Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.FCT under the Neuroclinomics2 project [PTDC/EEI-SII/1937/2014, SFRH/BD/95846/2013]; INESC-ID plurianual [UID/CEC/50021/2013]; LASIGE Research Unit [UID/CEC/00408/2013

Antenatal glucocorticoid treatment induces adaptations in adult midbrain dopamine neurons, which underpin sexually dimorphic behavioral resilience

We demonstrated previously that antenatal glucocorticoid treatment (AGT, gestational days 16-19) altered the size and organization of the adult rat midbrain dopaminergic (DA) populations. Here we investigated the consequences of these AGT-induced cytoarchitectural disturbances on indices of DA function in adult rats. We show that in adulthood, enrichment of striatal DA fiber density paralleled AGT-induced increases in the numbers of midbrain DA neurons, which retained normal basal electrophysiological properties. This was co-incident with changes in (i) striatal D2-type receptor levels (increased, both sexes); (ii) D1-type receptor levels (males decreased; females increased); (iii) DA transporter levels (males increased; females decreased) in striatal regions; and (iv) amphetamine-induced mesolimbic DA release (males increased; females decreased). However, despite these profound, sexually dimorphic changes in markers of DA neurotransmission, in-utero glucocorticoid overexposure had a modest or no effect on a range of conditioned and unconditioned appetitive behaviors known to depend on mesolimbic DA activity. These findings provide empirical evidence for enduring AGT-induced adaptive mechanisms within the midbrain DA circuitry, which preserve some, but not all, functions, thereby casting further light on the vulnerability of these systems to environmental perturbations. Furthermore, they demonstrate these effects are achieved by different, often opponent, adaptive mechanisms in males and females, with translational implications for sex biases commonly found in midbrain DA-associated disorders

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

Sex-specific disruption of murine midbrain astrocytic and dopaminergic developmental trajectories following antenatal GC treatment

The mammalian midbrain dopaminergic systems arising in the substantia nigra pars compacta (SNc) and ventral tegmental area (VTA) are critical for coping behaviours and are implicated in neuropsychiatric disorders where early life challenges comprise significant risk factors. Here, we aimed to advance our hypothesis that glucocorticoids (GCs), recognised key players in neurobiological programming, target development within these systems, with a novel focus on the astrocytic population. Mice received antenatal GC treatment (AGT) by including the synthetic GC, dexamethasone, in the mothers' drinking water on gestational days 16-19; controls received normal drinking water. Analyses of regional shapes and volumes of the adult SNc and VTA demonstrated that AGT induced long-term, dose-dependent, structural changes that were accompanied by profound effects on astrocytes (doubling/tripling of numbers and/or density). Additionally, AGT induced long-term changes in the population size and distribution of SNc/VTA dopaminergic neurons, confirming and extending our previous observations made in rats. Furthermore, glial/neuronal structural remodelling was sexually dimorphic and depended on the AGT dose and sub-region of the SNc/VTA. Investigations within the neonatal brain revealed that these long-term organisational effects of AGT depend, at least in part, on targeting perinatal processes that determine astrocyte density and programmed cell death in dopaminergic neurons. Collectively, our characterisation of enduring, AGT-induced, sex-specific cytoarchitectural disturbances suggests novel mechanistic links for the strong association between early environmental challenge (inappropriate exposure to excess GCs) and vulnerability to developing aberrant behaviours in later life, with translational implications for dopamine-associated disorders (such as schizophrenia, ADHD, autism, depression), which typically show a sex bia

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission

Understanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics