The Clavatulidae (Gastropoda, Conoidea) of the Miocene Paratethys Sea with considerations on fossil and extant Clavatulidae genera.

We present a revision of the Clavatulidae gastropods of the Neogene of the Central and Eastern Paratethys seas. In total, 111 species level names have been used in the literature for Clavatulidae of the Paratethys Sea. After revision, we document 83 species. Seventeen genus-level groups are recognized, of which eleven are formally described. Six species-groups cannot be clearly placed in a certain Clavatulidae genus. The presence of Clavatula, Perrona, Pusionella, Scaevatula and Tomellana in the Paratethyan assemblages documents a biogeographic relationship with extant Clavatulidae faunas from the tropical eastern Atlantic. No Indo-West Pacific influences were observed, as Paratethyan species previously placed in Turricula, which belongs within the Clavatulidae, do not belong within that genus. The Langhian (middle Miocene) diversity of 62 species is comparable to the number of extant species recorded from West Africa (~65 species), but displays a much higher diversity at genus level. This high biodiversity in Paratethyan assemblages suggests that the Central Paratethys was a major center of radiation for clavatulid gastropods, which is also expressed by a high endemicity of 98.8%. In contrast, the clavatulid diversity in the Eastern Paratethys was very low and stratigraphically restricted to the early Miocene Sakaraulian. Granulatocincta nov. gen., Megaclavatula nov. gen., Neoperrona nov. gen., Olegia nov. gen., Striopusionella nov. gen. are established as new genera. Clavatula sorini nov. sp., Clavatula irisae nov. sp., Tomellana dulaii nov. sp., Tomellana aueri nov. sp., Granulatocincta callim nov. sp., Granulatocincta theoderichi nov. sp., Megaclavatula grunerti nov. sp., Megaclavatula pilleri nov. sp., Neoperrona zoltanorum nov. sp., Olegia mandici nov. sp., Perrona koeberli nov. sp., Perrona loetschi nov. sp., Pusionella hofmanni nov. sp. are described as new species from the Miocene Paratethys, and Clavatula ariejansseni nov. sp., Clavatula atatuerki nov. sp. and Granulatocincta pelliscrocodili nov. sp., are described from the eastern Proto-Mediterranean Karaman Basin of Turkey. Clavatula jarzynkae nov. nom., Perrona grossi nov. nom., Perrona ilonae nov. nom. and Perrona wanzenboecki nov. nom. are introduced as new names for Clavatula auingeri Finlay, 1927 [non Hilber, 1879], Pleurotoma (Clavatula) auingeri Hilber, 1879 [non Hoernes, 1875], Clavatula vindobonensis nodosa Csepreghy-Meznerics, 1954 [non Bellardi, 1847] and Pleurotoma concinna Handmann, 1883 [non Scacchi, 1836] respectively. Clavatula kowalewskii Bauk, 2003, Clavatula letksensis Csepreghy-Meznerics, 1953, Pleurotoma aculeatum Eichwald, 1830, Pleurotoma subscalaris Handmann, 1882 and Pleurotoma (Clavatula) reginae Hoernes Auinger, 1891 are treated as junior subjective synonyms of Pleurotoma (Clavatula) antoniae Hoernes Auinger, 1891, Perrona emmae (Hoernes Auinger, 1879), Pleurotoma laevigata Eichwald, 1830, Pleurotoma schreibersi Hrnes, 1854 and Pleurotoma (Clavatula) apolloniae Hoernes Auinger, 1891 respectively.

Brain responses to anticipatory cues and milkshake taste in obesity, and their relationship to bariatric surgery outcome.

