Objective To investigate whether white matter network disruption underlies the pathogenesis of apathy, but not depression, in cerebral small vessel disease (SVD). Methods Three hundred thirty-one patients with SVD from the Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort (RUN DMC) study completed measures of apathy and depression and underwent structural MRI. Streamlines reflecting underlying white matter fibers were reconstructed with diffusion tensor tractography. First, path analysis was used to determine whether network measures mediated associations between apathy and radiologic markers of SVD. Next, we examined differences in whole-brain network measures between participants with only apathy, only depression, and comorbid apathy and depression and a control group free of neuropsychiatric symptoms. Finally, we examined regional network differences associated with apathy. Results Path analysis demonstrated that network disruption mediated the relationship between apathy and SVD markers. Patients with apathy, compared to all other groups, were impaired on whole-brain measures of network density and efficiency. Regional network analyses in both the apathy subgroup and the entire sample revealed that apathy was associated with impaired connectivity in premotor and cingulate regions. Conclusions Our results suggest that apathy, but not depression, is associated with white matter tract disconnection in SVD. The subnetworks delineated suggest that apathy may be driven by damage to white matter networks underlying action initiation and effort-based decision making.

Apathy is a reduction in goal-directed activity in the cognitive, behavioural, emotional, or social domains of a patient's life, and occurs in one out of three patients after stroke. Despite this, apathy is clinically under-recognised and poorly understood. This overview provides a contemporary introduction to apathy in stroke for researchers and practitioners, covering topics including diagnosis, neurobiological mechanisms, associated consequences, and potential treatments for apathy. Apathy is often misdiagnosed as other post-stroke conditions such as depression. Accurate differential diagnosis of apathy, which manifests as reductions in initiative, and depression, which manifests as negative emotionality, is important as it informs prognosis. Research on the neurobiology of apathy suggests that there are few consistent associations between stroke lesion location and the development of apathy. These may be resolved by adopting a network neuroscience approach, which conceptualises of apathy as a pathology arising from structural or functional damage to brain networks underlying motivated behaviour. Importantly, networks can be affected by physiological changes related to stroke, including the acute infarct but also diaschisis and neurodegeneration. Aside from neurobiological changes, apathy is also associated with other negative outcome measures such as functional disability, cognitive impairment, and emotional distress, suggesting that apathy is indicative of a worse prognosis following stroke. Unfortunately, high-quality trials aimed at treating apathy are scarce. Antidepressants may have limited effects on apathy. Acetylcholine and dopamine pharmacotherapy, behavioural interventions, and transcranial magnetic stimulation may be more promising avenues for treatment.

There has been much interest in how the hippocampus codes time in support of episodic memory. Notably, while rodent hippocampal neurons, including populations in subfield CA1, have been shown to represent the passage of time in the order of seconds between events, there is limited support for a similar mechanism in humans. Specifically, there is no clear evidence that human hippocampal activity during long-term memory processing is sensitive to temporal duration information that spans seconds. To address this gap, we asked participants to first learn short event sequences that varied in image content and interval durations. During fMRI, participants then completed a recognition memory task, as well as a recall phase in which they were required to mentally replay each sequence in as much detail as possible. We found that individual sequences could be classified using activity patterns in the anterior hippocampus during recognition memory. Critically, successful classification was dependent on the conjunction of event content and temporal structure information (with unsuccessful classification of image content or interval duration alone), and further analyses suggested that the most informative voxels resided in the anterior CA1. Additionally, a classifier trained on anterior CA1 recognition data could successfully identify individual sequences from the mental replay data, suggesting that similar activity patterns supported participants' recognition and recall memory. Our findings complement recent rodent hippocampal research, and provide evidence that long-term sequence memory representations in the human hippocampus can reflect duration information in the order of seconds. hippocampus | CA1 | episodic memory | time | functional magnetic resonance imaging S pace and time are significant dimensions of our episodic memories (1). However, while much is known about the neural substrates that contribute to spatial cognition and memory (2-4), relatively little is known about how the brain, in particular the medial temporal lobe (MTL), processes temporal information in the service of episodic memory. The discovery of rodent hippocampal time cells (5-7), which fire at specific moments during the empty delay between two events, suggests a potential hippocampal mechanism for representing the temporal structure of memories (8). Crucially, however, it is unclear whether a similar hippocampal mechanism supports human memory.Because time cells in the hippocampus (HPC) of the rodent signal the passage of time in the order of seconds, one would expect that a similar neural mechanism in humans would lead to the human HPC representing temporal duration information in the order of seconds in the context of episodic memory. To our knowledge, however, there is no existing evidence for this. No work has examined human HPC involvement in memory for temporal durations in the order of seconds within the context of long-term memory. While recent human investigations have focused on HPC contributions to the representation of temporal order,...

