Prediction and Topological Models in Neuroscience

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that topological predictions can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone

Loss of neuronal network resilience precedes seizures and determines the ictogenic nature of interictal synaptic perturbations

The mechanisms of seizure emergence, and the role of brief interictal epileptiform discharges (IEDs) in seizure generation are two of the most important unresolved issues in modern epilepsy research. Our study shows that the transition to seizure is not a sudden phenomenon,but a slow process characterized by the progressive loss of neuronal network resilience. From a dynamical perspective, the slow transition is governed by the principles of critical slowing, a robust natural phenomenon observable in systems characterized by transitions between dynamical regimes. In epilepsy, this process is modulated by the synchronous synaptic input from IEDs. IEDs are external perturbations that produce phasic changes in the slow transition process and exert opposing effects on the dynamics of a seizure-generating network, causing either anti-seizure or pro-seizure effects. We show that the multifaceted nature of IEDs is defined by the dynamical state of the network at the moment of the discharge occurrence

Multilocus ISSR Markers Reveal Two Major Genetic Groups in Spanish and South African Populations of the Grapevine Fungal Pathogen Cadophora luteo-olivacea

Cadophora luteo-olivacea is a lesser-known fungal trunk pathogen of grapevine which has been recently isolated from vines showing decline symptoms in grape growing regions worldwide. In this study, 80 C. luteo-olivacea isolates (65 from Spain and 15 from South Africa) were studied. Inter-simple-sequence repeat-polymerase chain reaction (ISSR-PCR) generated 55 polymorphic loci from four ISSR primers selected from an initial screen of 13 ISSR primers. The ISSR markers revealed 40 multilocus genotypes (MLGs) in the global population. Minimum spanning network analysis showed that the MLGs from South Africa clustered around the most frequent genotype, while the genotypes from Spain were distributed all across the network. Principal component analysis and dendrograms based on genetic distance and bootstrapping identified two highly differentiated genetic clusters in the Spanish and South African C. luteo-olivacea populations, with no intermediate genotypes between these clusters. Movement within the Spanish provinces may have occurred repeatedly given the frequent retrieval of the same genotype in distant locations. The results obtained in this study provide new insights into the population genetic structure of C. luteo-olivacea in Spain and highlights the need to produce healthy and quality planting material in grapevine nurseries to avoid the spread of this fungus throughout different grape growing regions

Image-based adaptive optics for in vivo imaging in the hippocampus

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