There is substantial variability in percent total weight loss (%TWL) following bariatric surgery. Functional brain imaging may explain more variance in post-surgical weight loss than psychological or metabolic information. Here we examined the neuronal responses during anticipatory cues and receipt of drops of milkshake in 52 pre-bariatric surgery men and women with severe obesity (OW, BMI = 35-60 kg/m2) (23 sleeve gastrectomy (SG), 24 Roux-en-Y gastric bypass (RYGB), 3 laparoscopic adjustable gastric banding (LAGB), 2 did not undergo surgery) and 21 healthy-weight (HW) controls (BMI = 19-27 kg/m2). One-year post-surgery weight loss ranged from 3.1 to 44.0 TWL%. Compared to HW, OW had a stronger response to milkshake cues (compared to water) in frontal and motor, somatosensory, occipital, and cerebellar regions. Responses to milkshake taste receipt (compared to water) differed from HW in frontal, motor, and supramarginal regions where OW showed more similar response to water. One year post-surgery, responses to high-fat milkshake cues normalized in frontal, motor, and somatosensory regions. This change in brain response was related to scores on a composite health index. We found no correlation between baseline response to milkshake cues or tastes and%TWL at 1-yr post-surgery. In RYGB participants only, a stronger response to low-fat milkshake and water cues (compared to high-fat) in supramarginal and cuneal regions respectively was associated with more weight loss. A stronger cerebellar response to high-fat vs low-fat milkshake receipt was also associated with more weight loss. We confirm differential responses to anticipatory milkshake cues in participants with severe obesity and HW in the largest adult cohort to date. Our brain wide results emphasizes the need to look beyond reward and cognitive control regions. Despite the lack of a correlation with post-surgical weight loss in the entire surgical group, participants who underwent RYGB showed predictive power in several regions and contrasts. Our findings may help in understanding the neuronal mechanisms associated with obesity.

Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach.

Schizophrenia and related disorders have heterogeneous outcomes. Individualized prediction of long-term outcomes may be helpful in improving treatment decisions. Utilizing extensive baseline data of 523 patients with a psychotic disorder and variable illness duration, we predicted symptomatic and global outcomes at 3-year and 6-year follow-ups. We classified outcomes as (1) symptomatic: in remission or not in remission, and (2) global outcome, using the Global Assessment of Functioning (GAF) scale, divided into good (GAF ≥ 65) and poor (GAF < 65). Aiming for a robust and interpretable prediction model, we employed a linear support vector machine and recursive feature elimination within a nested cross-validation design to obtain a lean set of predictors. Generalization to out-of-study samples was estimated using leave-one-site-out cross-validation. Prediction accuracies were above chance and ranged from 62.2% to 64.7% (symptomatic outcome), and 63.5-67.6% (global outcome). Leave-one-site-out cross-validation demonstrated the robustness of our models, with a minor drop in predictive accuracies of 2.3% on average. Important predictors included GAF scores, psychotic symptoms, quality of life, antipsychotics use, psychosocial needs, and depressive symptoms. These robust, albeit modestly accurate, long-term prognostic predictions based on lean predictor sets indicate the potential of machine learning models complementing clinical judgment and decision-making. Future model development may benefit from studies scoping patient's and clinicians' needs in prognostication.

Effects of weather and season on human brain volume.

We present an exploratory cross-sectional analysis of the effect of season and weather on Freesurfer-derived brain volumes from a sample of 3,279 healthy individuals collected on two MRI scanners in Hartford, CT, USA over a 15 year period. Weather and seasonal effects were analyzed using a single linear regression model with age, sex, motion, scan sequence, time-of-day, month of the year, and the deviation from average barometric pressure, air temperature, and humidity, as covariates. FDR correction for multiple comparisons was applied to groups of non-overlapping ROIs. Significant negative relationships were found between the left- and right- cerebellum cortex and pressure (t = -2.25, p = 0.049; t = -2.771, p = 0.017). Significant positive relationships were found between left- and right- cerebellum cortex and white matter between the comparisons of January/June and January/September. Significant negative relationships were found between several subcortical ROIs for the summer months compared to January. An opposing effect was observed between the supra- and infra-tentorium, with opposite effect directions in winter and summer. Cohen's d effect sizes from monthly comparisons were similar to those reported in recent psychiatric big-data publications, raising the possibility that seasonal changes and weather may be confounds in large cohort studies. Additionally, changes in brain volume due to natural environmental variation have not been reported before and may have implications for weather-related and seasonal ailments.

