717,529 research outputs found

    Recognition of Famous Names Predicts Cognitive Decline in Healthy Elders

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    Objective: The ability to recognize familiar people is impaired in both Mild Cognitive Impairment (MCI) and Alzheimer’s Dementia (AD). In addition, both groups often demonstrate a time-limited temporal gradient (TG) in which well known people from decades earlier are better recalled than those learned recently. In this study, we examined the TG in cognitively intact elders for remote famous names (1950–1965) compared to more recent famous names (1995–2005). We hypothesized that the TG pattern on a famous name recognition task (FNRT) would predict future cognitive decline, and also show a significant correlation with hippocampal volume. Method: Seventy-eight healthy elders (ages 65–90) with age-appropriate cognitive functioning at baseline were administered a FNRT. Follow-up testing 18 months later produced two groups: Declining (≥ 1 SD reduction on at least one of three measures) and Stable (\u3c 1 SD). Results: The Declining group (N = 27) recognized fewer recent famous names than the Stable group (N = 51), although recognition for remote names was comparable. Baseline MRI volumes for both the left and right hippocampi were significantly smaller in the Declining group than the Stable group. Smaller baseline hippocampal volume was also significantly correlated with poorer performance for recent, but not remote famous names. Logistic regression analyses indicated that baseline TG performance was a significant predictor of group status (Declining vs. Stable) independent of chronological age and APOE ε4 inheritance. Conclusions: The TG for famous name recognition may serve as an early preclinical cognitive marker of cognitive decline in healthy older individual

    False memory and delusions in Alzheimer's disease

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    Aims: This thesis aimed to investigate the relationship between memory errors and delusions in Alzheimer’s disease (AD), in order to further elucidate the mechanisms underlying delusion formation. This was achieved by undertaking narrative and systematic review of relevant literature, by exploring the relationship between performance on memory and metamemory tasks and delusions in AD patient populations and by investigating the neuroanatomical correlates of memory errors and delusions in AD patient populations. // Methods: I recruited 27 participants with and without delusions in AD and compared performance on measures of context memory, false memory and metamemory. I explored statistically significant behavioural findings further in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort of participants with AD (n = 733). I then conducted hypothesis-driven region of interest and exploratory voxel-based morphometric analyses to determine the relationship between false memory and delusions and regional brain volume in the ADNI cohort. This informed similar analyses of neuroimaging data in my own participants (n = 8). // Results: In both samples, individuals with delusions in AD had higher false recognition rates on recognition memory tasks than those without delusions. False recognition was inversely correlated with volume of medial temporal lobe, ventral visual stream and prefrontal cortex in both samples. In the ADNI sample, false recognition was also inversely correlated with anterior cingulate cortex (ACC) volume bilaterally. Participants with delusions had reduced volume of right ACC and increased volume of right parahippocampal gyrus compared to the control group. // Conclusions: These two complementary studies provide evidence of specific memory impairments associated with both delusions and a distinct pattern of brain atrophy in AD. Simple cognitive interventions can reduce false recognition rates in AD. Given the significant risks associated with antipsychotic drug treatment of delusions, exploring how these non-pharmacological interventions potentially affect psychosis symptoms in AD is an important next step

    STV-based Video Feature Processing for Action Recognition

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    In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end
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