Transcript Correlated neuronal activity and the flow of neural

Correlated neuronal activity
and the flow of neural
information
Jaeseung Jeong, Ph.D
Department of Bio and Brain Engineering
Nonlinear information transmission of the cerebral cortex
Conventional measure: cross-correlation
• Cross-correlation is used for quantifying correlations between
EEGs from different channels implying the information
transmission between two cortical regions (coherence analysis).
 ( x1   x1 )( x2   x2 ) 
 ( x1   x1 )2   ( x2   x2 )2 
[Example]
Jelic V et al., Quantitative electroencephalography power and
coherence in Alzheimer's disease and mild cognitive
impairment. Dementia. 1996;7(6):314-23.
Phase synchronization in chaotic systems
• Coupled chaotic oscillators can display phase synchronization even
when their amplitudes remain uncorrelated (Rosenblum et al., 1996).
Phase synchronization is characterized by a non uniform distribution
of the phase difference between two time series. It may be more
suitable to track nonstationary and nonlinear dynamics.
Phase synchronization and interdependence
Definition of synchronization: two or many subsystems sharing specific
common frequencies
Broader notion: two or many subsystems adjust some of their timevarying properties to a common behavior due to coupling or common
external forcing
Jansen et al., Phase synchronization of the ongoing EEG and
auditory EP generation. Clin Neurophysiol. 2003;114(1):79-85.
Le Van Quyen et al., Nonlinear interdependencies of EEG signals
in human intracranially recorded temporal lobe seizures. Brain Res.
(1998)
Breakspear and Terry. Detection and description of non-linear
interdependence in normal multichannel human EEG data. Clin
Neurophysiol (2002)
Neural Synchronization
• The brain can be conceived as a complex network of coupled and
interacting subsystems. Higher brain functions depend upon
effective processing and integration of information in this network.
This raises the question how functional interactions between
different brain areas take place, and how such interactions may be
changed in different types of pathology.
Nonlinear coupling among cortical areas
Mutual information of the EEG
•The MI between measurement xi generated from system X and
measurement yj generated from system Y is the amount of
information that measurement xi provides about yj.
J Jeong, JC Gore, BS Peterson. Mutual information analysis of the EEG
in patients with Alzheimer's disease. Clin Neurophysiol (2001)
What is the resting state
as a reference baseline?
What does the brain do when not actively engaged in
goal-directed cognitive tasks –
when, for want of a better term, we might say it is at “rest”?
What functions does the ‘resting’ brain subserve
and how do these impinge on more general aspects of cognition?
Functional connectivity in the motor cortex of
resting human brain using MRI.
•
An MRI time course of 512 images in resting human brain obtained every 250 ms reveals
fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the
sensorimotor cortex that were activated secondary to hand movement were identified using
functional MRI methodology (FMRI).
•
Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a
high degree of temporal correlation (P < 10(-3)) within these regions and also with time
courses in several other regions that can be associated with motor function.
•
It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations
in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
(Biswal B., Yetkin F., Haughton V. and Hyde J., (1995) Functional connectivity in the motor
cortex of resting human brain using echo-planar MRI. Magn. Res. Med. 34, 537–541)
•
Biswal et al. (1995) were the first to observe the coherence between such low f
requency oscillations and widely distributed neuro-anatomical networks.
•
This issue has since been explored in a wide range of tasks (e.g. [Gusnard et al
., 2001], [Kelly et al., 2008] and [McKiernan et al., 2006]), clinical pathologies (e
.g. [Bluhm et al., 2007], [Castellanos et al., 2008], [Greicius et al., 2007], [Greici
us et al., 2004], [Kennedy et al., 2006], [Lowe et al., 2002], [Tian et al., 2006] an
d [Tinaz et al., 2008]), and even in chimpanzees (Rilling et al., 2007).
Detection of functional connectivity using temporal correlations in MR images.
Michelle Hampson , Bradley S. Peterson, Pawel Skudlarski, James C. Gatenby, John C. Gore,
Human Brain Mapping 15(4):247 - 262, 2002
What is default mode network?
•
The default mode network (DMN) is a network of brain regions that are active when the
brain is at rest, which is characterized by coherent neuronal oscillations at a rate lower
than 0.1 Hz.
•
The DMN includes the posterior cingulate cortex (PCC) and the adjacent precuneus, the
medial prefrontal cortex (MPFC), and the medial, lateral and inferior parietal cortex.
•
Although deactivated during task performance, this network is active in the resting brain
with a high degree of functional connectivity between regions. This resting state activity
has been termed the default-mode of brain activity to denote a state in which an
individual is awake and alert, but not actively involved in an attention demanding or
goal-directed task (Raichle et al., 2001).
What is the neural substrate of default mode?
Key neuroanatomical components of anti-correlated task positive and task-negativ
e networks of the resting brain default network
How the Default modes are detected?