Searching for Imaging Biomarkers of Psychotic Dysconnectivity.

BACKGROUND: Progress in precision psychiatry is predicated on identifying reliable individual-level diagnostic biomarkers. For psychosis, measures of structural and functional connectivity could be promising biomarkers given consistent reports of dysconnectivity across psychotic disorders using magnetic resonance imaging. METHODS: We leveraged data from four independent cohorts of patients with psychosis and control subjects with observations from approximately 800 individuals. We used group-level analyses and two supervised machine learning algorithms (support vector machines and ridge regression) to test within-, between-, and across-sample classification performance of white matter and resting-state connectivity metrics. RESULTS: Although we replicated group-level differences in brain connectivity, individual-level classification was suboptimal. Classification performance within samples was variable across folds (highest area under the curve [AUC] range = 0.30) and across datasets (average support vector machine AUC range = 0.50; average ridge regression AUC range = 0.18). Classification performance between samples was similarly variable or resulted in AUC values of approximately 0.65, indicating a lack of model generalizability. Furthermore, collapsing across samples (resting-state functional magnetic resonance imaging, N = 888; diffusion tensor imaging, N = 860) did not improve model performance (maximal AUC = 0.67). Ridge regression models generally outperformed support vector machine models, although classification performance was still suboptimal in terms of clinical relevance. Adjusting for demographic covariates did not greatly affect results. CONCLUSIONS: Connectivity measures were not suitable as diagnostic biomarkers for psychosis as assessed in this study. Our results do not negate that other approaches may be more successful, although it is clear that a systematic approach to individual-level classification with large independent validation samples is necessary to properly vet neuroimaging features as diagnostic biomarkers.

Associations of cannabis use disorder with cognition, brain structure, and brain function in African Americans.

Although previous studies have highlighted associations of cannabis use with cognition and brain morphometry, critical questions remain with regard to the association between cannabis use and brain structural and functional connectivity. In a cross-sectional community sample of 205 African Americans (age 18-70) we tested for associations of cannabis use disorder (CUD, n = 57) with multi-domain cognitive measures and structural, diffusion, and resting state brain-imaging phenotypes. Post hoc model evidence was computed with Bayes factors (BF) and posterior probabilities of association (PPA) to account for multiple testing. General cognitive functioning, verbal intelligence, verbal memory, working memory, and motor speed were lower in the CUD group compared with non-users (p < .011; 1.9 < BF < 3,217). CUD was associated with altered functional connectivity in a network comprising the motor-hand region in the superior parietal gyri and the anterior insula (p < .04). These differences were not explained by alcohol, other drug use, or education. No associations with CUD were observed in cortical thickness, cortical surface area, subcortical or cerebellar volumes (0.12 < BF < 1.5), or graph-theoretical metrics of resting state connectivity (PPA < 0.01). In a large sample collected irrespective of cannabis used to minimize recruitment bias, we confirm the literature on poorer cognitive functioning in CUD, and an absence of volumetric brain differences between CUD and non-CUD. We did not find evidence for or against a disruption of structural connectivity, whereas we did find localized resting state functional dysconnectivity in CUD. There was sufficient proof, however, that organization of functional connectivity as determined via graph metrics does not differ between CUD and non-user group.

The Speed of Development of Adolescent Brain Age Depends on Sex and Is Genetically Determined.

Children and adolescents show high variability in brain development. Brain age-the estimated biological age of an individual brain-can be used to index developmental stage. In a longitudinal sample of adolescents (age 9-23 years), including monozygotic and dizygotic twins and their siblings, structural magnetic resonance imaging scans (N = 673) at 3 time points were acquired. Using brain morphology data of different types and at different spatial scales, brain age predictors were trained and validated. Differences in brain age between males and females were assessed and the heritability of individual variation in brain age gaps was calculated. On average, females were ahead of males by at most 1 year, but similar aging patterns were found for both sexes. The difference between brain age and chronological age was heritable, as was the change in brain age gap over time. In conclusion, females and males show similar developmental ("aging") patterns but, on average, females pass through this development earlier. Reliable brain age predictors may be used to detect (extreme) deviations in developmental state of the brain early, possibly indicating aberrant development as a sign of risk of neurodevelopmental disorders.

