A dispersal-induced paradox: synchrony and stability in stochastic metapopulations

Understanding how dispersal influences the dynamics of spatially distributed populations is a major priority of both basic and applied ecologists. Two well-known effects of dispersal are spatial synchrony (positively correlated population dynamics at different points in space) and dispersal-induced stability (the phenomenon whereby populations have simpler or less extinction-prone dynamics when they are linked by dispersal than when they are isolated). Although both these effects of dispersal should occur simultaneously, they have primarily been studied separately. Herein, I summarise evidence from the literature that these effects are expected to interact, and I use a series of models to characterise that interaction. In particular, I explore the observation that although dispersal can promote both synchrony and stability singly, it is widely held that synchrony paradoxically prevents dispersal-induced stability. I show here that in many realistic scenarios, dispersal is expected to promote both synchrony and stability at once despite this apparent destabilising influence of synchrony. This work demonstrates that studying the spatial and temporal impacts of dispersal together will be vital for the conservation and management of the many communities for which human activities are altering natural dispersal rates.     

Menstrual synchrony

Several studies have now documented menstrual synchrony in human females. There is a broad consensus that the phenomenon mainly occurs in women who spend a significant amount of time together, such as close friends and coworkers, and that social contact rather than a similar environment plays an important role in mediating the effect. However, the mechanisms involved and the adaptive function of menstrual synchrony are not understood. There is some evidence that olfactory cues between females might underlie the effect. More research is needed before the precise mechanisms that regulate menstrual synchrony are elucidated. Cynthia Graham received her MSc in clinical psychology from Glasgow University and her PhD from McGill University. Her previous research has involved menstrual synchrony and the effects of oral contraceptives on premenstrual changes. Currently she is working on a World Health Organization project assessing the effects of steroidal contraceptives on well-being and sexuality.     

Antibody-induced cell-cycle synchrony

Synergism between specific antibodies and cytotoxic drugs in killing tumor cells is an established phenomenon. When cells grown in a suspension culture are treated with antibody, a subsequent increase in susceptibility to cytosine arabinoside has been shown to be due to the induction of cell-cycle synchrony. This observation may lead to a novel way of investigating cell-cycle-dependent processes and also an aspect of tumor-antibody interactions that is not generally recognized.     

Canonical Analysis

Several fundamental properties of canonical variates are developed. In addition, the equivalence of the redundancy coefficient and the composite coefficient of determination is demonstrated; the latter is used in evaluating the prediction of input variables by canonical variates.     

A Canonical Circuit for Generating Phase-Amplitude Coupling

‘Phase amplitude coupling’ (PAC) in oscillatory neural activity describes a phenomenon whereby the amplitude of higher frequency activity is modulated by the phase of lower frequency activity. Such coupled oscillatory activity – also referred to as ‘cross-frequency coupling’ or ‘nested rhythms’ – has been shown to occur in a number of brain regions and at behaviorally relevant time points during cognitive tasks; this suggests functional relevance, but the circuit mechanisms of PAC generation remain unclear. In this paper we present a model of a canonical circuit for generating PAC activity, showing how interconnected excitatory and inhibitory neural populations can be periodically shifted in to and out of oscillatory firing patterns by afferent drive, hence generating higher frequency oscillations phase-locked to a lower frequency, oscillating input signal. Since many brain regions contain mutually connected excitatory-inhibitory populations receiving oscillatory input, the simplicity of the mechanism generating PAC in such networks may explain the ubiquity of PAC across diverse neural systems and behaviors. Analytic treatment of this circuit as a nonlinear dynamical system demonstrates how connection strengths and inputs to the populations can be varied in order to change the extent and nature of PAC activity, importantly which phase of the lower frequency rhythm the higher frequency activity is locked to. Consequently, this model can inform attempts to associate distinct types of PAC with different network topologies and physiologies in real data.     

Computing with neural synchrony

Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties.     

Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery

Genome-wide association studies have identified a wealth of genetic variants involved in complex traits and multifactorial diseases. There is now considerable interest in testing variants for association with multiple phenotypes (pleiotropy) and for testing multiple variants for association with a single phenotype (gene-based association tests). Such approaches can increase statistical power by combining evidence for association over multiple phenotypes or genetic variants respectively. Canonical Correlation Analysis (CCA) measures the correlation between two sets of multidimensional variables, and thus offers the potential to combine these two approaches. To apply CCA, we must restrict the number of attributes relative to the number of samples. Hence we consider modules of genetic variation that can comprise a gene, a pathway or another biologically relevant grouping, and/or a set of phenotypes. In order to do this, we use an attribute selection strategy based on a binary genetic algorithm. Applied to a UK-based prospective cohort study of 4286 women (the British Women''s Heart and Health Study), we find improved statistical power in the detection of previously reported genetic associations, and identify a number of novel pleiotropic associations between genetic variants and phenotypes. New discoveries include gene-based association of NSF with triglyceride levels and several genes (ACSM3, ERI2, IL18RAP, IL23RAP and NRG1) with left ventricular hypertrophy phenotypes. In multiple-phenotype analyses we find association of NRG1 with left ventricular hypertrophy phenotypes, fibrinogen and urea and pleiotropic relationships of F7 and F10 with Factor VII, Factor IX and cholesterol levels.     

Oscillations and hippocampal-prefrontal synchrony

The hippocampus and medial prefrontal cortex subserve spatial working memory in rodents. Recent evidence has demonstrated functional interactions between these brain regions in the form of sychronization of oscillatory activity during behavior. The nature of this synchrony and its relationship to behavioral performance suggests an important role in the function of the hippocampal-prefrontal circuit.     

Inhibitory synchrony as a mechanism for attentional gain modulation

Recordings from area V4 of monkeys have revealed that when the focus of attention is on a visual stimulus within the receptive field of a cortical neuron, two distinct changes can occur: The firing rate of the neuron can change and there can be an increase in the coherence between spikes and the local field potential (LFP) in the gamma-frequency range (30-50 Hz). The hypothesis explored here is that these observed effects of attention could be a consequence of changes in the synchrony of local interneuron networks. We performed computer simulations of a Hodgkin-Huxley type neuron driven by a constant depolarizing current, I, representing visual stimulation and a modulatory inhibitory input representing the effects of attention via local interneuron networks. We observed that the neuron's firing rate and the coherence of its output spike train with the synaptic inputs was modulated by the degree of synchrony of the inhibitory inputs. When inhibitory synchrony increased, the coherence of spiking model neurons with the synaptic input increased, but the firing rate either increased or remained the same. The mean number of synchronous inhibitory inputs was a key determinant of the shape of the firing rate versus current (f-I) curves. For a large number of inhibitory inputs (approximately 50), the f-I curve saturated for large I and an increase in input synchrony resulted in a shift of sensitivity-the model neuron responded to weaker inputs I. For a small number (approximately 10), the f-I curves were non-saturating and an increase in input synchrony led to an increase in the gain of the response-the firing rate in response to the same input was multiplied by an approximately constant factor. The firing rate modulation with inhibitory synchrony was highest when the input network oscillated in the gamma frequency range. Thus, the observed changes in firing rate and coherence of neurons in the visual cortex could be controlled by top-down inputs that regulated the coherence in the activity of a local inhibitory network discharging at gamma frequencies.     

