U.S. patent application number 17/028901 was filed with the patent office on 2021-01-07 for monitoring the effects of sleep deprivation using neuronal avalanches.
This patent application is currently assigned to The United States of America, as represented by the Secretary, Department of Health & Human Services. The applicant listed for this patent is The United States of America, as represented by the Secretary, Department of Health & Human Services, The United States of America, as represented by the Secretary, Department of Health & Human Services. Invention is credited to Christian Meisel, Dietmar Plenz, Oren Shriki, Giulio Tononi.
Application Number | 20210000362 17/028901 |
Document ID | / |
Family ID | |
Filed Date | 2021-01-07 |
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United States Patent
Application |
20210000362 |
Kind Code |
A1 |
Plenz; Dietmar ; et
al. |
January 7, 2021 |
MONITORING THE EFFECTS OF SLEEP DEPRIVATION USING NEURONAL
AVALANCHES
Abstract
The present invention is directed to a method of continuously
monitoring neuronal avalanches in a subject comprising (a)
determining a deviation in avalanche exponent (.alpha.) or
branching parameter (.sigma.) from a predetermined value at rest,
wherein the pre-determined value of a is a slope of a size
distribution of the synchronized neuronal activity and the
predetermined value is -3/2 and the pre-determined value of 6 is a
ratio of successively propagated synchronized neuronal activity and
the predetermined value is 1; and (b) repeating step (a) one or
more times to continuously monitor neuronal avalanches in a
subject. The invention also features methods of determining or
monitoring the degree of sleep deprivation in a subject, methods of
identifying subjects that are susceptible to a sleep disorder and
methods of diagnosing a sleep disorder in a subject.
Inventors: |
Plenz; Dietmar; (Chevy
Chase, MD) ; Shriki; Oren; (Rockville, MD) ;
Tononi; Giulio; (Verona, WI) ; Meisel; Christian;
(Washington, DC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The United States of America, as represented by the Secretary,
Department of Health & Human Services |
Rockville |
MD |
US |
|
|
Assignee: |
The United States of America, as
represented by the Secretary, Department of Health & Human
Services
Rockville
MD
|
Appl. No.: |
17/028901 |
Filed: |
September 22, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14912392 |
Feb 16, 2016 |
10820817 |
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PCT/US2014/051234 |
Aug 15, 2014 |
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17028901 |
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61866962 |
Aug 16, 2013 |
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Current U.S.
Class: |
1/1 |
International
Class: |
A61B 5/04 20060101
A61B005/04; A61B 5/0478 20060101 A61B005/0478; A61B 5/00 20060101
A61B005/00 |
Goverment Interests
STATEMENT OF RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSORED
RESEARCH
[0002] This work was supported by the National Institute of
Neurological Disorders and Stroke (NINDS) under grant number
RO1NS055185 and by the National Institute of Mental Health (NIMH)
under grant number 1P20MH-077967-01A1. The Government has certain
rights in this invention.
Claims
1. A method of identifying a subject that is susceptible to a sleep
disorder comprising: (a) performing an EEG using sensors provided
on the subject; (b) determining, by at least one processing device,
an avalanche exponent a or a branching parameter .sigma. from
signals output by the sensors, wherein .alpha. is a slope of a size
distribution of a synchronized neuronal activity and .sigma. is a
ratio of successively propagated synchronized neuronal activity;
(c) determining, by the at least one processing device, a deviation
in .alpha. or .sigma. as determined from a pre-determined value of
.alpha. or .sigma. at rest, wherein: the pre-determined value of
.alpha. is -3/2, and the pre-determined value of .sigma. is 1; (d)
repeating steps (a)-(c) one or more times; (e) identifying, by the
at least one processing devices, the subject as being susceptible
to a sleeping disorder when it is determined there is a deviation
in .alpha. or .sigma. from the pre-determined value of .alpha. or
.sigma.; (f) providing, by the at least one processing devices, a
notification that the subject is identified as being susceptible to
the sleeping disorder; and (g) providing intervention or guidance
of treatment to the subject in response to identification of the
subject as being susceptible to the sleeping disorder.
2. The method of claim 1, further comprising: (h) identifying the
time when .alpha. and .sigma. are determined to deviate from their
pre-determined values at rest.
3. The method of claim 1, wherein step (a) further comprises: (i)
filtering the signals output from the sensors; (ii) the sensors
include multiple EEG electrodes, and positive/negative threshold
crossings at each EEG electrode are detected; (iii) clustering
threshold crossings on the EEG electrodes on a pre-determined time
scale; and (iv) calculating the cluster size distribution for
determining .alpha. or calculating a ratio of successive threshold
crossings for determining .sigma..
4. The method of claim 3, wherein the EEG is continuously recorded
at more than one site.
5. The method of claim 3, wherein the EEG is filtered between 1-100
Hz or wherein the time scale is 1-50 ms.
6. The method of claim 1, wherein the subject with the deviation in
.alpha. or .sigma. from the pre-determined value of .alpha. or
.sigma. performs a psychomotor vigilance task.
7. The method of claim 1, further comprising determining, by the at
least one processing device, a magnitude and spatial distribution
of theta power in the signals output by the sensors.
8. The method of claim 1, further comprising gathering data from
other physiological sensors.
