Abstract
IntroductionApathy is a common yet under-recognised feature of cerebral small vessel disease (SVD), but its underlying neurobiological basis is not yet understood. We hypothesized that damage to the reward network is associated with an increase of apathy in patients with SVD. MethodsIn 114 participants with symptomatic SVD, defined as a magnetic resonance imaging confirmed lacunar stroke and confluent white matter hyperintensities, we used diffusion tensor imaging tractography to derive structural brain networks and graph theory to determine network efficiency. We determined which parts of the network correlated with apathy symptoms. We tested whether apathy was selectively associated with involvement of the reward network, compared with two “control networks” (visual and motor). ResultsApathy symptoms negatively correlated with connectivity in network clusters encompassing numerous areas of the brain. Network efficiencies within the reward network correlated negatively with apathy scores; (r = − 0.344, p < 0.001), and remained significantly correlated after co-varying for the two control networks. Of the three networks tested, only variability in the reward network independently explained variance in apathetic symptoms, whereas this was not observed for the motor or visual networks. LimitationsThe analysis refers only to cerebrum and not cerebellum. The apathy measure is derivative of depression measure. DiscussionOur results suggest that reduced neural efficiency, particularly in the reward network, is associated with increased apathy in patients with SVD. Treatments which improve connectivity in this network may improve apathy in SVD, which in turn may improve psychiatric outcome after stroke.
Highlights
Apathy is a common yet under-recognised feature of cerebral small vessel disease (SVD), but its underlying neurobiological basis is not yet understood
Our results suggest that reduced neural efficiency, in the reward network, is associated with increased apathy in patients with SVD
It results in pathological changes in the brain tissue and characteristic radiological features best detected using magnetic resonance imaging (MRI), including lacunar infarcts, T2-white matter hyperintensities (WMH), cerebral microbleeds, and more diffuse white matter damage measured using diffusion tensor imaging (DTI)
Summary
In 114 participants with symptomatic SVD, defined as a magnetic resonance imaging confirmed lacunar stroke and confluent white matter hyperintensities, we used diffusion tensor imaging tractography to derive structural brain networks and graph theory to determine network efficiency. We tested whether apathy was selectively associated with involvement of the reward network, compared with two “control networks” (visual and motor). Authors indicate that they will not make their data available to other researchers. The inclusion criteria for SCANS was a Fazekas of 2 score or more as determined by the local clinician. The Fazekas scores used in the analysis were based on these blinded assessments. Both periventricular and deep WMH were separately rated and an overall score generated which took into account both periventricular and deep WMH. Exclusion criteria were: any stroke mechanism other than SVD including intra/extra-cranial large artery stenosis >50%, cardio-embolic source, subcortical infarcts > 1.5cm in diameter as these are often embolic, or any cortical infarcts; history of major neurological or psychiatric condition excepting depression; non-fluent in English; not suitable for MRI; and unable to give informed consent
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