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

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium : medication matters

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters.

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters.

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker