Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker?

Abstract

Biomarkers in whichever modality are tremendously important in diagnosing of disease, tracking disease progression and clinical trials. This applies in particular for disorders with a long disease course including pre-symptomatic stages, in which only subtle signs of clinical progression can be observed. Magnetic resonance imaging (MRI) biomarkers hold particular promise due to their relative ease of use, cost-effectiveness and non-invasivity. Studies measuring resting-state functional MR connectivity have become increasingly common during recent years and are well established in neuroscience and related fields. Its increasing application does of course also include clinical settings and therein neurodegenerative diseases. In the present review, we critically summarise the state of the literature on resting-state functional connectivity as measured with functional MRI in neurodegenerative disorders. In addition to an overview of the results, we briefly outline the methods applied to the concept of resting-state functional connectivity.While there are many different neurodegenerative disorders cumulatively affecting a substantial number of patients, for most of them studies on resting-state fMRI are lacking. Plentiful amounts of papers are available for Alzheimer's disease (AD) and Parkinson's disease (PD), but only few works being available for the less common neurodegenerative diseases. This allows some conclusions on the potential of resting-state fMRI acting as a biomarker for the aforementioned two diseases, but only tentative statements for the others.For AD, the literature contains a relatively strong consensus regarding an impairment of the connectivity of the default mode network compared to healthy individuals. However, for AD there is no considerable documentation on how that alteration develops longitudinally with the progression of the disease. For PD, the available research points towards alterations of connectivity mainly in limbic and motor related regions and networks, but drawing conclusions for PD has to be done with caution due to a relative heterogeneity of the disease. For rare neurodegenerative diseases, no clear conclusions can be drawn due to the few published results. Nevertheless, summarising available data points towards characteristic connectivity alterations in Huntington's disease, frontotemporal dementia, dementia with Lewy bodies, multiple systems atrophy and the spinocerebellar ataxias.Overall at this point in time, the data on AD are most promising towards the eventual use of resting-state fMRI as an imaging biomarker, although there remain issues such as reproducibility of results and a lack of data demonstrating longitudinal changes. Improved methods providing more precise classifications as well as resting-state network changes that are sensitive to disease progression or therapeutic intervention are highly desirable, before routine clinical use could eventually become a reality.

Structural covariance across the lifespan: Brain development and aging through the lens of inter‐network relationships

“…These findings extend results in functional MRI studies on aging, which suggest that the shift from intra- to inter-network communication reflects de-specialization of function within brain areas and a resulting compensatory inter-network “cross-talk” to support regions with degraded functions (Brier et al, 2014; Chan et al, 2014). Specifically, patterns of desegregation, as measured by functional differences in modularity, show negative associations with memory in aging (Chan et al, 2014), and for some networks, have also been suggested to be potential biomarkers for neurodegenerative diseases (Hohenfeld et al, 2018). Future studies should use the present framework to examine patterns of inter-network modularity in clinical populations (see Future Directions).…”

“…Importantly, the formation of sub-groups along the continuum of modularity can either reflect organized or disorganized groupings, in which healthy organization preserves high modularity between functional, homotopic, and spatially proximal groupings , though many studies of brain modularity limit groupings to hypothesized organization strategies. Disruption of organized modularity is associated with a number of neurodegenerative diseases (Hohenfeld, Werner, & Reetz, 2018), particularly Alzheimer's Disease (Brier et al, 2014), as well as neural health in typical aging (Chan et al, 2014), FIGURE 1 Analytical pipeline. Analysis included multi-atlas segmentation of n = 5,019 images and subsequent division into 13 networks of interest, TICV-correction of gray matter volumes, growth curve fitting using a covariate-adjusted restricted cubic spline regression, and hierarchical clustering analysis to identify inter-network correlations [Color figure can be viewed at wileyonlinelibrary.com] leading some to suggest that the metric of modularity, and its underlying implication of global brain organization, is in fact a key predictive and diagnostic biomarker of neurodegenerative diseases (Brier et al, 2014;Hohenfeld et al, 2018), and brain health over aging more generally (Chan et al, 2014).…”

“…Disruption of organized modularity is associated with a number of neurodegenerative diseases (Hohenfeld, Werner, & Reetz, 2018), particularly Alzheimer's Disease (Brier et al, 2014), as well as neural health in typical aging (Chan et al, 2014), FIGURE 1 Analytical pipeline. Analysis included multi-atlas segmentation of n = 5,019 images and subsequent division into 13 networks of interest, TICV-correction of gray matter volumes, growth curve fitting using a covariate-adjusted restricted cubic spline regression, and hierarchical clustering analysis to identify inter-network correlations [Color figure can be viewed at wileyonlinelibrary.com] leading some to suggest that the metric of modularity, and its underlying implication of global brain organization, is in fact a key predictive and diagnostic biomarker of neurodegenerative diseases (Brier et al, 2014;Hohenfeld et al, 2018), and brain health over aging more generally (Chan et al, 2014). Previous work suggests that brain organization can be described through two primary metrics: (a) the parcellation characteristics of network sub-groupings (e.g., whether networks that are functionally or spatially related are correlated with each other), and (b) the lateral coordination (LC) of homotopic brain areas (e.g., how closely left and right hemisphere homologs correlate with each other).…”

MOU-EC: model-based whole-brain effective connectivity to extract biomarkers for brain dynamics from fMRI data and study distributed cognition

“…This is a problem since the parameters tuning of the model can be influenced by the specific noise of the data samples and, in turn, the results will be not generalizable to new samples -a phenomenon called overfitting. To evaluate the robustness of a classifier, a cross-validation procedure is the standard for voxel-wise studies for activation maps [109,152] and 385 for clinical applications [119,87]. This procedure consists in splitting the data samples in train and test sets that are respectively used to fit the classifier and to assess its performance, as described in Fig.…”

“…In parallel to the study of cognition, the presented framework can be applied to neuropathologies with the aim to inform clinical diagnosis [101]. The rationale is that BOLD activity specifically 710 reflects neuropathologies, even in the resting state [77,87]. If SC is increasingly used for strokes or Alzheimer disease that strongly impact the brain anatomy, fMRI may provide additional information [134,79].…”