12 February 2008 Data analysis and statistical tests for near-infrared functional studies of the brain
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We show some limitations of the standard t test when used together with typical data processing methods in functional Near Infrared Spectroscopy of the brain to assess the significance of multiple correlated points. We studied the occurrence of errors type I (that is the occurrence of false positive points) when typical processing methods are applied to time series of normal random numbers and to time series of simulated baseline systemic fluctuations. Since the results of the two studies are very similar we concluded that normal random numbers can be used to assess the occurrence of error type I due to certain algorithms of data processing. In order to decrease the occurrence of false positive points we propose to use some modified stepwise Bonferroni procedures, among which we studied the performance of Dubey/Armitage-Parmar algorithm. The results of the algorithm are shown for both simulated and experimental data.
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Angelo Sassaroli, Angelo Sassaroli, Yunjie Tong, Yunjie Tong, Christian Benes, Christian Benes, Sergio Fantini, Sergio Fantini, "Data analysis and statistical tests for near-infrared functional studies of the brain", Proc. SPIE 6850, Multimodal Biomedical Imaging III, 685008 (12 February 2008); doi: 10.1117/12.761707; https://doi.org/10.1117/12.761707

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