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Description, Identifying blood-based physical biomarkers for detection of Alzheimer’s disease., Quantifying physical differences of protein aggregates implicated in Alzheimer’s disease (AD), in blood, could provide crucial information on disease stages. Here, red blood cells (RBCs) from 50 patients with neurocognitive complaints and 16 healthy individuals were profiled using an atomic force microscope (AFM). AFM measurements revealed patient age– and stage of neurocognitive disorder–dependent differences in size, shape, morphology, assembly, and prevalence of protein aggregates on RBCs, referred to as physical biomarkers. Crystals composed of fibrils were exclusively detected on RBCs for AD patients aged above 80 years. Fibril prevalence was negatively correlated with the cerebrospinal fluid (CSF) β-amyloid (Aβ) 42/40 ratio and was observed to be higher in the Aβ-positive patient category. Using a cutoff of ≥40% fibril prevalence, the CSF Aβ status was classified with 88% accuracy (sensitivity 100%, specificity 73%). The merits and challenges in integrating physical biomarkers in AD diagnosis are discussed.

When we grasp and lift novel objects, we rely on visual cues and sensorimotor memories to predictively scale our finger forces and exert compensatory torques according to object properties. Recently, it was shown that object appearance, previous force scaling errors, and previous torque compensation errors strongly impact our percept. However, the influence of visual geometric cues on the perception of object torques and weights in a grasp to lift task is poorly understood. Moreover, little is known about how visual cues, prior expectations, sensory feedback, and sensorimotor memories are integrated for anticipatory torque control and object perception. Here, 12 young and 12 elderly participants repeatedly grasped and lifted an object while trying to prevent object tilt. Before each trial, we randomly repositioned both the object handle, providing a geometric cue on the upcoming torque, as well as a hidden weight, adding an unforeseeable torque variation. Before lifting, subjects indicated their torque expectations, as well as reporting their experience of torque and weight after each lift. Mixed-effect multiple regression models showed that visual shape cues governed anticipatory torque compensation, whereas sensorimotor memories played less of a role. In contrast, the external torque and committed compensation errors at lift-off mainly determined how object torques and weight were perceived. The modest effect of handle position differed for torque and weight perception. Explicit torque expectations were also correlated with anticipatory torque compensation and torque perception. Our main findings generalized across both age groups. Our results suggest distinct weighting of inputs for action and perception according to reliability. Electronic supplementary material The online version of this article (10.1007/s00221-019-05711-y) contains supplementary material, which is available to authorized users.

Predicting instead of only reacting to the properties of objects we grasp is crucial to dexterous object manipulation. Although we normally plan our grasps according to well-learned associations, we rely on implicit sensorimotor memories when we learn to interact with novel or ambiguous objects. However, little is known about the influence of sensorimotor predictions on subsequent perception and action. Here, young and elderly subjects repeatedly lifted an object in which the center of mass (CoM) was randomly varied between trials straight upward with the aim of preventing object tilts. After each lift, subjects indicated the location of the perceived CoM and reported how heavy the object felt. Surprisingly, we found that sensorimotor torque memories eventually causing initial lifting errors had substantial effects on the perception of torques, weight, and the torque planning for the next lift. Whereas subjects tended to partly retain their previous erroneous sensorimotor memories (instead of solely relying on the previously encountered torque for the upcoming motor plan), they perceived encountered torques to be stronger when they erroneously predicted them. Additionally, we found that torque prediction errors, as well as the actual torques, made the object feel heavier. By contrast, perception did not influence upcoming motor control. There were no major differences observed between the age groups. The sensorimotor impact on torque perception can be explained by internal feedforward prediction highlighting task-relevant errors, while the partial retention and adaptation of sensorimotor torque memories is reconciled with the trial-to-trial learning rule for motor adaptation. NEW & NOTEWORTHY The current study is the first to demonstrate in an object manipulation task in uncertainty that errors in the sensorimotor prediction of torques influence the perception of both torques and weight, whereas sensorimotor torque memories are partly retained and partly adapted to planning errors. Our results provide novel insights into the predictive mechanisms underpinning the common everyday task of object manipulation and further support theories about the predictive modulation of perception established in other neuroscientific disciplines.

Background: Alzheimer’s disease (AD) affects several cognitive functions and causes altered motor function. Fine motor deficits during object manipulation are evident in other neurological conditions, but have not been assessed in dementia patients yet. Objective: Investigate reactive and anticipatory grip force control in response to unexpected and expected load force perturbation in AD. Methods: Reactive and anticipatory grip force was investigated using a grip-device with force sensors. In this pilot study, fifteen AD patients and fourteen healthy controls performed a catching task. They held the device with one hand while a sandbag was dropped into an attached receptacle either by the experimenter or by the participant. Results: In contrast to studies of other neurological conditions, the majority of AD patients exerted lower static grip force levels than controls. Interestingly, patients who were slow in the Luria’s three-step test produced normal grip forces. The timing and magnitude of reactive grip force control were largely preserved in patients. In contrast, timing and extent of anticipatory grip forces were impaired in patients, although anticipatory control was generally preserved. These deficits were correlated with decreasing Mini-Mental State Examination scores. Apraxia scores, assessed by pantomime of tool-use, did not correlate with performance in the catching task. Conclusion: We interpreted the decreased grip force in AD in the context of loss of strength and lethargy, typical for patients with AD. The lower static grip force during object manipulation may emerge as a potential biomarker for early stages of AD, but more studies with larger sample sizes are necessary.

One way to foster the understanding of the impressively skilled fine motor control of human object manipulation is to investigate the rules of the underlying fundamental mechanisms. Examining the action of grasping and lifting objects of various characteristics reveals that one vital step to handle objects in a skilled and fluent way is to predict their properties and plan motor actions accordingly. A large number of behavioral studies have extracted the factors on which we rely when we interact with objects in an anticipatory mode of action. At the same time, considerable effort was taken to elucidate the neuroanatomical areas and networks involved and crucial for anticipatory behavior by conducting functional imaging and stimulation studies and examining the deficits of patients with localized brain damage. This chapter gives an overview of these studies and tries to reconcile their findings, in order to provide an insight into the basic principles of anticipatory motor control and their underlying neural substrates.