Methodology for computing white matter nerve fiber orientation in human histological slices

Transition from predictable to variable motor cortex and striatal ensemble patterning during behavioral exploration

Animals can capitalize on invariance in the environment by learning and automating highly consistent actions; however, they must also remain flexible and adapt to environmental changes. It remains unclear how primary motor cortex (M1) can drive precise movements, yet also support behavioral exploration when faced with consistent errors. Using a reach-to-grasp task in rats, along with simultaneous electrophysiological monitoring in M1 and dorsolateral striatum (DLS), we find that behavioral exploration to overcome consistent task errors is closely associated with tandem increases in M1 and DLS neural variability; subsequently, consistent ensemble patterning returns with convergence to a new successful strategy. We also show that compared to reliably patterned intracranial microstimulation in M1, variable stimulation patterns result in significantly greater movement variability. Our results thus indicate that motor and striatal areas can flexibly transition between two modes, reliable neural pattern generation for automatic and precise movements versus variable neural patterning for behavioral exploration.

It is not fully understood how behavioral flexibility is established in the context of automatic performance of a complex motor skill. Here the authors show that corticostriatal activity can flexibly transition between two modes during a reach to-grasp task in rats: reliable neural pattern generation for precise, automatic movements versus variable neural patterning for behavioral exploration.

Correlating new directional measures of myelin and axonal integrity in T2-weighted MRI with quantitative histology in multiple sclerosis

BACKGROUND

Imaging measurement of structure alignment has shown increasing importance in determining tissue properties. It is not known if a similar ability for characterizing neuropathology exists.

NEW METHODS

This study aimed to validate a new alignment-assessing method for measuring myelin and axonal properties using quantitative histological metrics. The new method involved analysis of the Fourier transform (FT) power spectrum in standard magnetic resonance imaging (MRI). T₂-weighted MRI were collected from 10 post-mortem multiple sclerosis (MS) brain samples. Three tissue types were examined: lesions, diffusely abnormal white matter, and normal appearing white matter. MRI analysis included computing the FT power spectrum; extracting alignment histograms; and calculating dominant orientation and alignment complexity (angular entropy). Post MRI, the brain samples were processed for myelin and axonal staining, and the stained images were used to derive quantitative orientation measures using structure tensor analysis for MRI comparison.

RESULTS

There were significant differences in orientation metrics between tissue types in both MRI and histology, and MRI measurements correlated strongly with histological indices. Moreover, the joint effect of myelin and axonal entropy explained over 95% of the variance of MRI angular entropy.

COMPARISON WITH EXISTING METHOD

There is no established method for characterizing myelin and axonal pathology using standard MRI. Advanced MRI methods have the potential to do this but are still in research development and are not yet routinely acquired in clinical practice.

CONCLUSIONS

Alignment measurement using clinical MRI scans may become a valuable new method for characterizing myelin and axonal properties in MS patients.

Design and validation of diffusion MRI models of white matter

Diffusion MRI is arguably the method of choice for characterizing white matter microstructure in vivo. Over the typical duration of diffusion encoding, the displacement of water molecules is conveniently on a length scale similar to that of the underlying cellular structures. Moreover, water molecules in white matter are largely compartmentalized which enables biologically-inspired compartmental diffusion models to characterize and quantify the true biological microstructure. A plethora of white matter models have been proposed. However, overparameterization and mathematical fitting complications encourage the introduction of simplifying assumptions that vary between different approaches. These choices impact the quantitative estimation of model parameters with potential detriments to their biological accuracy and promised specificity. First, we review biophysical white matter models in use and recapitulate their underlying assumptions and realms of applicability. Second, we present up-to-date efforts to validate parameters estimated from biophysical models. Simulations and dedicated phantoms are useful in assessing the performance of models when the ground truth is known. However, the biggest challenge remains the validation of the "biological accuracy" of estimated parameters. Complementary techniques such as microscopy of fixed tissue specimens have facilitated direct comparisons of estimates of white matter fiber orientation and densities. However, validation of compartmental diffusivities remains challenging, and complementary MRI-based techniques such as alternative diffusion encodings, compartment-specific contrast agents and metabolites have been used to validate diffusion models. Finally, white matter injury and disease pose additional challenges to modeling, which are also discussed. This review aims to provide an overview of the current state of models and their validation and to stimulate further research in the field to solve the remaining open questions and converge towards consensus.

Fourier transform power spectrum is a potential measure of tissue alignment in standard MRI: A multiple sclerosis study

Loss of tissue coherency in brain white matter is found in many neurological diseases such as multiple sclerosis (MS). While several approaches have been proposed to evaluate white matter coherency including fractional anisotropy and fiber tracking in diffusion-weighted imaging, few are available for standard magnetic resonance imaging (MRI). Here we present an image post-processing method for this purpose based on Fourier transform (FT) power spectrum. T₂-weighted images were collected from 19 patients (10 relapsing-remitting and 9 secondary progressive MS) and 19 age- and gender-matched controls. Image processing steps included: computation, normalization, and thresholding of FT power spectrum; determination of tissue alignment profile and dominant alignment direction; and calculation of alignment complexity using a new measure named angular entropy. To test the validity of this method, we used a highly organized brain white matter structure, corpus callosum. Six regions of interest were examined from the left, central and right aspects of both genu and splenium. We found that the dominant orientation of each ROI derived from our method was significantly correlated with the predicted directions based on anatomy. There was greater angular entropy in patients than controls, and a trend to be greater in secondary progressive MS patients. These findings suggest that it is possible to detect tissue alignment and anisotropy using traditional MRI, which are routinely acquired in clinical practice. Analysis of FT power spectrum may become a new approach for advancing the evaluation and management of patients with MS and similar disorders. Further confirmation is warranted.

Quantitative validation of a nonlinear histology-MRI coregistration method using generalized Q-sampling imaging in complex human cortical white matter

Advanced diffusion MRI methods have recently been proposed for detection of pathologies such as traumatic axonal injury and chronic traumatic encephalopathy which commonly affect complex cortical brain regions. However, radiological-pathological correlations in human brain tissue that detail the relationship between the multi-component diffusion signal and underlying pathology are lacking. We present a nonlinear voxel based two dimensional coregistration method that is useful for matching diffusion signals to quantitative metrics of high resolution histological images. When validated in ex vivo human cortical tissue at a 250×250×500 μm spatial resolution, the method proved robust in correlations between generalized q-sampling imaging and histologically based white matter fiber orientations, with r=0.94 for the primary fiber direction and r=0.88 for secondary fiber direction in each voxel. Importantly, however, the correlation was substantially worse with reduced spatial resolution or with fiber orientations derived using a diffusion tensor model. Furthermore, we have detailed a quantitative histological metric of white matter fiber integrity termed power coherence capable of distinguishing architecturally complex but intact white matter from disrupted white matter regions. These methods may allow for more sensitive and specific radiological-pathological correlations of neurodegenerative diseases affecting complex gray and white matter.