Apathy is a reduction in motivated goal-directed behavior (GDB) that is prevalent in cerebrovascular disease, providing an important opportunity to study the mechanistic underpinnings of motivation in humans. Focal lesions, such as those seen in stroke, have been crucial in developing models of brain regions underlying motivated behavior, while studies of cerebral small vessel disease (SVD) have helped define the connections between brain regions supporting such behavior. However, current lesion-based models cannot fully explain the neurobiology of apathy in stroke and SVD. To address this, we propose a network-based model which conceptualizes apathy as the result of damage to GDB-related networks. A review of the current evidence suggests that cerebrovascular disease-related pathology can lead to network changes outside of initially damaged territories, which may propagate to regions that share structural or functional connections. The presentation and longitudinal trajectory of apathy in stroke and SVD may be the result of these network changes. Distinct subnetworks might support cognitive components of GDB, the disruption of which results in specific symptoms of apathy. This network-based model of apathy may open new approaches for investigating its underlying neurobiology, and presents novel opportunities for its diagnosis and treatment. Recent research on the fundamental neuroanatomical and neurocognitive mechanisms underlying apathy has broadened our understanding of apathy in cerebrovascular disease and across neurological disorders. Despite this, theoretical work has primarily focused on linking neurodegenerative pathology to apathy, such as in Alzheimer's disease or Parkinson's disease (e.g., Lanctôt et al., 2017; Pagonabarraga et al., 2015). This has left the mechanisms underlying apathy in cerebrovascular disease comparably under-explored. Given the high prevalence of comorbid vascular pathology in neurodegenerative

Background and Purpose— Cerebrovascular disease contributes to age-related cognitive decline, but the mechanisms underlying this phenomenon remain incompletely understood. We hypothesized that vascular risk factors would lead to cognitive impairment through the disruption of brain white matter network efficiency. Methods— Participants were 19 346 neurologically healthy individuals from UK Biobank that underwent diffusion MRI and cognitive testing (mean age=62.6). Global efficiency, a measure of network integration, was calculated from white matter networks constructed using deterministic diffusion tractography. First, we determined whether demographics (age, sex, ethnicity, socioeconomic status, and education), vascular risk factors (hypertension, hypercholesterolemia, diabetes mellitus, smoking, body mass index), and white matter hyperintensities were related to global efficiency using multivariate linear regression. Next, we used structural equation modeling to model a multiple regression. The dependent variable was a latent cognition variable using all cognitive data, while independent variables were a latent factor including all vascular risk factors (vascular burden), demographic variables, white matter hyperintensities, and global efficiency. Finally, we used mediation analysis to determine whether global efficiency explained the relationship between vascular burden and cognition. Results— Hypertension and diabetes mellitus were consistently associated with reduced global efficiency even after controlling for white matter hyperintensities. Structural equation models revealed that vascular burden was associated with cognition ( P =0.023), but not after adding global efficiency to the model ( P =0.09), suggesting a mediation effect. Mediation analysis revealed a significant indirect effect of global efficiency on cognition through vascular burden ( P <0.001), suggesting a partial mediation effect. Conclusions— Vascular burden is associated with reduced global efficiency and cognitive impairment in the general population. Network efficiency partially mediates the relationship between vascular burden and cognition. This suggests that treating specific risk factors may prevent reductions in brain network efficiency and preserve cognitive functioning in the aging population.

