Unravelling the intrinsic functional boundaries of the macaque monkey cortex
Presented During:
Tuesday, June 27, 2017: 11:20 AM - 11:32 AM
Vancouver Convention Centre
Room:
Ballroom AB
Submission No:
1882
Submission Type:
Abstract Submission
On Display:
Monday, June 26 & Tuesday, June 27
Authors:
Ting Xu1, Alexander Opitz2, Arnaud Falchier2, Gary Linn2, Deborah Ross2, Julian Ramirez3, Darrick Sturgeon3, Eric Feczko3, Elinor Sullivan3, Jennifer Bagley3, Stan Colcombe2, Damien Fair3, Charles Schroeder4, Michael Milham1
Institutions:
1Child Mind Institute, New York, NY, 2Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 3Oregon Health and Science University, Oregon, United States, 4Columbia University College of Physicians and Surgeons & Nathan Kline Institute, New York; Orangeburg, NY
First Author:
Introduction:
A growing body of literature has demonstrated the ability to delineate cortical areas in the human brain based upon the detection of spatial transitions in intrinsic functional connectivity (iFC) profiles (Cohen et al., 2008; Wig et al., 2014). In particular, gradient-based parcellation approaches have gained popularity due to their ability to recapitulate previously established cytoarchitectonic brain areas. Here, we demonstrate the feasibility of extending the application of parcellation approaches to non-human primates (NHP), demonstrating the reliability of these parcellations and comparing the cortical areas revealed to those obtained in humans.
Methods:
We collected data from a male rhesus macaque monkey (age: 6 year) on a 3 Tesla Siemen Tim Trio scanner. Awake functional MRI scans were obtained during 6 sessions (4-7 scans for each session, 8 minutes per scan, 216 minutes in total, TR = 2 s, 1.46 x 1.46 x 2 mm); three of the sessions were carried out using a contrast agent (i.e., monocrystalline iron oxide particle (MION)) and 3 were without contrast. We obtained high-resolution T1-weighted anatomical images (0.5mm isotropic voxel) for surface registration. The native surface was reconstructed and registered to Yerkes19 macaque template (Donahue et al., 2016). We calculated iFC-similarity maps for each scan, followed by the spatial gradient and edge detection computation on native surface. The spatial correlations were calculated to investigate the reproducibility of boundaries across sessions and scans. We further explored the requirement of scan time for a relatively robust iFC and boundary map.
Results:
As expected, whole-brain gradient maps exhibited a higher degree of similarity among individuals within the same developmental period; differences were particularly notable at the extremes (i.e., childhood, older age) (see Figure 1A). To facilitate visualization, we defined 6 age groups and depicted mean gradient maps in Figure 1B. Next, at each voxel, we used univariate analyses to detect age-related linear and quadratic trends in global mean for the gradient map associated with that specific vertex. These analyses revealed linear age effects in posterior cortex, particularly in primary visual, sensorimotor, and default mode networks (Figure 1C). The quadratic effects were mainly located in the regions of network borders e.g. default mode, ventral attention (Figure 1C). Finally, at each vertex, we used MDMR to detect age-related variation (linear, quadratic) in the gradient maps defined across individuals. The linear and quadratic age-related effects were predominantly located in the regions of network borders, e.g. default mode, ventral attention, dorsal attention and frontoparietal network (Figure 2).
·Figure 1.
·Figure 2.
Conclusions:
By examining the transition pattern of iFC similarity in macaque, we have demonstrated the ability to detect functional boundaries and cortical areas in the macaque monkey cortex using awake R-fMRI in macaque, suggesting a reliable scheme for delineating cortical organization in macaque and potential utility for validating invasive individual parcellation.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Segmentation and Parcellation 1
Task-Independent and Resting-State Analysis
Keywords:
Other - macaque, parcellation, gradient, function connectivity
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Provide references in author date format
Gordon, T., et al., (2014), ‘Generation and evaluation of a cortical area parcellation from resting-state correlations’, Cerebral Cortex, vol. 26, pp. 288-303
Wig, G. S., et al., (2014), 'An approach for parcellating human cortical areas using resting-state correlations', NeuroImage, vol. 93, pp. 276–291
Glasser M. F., et al., (2016), ‘A multi-modal parcellation of human cerebral cortex’, Nature, vol. 536, pp. 171-178