstructural profiles. For both histology and myelin level, SG layer show higher gradient rank for primary visual cortex but relatively lower rank for association cortices (Fig. 1). At the system level, both SG and IG layers show a gradual transition of myeloarchitectural gradients from primary cortices to high-order systems, while SG layers showed a higher gradient rank than IG layers across subjects for primary cortices but a lower rank for high-order systems (Fig. 2). These results indicate that a distinct hierarchical characteristic may exist between SG and IG layers across the brain, suggesting their differentiated functional roles in cognitive processing.


We examined laminar-specific microstructural gradients in the human brain by using cyto- and myeloarchitectural profiles from BigBrain and HCP datasets respectively. Our results revealed a clear differentiated hierarchical organization between cortical sub-layers.

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Cortical Anatomy and Brain Mapping 2
Cortical Cyto- and Myeloarchitecture 1

Novel Imaging Acquisition Methods:

Anatomical MRI


Cortical Columns
Cortical Layers

1|2Indicates the priority used for review
Supporting Image: Fig1.png
Supporting Image: Fig2.png

My abstract is being submitted as a Software Demonstration.


Please indicate below if your study was a "resting state" or "task-activation” study.


Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.


Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Structural MRI
Postmortem anatomy

For human MRI, what field strength scanner do you use?


Which processing packages did you use for your study?

Other, Please list  -   CIVET, HCP Pipelines

Provide references using author date format

1. Paquola, Casey, et al (2019), ‘Microstructural and functional gradients are increasingly dissociated in transmodal cortices.’ PLoS biology 17.5: e3000284.
2. Amunts, Katrin, et al. (2013), ‘BigBrain: an ultrahigh-resolution 3D human brain model.’ Science 340.6139: 1472-1475.
3. Van Essen, David C., et al. (2013), ‘The WU-Minn human connectome project: an overview.’ Neuroimage 80: 62-79.
4. Wagstyl, Konrad, et al. (2018), ‘Mapping cortical laminar structure in the 3d bigbrain.’ Cerebral Cortex 28.7: 2551-2562.
5. Lewis, L.B., et al. (2022, submitted), ‘An Improved MSM surface registration pipeline to bridge atlases across the MNI and the FS/HCP worlds’, OHBM Montreal.
6. Mesulam, M-MARSEL. (2000), ‘Behavioral neuroanatomy.’ Principles of behavioral and cognitive neurology 2: 1-120.
7. von Economo, et al. (1925), Die cytoarchitektonik der hirnrinde des erwachsenen menschen. J. Springer.
8. Thomas Yeo, B. T., et al. (2011), ‘The organization of the human cerebral cortex estimated by intrinsic functional connectivity.’ Journal of neurophysiology 106.3: 1125-1165.