Research

Structural Plasticity

Brain plasticity is one of the enigmas of brain physiology. Brain plasticity is defined as the ability of brain tissue to re-organize its function to fit a new skill or compensate for a lost skill (1). The first evidences for brain plasticity are known from lesion studies in young children who compensated for severe functional loss following head injury or hemorrhage much better than adults (2). While functional aspects of neuro-plasticity can be studied using non-invasive techniques such as fMRI, EEF and MEG, investigation of the structural tissue characteristics of neuro-plasticity requires invasive histological approaches. Such studies found that long-term experience necessitates structural plasticity which, in the adult brain, is characterized by changes in the shape and number of the synapses (synaptogenesis) as well as other process (neurogenesis, gliogenesis and white matter plasticity) (3-7).

Structural MRI studies of brain plasticity reveal significant volumetric changes via voxel-based morphometry of T1 weighted scans (8-10). Yet, the micro-structure correlates of these changes are not well understood. Diffusion tensor imaging (DTI) became one of the most popular imaging techniques in neuroimaging and is regarded as a micro-structural probe. Recently, tract-based spatial statistics (TBSS) analysis of DTI scans before and after long-term motor coordination training (juggling) revealed regional fractional anisotropy (FA) increase in parietal pathways (11). In that study, FA changes were reported following few weeks of training.

An open question is what happens at shorter term learning and memory processes?

In a short term spatial navigation study performed both in humans and rodents, we found that DTI can also detect structural changes in cell morphology induced by plasticity within mere hours. Both in humans and rodents, the micro-structural changes, as observed by DTI, were localized to the anticipated brain regions: hippocampus, para-hippocampus, visual cortex, cingulate cortex and insular cortex.

In humans we have used a computer car racing game that included spatial and episodic learning and memory mechanisms. The subjects underwent two DTI session separated by only 2 hours of car-racing game (Figure 1). Their task included 16 laps of the car game track (Electronic Arts©), divided into 4 sessions. The subject's objective was to learn the track and achieve better lap times. Compared with the control groups (subjects that played the game but with different tracks or that did not perform any task between the DTI scans), a significant reduction in mean diffusivity was found in the left hippocampus and para-hippocampus (Figure 2).

To have a better understanding of the diffusion MRI changes in humans, we performed a similar study in rodents. Here we have used a short version of the water maze task in which rats had to find a hidden platform in a water pool based on spatial cues. The task included 12 trials (each lasting up to 1 minute). A significant reduction in the mean diffusivity was found, as in the humans study, in the hippocampus (Figure 3). Histological analysis of the rat's brains (Figure 4) found a link between changes at the synaptic level, changes in the morphology of astrocytes and LTP (indicated by increased level of BDNF).

Our results indicate that significant structural occur in the tissue within mere hours - an interesting result by itself from the neurophysiological point of view. However, by investigating the induced structural changes both by histology and MRI it is possible to elucidate the relations between tissue micro-structure and the diffusion MRI signal. Preliminary results of such comparison indicate that in gray matter tissue one of cellular correlates of diffusion MRI indices is the density and shape of astrocyte.

References:

1. Markham JA, Greenough WT. Experience-driven brain plasticity: beyond the synapse. Neuron Glia Biol 2004;1(4):351-363.
2. Duffau H. Brain plasticity: from pathophysiological mechanisms to therapeutic applications. J Clin Neurosci 2006;13(9):885-897.
3. Markus EJ, Petit TL. Synaptic structural plasticity: role of synaptic shape. Synapse 1989;3(1):1-11.
4. Shao Y, McCarthy KD. Plasticity of astrocytes. Glia 1994;11(2):147-155.
5. Lamprecht R, LeDoux J. Structural plasticity and memory. Nat Rev Neurosci 2004;5(1):45-54.
6. Matsuzaki M, Honkura N, Ellis-Davies GC, Kasai H. Structural basis of long-term potentiation in single dendritic spines. Nature 2004;429(6993):761-766.
7. Bruel-Jungerman E, Davis S, Laroche S. Brain plasticity mechanisms and memory: a party of four. Neuroscientist 2007;13(5):492-505.
8. May A, Gaser C. Magnetic resonance-based morphometry: a window into structural plasticity of the brain. Curr Opin Neurol 2006;19(4):407-411.
9.Johansen-Berg H. Structural plasticity: rewiring the brain. Curr Biol 2007;17(4):R141-144.
10. Lerch JP, Yiu AP, Martinez-Canabal A, Pekar T, Bohbot VD, Frankland PW, Henkelman RM, Josselyn SA, Sled JG. Maze training in mice induces MRI-detectable brain shape changes specific to the type of learning. Neuroimage 2011;54(3):2086-2095.
11. Scholtz J, Johansen-Berg H. White matter microstructure changes in response to training. Proc Human Brain Mapping 2009;15:362.

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