Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

Abstract

Neuroimaging techniques have greatly enhanced the understanding of neurodiversity (human brain variation across individuals) in both health and disease. The ultimate goal of using brain imaging biomarkers is to perform individualized predictions. Here we proposed a generalized framework that can predict explicit values of the targeted measures by taking advantage of joint information from multiple modalities. This framework also enables whole brain voxel-wise searching by combining multivariate techniques such as ReliefF, clustering, correlation-based feature selection and multiple regression models, which is more flexible and can achieve better prediction performance than alternative atlas-based methods. For 50 healthy controls and 47 schizophrenia patients, three kinds of features derived from resting-state fMRI (fALFF), sMRI (gray matter) and DTI (fractional anisotropy) were extracted and fed into a regression model, achieving high prediction for both cognitive scores (MCCB composite r = 0.7033, MCCB social cognition r = 0.7084) and symptomatic scores (positive and negative syndrome scale [PANSS] positive r = 0.7785, PANSS negative r = 0.7804). Moreover, the brain areas likely responsible for cognitive deficits of schizophrenia, including middle temporal gyrus, dorsolateral prefrontal cortex, striatum, cuneus and cerebellum, were located with different weights, as well as regions predicting PANSS symptoms, including thalamus, striatum and inferior parietal lobule, pinpointing the potential neuromarkers. Finally, compared to a single modality, multimodal combination achieves higher prediction accuracy and enables individualized prediction on multiple clinical measures. There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making.

Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders

“…In addition, to utilize as much information as possible, the features were derived from all subjects. Although the classification models were tested with 10-fold cross validation, more solid conclusions can be drawn by examining the performance of biomarkers on new subjects which were excluded from the feature extraction process (Du et al 2015; Meng et al 2016). …”

Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

“…There is increasing evidence that instead of using a single imaging modality to study its relationship with physiological or cognitive features, people are paying more attention to multimodal fusion, an approach that is able to capitalize on the strength of multiple imaging techniques, since it can uncover the hidden relationships that might be missed from separate unimodal imaging studies [1–4]. Compelling evidence has confirmed that neuropsychiatric disorders reflect fundamental differences in brain structure and function.…”

Quantifying the Interaction and Contribution of Multiple Datasets in Fusion: Application to the Detection of Schizophrenia

“…We should note that perfect classification is not the goal of this work, especially since the patients with schizophrenia were receiving antipsychotic and/or mood stabilizing medication during the scanning sessions, thus making it difficult to attribute observed differences to the disease or to the medication [31]–[33]. Instead, our goal is to enable unambiguous quantification of the additive value of each dataset, determination of the combination of datasets that achieves the greatest performance, and quantification of the interaction among datasets within an analysis.…”