Modelling cortical layer connectivity in the macaque brain

Poster No:


Submission Type:

Abstract Submission 


Ittai Shamir1, Yaniv Assaf2


1Tel Aviv University, Tel Aviv, Israel, 2Tel Aviv University, Tel Aviv-Jaffa, -

First Author:

Ittai Shamir  
Tel Aviv University
Tel Aviv, Israel


Yaniv Assaf  
Tel Aviv University
Tel Aviv-Jaffa, -


In recent years, significant progress has been made in the field of neuroimaging of not only global white matter connectivity (Sporns et al. 2005, Leemans et al. 2009, Setsompop et al. 2013, Van Essen et al. 2013), but also cortical grey matter structure (Barazany and Assaf 2012, Lifshits et al. 2018, Shamir et al. 2019). However, current techniques for exploring the brain's wiring diagram, also known as the connectome, remain biased by the definition of the cortex as one homogenous unit. Overcoming this bias demands expanding the basic unit used for connectome analysis to a more descriptive representation of the heterogeneous laminar substructure of the cortex.
Our aim is to expand the field of connectomics by introducing cortical layer connectivity. We explore MRI-based imaging of macaque cortical layer connectivity, by integrating multi-modal ex-vivo imaging via a simple model of layer connectivity, derived from a meta-analysis of published findings concerning interconnectivity of layers across the cortex. The result is then validated by comparison to the seminal work of Felleman and Van Essen regarding hierarchical processing in the primate cerebral cortex (Felleman and Van Essen 1991).


MRI Acquisition
MRI was performed on an excised Macaque brain, on a 7T/30 Bruker scanner with a 660 mT/m gradient system. The protocol was approved by the Tel Aviv University ethics committee on animal research. Three MRI sequences were performed:
1. DWI (diffusion weighted imaging) was performed with 4 segments diffusion weighted EPI sequence with a voxel size 0.48×0.48×0.48 mm3, image size 128×160×116 voxels, Δ/δ=20/3.3 ms, b=5000 s/mm2, with 96 gradient directions and additional 4 with b=0.
DWI dataset was used for global white matter connectivity (tractography), using constrained spherical deconvolution (CSD) in ExploreDTI (Leemans et al. 2009).
2. T1w sequence was performed with a 3D modified driven equilibrium Fourier transform (MDEFT) sequence with: voxel size 0.2×0.2×0.2 mm3, image size 300×360×220 voxels, TR/TE=1300/2.9 ms, TI=400 ms. This sequence was used as an anatomical reference with high gray/white matter contrast and used for segmentation and inner and outer surface estimation.
3. Inversion recovery was performed using a 3D FLASH sequence with: voxel size 0.67×0.67×0.67 mm3, image size 96×96×68 voxels, TR/TE=1300/4.672 ms and 44 inversion times spread between 25 ms up to 1,000 ms, each voxel fitted with up to 8 T1 values (similarly to Lifshits et al. 2018).
T1w and inversion recovery datasets were used for cortical laminar composition analysis (similarly to the framework presented in Shamir et al. 2019).
Model of cortical layer connectivity
Datasets were integrated using a model derived from a meta-analysis of 40 prominent published findings concerning interconnectivity of layers across the cortex (including, among others: Beul and Hilgetag 2014, Binzegger et al. 2004).


Macaque global white matter connectivity was extracted from DWI dataset (see figure 1 A) and cortical laminar composition was extracted from IR EPI dataset (see figure 1 B). Both datasets were analyzed using FVE91 parcellation (Felleman and Van Essen 1991). Two types of connectomes were extracted between thalamus, motor cortex and somatosensory cortex: standard white matter connectivity (figure 2 B) and expanded cortical layer connectivity (figure 2 C), integrated based on cortical laminar composition in both regions (figure 2 A) and using our simple model of layer connectivity.
Supporting Image: Figure1.jpg
   ·A- Macaque white matter connectivity: 1- tractography, 2- connectivity matrix; B- Cortical laminar composition analysis: mixture of t-distributions fit to T1 histogram used for tissue classification
Supporting Image: Figure2.jpg
   ·Macaque cortical layer connectivity between motor cortex (M1), somatosensory cortex (S1) and thalamus: A- cortical laminar composition; B- white matter connectivity; C- cortical layer connectivity


In this study we explore the laminar level connectivity of the macaque brain. We use a simple data-derived model of layer connectivity in order to integrate multi-modal neuroimaging techniques including tractography and cortical laminar composition analysis. By doing so, we address the heterogenous laminar structure of the cortex in connectomics and introduce a new and more detailed connectome, termed cortical layer connectivity.

