Response programming in dementia of the Alzheimer type: A kinematic analysis

Although planning is important for the functioning of patients with dementia of the Alzheimer Type (DAT), little is known about response programming in DAT. This study used a cueing paradigm coupled with quantitative kinematic analysis to document the preparation and execution of movements made by a group of 12 DAT patients and their age and sex matched controls. Participants connected a series of targets placed upon a WACOM SD420 graphics tablet, in response to the pattern of illumination of a set of light emitting diodes (LEDs). In one condition, participants could programme the upcoming movement, whilst in another they were forced to reprogramme this movement on-line (i.e. they were not provided with advance information about the location of the upcoming target). DAT patients were found to have programming deficits, taking longer to initiate movements; particularly in the absence of cues. While problems spontaneously programming a movement might cause a greater reliance upon on-line guidance, when both groups were required to guide the movement on-line, DAT patients continued to show slower and less efficient movements implying declining sensori-motor function; these differences were not simply due to strategy or medication status. (C) 1997 Elsevier Science Ltd.

The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations

A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern Emu-wren (Stipiturus malachurus intermedius), a critically endangered Australian bird. Using diserete-time Markov,chains to describe the dynamics of a metapopulation and stochastic dynamic programming (SDP) to find optimal solutions, we evaluated the following different management decisions: enlarging existing patches, linking patches via corridors, and creating a new patch. This is the first application of SDP to optimal landscape reconstruction and one of the few times that landscape reconstruction dynamics have been integrated with population dynamics. SDP is a powerful tool that has advantages over standard Monte Carlo simulation methods because it can give the exact optimal strategy for every landscape configuration (combination of patch areas and presence of corridors) and pattern of metapopulation occupancy, as well as a trajectory of strategies. It is useful when a sequence of management actions can be performed over a given time horizon, as is the case for many endangered species recovery programs, where only fixed amounts of resources are available in each time step. However, it is generally limited by computational constraints to rather small networks of patches. The model shows that optimal metapopulation, management decisions depend greatly on the current state of the metapopulation,. and there is no strategy that is universally the best. The extinction probability over 30 yr for the optimal state-dependent management actions is 50-80% better than no management, whereas the best fixed state-independent sets of strategies are only 30% better than no management. This highlights the advantages of using a decision theory tool to investigate conservation strategies for metapopulations. It is clear from these results that the sequence of management actions is critical, and this can only be effectively derived from stochastic dynamic programming. The model illustrates the underlying difficulty in determining simple rules of thumb for the sequence of management actions for a metapopulation. This use of a decision theory framework extends the capacity of population viability analysis (PVA) to manage threatened species.