Ittai Shamir1, Omri Tomer2, Ronnie Krupnik2, Yaniv Assaf2
1Tel Aviv University, Tel Aviv, Israel, 2Tel Aviv University, Tel Aviv, Tel Aviv
Despite growing progress in the field of connectomics (Van Essen et al. 2013), the field remains limited by the biased representation of the cortex as a single homogenous unit. Simultaneously, promising strides are being made in neuroimaging of the laminar composition of cortical grey matter (Barazany and Assaf 2012, Lifshits et al. 2018, Shamir et al. 2019, Assaf 2019). Integration of macrostructural white matter connectomics and microstructural grey matter laminar composition poses a promising development in connectomics (Johansen-Berg 2013).
Our aim is to model and explore the connectome of the healthy human brain on the cortical laminar level. The modelling process is completed by integrating white and grey matter multimodal MRI datasets using a recently published simplified model of cortical laminar connectivity (Shamir and Assaf 2020a). Following further examination and corroboration of the model in the visual cortex of the macaque brain (Shamir and Assaf 2020b), we present here a complex network exploration of the human laminar connectome.
Twenty healthy human subjects (N=20), including 8 male and 12 female, 18-78 years old.
Each subject was scanned on a 3T Magnetom Siemens Prisma (Siemens, Erlangen, Germany) scanner with a 64-channel RF coil. The sequences include:
1. A standard diffusion-weighted imaging (DWI) sequence, with the following parameters: Δ/δ=60/15.5 ms, b max=5000 (0 250 1000 3000 & 5000) s/mm2, with 87 gradient directions, FoV 204 mm, maxG= 7.2, TR=5200 ms, TE=118 ms, 1.5×1.5×1.5 mm^3, 128×128×94 voxels. This sequence was used to map the cortical connectome.
2. An MPRAGE sequence, with the following parameters: TR/TE = 1750/2.6 ms, TI = 900 ms, 1×1×1 mm^3, 224×224×160 voxels, each voxel fitted with a single T1 value.
3. An inversion recovery echo planar imaging (IR EPI) sequence, with the following parameters: TR/TE = 10,000/30 ms and 60 inversion times spread between 50 ms up to 3,000 ms, 3×3×3 mm^3, 68×68×42 voxels, each voxel fitted with up to 7 T1 values (Lifshits et al. 2018).
These two sequences (2 and 3) were used to characterize the cortical layers.
1. Global white matter connectivity analysis- DWI datasets were analyzed for global white matter connectivity using MRtrix3 software package (Tournier et al. 2019).
2. Cortical laminar composition analysis- MPRAGE and IR EPI datasets were analyzed for cortical laminar composition using our novel framework (Shamir et al. 2019).
Model of cortical laminar connectivity:
White and grey matter multimodal MRI datasets were integrated using our novel data-derived, granularity-based model of cortical laminar connectivity (Shamir and Assaf 2020a, 2020b). The resulting connectomes, representing both standard connectivity and laminar connectivity, are then explored via a set of neurobiologically meaningful complex network measures (Rubinov and Sporns 2010).
Analysis of both white matter connectivity and gray matter laminar composition were averaged across all 20 subjects (see figure 1). Datasets were then integrated, resulting in the cortical laminar connectome. Both standard and laminar connectomes were analyzed using neurobiologically meaningful complex network measures (see figure 2). Notice the varying distribution of degree values across laminar components, the prominence of clustering coefficient in the occipital lobe in the laminar connectome as opposed to the frontal lobe in the standard connectome, and the asymmetry of betweenness values in the laminar connectome.
In this study we model the laminar connectome of healthy human subjects and explore it via a set of neurobiologically meaningful complex network measures. The resulting micro-level connectome offers not only a more detailed and unbiased representation of the human connectome, but also new prospects in the field of structural and functional connectivity on the cortical laminar level.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Diffusion MRI Modeling and Analysis
Methods Development 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Novel Imaging Acquisition Methods:
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):
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:
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. Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K. The WU-Minn Human Connectome Project: An Overview. NeuroImage. 2013, 80, 62-79. doi: 10.1016/j.neuroimage.2013.05.041.
2. Johansen-Berg H. Human connectomics – What will the future demand? NeuroImage. 2013, 80, 541-544. doi: 10.1016/j.neuroimage.2013.05.082.
3. Barazany D, Assaf Y. Visualization of Cortical Lamination Patterns with Magnetic Resonance Imaging. Cerebral Cortex. 2012, 22, 2016–2023. doi: 10.1093/cercor/bhr277.
4. Lifshits S, Tomer O, Shamir I, Barazany D, Tsarfaty G, Rosset S, Assaf Y. Resolution considerations in imaging of the cortical layers. Neuroimage. 2018, 164, 112–120. doi: 10.1016/j.neuroimage.2017.02.086.
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6. Shamir I, Tomer O, Baratz Z, Tsarfaty G, Faraggi M, Horowitz A, Assaf Y. A framework for cortical laminar composition analysis using low-resolution T1 MRI images. Brain Structure and Function. 2019, 224, 4, 1457-1467. doi: 10.1007/s00429-019-01848-2.
7. Shamir I, Assaf Y. An MRI-based, data-driven model of cortical laminar connectivity. Neuroinformatics. 2020a, (), 1-14. doi: 10.1007/s12021-020-09491-7.
8. Shamir I, Assaf Y. Modelling cortical laminar connectivity in the macaque brain. 2020b. bioRxiv preprint. doi: 10.1101/2020.07.19.210526
9. Tournier JD, Smith RE, Raffelt D, Tabbara R, Dhollander T, Pietsch M, Christiaens D, Jeurissen B, Yeh C H, Connelly A. MRtrix3: A fast, flexible and open software framework for medical image processing and visualization. NeuroImage. 2019, 202, 116–37. doi: 10.1016/j.neuroimage.2019.116137
10. Rubinov M, Sporns O. Complex network measures of brain connectivity: Uses and interpretations, NeuroImage. 2010, 52(3): 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003.