Developmental Brain Connectivity in Chimpanzees using High-Resolution Diffusion MRI

Poster No:

2255 

Submission Type:

Abstract Submission 

Authors:

Cornelius Eichner1, Michael Paquette1, Guillermo Gallardo1, Christian Bock2, Tobias Gräßle3, Jenny Jaffe3, Carsten Jäger1, Evgeniya Kirilina1,4, Ilona Lipp1, Toralf Mildner1, Torsten Schlumm1, Felizitas Wermter2, Harald Möller1, Nikolaus Weiskopf1,5, Catherine Crockford6, Roman Wittig6, Angela Friederici1, Alfred Anwander1

Institutions:

1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany, 3Robert Koch Institute, Berlin, Germany, 4Free University of Berlin, Berlin, Germany, 5Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany, 6Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

First Author:

Cornelius Eichner  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany

Co-Author(s):

Michael Paquette  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany
Guillermo Gallardo  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany
Christian Bock  
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research
Bremerhaven, Germany
Tobias Gräßle  
Robert Koch Institute
Berlin, Germany
Jenny Jaffe  
Robert Koch Institute
Berlin, Germany
Carsten Jäger  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany
Evgeniya Kirilina  
Max Planck Institute for Human Cognitive and Brain Sciences|Free University of Berlin
Leipzig, Germany|Berlin, Germany
Ilona Lipp  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany
Toralf Mildner  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany
Torsten Schlumm  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany
Felizitas Wermter  
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research
Bremerhaven, Germany
Harald Möller  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany
Nikolaus Weiskopf  
Max Planck Institute for Human Cognitive and Brain Sciences|Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University
Leipzig, Germany|Leipzig, Germany
Catherine Crockford  
Max Planck Institute for Evolutionary Anthropology
Leipzig, Germany
Roman Wittig  
Max Planck Institute for Evolutionary Anthropology
Leipzig, Germany
Angela Friederici  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany
Alfred Anwander  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany

Introduction:

The evolutionary origin of human brain function and its connectivity is not yet well understood. This knowledge gap may be closed by comparing human brain connectivity with that of great apes 1,2. However, ethical concerns about primate research disallow neuroimaging research on great apes 3. Therefore, evolutionary neuroscience relies on a small number of previously acquired diffusion MRI (dMRI) data with limited resolution. The existing data predominantly focus on adult animals, making it difficult to analyze evolutionary aspects of brain development. We here present a novel approach for great ape dMRI data acquisition, utilizing post-mortem brains from chimps of various ages. The brains originate from animals in African wildlife field-sites, sanctuaries, and European zoos collected within the Evolution of Brain Connectivity Project, EBC. All animals were observed behaviorally throughout their life and died of natural cause without human interference. We optimized the achievable dMRI quality for post-mortem dMRI acquisitions using different sequences and systems. The resulting data quality allowed full brain measurements with 500μm resolution - the highest resolution dMRI data yet collected in apes.

Methods:

The brains were extracted after natural death within a post-mortem interval of only 2-24h. The brains were immersion-fixed with 4% paraformaldehyde in phosphate-buffered saline (PBS), shipped to Germany, washed in PBS, and immersed in perfluoropolyether for scanning.
For SNR comparison, high-resolution dMRI sequences with 500μm isotropic resolution were optimized on two different MRI systems (Fig 1A) – a Siemens 3T Connectom and a 9.4T Bruker Biospec 94/30. Final data were acquired with the higher SNR MRI system.
The required formalin fixation of post-mortem tissue alters its properties and diffusion coefficients by varying factors 4. Optimal tissue diffusion-weighting was determined using pre-scans, acquired on a 3T Siemens Connectom MRI System with Gmax=300mT/m. The voxel-wise diffusion contrast, ΔSDTI, was computed as parallel versus radial signal intensity, predicted by DTI. The b-value with highest median whole-brain contrast across 22 b-shells, ranging from b=1000s/mm2 to b=10000s/mm2, was chosen (Fig 1B).
Processing of the dMRI data included debiasing 6, denoising 7, temperature drift correction, and correction of sample displacement and distortions 8. The temperature drift correction compensated for increasing tissue diffusivity with temperature by computing a time-specific scaling factor, scaling signal intensities to match steady-state temperature conditions. Initial tractography and visualization of the processed dataset were performed using brainGL.

