Mapping directional functional connectivity across brain-wide networks with layer-specific CBV-fMR

Poster No:


Submission Type:

Abstract Submission 


Laurentius Huber1, Emily Finn2, Denizhan Kurban1, Sean Marrett2, Arman Khojandi2, Rainer Goebel3, Peter Bandettini2, Benedikt Poser1


1MR-Methods group, CN, MBIC, FPN, Uni Maastricht, Maastricht, The Netherlands, 2NIMH, Bethesda, USA, 3CN, MBIC, FPN, Uni Maastricht, Maastricht, The Netherlands

First Author:

Laurentius Huber  
MR-Methods group, CN, MBIC, FPN, Uni Maastricht
Maastricht, The Netherlands


Emily Finn, PhD  
Bethesda, USA
Denizhan Kurban  
MR-Methods group, CN, MBIC, FPN, Uni Maastricht
Maastricht, The Netherlands
Sean Marrett  
Bethesda, USA
Arman Khojandi, B.S.  
Bethesda, USA
Rainer Goebel, Ph.D.  
CN, MBIC, FPN, Uni Maastricht
Maastricht, The Netherlands
Peter Bandettini  
Bethesda, USA
Benedikt Poser  
MR-Methods group, CN, MBIC, FPN, Uni Maastricht
Maastricht, The Netherlands


With recent advances in ultra-high-field MRI hardware and sequence mechanisms, it has become possible to measure CBV-weighted fMRI signal across cortical layers. While initial proof-of-principle layer-fMRI studies in primary brain areas with conventional fMRI task designs are promising, layer-fMRI has not yet realized its full potential to map layer-dependent functional connectivity across large-scale brain networks. In this study, we investigate the applicability of CBV-weighted layer-fMRI to assess functional connectivity during resting-state and naturalistic tasks. We can map common resting-state networks and characterize their internal layer-dependent signatures with respect to directionality and cortical hierarchy.


Multiple 14-min acquisitions were conducted at 7T during resting-state and/or movie watching in N=11 participants.
Sequence development and fMRI acquisition were performed on 7T Siemens Magnetom ' Classic' scanners at Maastricht University and NIH, respectively, under the corresponding local IRB approvals.

Functional data of GE-BOLD and cerebral blood volume (CBV) were concomitantly acquired with MAGEC SS-SI-VASO. A slab-selective (28 slices) and a whole brain VASO sequence (104 slices) was used (details on the novel whole-brain VASO sequence are given in Huber et al., submitted to ISMRM Acquisition parameters were: in-plane resolution 0.8mm iso., 216x162 matrix, TE=25ms, in-plane PF=6/8 with POCS8, FLASH-GRAPPA=3, TR=8.4s, 3D-EPI readout (Poser 2010), remaining sequence parameters on github:, 32-ch NOVA coil.


Fig 1 depicts characteristic large-scale networks at 0.8mm. CBV maps show that 7T VASO can provide sufficient signal to extract connectivity maps across the entire brain.
Fig. 2 depicts the results of the proposed whole brain layer-dependent connectomics mapping Panels A-B) illustrate the approach to generate connectivity matrices: First, the EPI brain is parcelated into a number of atlas brain areas (Shen 2013). Then the average time courses within each brain area is correlated against all other brain area's time courses. The combinations of all correlations are summarized in the connectivity matrix. Panel C) shows that the layering adds an additional dimension to this. Rows and columns refer to layers. Off-diagonal elements can be used to interpret directional connectivity.
Panels D-G depict commonly observed networks:
⇒ 'Visual network': V1 receives top-down feedback layers from V5, while V5 receives bottom-up input in the middle/deeper layers (red circles).
⇒ 'Sensory motor network': M1 receives input from S1, solely in superficial layers.
⇒ 'Default mode network': Ellipses highlight that the PCC is the only middle-layer dominated ROI. Other ROIs are feedback driven. Thus, PCC is the hub of the 'default mode network', while the other areas are being passively dragged along with feedback connections.
⇒ 'Fronto-Parietal network' with a characteristically isolated connectivity within superficial and deeper layers only.
Supporting Image: Fig3_coverage-01.png
   ··Fig 1: 7T-MAGEC-VASO provides sufficient contrast-to-noise ratios to image connectivity maps during naturalistic movie watching tasks. Panels refer to representative functional networks.
Supporting Image: Fig6_WB_connectome-01.png
   ··Fig 2: Whole brain layer-dependent connectomics: Analysis approach and representative results for selected functional networks from (Smith 2009).


Layer-dependent fMRI is limited by SNR and venous signal leakage, making it challenging to map the functional connectome in humans. However, with the application of spatially specific CBV-weighted fMRI, we found that it is possible to measure functional connectivity across the entire brain without biases of venous signal leakage, yielding layer-dependent localization specificity.
The ability to reliably measure the whole brain layer-dependent connectome without venous biases constitutes a paradigm shift in human neuroscience.
It can be a valuable tool:
⇒ to obtain information about directional information flow across the entire brain
⇒ to reveal causal relationships in large scale networks

Modeling and Analysis Methods:

Activation (eg. BOLD task-fMRI)
Methods Development

Novel Imaging Acquisition Methods:



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.

Resting state

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

Healthy subjects

Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.


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:

Functional MRI

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

De Martino et al. 2015. PNAS 112(52): 16036–41.
Huber et al. (2019). NeuroImage,​1016/​j.​neuroimage.​2019.​116463
Finn et al. (2019). Nature Neuro.
Huber et al. (2018). Neuroimage in press.
Huber et al. (2017). Neuron.
Huber et al. (2017). Neuroimage, doi: 10.1016/j.neuroimage.2017.07
Muckli et al. (2015). Curr. Biol. 25, 2690–2695.
Kok et al. (2016). Current Biology 26(3): 371–76.
Koopmans et al. (2010). Human Brain Mapping 31(9): 1297–1304.
Poser et al. (2010). NeuroImage 51(1): 261–66.
Polimeni et al. (2010). ISMRM (Vol. 18, p. 353).
Sharoh et al. (2019). PNAS: 1907858116.
Shen, et al., (2013).
Smith et al. (2009). PNAS 106(31): 13040–45.