Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability

Background Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models. Methods We propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs. Results The best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD. Conclusions The FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task.PTDC/EEI-SII/1937/2014; SFRH/BD/95846/2013; SFRH/BD/118872/2016info:eu-repo/semantics/publishedVersio

Childhood trauma and mixed episodes are associated with poor response to lithium in bipolar disorders

International audienceObjectives: Reliable predictors of response to lithium are still lacking in bipolar disorders (BDs). However, childhood trauma has been hypothesized to be associated with poor response to lithium.Methods: We included 148 patients with BD, euthymic when retrospectively and clinically assessed for response to lithium and childhood trauma using reliable scales.Results: According to the 'Alda scale', the sample consisted in 20.3% of excellent responders, 49.3% of partial responders and 30.4% of non-responders to lithium. A higher level of physical abuse significantly correlated with a lower level of response to lithium (P = 0.009). As compared to patients not exposed to any abuse, patients with at least two trauma abuses (emotional, physical or sexual) were more at risk of belonging to the non-responders group (OR = 4.91 95% CI (1.01-27.02)). Among investigated clinical variables, lifetime presence of mixed episodes and alcohol misuse were associated with non-response to lithium. Multivariate analyses demonstrated that physical abuse and mixed episodes were independently associated with poor response to lithium (P = 0.005 and P = 0.013 respectively).Conclusions: Childhood physical abuse might be involved in a poor future response to lithium prophylaxis, this effect being independent of the association between clinical expression of BD and poor response to lithium

Posttraumatic Stress Symptoms, Intrusive Thoughts, and Disruption Are Longitudinally Related to Elevated Cortisol and Catecholamines Following a Major Hurricane

This is the first study of a natural disaster (Hurricane Andrew) in which psychological and neuroendocrine data were collected 1–4 and 9–12 months afterward. Data were assessed using a community sample (N = 111) of hurricane survivors. Elevated posttraumatic stress symptoms (intrusive and avoidant thoughts) and stress hormones that initially were twice normal control values decreased significantly over time and returned to levels of non‐hurricane controls by the end of the year. In contrast to previous reports, suggesting low cortisol in posttraumatic stress disorder (PTSD), our sample had elevated cortisol, perhaps due to the nature of the trauma (i.e., natural disaster vs. crime, rape or war), our timing, or getting samples a few months after the event. In addition, the decrease in stress hormones over the year (cortisol and epinephrine [E]) was related to a decrease in psychological symptoms of trauma. Cortisol and norepinephrine (NE) were both related to the hurricane experience as well (damage and rebuilding; damage and disruption). Gender differences showed women reported more distress, but men had higher NE and cortisol. Finally, cortisol correlated most consistently both cross sectionally and longitudinally with reported days ill