Bootstrap methods for estimating spatial synchrony of fluctuating populations

We describe and examine methods for estimating spatial correlations used in population ecology. We base our analyses on a hypothetical example of a species that has been censured at 30 different locations for 20 years. We assume that the population fluctuations can be described by a simple linear model on logarithmic scale. Stochastic simulations is utilized to check how seven different ways of resampling perform when the goal is to find nominal 95% confidence intervals for the spatial correlation in growth rates at given distances. It turns out that resampling of locations performs badly, with true coverage level as low as 30–40%, especially for small correlations at long distances. Resampling of timepoints performs much better, with coverage varying from 80 to 90%, depending on the strength of density regulation and whether the spatial correlation is estimated for the response variable or for the error terms in the model. Assuming that the underlying model is known, the best results are achieved for parametric bootstrapping, a result that strongly emphasize the importance of defining and estimating a proper population model when studying spatial processes.     

High temporal frequency synchrony is insufficient for perceptual grouping

We used textures of randomly moving grating patches to assess the role of fine-grain temporal synchrony in texture segregation. In the target area, patches reversed direction simultaneously. In the surround, patches changed direction at random times. Thus, phase changes in the target area were precisely synchronous, whereas those in the surround were not. In agreement with work carried out by Lee and Blake, we found that the target area was frequently visible, and that observers could discriminate its shape (horizontal versus vertical) at frame rates of 100 Hz in brief exposures (200 ms). Further experiments suggested that the length of unidirectional motion sequences in the target area, rather than synchrony, determined its visibility. To eliminate completely contrast and motion cues, we made all the background elements identical to the target elements, but with a random starting phase. Despite the presence of synchrony in the target area but not the background, the target was generally very hard to see. Targets that remained visible contained low temporal frequency modulations of direction. We conclude that the human observer can detect synchrony, but only at modest temporal frequencies once motion and contrast artefacts have been eliminated.     

Canonical phylogenetic ordination

A phylogenetic comparative method is proposed for estimating historical effects on comparative data using the partitions that compose a cladogram, i.e., its monophyletic groups. Two basic matrices, Y and X, are defined in the context of an ordinary linear model. Y contains the comparative data measured over t taxa. X consists of an initial tree matrix that contains all the xj monophyletic groups (each coded separately as a binary indicator variable) of the phylogenetic tree available for those taxa. The method seeks to define the subset of groups, i.e., a reduced tree matrix, that best explains the patterns in Y. This definition is accomplished via regression or canonical ordination (depending on the dimensionality of Y) coupled with Monte Carlo permutations. It is argued here that unrestricted permutations (i.e., under an equiprobable model) are valid for testing this specific kind of groupwise hypothesis. Phylogeny is either partialled out or, more properly, incorporated into the analysis in the form of component variation. Direct extensions allow for testing ecomorphological data controlled by phylogeny in a variation partitioning approach. Currently available statistical techniques make this method applicable under most univariate/multivariate models and metrics; two-way phylogenetic effects can be estimated as well. The simplest case (univariate Y), tested with simulations, yielded acceptable type I error rates. Applications presented include examples from evolutionary ethology, ecology, and ecomorphology. Results showed that the new technique detected previously overlooked variation clearly associated with phylogeny and that many phylogenetic effects on comparative data may occur at particular groups rather than across the entire tree.     

Canonical structures for the hypervariable regions of immunoglobulins

We have analysed the atomic structures of Fab and VL fragments of immunoglobulins to determine the relationship between their amino acid sequences and the three-dimensional structures of their antigen binding sites. We identify the relatively few residues that, through their packing, hydrogen bonding or the ability to assume unusual phi, psi or omega conformations, are primarily responsible for the main-chain conformations of the hypervariable regions. These residues are found to occur at sites within the hypervariable regions and in the conserved beta-sheet framework. Examination of the sequences of immunoglobulins of unknown structure shows that many have hypervariable regions that are similar in size to one of the known structures and contain identical residues at the sites responsible for the observed conformation. This implies that these hypervariable regions have conformations close to those in the known structures. For five of the hypervariable regions, the repertoire of conformations appears to be limited to a relatively small number of discrete structural classes. We call the commonly occurring main-chain conformations of the hypervariable regions "canonical structures". The accuracy of the analysis is being tested and refined by the prediction of immunoglobulin structures prior to their experimental determination.