Phylogenetic analyses indicate that viviparity (live-bearing reproduction) has originated independently in more than 150 vertebrate lineages, including a minimum of 115 clades of extant squamate ...reptiles. Other evolutionary origins of viviparity include 13 origins among bony fishes, nine among chondrichthyans, eight in amphibians, one in Paleozoic placoderms, six among extinct reptiles, and one in mammals. The origins of viviparity range geologically from the mid-Paleozoic through the Mesozoic to the Pleistocene. Substantial matrotrophy (maternal provision of nutrients to embryos during pregnancy) has arisen at least 33 times in these viviparous clades, with most (26) of these origins having occurred among fishes and amphibians. Convergent evolution in patterns of matrotrophy is widespread, as reflected by multiple independent origins of placentotrophy, histotrophy, oophagy, and embryophagy. Specializations for nutrient transfer to embryos are discontinuously distributed, reflecting the roles of phylogenetic inertia, exaptation (preadaptation), and constraint. Ancestral features that function in gas exchange and nutrition repeatedly and convergently have been co-opted for nutrient transfer, often through minor modification of their components and changes in the timing of their expression (heterochrony). Studies on functional and evolutionary morphology continue to play a central role in our attempts to understand viviparity and mechanisms of fetal nutrition.
Viviparity in squamate reptiles is widely recognized as having evolved convergently from oviparity more than 100 times. However, questions persist as to whether reversals from viviparity back to ...oviparity have ever occurred. Based on a theoretical model, a recent paper (Pyron and Burbrink, 2014) has proposed that viviparity is ancestral for squamates and that viviparity-oviparity reversals have far outnumbered origins of viviparity in reproductive history. Close examination of this analysis reveals features that cast doubt on its plausibility, notably the requirement of repeated, sequential transformations back and forth between these reproductive modes, as well as numerous, uncounted evolutionary transformations that have produced inaccurate estimates of parsimony. Evidence derived from studies of anatomy, physiology, and developmental biology strongly supports the inference that oviparity is ancestral for squamates and has given rise to viviparity on numerous occasions. Biological data provide important insights into the likelihood of evolutionary transformations, and deserve to be incorporated fully into future analyses of the evolution of reproductive modes.
ABSTRACT
The reproductive diversity of extant cartilaginous fishes (class Chondrichthyes) is extraordinarily broad, reflecting more than 400 million years of evolutionary history. Among their many ...notable reproductive specialisations are viviparity (live‐bearing reproduction) and matrotrophy (maternal provision of nutrients during gestation). However, attempts to understand the evolution of these traits have yielded highly discrepant conclusions. Here, we compile and analyse the current knowledge on the evolution of reproductive diversity in Chondrichthyes with particular foci on the frequency, phylogenetic distribution, and directionality of evolutionary changes in their modes of reproduction. To characterise the evolutionary transformations, we amassed the largest empirical data set of reproductive parameters to date covering nearly 800 extant species and analysed it via a comprehensive molecular‐based phylogeny. Our phylogenetic reconstructions indicated that the ancestral pattern for Chondrichthyes is ‘short single oviparity’ (as found in extant holocephalans) in which females lay successive clutches (broods) of one or two eggs. Viviparity has originated at least 12 times, with 10 origins among sharks, one in batoids, and (based on published evidence) another potential origin in a fossil holocephalan. Substantial matrotrophy has evolved at least six times, including one origin of placentotrophy, three separate origins of oophagy (egg ingestion), and two origins of histotrophy (uptake of uterine secretions). In two clades, placentation was replaced by histotrophy. Unlike past reconstructions, our analysis reveals no evidence that viviparity has ever reverted to oviparity in this group. Both viviparity and matrotrophy have arisen by a variety of evolutionary sequences. In addition, the ancestral pattern of oviparity has given rise to three distinct egg‐laying patterns that increased clutch (brood) size and/or involved deposition of eggs at advanced stages of development. Geologically, the ancestral oviparous pattern arose in the Paleozoic. Most origins of viviparity and matrotrophy date to the Mesozoic, while a few that are represented at low taxonomic levels are of Cenozoic origin. Coupled with other recent work, this review points the way towards an emerging consensus on reproductive evolution in chondrichthyans while offering a basis for future functional and evolutionary analyses. This review also contributes to conservation efforts by highlighting taxa whose reproductive specialisations reflect distinctive evolutionary trajectories and that deserve special protection and further investigation.
