A six-month longitudinal evaluation significantly improves accuracy of predicting incipient Alzheimer's disease in mild cognitive impairment.

RATIONALE AND OBJECTIVES: Early prediction of incipient Alzheimer's disease (AD) dementia in individuals with mild cognitive impairment (MCI) is important for timely therapeutic intervention and identifying participants for clinical trials at greater risk of developing AD. Methods to predict incipient AD in MCI have mostly utilized cross-sectional data. Longitudinal data enables estimation of the rate of change of variables, which along with the variable levels have been shown to improve prediction power. While some efforts have already been made in this direction, all previous longitudinal studies have been based on observation periods longer than one year, hence limiting their practical utility. It remains to be seen if follow-up evaluations within shorter intervals can significantly improve the accuracy of prediction in this problem. Our aim was to determine the added value of incorporating 6-month longitudinal data for predicting progression from MCI to AD. MATERIALS AND METHODS: Using 6-months longitudinal data from 247 participants with MCI, we trained two Random Forest classifiers to distinguish between progressive MCI (n=162) and stable MCI (n=85) cases. These models utilized structural MRI, neurocognitive assessments, and demographic information. The first model (cross-sectional) only used baseline data. The second model (longitudinal) used data from both baseline and a 6-month follow-up evaluation allowing the model to additionally incorporate biomarkers' rate of change. RESULTS: The longitudinal model (AUC=0.87; accuracy=80.2%) performed significantly better (P<0.05) than the cross-sectional model (AUC=0.82; accuracy=71.7%). CONCLUSION: Short-term longitudinal assessments significantly enhance the performance of AD prediction models.

Performance and Function Meet Structure: A White Matter Connection Tuned for Vocal Production.

Contemporary imaging techniques have increased the potential for establishing how brain regions interact during spoken language. Some imaging methods report bilateral changes in brain activity during speech, whereas another approach finds that the relationship between individual variability in speech measures and individual variability in brain activity more closely resembles clinical observations. This approach has repeatedly demonstrated that speaking rate for phonological and lexical items can be predicted by an inverse relationship between cerebral blood flow in the left inferior frontal region and the right caudate nucleus. To determine whether morphology contributes to this relationship, we examined ipsilateral and contralateral white matter connections between these structures using diffusion tensor imaging, and we further assessed possible relationships between morphology and selected acoustic measures of participants' vocal productions. The ipsilateral connections between the inferior frontal regions and the caudate nuclei had higher average fractional anisotropy and mean diffusivity values than the contralateral connections. Neither contralateral connection between inferior frontal and caudate regions showed a significant advantage on any of the average morphology measures. However, individual differences in white matter morphology were significantly correlated with individual differences in vocal amplitude and frequency stability in the left frontal-right caudate connection. This cortical-striatal connection may be "tuned" for a role in the coordination of cortical and subcortical activity during speech. The structure-function relationship in this cortical-subcortical pathway supports the previous observation of a predictive pattern of cerebral blood flow during speech and may reflect a mechanism that ensures left-hemisphere control of the vocal expression of propositional language.