Discriminatory Analysis of Biochip-Derived Protein Patterns in CSF and Plasma in Neurodegenerative Diseases

The role of biomarkers in neurodegenerative diseases has been emphasized by recent research. Future clinical demands for identifying diseases at an early stage may render them essential. The aim of this pilot study was to test the analytical performance of two multiplex assays of cerebral markers on a well-defined clinical material consisting of patients with various neurodegenerative diseases. We measured 10 analytes in plasma and cerebrospinal fluid (CSF) from 60 patients suffering from Alzheimer's disease (AD), vascular dementia, frontotemporal dementia, dementia with Lewy bodies, or mild cognitive impairment, as well as 20 cognitively healthy controls. We used the Randox biochip-based Evidence Investigator™ system to measure the analytes. We found it possible to measure most analytes in both plasma and CSF, and there were some interesting differences between the diagnostic groups, although with large overlaps. CSF heart-type fatty acid-binding protein was increased in AD. Glial fibrillary acidic protein and neutrophil gelatinase-associated lipocalin in CSF and D-dimer in plasma were elevated in patients with cerebrovascular disease. A multivariate statistical analysis revealed that the pattern of analytes could help to differentiate the conditions, although more studies are required to verify this

Finding Your Way from the Bed to the Kitchen: Re-enacting and Re-combining Sensorimotor Episodes Learned from Human Demonstration

Several simulation theories have been proposed as an explanation for how humans and other agents internalize an inner world that allows them to simulate interactions with the external real world -- prospectively and retrospectively. Such internal simulation of interaction with the environment has been argued to be a key mechanism behind mentalizing and planning. In the present work, we study internal simulations in a robot acting in a simulated human environment. A model of sensory-motor interactions with the environment is generated from human demonstrations, and tested on a Robosoft Kompai robot. The model is used as a controller for the robot, reproducing the demonstrated behavior. Information from several different demonstrations is mixed, allowing the robot to produce novel paths through the environment, towards a goal specified by top-down contextual information. The robot model is also used in a covert mode, where actions are inhibited and perceptions are generated by a forward model. As a result, the robot generates an internal simulation of the sensory-motor interactions with the environment. Similar to the overt mode, the model is able to reproduce the demonstrated behavior as internal simulations. When experiences from several demonstrations are combined with a top-down goal signal, the system produces internal simulations of novel paths through the environment. These results can be understood as the robot imagining an inner world generated from previous experience, allowing it to try out different possible futures without executing actions overtly. We found that the success rate in terms of reaching the specified goal was higher during internal simulation, compared to overt action. These results are linked to a reduction in prediction errors generated during covert action. Despite the fact that the model is quite successful in terms of generating covert behavior towards specified goals, internal simulations display different temporal distributions compared to their overt counterparts. Links to human cognition and specifically mental imagery are discussed