Publications

An ANOVA approach for statistical comparisons of brain networks

Daniel Fraiman and Ricardo Fraiman

The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.

Computational models of memory consolidation and long-term synaptic plasticity during sleep

César Rennó-Costa, Ana Cláudia Costa da Silva, Wilfredo Blanco and Sidarta Ribeiro

The brain stores memories by persistently changing the connectivity between neurons. Sleep is known to be critical for these changes to endure. Research on the neurobiology of sleep and the mechanisms of long-term synaptic plasticity has provided data in support of various theories of how brain activity during sleep affects long-term synaptic plasticity. The experimental findings – and therefore the theories – are apparently quite contradictory, with some evidence pointing to a role of sleep in the forgetting of irrelevant memories, whereas other results indicate that sleep supports the reinforcement of the most valuable recollections. A unified theoretical framework is in need. Computational modeling and simulation provide grounds for the quantitative testing and comparison of theoretical predictions and observed data, and might serve as a strategy to organize the rather complicated and diverse pool of data and methodologies used in sleep research. This review article outlines the emerging progress in the computational modeling and simulation of the main theories on the role of sleep in memory consolidation.

Estimating the interaction graph of stochastic neural dynamics

Aline Duarte, Antonio Galves, Eva Löcherbach and Guilherme Ost

In this paper, we address the question of statistical model selection for a class of stochastic models of biological neural nets. Models in this class are systems of interacting chains with memory of variable length. Each chain describes the activity of a single neuron, indicating whether it spikes or not at a given time. The spiking probability of a given neuron depends on the time evolution of its presynaptic neurons since its last spike time. When a neuron spikes, its potential is reset to a resting level and postsynaptic current pulses are generated, modifying the membrane potential of all its postsynaptic neurons. The relationship between a neuron and its pre- and postsynaptic neurons defines an oriented graph, the interaction graph of the model. The goal of this paper is to estimate this graph based on the observation of the spike activity of a finite set of neurons over a finite time. We provide explicit exponential upper bounds for the probabilities of under- and overestimating the interaction graph restricted to the observed set and obtain the strong consistency of the estimator. Our result does not require stationarity nor uniqueness of the invariant measure of the process.

Plasticity in the Brain after a Traumatic Brachial Plexus Injury in Adults

Claudia Vargas, Fernanda Torres, Bia Ramalho, Cristiane Patroclo, Lidiane Souza, Fernanda Guimarães, José Vicente Martins and Maria Luíza Rangel

In this chapter, we aim to discuss the neurophysiological basis of the brain reorganization (also called plasticity) that associates with a traumatic brachial plexus injury (TBPI), as well as following the brachial plexus surgical reconstruction and its physical rehabilitation. We start by reviewing core aspects of plasticity following peripheral injuries such as amputation and TBPI as well as those associated with chronic pain conditions. Then, we present recent results collected by our team centered on physiological measurements of plasticity after TBPI. Finally, we discuss that an important limitation in the field is the lack of systematic measurement of TBPI clinical features. We finish by proposing possible future venues in the domain of brain plasticity following a TBPI.

Firing properties of ventral medullary respiratory neurons in sino-aortic denervated rats

Amorim MR, Pena RFO, Souza GMPR, Bonagamba LGH, Roque AC and Machado BH

In previous studies we documented that after sino-aortic denervation (SAD) in rats there are significant changes in the breathing pattern, but no significant changes in sympathetic activity and mean arterial pressure (MAP) compared with Sham-operated rats. However, the neural mechanisms involved in the respiratory changes after SAD and the extent to which they may contribute to the observed normal sympathetic activity and MAP remain unclear. Here, we hypothesized that after SAD, rats present with changes in the firing frequency of the ventral medullary inspiratory and post-inspiratory neurons. To test this hypothesis, male Wistar rats underwent SAD or Sham surgery and 3 days later were surgically prepared for an in situ preparation. The duration of inspiration significantly increased in SAD rats. During inspiration, the total firing frequency of Ramp-I, Pre-I / I, and Late-I neurons was not different between groups. During post-inspiration, the total firing frequency of Post-I neurons was also not different between groups. Furthermore, the data demonstrate a reduced interburst frequency of Pre-I/I and an increased long-term variability of Late-I neurons in SAD compared with Sham rats. These findings indicate that the SAD-induced prolonged inspiration was not accompanied by alterations in the total firing frequency of the ventral medullary respiratory neurons, but it was associated with changes in the long-term variability of Late-I neurons. We suggest that the timing imbalance in the respiratory network in SAD rats may contribute to the modulation of presympathetic neurons after removal of baroreceptor afferents.

Anticipated Synchronization and Zero-Lag Phases in Population Neural Models

Germán César Dima, Mauro Copelli and Gabriel Bernardo Mindlin

Anticipated synchronization is a counterintuitive synchronization regime between a master and a slave dynamical system in which there is a negative phase difference between the driver and the driven system. By studying a set of simple neural oscillators, we unveil the dynamical mechanisms required to generate this phenomenon. We study master–slave configurations where the slave system is, when uncoupled, in a quiescent excitable state. We exemplify our results by describing the dynamics of a dynamical system proposed to model the part of a songbird’s brain involved in song production.