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural) 1
Diffusion MRI Modeling and Analysis
Methods Development

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Cortical Anatomy and Brain Mapping
Cortical Cyto- and Myeloarchitecture 2


Computational Neuroscience
Cortical Layers

1|2Indicates the priority used for review

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.


Please indicate which methods were used in your research:

Structural MRI
Diffusion MRI
Computational modeling

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


Which processing packages did you use for your study?


Provide references using author date format

1. Sporns, O., Tononi, G., Kotter, R. (2005). ‘The Human Connectome: A Structural Description of the Human Brain’, PLoS Computational Biology, 1(4): e42, 0245-0251.
doi: 10.1371/journal.pcbi.0010042
2. Leemans, A., Jeurissen, B., Sijbers, J., Jones, D.K. (2009), ‘ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data’, In: 17th Annual Meeting of International Society of Magnetic Resonance in Medicine, p. 3537, Hawaii, USA.
3. Setsompop, K., Kimmlingen, R., Eberlein, E., Witzel, T., Cohen-Aded, J., McNab, J.A., Keil, B., Tisdall, M.D., Hoecht, P., Dietz, P., Cauley, S.F., Tountcheva, V., Matschl, V., Lenz, V.H., Heberlein, K., Potthast, A., Thein, H., Van Horn, J., Toga, A., Schmitt, F., Lehne, D., Rosen, B.R., Wedeen, V., Wald, L.L. (2013), ‘Pushing the limits of in vivo diffusion MRI for the Human Connectome Project’, NeuroImage, 80. 220-233.
doi: 10.1016/j.neuroimage.2013.05.078
4. Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E.J., Yacoub, E., Ugurbil, K. (2013), ‘The WU-Minn Human Connectome Project: An Overview’, NeuroImage, 80, 62-79.
doi: 10.1016/j.neuroimage.2013.05.041
5. Barazany, D., Assaf, Y. (2012), ‘Visualization of Cortical Lamination Patterns with Magnetic Resonance Imaging’, Cerebral Cortex, 22, 2016-2023.
doi: 10.1093/cercor/bhr277
6. Lifshits, S., Tomer, O., Shamir, I., Barazany, D., Tsarfaty, G., Rosset, S., Assaf, Y. (2018), ‘Resolution considerations in imaging of the cortical layers’, Neuroimage, 164, 112-120.
doi: 10.1016/j.neuroimage.2017.02.086
7. Shamir, I., Tomer, O., Baratz, Z., Tsarfaty, G., Faraggi, M., Horowitz, A., Assaf, Y. (2019), ‘A framework for cortical laminar composition analysis using low-resolution T1 MRI images’, Brain Structure and Function, vol. 224, is. 4, 1457-1467.
doi: 10.1007/s00429-019-01848-2
8. Felleman, D.J., Van Essen, D.C. (1991), ‘Distributed Hierarchical Processing in the Primate Cerebral Cortex’, Cerebral Cortex, 1, 1-47.
9. Beul, S.F., Hilgetag, C.C. (2014). ‘Towards a "canonical" agranular cortical microcircuit’, Frontiers in Neuroanatomy, 8:165.
doi: 10.3389/fnana.2014.00165
10. Binzegger, T., Douglas, R.J., Martin, K.A.C. (2004), ‘A Quantitative Map of the Circuit of Cat Primary Visual Cortex’, The Journal of Neuroscience, 24(39), 8441-8453.
doi: 10.1523/JNEUROSCI.1400-04.2004