9. The method of claim 1, wherein the method is operational with
hardware or software or a combination thereof.
10. The method of claim 1, wherein the sensors include multiple dry
electrodes of a headset, wherein the dry electrodes are
individually isolated and amplified, wherein the headset is
wearable in a non-laboratory setting.
11. A method of diagnosing a sleep disorder in a subject
comprising: (a) performing an EEG using sensors provided on the
subject (b) determining, by at least one processing device, an
avalanche exponent .alpha. or a branching parameter .sigma. from
signals output by the sensors, wherein .alpha. is a slope of a size
distribution of a synchronized neuronal activity and .sigma. is a
ratio of successively propagated synchronized neuronal activity;
(c) determining, by the at least one processing device, a deviation
in .alpha. or .sigma. as determined from a pre-determined value of
.alpha. or .sigma. at rest, wherein: the pre-determined value of
.alpha. is -3/2, and the pre-determined value of .delta. is 1; (d)
repeating step (a)-(c) one or more times; (e) diagnosing, by the at
least one processing device, the subject as having a sleeping
disorder when it is determined there is a deviation in .alpha. or
.sigma. from the pre-determined value of .alpha. or .sigma.; (f)
providing, by the at least one processing devices, a notification
that the subject is diagnosed as having the sleeping disorder; and
(g) providing intervention or guidance of treatment to the subject
in response to diagnosing the subject as having the sleeping
disorder.
12. The method of claim 11, further comprising: (h) identifying the
time when .alpha. and .sigma. are determined to deviate from their
pre-determined values at rest.
13. The method of claim 11, wherein step (a) further comprises: (i)
filtering the signals output from the sensors; (ii) the sensors
include multiple EEG electrodes, and positive/negative threshold
crossings at each EEG electrode are detected; (iii) clustering
threshold crossings on the EEG electrodes on a pre-determined time
scale; and (iv) calculating the cluster size distribution for
determining a or calculating a ratio of successive threshold
crossings for determining .sigma..
14. The method of claim 13, wherein the EEG is continuously
recorded at more than one site.
15. The method of claim 13, wherein the EEG is filtered between
1-100 Hz or wherein the time scale is 1-50 ms.
16. The method of claim 11, wherein the subject with the deviation
in .alpha. or .sigma. from the pre-determined value of .alpha. or
.sigma. performs a psychomotor vigilance task.
17. The method of claim 11, further comprising determining, by the
at least one processing device, a magnitude and spatial
distribution of theta power in the signals output by the
sensors.
18. The method of claim 11, further comprising gathering data from
other physiological sensors.
19. The method of claim 11, wherein the method is operational with
hardware or software or a combination thereof.
20. The method of claim 11, wherein the sensors include multiple
dry electrodes of a headset, wherein the dry electrodes are
individually isolated and amplified, wherein the headset is
wearable in a non-laboratory setting.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of application Ser. No.
14/912,392 filed on Feb. 16, 2016, which is the U.S. National Stage
of PCT International Application No. PCT/US2014/051234, filed Aug.
15, 2014, which claims the benefit of priority under 35 U.S.C.
.sctn. 119(e) to U.S. Provisional Application No: 61/866,962, filed
Aug. 16, 2013, which applications are incorporated herein by
reference in their entirety.
FIELD OF THE INVENTION
[0003] This invention relates generally to the field of sleep and
sleep deprivation.
BACKGROUND OF THE INVENTION
[0004] The beneficial role of sleep for brain function and
cognitive processing is well documented [1-3]. For example,
subjects who were trained on a motor-learning task in the evening
and were tested in the morning 12 hours later, showed greater
improvement in performance compared with subjects who were trained
on the same task in the morning and were tested 12 hours later in
the evening [3]. Conversely, it is also well demonstrated that
deprivation from sleep has detrimental effects on cognitive
function [4-6].
[0005] The breadth of mechanisms through which sleep exerts its
beneficial functions is not well understood. At the level of the
brain, though, the synaptic homeostasis hypothesis [7], has gained
recognition in recent years. The hypothesis suggests that sleep
regulates the strength of synaptic connections in the brain. In
particular, synapses strengthened during wakefulness, e.g. during
memory formation, eventually lead to an over-excitability of brain
networks unless the balance between excitation and inhibition is
appropriately re-established during sleep. Accordingly, sleep
deprivation is predicted to lead to an imbalance between excitation
and inhibition towards an excitation-dominated state, in line with
an increased risk for epileptic seizures and hallucinations during
prolonged periods of wakefulness.
[0006] Sleep deprivation increases the EEG power in the slow wave
(0.5-4.5 Hz) and theta (5-9 Hz) range [8-10]. Although such
increases in select frequency bands have been hypothesized to
reflect changes in synaptic strength in the underlying neuronal
circuits [7,9], the relationship between EEG power and synaptic
plasticity remains unclear. EEG power also depends on recording
technique and differs among individuals, making it hard to devise
absolute criteria for quantifying the effect of sleep
deprivation.
[0007] An alternative metric for brain activity that has been shown
to be sensitive to the balance of excitation and inhibition is
based on the concept of neuronal avalanches [11].
[0008] Neuronal avalanches are intermittent, spatiotemporal
activity bursts that emerge spontaneously in vitro [2,12] , in vivo
[6,10] , and, more recently, in human resting brain activity [13].
