The “lifestyle” research group aims at understanding the brain mechanisms underlying the effects of lifestyle factors from normal aging to Alzheimer’s disease dementia.
To this aim we use multimodal neuroimaging – amyloid and FDG-PET, structural MRI, resting fMRI and task-related fMRI – and lifestyle assessments -cognitive, physical, social engagement and diet.
We are especially interested in (i) understanding the brain mechanisms of reserve across disease stages, (ii) assessing the combined/relative effects of lifestyle factors on neuroimaging measurements and in (iii) identifying critical windows of time where exposure to positive lifestyle factors may have a greater impact.
IN MORE DETAILS
There is growing evidence in the literature that we could modify the course of the disease, and brain and mental health in general, by modifying our lifestyle. We showed for example that higher education was able to counteract the effects of APOE ε4 on metabolism independently of Aβ deposition, as increased metabolism with education was found in APOE ε4 carriers in critical regions that sustain episodic memory performance (Arenaza-Urquijo et al., Neurology, 2015). In another study, we assessed the links between lifestyle factors and different neuroimaging measures including markers of AD. Thus, assessing the relationships between years of education and brain volume, metabolism and connectivity, we showed that, in healthy elderly with no evidence for Aβ deposition, there was a positive relationship between education and brain volume and metabolism, especially in the anterior cingulate cortex (Arenaza-Urquijo et al., Neuroimage, 2013). Moreover, the connectivity of this region increased with increasing years of education especially with the hippocampus and posterior cingulate cortex, two regions particularly important in AD. By contrast, in a collaborative project, we found negative relationships between education and brain metabolism and connectivity in asymptomatic older adults including individuals with Aβ deposition (Bastin et al., Neuroimage, 2012). We think that these apparently discrepant findings with positive versus negative relationships, also found in the literature, reflect the progression from neuroprotective to compensation processes over the course of the disease, which we summarized in an integrative model (Arenaza-Urquijo et al., Front. Aging Neurosci., 2015) (Figure 8). Thus, in individuals without AD lesions, education is related with increased brain performances while when AD-related pathology appears, education is related with increased resistance to brain lesions so that at the same level of cognitive impairment, more lesions will be found in those with higher education. This model was supported by a recent study where we showed that higher education was associated with lower Aβ deposition in normal older adults but with higher Aβ deposition in mild cognitive impairment (Arenaza-Urquijo et al., Neurobiol Aging., 2017). Moreover, in the same study we found increased FDG-PET uptake with education in MCI patients within the regions of higher Florbetapir-PET uptake, suggesting a compensatory increase in glucose metabolism. The findings suggest that early intellectual enrichment before the onset of dementia may be associated with protection in healthy asymptomatic elderly, and then with compensation from Aβ at the symptomatic stage.
Another relevant aspect to be further investigated in this area is the relative impact of different lifestyle factors. We started to assess this question by investigating the specific relationships between cognitive versus physical activity engagement during late-adulthood and gray matter volume (GM) in normal older adults. We showed independent relationships of the two lifestyle factors in both common and distinct brain areas, and found that the effects of late life cognitive and physical activity were independent from early cognitive engagement as reflected by years of education (Brain Imaging Behav., 2017). Further works are needed to understand the specific and synergic effects of different lifestyle factors, in different lifetime periods, as this information is crucial to design optimal non-pharmacological (preventive and therapeutic) intervention programs.