Maturation trajectories of cortical resting-state networks depend on the mediating frequency band

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13–30 Hz) and gamma (31–80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development.This work was supported by grants from the Nancy Lurie Marks Family Foundation (TK, SK, MGK), Autism Speaks (TK), The Simons Foundation (SFARI 239395, TK), The National Institute of Child Health and Development (R01HD073254, TK), National Institute for Biomedical Imaging and Bioengineering (P41EB015896, 5R01EB009048, MSH), and the Cognitive Rhythms Collaborative: A Discovery Network (NFS 1042134, MSH). (Nancy Lurie Marks Family Foundation; Autism Speaks; SFARI 239395 - Simons Foundation; R01HD073254 - National Institute of Child Health and Development; P41EB015896 - National Institute for Biomedical Imaging and Bioengineering; 5R01EB009048 - National Institute for Biomedical Imaging and Bioengineering; NFS 1042134 - Cognitive Rhythms Collaborative: A Discovery Network

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band.

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13-30 Hz) and gamma (31-80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development

A roadmap towards a functional paradigm for learning & memory in plants

In plants, the acquisition, processing and storage of empirical information can result in the modification of their behavior according to the nature of the stimulus, and yet this area of research remained relatively understudied until recently. As the body of evidence supporting the inclusion of plants among the higher organisms demonstrating the adaptations to accomplish these tasks keeps increasing, the resistance by traditional botanists and agricultural scientists, who were at first cautious in allowing the application of animal models onto plant physiology and development, subsides. However, the debate retains much of its heat, a good part of it originating from the controversial use of nervous system terms to describe plant processes. By focusing on the latest findings on the cellular and molecular mechanisms underlying the well established processes of Learning and Memory, recognizing what has been accomplished and what remains to be explored, and without seeking to bootstrap neuronal characteristics where none are to be found, a roadmap guiding towards a comprehensive paradigm for Learning and Memory in plants begins to emerge. Meanwhile the applications of the new field of Plant Gnosophysiology look as promising as ever. © 2018 Elsevier Gmb

Parameter identification for a local field potential driven model of the Parkinsonian subthalamic nucleus spike activity

Several models, with various degrees of complexity have been proposed to model the neuronal activity from different parts of the human brain. We have shown before that various modeling approaches, including a Hammerstein-Wiener (H-W) model, can be used to predict the spike trains from a deep nucleus, the subthalamic nucleus, using the underlying local field potentials. In this article, we present, in depth, the various choices one has to make, and the limitations that they introduce, during the H-W model parameter identification process. From a segment of the recorded data, which contains information about the spike times of a single neuron, we identify and extract the model parameters. We then use those parameters to numerically simulate the spike timing, the rhythm and the inter-spike intervals for the rest of the recording. To assess how well the model fits to the measured data we combine measures of spike train synchrony, namely the Victor-Purpura distance and the Gaussian similarity measure, with time-scale independent train distances. We show that a wise combination of metrics results in models that predict the spikes with temporal accuracy ranging, on average, from 53% to more than 80%, depending on the number of the neurons' spikes recorded. The model's prediction is adequate for estimating accurately the spike rhythm. Quantitative results establish the model's validity as a simple yet biologically plausible model of the spike activity recorded from a deep nucleus inside the human brain. © 2012 Elsevier Ltd

Prediction of the timing and the rhythm of the parkinsonian subthalamic nucleus neural spikes using the local field potentials

In this paper, we discuss the use of a nonlinear cascade model to predict the subthalamic nucleus spike activity from the local field potentials recorded in the motor area of the nucleus of Parkinsons disease patients undergoing deep brain stimulation. We use a segment of appropriately selected and processed data recorded from five nuclei to acquire the information of the spike timing and rhythm of a single neuron and estimate the model parameters. We then use the rest of each recording to assess the models accuracy in predicting spike timing, rhythm, and interspike intervals. We show that the cumulative distribution function (CDF) of the predicted spikes remains inside the 95 confidence interval of the CDF of the recorded spikes. By training the model appropriately, we prove its ability to provide quite accurate predictions for multiple-neuron recordings as well, and we establish its validity as a simple yet biologically plausible model of the intranuclear spike activity recorded from Parkinsons disease patients. © 2012 IEEE

Toward relating the subthalamic nucleus spiking activity to the local field potentials acquired intranuclearly

Studies on neurophysiological correlates of the functional magnetic resonance imaging (fMRI) signals reveal a strong relationship between the local field potential (LFP) acquired invasively and metabolic signal changes in fMRI experiments. Most of these studies failed to reveal an analogous relationship between metabolic signals and the spiking activity. That would allow for the prediction of the neural activity exclusively from the fMRI signals. However, the relationship between fMRI signals and spiking activity can be inferred indirectly provided that the LFPs can be used to predict the spiking activity of the area. Until now, only the LFP-spike relationship in cortical areas has been examined. Herein, we show that the spiking activity can be predicted by the LFPs acquired in a deep nucleus, namely the subthalamic nucleus (STN), using a nonlinear cascade model. The model can reproduce the spike patterns inside the motor area of the STN that represent information about the motor plans. Our findings expand the possibility of further recruiting non-invasive neuroimaging techniques to understand the activity of the STN and predict or even control movement. © 2011 IOP Publishing Ltd

Synaptic Plasticity: A Unifying Model to Address Some Persisting Questions

Since it was first observed, synaptic plasticity has been considered as the experimental paradigm most likely to provide us with an understanding of how information is stored in the vertebrate brain. Various types have been demonstrated over these past 45 years, most notably long-term potentiation and long-term depression, and their established characteristics as well as their induction and consolidation requirements are highly indicative of this plasticity being the substrate for skills acquisition and mnemonic engraving. The molecular, biochemical, and structural models that have been proposed in the past, although most accommodate some aspect of synaptic plasticity observations, admittedly cannot offer a universally functional connection between all the phenomena that surround and result in the different modifications of synaptic efficacy. As a result, there are a number of persisting questions. In an attempt toward synthesis, we reviewed the most important studies in the field and believe that we can now propose a unifying Model for synaptic plasticity that can accommodate the experimental evidence and reconcile most of the contradictions. Moreover, from this model emerge potential answers to several unyielding questions, namely, accounting for the induction and expression of long-term depression, identifying the plasticity switch, offering a possible explanation for the sliding modification threshold, and proposing a new mechanism for synaptic tagging