Detection and forecasting of neurocognitive decline using functional neuroimaging and machine learning

A system comprising:

• a memory;
• and
• a processor coupled to the memory and configured to:
• obtain neuroimaging data from a test subject, the neuroimaging data including active-state functional neuroimaging data collected while the test subject performs a naturalistic language-based task;
• extract a feature set from the functional neuroimaging data;
• define an input data set that includes at least the feature set;
• and
• determine a predicted NCD status for the test subject by analyzing the input data set using a classifier that has been trained using machine learning to predict the NCD status for an individual, wherein training of the classifier is based on corresponding input data sets obtained for a plurality of training subjects having known NCD status based on a neurocognitive assessment.

A computer-readable storage medium having stored therein program code instructions that, when executed by a processor in a computer system, cause the processor to perform a method comprising:

• obtaining neuroimaging data from a test subject, the neuroimaging data including one or more of:
• active-state functional neuroimaging data collected while the test subject performs a naturalistic language-based task;
• resting-state functional neuroimaging data collected while the test subject is at rest;
• or
• anatomical neuroimaging data characterizing one or more brain structures of the test subject;
• extracting a feature set from the functional neuroimaging data;
• defining an input data set that includes at least the feature set;
• and
• determining a predicted NCD status for the test subject by analyzing the input data set using a classifier that has been trained using machine learning to predict the NCD status for an individual, wherein training of the classifier is based on corresponding input data sets obtained for a plurality of training subjects having known NCD status based on a neurocognitive assessment.