Low frequency oscillation of fMRI signal
and spontaneous activity at rest
• When a long MRI time series data are analyzed in terms of frequency
distribution, one can see the oscillation power is largely in the low
frequency region, far below respiration rate. There are some peaks at
0.1Hz or at a lower frequency.
• Such 0.1Hz oscillations used to be attributed to so-called vaso-motion,
of the sort seen in in-vivo optical measurements. Any vascular
modulation could lead to CBF variations. If this is the case, the
modulation is not likely due to the local neuronal activity, but some
signal to the vascular system from remote areas.
• However, the presence of connectivity between functionally related sites
was shown by correlations between these low frequency oscillations in
time series MRI data at resting state (Biswal et al 1995). Furthermore,
there is a slow modulation of the power of neural oscillations in the
gamma range; such modulations can induce low frequency BOLD signal
variation (Leopold et al 2003).
The blood oxygen level dependent (BOLD)
signal in DMN
• Empirical research has largely focused on the functional
connectivity of the DMN within the parameters of functional
magnetic resonance imaging (fMRI) data and the blood oxygen
level dependent (BOLD) signal; an indirect measure of neuronal
activity reflecting changes in blood oxygen level contrasts within
the brain (Fox and Raichle, 2007).
• Low-frequency oscillations are likely associated with connectivity
of larger scale neuronal networks, while higher frequencies are
constrained in smaller networks, and may be modulated by
activity in the slower oscillating larger networks ([Buzsáki and
Draguhn, 2004], [Fox and Raichle, 2007] and Penttonen and
Buzsáki, 2003]).
EEG and DMN
• Researchers have examined DMN activity in terms of traditional bands
of EEG activity (Chen et al., 2008), and in terms of very slow EEG
frequencies ([Helps et al., 2008] and [Vanhatalo et al., 2004]).
• Vanhatalo reported pervasive very low frequency oscillations (0.02–
0.2 Hz) across diverse scalp regions, in combination with evidence of
robust phase-locking between these low frequency oscillations and
traditional EEG bands of activity.
• Chen et al. (2008) compared the spatial distribution and spectral power
of seven bands of resting state EEG activity, in an eyes closed and eyes
open condition:
• In the eyes closed condition, the authors report delta (0.5–3.5 Hz)
activity in the prefrontal area, theta (4–7 Hz) activity at frontocentral
sites, and alpha-1 (7.5–9.5 Hz) activity distributed in the anterior–
posterior region. Further, alpha-2 (10–12 Hz) and beta-1 (13–23 Hz)
activity were evident in posterior regions, and high frequency beta-2
(24–34 Hz) and gamma (34–45 Hz) in the prefrontal area.
• Comparatively, in the eyes open condition, delta activity was enhanced,
and theta, alpha-1, alpha-2 and beta-1 were reduced in the respective
regions.
• They term this defined set of regional and frequency specific activity,
the EEG default-mode network (EEG-DMN), and propose that the
EEG-DMN should now be examined in the context of task-induced
demands and in patient groups.
When is the DMN formed?
• The limited evidence of DMN in the infant brain (Fransson et al., 2007),
fragmented connectivity between DMN regions during rest in young
children (7–9 years; Fair et al., 2008), and more consistent DMN
connectivity in children aged 9–12 years (Thomason et al., 2008),
suggests that this network of spontaneous low frequency activity
undergoes developmental change and maturation.
Properties of Default mode networks
• Research has concentrated on the patterns of activity within and
interconnectivity between DMN brain regions during rest, and the
impact that the commencement of goal-directed activity has on this.
• Significantly, DMN activity is attenuated rather than extinguished
during this transition between states, and is observed, albeit at lower
levels, alongside task-specific activations ([Eichele et al., 2008],
[Fransson, 2006], [Greicius et al., 2003] and Greicius and Menon, 2004).
• The more demanding the task the stronger the deactivation appears
to be ([McKiernan et al., 2006] and [Singh and Fawcett, 2008]).
• Increased PCC activity, or reduced deactivation, systematically
preceded and predicted response errors in a flanker task, up to 30 s
before the error was made (Eichele et al., 2008).
A notable exception to this general pattern of
deactivation during goal-directed activity
•
Attenuation of the ventral MPFC occurred with tasks involving
judgments that were self-referential, while activity in the dorsal
MPFC increased for self-referential stimuli, suggesting the dorsal
MPFC is associated with introspective orientated thought (Gusnard et
al., 2001).
•
Working memory tasks differentially deactivate the PCC. One study
observed a signal increase and spatial decrease in the PCC and a
signal decrease but spatial increase in the ACC with increasing
working memory load in an n-back task (Esposito et al., 2006).