Importantly, the sizes of neuronal avalanches distribute according
to a power law with an exponent .alpha.=-3/2. This signature of
avalanches is in line with the finding that avalanches reflect
exquisitely balanced propagation of neural activity captured by a
critical branching parameter of .sigma.=1 [11,12,14]. Manipulating
the balance of excitation and inhibition leads to deviations from
the power law behavior [15]. For example, blocking inhibition in
cortical networks leads to epileptic-like behavior and to more
large-size avalanches than what would be expected from a power law
distribution [15]. Moreover, these deviations are accompanied by a
decrease in performance in terms of various measures of information
representation, transmission and storage [15, 16].
[0009] Sleep deprivation is known to adversely affect basic
cognitive abilities such as object recognition and decision making,
even leading to hallucinations and epileptic seizures. At the
neuronal level, sleep deprivation is associated with an increase in
synaptic excitation, suggesting that sleep restores the normal
balance of excitation and inhibition in the brain.
[0010] Currently, there is no easy way to identify the detrimental
effects of sleep deprivation from monitoring brain activity
directly. Accordingly, there is a need in the art for new methods
for monitoring the effects of sleep deprivation.
SUMMARY OF THE INVENTION
[0011] The present invention provides a robust method to
continuously monitor brain activity in order to estimate the
potential decrease in behavioral and cognitive performance that can
result from sleep deprivation. It has been shown that ongoing brain
activity in humans organizes as neuronal avalanche dynamics.
Avalanche dynamics are characterized by a power law in avalanche
size distribution with an exponent of alpha (.alpha.)=-3/2 and a
critical branching parameter of sigma (.sigma.)=1. It has been
shown that neuronal networks that maintain neuronal avalanche
dynamics, i.e. (alpha, sigma)=(-3/2, 1) optimize numerous aspects
of information processing [14-16]. The present application
demonstrates that a deviation from the optimal set of avalanche
parameters correlates with duration of wakefulness and decrease in
behavioral performances as measured in a simple reaction time
task.
[0012] Accordingly, the invention monitors continuously brain
activity by non-invasive means, e.g. EEG electrodes embedded into a
helmet, and estimates neuronal avalanche parameters continuously.
The deviation of current parameters from the optimal set (-3/2, 1)
of avalanche parameters is continuously calculated. Decrease in
performance positively correlates with deviation from the optimal
set. A tolerance range will be introduced for tolerable degrees of
deviation (e.g. 10%). Feedback signals to the human about deviation
of the current brain state from avalanche dynamics will be
provided. In certain embodiments, continuously recorded EEG will be
evaluated on line, and if the deviation from expected avalanche
dynamics reaches a critical threshold, an alert will be issued
signaling, for example, that the subject is at risk of
underperformance, or in post-exercise analysis to identify
individuals resilient to sleep deprivation or risk. In a first
aspect, the method features methods of continuously monitoring
neuronal avalanches in a subject comprising (a) determining a
deviation in avalanche exponent (.sigma.) or branching parameter
(.sigma.) from a predetermined value at rest, wherein the
pre-determined value of a is a slope of a size distribution of the
synchronized neuronal activity and the predetermined value is -3/2
and the pre-determined value of 6 is a ratio of successively
propagated synchronized neuronal activity and the predetermined
value is 1; and (b) repeating step (a) one or more times to
continuously monitor neuronal avalanches in a subject.
[0013] In one embodiment, the method further comprises (c)
identifying the time when .alpha. and .delta. deviate from their
predetermined values at rest. In another embodiment, step (a)
comprises (i) continuously recording the electroencephalogram
(EEG); (ii) filtering the EEG; (iii) detecting positive/negative
threshold crossings at each EEG electrode; (iv) clustering
threshold crossings on the EEG array on a predetermined time scale;
(v) calculating the cluster size distribution and determining the
slope alpha (.alpha.); and (vi) calculating the ratio of successive
threshold crossing to obtain sigma (.sigma.).
[0014] In another embodiment, the EEG is continuously recorded at
more than one site, for example 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19 , 20, 21, 22, 23, 24, 25 or more sites.
In a related embodiment, the EEG is continuously recorded at more
than 10 sites.
[0015] In a further embodiment, the EEG is filtered between 1-100
Hz.
[0016] In another further embodiment, the time scale is 1-50 ms,
for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50
ms.
[0017] In another aspect, the invention features a method of
determining the degree of sleep deprivation in a subject comprising
(a) determining a deviation in avalanche exponent (.alpha.) or
branching parameter (.sigma.) from a predetermined value at rest,
wherein the pre-determined value of a is a slope of a size
distribution of the synchronized neuronal activity and the
predetermined value is -3/2 and the pre-determined value of 6 is a
ratio of successively propagated synchronized neuronal activity and
the predetermined value is 1; and (b) repeating step (a) one or
more times, wherein a change in a or 6 from the pre-determined
value indicates the degree of sleep deprivation in a subject.
