Earliest Amyloid and Tau Deposition Modulate the Influence of Limbic Networks During Closed-loop Hippocampal Downregulation

Stavros Skouras; Jordi Torner; Patrik Andersson; Yury Koush; Carles Falcon; Carolina Minguillon; Karine Fauria; Francesc Alpiste; Kaj Blenow; Henrik Zetterberg; Juan D. Gispert; José L. Molinuevo

Disclosures

Brain. 2020;143(3):976-992. 

In This Article

Abstract and Introduction

Abstract

Research into hippocampal self-regulation abilities may help determine the clinical significance of hippocampal hyperactivity throughout the pathophysiological continuum of Alzheimer's disease. In this study, we aimed to identify the effects of amyloid-β peptide 42 (amyloid-β42) and phosphorylated tau on the patterns of functional connectomics involved in hippocampal downregulation. We identified 48 cognitively unimpaired participants (22 with elevated CSF amyloid-β peptide 42 levels, 15 with elevated CSF phosphorylated tau levels, mean age of 62.705 ± 4.628 years), from the population-based 'Alzheimer's and Families' study, with baseline MRI, CSF biomarkers, APOE genotyping and neuropsychological evaluation. We developed a closed-loop, real-time functional MRI neurofeedback task with virtual reality and tailored it for training downregulation of hippocampal subfield cornu ammonis 1 (CA1). Neurofeedback performance score, cognitive reserve score, hippocampal volume, number of apolipoprotein ε4 alleles and sex were controlled for as confounds in all cross-sectional analyses. First, using voxel-wise multiple regression analysis and controlling for CSF biomarkers, we identified the effect of healthy ageing on eigenvector centrality, a measure of each voxel's overall influence based on iterative whole-brain connectomics, during hippocampal CA1 downregulation. Then, controlling for age, we identified the effects of abnormal CSF amyloid-β42 and phosphorylated tau levels on eigenvector centrality during hippocampal CA1 downregulation. Across subjects, our main findings during hippocampal downregulation were: (i) in the absence of abnormal biomarkers, age correlated with eigenvector centrality negatively in the insula and midcingulate cortex, and positively in the inferior temporal gyrus; (ii) abnormal CSF amyloid-β42 (<1098) correlated negatively with eigenvector centrality in the anterior cingulate cortex and primary motor cortex; and (iii) abnormal CSF phosphorylated tau levels (>19.2) correlated with eigenvector centrality positively in the ventral striatum, anterior cingulate and somatosensory cortex, and negatively in the precuneus and orbitofrontal cortex. During resting state functional MRI, similar eigenvector centrality patterns in the cingulate had previously been associated to CSF biomarkers in mild cognitive impairment and dementia patients. Using the developed closed-loop paradigm, we observed such patterns, which are characteristic of advanced disease stages, during a much earlier presymptomatic phase. In the absence of CSF biomarkers, our non-invasive, interactive, adaptive and gamified neuroimaging procedure may provide important information for clinical prognosis and monitoring of therapeutic efficacy. We have released the developed paradigm and analysis pipeline as open-source software to facilitate replication studies.

Introduction

Alzheimer's disease poses a global threat to millions of lives and the sustainability of public healthcare (Prince et al., 2015). Recent progress is shifting the mainstream dual clinic-pathological concept of Alzheimer's disease towards a pathophysiological 'continuum', with progression being monitored in vivo through the study of CSF and PET biomarkers (Jack et al., 2018). The study of the brain alterations associated with the presence of abnormal levels of biomarkers in unimpaired individuals may shed light on factors and mechanisms associated with cerebral resilience or vulnerability to early Alzheimer's disease pathology.

According to the 'compensation-related utilization of neural circuits hypothesis' (CRUNCH; Reuter-Lorenz and Cappell, 2008) and the 'network-based degeneration' theory (Seeley et al., 2009), functional alterations such as hippocampal hyperactivity during the preclinical stage are likely to be initially compensated by specific changes in functional connectomics (Jacobs et al., 2013). In network science, eigenvector centrality (EC) is one of the most advanced connectomic metrics (Bonacich, 1972; Langville and Meyer, 2006) that is designed to reflect the influence of each node on the overall connectomic patterns of a network (Borgatti, 2005; Lohmann et al., 2010; Wink et al., 2012). For example, when applied to social interactions, EC can determine the members of a social network exerting the most influence (Bonacich, 1972) and EC is also the metric subserving the most successful internet search engine (Langville and Meyer, 2006). In functional neuroimaging, eigenvector centrality mapping (ECM) is an assumption-free, data-driven procedure that can be performed in high image resolution, for each participant, using the time series from every individual voxel while accounting for global brain patterns of functional connectivity. ECM derives estimates of relative influence on whole brain connectomics, per voxel or cluster of voxels (Lohmann et al., 2010; Wink et al., 2012). Importantly, EC values can be processed to meet parametric assumptions, enabling group-level regression with biomarker covariates to be performed. These features make ECM particularly promising for the investigation of changes along disease continuums such as the pathophysiological continuum of Alzheimer's disease (Skouras et al., 2019a).

