The Semantic Memory Imaging In Late Life Pilot Study

Introduction: Several functional magnetic resonance imaging (fMRI) studies have analyzed the famous name discrimination task (FNDT), an uncontrolled semantic memory probe requiring discrimination between famous and unfamiliar individuals. Completion of this simple task recruits a semantic memory network that has shown utility in determining risk for Alzheimer\u27s disease (AD). Specific semantic memory probes using biographical information associated with famous individuals may build on previous findings and yield superior information regarding risk for AD. Method: Sixteen cognitively intact elders completed the FNDT and two novel tasks during fMRI: Categories (matching famous individuals to occupational categories) and Attributes (matching famous individuals to specific bodies of work or life events). Five participants were carriers of the Apolipoprotein E (APOE) ε4 allele. Results: Relative to their respective control tasks, participants recruited brain regions for all three tasks consistent with previous research, including left temporal lobe, left angular gyrus, precuneus, posterior cingulate, and anterior cingulate. The FNDT generated significantly more activity than the other tasks in anterior cingulate and several posterior regions. Categories had significantly lesser activity than other tasks in inferior parietal lobe, precuneus, and posterior cingulate. Attributes, the most specific semantic probe, demonstrated the strongest left lateralization with significantly greater activity in left inferior frontal gyrus and anterior temporal lobe. APOE ε4 carriers had regions with greater activity across all three tasks, with the greatest number of regions for Attributes, including in left anterior temporal lobe. Discussion: This pilot study identified neural correlates of different levels of semantic processing. The FNDT, an unconstrained semantic knowledge probe, demonstrated greater activity across most regions. The Attributes task, a specific semantic probe, had focused left-lateralized activity, including anterior temporal lobe and inferior frontal gyrus. APOE ε4 carriers demonstrated significantly greater activity in left anterior temporal lobe during Attributes only, demonstrating this task\u27s potential utility for determination of AD risk

Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease

Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity

Task switching ability in mild cognitive impairment

There is growing evidence of executive function deficits in mild cognitive impairment (MCI) and task switching ability has been shown to predict MCI transition to Alzheimer's disease. We tested task switching ability using a cued task switching paradigm in 27 MCI patients. Sixteen patients could perfume the task (MCI-able) and 11 could not (MCI-unable). Neuropsychological, electrophysiological, neuroanatomical, genetic, demographic, health-related data are presented for the MCI sub-groups and normal controls. The most significant finding of this study is that task-switching ability can be a powerful tool in characterizing this heterogeneous population. We found that most MCI patients exhibit some form of task-switching deficits, but to vastly different degrees. On the one hand there are individuals closer to the normal aging end of the cognitive spectrum; these individuals may present with memory deficits relative to their normal age peers but can compensate these with quasi-intact executive functions and have a high probability of remaining dementia free as long as their executive functions remain adequate. On the other side of the spectrum, there are individuals who perfume poorly on executive tasks as well as having significant episodic memory deficits. These individuals appear to have a high probability of developing AD or dying within four years. ii

Molecular neuroanatomy: mouse-human homologies and the landscape of genes implicated in language disorders

The distinctiveness of brain structures and circuits depends on interacting gene products, yet the organization of these molecules (the "transcriptome") within and across brain areas remains unclear. High-throughput, neuroanatomically-specific gene expression datasets such as the Allen Human Brain Atlas (AHBA) and Allen Mouse Brain Atlas (AMBA) have recently become available, providing unprecedented opportunities to quantify molecular neuroanatomy. This dissertation seeks to clarify how transcriptomic organization relates to conventional neuroanatomy within and across species, and to introduce the use of gene expression data as a bridge between genotype and phenotype in complex behavioral disorders. The first part of this work examines large-scale, regional transcriptomic organization separately in the mouse and human brain. The use of dimensionality reduction methods and cross-sample correlations both revealed greater similarity between samples drawn from the same brain region. Sample profiles and differentially expressed genes across regions in the human brain also showed consistent anatomical specificity in a second human dataset with distinct sampling properties. The frequent use of mouse models in clinical research points to the importance of comparing molecular neuroanatomical organization across species. The second part of this dissertation describes three comparative approaches. First, at genome scale, expression profiles within homologous brain regions tended to show higher similarity than those from non-homologous regions, with substantial variability across regions. Second, gene subsets (defined using co-expression relationships or shared annotations), which provide region-specific, cross-species molecular signatures were identified. Finally, brain-wide expression patterns of orthologous genes were compared. Neuron and oligodendrocyte markers were more correlated than expected by chance, while astrocyte markers were less so. The localization and co-expression of genes reflect functional relationships that may underlie high-level functions. The final part of this dissertation describes a database of genes that have been implicated in speech and language disorders, and identifies brain regions where they are preferentially expressed or co-expressed. Several brain structures with functions relevant to four speech and language disorders showed co-expression of genes associated with these disorders. In particular, genes associated with persistent developmental stuttering showed stronger preferential co-expression in the basal ganglia, a structure of known importance in this disorder

