Objective Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features. Methods Late-life depression patients (medicated post-recruitment) [n=33] and elderly non-depressed individuals [n=35] were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pre-treatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models. Results A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity. Conclusions Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures—rather than region-based differences—are associated with depression versus depression recovery since to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps towards personalized late-life depression treatment.

Objective This study tests whether or not the structural white matter lesions that are characteristic of late-life depression are associated with alterations in the functional affective circuits of late-life depression. This study used an emotional faces paradigm that has been shown to engage the affective limbic brain regions. Method Thirty-three elderly depressed patients and 27 nondepressed comparison subjects participated in this study. The patients were recruited through the NIMH-sponsored Advanced Center for Interventions and Services Research for the Study of Late-Life Mood Disorders at the University of Pittsburgh Center for Bioethics and Health Law. Structural and functional MRI was used to assess white matter hyperintensity (WMH) burden and functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) response on a facial expression affective-reactivity task in both elderly participants with nonpsychotic and non-bipolar major depression (unmedicated) and nondepressed elderly comparison subjects. Results As expected, greater subgenual cingulate activity was observed in the depressed patients relative to the nondepressed comparison subjects. This same region showed greater task-related activity associated with a greater burden of cerebrovascular white matter change in the depressed group. Moreover, the depressed group showed a significantly greater interaction of WMH by fMRI activity effect than the nondepressed group. Conclusions The observation that high WMH burden in late-life depression is associated with greater BOLD response on the affective-reactivity task supports the model that white matter ischemia in elderly depressed patients disrupts brain mechanisms of affective regulation and leads to limbic hyperactivation.

Previously recalcitrant patients with chronic complex mTBI demonstrated significant improvement in symptoms, cognitive, vestibular, oculomotor, and balance function following targeted interventions.

White matter hyperintensities (WMHs) have been shown to be associated with the development of late-life depression (LLD) and eventual treatment outcomes. This study sought to investigate longitudinal WMH changes in patients with LLD during a 12-week antidepressant treatment course. Forty-seven depressed elderly patients were included in this analysis. All depressed subjects started pharmacological treatment for depression shortly after a baseline magnetic resonance imaging (MRI) scan. At 12 weeks, patients underwent a follow-up MRI scan, and were categorized as either treatment remitters (n = 23) or non-remitters (n = 24). Among all patients, there was as a significant increase in WMHs over 12 weeks (t(46) = 2.36, P = 0.02). When patients were stratified by remission status, non-remitters demonstrated a significant increase in WMHs (t(23) = 2.17, P = 0.04), but this was not observed in remitters (t(22) = 1.09, P = 0.29). Other markers of brain integrity were also investigated including whole brain gray matter volume, hippocampal volume, and fractional anisotropy. No significant differences were observed in any of these markers during treatment, including when patients were stratified based on remission status. These results add to existing literature showing the association between WMH accumulation and LLD treatment outcomes. Moreover, this is the first study to demonstrate similar findings over a short interval (ie 12 weeks), which corresponds to the typical length of an antidepressant trial. These findings serve to highlight the acute interplay of cerebrovascular ischemic disease and LLD.