Alzheimer's disease (AD) is the leading form of dementia worldwide. AD disrupts neuronal pathways and thus is commonly viewed as a network disorder. Many studies demonstrate the power of functional ...connectivity (FC) graph-based biomarkers for automated diagnosis of AD using electroencephalography (EEG). However, various FC measures are commonly utilised, as each aims to quantify a unique aspect of brain coupling. Graph neural networks (GNN) provide a powerful framework for learning on graphs. While a growing number of studies use GNN to classify EEG brain graphs, it is unclear which method should be utilised to estimate the brain graph. We use eight FC measures to estimate FC brain graphs from sensor-level EEG signals. GNN models are trained in order to compare the performance of the selected FC measures. Additionally, three baseline models based on literature are trained for comparison. We show that GNN models perform significantly better than the other baseline models. Moreover, using FC measures to estimate brain graphs improves the performance of GNN compared to models trained using a fixed graph based on the spatial distance between the EEG sensors. However, no FC measure performs consistently better than the other measures. The best GNN reaches 0.984 area under sensitivity-specificity curve (AUC) and 92% accuracy, whereas the best baseline model, a convolutional neural network, has 0.924 AUC and 84.7% accuracy.
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•New EEG channel selection method integrating (non) linear functional connectivity (FC).•A generic measure of similarity using kernel matrix from nonlinear manifold learning.•FC ...changes in Alzheimer’s between the occipital and regions along the frontoparietal.•FC changes along the frontoparietal and rest of the EEG are important in Alzheimer’s.
Dynamical, causal, and cross-frequency coupling analysis using the electroencephalogram (EEG) has gained significant attention for diagnosing and characterizing neurological disorders. Selecting important EEG channels is crucial for reducing computational complexity in implementing these methods and improving classification accuracy. In neuroscience, measures of (dis) similarity between EEG channels are often used as functional connectivity (FC) features, and important channels are selected via feature selection. Developing a generic measure of (dis) similarity is important for FC analysis and channel selection. In this study, learning of (dis) similarity information within the EEG is achieved using kernel-based nonlinear manifold learning. The focus is on FC changes and, thereby, EEG channel selection. Isomap and Gaussian Process Latent Variable Model (Isomap-GPLVM) are employed for this purpose. The resulting kernel (dis) similarity matrix is used as a novel measure of linear and nonlinear FC between EEG channels. The analysis of EEG from healthy controls (HC) and patients with mild to moderate Alzheimer’s disease (AD) are presented as a case study. Classification results are compared with other commonly used FC measures. Our analysis shows significant differences in FC between bipolar channels of the occipital region and other regions (i.e. parietal, centro-parietal, and fronto-central) between AD and HC groups. Furthermore, our results indicate that FC changes between channels along the fronto-parietal region and the rest of the EEG are important in diagnosing AD. Our results and its relation to functional networks are consistent with those obtained from previous studies using fMRI, resting-state fMRI and EEG.
Neurodegenerative diseases are a group of nervous system conditions characterised pathologically by the abnormal deposition of protein throughout the brain and spinal cord. One common ...pathophysiological change seen in all neurodegenerative disease is a change to the metabolic function of nervous system and peripheral cells. Glycolysis is the conversion of glucose to pyruvate or lactate which results in the generation of ATP and has been shown to be abnormal in peripheral cells in Alzheimer's disease, Parkinson's disease, and Amyotrophic Lateral Sclerosis. Changes to the glycolytic pathway are seen early in neurodegenerative disease and highlight how in multiple neurodegenerative conditions pathology is not always confined to the nervous system. In this paper, we review the abnormalities described in glycolysis in the three most common neurodegenerative diseases. We show that in all three diseases glycolytic changes are seen in fibroblasts, and red blood cells, and that liver, kidney, muscle and white blood cells have abnormal glycolysis in certain diseases. We highlight there is potential for peripheral glycolysis to be developed into multiple types of disease biomarker, but large-scale bio sampling and deciphering how glycolysis is inherently altered in neurodegenerative disease in multiple patients' needs to be accomplished first to meet this aim.
