Global climate change and biota: evidence from foraminifera during the middle eocene climatic optimum

Paleonviromental and paleoceanographic reconstructions of the repercussions related to the middle eocene climatic optimum global warming event. The study has been focused on two mid-latitude locations (Alano section, NE Italy) and ODP Site 1263 (SE Atlantic) using micropaleontological (benthic foraminifera) and geochemical proxies

What PLS can still do for Imaging Genetics in Alzheimer's disease

In this work we exploited Partial Least Squares (PLS) model for analyzing the genetic underpinning of grey matter atrophy in Alzheimer's Disease (AD). To this end, 42 features derived from T1-weighted Magnetic Resonance Imaging, including cortical thicknesses and subcortical volumes were considered to describe the imaging phenotype, while the genotype information consisted of 14 recently proposed AD related Polygenic Risk Scores (PRS), calculated by including Single Nucleotide Polymorphism passing different significance thresholds. The PLS model was applied on a large study cohort obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database including both healthy individuals and AD patients, and validated on an independent ADNI Mild Cognitive Impairment (MCI) cohort, including Early (EMCI) and Late MCI (LMCI). The experimental results confirm the existence of a joint dynamics between brain atrophy and genotype data in AD, while providing important generalization results when tested on a clinically heterogeneous cohort. In particular, less AD specific PRS scores were negatively correlated with cortical thicknesses, while highly AD specific PRSs showed a peculiar correlation pattern among specific subcortical volumes and cortical thicknesses. While the first outcome is in line with the well known neurodegeneration process in AD, the second could be revealing of different AD subtypes

Temperature dependency of metabolic rates in the upper ocean: A positive feedback to global climate change?

The temperature of seawater can affect marine plankton in various ways, including by affecting rates of metabolic processes. This can change the way carbon and nutrients are fixed and recycled and hence the chemical and biological profile of the water column. A variety of feedbacks on global climate are possible, especially by altering patterns of uptake and return of carbon dioxide to the atmosphere. Here we summarize and synthesize recent studies in the field of ecology, oceanography and ocean carbon cycling pertaining to possible feedbacks involving metabolic processes. By altering the rates of cellular growth and respiration, temperature-dependency may affect nutrient uptake and food demand in plankton and ultimately the equilibrium of pelagic food webs, with cascade effects on the flux of organic carbon between the upper and inner ocean (the “biological carbon pump”) and the global carbon cycle. Insights from modern ecology can be applied to investigate how temperature-dependent changes in ocean biogeochemical cycling over thousands to millions of years may have shaped the long-term evolution of Earth's climate and life. Investigating temperature-dependency over geological time scales, including through globally warm and cold climate states, can help to identify processes that are relevant for a variety of future scenarios

On the Viability of Diffusion MRI-Based Microstructural Biomarkers in Ischemic Stroke

Recent tract-based analyses provided evidence for the exploitability of 3D-SHORE microstructural descriptors derived from diffusion MRI (dMRI) in revealing white matter (WM) plasticity. In this work, we focused on the main open issues left: (1) the comparative analysis with respect to classical tensor-derived indices, i.e., Fractional Anisotropy (FA) and Mean Diffusivity (MD); and (2) the ability to detect plasticity processes in gray matter (GM). Although signal modeling in GM is still largely unexplored, we investigated their sensibility to stroke-induced microstructural modifications occurring in the contralateral hemisphere. A more complete picture could provide hints for investigating the interplay of GM and WM modulations. Ten stroke patients and ten age/gender-matched healthy controls were enrolled in the study and underwent diffusion spectrum imaging (DSI). Acquisitions at three and two time points (tp) were performed on patients and controls, respectively. For all subjects and acquisitions, FA and MD were computed along with 3D-SHORE-based indices [Generalized Fractional Anisotropy (GFA), Propagator Anisotropy (PA), Return To the Axis Probability (RTAP), Return To the Plane Probability (RTPP), and Mean Square Displacement (MSD)]. Tract-based analysis involving the cortical, subcortical and transcallosal motor networks and region-based analysis in GM were successively performed, focusing on the contralateral hemisphere to the stroke. Reproducibility of all the indices on both WM and GM was quantitatively proved on controls. For tract-based, longitudinal group analyses revealed the highest significant differences across the subcortical and transcallosal networks for all the indices. The optimal regression model for predicting the clinical motor outcome at tp3 included GFA, PA, RTPP, and MSD in the subcortical network in combination with the main clinical information at baseline. Region-based analysis in the contralateral GM highlighted the ability of anisotropy indices in discriminating between groups mainly at tp1, while diffusivity indices appeared to be altered at tp2. 3D-SHORE indices proved to be suitable in probing plasticity in both WM and GM, further confirming their viability as a novel family of biomarkers in ischemic stroke in WM and revealing their potential exploitability in GM. Their combination with tensor-derived indices can provide more detailed insights of the different tissue modulations related to stroke pathology

