Background/Aims: Alzheimer's disease (AD) patients have an impaired ability to quickly reweight central sensory dependence in response to unexpected body perturbations. Herein, we aim to study provoked compensatory postural adjustments (CPAs) in a conflicting sensory paradigm with unpredictable visual displacements using virtual reality goggles. Methods: We used kinematic time-frequency analyses of two frequency bands: a low-frequency band (LB; 0.3-1.5 Hz; mechanical strategy) and a high-frequency band (HB; 1.5-3.5 Hz; cognitive strategy). We enrolled 19 healthy subjects (controls) and 21 AD patients, divided according to their previous history of falls. Results: The AD faller group presented higher-power LB CPAs, reflecting their worse inherent postural stability. The AD patients had a time lag in their HB CPA reaction. Conclusion: The slower reaction by CPA in AD may be a reflection of different cognitive resources including body schema self-perception, visual motion, depth perception, or a different state of fear and/or anxiety.

Postural control is a complex dynamic mechanism, which integrates information from visual, vestibular and somatosensory systems. Idiopathic Parkinson's disease (IPD) patients are unable to produce appropriate reflexive responses to changing environmental conditions. Still, it is controversial what is due to voluntary or involuntary postural control, even less what is the effect of levodopa. We aimed to evaluate compensatory postural adjustments (CPA), with kinematic and time-frequency analyzes, and further understand the role of dopaminergic medication on these processes. 19 healthy subjects (Controls) and 15 idiopathic Parkinson's disease (IPD) patients in the OFF and ON medication states, wearing IMUs, were submitted to a virtual reality scenario with visual downward displacements on a staircase. We also hypothesized if CPA would involve mechanisms occurring in distinct time scales. We subsequently analyzed postural adjustments on two frequency bands: low components between 0.3 and 1.5 Hz (LB), and high components between 1.5 and 3.5 Hz (HB). Vertical acceleration demonstrated a greater power for discriminating IPD patients from healthy subjects. Visual perturbation significantly increased the power of the HB in all groups, being particularly more evident in the OFF state. Levodopa significantly increased their basal power taking place on the LB. However, controls and IPD patients in the ON state revealed a similar trend of the control mechanism. Results indicate an improvement in muscular stiffness provided by levodopa. They also suggest the role of different compensatory postural adjustment patterns, with LB being related to inertial properties of the oscillating mass and HB representing reactions to the ongoing visual input-changing scenario.

The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer's disease (AD). In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support Vector Machines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%). Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.

Audition, albeit less significant than vision, also plays a role in the multi-sensorial dynamic control of postural stability by the central nervous system. In everyday life, audition is likely to be a relevant factor in postural stability. This is especially relevant in AD in which, even when the peripheral sensory system is intact, the central processing is impaired and sensory dependence is re-weighted.