1,721,053 research outputs found
Attaining the recesses of the cognitive space
No full textExisting neuropsychological tests of executive function often manifest a difficulty pinpointing cognitive deficits when these are intermittent and come in the form of omissions. We discuss the hypothesis that two partially interrelated reasons for this failure stem from relative inability of neuropsychological tests to explore the cognitive space and to explicitly take into account strategic and opportunistic resource allocation decisions, and to address the temporal aspects of both behaviour and task-related brain function in data analysis. Criteria for tasks suitable for neuropsychological assessment of executive function, as well as appropriate ways to analyse and interpret observed behavioural data are suggested. It is proposed that experimental tasks should be devised which emphasize typical rather than optimal performance, and that analyses should quantify path-dependent fluctuations in performance levels rather than averaged behaviour. Some implications for experimental neuropsychology are illustrated for the case of planning and problem-solving abilities and with particular reference to cognitive impairment in closed-head injury
How can we study reasoning in the brain?
No full textThe brain did not develop a dedicated device for reasoning. This fact bears dramatic consequences. While for perceptuo-motor functions neural activity is shaped by the input’s statistical properties, and processing is carried out at high speed in hardwired spatially segregated modules, in reasoning, neural activity is driven by internal dynamics and processing times, stages, and functional brain geometry are largely unconstrained a priori. Here, it is shown that the complex properties of spontaneous activity, which can be ignored in a short-lived event-related world, become prominent at the long time scales of certain forms of reasoning. It is argued that the neural correlates of reasoning should in fact be defined in terms of non-trivial generic properties of spontaneous brain activity, and that this implies resorting to concepts, analytical tools, and ways of designing experiments that are as yet non-standard in cognitive neuroscience. The implications in terms of models of brain activity, shape of the neural correlates, methods of data analysis, observability of the phenomenon, and experimental designs are discussed
Neurofeedback: Principles, appraisal, and outstanding issues
No full textNeurofeedback is a form of brain training in which subjects are fed back information about some measure of their brain activity which they are instructed to modify in a way thought to be functionally advantageous. Over the last 20 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts. However, in spite of a growing popularity, neurofeedback protocols typically make (often covert) assumptions on what aspects of brain activity to target, where in the brain to act and how, which have far-reaching implications for the assessment of its potential and efficacy. Here we critically examine some conceptual and methodological issues associated with the way neurofeedback's general objectives and neural targets are defined. The neural mechanisms through which neurofeedback may act at various spatial and temporal scales, and the way its efficacy is appraised are reviewed, and the extent to which neurofeedback may be used to control functional brain activity discussed. Finally, it is proposed that gauging neurofeedback's potential, as well as assessing and improving its efficacy will require better understanding of various fundamental aspects of brain dynamics and a more precise definition of functional brain activity and brain-behaviour relationships
Commentary: The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs
No full textThe “entropic brain hypothesis” holds that the quality of conscious states depends on the system’s
entropy (Carhart-Harris et al., 2014). Brain activity is said to become “more random and so harder
to predict in primary states – of which the psychedelic state is an exemplar.” Psychedelic-induced
brain activity would be associated with elevated entropy in some of its aspects with respect to
normal wakeful consciousness. This would indicate that psychedelic-induced brain activity would
exhibit criticality, while normal wakeful consciousness would be subcritical.
But can entropy be a unique indicator of the “quality of consciousness?” Are there reasons to
believe that psychedelic-induced activity is not critical
Gauging Functional Brain Activity: From Distinguishability to Accessibility
Full text linkStandard neuroimaging techniques provide non-invasive access not only to human brain anatomy but also to its physiology. The activity recorded with these techniques is generally called functional imaging, but what is observed per se is an instance of dynamics, from which functional brain activity should be extracted. Distinguishing between bare dynamics and genuine function is a highly non-trivial task, but a crucially important one when comparing experimental observations and interpreting their significance. Here we illustrate how neuroimaging’s ability to extract genuine functional brain activity is bounded by functional representations’ structure. To do so, we first provide a simple definition of functional brain activity from a system-level brain imaging perspective. We then review how the properties of the space on which brain activity is represented induce relations on observed imaging data which allow determining the extent to which two observations are functionally distinguishable and quantifying how far apart they are. It is also proposed that genuine functional distances would require defining accessibility, i.e., how a given observed condition can be accessed from another given one, under the dynamics of some neurophysiological process. We show how these properties result from the structure defined on dynamical data and dynamics-to-function projections, and consider some implications that the way and extent to which these are defined have for the interpretation of experimental data from standard system-level brain recording techniques
Projective mechanisms subtending real world phenomena wipe away cause effect relationships
No full textCausal relationships lie at the very core of scientific description of biophysical phenomena. Nevertheless, observable facts involving changes in system shape, dimension and symmetry may elude simple cause and effect inductive explanations. Here we argue that numerous physical and biological phenomena such as chaotic dynamics, symmetry breaking, long-range collisionless neural interactions, zero-valued energy singularities, and particle/wave duality can be accounted for in terms of purely topological mechanisms devoid of causality. We illustrate how simple topological claims, seemingly far away from scientific inquiry (e.g., “given at least some wind on Earth, there must at all times be a cyclone or anticyclone somewhere”; “if one stirs to dissolve a lump of sugar in a cup of coffee, it appears there is always a point without motion”; “at any moment, there is always a pair of antipodal points on the Earth's surface with equal temperatures and barometric pressures”) reflect the action of non-causal topological rules. To do so, we introduce some fundamental topological tools and illustrate how phenomena such as double slit experiments, cellular mechanisms and some aspects of brain function can be explained in terms of geometric projections and mappings, rather than local physical effects. We conclude that unavoidable, passive, spontaneous topological modifications may lead to novel functional biophysical features, independent of exerted physical forces, thermodynamic constraints, temporal correlations and probabilistic a priori knowledge of previous cases
Why should cognitive neuroscientists study the brain's resting state?
