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Introduction Previous studies have reported either that statins are protective against dementia or that they increase the risk of cognitive decline. We focus on the effect of statins on dementia-associated neuropsychiatric symptoms (NPS), rather than cognition. Methods 164 patients referred for cognitive assessment from the St. Michael's Hospital memory clinic were allocated into 4 groups based on statin use: 1= atorvastatin 2= rosuvastatin; 3= nonspecific cholesterol medication; 4= not taking a statin. We then compared cross sectionally their Neuropsychiatric Inventory (NPI) severity scores (total and subdomains scores). All statistical analysis was done on SPSS 25. NPI total severity score, and NPI severity subdomain scores, were tested among the statin groups using Kruskal Wallis test. Chi-squared and Fisher exact tests were used to test the association between gender and education level of statin groups. Results There was no association between gender and statin use (p= 0.537). In addition, there was no association between education level and statin use (p= 0.330). For NPI scores, there was no difference between the groups H(3)= 3.725, p=0.293. Examining the 12 NPI subdomain scores, aberrant motor behavior was significantly different among the statin groups (p= 0.005). Further analysis revealed that taking nonspecific cholesterol medication had the most frequent occurrence of aberrant motor behavior. Conclusions While there was no difference in NPI severity total score between statin groups, the aberrant motor behavior subdomain score was significantly different among the four statin groups, with unspecified statin use associated with higher rates of aberrant motor behavior. This study suggests that statin use in general and type of statin selected may contribute differentially to NPS domains. Further studies in larger cohorts adjusting for dose and comorbities are required to clarify this finding. This research was funded by: N/A

Introduction Background: Delusions occur in older patients and may be a harbinger of later cognitive impairment. Studies to date have suggested a correlation with white matter lesion burden [2] [3] but the correlation between delusions and white matter texture (WMT) is not well understood. White matter texture refers to tissue organization in normal-appearing brain matter (NABM) related to the overall diffusion capabilities of brain tissue. There is growing interest in analyzing these regions of the brain since they may contain early signs of disease [4]. Whether delusion severity is correlated with WMT is not so clear. Knowing the relationship between delusional severity and WMT may provide insight into potential mechanisms. Objective: Using neuroimaging biomarkers measured from 212 FLAIR MRI volumes, we investigated the relationship between delusional level and texture of the NABM in a cognitively mixed cohort extracted from the Alzheimer's Disease Neuroimaging (ADNI) data set. Methods Method: We extracted a cognitively diverse sample of patients with delusions based on NPI-Q scores from the ADNI data set. The delusion group was organized according to severity (1, 2, 3) based on Neuropsychiatric Inventory-Quick (NPI-Q) scores, with 1 being the lowest severity, and FLAIR MRI volumes for each subject was downloaded. There were 32 subjects (130 volumes) in group 1, 11 subjects (49 volumes) in group 2 and 9 subjects (33 volumes) in group 3 for analysis. Using validated, in-house brain extraction and intensity standardization algorithms [5][6][7], the normal-appearing brain matter (NABM) of FLAIR images were extracted. Texture analysis was performed on the NABM with a 2D texture feature that measures spatial correlation among pixel intensities [1]. This 2D spatial correlation metric was extracted on a per pixel basis, using 3mm x 3mm windows to find local, microstructural texture differences. The spatial feature maps were averaged over all slices to find a volume representation and a slight erosion was applied to remove outliers caused by the large brain-background edge. From the volume's correlation histogram, the bottom 2% and top 98% of the features are removed to further improve robustness to outliers. The mean and median of these feature maps were statistically compared between delusion groups (1, 2, 3). High spatial correlation indicates neighboring pixels have similar intensities (and is more homogeneous). Results Results: Boxplots for the NABM texture measures are shown for groups 1, 2 and 3 in Figure 1. Subjects with high delusional severity (group 3) had the most noticeable change in WMT when compared to lowest delusional severity (group 1). Group 3 had a higher spatial correlation value of 6.0947 e+03 +/- 592.6178 than group 1 with values of 5.7630 e+03 +/- 832.5040. A higher value indicates more spatial correlation among pixels (i.e. local homogeneity since closer pixels are more likely to have similar intensity values). This suggests that there are larger regions that are smooth in Group 3 compared to Group 1, which could indicate less WM “structure” in the more severe delusions group. This effect is shown in Figure 2. T-test results in Table 1 shows the highest difference is between groups 1 and 3 when examining the mean and median values with p-values both Conclusions Discussion: This study provides the first evidence that severity of delusions in a cognitively diverse sample of older adults is correlated with abnormalities in white matter texture. This finding suggests that alterations in white matter texture may contribute to the development of delusions in older patients. There is a notable difference between an individual with mild symptoms and severe symptoms based on their white matter texture as shown. Due to the subjective nature of an individual being scored either NPI-Q 1 or 2, or, 2 or 3, differentiating between mild and moderate, or moderate and severe disease is often ill-defined. This may contribute to challenges in differentiating between these groups. Further study comparing our results to patients without delusions and controlling for severity of cognitive impairment is required to further validate our findings. This research was funded by: St. Michael's Hospital Foundation, Heather and Eric Donnelly endowment

Introduction: Chronic stroke may lead to persistent cognitive deficits in multiple domains. Despite this issue, there are no clear guidelines about post-stroke fitness to drive, due to a limited understanding of how stroke impairs brain networks essential for driving. We hypothesize reduced activity in driving-related brain regions, for more cognitively demanding tasks (e.g. left turns with traffic, distraction) and individuals with poorer neurocognitive test scores. Methods: we tested 7 adults with prior ischemic stroke (44-71 yrs.) and imaged brain function using functional Magnetic Resonance Imaging (fMRI) while they performed simulated driving scenarios, using our novel MRI-compatible simulator system. Performance was compared to 10 healthy age-matched controls (56-75 yrs.). Results: chronic stroke patients showed similar posterior brain activations as controls (e.g. visual and prefrontal lobes, precuneus). However, individuals with lower neurocognitive test scores (e.g. Montreal Cognitive Assessment, Trail-Making Test) had greater posterior activation during turning tasks, potentially due to increased cognitive effort. They also showed greater reductions in activity during distraction tasks, indicating brain function is more sensitive to distracting stimuli. Functional abnormalities were not proximal to infarcts, indicating non-focal alterations of brain networks following stroke. Conclusions: This is the first demonstration of systematic changes in brain function for stroke patients, and relation to clinical measures. This may have implications in our understanding of how stroke affects brain function during integrative, real-world tasks.

We use novel hyperspectral NIRS technique in studies of functional brain activity and cerebral monitoring in cardiac arrest patients. The technique appears to be more sensitive to cerebral hemodynamic changes than the multispectral NIRS, capable to directly assess neuronal oxygen metabolism.