[0018] In yet another aspect, the invention features a method of
identifying subjects that are susceptible to a sleep disorder
comprising (a) determining a deviation in avalanche exponent
(.alpha.) or branching parameter (.sigma.) from a predetermined
value at rest, wherein the pre-determined value of is a slope of a
size distribution of the synchronized neuronal activity and the
predetermined value is -3/2 and the pre-determined value of .sigma.
is a ratio of successively propagated synchronized neuronal
activity and the predetermined value is 1; and (b) repeating step
(a) one or more times, wherein a change in .alpha. or .sigma. from
the pre-determined value indicates that the subject is susceptible
to a sleep disorder.
[0019] In still another aspect, the invention features a method of
diagnosing a sleep disorder in a subject comprising (a) determining
a deviation in avalanche exponent (.alpha.) or branching parameter
(.sigma.) from a predetermined value at rest, wherein the
pre-determined value of a is a slope of a size distribution of the
synchronized neuronal activity and the predetermined value is -3/2
and the pre-determined value of 6 is a ratio of successively
propagated synchronized neuronal activity and the predetermined
value is 1; and (b) repeating step (a) one or more times, wherein a
change in .alpha. or .sigma. from the pre-determined value
indicates that the subject is suffering from a sleep disorder.
[0020] In one embodiment of the above aspects, the determined value
.alpha. is a slope of a size distribution that is steeper or more
shallow than the pre-determined slope. In another embodiment of the
above aspects, the determined value .sigma. is a branching ratio
that is smaller or larger than 1. In a further embodiment of the
above aspects, the change in in .alpha. or .sigma. from the
pre-determined value is correlated with an increased reaction time
in a psychomotor vigilance task. In another further embodiment of
the above aspects, the change in .alpha. or .sigma. from the
pre-determined value is correlated with a decrease in behavioral
performance.
[0021] In one embodiment, the subject is suffering from a sleep
disorder.
[0022] In one embodiment, the subject has not slept for 24 or more
hours, for example 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
70, 71, 72 or more hours.
[0023] In one embodiment of the above aspects, the method further
comprises determining the magnitude and spatial distribution of
theta power.
[0024] In another embodiment of the above aspects, the method
further comprises gathering data from other physiological
sensors.
[0025] In a further embodiment of the above aspects, the method is
operational with hardware or software or a combination thereof.
Definitions
[0026] To facilitate an understanding of the present invention, a
number of terms and phrases are defined below.
[0027] As used herein, the singular forms "a", "an", and "the"
include plural forms unless the context clearly dictates otherwise.
Thus, for example, reference to "a biomarker" includes reference to
more than one biomarker.
[0028] Unless specifically stated or obvious from context, as used
herein, the term "or" is understood to be inclusive.
[0029] As used herein, the terms "comprises," "comprising,"
"containing," "having" and the like can have the meaning ascribed
to them in U.S. Patent law and can mean "includes," "including,"
and the like; "consisting essentially of" or "consists essentially"
likewise has the meaning ascribed in U.S. Patent law and the term
is open-ended, allowing for the presence of more than that which is
recited so long as basic or novel characteristics of that which is
recited is not changed by the presence of more than that which is
recited, but excludes prior art embodiments.
[0030] The term "behavioral performance" is meant to refer to
performance in a cognitive task, such as, but not limited to,
reaction time in a typical psychomotor vigilance task (PVT), a
sensorimotor coordination task, such as steering a vehicle through
demanding environment, or cognitive functions, such as decision
making
[0031] The term "continuously monitoring" is meant to refer to
determining a value or output more than one time, for example two,
three, four, five, six, seven, eight, nine, ten or more times with
relatively short intervals between consecutive measurements.
[0032] The term "electroencephalogram (EEG)" is meant to refer to
the recording of electrical activity, typically along the
scalp.
[0033] The term "event" is meant to refer to a significantly large
signal deflection in one of the recorded channels.
[0034] The term "neuronal avalanche" is meant to refer to a cascade
of bursts of activity in neuronal networks. Neuronal avalanches
reflect normal brain activity in the awake state.
[0035] The term "avalanche exponent" is meant to refer to the slope
(.alpha.) of the power law in neuronal avalanche dynamics
Preferably, the sizes of neuronal avalanches distribute according
to a power law with an exponent .alpha.=-3/2. A deviation in
avalanche exponent (.alpha.) from a predetermined value at rest is
any change in .alpha..
[0036] The term "branching parameter" is meant to refer to a value
(.sigma.) that reflects the intrinsic amplification in cascades of
activity. A deviation in branching parameter (.sigma.) from a
predetermined value at rest is any change in .sigma..
[0037] The term "sleep disorder" is meant to refer to generally any
abnormal sleeping pattern. Examples of sleep disorders include, but
are not limited to, dyssomnia, insomnia, sleep apnea, narcolepsy,
and circadian rhythmic disorders.
[0038] The term "subject" is meant to refer to any form of animal.
Preferably the subject(s) are mammal, and most preferably
human.
[0039] The term "synchronized neuronal activity" is meant to refer
to activity of a group of neurons. Synchronized neuronal activity
can give rise to macroscopic oscillations or transient deflections,
which can be observed in an electroencephalogram (EEG).
[0040] The term "theta power" is meant to refer to the power
spectrum of the EEG signal in the theta frequency range, which is
typically from 4-5 Hz to 8-9 Hz.
