Magnetic resonance imaging of mouse brain networks plasticity following motor learning

• Published in: PloS one Vol. 14; no. 5; p. e0216596
• Main Authors: Badea, Alexandra, Ng, Kwan L, Anderson, Robert J, Zhang, Jiangyang, Miller, Michael I, O'Brien, Richard J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08-05-2019; Public Library of Science (PLoS)
• Subjects: Aging; Animal cognition; Animal models; Animals; Anisotropy; Biology and Life Sciences; Brain; Brain - physiology; Brain injuries; Brain injury; Brain Mapping - methods; Brain research; Cerebellum; Circuits; Clusters; Cortex; Cortex (somatosensory); Cortex (temporal); Decision making; Diagnostic imaging; Diffusion; Diffusion coefficient; Environment models; Exploitation; Genetics; Growth factors; Head injuries; Histology; Information processing; Ischemia; Learning; Learning - physiology; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mapping; Medical imaging; Medicine and Health Sciences; Mice; Mice, Inbred C57BL; Microscopy; Motor learning; Motor skill; Motor skill learning; Motor Skills - physiology; Motor task performance; Nerve Net - physiology; Networks; Neural networks; Neuroimaging; Neurology; Neuronal Plasticity - physiology; Neuroplasticity; NMR; Nuclear magnetic resonance; Parafascicular nucleus; Physical Sciences; Physiological aspects; Plastic properties; Plasticity; Rehabilitation; Research and Analysis Methods; Rodents; Sensorimotor integration; Sensory integration; Shrinkage; Social Sciences; Somatosensory cortex; Statistical analysis; Temporal cortex; Tensors; Thickening; Visual cortex; Volumetric analysis; New York; United States > US; Baltimore Maryland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0216596

We do not have a full understanding of the mechanisms underlying plasticity in the human brain. Mouse models have well controlled environments and genetics, and provide tools to help dissect the mechanisms underlying the observed responses to therapies devised for humans recovering from injury of ischemic nature or trauma. We aimed to detect plasticity following learning of a unilateral reaching movement, and relied on MRI performed with a rapid structural protocol suitable for in vivo brain imaging, and a longer diffusion tensor imaging (DTI) protocol executed ex vivo. In vivo MRI detected contralateral volume increases in trained animals (reachers), in circuits involved in motor control, sensory processing, and importantly, learning and memory. The temporal association area, parafascicular and mediodorsal thalamic nuclei were also enlarged. In vivo MRI allowed us to detect longitudinal effects over the ~25 days training period. The interaction between time and group (trained versus not trained) supported a role for the contralateral, but also the ipsilateral hemisphere. While ex vivo imaging was affected by shrinkage due to the fixation, it allowed for superior resolution and improved contrast to noise ratios, especially for subcortical structures. We examined microstructural changes based on DTI, and identified increased fractional anisotropy and decreased apparent diffusion coefficient, predominantly in the cerebellum and its connections. Cortical thickness differences did not survive multiple corrections, but uncorrected statistics supported the contralateral effects seen with voxel based volumetric analysis, showing thickening in the somatosensory, motor and visual cortices. In vivo and ex vivo analyses identified plasticity in circuits relevant to selecting actions in a sensory-motor context, through exploitation of learned association and decision making. By mapping a connectivity atlas into our ex vivo template we revealed that changes due to skilled motor learning occurred in a network of 35 regions, including the primary and secondary motor (M1, M2) and sensory cortices (S1, S2), the caudate putamen (CPu), visual (V1) and temporal association cortex. The significant clusters intersected tractography based networks seeded in M1, M2, S1, V1 and CPu at levels > 80%. We found that 89% of the significant cluster belonged to a network seeded in the contralateral M1, and 85% to one seeded in the contralateral M2. Moreover, 40% of the M1 and S1 cluster by network intersections were in the top 80th percentile of the tract densities for their respective networks. Our investigation may be relevant to studies of rehabilitation and recovery, and points to widespread network changes that accompany motor learning that may have potential applications to designing recovery strategies following brain injury.