Background: Apathy is a common symptom in various neuropsychiatric diseases including mild cognitive impairment (MCI) and dementia. Apathy may be associated with an increased risk of cognitive decline. The objective of this study was to investigate if apathy predicts the progression from MCI to Alzheimer’s disease (AD). Methods: The Alzheimer’s Disease Neuroimaging Initiative is a prospective multicentre cohort study. At baseline, 397 patients with MCI without major depression were included. Clinical data and the Geriatric Depression Scale at baseline were used. Apathy was defined based on the 3 apathy items of the 15-item Geriatric Depression Scale. The main outcome measure was the association of apathy with progression from MCI to AD. Results: During an average follow-up of 2.7 years (SD 1.0), 166 (41.8%) patients progressed to AD. The presence of symptoms of apathy without symptoms of depressive affect increased the risk of progression from MCI to AD (hazard ratio = 1.85, 95% CI = 1.09–3.15). Apathy in the context of symptoms of depressive affect or symptoms of depressive affect alone, without apathy, did not increase the risk of progression to AD. Conclusions: Symptoms of apathy, but not symptoms of depressive affect, increase the risk of progression from MCI to AD. Apathy in the context of symptoms of depressive affect does not increase this risk. Symptoms of apathy and depression have differential effects on cognitive decline.

IntroductionPoststroke depression (PSD) is one of the most common emotional disorders afflicting those who experience stroke. A meta-analysis has indicated that the prevalence of major or mild depression is approximately 18% (range 8%-46%), 1 with the presence of PSD being associated with increased mortality.2 Converging evidence has implicated particular neural networks in the pathophysiology of mood disorders.3 However, despite being one of the direct causes of depression, whether stroke-induced neuroanatomical deterioration actually plays an important role in the onset of PSD is still controversial. Previous neuroimaging studies have focused mainly on regional differences and severity of local brain lesions. 4 In addition, an often-cited meta-analysis that reported no clear association between PSD and any specific lesion location or hemisphere 4 has fueled intense debate. Recently, although statistical parametric mapping linked affective depression to lesions centred in the left basal ganglia and left frontal cortex, the conclusions remain in doubt because results were not corrected for multiple comparisons. 5,6 Diffusion tensor imaging (DTI) is a noninvasive technique that assesses white matter connectivity, particularly fibre density and myelination. In addition, structural brain network interactions can be quantified using brain graphs 7,8 in which neuroanatomical regions are defined as a set of nodes and DTI-derived white matter connections act as interconnecting edges.7 Using this approach, a disruption of neural topology has been shown in several brain diseases, 9-11 including those occurring in chronic stroke patients. 12 Here, we evaluated 3 markers of brain-damage severity: lesion index, fractional anisotropy (FA) reduction and brain structural networks. We hypothesized that a specific brain subnetwork is associated with PSD and that the damage to it might serve as a predictor of poststroke major depression. We constructed a depression-related subnetwork based on Background: Despite being one of the direct causes of depression, whether stroke-induced neuroanatomical deterioration actually plays an important role in the onset of poststroke depression (PSD) is controversial. We assessed the structural basis of PSD, particularly with regard to white matter connectivity. Methods: We evaluated lesion index, fractional anisotropy (FA) reduction and brain structural networks and then analyzed whole brain voxel-based lesions and FA maps. To understand brain damage in the context of brain connectivity, we used a graph theoretical approach. We selected nodes whose degree correlated with the Hamilton Rating Scale for Depression score (p < 0.05, false discovery rate-corrected), after controlling for age, sex, years of education, lesion size, Mini Mental State Examination score and National Institutes of Health Stroke Scale score. We used Poisson regression with robust standard errors to assess the contribution of the identified network toward poststroke major depression. Results: We included 116 stroke patients ...