•
In contrast, earlier research reported a significant task-related
decrease in PCC (Greicius et al., 2003), and although Hampson et al.,
(2006) did not find functional connectivity between the ventral
ACC and PCC to differ between rest and a working memory task,
performance was positively correlated with the degree of ventral ACC
The issue of how different brain regions are connected
functionally, that is, how the interplay of different areas
subserves cognitive function, has become a key concern
in neuroscience.
Anti-correlated task-positive and task-negative
resting networks
• The DMN has been described as a ‘task-negative network’ given the
apparent antagonism between its activation and task performance.
• A second network also characterized by spontaneous low frequency
activity has been identified as a task-positive network. This network
includes the dorsolateral prefrontal cortex (DLPFC), inferior parietal
cortex (IPC) and supplementary motor area (SMA).
• Interestingly, the task-positive network and the DMN are temporally
anti-correlated, such that task-specific activation of the task-positive
network is affiliated with attenuation of the DMN.
This has led to a certain confusion with regard to terminology.
Should only the task-negative network be termed the DMN and
contrasted with the task-positive network?
Or should both task-positive and task-negative networks be
regarded as elements of the DMN?
Task-positive and negative components
• The case for including task-positive and negative components as part
of the same default-mode network system is supported by a
considerable amount of evidence. Fox et al., 2005)
• This proposition allows for naturally occurring competition between
the task-negative and task-positive component, such that spontaneous
anti-correlated interactions between the networks will result in
periodic task interference, and importantly, does not necessitate the
involvement of a central executive.
• Indeed, it has been suggested on a number of occasions that the
anti-correlation between the two networks may prove to be
functionally more important, than DMN activity itself ([Fox et al.,
2005]).
• We use the DMN term to describe the task-negative network
specifically. We use the term Low Frequency Resting State Networks
The functional significance of DMN activity
• PCC (and adjacent precuneus) and MPFC, are the two most clearly
delineated regions within the DMN in terms of their functional roles
(Raichle et al., 2001).
• PCC appears to serve an important adaptive function and is implicated
in broad-based continuous sampling of external and internal
environments (Raichle et al., 2001).
• Reduced connectivity with anterior DMN regions in attention
deficit/hyperactivity disorder (ADHD) participants ([Castellanos et al.,
2008] and [Uddin et al., 2008a]) suggests that this region may be
implicated in working memory or attention dysfunction.
• Finally, PCC and retrosplenial cortex are also associated with the
processing of emotionally salient stimuli, and may play a role in
emotional processing related to episodic memory (Maddock, 1999).
• MPFC has been associated with social cognition involving the
monitoring of ones own psychological states, and mentalising about
the psychological states of others ([Blakemore, 2008], ).
In the context of DMN activity, MPFC is thought to mediate a dynamic
interplay between emotional processing and cognition functions which
map on to the ventral and dorsal regions, respectively ([Gusnard et al.,
2001], [Raichle et al., 2001] and [Simpson et al., 2001]).
The significance of TNN and TNP
• Slow oscillations of power may reflect long range coordination in a
functional network. Spontaneous fluctuations of fMRI signals at resting
state have been explored to find functional networks among functional
sites on the basis of the connectivity.
• It is thought that the TNN corresponds to task-independent
introspection, or self-referential thought, while the TPN corresponds to
action, and that perhaps the TNN and TPN should be considered
elements of a single default mode network with anti-correlated
components.
One hypothesis for DMN and task-positive
network
• One hypothesis is that task-positive activity is thought to be
associated with preparedness for unexpected or novel
environmental events.
• According to this account the reciprocal relationship between the
task-positive component and DMN has been described as low
frequency toggling between a task-independent, self-referential
and introspective state and an extrospective state that ensures the
individual is alert and attentive to unexpected or novel environmental
events ([Fox et al., 2005], [Fransson, 2005] and Fransson, 2006).
The functional role of low frequency oscillations
• The functional role of low frequency oscillations coherent across
resting state networks, and particularly the DMN, remains speculative.
• Possible candidates include the temporal binding of information
(Engel et al., 2001), particularly related to the coordination and
neuronal organisation of brain activity between regions that
frequently work in combination (Fox and Raichle, 2007);
The functional role of DMN
• The ability to maintain attentional focus and resist distraction or
lapses of attention is conventionally considered to underlie higher
order top–down control.
• Attentional lapses during goal-directed action may be a result of
interference arising from spontaneous, and most likely selfreferential, thought.
• The degree and maintenance of attenuation in DMN will relate
specifically to both state factors such as motivation, and trait factors,
such as disorder.
Mental disorders and DMN
• In mental disorder, the absence of, or reductions in, the anticorrelation between the DMN and task-positive network manifest as
reduced introspective thought (ASD) and attentional lapses (ADHD);
while excessive antagonism will likely result in zealous toggling
between extrospective and introspective processes (Schizophrenia).
