Efficient interhemispheric integration of neural activity between left and right primary motor cortex (M1) is critical for inter-limb motor control. We employed optogenetic stimulation to establish a ...framework for probing transcallosal M1–M1 interactions in rats. We performed optogenetic stimulation of excitatory neurons in right M1 of male Sprague-Dawley rats. We recorded the transcallosal evoked potential in contralateral left M1 via chronically implanted electrodes. Recordings were performed under anesthesia combination of dexmedetomidine and a low concentration of isoflurane. We systematically varied the stimulation intensity and duration to characterize the relationship between stimulation parameters in right M1 and the characteristics of the evoked intracortical potentials in left M1. Optogenetic stimulation of right M1 consistently evoked a transcallosal response in left M1 with a consistent negative peak (N1) that sometimes was preceded by a smaller positive peak (P1). Higher stimulation intensity or longer stimulation duration gradually increased N1 amplitude and reduced N1 variability across trials. A combination of stimulation intensities of 5–10 mW with stimulus durations of 1–10 ms were generally sufficient to elicit a robust transcallosal response in most animal, with our optic fiber setup. Optogenetically stimulated excitatory neurons in M1 can reliably evoke a transcallosal response in anesthetized rats. Characterizing the relationship between “stimulation dose” and “response magnitude” (i.e., the gain function) of transcallosal M1-to-M1 excitatory connections can be used to optimize the variables of optogenetic stimulation and ensure stimulation efficacy.
Magnetic resonance imaging (MRI) of the human brain has provided converging evidence that visual deprivation induces regional changes in white matter (WM) microstructure. It remains unclear how these ...changes modify network connections between brain regions. Here we used diffusion-weighted MRI to relate differences in microstructure and structural connectedness of WM in individuals with congenital or late-onset blindness relative to normally sighted controls. Diffusion tensor imaging (DTI) provided voxel-specific microstructural features of the tissue, while anatomical connectivity mapping (ACM) assessed the connectedness of each voxel with the rest of the brain. ACM yielded reduced anatomical connectivity in the corpus callosum in individuals with congenital but not late-onset blindness. ACM did not identify any brain region where blindness resulted in increased anatomical connectivity. DTI revealed widespread microstructural differences as indexed by a reduced regional fractional anisotropy (FA). Blind individuals showed lower FA in the primary visual and the ventral visual processing stream relative to sighted controls regardless of the blindness onset. The results show that visual deprivation shapes WM microstructure and anatomical connectivity, but these changes appear to be spatially dissociated as changes emerge in different WM tracts. They also indicate that regional differences in anatomical connectivity depend on the onset of blindness.
Non-invasive axon diameter distribution (ADD) mapping using diffusion MRI is an ill-posed problem. Current ADD mapping methods require knowledge of axon orientation before performing the acquisition. ...Instead, ActiveAx uses a 3D sampling scheme to estimate the orientation from the signal, providing orientationally invariant estimates. The mean diameter is estimated instead of the distribution for the solution to be tractable. Here, we propose an extension (ActiveAx
) that provides non-parametric and orientationally invariant estimates of the whole distribution.
The accelerated microstructure imaging with convex optimization (AMICO) framework accelerates mean diameter estimation using a linear formulation combined with Tikhonov regularization to stabilize the solution. Here, we implement a new formulation (ActiveAx
) that uses Laplacian regularization to provide robust estimates of the whole ADD.
The performance of ActiveAx
was evaluated using Monte Carlo simulations on synthetic white matter samples mimicking axon distributions reported in histological studies.
ActiveAx
provided robust ADD reconstructions when considering the isolated intra-axonal signal. However, our formulation inherited some common microstructure imaging limitations. When accounting for the extra axonal compartment, estimated ADDs showed spurious peaks and increased variability because of the difficulty of disentangling intra and extra axonal contributions.
Laplacian regularization solves the ill-posedness regarding the intra axonal compartment. ActiveAx
can potentially provide non-parametric and orientationally invariant ADDs from isolated intra-axonal signals. However, further work is required before ActiveAx
can be applied to real data containing extra-axonal contributions, as disentangling the 2 compartment appears to be an overlooked challenge that affects microstructure imaging methods in general.
Purpose
Non‐invasive axon diameter distribution (ADD) mapping using diffusion MRI is an ill‐posed problem. Current ADD mapping methods require knowledge of axon orientation before performing the ...acquisition. Instead, ActiveAx uses a 3D sampling scheme to estimate the orientation from the signal, providing orientationally invariant estimates. The mean diameter is estimated instead of the distribution for the solution to be tractable. Here, we propose an extension (ActiveAxADD) that provides non‐parametric and orientationally invariant estimates of the whole distribution.
Theory
The accelerated microstructure imaging with convex optimization (AMICO) framework accelerates mean diameter estimation using a linear formulation combined with Tikhonov regularization to stabilize the solution. Here, we implement a new formulation (ActiveAxADD) that uses Laplacian regularization to provide robust estimates of the whole ADD.
Methods
The performance of ActiveAxADD was evaluated using Monte Carlo simulations on synthetic white matter samples mimicking axon distributions reported in histological studies.
Results
ActiveAxADD provided robust ADD reconstructions when considering the isolated intra‐axonal signal. However, our formulation inherited some common microstructure imaging limitations. When accounting for the extra axonal compartment, estimated ADDs showed spurious peaks and increased variability because of the difficulty of disentangling intra and extra axonal contributions.
Conclusion
Laplacian regularization solves the ill‐posedness regarding the intra axonal compartment. ActiveAxADD can potentially provide non‐parametric and orientationally invariant ADDs from isolated intra‐axonal signals. However, further work is required before ActiveAxADD can be applied to real data containing extra‐axonal contributions, as disentangling the 2 compartment appears to be an overlooked challenge that affects microstructure imaging methods in general.
We propose a method for the segmentation of Multiple Sclerosis lesions. The method is based on probability maps derived from a K-Nearest Neighbours classification. These are used as a non parametric ...likelihood in a Bayesian formulation with a prior that assumes connectivity of neighbouring voxels. The formulation is solved using the method of Iterated Conditional Modes (ICM). The parameters of the method are found through leave-one-out cross validation on training data after which it is evaluated on previously unseen test data. The multi modal features investigated are 3 structural MRI modalities, the diffusion MRI measures of Fractional Anisotropy (FA), Mean Diffusivity (MD) and several spatial features. Results show a benefit from the inclusion of diffusion primarily to the most difficult cases. Results shows that combining probabilistic K-Nearest Neighbour with a Markov Random Field formulation leads to a slight improvement of segmentations.
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