Dopaminergic denervation is not necessary to induce gait disorders in atypical parkinsonian syndrome

Parkinsonian gait in aging: A feature of Alzheimer's pathology?

INTRODUCTION

Central neurological gait abnormalities (CNGA; i.e. frontal or parkinsonian) are frequently associated with neurodegenerative conditions in older adults, but their pathophysiological substrates remain poorly described. This cross-sectional study aims to assess the association between cerebrospinal fluid (CSF) Alzheimer's biomarkers and CNGA.

METHODS

CSF biomarkers (phosphor-tau, total tau and Aβ1-42) were measured in 52 patients with CNGA (77.33 ± 6.09 years; 28.8% female). Gait phenotypes were evaluated by two diagnosis-blinded assessors and classified as frontal gait, parkinsonian gait or other gait abnormalities.

RESULTS

Parkinsonian gait was significantly associated with a decreased CSF Aβ42 even after adjusting on age, gender, comorbidities and white matter changes (β: -0.32; 95% CI: [-340.6; -22.9]; p value: 0.026). Phosphor-tau and total tau were not associated with any other CNGA (i.e. frontal gait and other gait abnormalities).

DISCUSSION

Parkinsonian gait represents a gait phenotype of Alzheimer's pathology in patients with CNGA.

Brain Structure Covariance Associated With Gait Control in Aging

BACKGROUND

Structural and functional brain imaging methods have identified age-related changes in brain structures involved in gait control. This cross-sectional study aims to investigate gray matter networks associated with gait control in aging using structural covariance analysis.

METHODS

Walking speed were measured in 326 nondemented older community-dwellers (age 71.3 ± 4.5; 41.7% female) under three different walking conditions: normal walking and two challenging tasks: motor (ie, fast speed) and an attention-demanding dual task (ie, backward counting).

RESULTS

Three main individual gray matter regions were positively correlated with walking speed (ie, slower walking speed was associated with lower brain volumes): right thalamus, right caudate nucleus, and left middle frontal gyrus for normal walking, rapid walking, and dual-task walking condition, respectively. The structural covariance analysis revealed that prefrontal regions were part of the networks associated with every walking condition; the right caudate was associated specifically with the hippocampus, amygdala and insula for the rapid walking condition, and the left middle frontal gyrus with a network involving the cuneus for the dual-task condition.

CONCLUSION

Our results suggest that brain networks associated with gait control vary according to walking speed and depend on each walking condition. Gait control in aging involved a distributed network including regions for emotional control that are recruited in challenging walking conditions.

Brain imaging of locomotion in neurological conditions

Impaired locomotion is a frequent and major source of disability in patients with neurological conditions. Different neuroimaging methods have been used to understand the brain substrates of locomotion in various neurological diseases (mainly in Parkinson's disease) during actual walking, and while resting (using mental imagery of gait, or brain-behavior correlation analyses). These studies, using structural (i.e., MRI) or functional (i.e., functional MRI or functional near infra-red spectroscopy) brain imaging, electrophysiology (i.e., EEG), non-invasive brain stimulation (i.e., transcranial magnetic stimulation, or transcranial direct current stimulation) or molecular imaging methods (i.e., PET, or SPECT) reveal extended brain networks involving both grey and white matters in key cortical (i.e., prefrontal cortex) and subcortical (basal ganglia and cerebellum) regions associated with locomotion. However, the specific roles of the various pathophysiological mechanisms encountered in each neurological condition on the phenotype of gait disorders still remains unclear. After reviewing the results of individual brain imaging techniques across the common neurological conditions, such as Parkinson's disease, dementia, stroke, or multiple sclerosis, we will discuss how the development of new imaging techniques and computational analyses that integrate multivariate correlations in "large enough datasets" might help to understand how individual pathophysiological mechanisms express clinically as an abnormal gait. Finally, we will explore how these new analytic methods could drive our rehabilitative strategies.