What the geological past can tell us about the future of the ocean’s twilight zone

Paleontological reconstructions of plankton community structure during warm periods of the Cenozoic (last 66 million years) reveal that deep-dwelling ‘twilight zone’ (200–1000 m) plankton were less abundant and diverse, and lived much closer to the surface, than in colder, more recent climates. We suggest that this is a consequence of temperature’s role in controlling the rate that sinking organic matter is broken down and metabolized by bacteria, a process that occurs faster at warmer temperatures. In a warmer ocean, a smaller fraction of organic matter reaches the ocean interior, affecting food supply and dissolved oxygen availability at depth. Using an Earth system model that has been evaluated against paleo observations, we illustrate how anthropogenic warming may impact future carbon cycling and twilight zone ecology. Our findings suggest that significant changes are already underway, and without strong emissions mitigation, widespread ecological disruption in the twilight zone is likely by 2100, with effects spanning millennia thereafter

Age-Specific 18F-FDG Image Processing Pipelines and Analysis Are Essential for Individual Mapping of Seizure Foci in Paediatric Patients with Intractable Epilepsy

Fluoro-18-deoxyglucose positron emission tomography (FDG-PET) is an important tool for the pre-surgical assessment of children with drug-resistant epilepsy. Standard assessment is carried out visually and this is often subjective and highly user-dependent. Voxel-wise statistics can be used to remove user-dependent biases by automatically identifying areas of significant hypo/hyper-metabolism, associated to the epileptogenic area. In the clinical settings, this analysis is carried out using commercially available software. These software packages suffer from two main limitations when applied to paediatric PET data: 1) paediatric scans are spatially normalised to an adult standard template and 2) statistical comparisons use an adult control dataset. The aim of this work is to provide a reliable observer-independent pipeline for the analysis of paediatric FDG-PET scans, as part of pre-surgical planning in epilepsy. METHODS: A pseudo-control dataset (n = 19 for 6-9y, n = 93 for 10-20y) was used to create two age-specific FDG-PET paediatric templates in standard paediatric space. The FDG-PET scans of 46 epilepsy patients (n = 16 for 6-9y, n = 30 for 10-17y) were retrospectively collated and analysed using voxel-wise statistics. This was implemented with the standard pipeline available in the commercial software Scenium and an in-house Statistical Parametric Mapping v.8 (SPM8) pipeline (including age-specific paediatric templates and normal database). A kappa test was used to assess the level of agreement between findings of voxel-wise analyses and the clinical diagnosis of each patient. The SPM8 pipeline was further validated using post-surgical seizure-free patients. RESULTS: Improved agreement with the clinical diagnosis was reported using SPM8, in terms of focus localisation, especially for the younger patient group: kScenium=0.489 versus kSPM=0.805. The proposed pipeline also showed a sensitivity of ~70% in both age ranges, for the localisation of hypo-metabolic areas on paediatric FDG-PET scans in post-surgical seizure-free patients. CONCLUSION: We show that by creating age-specific templates and using paediatric control databases, our pipeline provides an accurate and sensitive semi-quantitative method for assessing FDG-PET scans of patients under 18y

Microstructural MRI Correlates of Cognitive Impairment in Multiple Sclerosis: The Role of Deep Gray Matter

