494 research outputs found
On the molecular mechanism of surface charge amplification and related phenomena at aqueous polyelectrolyte-graphene interfaces
In this communication we illustrate the occurrence of a recently reported new
phenomenon of surface-charge amplification, SCA, (originally dubbed
overcharging, OC), [Jimenez-Angeles F. and Lozada-Cassou M., J. Phys. Chem. B,
2004, 108, 7286] by means of molecular dynamics simulation of aqueous
electrolytes solutions involving multivalent cations in contact with charged
graphene walls and the presence of short-chain lithium polystyrene sulfonates
where the solvent water is described explicitly with a realistic molecular
model. We show that the occurrence of SCA in these systems, in contrast to that
observed in primitive models, involves neither contact co-adsorption of the
negatively charged macroions nor divalent cations with a large size and charge
asymmetry as required in the case of implicit solvents. In fact the SCA
phenomenon hinges around the preferential adsorption of water (over the
hydrated ions) with an average dipolar orientation such that the charges of the
water's hydrogen and oxygen sites induce magnification rather than screening of
the positive-charged graphene surface, within a limited range of surface-charge
density.Comment: 10 pages, 6 figure
Self-similar correlation function in brain resting-state fMRI
Adaptive behavior, cognition and emotion are the result of a bewildering
variety of brain spatiotemporal activity patterns. An important problem in
neuroscience is to understand the mechanism by which the human brain's 100
billion neurons and 100 trillion synapses manage to produce this large
repertoire of cortical configurations in a flexible manner. In addition, it is
recognized that temporal correlations across such configurations cannot be
arbitrary, but they need to meet two conflicting demands: while diverse
cortical areas should remain functionally segregated from each other, they must
still perform as a collective, i.e., they are functionally integrated. Here, we
investigate these large-scale dynamical properties by inspecting the character
of the spatiotemporal correlations of brain resting-state activity. In physical
systems, these correlations in space and time are captured by measuring the
correlation coefficient between a signal recorded at two different points in
space at two different times. We show that this two-point correlation function
extracted from resting-state fMRI data exhibits self-similarity in space and
time. In space, self-similarity is revealed by considering three successive
spatial coarse-graining steps while in time it is revealed by the 1/f frequency
behavior of the power spectrum. The uncovered dynamical self-similarity implies
that the brain is spontaneously at a continuously changing (in space and time)
intermediate state between two extremes, one of excessive cortical integration
and the other of complete segregation. This dynamical property may be seen as
an important marker of brain well-being both in health and disease.Comment: 14 pages 13 figures; published online before print September 2
Signal integration enhances the dynamic range in neuronal systems
The dynamic range measures the capacity of a system to discriminate the
intensity of an external stimulus. Such an ability is fundamental for living
beings to survive: to leverage resources and to avoid danger. Consequently, the
larger is the dynamic range, the greater is the probability of survival. We
investigate how the integration of different input signals affects the dynamic
range, and in general the collective behavior of a network of excitable units.
By means of numerical simulations and a mean-field approach, we explore the
nonequilibrium phase transition in the presence of integration. We show that
the firing rate in random and scale-free networks undergoes a discontinuous
phase transition depending on both the integration time and the density of
integrator units. Moreover, in the presence of external stimuli, we find that a
system of excitable integrator units operating in a bistable regime largely
enhances its dynamic range.Comment: 5 pages, 4 figure
Single subject pharmacological-MRI (phMRI) study: Modulation of brain activity of psoriatic arthritis pain by cyclooxygenase-2 inhibitor
We use fMRI to examine brain activity for pain elicited by palpating joints in a single patient suffering from psoriatic arthritis. Changes in these responses are documented when the patient ingested a single dose of a selective cyclooxygenase-2 inhibitor (COX-2i). We show that mechanical stimulation of the painful joints exhibited a cortical activity pattern similar to that reported for acute pain, with activity primarily localized to the thalamus, insular, primary and secondary somatosensory cortices and the mid anterior cingulum. COX-2i resulted in significant decreased in reported pain intensity and in brain activity after 1 hour of administration. The anterior insula and SII correlated with pain intensity, however no central activation site for the drug was detected. We demonstrate the similarity of the activation pattern for palpating painful joints to brain activity in normal subjects in response to thermal painful stimuli, by performing a spatial conjunction analysis between these maps, where overlap is observed in the insula, thalamus, secondary somatosensory cortex, and anterior cingulate. The results demonstrate that one can study effects of pharmacological manipulations in a single subject where the brain activity for a clinical condition is delineated and its modulation by COX-2i demonstrated. This approach may have diagnostic and prognostic utility