An increasing proportion of cognitive difficulties are recognized to have a functional cause, the chief clinical indicator of which is internal inconsistency. When these symptoms are impairing or ...distressing, and not better explained by other disorders, this can be conceptualized as a cognitive variant of functional neurological disorder, termed functional cognitive disorder (FCD). FCD is likely very common in clinical practice but may be under-diagnosed. Clinicians in many settings make liberal use of the descriptive term mild cognitive impairment (MCI) for those with cognitive difficulties not impairing enough to qualify as dementia. However, MCI is an aetiology-neutral description, which therefore includes patients with a wide range of underlying causes. Consequently, a proportion of MCI cases are due to non-neurodegenerative processes, including FCD. Indeed, significant numbers of patients diagnosed with MCI do not 'convert' to dementia. The lack of diagnostic specificity for MCI 'non-progressors' is a weakness inherent in framing MCI primarily within a deterministic neurodegenerative pathway. It is recognized that depression, anxiety and behavioural changes can represent a prodrome to neurodegeneration; empirical data are required to explore whether the same might hold for subsets of individuals with FCD. Clinicians and researchers can improve study efficacy and patient outcomes by viewing MCI as a descriptive term with a wide differential diagnosis, including potentially reversible components such as FCD. We present a preliminary definition of functional neurological disorder-cognitive subtype, explain its position in relation to other cognitive diagnoses and emerging biomarkers, highlight clinical features that can lead to positive diagnosis (as opposed to a diagnosis of exclusion), and red flags that should prompt consideration of alternative diagnoses. In the research setting, positive identifiers of FCD will enhance our recognition of individuals who are not in a neurodegenerative prodrome, while greater use of this diagnosis in clinical practice will facilitate personalized interventions.
Stroke is associated with the development of cognitive impairment and dementia. We assessed the effect of intensive blood pressure (BP) and/or lipid lowering on cognitive outcomes in patients with ...recent stroke in a pilot trial.
In a multicentre, partial-factorial trial, patients with recent stroke, absence of dementia, and systolic BP (SBP) 125-170 mmHg were assigned randomly to at least 6 months of intensive (target SBP <125 mmHg) or guideline (target SBP <140 mmHg) BP lowering. The subset of patients with ischaemic stroke and total cholesterol 3.0-8.0 mmol/l were also assigned randomly to intensive (target LDL-cholesterol <1.3 mmol/l) or guideline (target LDL-c <3.0 mmol/l) lipid lowering. The primary outcome was the Addenbrooke's Cognitive Examination-Revised (ACE-R).
We enrolled 83 patients, mean age 74.0 (6.8) years, and median 4.5 months after stroke. The median follow-up was 24 months (range 1-48). Mean BP was significantly reduced with intensive compared to guideline treatment (difference -10·6/-5·5 mmHg; p<0·01), as was total/LDL-cholesterol with intensive lipid lowering compared to guideline (difference -0·54/-0·44 mmol/l; p<0·01). The ACE-R score during treatment did not differ for either treatment comparison; mean difference for BP lowering -3.6 (95% CI -9.7 to 2.4), and lipid lowering 4.4 (95% CI -2.1 to 10.9). However, intensive lipid lowering therapy was significantly associated with improved scores for ACE-R at 6 months, trail making A, modified Rankin Scale and Euro-Qol Visual Analogue Scale. There was no difference in rates of dementia or serious adverse events for either comparison.
In patients with recent stroke and normal cognition, intensive BP and lipid lowering were feasible and safe, but did not alter cognition over two years. The association between intensive lipid lowering and improved scores for some secondary outcomes suggests further trials are warranted.
ISRCTN ISRCTN85562386.
Neurogenerative disorders, like dementia, can affect a person’s speech, language and as a consequence, conversational interaction capabilities. A recent study, aimed at improving dementia detection ...accuracy, investigated the use of conversation analysis (CA) of interviews between patients and neurologists as a means to differentiate between patients with progressive neurodegenerative memory disorder (ND) and those with (non-progressive) functional memory disorders (FMD). However, doing manual CA is expensive and difficult to scale up for routine clinical use. In this paper, we present an automatic classification system using an intelligent virtual agent (IVA). In particular, using two parallel corpora of respectively neurologist- and IVA-led interactions, we show that using acoustic, lexical and CA-inspired features enable ND/FMD classification rates of 90.0% for the neurologist-patient conversations, and 90.9% for the IVA-patient conversations. Analysis of the differentiating potential of individual features show that some differences exist between the IVA and human-led conversations, for example in average turn length of patients.
Alzheimer's disease (AD) accounts for 60%-70% of all dementia cases, and clinical diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify disease progression or ...alleviate symptoms are being developed, to assess their efficacy, novel robust biomarkers of brain function are urgently required. This paper aims to explore a routine to gain such biomarkers using the quantitative analysis of electroencephalography (QEEG). This paper proposes a supervised classification framework that uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-nearest neighbor (KNN) classification, quantitative evaluation, and topographic visualization. Considering the human brain either as a stationary or a dynamical system, both the frequency-based and time-frequency-based features were tested in 40 participants. The results show that: 1) the proposed method can achieve up to a 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared with alternative machine learning approaches; 2) the features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; and 3) in the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD. The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and the localization of significant QEEG features. These important findings and the proposed classification framework could be used for the development of a biomarker for the diagnosis and monitoring of disease progression in AD.
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