Discrete One-Dimensional Coverage Process on a Renewal Process

Sandro Gallo and Nancy L. Garcia

Consider the following coverage model on N, for each site i ∈ N associate a pair (ξi, Ri) where (ξi)i≥0 is a 1-dimensional undelayed discrete renewal point process and (Ri)i≥0 is an i.i.d. sequence of N-valued random variables. At each site where ξi = 1 start an interval of length Ri . Coverage occurs if every site of N is covered by some interval. We obtain sharp conditions for both, positive and null probability of coverage. As corollaries, we extend results of the literature of rumor processes and discrete one-dimensional Boolean percolation.

Dynamic uniqueness for stochastic chains with unbounded memory

Christophe Gallesco, Sandro Gallo and Daniel Y. Takahashi

We say that a probability kernel exhibits dynamic uniqueness (DU) if all the stochastic chains starting from a fixed past coincide on the future tail σ-algebra. Our first theorem is a set of properties that are pairwise equivalent to DU which allow us to understand how it compares to other more classical concepts. In particular, we prove that DU is equivalent to a weak-ℓ2 summability condition on the kernel. As a corollary to this theorem, we prove that the Bramson–Kalikow and the long-range Ising models both exhibit DU if and only if their kernels are ℓ2 summable. Finally, if we weaken the condition for DU, asking for coincidence on the future σ-algebra for almost every pair of pasts, we obtain a condition that is equivalent to β-mixing (weak-Bernoullicity) of the compatible stationary chain. As a consequence, we show that a modification of the weak-ℓ2 summability condition on the kernel is equivalent to the β-mixing of the compatible stationary chain.

Post-class naps boost declarative learning in a naturalistic school setting

Thiago Cabral, Natália B. Mota, Lucia Fraga, Mauro Copelli, Mark A. McDaniel and Sidarta Ribeiro

Laboratory evidence of a positive effect of sleep on declarative memory consolidation suggests that naps can be used to boost school learning in a scalable, low-cost manner. The few direct investigations of this hypothesis have so far upheld it, but departed from the naturalistic setting by testing non-curricular contents presented by experimenters instead of teachers. Furthermore, nap and non-nap groups were composed of different children. Here we assessed the effect of post-class naps on the retention of Science and History curricular contents presented by the regular class teacher to 24 students from 5th grade. Retention was repeatedly measured 3–4 days after content learning, with weekly group randomization over 6 consecutive weeks. Contents followed by long naps (>30 min), but not short naps (

Can the Recording of Motor Potentials Evoked by Transcranial Magnetic Stimulation Be Optimized?

Marco A. C. Garcia, Victor H. Souza and Claudia D. Vargas

Transcranial magnetic stimulation (TMS) combined with surface electromyography (sEMG) has been for a long time an important non-invasive tool to investigate and better understand how brain controls the skeletal muscles. However, the present literature still lacks standardization protocols and comprehensive discussions about possible influences of sEMG electrode placement and montages on TMS evoked responses. With the advent of TMS by Barker et al. (1985), several advances have been made in basic and clinical neurophysiology (Rossini et al., 2015). In TMS, a high-intensity brief magnetic pulse applied with a coil over the subject's scalp, induces an electric field across the cortical tissue that depolarizes a group of neuronal pools. Therefore, if a single pulse is applied over a particular spot of the primary motor cortex (M1), the generated action potentials travel down the corticospinal tract reaching a specific muscle or group of muscles, which in turn can be achieved by recording their myoelectric activities. Such myoelectric activity may contain potentials varying from a few micro to millivolts and are recognized as motor evoked potentials (MEPs). MEPs can be recorded by means of sEMG with different electrode types, e.g. surface or indwelling, and montages, e.g. mono and bipolar. Most TMS applications take advantage of MEP amplitude and latency to evaluate the integrity and/or excitability of the motor corticospinal pathway to study normal and abnormal aspects of neurophysiology, including the pathophysiology of many neurological and motor disorders. Some may believe that differences in electrode arrangement for recording MEPs can offer a small impact in data quality; in this case he/she may be a victim of an ordinary pitfall. Thus, we may ask and discuss along this manuscript, what are the disadvantages and advantages of recording MEPs from different surface electrode montages? Do they provide a robust and similar comprehension of motor corticospinal excitability?

Pages

 

NeuroMat

The Research, Innovation and Dissemination Center for Neuromathematics is hosted by the University of São Paulo and funded by FAPESP (São Paulo Research Foundation).

 

User login

 

Contact

Address:
1010 Matão Street - Cidade Universitária - São Paulo - SP - Brasil. 05508-090. See map.

Phone:
55 11 3091-1717

General contact email:
neuromat@numec.prp.usp.br

Media inquiries email:
comunicacao@numec.prp.usp.br