Imaging white matter diffusion changes with development and recovery from brain injury.

PURPOSE: This study reviews the application of diffusion tensor imaging (DTI) to the study of developmental and pathological changes in brain white matter. The ability to measure and monitor such changes in vivo would provide important opportunities for charting disease progression and monitoring response to therapeutic intervention. This study first reviews the use of DTI in studying normal human brain development. It goes on to illustrate how DTI has been used to provide insights into recovery from damage in selected brain disorders. CONCLUSIONS: It is concluded that potential clinical applications of DTI include: (i) monitoring pathological change, (ii) providing markers that predict recovery and allow for individual targeting of therapy, (iii) providing outcome measures, (iv) providing measures of potentially compensatory structural changes and (v) improving understanding of normal brain anatomy to aid in interpretation of the consequences of localized damage

Network analysis detects changes in the contralesional hemisphere following stroke

Changes in brain structure occur in remote regions following focal damage such as stroke. Such changes could disrupt processing of information across widely distributed brain networks. We used diffusion MRI tractography to assess connectivity between brain regions in 9 chronic stroke patients and 18 age-matched controls. We applied complex network analysis to calculate ‘communicability’, a measure of the ease with which information can travel across a network. Clustering individuals based on communicability separated patient and control groups, not only in the lesioned hemisphere but also in the contralesional hemisphere, despite the absence of gross structural pathology in the latter. In our highly selected patient group, lesions were localised to the left basal ganglia/internal capsule. We found reduced communicability in patients in regions surrounding the lesions in the affected hemisphere. In addition, communicability was reduced in homologous locations in the contralesional hemisphere for a subset of these regions. We interpret this as evidence for secondary degeneration of fibre pathways which occurs in remote regions interconnected, directly or indirectly, with the area of primary damage. We also identified regions with increased communicability in patients that could represent adaptive, plastic changes post-stroke. Network analysis provides new and powerful tools for understanding subtle changes in interactions across widely distributed brain networks following stroke

Model-free characterization of brain functional networks for motor sequence learning using fMRI.

Neuroimaging experiments have identified several brain regions that appear to play roles in motor learning. Here we apply a novel multivariate analytical approach to explore the dynamic interactions of brain activation regions as spatio-temporally coherent functional networks. We acquired BOLD fMRI signal during explicit motor sequence learning task to characterize the adaptive functional changes in the early phase of motor learning. Subjects practiced a 10-digit, visually cued, fixed motor sequence during 15 consecutive 30 s practice blocks interleaved with similarly cued random sequence blocks. Tensor Independent Component Analysis (TICA) decomposed the data into statistically independent spatio-temporal processes. Two components were identified that represented task-related activations. The first component showed decreasing activity of a fronto-parieto-cerebellar network during task conditions. The other exclusively related to sequence learning blocks showed activation in a network including the posterior parietal and premotor cortices. Variation in expression of this component across individual subjects correlated with differences in behavior. Relative deactivations also were found in patterns similar to those described previously as "resting state" networks. Some of these deactivation components also showed task- and time-related modulations and were related to the behavioral improvement. The spatio-temporal coherence within these networks suggests that their elements are functionally integrated. Their anatomical plausibility and correlation with behavioral measures also suggest that this approach allows characterization of the interactions of functional networks relevant to the task. Particular value for multi-variant, model-free methods such as TICA lies in the potential for generating hypotheses regarding functional anatomical networks underlying specific behaviors

Age-related changes in grey and white matter structure throughout adulthood.

Normal ageing is associated with gradual brain atrophy. Determining spatial and temporal patterns of change can help shed light on underlying mechanisms. Neuroimaging provides various measures of brain structure that can be used to assess such age-related change but studies to date have typically considered single imaging measures. Although there is consensus on the notion that brain structure deteriorates with age, evidence on the precise time course and spatial distribution of changes is mixed. We assessed grey matter (GM) and white matter (WM) structure in a group of 66 adults aged between 23 and 81. Multimodal imaging measures included voxel-based morphometry (VBM)-style analysis of GM and WM volume and diffusion tensor imaging (DTI) metrics of WM microstructure. We found widespread reductions in GM volume from middle age onwards but earlier reductions in GM were detected in frontal cortex. Widespread age-related deterioration in WM microstructure was detected from young adulthood onwards. WM decline was detected earlier and more sensitively using DTI-based measures of microstructure than using markers of WM volume derived from conventional T1-weighted imaging

Structural and functional bases for individual differences in motor learning.