Seeking for a fingerprint: analysis of point processes in actigraphy recording
Motor activity of humans displays complex temporal fluctuations which can be
characterized by scale-invariant statistics, thus documenting that structure
and fluctuations of such kinetics remain similar over a broad range of time
scales. Former studies on humans regularly deprived of sleep or suffering from
sleep disorders predicted change in the invariant scale parameters with respect
to those representative for healthy subjects. In this study we investigate the
signal patterns from actigraphy recordings by means of characteristic measures
of fractional point processes. We analyse spontaneous locomotor activity of
healthy individuals recorded during a week of regular sleep and a week of
chronic partial sleep deprivation. Behavioural symptoms of lack of sleep can be
evaluated by analysing statistics of duration times during active and resting
states, and alteration of behavioural organization can be assessed by analysis
of power laws detected in the event count distribution, distribution of waiting
times between consecutive movements and detrended fluctuation analysis of
recorded time series. We claim that among different measures characterizing
complexity of the actigraphy recordings and their variations implied by chronic
sleep distress, the exponents characterizing slopes of survival functions in
resting states are the most effective biomarkers distinguishing between healthy
and sleep-deprived groups.Comment: Communicated at UPON2015, 14-17 July 2015, Barcelona. 21 pages, 11
figures; updated: figures 4-7, text revised, expanded Sec. 1,3,
Unstable Dynamics, Nonequilibrium Phases and Criticality in Networked Excitable Media
Here we numerically study a model of excitable media, namely, a network with
occasionally quiet nodes and connection weights that vary with activity on a
short-time scale. Even in the absence of stimuli, this exhibits unstable
dynamics, nonequilibrium phases -including one in which the global activity
wanders irregularly among attractors- and 1/f noise while the system falls into
the most irregular behavior. A net result is resilience which results in an
efficient search in the model attractors space that can explain the origin of
certain phenomenology in neural, genetic and ill-condensed matter systems. By
extensive computer simulation we also address a relation previously conjectured
between observed power-law distributions and the occurrence of a "critical
state" during functionality of (e.g.) cortical networks, and describe the
precise nature of such criticality in the model.Comment: 18 pages, 9 figure
Rate-dependent propagation of cardiac action potentials in a one-dimensional fiber
Action potential duration (APD) restitution, which relates APD to the
preceding diastolic interval (DI), is a useful tool for predicting the onset of
abnormal cardiac rhythms. However, it is known that different pacing protocols
lead to different APD restitution curves (RCs). This phenomenon, known as APD
rate-dependence, is a consequence of memory in the tissue. In addition to APD
restitution, conduction velocity restitution also plays an important role in
the spatiotemporal dynamics of cardiac tissue. We present new results
concerning rate-dependent restitution in the velocity of propagating action
potentials in a one-dimensional fiber. Our numerical simulations show that,
independent of the amount of memory in the tissue, waveback velocity exhibits
pronounced rate-dependence and the wavefront velocity does not. Moreover, the
discrepancy between waveback velocity RCs is most significant for small DI. We
provide an analytical explanation of these results, using a system of coupled
maps to relate the wavefront and waveback velocities. Our calculations show
that waveback velocity rate-dependence is due to APD restitution, not memory.Comment: 17 pages, 7 figure
Noise in neurons is message-dependent
Neuronal responses are conspicuously variable. We focus on one particular
aspect of that variability: the precision of action potential timing. We show
that for common models of noisy spike generation, elementary considerations
imply that such variability is a function of the input, and can be made
arbitrarily large or small by a suitable choice of inputs. Our considerations
are expected to extend to virtually any mechanism of spike generation, and we
illustrate them with data from the visual pathway. Thus, a simplification
usually made in the application of information theory to neural processing is
violated: noise {\sl is not independent of the message}. However, we also show
the existence of {\sl error-correcting} topologies, which can achieve better
timing reliability than their components.Comment: 6 pages,6 figures. Proceedings of the National Academy of Sciences
(in press
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