[0041] Other features and advantages of the invention will be
apparent from the following description of the preferred
embodiments thereof, and from the claims. Unless otherwise defined,
all technical and scientific terms used herein have the same
meaning as commonly understood by one of ordinary skill in the art
to which this invention belongs. Although methods and materials
similar or equivalent to those described herein can be used in the
practice or testing of the present invention, suitable methods and
materials are described below. All published foreign patents and
patent applications cited herein are incorporated herein by
reference. Genbank and NCBI submissions indicated by accession
number cited herein are incorporated herein by reference. All other
published references, documents, manuscripts and scientific
literature cited herein are incorporated herein by reference. In
the case of conflict, the present specification, including
definitions, will control. In addition, the materials, methods, and
examples are illustrative only and not intended to be limiting.
BRIEF DESCRIPTION OF THE DRAWINGS
[0042] FIG. 1A and FIG. 1B show cascade size distributions follow
power laws, as expected for neuronal avalanches. FIG. 1A is a line
graph showing the cascade size distribution of a single subject
using .DELTA.t=6 ms. Solid black line depicts original data and
broken red line corresponds to a control, i.e. phase-shuffled data
in which temporal correlations among EEG sensors are destroyed and
avalanches are absent. Broken black line represents a reference
power law with an exponent of -3/2. FIG. 1B is a line graph
demonstrating the cascade size distributions for subsamples of the
sensor array. Line color indicates the number of sensors used for
the analysis.
[0043] FIG. 2 shows the power law exponent is close to .alpha.=-3/2
at the critical branching parameter .sigma.=1. Phase plot of the
power law exponent, a, versus the branching parameter, .sigma..
Each point represents the mean across datasets and error bars
represent SEM.
[0044] FIG. 3A-FIG. 3F show avalanche metrics correlate with sleep
deprivation and decrease in behavioral performance. FIG. 3A, FIG.
3C, and FIG. 3D are line graphs that show dependence of branching
parameter (FIG. 3A), avalanche exponent (FIG. 3B) and reaction time
(FIG. 3C) with time awake (mean across datasets). The red dot
represents measurement after 8 hours of recovery sleep.
Importantly, even a transient improvement in reaction time is
accompanied with a corresponding transient return of the avalanche
metrics towards the optimal values (alpha, sigma)=(-3/2, 1)
(4.sup.th time point=12 hrs wakefulness).
[0045] FIG. 3D is a line graph that shows correlation between
branching parameter and avalanche exponent during time awake. FIG.
3E is a line graph showing the correlation between branching
parameter and RT during time awake. FIG. 3F is a line graph showing
the dependence of mean inter event interval (IEI) on time awake.
The p-values of the linear regression are displayed in each
panel.
[0046] FIG. 4A and FIG. 4B are a series of line graphs showing an
increase in deviation from avalanche dynamics with sleep
deprivation in rats. FIG. 4A is a line graph showing the change in
power law of avalanche sizes with increase sleep deprivation in
rats. Note tendency to supercritical dynamics (arrow; single
experiment; shifted for visibility). Broken line: -3/2. FIG. 4B is
a line graph showing avalanche metrics .kappa., i.e. the deviation
from a power law with slope of -3/2, and .sigma. (z-scores)
quantify the progressive deviation from avalanche dynamics for
baseline (B), sleep deprivation in h, and after sleep (R) for n=3
rats.
[0047] FIG. 5A-FIG. 5D are a series of bar charts and line graphs
showing changes in the organization of neuronal avalanches with
increasing duration of wakefulness. FIG. 5A is a line graph showing
the probability distribution of cascade sizes deviates from a
power-law distribution in the tail (arrow) after sustained
wakefulness. Curves represent combined avalanches from a subject
for the first four EEG recordings (blue, 0-9 h of wakefulness) and
the last four recordings (red, 30-39 h of wakefulness). FIG. 5B is
a series of bar charts showing both .sigma..sub.n (branching
parameter, left plot) and .DELTA.D.sub.n (indexing the deviation
from a power-law function), right plot) progressively increased
during sustained wakefulness (hours 0-39) and return to lower
values after recovery sleep (ps). Values were normalized to
z-scores in each subject before averaging over the different
subjects, which is denoted by subscripts n. Gray curves show the
continuous measurements, the colored bars correspond to averages of
the four EEG recordings at 0-9 h (blue) and at 30-39 h (red) of
wakefulness. The plot in the middle shows absolute means values of
.sigma. over all individuals without prior normalization of the
data at the beginning of sleep deprivation (blue) and toward the
end (red). Error bars indicate SE. Parameters were .DELTA.t=39 ms,
threshold=4.0, r=0.75; p values indicate the difference between red
and blue bars (two-tailed paired t test). FIG. 5C is a bar chart
showing the increase in .sigma..sub.n (gray bars) and
.DELTA.D.sub.n (brown bars) is illustrated as the difference (Diff)
between values at the end (30-39 h of wakefulness) and the
beginning (0-9 h) of the sleep deprivation period. Positive values
therefore indicate an increase of .sigma..sub.n or .DELTA.D.sub.n
in the course of sleep deprivation, which was significant for a
broad range of thresholds (th) and correlations (R, *p.ltoreq.0.05,
**p.ltoreq.0.01; two-tailed paired t test). FIG. 5D is a bar chart
showing recovery after sleep. Bars illustrate the decrease in
.sigma..sub.n (gray bars) and .DELTA.D.sub.n (brown bars) after
recovery sleep (ps) compared with the last value of the sleep
deprivation period (after 39 h of wakefulness). Negative values
therefore indicate a decrease of .sigma..sub.n or .DELTA.D.sub.n
after recovery sleep following the 40 h sleep deprivation period.
