Beyond network connectivity: A classification approach to brain age prediction with resting-state fMRI

What is it about?

Using machine learning to investigate how neural signals are altered by and predictive of a person's age, sex, and the recording center. Classically, regression analysis has been used in this domain. This work uses SVM classification to do a pairwise analysis and present accuracy rates for the comparisons.

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Photo by Growtika on Unsplash

Photo by Growtika on Unsplash

Why is it important?

The use of machine learning to investigate how neural signals are altered by and predictive of age and sex via a pairwise classification methodology. Determine what properties of fMRI signals are the most predictive of age as well as which brain networks are most affected by aging. Quantify the degree of a prospective fingerprint introduced by a recording center into the fMRI recordings of its subjects.