We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions - including prefrontal, parietal and hippocampal cortices - exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancellation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures.

Thirteen autistic and 14 typically developing children (controls) imitated hand/arm gestures and performed mirror drawing; both tasks assessed ability to reorganize the relationship between spatial goals and the motor commands needed to acquire them. During imitation, children with autism were less accurate than controls in replicating hand shape, hand orientation, and number of constituent limb movements. During shape tracing, children with autism performed accurately with direct visual feedback, but when viewing their hand in a mirror, some children with autism generated fewer errors than controls whereas others performed much worse. Large mirror drawing errors correlated with hand orientation and hand shape errors in imitation, suggesting that visuospatial information processing deficits may contribute importantly to functional motor coordination deficits in autism.

Electroencephalogram coherence was measured in children with autism spectrum disorders (ASD) and control children at baseline and while watching videos of a familiar and unfamiliar person reading a story. Coherence was measured between the left and right hemispheres of the frontal, parietal, and temporal-parietal lobes (interhemispheric) and between the frontal and parietal lobes in each hemisphere (intrahemispheric). A data-reduction technique was employed to identify the frequency (alpha) that yielded significant differences in video conditions. Children with ASD displayed reduced coherence at the alpha frequency between the left and right temporal-parietal lobes in all conditions and reduced coherence at the alpha frequency between left and right frontal lobes during baseline. No group differences in intrahemispheric coherence at the alpha frequency emerged at the chosen statistical threshold. Results suggest decreased interhemispheric connectivity in frontal and temporal-parietal regions in children with ASD compared to controls.

IntroductionCurrent research suggests that the neuropathology of dementia—including brain changes leading to memory impairment and cognitive decline—is evident years before the onset of this disease. Older adults with cognitive decline have reduced functional independence and quality of life, and are at greater risk for developing dementia. Therefore, identifying biomarkers that can be easily assessed within the clinical setting and predict cognitive decline is important. Early recognition of cognitive decline could promote timely implementation of preventive strategies.MethodsWe included 89 community-dwelling adults aged 70 years and older in our study, and collected 32 measures of physical function, health status and cognitive function at baseline. We utilized an L1–L2 regularized regression model (elastic net) to identify which of the 32 baseline measures were strongly predictive of cognitive function after one year. We built three linear regression models: 1) based on baseline cognitive function, 2) based on variables consistently selected in every cross-validation loop, and 3) a full model based on all the 32 variables. Each of these models was carefully tested with nested cross-validation.ResultsOur model with the six variables consistently selected in every cross-validation loop had a mean squared prediction error of 7.47. This number was smaller than that of the full model (115.33) and the model with baseline cognitive function (7.98). Our model explained 47% of the variance in cognitive function after one year.DiscussionWe built a parsimonious model based on a selected set of six physical function and health status measures strongly predictive of cognitive function after one year. In addition to reducing the complexity of the model without changing the model significantly, our model with the top variables improved the mean prediction error and R-squared. These six physical function and health status measures can be easily implemented in a clinical setting.

• Trouble laying still limits children with autism from completing research MRI. • Scheduling conflicts limit availability of typically developing children for research MRI. • Social script app associated with MRI completion.