Treacher, A. | Search bioRxiv

bioRxiv2020

Predicting Longitudinal Disease Severity for Individuals with Parkinson's Disease using Functional MRI and Machine Learning Prognostic Models

Parkinsons disease is the second most common neurodegenerative disorder and is characterized by the loss of ability to control voluntary movements. Predictive biomarkers of progression in Parkinsons Disease are urgently needed to expedite the development of neuroprotective treatments and facilitate discussions about disease prognosis between clinicians and patients. Resting-state functional magnetic resonance imaging (rs-fMRI) shows promise in predicting progression, with derived measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF), having been previously been associated with current disease severity. In this work, ReHo and fALFF features from 82 Parkinsons Disease subjects are used to train machine learning predictors of baseline clinical severity and progression at 1 year, 2 years, and 4 years follow-up as measured by the Movement Disorder Society Unified Depression Rating Scale (MDS-UPDRS) score. This is the first time that rs-fMRI and machine learning have been combined to predict future disease progression. The machine learning models explain up to 30.4% (R2 = 0.304) of the variance in baseline MDS-UPDRS scores, 55.8% (R2 = 0.558) of the variance in year 1 scores, and 47.1% (R2 = 0.471) of the variance in year 2 scores with high statistical significance (p < 0.0001). For distinguishing high- and low-progression individuals (MDS-UPDRS score above or below the median), the models achieve positive predictive values of up to 71% and negative predictive values of up to 84%. The models learn patterns of ReHo and fALFF measures that predict better and worse prognoses. Higher ReHo and fALFF in regions of the default motor network predicted lower current severity and lower future progression. The rs-fMRI features in the temporal lobe, limbic system, and motor cortex were also identified as predictors. These results present a potential neuroimaging biomarker that accurately predicts progression, which may be useful as a clinical decision-making tool and in future trials of neuroprotective treatments.

neuroscience↗

bioRxiv2020

Distinct Predictive Patterns of Antidepressant Response to Sertraline, Placebo and Bupropion using Pre-Treatment Reward Processing as examined through Functional MRI and Deep Learning: Key Results from the EMBARC Study

ImportanceThe lack of antidepressant-specific biomarkers to inform treatment selection is a key obstacle in the treatment of Major Depressive Disorder. Quantitative measurements of reward processing neural activity with task-based functional magnetic resonance imaging (fMRI) may allow prediction of individual outcomes for specific antidepressants. ObjectiveTo build and validate predictors of individual outcomes for sertraline, bupropion, and placebo using reward processing task-based fMRI, clinical assessments, and deep learning. DesignThis is a secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a placebo-controlled, double-blind randomized clinical trial. The study ran from July 2011 to December 2015 and this analysis was performed between December 2018 and July 2019. SettingThe EMBARC study was conducted at 4 academic medical centers. ParticipantsA random sample of 296 un-medicated participants meeting DSIM-IV criteria for depression was enrolled. InterventionSubjects were randomized to sertraline or placebo during phase 1. In phase 2, non-responders to placebo were treated with sertraline and non-responders to sertraline were treated with bupropion. Each phase lasted for 8 weeks. Main outcomes and measuresThe primary outcome was the change in the 17-item Hamilton Rating Scale for Depression ({Delta}HAMD) after 8 weeks of treatment. Task-related brain activation measures, computed from pre-treatment reward processing task fMRI, and clinical measurements were used to train deep learning predictive models for each treatment group. ResultsThe current analysis includes 222 participants with complete imaging, clinical, and treatment outcome data (146 female, mean age 16.4 {+/-} 13.4 years). The predictive model for sertraline, trained on 106 participants, achieved an R2 of 35% (95% CI 20-50%, p < 10-3) in predicting {Delta}HAMD and a number-needed-to-treat (NNT) of 4.31 in predicting remission. The placebo model, trained on 116 participants, achieved an R2 of 23% (95% confidence interval of 11-37%, p < 10-3) and an NNT of 2.78. The bupropion model achieved an R2 of 37% (95% CI 12-61%, p < 10-3) and an NNT of 2.35. Reward processing activity in regions such as the medial frontal cortex, insula, and thalamus were important predictors of sertraline outcome, while the anterior cingulate cortex, striatum, insula, and thalamus were important regions for predicting bupropion outcome. These are consistent with previously reported findings, while regions not previously implicated such as the temporal pole and paracentral lobule were also found to be predictive. Conclusions and relevanceThese findings demonstrate the utility of reward processing measurements and deep learning in predicting individual antidepressant outcomes with high accuracy. They also present potential composite biomarkers for these treatments based on neuroimaging and clinical features. Trial registrationClinicalTrials.gov identifier: NCT01407094 KEY POINTSO_ST_ABSQuestionC_ST_ABSCan pre-treatment fMRI measurements of reward processing, in combination with multi-dimensional clinical assessments, be used to form personalized predictions of antidepressant outcomes? FindingsDeep learning predictive models trained on pre-treatment reward processing task-based neuroimaging and clinical data from a randomized clinical trial were able to explain up to 37% of the variance in individual treatment outcomes and predict remission with NNT of 2-4. Specific clinical variables and brain regions with reward processing activity important for the model predictions were identified, which may form composite biomarkers of antidepressant response. MeaningQuantitative measures of reward processing, as identified through deep learning, may move us closer to a precision medicine approach that enables clinicians to select the appropriate antidepressant for each patient with greater certainty.

bioinformatics↗