Functional regeneration and recovery of locomotor activity in spinally transected lamprey.

Spinally transected lamprey recovery locomotor function within 3-6 weeks, and recovery is due, in part, to functional regeneration of neural pathways in the central nervous system (CNS). Our data demonstrate for the first time in the lamprey that descending axons arising from brainstem command neurons can functionally regenerate and restore locomotor initiation below a healed spinal transection site. Immediately after behavioral recovery (3-6 weeks) the locomotor pattern was incomplete but returned to normal during the remainder of the recovery period (6-40 weeks). Initially, the extent of regeneration of descending axons was limited but increased to at least 30-50 mm at recovery times of 24-40 weeks. Regenerated giant Muller axons do not contribute significantly to recovery of locomotor function; rather, regenerated axons of smaller reticulospinal neurons appear to restore locomotor initiation. The restoration of locomotor coordination across a spinal lesion is dependent on two mechanisms: regeneration of spinal coordinating neurons and mechanosensory inputs. Comparisons are made to spinal cord regeneration in other lower vertebrates and to the relative lack of CNS regeneration and behavioral recovery in higher vertebrates.     

Protein recovery from the all-agarose ProSieve gel system.

Electrophoresis in polyacrylamide gels is one of the most powerful tools used for the analysis of proteins. However, this technique is not widely used for protein purification for a variety of reasons such as the following: less than quantitative recoveries; involved, time-consuming methodologies; and impurities in the protein preparations from gel-polymerization by-products that can modify the proteins and interfere with subsequent experiments. As an alternative, we have developed a simple and quantitative recovery procedure for proteins separated by electrophoresis in the all-agarose ProSieve gel system. Using this procedure, greater than 90% of each protein examined was recovered, and these proteins were unaffected by the recovery procedure.     

Functional activation studies of word processing in the recovery from aphasia

Some reviews on theories of recovery in aphasia put an emphasis on neural network models based on empirical data from evoked-potentials in aphasia as an approach to mapping recovery of cognitive function to neural structure. We will focus here on what we call an "anatomical" approach to look at recovery in aphasia. "Anatomical" theories of recovery stated by classical aphasiologists have contributed to the understanding of language representations in the human brain. But many aspects of these theories can only be investigated by using modern techniques of lesion analysis, psychometric assessment and functional imaging. Whereas structure-function relations have been primarily established by looking for the association of deficit symptoms with certain lesions, functional activation methods offer a means to study more directly the functional anatomy of recovered or retained functions in neuropsychological patients. To falsify or build up anatomical theories of recovery we will propose a stepwise approach of inference. The methodological pitfalls of this approach will be discussed by focussing on anatomical hypotheses of semantic word comprehension and its impairment and recovery in aphasia.     

Functional connectivity metrics during stroke recovery

We explore functional connectivity in nine subjects measured with 1.5T fMRI-BOLD in a longitudinal study of recovery from unilateral stroke affecting the motor area (Small et al., 2002). We found that several measures of complexity of covariance matrices show strong correlations with behavioral measures of recovery. In Schmah et al. (2010), we applied Linear and Quadratic Discriminants (LD and QD) computed on a principal components (PC) subspace to classify the fMRI volumes into "early" and "late" sessions. We demonstrated excellent classification accuracy with QD but not LD, indicating that potentially important differences in functional connectivity exist between the early and late sessions. Motivated by Mclntosh et al. (2008), who showed that EEG brain-signal variability and behavioral performance both increased with age during development, we investigated complexity of the covariance matrix for this longitudinal stroke recovery data set. We used three complexity measures: the sphericity index described by Abdi (2010); "unsupervised dimensionality", which is the number of PCs that minimizes unsupervised generalization error of a covariance matrix (Hansen et al., 1999); and "QD dimensionality", which is the number of PCs that minimizes the classification accuracy of QD. Although these approaches measure different kinds of complexity, all showed strong correlations with one or more behavioral tests: nine-hole peg test, hand grip test and pinch test. We could not demonstrate that either sphericity or unsupervised dimensionality were significantly different for the "early" and "late" sessions using a paired Wilcoxon test. However, the amount of relative behavioral improvement was correlated with sphericity of the overall covariance matrix (pooled across all sessions), as well as with the divergence of the eigenspectra between the "early" and "late" covariance matrices. Complexity measures that use the number of PCs (which optimize QD classification or unsupervised generalization) were correlated with the behavioral performance of the final session, but not with the relative improvement. These are suggestive, but limited, results given the sample size, restricted behavioral measurements and older 1.5T BOLD data sets. Nevertheless, they indicate one potentially fruitful direction for future data-driven fMRI studies of stroke recovery in larger, better-characterized longitudinal stroke data sets recorded at higher field strength. Finally, we produced sensitivity maps (Kjems et al., 2002) corresponding to both linear and quadratic discriminants for the "early" vs. "late" classification. These maps measure the influence of each voxel on the class assignments for a given classifier. Differences between the scaled sensitivity maps for the linear and quadratic discriminants indicate brain regions involved in changes in functional connectivity. These regions are highly variable across subjects, but include the cerebellum and the motor area contralateral to the lesion.