Results:

Samples indicated a consistent maximum diffusion contrast, ΔSDTI, at b=5000s/mm2 (Fig 1B). Consequently, this diffusion-weighting was chosen to be used as the standard for dMRI acquisitions. The SNR comparison indicated a higher SNR for the preclinical 9.4T MRI system (Fig 1C). Hence, remaining dMRI data were acquired using the preclinical MRI setting (Fig 1D).
Currently, data were acquired from nine chimpanzees (age range 0 - 47y, 3f). The resulting dMRI data were of unprecedented image resolution for chimpanzee whole-brain scans and allow tractography reconstructions for various ages. Fig 2B displays color FA data from representative brains of three different age groups (newborn, child, adult).

Conclusions:

In this work, we present the highest resolution dMRI data yet collected in chimpanzees. The dMRI data allow tractography reconstructions to compare structural connectivity between apes and humans. The non-invasive selection of naturally deceased animals from the wild provides access to brains of all ages. This enables novel access to the development of ape brain connectivity and allows to relate the brain structure to behavioral characteristics.

Lifespan Development:

Lifespan Development Other

Modeling and Analysis Methods:

Diffusion MRI Modeling and Analysis
Methods Development

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Normal Development 2

Novel Imaging Acquisition Methods:

Diffusion MRI 1

Keywords:

Development
HIGH FIELD MR
Language
MRI
MRI PHYSICS
STRUCTURAL MRI
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Evolution

1|2Indicates the priority used for review
Supporting Image: Fig1.png
   ·Figure 1
Supporting Image: Fig2.png
   ·Figure 2
 

My abstract is being submitted as a Software Demonstration.

No

Please indicate below if your study was a "resting state" or "task-activation” study.

Other

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

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.

Not applicable

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.

Yes

Please indicate which methods were used in your research:

Structural MRI
Diffusion MRI
Postmortem anatomy

Which processing packages did you use for your study?

FSL

Provide references using author date format

1. Rilling JK, Glasser MF, Preuss TM, et al. The evolution of the arcuate fasciculus revealed with comparative DTI. Nat Neurosci. 2008;11(4):426-428. doi:10.1038/nn2072
2. Ardesch DJ, Scholtens LH, Li L, Preuss TM, Rilling JK, van den Heuvel MP. Evolutionary expansion of connectivity between multimodal association areas in the human brain compared with chimpanzees. Proc Natl Acad Sci U S A. 2019;116(14):7101-7106. doi:10.1073/pnas.1818512116
3. Cohen J. Animal studies. NIH to end chimp breeding for research. Science. 2007;316(5829):1265. doi:10.1126/science.316.5829.1265
4. Roebroeck A, Miller KL, Aggarwal M. Ex vivo diffusion MRI of the human brain: Technical challenges and recent advances. NMR Biomed. 2019;32(4):e3941. doi:10.1002/nbm.3941
5. Eichner C, Paquette M, Mildner T, et al. Increased sensitivity and signal-to-noise ratio in diffusion-weighted MRI using multi-echo acquisitions. Neuroimage. 2020;221(117172):117172. doi:10.1016/j.neuroimage.2020.117172
6. Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. Magn Reson Med. 1995;34(6):910-914. doi:10.1002/mrm.1910340618
7. Cordero-Grande L, Christiaens D, Hutter J, Price AN, Hajnal JV. Complex diffusion-weighted image estimation via matrix recovery under general noise models. Neuroimage. 2019;200:391-404. doi:10.1016/j.neuroimage.2019.06.039
8. Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063-1078. doi:10.1016/j.neuroimage.2015.10.019