BACKGROUNDEarly discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency.METHODSOur research is based on resting‐state electroencephalography (EEG) and the current dataset includes 137 consensus‐diagnosed, community‐dwelling Black Americans (ages 60–90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time‐varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity‐based score.RESULTSThe leave‐one‐out cross‐validation accuracy is 91.97% and 3‐fold accuracy is 91.17%. The 9 to 18 months’ progression trend prediction accuracy over an availability‐limited subset sample is 84.61%.CONCLUSIONThe EEG‐based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.
Abstract-Based on field geological investigation, combined with the regional geology datas and study results from the former researchers, this paper has researched and analyzed the tectonic geomorphology types of Pinggu area and its adjacent area. The results indicated that the major tectonic geomorphological types include the fault block mountains and Pinggu Basin. Orogeny in this area was based on Yanshanian, and formed at tectonic movement in Cenozoic. For Pinggu Basin, it was controlled by Ershilichangshan Fault during the whole Quaternary, at least the west boundary, although the sediment center in Pinggu hollow was far from Ershilichangshan Fault. Another important fault in this area, the movement of Xiadian Fault has slightly influence on the evolution of Pinggu Basin during Quaternary.
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