It appears that delayed recall with distractors may be too difficult a task to discriminate risk very well. On the other hand, for those that can perform well at level 3 relative to others with MCI, risk of conversion is much lower, Enzalutamide order as illustrated in Table ?Table55. Poset modeling also appears to be helpful in further clarifying the notion of multidomain MCI. Our analyses suggest that perceptual motor speed functioning may have a stronger link to subsequent risk of AD progression than other cognitive functions when considered in conjunction with relatively reduced function of level-2 episodic memory. On the other hand, additional impairment with cognitive flexibility does not appear to increase risk beyond that due to episodic memory impairment.
These results suggest that having relatively lower functioning across multiple functions can indeed increase risk for AD, but, that it may matter which of the functions are impaired. Because corresponding samples become smaller as specific combinations of deficits are analyzed, however, these analyses must be viewed as preliminary. A possible concern is that the ranges of performance levels for functions in the poset model are limited, particularly when a functioning level is either high or low. However, it should be kept in mind that there are also limited NP response data available, due to the time-consuming and burdensome nature of NP measurement. Hence, there are statistical limitations to the granularity of information on functioning that can be assessed accurately.
Our model was shown to be feasible, while still being able to provide discriminating information relating to AD conversion. Another limitation is the scope of inference that can be made from the ADNI sample, which was overwhelmingly Caucasian, and imbalanced towards males. Certainly, these findings should be validated in other datasets. Future directions Since the poset model allows for precise and detailed cognitive profiles, this approach can be used in conjunction with imaging and genetic studies. As an example, a cluster analysis approach has recently been applied to characterize MCI subgroups by cognitive characteristics [25]. While white matter lesion burden was found to differ by these groupings, in general, cognitive subtypes resulting from cluster analysis can at times be more difficult to interpret than GSK-3 profiles generated with poset models. Also, note the persistent heterogeneity of conversion outcomes among MCI subjects even within the cognitively homogeneous subgroups generated here. These subgroups could be an interesting selleck chem basis for functional magnetic resonance imaging (fMRI) studies of cognitive reserve, which may improve understanding of how this heterogeneity arises [26,27].