Neural Correlates of Stepping in Healthy Elderly: Parietal and Prefrontal Cortex Activation Reflects Cognitive-Motor Interference Effects.

Gait analysis involving cognitive-motor dual task (DT) is a diagnostic tool in geriatrics. Cognitive-motor interference effects during DT, such as decreased walking speed and increased step-to-step variability, have a high predictive value for fall risk and cognitive decline. Previously we showed the feasibility of DT during functional magnetic resonance imaging (fMRI) using an MRI-compatible stepping device. Here, we improved the DT-fMRI protocol with respect to task difficulty and signal robustness, making it more suitable for individualized analysis to better understand the neuronal substrates of cognitive-motor interference effects. Thirty healthy elderly subjects performed cognitive and motor single tasks (ST; stepping or finger tapping), as well as combined cognitive-motor DT during fMRI. After whole brain group level analysis, a region-of-interest (ROI) analysis and the computation of dual task costs (DTC = activation difference ratio ST/DT) at individual level were performed. Activations in the primary (M1) and secondary motor as well as in parietal and prefrontal cortex were measured at the group level during DT. Motor areas showed decreased activation whereas parietal and prefrontal areas showed increased activation in DT vs. ST. Stepping yielded more distinctive activations in DT vs. ST than finger tapping. At the individual level, the most robust activations (based on occurrence probability and signal strength) were measured in the stepping condition, in M1, supplementary motor area (SMA) and superior parietal lobule/intraparietal sulcus (SPL/IPS). The distribution of individual DTC in SPL/IPS during stepping suggested a separation of subjects in groups with high vs. low DTC. This study proposes an improved cognitive-motor DT-fMRI protocol and a standardized analysis routine of functional neuronal markers for cognitive-motor interference at the individual level.

Imaging gait analysis: An fMRI dual task study.

INTRODUCTION: In geriatric clinical diagnostics, gait analysis with cognitive-motor dual tasking is used to predict fall risk and cognitive decline. To date, the neural correlates of cognitive-motor dual tasking processes are not fully understood. To investigate these underlying neural mechanisms, we designed an fMRI paradigm to reproduce the gait analysis. METHODS: We tested the fMRI paradigm's feasibility in a substudy with fifteen young adults and assessed 31 healthy older adults in the main study. First, gait speed and variability were quantified using the GAITRite© electronic walkway. Then, participants lying in the MRI-scanner were stepping on pedals of an MRI-compatible stepping device used to imitate gait during functional imaging. In each session, participants performed cognitive and motor single tasks as well as cognitive-motor dual tasks. RESULTS: Behavioral results showed that the parameters of both gait analyses, GAITRite© and fMRI, were significantly positively correlated. FMRI results revealed significantly reduced brain activation during dual task compared to single task conditions. Functional ROI analysis showed that activation in the superior parietal lobe (SPL) decreased less from single to dual task condition than activation in primary motor cortex and in supplementary motor areas. Moreover, SPL activation was increased during dual tasks in subjects exhibiting lower stepping speed and lower executive control. CONCLUSION: We were able to simulate walking during functional imaging with valid results that reproduce those from the GAITRite© gait analysis. On the neural level, SPL seems to play a crucial role in cognitive-motor dual tasking and to be linked to divided attention processes, particularly when motor activity is involved.

Individual differences in cognitive plasticity: an investigation of training curves in younger and older adults.

To date, cognitive intervention research has provided mixed but nevertheless promising evidence with respect to the effects of cognitive training on untrained tasks (transfer). However, the mechanisms behind learning, training effects and their predictors are not fully understood. Moreover, individual differences, which may constitute an important factor impacting training outcome, are usually neglected. We suggest investigating individual training performance across training sessions in order to gain finer-grained knowledge of training gains, on the one hand, and assessing the potential impact of predictors such as age and fluid intelligence on learning rate, on the other hand. To this aim, we propose to model individual learning curves to examine the intra-individual change in training as well as inter-individual differences in intra-individual change. We recommend introducing a latent growth curve model (LGCM) analysis, a method frequently applied to learning data but rarely used in cognitive training research. Such advanced analyses of the training phase allow identifying factors to be respected when designing effective tailor-made training interventions. To illustrate the proposed approach, a LGCM analysis using data of a 10-day working memory training study in younger and older adults is reported.