No full textCognitive neuroscience investigates how
cognitive function is produced by the
brain. Seen from a reverse angle, cognitive
neuroscience studies how brain activity
is modulated by the execution of cognitive
tasks. In the former case, cognitive
function is characterized in terms
of neural properties associated with the
execution of given cognitive tasks, while
in the latter it can be thought of as
a probe exposing information on brain
dynamics.
Brain activity displays dynamics independently
of whether a particular task is
carried out or not. The question is then:
should cognitive neuroscience get interested
in the properties of resting brain
activity? And, if so, how and to what extent
can studying resting brain activity help
characterizing the neural correlates of cognitive
processes
Can multilayer brain networks be a real step forward?: Comment on “Network science of biological systems at different scales: A review” by M. Gosak et al
No full textVarious aspects of functional brain activity seem to capture genuine aspects of the functional organization of brain networks, making a MN representation more than a convenient representation tool. However, a series of fundamental problems arise with this new approach, which make the interpretation of multilayer brain networks a
terra incognita that will need to be explored in the near future
Brain synchronizability, a false friend
No full textSynchronization plays a fundamental role in healthy cognitive and motor function. However, how synchronization depends on the interplay between local dynamics, coupling and topology and how prone to synchronization a network is, given its topological organization, are still poorly understood issues. To investigate the synchronizability of both anatomical and functional brain networks various studies resorted to the Master Stability Function (MSF) formalism, an elegant tool which allows analysing the stability of synchronous states in a dynamical system consisting of many coupled oscillators. Here, we argue that brain dynamics does not fulfil the formal criteria under which synchronizability is usually quantified and, perhaps more importantly, this measure refers to a global dynamical condition that never holds in the brain (not even in the most pathological conditions), and therefore no neurophysiological conclusions should be drawn based on it. We discuss the meaning of synchronizability and its applicability to neuroscience and propose alternative ways to quantify brain networks synchronization
Measuring brain temperature without a thermometer
No full textTemperature has profound effects on a
wide range of parameters of neural activity
at various scales (Hodgkin and Katz,
1949). At the cell level, ionic currents,
membrane potential, input resistance,
action potential amplitude, duration and
propagation, and synaptic transmission
have all been shown to be affected
by temperature variations (Hodgkin and
Katz, 1949; Kullmann and Asztely, 1998;
Volgushev et al., 2000a,b; Fujii et al.,
2002). At mesoscopic scales of neural
activity, temperature changes can steer
network activity toward different functional
regimes (Reig et al., 2010), affecting
the duration, frequency and firing rate
of activated states during slow frequency
oscillations, and the ability to end these
states (Compte et al., 2003). Temperature
also has a substantial effect on chemical
reaction rates (Swan, 1974), and
affects the blood oxygen saturation level
by changing haemoglobin affinity for oxygen
(Guyton, 1987). Furthermore, cooling
reduces metabolic processes (Esmann
and Skou, 1988), and has been used to
silence cortical areas to study their function
(Uyeda and Fuster, 1967).
While from single cell to mesoscopic
levels temperature can directly be measured,
at the system level of non-invasive
studies using electroencephalogram or
functional magnetic resonance, it can
only be estimated indirectly, using the
temperature dependence of the magnetic
resonance signal’s frequency (Hindman,
1966; Parker et al., 1983; Kuroda et al.,
1996). Furthermore, a theoretical model
of brain temperature (Yablonskiy et al.,
2000; Sukstankii and Yablonskiy, 2006)
allows inferring from functional magnetic
resonance data that functional stimulation
can induce local brain temperature
fluctuationsofupto±1
◦
Cwithrespect
to resting temperature, by locally changing
the balance between metabolic heat
production and heat removal by blood
flow.
The potential impact of temperature
modulations on functional brain
activity is significant. Given a temperature
effect on blood oxygen saturation
levels of several percent/1
◦
C(Guyton,
1987), and an estimated average brain
van’t Hoff temperature coefficient Q
(the factor by which a reaction rate
increases for 10
◦
C increases) of 2,3 (Swan,
1974),theobservedtemperaturefluctuations
may lead to sizeable changes
in blood oxygen saturation levels and
to >2% variations in chemical reaction
rates.
Here we propose a way to directly quantify
temperature from system-level brain
recordings, and show how it can be used to
characterize neural activity associated with
cognitive function
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