Abstract
The selective destruction of large-scale brain networks by pathogenic protein spread is a ubiquitous theme in neurodegenerative disease. Characterising the circuit architecture of these diseases could illuminate both their pathophysiology and the computational architecture of the cognitive processes they target. However, this is challenging using standard neuroimaging techniques. Here we addressed this issue using a novel technique—spectral dynamic causal modelling—that estimates the effective connectivity between brain regions from resting-state fMRI data. We studied patients with semantic dementia—the paradigmatic disorder of the brain system mediating world knowledge—relative to healthy older individuals. We assessed how the effective connectivity of the semantic appraisal network targeted by this disease was modulated by pathogenic protein deposition and by two key phenotypic factors, semantic impairment and behavioural disinhibition. The presence of pathogenic protein in SD weakened the normal inhibitory self-coupling of network hubs in both antero-mesial temporal lobes, with development of an abnormal excitatory fronto-temporal projection in the left cerebral hemisphere. Semantic impairment and social disinhibition were linked to a similar but more extensive profile of abnormally attenuated inhibitory self-coupling within temporal lobe regions and excitatory projections between temporal and inferior frontal regions. Our findings demonstrate that population-level dynamic causal modelling can disclose a core pathophysiological feature of proteinopathic network architecture—attenuation of inhibitory connectivity—and the key elements of distributed neuronal processing that underwrite semantic memory.
Highlights
The selective destruction of large-scale brain networks by pathogenic protein spread is a ubiquitous theme in neurodegenerative disease
Spectral dynamic causal modelling (DCM) employs generative modelling to partition the BOLD signal into three components: a neuronal state model, describing effective connectivity; a hemodynamic state model that characterises how neural activity is transformed into the BOLD signal; and observation or measurement noise[22]
The average percentage variance-explained by DCM model estimation when fitted to the observed data was 82.8% for left hemisphere regions of interest (ROI) and 81.1% for right hemisphere ROIs
Summary
The selective destruction of large-scale brain networks by pathogenic protein spread is a ubiquitous theme in neurodegenerative disease. Functional connectivity reflects statistical dependencies between spatially remote neurophysiological events[10], generally based on seed correlation or independent component analysis—and assessed post hoc using connectomic constructs from graph theory Such metrics have certain limitations: they are not grounded in neuroanatomical frameworks of inter-regional (extrinsic) connectivity, cannot identify directed connections, cannot measure within-region (intrinsic) connectivity and are potentially confounded by age-related or neurodegenerative changes in neurovascular coupling. The SD syndrome arises from pathogenic protein deposition; principally targeting one canonical, large-scale connectivity network: the ‘semantic appraisal network’ This network is anchored in anterior temporal lobe cortex and encompasses mesial, inferior and lateral temporal and inferior frontal lobe regions in both cerebral hemispheres, albeit generally with an asymmetric, left-sided e mphasis[2,4,28,29]. SD constitutes a neurodegenerative proteinopathy with a uniquely coherent clinical, neuroanatomical and molecular pathological signature: a cardinal ‘molecular nexopathy’[3,24]
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