People vary in their ability to learn new motor skills. We hypothesize that between-subject variability in brain structure and function can explain differences in learning. We use brain functional and structural MRI methods to characterize such neural correlates of individual variations in motor learning. Healthy subjects applied isometric grip force of varying magnitudes with their right hands cued visually to generate smoothly-varying pressures following a regular pattern. We tested whether individual variations in motor learning were associated with anatomically colocalized variations in magnitude of functional MRI (fMRI) signal or in MRI differences related to white and grey matter microstructure. We found that individual motor learning was correlated with greater functional activation in the prefrontal, premotor, and parietal cortices, as well as in the basal ganglia and cerebellum. Structural MRI correlates were found in the premotor cortex [for fractional anisotropy (FA)] and in the cerebellum [for both grey matter density and FA]. The cerebellar microstructural differences were anatomically colocalized with fMRI correlates of learning. This study thus suggests that variations across the population in the function and structure of specific brain regions for motor control explain some of the individual differences in skill learning. This strengthens the notion that brain structure determines some limits to cognitive function even in a healthy population. Along with evidence from pathology suggesting a role for these regions in spontaneous motor recovery, our results also highlight potential targets for therapeutic interventions designed to maximize plasticity for recovery of similar visuomotor skills after brain injury

Cognitive context determines dorsal premotor cortical activity during hand movement in patients after stroke.

BACKGROUND AND PURPOSE: Stroke patients often have difficulties in simultaneously performing a motor and cognitive task. Functional imaging studies have shown that movement of an affected hand after stroke is associated with increased activity in multiple cortical areas, particularly in the contralesional hemisphere. We hypothesized patients for whom executing simple movements demands greater selective attention will show greater brain activity during movement. METHODS: Eight chronic stroke patients performed a behavioral interference test using a visuo-motor tracking with and without a simultaneous cognitive task. The magnitude of behavioral task decrement under cognitive motor interference (CMI) conditions was calculated for each subject. Functional MRI was used to assess brain activity in the same patients during performance of a visuo-motor tracking task alone; correlations between CMI score and movement-related brain activation were then explored. RESULTS: Movement-related activation in the dorsal precentral gyrus of the contralesional hemisphere correlated strongly and positively with CMI score (r(2) at peak voxel=0.92; P<0.05). Similar but weaker relationships were observed in the ventral precentral and middle frontal gyrus. There was no independent relationship between hand motor impairment and CMI. CONCLUSIONS: Results suggest that variations in the degree to which a cognitive task interferes with performance of a concurrent motor task explains a substantial proportion of the variations in movement-related brain activity in patients after stroke. The results emphasize the importance of considering cognitive context when interpreting brain activity patterns and provide a rationale for further evaluation of integrated cognitive and movement interventions for rehabilitation in stroke

Structural and functional bases for individual differences in motor learning.

People vary in their ability to learn new motor skills. We hypothesize that between-subject variability in brain structure and function can explain differences in learning. We use brain functional and structural MRI methods to characterize such neural correlates of individual variations in motor learning. Healthy subjects applied isometric grip force of varying magnitudes with their right hands cued visually to generate smoothly-varying pressures following a regular pattern. We tested whether individual variations in motor learning were associated with anatomically colocalized variations in magnitude of functional MRI (fMRI) signal or in MRI differences related to white and grey matter microstructure. We found that individual motor learning was correlated with greater functional activation in the prefrontal, premotor, and parietal cortices, as well as in the basal ganglia and cerebellum. Structural MRI correlates were found in the premotor cortex [for fractional anisotropy (FA)] and in the cerebellum [for both grey matter density and FA]. The cerebellar microstructural differences were anatomically colocalized with fMRI correlates of learning. This study thus suggests that variations across the population in the function and structure of specific brain regions for motor control explain some of the individual differences in skill learning. This strengthens the notion that brain structure determines some limits to cognitive function even in a healthy population. Along with evidence from pathology suggesting a role for these regions in spontaneous motor recovery, our results also highlight potential targets for therapeutic interventions designed to maximize plasticity for recovery of similar visuomotor skills after brain injury