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning.

Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the future, as opposed to making a diagnosis, which is concerned with the current state. During the follow-up period, many factors will influence the course of the disease. Combined with the usually scarcer longitudinal data and the variability in the definition of outcomes/transition, this makes prognostic predictions a challenging endeavor. Employing neuroimaging data in this endeavor introduces the additional hurdle of high dimensionality. Machine learning techniques are especially suited to tackle this challenging problem. This review starts with a brief introduction to machine learning in the context of its application to clinical neuroimaging data. We highlight a few issues that are especially relevant for prediction of outcome and transition using neuroimaging. We then review the literature that discusses the application of machine learning for this purpose. Critical examination of the studies and their results with respect to the relevant issues revealed the following: 1) there is growing evidence for the prognostic capability of machine learning-based models using neuroimaging; and 2) reported accuracies may be too optimistic owing to small sample sizes and the lack of independent test samples. Finally, we discuss options to improve the reliability of (prognostic) prediction models. These include new methodologies and multimodal modeling. Paramount, however, is our conclusion that future work will need to provide properly (cross-)validated accuracy estimates of models trained on sufficiently large datasets. Nevertheless, with the technological advances enabling acquisition of large databases of patients and healthy subjects, machine learning represents a powerful tool in the search for psychiatric biomarkers.

Resting-state subcortical functional connectivity in HIV-infected patients on long-term cART.

Despite long-term successful treatment with cART, impairments in cognitive functioning are still being reported in HIV-infected patients. Since changes in cognitive function may be preceded by subtle changes in brain function, neuroimaging techniques, such as resting-state functional magnetic resonance imaging (rs-fMRI) have become useful tools in assessing HIV-associated abnormalities in the brain. The purpose of the current study was to examine the extent to which HIV infection in virologically suppressed patients is associated with disruptions in subcortical regions of the brain in comparison to a matched HIV-negative control group. The sample consisted of 72 patients and 39 controls included between January 2012 and January 2014. Resting state functional connectivity was determined between fourteen regions-of-interest (ROI): the left and right nucleus accumbens, amygdala, caudate nucleus, hippocampus, putamen, pallidum and thalamus. A Bayesian method was used to estimate resting-state functional connectivity, quantified in terms of partial correlations. Both groups showed the strongest partial correlations between the left and right caudate nucleus and the left and right thalamus. However, no differences between the HIV patients and controls were found between the posterior expected network densities (control network density = 0.26, SD = 0.05, patient network density = 0.26, SD = 0.04, p = 0.58). The results of the current study show that HIV does not affect subcortical connectivity in virologically controlled patients who are otherwise healthy.

Let's Not Waste Time: Using Temporal Information in Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR) for Parcellating FMRI Data.

We have proposed a Bayesian approach for functional parcellation of whole-brain FMRI measurements which we call Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR). We use distance-dependent Chinese restaurant processes (dd-CRPs) to define a flexible prior which partitions the voxel measurements into clusters whose number and shapes are unknown a priori. With dd-CRPs we can conveniently implement spatial constraints to ensure that our parcellations remain spatially contiguous and thereby physiologically meaningful. In the present work, we extend CAESAR by using Gaussian process (GP) priors to model the temporally smooth haemodynamic signals that give rise to the measured FMRI data. A challenge for GP inference in our setting is the cubic scaling with respect to the number of time points, which can become computationally prohibitive with FMRI measurements, potentially consisting of long time series. As a solution we describe an efficient implementation that is practically as fast as the corresponding time-independent non-GP model with typically-sized FMRI data sets. We also employ a population Monte-Carlo algorithm that can significantly speed up convergence compared to traditional single-chain methods. First we illustrate the benefits of CAESAR and the GP priors with simulated experiments. Next, we demonstrate our approach by parcellating resting state FMRI data measured from twenty participants as taken from the Human Connectome Project data repository. Results show that CAESAR affords highly robust and scalable whole-brain clustering of FMRI timecourses.

Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates.

Functional connectivity concerns the correlated activity between neuronal populations in spatially segregated regions of the brain, which may be studied using functional magnetic resonance imaging (fMRI). This coupled activity is conveniently expressed using covariance, but this measure fails to distinguish between direct and indirect effects. A popular alternative that addresses this issue is partial correlation, which regresses out the signal of potentially confounding variables, resulting in a measure that reveals only direct connections. Importantly, provided the data are normally distributed, if two variables are conditionally independent given all other variables, their respective partial correlation is zero. In this paper, we propose a probabilistic generative model that allows us to estimate functional connectivity in terms of both partial correlations and a graph representing conditional independencies. Simulation results show that this methodology is able to outperform the graphical LASSO, which is the de facto standard for estimating partial correlations. Furthermore, we apply the model to estimate functional connectivity for twenty subjects using resting-state fMRI data. Results show that our model provides a richer representation of functional connectivity as compared to considering partial correlations alone. Finally, we demonstrate how our approach can be extended in several ways, for instance to achieve data fusion by informing the conditional independence graph with data from probabilistic tractography. As our Bayesian formulation of functional connectivity provides access to the posterior distribution instead of only to point estimates, we are able to quantify the uncertainty associated with our results. This reveals that while we are able to infer a clear backbone of connectivity in our empirical results, the data are not accurately described by simply looking at the mode of the distribution over connectivity. The implication of this is that deterministic alternatives may misjudge connectivity results by drawing conclusions from noisy and limited data.

Probabilistic clustering of the human connectome identifies communities and hubs.

A fundamental assumption in neuroscience is that brain function is constrained by its structural properties. This motivates the idea that the brain can be parcellated into functionally coherent regions based on anatomical connectivity patterns that capture how different areas are interconnected. Several studies have successfully implemented this idea in humans using diffusion weighted MRI, allowing parcellation to be conducted in vivo. Two distinct approaches to connectivity-based parcellation can be identified. The first uses the connection profiles of brain regions as a feature vector, and groups brain regions with similar connection profiles together. Alternatively, one may adopt a network perspective that aims to identify clusters of brain regions that show dense within-cluster and sparse between-cluster connectivity. In this paper, we introduce a probabilistic model for connectivity-based parcellation that unifies both approaches. Using the model we are able to obtain a parcellation of the human brain whose clusters may adhere to either interpretation. We find that parts of the connectome consistently cluster as densely connected components, while other parts consistently result in clusters with similar connections. Interestingly, the densely connected components consist predominantly of major cortical areas, while the clusters with similar connection profiles consist of regions that have previously been identified as the 'rich club'; regions known for their integrative role in connectivity. Furthermore, the probabilistic model allows quantification of the uncertainty in cluster assignments. We show that, while most clusters are clearly delineated, some regions are more difficult to assign. These results indicate that care should be taken when interpreting connectivity-based parcellations obtained using alternative deterministic procedures.

Lectotype designation for Murex nebula Montagu 1803 (Mangeliidae) and its implications for Bela Leach in Gray 1847.

Bela Leach in Gray is a misapplied and broadly defined genus within the family Mangeliidae Fischer, 1883. Examination of material from the Montagu collection at the Royal Albert Memorial Museum & Art Gallery (RAMM) in Exeter (UK) led to the discovery of six specimens of Murex nebula Montagu 1803 (the type species of Bela). This material is considered to belong to the original lot used by Montagu to define his species. We selected the best-preserved specimen as a lectotype. The lectotype and paralectotypes deposited at the RAMM are fully described and illustrated. Furthermore, diagnostic characters for recognizing B. nebula specimens are presented: protoconch shows weak ornamentation; teleoconch is fusiform with slightly convex whorls characterized by broad, suture-to-suture ribs and dense but weak spiral elements; outer lip is thin; anal sinus is shallow, placed on the shoulder ramp. These key features are of basic importance for: i) restricting the usage of the genus Bela and promoting its stability and consistent usage in literature and ii) separating two allied (and sometimes interchanged) genera: Bela and Mangelia Risso 1826.