Differences were significant over a range of parameters.
[0048] FIG. 6A is a bar chart. The colored bars correspond to
averages of the four EEG recordings at 0-9 h (blue) and at 30-39 h
(red) of wakefulness; p values indicate the difference between red
and blue bars (two-tailed paired t test). FIG. 6B is a bar chart.
Of all metrics, theta spectral power and 6 exhibit the highest
correlation with alertness as quantified by the absolute
correlation coefficient [R].
DETAILED DESCRIPTION
[0049] Previous studies indicate that sleep deprivation increases
the EEG power in the slow wave (0.5-4.5 Hz) and theta 4-5 Hz to 8-9
Hz range. Current approaches rely on analysis of these frequency
bands. However, EEG power depends on recording technique and
differs among individuals, making it hard to devise absolute
criteria for quantifying the effect of sleep deprivation. Neuronal
avalanches reflect normal brain activity in the awake state and are
characterized by a power law in avalanche sizes with slope of -3/2
and a critical branching parameter of 1. In contrast to EEG power,
the avalanche metric is an absolute metric that has been shown to
be identical across individuals and it is directly related to the
potential underlying imbalance of excitation and inhibition that
results from sleep deprivation. This allows the metric to be used
in absolute terms, i.e. no control group is required to identify
performance changes. The present invention is based, at least in
part, on the finding that after monitoring neuronal avalanches in
the EEG of human subjects over the course of 36 hours of sleep
deprivation, both the slope in size distribution and the branching
parameter correlate with the duration of sleep deprivation as well
as the increase in reaction time in a psychomotor vigilance task.
The present invention demonstrates that avalanche dynamics provide
absolute benchmarks to identify the decrease in performance for
sleep deprived subjects.
[0050] Described herein is a clinical biomarker to monitor the
quality and cumulative effect of sleep. Sleeping disorders
constitute a broad spectrum of neurological and other medical
conditions, whereby impaired quality of sleep often leads to severe
health and cognitive defects. As such, effective clinical treatment
and diagnosis would benefit from objective biomarkers quantifying
the recuperational effect of sleep or its deficits. However, prior
to the invention described herein, there was a lack of objective
biomarkers measuring the quality of sleep. The results described
herein demonstrate a marker relating cortical dynamics to optimal
cognitive performance. As described herein, avalanche dynamics are
re-established by sleep after prolonged wakefulness and highly
correlate with behavioral performance Avalanche dynamics have also
been shown to optimize a number of aspects in information
processing in cortical networks suggesting a beneficial effect of
sleep induced reset of avalanche dynamics for cognitive
performance. In this regard, the metrics provide an objective
marker for the quality of sleep and have the potential to guide and
monitor the effect of treatment to improve sleep quality in
clinical settings. Software extracting the markers related to
neuronal avalanches is a useful tool for diagnostic and monitoring
treatment progress in sleep laboratories and other clinical
settings.
[0051] Also described herein is a biomarker for optimal cortical
function under conditions of sustained wakefulness. In many
professions long, uninterrupted shifts are required while
maintaining high cognitive functioning, e.g., medical professions
on call, military personnel, and pilots. The quantification of
cortical dynamics by neuronal avalanches metrics has the
opportunity to provide valuable and objective markers for cognitive
performance under sleep deprivation conditions.
Neuronal Avalanches
[0052] Neuronal avalanches are intermittent, spatiotemporal
activity bursts that emerge spontaneously in vitro, in vivo and in
human resting brain activity. A definition of neuronal avalanches
is provided in US Patent Application 20090036791, filed Feb. 5,
2009 and incorporated by reference in its entirety herein. A
neuronal avalanche is a sequence of consecutive time bins of
duration At with at least one event, which is preceded and
terminated by at least one time bin with no activity. The absence
of activity for a period of .DELTA.t thus indicates the end of an
avalanche. If the decision of whether an avalanche has ended is
made too early (.DELTA.t too short), avalanches will be terminated
prematurely; if the .DELTA.t chosen is too long, avalanches will be
falsely concatenated. If avalanches did simply propagate like a
wave, an approximation for .DELTA.t (.DELTA.tavg) could be obtained
by averaging the time between one event at one sensor and the next
event at neighboring sensors only. Because events in avalanches
occur in irregular patterns across sensors on the array, a pair
wise approximation can be used in order to assess the average time
that is required for events to propagate between electrodes. It is
noted that neuronal avalanches are scale-invariant. The
identification of a particular .DELTA.t for the analysis is only
required because of the choice of a particular spatial sampling
grid imposed by the sensor or electrode array used to measure
events. It was shown [e.g. 11] that the choice of .DELTA.t linearly
scales with the choice of interelectrode or intersensor distance
.DELTA.t and thus the ratio .DELTA.d/.DELTA.t=constant and
approximates the average propagation velocity of neuronal
synchronized bursts.
