Multimodal biomarkers predictive of transdiagnostic symptom severity

Abstract

The method for evaluating mental health of a patient includes displaying a series of inquiries from mental health questionnaires on a display device. Each inquiry of the series of inquiries includes text and a set of answers. A series of selections is received from a user interface. Each selection of the series of selections is representative of an answer of the set of answers for each corresponding inquiry in the series of inquiries. Unprocessed MRI data are received. The unprocessed MRI data correspond to a set of MRI images of a biological structure associated with a patient. Using a machine learning model, the series of selections and the unprocessed MRI data are processed. The series of selections being processed corresponds to the series of inquiries. A symptom severity indicator for a mental health category of the patient is outputted.

What is claimed is: 
     
         1 . A system for evaluating mental health of a patient, the system comprising:
 a display device;   a user interface;   a memory containing machine readable medium comprising machine executable code having stored thereon instructions for performing a method; and   a control system coupled to the memory comprising one or more processors, the control system configured to execute the machine executable code to cause the control system to:
 display, on the display device, a series of inquiries from mental health questionnaires, each inquiry of the series of inquiries comprising text and a set of answers; 
 receive, from the user interface, a series of selections, each selection of the series of selections being representative of an answer of the set of answers for each corresponding inquiry in the series of inquiries; 
 receive, unprocessed MRI data corresponding to a set of MRI images of a biological structure associated with the patient; and 
 process, using a machine learning model, the series of selections corresponding to the series of inquiries and the unprocessed MRI data to output a symptom severity indicator for a mental health category of the patient. 
   
     
     
         2 . The system of  claim 1 , wherein the unprocessed MRI data corresponds to MRI data for a brain of the patient. 
     
     
         3 . The system of  claim 1 , wherein the unprocessed MRI data comprises fMRI data. 
     
     
         4 . The system of  claim 1 , wherein the control system is further configured to preprocess the unprocessed MRI data to identify a plurality of features. 
     
     
         5 . The system of  claim 1 , wherein the mental health category of the patient comprises at least one of: depression, anxiety, and anhedonia. 
     
     
         6 . The system of  claim 1 , wherein the machine learning model is at least one of: a generalized linear model, a regression model, a supervised regression method, a logistical regression model, random forest, lasso, and an elastic net. 
     
     
         7 . A system for evaluating mental health of a patient, the system comprising:
 a display device;   a user interface;   a memory containing machine readable medium comprising machine executable code having stored thereon instructions for performing a method;   a control system coupled to the memory comprising one or more processors, the control system configured to execute the machine executable code to cause the control system to:
 receive, from the user interface, a selection of answers corresponding to each question in a series of questions from mental health questionnaires; 
 receive, unprocessed MRI data corresponding to a set of MRI images of a biological structure; 
 process, using a machine learning model, multimodal feature sets derived from (i) the selection of answers, and (ii) the unprocessed MRI data to output a symptom severity indicator for a mental health category of the patient. 
   
     
     
         8 . The system of  claim 7 , wherein the unprocessed MRI data corresponds to MRI data for a brain of the patient. 
     
     
         9 . The system of  claim 7 , wherein the unprocessed MRI data comprises fMRI data. 
     
     
         10 . The system of  claim 7 , wherein the control system is further configured to preprocess the unprocessed MRI data to identify a plurality of features. 
     
     
         11 . The system of  claim 7 , wherein the mental health category of the patient comprises at least one of: depression, anxiety, and anhedonia. 
     
     
         12 . The system of  claim 7 , wherein the machine learning model is at least one of: a generalized linear model, a regression model, a supervised regression method, a logistical regression model, random forest, lasso, and an elastic net. 
     
     
         13 . A machine learning training system, comprising:
 at least one nontransitory processor-readable storage medium that stores at least one of processor-executable instructions or data; and   at least one processor communicatively coupled to the at least one nontransitory processor-readable storage medium, in operation, the at least one processor configured to:
 receive labeled training data for a plurality of individuals indicating whether each of the plurality of individuals has one or more mental health disorders, the labeled training data comprising:
 MRI data recorded for each of the plurality of individuals; and 
 a selection of answers to a series of questions for each of the plurality of individuals; 
 
 determine a plurality of features from the labeled training data; 
 train an initial machine learning model in a supervised manner, based on the plurality of features; 
 extract importance measures for each of the plurality of features, based on the training of the initial machine learning model; 
 generate a plurality of subset machine learning models based on the extracted importance measures for the plurality of features; 
 evaluate a classification performance of the generated plurality of subset machine learning models; 
 select at least one of the subset machine learning models as the machine learning model; and 
 store the plurality of features of the machine learning model in the at least one nontransitory processor-readable storage medium for subsequent use as a screening tool. 
   
     
     
         14 . The machine learning system of  claim 13 , wherein each feature in the plurality of features comprises an importance measure; wherein each of the subset machine learning models includes a sequentially lower number of features than a following subset machine learning model; and wherein the features are selected for each subset machine learning model based on a highest importance measure. 
     
     
         15 . The machine learning system of  claim 13 , wherein the selected subset machine learning model includes a portion of the plurality of features, the portion selected from features having an importance measure above a threshold value. 
     
     
         16 . The machine learning system of  claim 13 , wherein each of the subset machine learning models includes (i) a different selection of a portion of the plurality of features, or (ii) a different combination of the plurality of features. 
     
     
         17 . The machine learning system of  claim 13 , wherein training the initial machine learning model further comprises using k-fold cross validation with logistic regression. 
     
     
         18 . The machine learning system of  claim 13 , wherein the labeled training data further comprises at least one of functional measurement data or physiological measurement data. 
     
     
         19 . The machine learning system of  claim 13 , further comprising:
 using the features of the machine learning model as a screening tool to assess at least one of intermediate or end-point outcomes in at least one clinical trial testing for treatment responses.   
     
     
         20 . The machine learning system of  claim 13 , wherein the machine learning model is trained on (i) clinical scales data corresponding to the plurality of individuals; (ii) fMRI full connectivity data corresponding to the plurality of individuals; (iii) sMRI data corresponding to a plurality of individuals, the sMRI data comprising cortical volume data, cortical thickness data, and cortical surface area data; (iv) input data corresponding to the plurality of individuals, wherein, for each individual, the input data comprises clinical scales data and fMRI data; (v) input data corresponding to the plurality of individuals, wherein, for each individual, the input data comprises clinical scales data and sMRI data; (vi) input data corresponding to the plurality of individuals, wherein, for each individual, the input data comprises fMRI data and sMRI data; or (vii) input data corresponding to the plurality of individuals, wherein, for each individual, the input data comprises fMRI data, clinical scales data, and sMRI data.