Previous studies have shown that EC reveals similar patterns to fluorodeoxyglucose (FDG)-PET when comparing patients with Alzheimer's disease to healthy controls (Adriaanse et al., 2016), and that patients with Alzheimer's disease present significant differences in EC, in the anterior cingulate cortex (ACC), paracingulate gyrus, cuneus and occipital cortex (Binnewijzend et al., 2014). An independent study found that increased EC in the cingulate cortex and thalamus is related to compensatory mechanisms across the pathophysiological continuum of Alzheimer's disease, and evidenced a relation of these EC patterns to reduced functional connectivity between the midcingulate cortex and the left hippocampus during the preclinical phase of Alzheimer's disease (Skouras et al., 2019a). The latter finding suggested that EC changes may comprise a sensitive indicator of preclinical changes in functional connectomics related to hippocampal function. However, all previous investigations of EC in Alzheimer's disease have been limited to task-free (i.e. resting state) functional MRI, even though EC is particularly suited for the explorative investigation of differences in task-related connectivity with functional MRI designs featuring long, continuous experimental conditions (Koelsch and Skouras, 2014; Alnæs et al., 2015). Recent evidence suggests that functional tasks that engage specific brain regions affected by a disease lead to stronger effects and consequently to higher discriminative power between patients and controls (Finn et al., 2017; Greene et al., 2018). In the present study, we aimed to investigate preclinical differences of EC related to hippocampal function, by harnessing the sensitivity of CSF biomarkers.

Across Alzheimer's disease patient studies and healthy memory studies, the hippocampus has been established to be the single brain region with the most important role (Schwindt and Black, 2009; Kim, 2011). Hippocampal subfield CA1 in particular, appears to be a region that is integral to contextual episodic memory (Mizumori et al., 1999; Leutgeb et al., 2004; Vazdarjanova and Guzowski, 2004; Penner and Mizumori, 2012). CA1 presents atrophy linked to Alzheimer's disease pathology but no volume loss related to normal ageing (Wilson et al., 2004; Frisoni et al., 2008; Yushkevich et al., 2015). Here, we focus on hippocampal subfield CA1 because we reasoned that if preclinical alterations in brain function indeed occur prior to neurodegeneration as has been suggested (Pihlajamäki and Sperling, 2008; Teipel et al., 2015), it is probable that they are most prominent in areas that later undergo atrophy due to Alzheimer's disease. Hippocampal hyperactivity appears to precede amyloid accumulation (Leal et al., 2017), which is accepted as the earliest sign of preclinical Alzheimer's disease (Jack et al., 2018). Hippocampal hyperactivity also appears to be more prominent in subjects at genetic risk for Alzheimer's disease (Tran et al., 2017). The lack of concurrent neurodegeneration or decline in cognitive performance (Leal et al., 2017; Tran et al., 2017) implies: (i) that hippocampal hyperactivity is the result of compromised neural efficiency, as defined in the context of neural reserve (Barulli and Stern, 2013; Stern et al., 2018); and (ii) a potential link between hippocampal hyperactivity and compromised hippocampal downregulation abilities. In this context, developing a standard framework for the investigation of the ability to downregulate hippocampal activation may bring us a step closer to detecting Alzheimer's disease-specific signs earlier along the pathophysiological continuum of Alzheimer's disease.

Real-time neurofeedback enables focusing investigations on specific brain regions and their self-regulation during interactive functional tasks (Sitaram et al., 2017). Neurofeedback with real-time functional MRI (rt-fMRI) is particularly suited to study hippocampal downregulation due to the spatiotemporal resolution of functional MRI and the deep-brain location of the hippocampus. In a previous rt-fMRI neurofeedback study of healthy young adults, memory performance correlated with downregulation, but not upregulation, of the parahippocampal formation (Weiskopf et al., 2004). Moreover, a recent study has demonstrated that rt-fMRI neurofeedback is suitable for the downregulation of deep-brain structures (e.g. the amygdala that is adjacent to the hippocampus), as well as a link between amygdala downregulation and clinical symptoms (Nicholson et al., 2017). In addition, neurofeedback tasks can be combined with virtual reality (VR). VR has shown promise for the development of immersive experimental tasks with increased ecological validity (Krokos et al., 2018), which have revealed significant insights regarding hippocampal function (Duarte et al., 2014; Igloi et al., 2014; Dimsdale-Zucker et al., 2018). Furthermore, VR promotes participants' experimental compliance, through perceptual immersion in engaging tasks (Chirico et al., 2017) that in turn can result in higher engagement (Torner et al., 2019) and improved performance (Krokos et al., 2018). The increased engagement and compliance afforded by VR tasks, facilitates focused participation in long functional tasks. Combined with closed-loop neurofeedback paradigms, long tasks enable the acquisition of continuous whole-brain datasets that are optimal for the reliable estimation of functional connectomics (Gonzalez-Castillo et al., 2014; Finn et al., 2015), including EC.

Here, we fused state-of-the-art, disruptive technologies (i.e. fully automated electrochemiluminescence immunoassay, real-time functional neuroimaging and VR), aiming to create an interactive and entertaining task to investigate the neural correlates of hippocampal downregulation, associated with core Alzheimer's disease CSF biomarkers and healthy ageing, in a sample of cognitively unimpaired participants at risk for Alzheimer's disease. We hypothesized that: (i) to downregulate hippocampal activation, participants with hippocampal hyperactivity engage their brain differently or more strongly than healthy agers and that such differences can be captured by EC during hippocampal downregulation; and (ii) we may identify patterns of functional connectomics during hippocampal downregulation that resemble those found during resting state at advanced Alzheimer's disease stages. Specifically, we expected to find measurable differences of EC in the occipital cortex (Binnewijzend et al., 2014), the inferior parietal lobule, the thalamus and the cingulate cortex (Binnewijzend et al., 2014; Skouras et al., 2019a).

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