Structural integrity of the corpus callosum predicts long-term transfer of fluid intelligence-related training gains in normal aging.

Although cognitive training usually improves cognitive test performance, the capability to transfer these training gains into respective or functionally related cognitive domains varies significantly. Since most studies demonstrate rather limited transfer effects in older adults, aging might be an important factor in transfer capability differences. This study investigated the transfer capability of logical reasoning training gains to a measure of Fluid Intelligence (Gf) in relation to age, general intelligence, and brain structural integrity as measured by diffusion tensor imaging. In a group of 41 highly educated healthy elderly, 71% demonstrated successful transfer immediately after a 4-week training session (i.e. short-term transfer). In a subgroup of 22% of subjects transfer maintained over a 3-month follow-up period (i.e. long-term transfer). While short-term transfer was not related to structural integrity, long-term transfer was associated with increased structural integrity in corpus and genu of the corpus callosum. Since callosal structural integrity was also related to age (in the present and foregoing studies), previously observed associations between age and transfer might be moderated by the structural integrity. Surprisingly, age was not directly associated with transfer in this study which could be explained by the multi-dependency of the structural integrity (modulating factors beside age, e.g. genetics). In this highly educated sample, general intelligence was not related to transfer suggesting that high intelligence is not sufficient for transfer in normal aging. Further studies are needed to reveal the interaction of transfer, age, and structural integrity and delineate mechanisms of age-dependent transfer capabilities.

A methodological approach to studying resilience mechanisms: demonstration of utility in age and Alzheimer's disease-related brain pathology.

The present work aims at providing a methodological approach for the investigation of resilience factors and mechanisms in normal aging, Alzheimer's disease (AD) and other neurodegenerative disorders. By expanding and re-conceptualizing traditional regression approaches, we propose an approach that not only aims at identifying potential resilience factors but also allows for a differentiation between general and dynamic resilience factors in terms of their association with pathology. Dynamic resilience factors are characterized by an increasing relevance with increasing levels of pathology, while the relevance of general resilience factors is independent of the amount of pathology. Utility of the approach is demonstrated in age and AD-related brain pathology by investigating widely accepted resilience factors, including education and brain volume. Moreover, the approach is used to test hippocampal volume as potential resilience factor. Education and brain volume could be identified as general resilience factors against age and AD-related pathology. Beyond that, analyses highlighted that hippocampal volume may not only be disease target but also serve as a potential resilience factor in age and AD-related pathology, particularly at higher levels of tau-pathology (i.e. dynamic resilience factor). Given its unspecific and superordinate nature the approach is suitable for the investigation of a wide range of potential resilience factors in normal aging, AD and other neurodegenerative disorders. Consequently, it may find a wide application and thereby promote the comparability between studies.

Altered whole-brain white matter networks in preclinical Alzheimer's disease.

Surrogates of whole-brain white matter (WM) networks reconstructed using diffusion tensor imaging (DTI) are novel markers of structural brain connectivity. Global connectivity of networks has been found impaired in clinical Alzheimer's disease (AD) compared to cognitively healthy aging. We hypothesized that network alterations are detectable already in preclinical AD and investigated major global WM network properties. Other structural markers of neurodegeneration typically affected in prodromal AD but seeming largely unimpaired in preclinical AD were also examined. 12 cognitively healthy elderly with preclinical AD as classified by florbetapir-PET (mean age 73.4 ± 4.9) and 31 age-matched controls without cerebral amyloidosis (mean age 73.1 ± 6.7) from the ADNI were included. WM networks were reconstructed from DTI using tractography and graph theory. Indices of network capacity and the established imaging markers of neurodegeneration hippocampal volume, and cerebral glucose utilization as measured by fludeoxyglucose-PET were compared between the two groups. Additionally, we measured surrogates of global WM integrity (fractional anisotropy, mean diffusivity, volume). We found an increase of shortest path length and a decrease of global efficiency in preclinical AD. These results remained largely unchanged when controlling for WM integrity. In contrast, neither markers of neurodegeneration nor WM integrity were altered in preclinical AD subjects. Our results suggest an impairment of WM networks in preclinical AD that is detectable while other structural imaging markers do not yet indicate incipient neurodegeneration. Moreover, these findings are specific to WM networks and cannot be explained by other surrogates of global WM integrity.