White matter hyperintensities (WMH) constitute the visible spectrum of cerebral small vessel disease (SVD) markers and are associated with cognitive decline, although they do not fully account for memory decline observed in individuals with SVD. We hypothesize that WMH might exert their effect on memory decline indirectly by affecting remote brain structures such as the hippocampus. We investigated the temporal interactions between WMH, hippocampal atrophy and memory decline in older adults with SVD. Five hundred and three participants of the RUNDMC study underwent neuroimaging and cognitive assessments up to 3 times over 8.7 years. We assessed WMH volumes semi‐automatically and calculated hippocampal volumes (HV) using FreeSurfer. We used linear mixed effects models and causal mediation analyses to assess both interaction and mediation effects of hippocampal atrophy in the associations between WMH and memory decline, separately for working memory (WM) and episodic memory (EM). Linear mixed effect models revealed that the interaction between WMH and hippocampal volumes explained memory decline (WM: β = .067; 95%CI[.024–0.111]; p < .01; EM: β = .061; 95%CI[.025–.098]; p < .01), with better model fit when the WMH*HV interaction term was added to the model, for both WM (likelihood ratio test, χ2[1] = 9.3, p < .01) and for EM (likelihood ratio test, χ2[1] = 10.7, p < .01). Mediation models showed that both baseline WMH volume (β = −.170; p = .001) and hippocampal atrophy (β = 0.126; p = .009) were independently related to EM decline, but the effect of baseline WMH on EM decline was not mediated by hippocampal atrophy (p value indirect effect: 0.572). Memory decline in elderly with SVD was best explained by the interaction of WMH and hippocampal volumes. The relationship between WMH and memory was not causally mediated by hippocampal atrophy, suggesting that memory decline during aging is a heterogeneous condition in which different pathologies contribute to the memory decline observed in elderly with SVD.

ObjectivesTo investigate whether longitudinal structural network efficiency is associated with cognitive decline and whether baseline network efficiency predicts mortality in cerebral small vessel disease (SVD).MethodsA prospective, single-centre cohort consisting of 277 non-demented individuals with SVD was conducted. In 2011 and 2015, all participants were scanned with MRI and underwent neuropsychological assessment. We computed network properties using graph theory from probabilistic tractography and calculated changes in psychomotor speed and overall cognitive index. Multiple linear regressions were performed, while adjusting for potential confounders. We divided the group into mild-to-moderate white matter hyperintensities (WMH) and severe WMH group based on median split on WMH volume.ResultsThe decline in global efficiency was significantly associated with a decline in psychomotor speed in the group with severe WMH (β=0.18, p=0.03) and a trend with change in cognitive index (β=0.14, p=0.068), which diminished after adjusting for imaging markers for SVD. Baseline global efficiency was associated with all-cause mortality (HR per decrease of 1 SD 0.43, 95% CI 0.23 to 0.80, p=0.008, C-statistic 0.76).ConclusionDisruption of the network efficiency, a metric assessing the efficiency of network information transfer, plays an important role in explaining cognitive decline in SVD, which was however not independent of imaging markers of SVD. Furthermore, baseline network efficiency predicts risk of mortality in SVD that may reflect the global health status of the brain in SVD. This emphasises the importance of structural network analysis in the context of SVD research and the use of network measures as surrogate markers in research setting.

While preoperative radiation therapy is advantageous for reducing rectal cancer recurrence, this study identifies advantages of postoperative radiation for reducing second primary cancers. This research will help improve recommendations for postdiagnosis surveillance in patients with rectal cancer.