ORCID Profile
0000-0002-6347-3986
Current Organisations
University of Sheffield
,
University of Cambridge
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Publisher: Oxford University Press (OUP)
Date: 05-2020
Abstract: Tau pathology, neuroinflammation, and neurodegeneration are key aspects of Alzheimer’s disease. Understanding whether these features predict cognitive decline, alone or in combination, is crucial to develop new prognostic measures and enhanced stratification for clinical trials. Here, we studied how baseline assessments of in vivo tau pathology (measured by 18F-AV-1451 PET), neuroinflammation (measured by 11C-PK11195 PET) and brain atrophy (derived from structural MRI) predicted longitudinal cognitive changes in patients with Alzheimer’s disease pathology. Twenty-six patients (n = 12 with clinically probable Alzheimer’s dementia and n = 14 with amyloid-positive mild cognitive impairment) and 29 healthy control subjects underwent baseline assessment with 18F-AV-1451 PET, 11C-PK11195 PET, and structural MRI. Cognition was examined annually over the subsequent 3 years using the revised Addenbrooke’s Cognitive Examination. Regional grey matter volumes, and regional binding of 18F-AV-1451 and 11C-PK11195 were derived from 15 temporo-parietal regions characteristically affected by Alzheimer’s disease pathology. A principal component analysis was used on each imaging modality separately, to identify the main spatial distributions of pathology. A latent growth curve model was applied across the whole s le on longitudinal cognitive scores to estimate the rate of annual decline in each participant. We regressed the in iduals’ estimated rate of cognitive decline on the neuroimaging components and examined univariable predictive models with single-modality predictors, and a multi-modality predictive model, to identify the independent and combined prognostic value of the different neuroimaging markers. Principal component analysis identified a single component for the grey matter atrophy, while two components were found for each PET ligand: one weighted to the anterior temporal lobe, and another weighted to posterior temporo-parietal regions. Across the whole-s le, the single-modality models indicated significant correlations between the rate of cognitive decline and the first component of each imaging modality. In patients, both stepwise backward elimination and Bayesian model selection revealed an optimal predictive model that included both components of 18F-AV-1451 and the first (i.e. anterior temporal) component for 11C-PK11195. However, the MRI-derived atrophy component and demographic variables were excluded from the optimal predictive model of cognitive decline. We conclude that temporo-parietal tau pathology and anterior temporal neuroinflammation predict cognitive decline in patients with symptomatic Alzheimer’s disease pathology. This indicates the added value of PET biomarkers in predicting cognitive decline in Alzheimer’s disease, over and above MRI measures of brain atrophy and demographic data. Our findings also support the strategy for targeting tau and neuroinflammation in disease-modifying therapy against Alzheimer’s disease.
Publisher: American Psychological Association (APA)
Date: 09-2018
DOI: 10.1037/NEU0000425
Abstract: Impaired nucleus accumbens (NAcc) activation is associated with amotivation and anhedonia, which are resistant to treatment with antipsychotics and antidepressants in schizophrenia. In this study, healthy participants were trained to self-regulate the activation of their NAcc, a brain region that plays an important role in motivation, using real-time functional magnetic resonance imaging (fMRI) neurofeedback. The experimental group ( For the learning effect of NAcc regulation, we found that the majority of participants (74%) in the experimental group successfully learned to self-regulate the NAcc. They also showed improved behavioral performance in motivation and decreased functional connectivity between the NAcc and the ventral medial prefrontal cortex and an increase in small-world properties in the reward circuit after training, indicating improved information integration in reward processing. However, improvement in motivation and modification of function connectivity were not observed in the These findings suggest that the NAcc could be self-regulated using real-time fMRI neurofeedback and can result in improved motivation in cognitive tasks. (PsycINFO Database Record
Publisher: American Psychological Association (APA)
Date: 05-2014
DOI: 10.1037/NEU0000035
Abstract: Prospective memory (PM) refers to the ability to remember to perform actions in the future. Schizophrenia spectrum disorders show impairments in PM but neural correlates of these impairments remain unclear. The present study aimed to examine brain activation during PM to identify impairments in in iduals with schizotypal personality features. Nineteen participants with schizotypal features and 22 healthy controls participated in a functional MRI experiment while performing a PM task. Results showed that the prefrontal cortex (including Brodmann Area [BA] 10), middle temporal gyrus, and precuneus were activated when performing the PM task compared with baseline. The schizotypal and control groups did not differ in behavioral PM performance. However, participants with schizotypal features showed decreased activations in the inferior and medial frontal lobes (BA 45, and 8). These results confirmed that the PM network involves prefrontal cortex, including BA 10. The lower activation in prefrontal cortex of in iduals with schizotypal features when performing a PM task indicates brain activation abnormality. Notably, this abnormality may occur in the absence of any behavioral manifestation. Our findings support the hypothesis of frontal lobe involvement in PM deficits observed in in iduals with schizotypal features.
Publisher: Cold Spring Harbor Laboratory
Date: 05-05-2022
DOI: 10.1101/2022.04.28.22274107
Abstract: Although a variety of brain lesions may contribute to the pathological diagnosis of dementia, the relationship of these lesions to dementia, how they interact and how to quantify them remain uncertain. Systematically assessing neuropathological measures in relation to the cognitive and functional definitions of dementia may enable the development of better diagnostic systems and treatment targets. The objective of this study is to apply machine learning approaches for feature selection to identify key features of Alzheimer-related pathologies associated with dementia. We applied machine learning techniques for feature ranking and classification as an unbiased comparison of neuropathological features and assessment of their diagnostic performance using a cohort (n=186) from the Cognitive Function and Ageing Study (CFAS). Seven feature ranking methods using different information criteria consistently ranked 22 out of the 34 neuropathology features for importance to dementia classification. Braak neurofibrillary tangle stage, Beta-amyloid and cerebral amyloid angiopathy features were the most highly ranked, although were highly correlated with each other. The best performing dementia classifier using the top eight ranked neuropathology features achieved 79% sensitivity, 69% specificity, and 75% precision. A substantial proportion (40.4%) of dementia cases was consistently misclassified by all seven algorithms and any combination of the 22 ranked features. These results highlight the potential of using machine learning to identify key indices of plaque, tangle and cerebral amyloid angiopathy burdens that may be useful for the classification of dementia.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Li Su.