Mesoscopic functional connectivity between cortex and globus pallidus nuclei
Poster No:
2163
Submission Type:
Abstract Submission
Authors:
Vinod Kumar1, Christian Beckmann2, Jonas Bause1, Edyta Charyasz1, Klaus Scheffler3, Wolfgang Grodd1
Institutions:
1Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, 2Donders Institute for Brain, Cognition, and Behavior, Radboud University Nijmegen, Nijmegen, Netherlands, 3Max Planck Institute for Biological Cybernetics & University of Tübingen, Tuebingen, Germany
First Author:
Co-Author(s):
Christian Beckmann
Donders Institute for Brain, Cognition, and Behavior, Radboud University Nijmegen
Nijmegen, Netherlands
Donders Institute for Brain, Cognition, and Behavior, Radboud University Nijmegen
Nijmegen, Netherlands
Klaus Scheffler
Max Planck Institute for Biological Cybernetics & University of Tübingen
Tuebingen, Germany
Max Planck Institute for Biological Cybernetics & University of Tübingen
Tuebingen, Germany
Introduction:
The globus pallidus/pallidum (GP) receives cortical feedback from deeper layer V via the striatum and connects with the thalamus and other subcortical nuclei [1,2]. The GP connected thalamus further engages in feedforward input to the medial layer 4 of the cortex [3]. With such a connectivity scheme within the cortico-basal ganglia-thalamo-cortical loop, GP engages in a broad array of brain functions, including motor control and cognitive functions. However, the GP's functional connectivity concerning cortical laminar organization has not been investigated in the human brain. To address this gap, our study employed high-resolution fMRI to map the functional connectivity of the GP with respect to the cortical depth in the human brain.
Methods:
Subjects: 16 healthy subjects (Age 23-39 Y, Mean age: 27 Y, 9 F, 7 M) participated in the study with written informed consent before participation. The local research ethics committee approved the study.
Data acquisition: MRI was performed at 9.4 Tesla (Magnetom, Siemens) using a custom-built 16 transmit and 31 receive channel head coil. We acquired structural MP2RAGE (0.6 mm isotropic) and rsfMRI data (SMS EPI, FLEET pre-scan, MB 3, 1mm isotropic, TR 2200 ms, TE 27 ms, 117 slices, 300-360 scans, 10 scans with reversed phase encoding) [4].
Data Analysis:
i). Laminar Delineation: The MP2RAGE was reconstructed using raw data with in-house Matlab code The uniform contrast was analyzed using presurfer (https://github.com/srikash/presurfer) [5]. The layer delineation in 11 equidistance bins was performed after the freesurfer segmentation and manual quality control using LAYNII [6].
ii). rs-fMRI analysis: The thermal noise correction of the fMRI data was performed using the NORDIC Matlab toolbox [7]. The fMRI preprocessing was performed in native subject space using AFNI with despiking, slice time correction, distortion correction, coregistration, motion correction, anaticor (noise correction), and without smoothing [8]. The correlations between the GP and cortex were computed using AFNI [8]. Subsequently, smoothing within layers (0.7 FWHM was performed using LAYNII (Figure 1). The computed correlation maps (z) were then corrected for multiple comparisons (FDR p 0.05) with AFNI.
iii). Group Profile Tracking Analysis: The depth profile of the functional connectivity of the GP with each cortical area was determined using non-linear modeling [9,10]. In the next step, the predicted similar depth profiles with the peak connectivity in the medial and deeper cortical depth were segregated and visualized (Figure 2).
Data acquisition: MRI was performed at 9.4 Tesla (Magnetom, Siemens) using a custom-built 16 transmit and 31 receive channel head coil. We acquired structural MP2RAGE (0.6 mm isotropic) and rsfMRI data (SMS EPI, FLEET pre-scan, MB 3, 1mm isotropic, TR 2200 ms, TE 27 ms, 117 slices, 300-360 scans, 10 scans with reversed phase encoding) [4].
Data Analysis:
i). Laminar Delineation: The MP2RAGE was reconstructed using raw data with in-house Matlab code The uniform contrast was analyzed using presurfer (https://github.com/srikash/presurfer) [5]. The layer delineation in 11 equidistance bins was performed after the freesurfer segmentation and manual quality control using LAYNII [6].
ii). rs-fMRI analysis: The thermal noise correction of the fMRI data was performed using the NORDIC Matlab toolbox [7]. The fMRI preprocessing was performed in native subject space using AFNI with despiking, slice time correction, distortion correction, coregistration, motion correction, anaticor (noise correction), and without smoothing [8]. The correlations between the GP and cortex were computed using AFNI [8]. Subsequently, smoothing within layers (0.7 FWHM was performed using LAYNII (Figure 1). The computed correlation maps (z) were then corrected for multiple comparisons (FDR p 0.05) with AFNI.
iii). Group Profile Tracking Analysis: The depth profile of the functional connectivity of the GP with each cortical area was determined using non-linear modeling [9,10]. In the next step, the predicted similar depth profiles with the peak connectivity in the medial and deeper cortical depth were segregated and visualized (Figure 2).
Results:
The majority of cortical areas show association with GP in the medial and deeper cortical depth (Figure 2). The GP connectivity with medial depth in cortical regions covers mainly the default mode, visual, fronto-parietal, and salience networks, suggesting the GP is more involved in the feedforward flow of information to these cortical networks during rest (Figure 2). In contrast, deeper depth-specific highly correlated cortical regions cover mainly somatosensory, visual, cingulate, temporal, and frontal areas, suggesting the GP is more involved in the feedback flow of information from these cortical areas during rest. The left and right pallidum show similar depth preferences with cortical areas, with slight laterality differences.
Conclusions:
The analysis reveals a depth-specific spatial precision of the functional connectivity between cortical areas and pallidum. The depth-specificity with different cortical regions hints a feedforward and feedback communication at rest. The study allows a coarser interpretation concerning cortical laminar profiles, as individual layers may vary with size and density in different cortical areas. GRE BOLD fMRI has limitations to the spatial specificity of the signal and can be biased by cortical orientation; therefore, depth-specificity may be enhanced with other imaging functional contrasts, i.e., VASO and bSSFP.
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems 2
Subcortical Structures 1
Keywords:
Basal Ganglia
Cortical Layers
Sub-Cortical
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