Florida Undergraduate Research Conference

FURC serves as a multi-disciplinary conference through which undergraduate students from the state of Florida can present their research. February 16-17, 2024https://digitalcommons.unf.edu/university_events/1006/thumbnail.jp

06. 2000 Twelfth Annual IMSA Presentation Day

https://digitalcommons.imsa.edu/class_of_2000/1003/thumbnail.jp

2000 Twelfth Annual IMSA Presentation Day

... professional research journals such as Nature, Biology of Reproduction, Neuroscience Research Communications and Ceramic Engineering and Science Proceedings have all featured the research work of MSA students through presentations and publications.https://digitalcommons.imsa.edu/archives_sir/1024/thumbnail.jp

Jefferson Alumni Bulletin – Volume XLVII, Number 1, December 1997

Jefferson Alumni Bulletin – Volume XLVII, Number 1, December 1997 Annual Giving Celebrates a Half-Century of Strengthening Jefferson, Page 4 Screening for Breast Cancer: A Continuing Dilemma, Page 8 The McClellan house: A Link to the University’s Past, Page 12 The Bulletin Reaches its 75th Year, Page 17 Landmark Breast Cancer Meeting at Jefferson, Page 21 University and SmithKline Beecham Link Clinical Research, Page 22 Missing Cancer-Suppressor Genes May Have Deadly Effects, Page 23 Goldstein is Vice President for Research at JDFI, Page 27 Brezinski Develops Noninvasive Method to Detect Early Signs of Cancer and Heart Attacks, Page 2

Harnessing brain imaging data to personalise management of fatigue in inflammatory arthritis

Rheumatoid arthritis and psoriatic arthritis are chronic inflammatory conditions in which chronic fatigue persists in the majority of patients despite successful management of disease activity. This multidimensional, disabling fatigue correlates with various brain characteristics. Current treatments inadequately address fatigue, emphasising the importance of exploring its neural underpinnings and what potential imaging the brain has to inform the management of fatigue in these inflammatory arthritis conditions. To do so, I applied brain measures to stratify inflammatory arthritis patients into fatigue-related subgroups with potentially amendable biological differences, identify correlates of different subdimensions of fatigue, and predict fatigue follow-up after fatigue-specific or pharmacological treatments in different inflammatory arthritis cohorts of rheumatoid and psoriatic arthritis. I hypothesised that there are (1) subtypes of fatigue in patients with rheumatoid arthritis, illustrated by distinct subgroups stratified by a relationship between neuroimaging brain characteristics and fatigue; (2) statistically significant correlates of subcomponents of fatigue; (3) statistically significant predictors of fatigue scores after non-pharmacological treatments in rheumatoid arthritis; (4) statistically significant predictors of fatigue scores after pharmacological treatments in rheumatoid and psoriatic arthritis; (5) models that can predict individual fatigue outcomes above chance in a trial of non-pharmacological treatments in rheumatoid arthritis using machine learning to combine multiple neuroimaging and clinical variables. I found a link between neuroimaging brain connectivity and distinct subgroups in rheumatoid arthritis related to fatigue subdimensions, albeit only within a specific cohort. Associations emerged between brain imaging metrics and baseline fatigue subcomponents, showing varied correlations with different metrics. In rheumatoid arthritis patients undergoing exercise or cognitive-behavioural interventions, baseline brain imaging predictors of fatigue centred on structural connectivity from the precuneus to the anterior cingulate cortex. In contrast, I did not find significant neuroimaging predictors of fatigue in rheumatoid arthritis patients who started a new disease-modifying antirheumatic drug. However, I did find such predictors in psoriatic arthritis patients, encompassing cortical thickness of the visual pericalcarine cortex and functional connectivity within the default mode and salience networks, involving the inferior parietal lobule and anterior cingulate cortex. Finally, models using diverse neuroimaging and clinical modalities along with different machine learning algorithms outperformed models using solely the baseline median fatigue. Significantly, these models did not surpass chance level or replicate their utility in usual care patients in an independent rheumatoid arthritis cohort. Overall, despite not finding a model that can predict individual fatigue outcomes, this research advanced our understanding by pinpointing different fatigue-related brain circuits, delineating associations with subcomponents, and identifying group-level predictors of fatigue. If such findings are utilised by future studies using molecular and brain stimulation techniques, neuroimaging can offer innovative solutions to patients to significantly improve their quality of life