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High dynamicity of cortical depth-dependent connectivity states1INM-4, Forschungszentrum Juelich, Juelich, Germany, 2Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany, 3INM-11, Forschungszentrum Juelich, Juelich, Germany, 4Translational Medicine, JARA-BRAIN, Aachen, Germany, 5Department of Neurology, RWTH Aachen University, Aachen, Germany
Synopsis
We assessed resting-state fMRI acquired with high spatial resolution with a dynamic functional connectivity analysis involving multiple cortical ROIs and depths. Two states relevant to the normalized global laminar connectivity were identified in healthy volunteers (superficial vs. deep layer connectivity predominance). Mean laminar connectivity states were much more dynamic than the states identified in a routine ROI-based dynamic connectivity analysis, suggesting the potential of high-spatial-resolution fMRI to reveal time-varying features in the human brain.
Introduction
The fMRI community is continually striving to develop new analysis methods to measure different features of brain function. In the past two decades, the analysis of functional connectivity with time sensitivity, i.e. dynamic functional connectivity, has demonstrated that animal and human brains at rest transit between varying states1,2. In parallel, developments in MR sequencing, together with the emergence of ultra-high-field MRI scanners, have made it possible to obtain images of the brain with a level of precision that enables the assessment of fine structures, such as the cerebral cortex at multiple depths (e.g. 3-5). Each cortical lamina is known to hold particular cell types and participate in different steps of information processing, e.g. in the somatic cortex, the intermediate layers are the main target of sensory input, while the deep layers initiate efferent motor responses from the cortex to the spinal cord6, hence, being able to track connectivity along the cortical depth could bring new insights into the brain working mode, not only during task performance but also during rest. In the last year, the field-of-view of high-resolution sequences has been expanded to cover a big portion of the human brain, allowing for resting-state network evaluation with laminar specificity7. In this work, we evaluated the potential of a dynamic connectivity analysis in cortical depth specific resting-state data.Methods
We analyzed a set of data consisting of whole-brain rs-fMRI scans (~10 min) from 12 volunteers, acquired at a Siemens Magnetom Terra 7T scanner with a 1-channel Tx / 32-channel Rx Nova Medical head coil. The fMRI sequence was GE EPIK (GE EPI with keyhole)8-12. TR/TE = 3500/22 ms, FA = 85°, partial Fourier = 5/8, 3-fold in-plane/3-fold inter-plane (multi-band) acceleration, matrix = 336 × 336 × 123 slices, voxel size = 0.63 × 0.63 × 0.63 mm3. Pre-processing included slice-timing correction, realignment, regression of motion parameters, regression of the cardiac and respiratory cycles13, regression of the mean signal of the cerebrospinal fluid and white matter, and regression of the pre-processed phase image14. For each scan, 134 connectivity matrices containing 46 ROIs and six cortical depths per ROI were computed in ~120s (34 TRs) sliding windows (w) spaced by 1 TR, and all matrices, normalized per time point, were concatenated in time and along all subjects (time-connectivity matrix = [w1..w134]-subj1…[w1..w134]-subj12). This ROI-layer time-connectivity matrix, a version containing only ROIs (standard procedure) and a condensed version where laminar connectivity was averaged across ROIs (mean layer-to-layer connectivity, no ROI information) were fed to a k-means algorithm to identify different brain states relevant to different input information. An index of state dynamicity was computed per condition as the number of state switches, and the time spent in each state was computed per scan and averaged across subjects.Results
Compared to the typical ROI connectivity or the ROI with added laminar information, the number of state switches was significantly higher for states that were determined based on the different involvement of the cortical laminae on global brain connectivity (Fig. 1). The mean whole-brain functional connectivity across the cortical depth (global layer-to-layer analysis) oscillated between two states, one characterized by stronger connectivity between superficial layers and another where the deep layers were more strongly connected, the latter being more prevalent (although not statistically significantly) (Fig. 2).Discussion & conclusion
The higher dynamicity of layer connectivity compared to ROI connectivity suggests the potential of layer fMRI to detect changes that are not easily identified with common analysis procedures, i.e., the physiological differences along the cortical depth that contribute to the global brain connectivity seem to be sufficiently relevant to determine different brain states.Acknowledgements
We thank Ms. Rick for text revision and all volunteers involved in the study for their cooperation.References
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