[0053] US Patent Application 20090036791 sets forth how to
calculate .DELTA.t avg for fixed intersensor/interelectrode
distances of the array, and how to calculate avalanche size and
avalanche branching parameter. In order to calculate .DELTA.t avg,
the distribution of time intervals T for successive events on the
array can be obtained. Starting with the first event, e.g.
A.sup.k(t.sub.i) on electrode k at time t.sub.i, the next
occurrence of an event on the array can be searched for, e.g.
A.sup.l(t.sub.j) on electrode 1 at time t.sub.j, and calculated the
time interval T.sub.m-.sub..DELTA.t.sup.k,l, where
m=(t.sub.j-t.sub.i)/.DELTA.t. This process can be repeated for all
occurrences of events on electrode k and for all electrodes. The
resulting values can be combined into one density distribution
P(T.sub.m.DELTA.t), which captures how often successive events
occurred with a particular delay m times .DELTA.t on the array
irrespective of their spatial location. Consequently, the average
value of T provides an approximation for .DELTA.tavg, the average
time to wait before making a decision whether an event propagated
on the array. However, this interval distribution is highly skewed,
particularly when one compares the last event with the first event
in successive avalanches that are separated by long times. In order
to exclude time intervals from successive events that are barely
correlated, a cut-off time .tau..sub.max can be calculated for
which the average crosscorrelation function (ccf) for pair wise
electrode comparisons R.sub.ccf(.tau.) had decayed to negligible
values. The ccf between electrodes k, l (R.sub.ccf.sup.k,l(.tau.))
can be calculated as
R ccf k , l ( .tau. ) = 1 m ax = 1 ma x A L F P ' k ( .DELTA. t +
.tau. ) A L F P ' l ( .DELTA. t ) .sigma. ( A L F P ' k ) .sigma. (
A L F P ' l ) ( 1 ) ##EQU00001##
[0054] at .DELTA.t=1 ms fort .tau..di-elect cons.[-100,100], where
m is an integer value up to m.sub.max
.DELTA.t+.tau.<T.sub.totA'.sup.k(t.sub.i)=A.sup.k(t.sub.i)-E(A.sup.k),
E(.) is the expected value for A.sup.k(t.sub.i), and
.sigma..sup.2(.) is the variance. Finally, the population ccf
(R.sub.ccf(.tau.)) can be calculated as
R ccf ( .tau. ) = 2 n e l e c ( n e l e c - 1 ) k = 1 n elec l = k
+ 1 n e l e c R ccf k , l ( .tau. ) ( 2 ) ##EQU00002##
[0055] The estimate of the average time between successive,
correlated nLFPs on the array, i.e. .DELTA.t'.sub.avg, can be
obtained by integrating the density distribution of intervals for
each network up to .tau..sub.max,
.DELTA. t avg ' ( .tau. m .alpha. x ) = m = 1 m .DELTA. t = .tau.
ma x T m .DELTA. t P ( T m .DELTA. t ) ( 3 ) ##EQU00003##
[0056] If the exemplary maximal sampling rate was 1 kHz, the actual
.DELTA.tavg to calculate avalanches can be taken as the nearest
multiple of .DELTA.t=1 ms. In short, after .DELTA.tavg is
calculated for a particular network and experimental condition, the
electrode signals can be re-sampled at the new temporal resolution
of .DELTA.tavg. Time and amplitude of event peaks can be extracted
as A.sup.k(t.sub.i), where t.sub.i=itavg, i .[1, n'.sub.max] and
T.sub.tot=n'.sub.max.DELTA.tavg. Avalanches can be determined on
the downsampled data set at bin width .DELTA.tavg.
[0057] Avalanche sizes can be calculated in two different ways. By
taking into account event peak amplitudes A.sub.i.sup.k, the
avalanche size s.sub.LFP.sup.Avalj can be calculated by summing up
A.sub.i.sup.k on active electrodes for the lifetime
T.sup.avaljLFP=m times .DELTA.tavg of Aval.sub.j, defined as the
number of m bins of width .DELTA.tavg that were occupied by
avalanche Aval.sub.j that started at time t.sub.i and stopped at
time t.sub.i+(m-1).DELTA.tavg
S L F P A v a l j = i = 0 m - 1 k = 1 n elec A t i k + m .DELTA. t
avg ( 4 ) ##EQU00004##
For the density distribution of s.sub.LFP or S.sub.event, the range
in sizes s.sub.LFP or S.sub.event, was covered by 100 bins that
increased logarithmically from 3-3000 .mu.V(or any other event
amplitude measure), which results in equidistant sampling of the
data in logarithmic coordinates. Avalanche sizes can also be
calculated as the number of active electrodes within an avalanche,
s.sub.ele, using (4), but setting all non-zero A.sub.i.sup.k values
to 1. For the density distribution of s.sub.ele, linear binning
from 1 to the maximal number of array electrodes can be used. In
the critical state, the distribution of avalanche sizes forms a
power law with slope .alpha.=-3/2. From the experimental data, the
exponent a of the power law represents the slope of the log-log
transformed size distribution and can be estimated using linear
regression analysis. Estimating a is not limited to regression
analysis only. For example, .alpha. can be estimated using a
maximum likelihood estimation
- .alpha. = 1 + n [ i = 1 n ln N ( S i ) N ( S m i n ) ] - 1 ( 5 )
##EQU00005##
where N(s.sub.i) represents the number of avalanches of size
s.sub.i, N(s.sub.min) represents the number of minimal avalanche
size s.sub.min measured, and n represents the number of size
categories up to the cut-off imposed by the array size.