Quantifying uncertainty in brain network measures using Bayesian connectomics.

The wiring diagram of the human brain can be described in terms of graph measures that characterize structural regularities. These measures require an estimate of whole-brain structural connectivity for which one may resort to deterministic or thresholded probabilistic streamlining procedures. While these procedures have provided important insights about the characteristics of human brain networks, they ultimately rely on unwarranted assumptions such as those of noise-free data or the use of an arbitrary threshold. Therefore, resulting structural connectivity estimates as well as derived graph measures fail to fully take into account the inherent uncertainty in the structural estimate. In this paper, we illustrate an easy way of obtaining posterior distributions over graph metrics using Bayesian inference. It is shown that this posterior distribution can be used to quantify uncertainty about graph-theoretical measures at the single subject level, thereby providing a more nuanced view of the graph-theoretical properties of human brain connectivity. We refer to this model-based approach to connectivity analysis as Bayesian connectomics.

Structurally-informed Bayesian functional connectivity analysis.

Functional connectivity refers to covarying activity between spatially segregated brain regions and can be studied by measuring correlation between functional magnetic resonance imaging (fMRI) time series. These correlations can be caused either by direct communication via active axonal pathways or indirectly via the interaction with other regions. It is not possible to discriminate between these two kinds of functional interaction simply by considering the covariance matrix. However, the non-diagonal elements of its inverse, the precision matrix, can be naturally related to direct communication between brain areas and interpreted in terms of partial correlations. In this paper, we propose a Bayesian model for functional connectivity analysis which allows estimation of a posterior density over precision matrices, and, consequently, allows one to quantify the uncertainty about estimated partial correlations. In order to make model estimation feasible it is assumed that the sparseness structure of the precision matrices is given by an estimate of structural connectivity obtained using diffusion imaging data. The model was tested on simulated data as well as resting-state fMRI data and compared with a graphical lasso analysis. The presented approach provides a theoretically solid foundation for quantifying functional connectivity in the presence of uncertainty.

The Quest for EEG Power Band Correlation with ICA Derived fMRI Resting State Networks.

The neuronal underpinnings of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) resting state networks (RSNs) are still unclear. To investigate the underlying mechanisms, specifically the relation to the electrophysiological signal, we used simultaneous recordings of electroencephalography (EEG) and fMRI during eyes open resting state (RS). Earlier studies using the EEG signal as independent variable show inconclusive results, possibly due to variability in the temporal correlations between RSNs and power in the low EEG frequency bands, as recently reported (Goncalves et al., 2006, 2008; Meyer et al., 2013). In this study we use three different methods including one that uses RSN timelines as independent variable to explore the temporal relationship of RSNs and EEG frequency power in eyes open RS in detail. The results of these three distinct analysis approaches support the hypothesis that the correlation between low EEG frequency power and BOLD RSNs is instable over time, at least in eyes open RS.

Compound response of marine and terrestrial ecosystems to varying climate: pre-anthropogenic perspective from bivalve shell growth increments and tree-rings.

Annual growth increments were examined from shells of the ocean quahog (Arctica islandica L.) from northwest Norway and from tree-ring samples of the Scots pine (Pinus sylvestris L.) from nearby coastal areas. The reconstructed annual growth increments were used to compare growth variability in marine and terrestrial ecosystems. Spatiotemporal comparison of the growth records showed statistically significant correlation during the 19th century A.D., indicative of ecosystem-independent response to pre-anthropogenic climate variations. Geographical correlation between marine and terrestrial records was only observed at the local scale. Years with particularly low winter or high summer North Atlantic Oscillation (NAO) indices showed the best synchronization of marine and terrestrial growth. Despite strong correlation during historical time, our palaeoecological evidence suggests that marine and terrestrial ecosystems may show dissimilar growth reaction to recently observed positive winter-NAO phases.