Structural Network Efficiency Predicts Resilience to Cognitive Decline in Elderly at Risk for Alzheimer’s Disease

 So how EXACTLY do you create this structural network efficiency? Since you gave us no answer, USELESS!

Structural Network Efficiency Predicts Resilience to Cognitive Decline in Elderly at Risk for Alzheimer’s Disease

Florian U. Fischer^1,2*, Dominik Wolf^1,2, Oliver Tüscher^1,3 and Andreas Fellgiebel^1,2 on behalf of Alzheimer’s Disease Neuroimaging Initiative^†

• ¹Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Johannes Gutenberg University Mainz, Mainz, Germany
• ²Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Mainz, Germany
• ³Leibniz Institute for Resilience Research (LIR), Mainz, Germany

Introduction: 

Functional imaging studies have demonstrated the recruitment of additional neural resources as a possible mechanism to compensate for age and Alzheimer’s disease (AD)-related cerebral pathology, the efficacy of which is potentially modulated by underlying structural network connectivity. Additionally, structural network efficiency (SNE) is associated with intelligence across the lifespan, which is a known factor for resilience to cognitive decline. We hypothesized that SNE may be a surrogate of the physiological basis of resilience to cognitive decline in elderly persons without dementia and with age- and AD-related cerebral pathology. 

Methods: 

We included 85 cognitively normal elderly subjects or mild cognitive impairment (MCI) patients submitted to baseline diffusion imaging, liquor specimens, amyloid-PET and longitudinal cognitive assessments. SNE was calculated from baseline MRI scans using fiber tractography and graph theory. Mixed linear effects models were estimated to investigate the association of higher resilience to cognitive decline with higher SNE and the modulation of this association by increased cerebral amyloid, liquor tau or WMHV.  

Results: 

For the majority of cognitive outcome measures, higher SNE was associated with higher resilience to cognitive decline (p-values: 0.011–0.039). Additionally, subjects with higher SNE showed more resilience to cognitive decline at higher cerebral amyloid burden (p-values: <0.001–0.036) and lower tau levels (p-values: 0.002–0.015). 

Conclusion: 

These results suggest that SNE to some extent may quantify the physiological basis of resilience to cognitive decline most effective at the earliest stages of AD, namely at increased amyloid burden and before increased tauopathy.

Introduction

In-vivo amyloid imaging has profoundly improved the diagnosis of Alzheimer’s disease (AD) at its pre-dementia stages. However, individual predictions of cognitive decline are unsatisfactory from a clinical perspective due to considerable variance (Vos et al., 2013; Insel et al., 2016; Jack et al., 2016; Donohue et al., 2017), which presumably refers to an individual’s capacity to tolerate or compensate cerebral pathology, commonly termed reserve or—more generally—resilience (Yaffe et al., 2011; Barulli and Stern, 2013; Cabeza et al., 2018; Wolf et al., 2018). The identification and quantification of an MRI-based surrogate of the physiological basis of this resilience could thus complement and significantly improve individual predictions of cognitive decline based on cerebral pathology. However, the underlying physiological basis of resilience to cognitive decline has not conclusively been identified so far.

For an extensive discussion of hypotheses, please refer to Barulli and Stern (2013) and Cabeza et al. (2018). Briefly, functional imaging studies have demonstrated that sustained cognition in aging is associated with maintained functional connectivity (Tsvetanov et al., 2016, 2018). Furthermore, in higher age and in the presence of cerebral pathology, the brain seems to recruit more neural resources for given cognitive tasks as compared to younger subjects or those with less pathology present, which may be a resilience mechanism (Sebastian et al., 2013; Reuter-Lorenz and Park, 2014; Stargardt et al., 2015; Fernández-Cabello et al., 2016).

As functional imaging is limited to the specific tasks or situations of the experiment performed, it is complementary to these experiments to investigate the brain structures underlying brain functions, whose structural organization demonstrably coincides with and possibly modulates functional connectivity (Damoiseaux, 2017). However, rather than being specific to tasks brain structure is arguably the product of a lifetime’s individual cognitive profile and thus reflects a convolute of general everyday cognition. This notion is supported by the repeated finding of the association of the brain’s structural organization and intelligence (Li et al., 2009; Fischer et al., 2014; Bathelt et al., 2018; Koenis et al., 2018), which aims to measure the underlying general factor of cognitive performance across different cognitive domains and tasks (Deary et al., 2010). Interestingly, intelligence is also a known resilience factor to cognitive decline (Schmand et al., 1997; Whalley et al., 2004; Stern, 2012). These findings led us to hypothesize that it may be worthwhile to investigate the brain’s structural connectome as a potential predictor for resilience to cognitive decline. In light of the repeatedly demonstrated associations with intelligence cited above (Li et al., 2009; Fischer et al., 2014), we chose global efficiency of the network constructed from GM segments and reconstructed WM connections, a measure that aims to model and quantify parallel information transfer capacity (Li et al., 2009), for the present study over the myriad of other available graph theory-based options.