Abstract
The disruption of brain networks is characteristic of neurodegenerative dementias. However, it is controversial whether changes in connectivity reflect only the functional anatomy of disease, with selective vulnerability of brain networks, or the specific neurophysiological consequences of different neuropathologies within brain networks. We proposed that the oscillatory dynamics of cortical circuits reflect the tuning of local neural interactions, such that different pathologies are selective in their impact on the frequency spectrum of oscillations, whereas clinical syndromes reflect the anatomical distribution of pathology and physiological change. To test this hypothesis, we used magnetoencephalography from five patient groups, representing dissociated pathological subtypes and distributions across frontal, parietal and temporal lobes: amnestic Alzheimer's disease, posterior cortical atrophy, and three syndromes associated with frontotemporal lobar degeneration. We measured effective connectivity with graph theory-based measures of local efficiency, using partial directed coherence between sensors. As expected, each disease caused large-scale changes of neurophysiological brain networks, with reductions in local efficiency compared to controls. Critically however, the frequency range of altered connectivity was consistent across clinical syndromes that shared a likely underlying pathology, whilst the localization of changes differed between clinical syndromes. Multivariate pattern analysis of the frequency-specific topographies of local efficiency separated the disorders from each other and from controls (accuracy 62% to 100%, according to the groups' differences in likely pathology and clinical syndrome). The data indicate that magnetoencephalography has the potential to reveal specific changes in neurophysiology resulting from neurodegenerative disease. Our findings confirm that while clinical syndromes have characteristic anatomical patterns of abnormal connectivity that may be identified with other methods like structural brain imaging, the different mechanisms of neurodegeneration also cause characteristic spectral signatures of physiological coupling that are not accessible with structural imaging nor confounded by the neurovascular signalling of functional MRI. We suggest that these spectral characteristics of altered connectivity are the result of differential disruption of neuronal microstructure and synaptic physiology by Alzheimer's disease versus frontotemporal lobar degeneration.
Highlights
The impact of neurodegeneration can be understood in terms of its effect on the structure and function of brain networks
The posterior cortical atrophy (PCA) variant of Alzheimer’s disease caused a similar reduction in gamma band local efficiency, but in a different distribution that lay over more posterior regions (Fig. 2B)
We have identified distinctive neurophysiological signatures associated with five neurodegenerative disorders resulting from Alzheimer’s disease and frontotemporal lobar degeneration (FTLD)
Summary
The impact of neurodegeneration can be understood in terms of its effect on the structure and function of brain networks. There are structural anatomical fingerprints for Alzheimer’s disease and frontotemporal dementia, and disease-specific changes in their functional connectivity with ‘epicentres’ of disease (Seeley et al, 2009; Zhou et al, 2010; Crossley et al, 2014). The distribution of abnormal connectivity mirrors the anatomical and functional networks in health, suggesting selective vulnerability of brain networks to neuropathology (Pievani et al, 2011). Functional MRI can detect the late consequences of this cascade on connectivity (Bullmore and Sporns, 2009; Fornito et al, 2015), but it is limited by slow and indirect neurovascular signalling (Hillman, 2014; Tsvetanov et al, 2015). Magnetoencephalography (MEG) and EEG offer a temporal resolution that can resolve changes in neural dynamics that are indistinguishable by functional MRI, and that are independent of effects of age or medication on the neurovascular response (de Haan et al, 2012a; Hughes et al, 2013; Tsvetanov et al, 2015)
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