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However, few studies have explored interregional influences of brain activity during the aging process. We proposed a causality analysis framework combining high dimensionality independent component analysis (ICA), Granger causality, and least absolute shrinkage and selection operator regression on longitudinal brain metabolic activity data measured by Fludeoxyglucose positron emission tomography (FDG\u2013PET). We analyzed FDG\u2013PET images from healthy old subjects, who were scanned for at least five sessions with an averaged intersession interval of 1\u2009year. The longitudinal data were concatenated across subjects to form a time series, and the first\u2010order autoregressive model was used to measure interregional causality among the independent sources of metabolic activity identified using ICA. Several independent sources with reduced metabolic activity in aging, including the anterior temporal lobe and orbital frontal cortex, demonstrated causal influences over many widespread brain regions. On the other hand, the influenced regions were more distributed, and had smaller age\u2010related declines or even relatively increased metabolic activity. 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