COURSE OBJECTIVES:
Students should acquire capacity to understand:
1. Cognitive decline in humans and experimental models
2. Statistical methods, neuroimaging methods, molecular methods and hidden goal task used in prediction of cognitive decline
COURSE CONTENT:
1. Molecular mechanisms of aging from cells to organisms: evolutionary theories of aging, theoretical models of aging, Gompertz-Makeham mortality curves, and progeroid syndromes
2. Genetic influence on lifespan and longevity: results from twin studies; neurodevelopmental origin of individual differences in cortical architecture in middle-aged adults: genetic dependence of cortical thickness
3. Molecular mechanisms of aging from cells to organisms: evolutionary theories of aging, theoretical models of aging, Gompertz-Makeham mortality curves, and progeroid syndromes
4. Genomic mosaicism of developing and adult brain (unlinked and linked to germline mutations); somatic APP gene recombination in Alzheimer's disease
5. Experimental models of aging in Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Mus musculus
6. Genome-wide association studies of cognitive capabilities and educational attainment: heritability and polygenic nature of human intelligence (with emphasis on differences between crystallized and fluid-type intelligence)
7. Clinical variables and biomarkers in prediction of cognitive decline and slowing of perceptual processing speed in older adults using logistic, linear, and multivariate regression models
8. Subjective cognitive impairment, imaging cognitive decline by visualization of brain atrophy (MRI) and decreased functional connectivity and metabolic activity (fMRI, PET), differential diagnosis of minor neurocognitive disorder (mild cognitive impairment) and major neurocognitive disorder (dementia)
9. Horvath's and Hannum's epigenetic aging clocks and their applications: positive and negative epigenetic drifts, risk factors and epigenetic rejuvenation (reversibility of epigenetic changes and reprogramming of aged cells), epigenetic prediction of time to death
10. Description of all steps in development of a new system to detect early mild cognitive impairment (MCI) based on a hidden goal task (HGT) test will be described; Upon explanation of calculation of positive and negative predictive values at different cohort prevalences for MCI, students will have to determine potential of the test for diagnostic and/or screening purposes based on a given data set
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Cognitive neuroscience, Banich MT, Compton RJ, Cambridge University Press, 2018.
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Cognitive neuroscience: the biology of the mind, Gazzaniga MS, Ivry R, Mangun GR, 2019.
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Neuroscience, Purves D et al, Sunderland (MA): Sinauer Associates, 2018.
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Handbook of the neuroscience of aging, Hof PR, Mobbs C, Cambridge (MA): Academic Press, 2009.
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Computational modeling in cognition: principles and practice, Lewandowsky S, Farrell S, Los Angeles: SAGE Publications, 2010.
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Animal models of human cognitive aging, Bizon JL, Woods A, New York: Humana Press, 2009.
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https://www.ncbi.nlm.nih.gov/pubmed/22814030 (Mini-mental state examination).
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https://www.ncbi.nlm.nih.gov/pubmed/21788513 (neoteny of synaptic spines in humans).
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https://www.ncbi.nlm.nih.gov/pubmed/15652989 (age-related changes of the human brain).
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https://www.ncbi.nlm.nih.gov/pubmed/22180840 (functional alterations in autism).
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https://www.ncbi.nlm.nih.gov/pubmed/9067838 (changes in volume and number of neurons of the human hippocampal formation in normal aging and Alzheimer's disease).
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https://www.ncbi.nlm.nih.gov/pubmed/31767039 (DNA methylation aging clock).
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https://www.ncbi.nlm.nih.gov/pubmed/31796892 (epigenetic predictor of death).
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Genomic mosaicism in the developing and adult brain, Dev Neurobiol. 2018; 78: 1026-1048, Rohrback S et al.
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Somatic APP gene recombination in Alzheimer's disease and normal neurons. Nature 2018; 563: 639-645, Lee MH et al..
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