Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

Abstract

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

Keywords: Alzheimer's disease; The Virtual Brain; brain simulation; connectomics; multi-scale brain modeling.

Figure 1

Flowchart for the structure of this article.

Figure 2

Basic epidemiology of different types of dementia. Data and p-values from Robinson et al. (2018a). Shown is an elderly cohort (n = 185) with a mean age of 97.7 years in an autopsy study. On the left, we see the prevalence of the cognitive states within this cohort at the time of death. More than half of the people suffered from dementia in this age group, while a quarter suffered from mild cognitive impairment (MCI), and another quarter had no cognitive disturbances. On the right, the clinical diagnosis (ante mortem) for the subpopulation that suffered from dementia is shown. Alzheimer's Disease (AD) is the most prevalent form of dementia; however, mixed forms and other primary neurodegenerative dementias as synucleinopathies or frontotemporal lobar degeneration (FTLD) spectrum also play a role as well as vascular dementia (VD). In the post mortem analysis, the full cohort showed at least partial AD-related pathologic changes: 100% had neurofibrillary tangles of at least Braak stage I, and 63% had neuritic plaques. The mean Braak stage was in the dementia group 4.1, in the non-dementia group 3.2 (p < 0.001). However, the dementia group also showed a significant higher Lewy-body pathology (p = 0.018) and transactive response DNA-binding protein 43 kDa (TDP-43) pathology (p < 0.001) as well as a higher rate of definitive cerebrovascular disease (p = 0.016). These findings indicate that in particular in the “super old,” different neuropathologic changes are probably concomitant and contribute to the development of cognitive decline in dementia—in contrast to the concept of “pure” AD as an isolated neurodegenerative disease.

Figure 3

Mind map of the dementia syndrome and its differential diagnoses. The possible etiologies are widely spread across cerebral and systemic diseases. It is important to mention that Alzheimer's Disease (AD) is the most common form of dementia, but AD is not trivial to diagnose, in particular, if it requires to forgo some invasive tests in the elderly. However, the exact diagnosis is of enormous relevance because some possible causes of dementia are curable, such as normal pressure hydrocephalus, metabolic disorders, and immunologic or infectious causes. In the clinic, most patients are diagnosed with AD, vascular dementia, Lewy-body dementia, frontotemporal dementia, or a mixed form thereof (Figure 2). None of the primary neurodegenerative diseases can be treated in a causal and disease-modifying way, besides the treatment of vascular dementia with general atherosclerosis therapy. Their leading proteinopathy sorts the neurodegenerative diseases—caused by Abeta, Tau, prion protein, transactive response DNA binding protein 43 kDa (TDP-43), and alpha-synuclein (Wallesch and Förstl, 2012). Protein images modified from http://www.ebi.ac.uk/. FTD-TDP, frontotemporal degeneration caused by TDP-43; PPA, primary progressive aphasia; FTD-ALS, frontotemporal degeneration with amyotrophic lateral sclerosis; LATE, limbic-predominant age-related TDP-43 encephalopathy; CJD, Creutzfeldt-Jakob's disease; GSS, Gerstmann-Sträußler-Scheinker (syndrome); CAA, cerebral amyloid angiopathy; PCA, posterior cortical atrophy; M. Pick, Pick's Disease.

Figure 4

Visual representation of Abeta and Tau stages according to Braak and Braak (1991, 1997), and Braak et al. (2006). The darker color indicates a higher load of this protein in the respective brain area. The regions are listed in Tables 3, 4.

Figure 5

Overview of contributing factors in AD and potential intervention strategies. Shown are only the most important factors, which are also described in more detail in this article's main text. In the upper left corner, we see the neurovascular system. Both characteristics of blood vessels (e.g., atherosclerosis and endothelial dysfunction) (Love and Miners, 2016), as well as aspects of the blood-brain barrier (Sweeney et al., 2018), play a role in AD. A particular aspect here is the role of neural immunity, both with the brain-own microglia cells and the effect of systemic immune cells, e.g., mediated by antibodies (Heneka et al., 2015a,c). On the upper right corner, we see an illustration of the multiscale network structure of the brain. Stimulation approaches as deep brain stimulation (DBS), transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS) act on the larger scale of a network-level; nevertheless, the actual changes happen on the level of synapses. Also, transmitter interventions develop their effects mainly at the micro-scale of synapses. In the lower right corner, basic molecular pathways in the extra- and intracellular space of a neuron are shown. We focused here on the processing of the two hallmark proteins Abeta and Tau, as well as the Notch-1 pathway, which is involved in memory (Marathe and Alberi, 2015) and plasticity (Brai et al., 2015). We illustrate the APP procession by the amyloidogenic or non-amyloidogenic way and its interaction with Notch-1 processing and, second, in the axon, the hyperphosphorylation and aggregation of Tau. A more detailed description of the named treatment strategies presently under development is provided in Table 1. NGF, nerve growth factor; Abeta, amyloid-beta; p-tau, phosphorylized Tau protein; APP, amyloid precursor protein; APPα, APP in alpha-helix configuration; NECD, Notch extracellular domain; NICD, Notch intracellular domain.

Figure 6

Neurodegeneration in Alzheimer's Disease (AD) from a network perspective. In this schematic example network, the red links (edges) are being weakened and progressively disconnected by AD. Preferentially, edges attached to nodes with high degree (hubs) are being targeted (here node A) (Stam et al., ; Lo et al., ; Yan et al., 2018). Besides, lower clustering in AD has repeatedly been observed (Brier et al., ; Minati et al., ; Pereira et al., ; Dai et al., 2019), i.e., links involved in triangles are broken off (here, e.g., the link between nodes B and C forming the triangle A-B-C). These two “attacks” of AD on the network lead not only to a lower clustering coefficient but also evoke a lower efficiency, defined here as the inverse of the global path length. This lower efficiency is demonstrated in the example network by the shortest path length between the blue nodes D and E before and after the deletion of the red links (before: 4 links, after: 6 links).

Figure 7

Potential applications of The Virtual Brain in the investigation of Alzheimer's Disease (AD). As we outline in this article, computational modeling provides a powerful tool to link empirical findings from different scales and disciplines to new insights for improved diagnostics and treatments. PET, positron emission tomography; DBS, deep brain stimulation; tDCS, transcranial direct current stimulation; TMS, transcranial magnetic stimulation.