• Second, the integrity of the DMN is affected by reductions in
connectivity, and is associated with deficits in attention and working
memory (Alzheimer’s disease, ADHD, schizophrenia), as well as
problems with self-referential and introspective mental processing
(ASD).
• In contrast, increased connectivity has been associated with
maladaptive emotional and introspective processing (depression,
schizophrenia).
Mental disorders and DMN
• Third, altered patterns of DMN functional connectivity commonly
characterize dysfunctional introspective processing –
connectivity in the DMN is negatively related to the positive
symptoms of schizophrenia, while enhanced connectivity in the
subgenual cingulate is associated with the length of depressive
episode.
• Finally, altered patterns of connectivity, atypical anti-correlations
between the DMN and task-positive network, and reduced
integrity of DMN functions, observed in a range of mental
disorders, are all potential and pervasive sources of
interference during goal-directed activity.
Information processing of the brain
for binding problem
• The BINDING PROBLEM, the receptive fields of two visual
neurons were stimulated in two conditions, one in which a
single object was presented, and another in which two
objects were presented, but in a way that evoked
practically the same firing rates as the single stimulus.
• In this case, the synchrony between pairs of neurons
reflected whether one or two stimuli were shown, even
when both firing rates did not vary across conditions.
cross-correlogram
• A popular analytical tool used by neuroscientists to study the joint
activity of neurons is the cross-correlation histogram or crosscorrelogram.
• It is constructed from the spike trains of two neurons, and shows the
probability (or some quantity proportional to it) that neuron B fires a
spike milliseconds before or after a spike from neuron A; is called the
time shift or time lag.
• When the two spike trains are independent, the cross-correlogram is
flat; if there is any covariation in the spike trains, one or more peaks
appear.
• For instance, a peak at zero time shift means that the two neurons
tend to fire at the same time more often than expected by chance.
• Usually, cross-correlograms are corrected so that peaks caused by
covariations in mean firing rate, computed over several tens or
hundreds of milliseconds, are eliminated.
Coincidence detection
• In theory, neurons might be exquisitely sensitive to certain
temporal input patterns. The classical mechanism
proposed for this is coincidence detection, which occurs
when a neuron is sensitive to the arrival of spikes from
two or more inputs within a short time window.
• There are examples, most notably in the auditory system,
in which highly accurate coincidence detection takes
place, but the question is whether this mechanism is
commonly used throughout the cortex.
• a | All input spike trains were independent. In the middle traces, both
postsynaptic neurons are shown to fire at about 30 spikes s-1.
• b | Excitatory inputs were synchronous, with 10% shared inputs, as in
Fig. 1a. Balanced and unbalanced neurons fired at 67 and 45 spikes s-1,
respectively.
• c | Inhibitory inputs oscillated with an amplitude equal to 50% of the
mean rate, as in Fig. 1e. Balanced and unbalanced neurons fired at 59
and 30 spikes s-1, respectively.
• d | All inputs were synchronous, with 10% shared inputs. Balanced and
unbalanced neurons fired at 31 and 41 spikes s-1, respectively. For
comparison, broken lines in the input–output rate plots (b–d) are the
curves obtained with independent inputs (a). The balanced neuron is
much more sensitive to correlations than the unbalanced one.
• The y axis indicates the rate of spike coincidences when the spike
trains from the two neurons are shifted in time by the amount shown
on the x axis. These correlograms have been normalized so that a zero
rate corresponds to independent spike trains. The three panels
correspond to three different pairs.
• Red traces were calculated from trials in which the monkey paid
attention to a ‘tactile stimulus’ (the cross on the table); blue traces
were calculated from trials in which the same tactile stimulus was
presented, but the monkey had to pay attention to a visual stimulus on
the screen.
In the top two examples, more synchrony
was observed when attention was focused
on the tactile stimuli; this was the more
prevalent effect. An example of lower
synchrony with attention on the tactile
stimulus — the less frequent effect — is
shown in the lower plot.
• Monkeys were trained to fixate on a central spot and
to attend to either of two stimuli presented
simultaneously and at the same eccentricity.
• One of the stimuli fell inside the receptive field of a
neuron, the activity of which was recorded.
• So, the responses to the same stimulus could be
compared in two conditions, with attention inside or
outside the neuron's receptive field.
• a and b | The continuous traces show the stimulus-driven local field
potentials (LFPs). The spikes below were recorded simultaneously from
different electrodes.
• c and d | Spike-triggered averages (STAs) computed during the
stimulus presentation period. The STA corresponds to the average LFP
waveform that is seen at the time of a spike. The y axes indicate the
mean LFP; the x axes indicate time relative to the occurrence of a
spike.
• e | Power spectra of the two STAs shown in c and d. When attention
is focused inside the receptive field, the recorded neuron tends to fire
more in phase with the frequency components around 50 Hz, and less
so with respect to the frequencies around 10 Hz.