A double-dichotomy clustering of dual pathology dementia patients

Introduction: Subcortical ischemic vascular disease (SIVD) and Alzheimer's disease (AD) related dementia can coexist in older subjects, leading to mixed dementia (MX). Identification of dementia sub-groups is important for designing proper treatment plans and clinical trials. Method: An Alzheimer's disease severity (ADS) score and a vascular disease severity (VDS) score are calculated from CSF and MRI biomarkers, respectively. These scores, being sensitive to different Alzheimer's and vascular disease processes are combined orthogonally in a double-dichotomy plot. This formed an objective basis for clustering the subjects into four groups, consisting of AD, SIVD, MX and leukoaraiosis (LA). The relationship of these four groups is examined with respect to cognitive assessments and clinical diagnosis. Results: Cluster analysis had at least 83% agreement with the clinical diagnosis for groups based either on Alzheimer's or on vascular sensitive biomarkers, and a combined agreement of 68.8% for clustering the four groups. The VDS score was correlated to executive function (r = -0.28, p < 0.01) and the ADS score to memory function (r = −0.35, p < 0.002) after adjusting for age, sex, and education. In the subset of patients for which the cluster scores and clinical diagnoses agreed, the correlations were stronger (VDS score-executive function: r = −0.37, p < 0.006 and ADS score-memory function: r = −0.58, p < 0.0001). Conclusions: The double-dichotomy clustering based on imaging and fluid biomarkers offers an unbiased method for identifying mixed dementia patients and selecting better defined sub-groups. Differential correlations with neuropsychological tests support the hypothesis that the categories of dementia represent different etiologies

Quinolinate and related excitotoxins: mechanisms of neurotoxicity and disease relevance

There are many ways in which neuronal damage can be produced in the brain, including the overactivation of depolarizing receptors, exposure to high levels of pro-inflammatory proteins such as cytokines, or miscellaneous toxins, but the kynurenine pathway has emerged as a novel but potentially major factor in regulating neuronal viability or death. It is the major route for the metabolism of the essential amino acid tryptophan, which is oxidized by indoleamine-2,3-dioxygenase (IDO) to a series of compounds which can activate, block, or modulate conventional neurotransmitter receptors. Quinolinic acid is an agonist at N-methyl-d-aspartate receptors, kynurenic acid is an antagonist at these and other glutamate receptors, and other kynurenine metabolites are highly redox-active. Superimposed on the discovery of this neuromodulatory pathway have been observations that activity in the pathway is linked to neurological and psychiatric disorders, correlating with disease state (as in Huntington’s disease) or cognitive function (as following bypass surgery). Together, the data accumulated to date make a strong case for this hitherto obscure pathway being a major factor in determining cell damage, death, or recovery in health and disease

Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Neuroanatomical findings on youth anxiety disorders are notoriously difficult to replicate, small in effect size and have limited clinical relevance. These concerns have prompted a paradigm shift toward highly powered (that is, big data) individual-level inferences, which are data driven, transdiagnostic and neurobiologically informed. Here we built and validated supervised neuroanatomical machine learning models for individual-level inferences, using a case–control design and the largest known neuroimaging database on youth anxiety disorders: the ENIGMA-Anxiety Consortium (N = 3,343; age = 10–25 years; global sites = 32). Modest, yet robust, brain-based classifications were achieved for specific anxiety disorders (panic disorder), but also transdiagnostically for all anxiety disorders when patients were subgrouped according to their sex, medication status and symptom severity (area under the receiver operating characteristic curve, 0.59–0.63). Classifications were driven by neuroanatomical features (cortical thickness, cortical surface area and subcortical volumes) in fronto-striato-limbic and temporoparietal regions. This benchmark study within a large, heterogeneous and multisite sample of youth with anxiety disorders reveals that only modest classification performances can be realistically achieved with machine learning using neuroanatomical data