33 research outputs found
The genetic architecture of the human cerebral cortex
The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder
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The impact of PICALM genetic variations on reserve capacity of posterior cingulate in AD continuum
Phosphatidylinositolbinding clathrin assembly protein (PICALM) gene is one novel genetic player associated with late-onset Alzheimer’s disease (LOAD), based on recent genome wide association studies (GWAS). However, how it affects AD occurrence is still unknown. Brain reserve hypothesis highlights the tolerant capacities of brain as a passive means to fight against neurodegenerations. Here, we took the baseline volume and/or thickness of LOAD-associated brain regions as proxies of brain reserve capacities and investigated whether PICALM genetic variations can influence the baseline reserve capacities and the longitudinal atrophy rate of these specific regions using data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In mixed population, we found that brain region significantly affected by PICALM genetic variations was majorly restricted to posterior cingulate. In sub-population analysis, we found that one PICALM variation (C allele of rs642949) was associated with larger baseline thickness of posterior cingulate in health. We found seven variations in health and two variations (rs543293 and rs592297) in individuals with mild cognitive impairment were associated with slower atrophy rate of posterior cingulate. Our study provided preliminary evidences supporting that PICALM variations render protections by facilitating reserve capacities of posterior cingulate in non-demented elderly
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Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis
Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions
The Learning Healthcare (Data) System: Virtual Data Warehouse Data Capture Revisited
Background/Aims: At the 2014 HCSRN annual meeting, Bachman and colleagues presented an excellent investigation into rates of encounters and drug fills at Virtual Data Warehouse (VDW) sites in order to evaluate (among other things) the VDW enrollment file’s “OUTSIDE_UTILIZATION” field, which purported to flag periods during which complete data capture of either pharmacy or encounter data was suspect. That investigation revealed serious problems with the flag, calling its usefulness into question. Taking this to heart, the VDW enrollment workgroup proposed removing this field and adding a suite of six new flags intended to express confidence in the capture of pharmacy, laboratory, outpatient encounter, inpatient encounter, tumor and electronic medical record data individually. These flags are assigned by local VDW analysts on the basis of their knowledge of data capture limitations at their respective sites for identifiable subgroups of patients. VDW programs were written and tested for creating these new data incompleteness variables. All HCSRN sites were invited to run these programs and share their results.
Methods: Modeled after Bachman et al.’s work, we calculated rates of pharmacy fills, lab results, encounters, tumor records and vital signs by the appropriate new flag. We then plotted these rates over time to see whether in fact the people/periods flagged as having suspect data capture did in fact have lower rates compared to those who/that were not.
Results: At the sites that implemented the flags, data capture rates generally varied in line with expectations — suspected incomplete groups had markedly lower rates. Of the six flags, “incomplete_rx” saw the best implementations, with all seven implementing sites showing clear distinctions between people whose data capture was suspect and those for whom it was not. “Incomplete_tumor” had the most variable implementations, with clear distinctions at some sites but not others.
Conclusion: On balance, the new flags stand to improve the quality of data-based research in the HCSRN. Projects needing to define populations at risk of exposure to particular pharmacy fills, tumors or lab result values, for example, would do well to use the new flags to screen out people for whom exposure risk may not be completely captured