Multimodal Neuroimaging in Alzheimer's Disease: Early Diagnosis, Physiopathological Mechanisms, and Impact of Lifestyle

Abstract

Over the last ten years, we have conducted research in Alzheimer's disease (AD) using multimodal neuroimaging techniques to improve diagnosis, further our understanding of the pathological mechanisms underlying the disease, and support the development of innovative non-pharmacological preventive strategies. Our works emphasized the interest of hippocampal subfield volumetry in early diagnosis and the need for further development in this field including optimization, standardization, and automatization of the techniques. Also, we conducted several studies in cognitively intact at-risk elderly (e.g., subjective cognitive decline patients and APOE4 carriers) to better identify biomarkers associated with increased risk of developing AD. Regarding the physiopathological mechanisms, specific multimodal neuroimaging techniques allowed us to highlight the relevance of diaschisis, the mismatch between neurodegeneration and local Aβ deposition and the regional variation in the mechanisms underlying structural or functional alterations. Further works integrating other biomarkers known to play a role in the physiopathology of AD (tau, TDP-43, inflammation, etc.) in a longitudinal design would be useful to get a comprehensive understanding of their relative role, sequence, and causal relationships. Our works also highlighted the relevance of functional connectivity in further understanding the specificity of cognitive deficits in AD and how connectivity differentially influences the propagation of the different AD biomarkers. Finally, we conducted several studies on the links between lifestyle factors and neuroimaging biomarkers to unravel mechanisms of reserve. Further efforts are needed to better understand which lifestyle factor, or combination of factors, impact on AD pathology, and when, to help translating our knowledge to training programs that might prevent or delay brain and cognitive changes leading to AD dementia.

Keywords: Aging; Alzheimer’s disease; FDG-PET; diagnosis; disconnection; lifestyle; meditation; multimodal neuroimaging; prevention; structural MRI.

Fig.1

Differential alteration of hippocampal subfields in AD versus normal aging. The hippocampal subfields can be distinguished on 3D hippocampal surface views (A), and this technique showed predominant atrophy of the CA1 subfield in AD (B). Compared to standard resolution T1 MRI (C), a high-resolution proton density MRI sequence allows to visualize the hippocampus fine anatomy (D) and thus to delineate the different hippocampal subfields (E). This approach is promising for early AD diagnosis as it allows to distinguish the effects of AD from that of other conditions such as normal aging (F).

Fig.2

Hypothetical model illustrating the links between the main AD biomarkers and the underlying neuropathological processes. In this multidetermined perspective of the disease, Aβ and tau pathologies appear as at least partly independent processes, under the influence of genetic and environmental factors, and interact to lead to AD disease. Other neuropathological processes, some of which are still unknown, are likely also involved in the physiopathology of the disease. Adapted from [21, 22].

Fig.3

Schematic representation of the graded effect of APOE4 on structural MRI (atrophy), FDG-PET (metabolism), and molecular (Aβ deposition) cortical changes. APOE4 effects clearly predominate on Aβ deposition (thick arrows), while the effects are more modest on cortical metabolism and volume (thin arrows). This figure also illustrates that APOE4 operates through both Aβ-dependent and Aβ-independent processes. From [31].

Fig.4

Comparison of the rate of atrophy over two years between cognitively intact older adults with (PIB+) and without (PIB-) Aβ deposition in their brain (as measured with PIB-PET) (Left). This study shows a greater rate of atrophy in PIB+ individuals, especially in the temporal neocortex and posterior and middle cingulate cortex (Right). From [35].

Fig.5

Regional variation in the degree of biomarkers. Some regions show predominant atrophy (left panel), others have higher hypometabolism than atrophy (middle panel), and Aβ deposition predominates in other areas (right panel). This suggests differences in the underlying pathophysiological mechanisms. From [39].

Fig.6

Distant relationships between atrophy and hypometabolism in AD. Using original methods especially developed for this purpose, we showed that hippocampal atrophy (red) was at least partly responsible for the disruption of white matter fibers (the perforant path in blue and the uncinate fasciculus in yellow) (1) itself responsible for hypometabolism in the posterior cingulate (green) and medial orbitofrontal cortex (purple and light blue) (2). From [42].

Fig.7

Relationships between medial temporal lobe atrophy common to AD and SD (center panel) and whole-brain white matter density maps in patients with AD (top panel) and semantic dementia (bottom panel). From [45].

Fig.8

Schematic theoretical representation of the differential expression of reserve mechanisms (neuroprotection versus compensation) across the spectrum from cognitively normal healthy adults to AD dementia. We propose that neuroprotection and brain maintenance predominates in healthy elderly while compensation processes predominate as AD progresses to dementia (probably up to a certain stage where compensation is not possible anymore). [54, 55].