Although cognitive impairment (CI) is frequently observed in people with multiple sclerosis (pwMS), its pathogenesis is still controversial. Conflicting results emerged concerning the role of microstructural gray matter (GM) damage especially when involving the deep GM structures. In this study, we aimed at evaluating whether differences in cortical and deep GM structures between apparently cognitively normal (ACN) and CI pwMS (36 subjects in total) are present, using an extensive set of diffusion MRI (dMRI) indices and conventional morphometry measures. The results revealed increased anisotropy and restriction over several deep GM structures in CI compared with ACN pwMS, while no changes in volume were present in the same areas. Conversely, reduced anisotropy/restriction values were detected in cortical regions, mostly the pericalcarine cortex and precuneus, combined with reduced thickness of the superior frontal gyrus and insula. Most of the dMRI metrics but none of the morphometric indices correlated with the Symbol Digit Modality Test. These results suggest that deep GM microstructural damage can be a strong anatomical substrate of CI in pwMS and might allow identifying pwMS at higher risk of developing CI

Editorial: Brain-connectivity-based computer interfaces

Editorial on the Research Topic Brain-connectivity-based computer interface

Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants

Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue focusing on five WM bundles holding that feature that is Association, Brainstem, Commissural, Limbic and Projection fibers, respectively. For each tract group, we estimated brain age for 15,335 healthy participants from United Kingdom Biobank relying on diffusion MRI data derived endophenotypes, Bayesian ridge regression modeling and 10 fold-cross validation. Furthermore, we estimated brain age for an Ensemble model that gathers all the considered WM bundles. Association analysis was subsequently performed between the estimated brain age delta as resulting from the six models, that is for each tract group as well as for the Ensemble model, and 38 daily life style measures, 14 cardiac risk factors and cardiovascular magnetic resonance imaging features and genetic variants. The Ensemble model that used all tracts from all fiber groups (FG) performed better than other models to estimate brain age. Limbic tracts based model reached the highest accuracy with a Mean Absolute Error (MAE) of 5.08, followed by the Commissural ([Formula: see text]), Association ([Formula: see text]), and Projection ([Formula: see text]) ones. The Brainstem tracts based model was the less accurate achieving a MAE of 5.86. Accordingly, our study suggests that the Limbic tracts experience less brain aging or allows for more accurate estimates compared to other tract groups. Moreover, the results suggest that Limbic tract leads to the largest number of significant associations with daily lifestyle factors than the other tract groups. Lastly, two SNPs were significantly (p value [Formula: see text]) associated with brain age delta in the Projection fibers. Those SNPs are mapped to HIST1H1A and SLC17A3 genes

Non-Aβ-dependent factors associated with global cognitive and physical function in alzheimer's disease: a pilot multivariate analysis

Recent literature highlights the importance of identifying factors associated with mild cognitive impairment (MCI) and Alzheimer's Disease (AD). Actual validated biomarkers include neuroimaging and cerebrospinal fluid assessments; however, we investigated non-Aβ-dependent factors associated with dementia in 12 MCI and 30 AD patients. Patients were assessed for global cognitive function (Mini-Mental state examination-MMSE), physical function (Physical Performance Test-PPT), exercise capacity (6-min walking test-6MWT), maximal oxygen uptake (VO₂max), brain volume, vascular function (flow-mediated dilation-FMD), inflammatory status (tumor necrosis factor-α ,TNF- α, interleukin-6, -10 and -15) and neurotrophin receptors (p75NTR and Tropomyosin receptor kinase A -TrkA). Baseline multifactorial information was submitted to two separate backward stepwise regression analyses to identify the variables associated with cognitive and physical decline in demented patients. A multivariate regression was then applied to verify the stepwise regression. The results indicated that the combination of 6MWT and VO₂max was associated with both global cognitive and physical function (MMSE = 11.384 + (0.00599 × 6MWT) - (0.235 × VO₂max)); (PPT = 1.848 + (0.0264 × 6MWT) + (19.693 × VO₂max)). These results may offer important information that might help to identify specific targets for therapeutic strategies (NIH Clinical trial identification number NCT03034746)