Alternatively, a can be estimated from the cumulative size
distribution up to the power law cut-off, which forms a slope of
a.alpha.+1. Because no significant differences exist between
estimates of .alpha. based on s.sub.LFP (.alpha..sub.LFP) or
(s.sub.ele) (.alpha..sub.ele) slope values can be given as
.alpha..sub.LFP unless a particular emphasis is placed on the area
an avalanche covers on the array. Average avalanche size
distributions can be plotted as mean +/-S.E.M.
[0058] The branching parameter .sigma. can be used to describe the
balance of propagated synchronized activity in cortical tissue. The
general definition of 6 refers to the ratio between successive
generations, for example the average number of descendants from one
ancestor. When .alpha.=-3/2, the neuronal tissue is critical and,
correspondingly, .sigma.=1. The branching parameter .sigma. can be
defined in binary and analog terms.
[0059] In the binary case, .sigma. is defined as the average ratio
of electrodes activated in time bin .DELTA.t.sub.n+1 (descendants;
n.sub.d), divided by the number of electrodes active in time bin
.DELTA.t.sub.n (ancestor; n.sub.a). Mathematically, the average
branching parameter .sigma. for the electrode array in the case of
one ancestor electrode (n.sub.a=1) is simply given by
.sigma. = d = 0 n ma x d p ( d ) ( 6 ) ##EQU00006##
where d is the number of electrode descendants, p(d) is the
probability of observing d descendants, and n.sub.max is the
maximal number of active electrodes. Note that formula (6) does not
describe a probability density and theoretically 6 can take any
valueless than or equal to zero. In the binary case, .sigma. is
best estimated from the first and second time bin of an avalanche.
Although strictly speaking, .sigma. is only defined for one
ancestor, .sigma. can also be estimated when there are multiple
ancestors. Under these conditions, d is given by
n = ( n d n a ) ( 7 ) ##EQU00007##
where n.sub.a is the number of electrode ancestors observed in the
first time bin and n.sub.d is the number of active electrodes in
the second time bin of an avalanche and round is the rounding
operation to the nearest integer. The likelihood of observing d
descendants can be approximated by:
p ( d ) = avalanches ( n .SIGMA. a | d n .SIGMA. a ) ( n m ax - 1 n
ma x - n a ) ( 8 ) ##EQU00008##
where n.sub..SIGMA.nld is the total number of electrode ancestors
in all avalanches when n.sub.d descendants were observed,
n.sub..SIGMA.a is the total number of ancestors observed in all
avalanches, and
( n ma x - 1 n ma x - n a ) ( 9 ) ##EQU00009##
is a factor that provided an approximate correction for the reduced
number of electrodes available in the next time bin because of
electrode refractoriness. Note that the branching parameter is not
defined for zero ancestors and thus does not provide information
about the initiation of bursts. In cases where there is only one
ancestor, formula (8) is equivalent to (6). In the analog case, the
branching parameter .sigma. includes analog information about the
event, e.g. its negative peak value (event amplitude) or the event
area (e.g. integrated event amplitude from crossing negative
threshold to return to threshold).
[0060] For the analog calculation, each event in an avalanche is
normalized to the amplitude or area of the first event in the
avalanche. For each time bin, the corresponding event distributions
from all avalanches can be combined. The succession of event
distributions during the life time of the avalanche then
approximates the branching parameter .sigma.. More specifically, if
the mode (mod) of the event distributions equals 1 for each time
bin, events do not grow nor do they decay within an avalanche,
which is equivalent to the binary case of .sigma.=1. Because the
distribution of ratios is better expressed in log values, in the
analog case, one can state log(1)=0. Thus, log(mod)=0 demonstrates
that the brain dynamics is critical and fulfills the criteria for
neuronal avalanches.
[0061] US Patent Application 20090036791 also provides methods and
systems for performing a Neuronal Avalanche Assay (NAS-assay). The
NAS-assay uses the spatial distribution of synchronized activity,
in neuronal tissue. For exemplary purposes, the description is
directed toward the NAS-assay using local field potentials (LFP).
In the LFP, the activity of a single neuron is barely detectable,
however, if many neurons synchronize their activities, the LFP is
large enough to be registered by a local recording device, in this
case, the microelectrode. As LFPs propagate along an array of
microelectrodes, the neuronal activity can be analyzed for neuronal
avalanches. However, any method with which synchronized neuronal
activity can be detected locally in the living brain and which
allows for the monitoring of the spread of synchronized activity,
can be used in the NAS-assay. The NAS-assay, in its principle
design, is not limited to the use of LFPs only.
Methods
[0062] In certain aspects, the present invention features methods
of continuously monitoring neuronal avalanches in a subject. In
preferred embodiments, the method comprises (a) determining a
deviation in avalanche exponent (.alpha.) or branching parameter
(.sigma.) from a predetermined value at rest, wherein the
pre-determined value of a is a slope of a size distribution of the
synchronized neuronal activity and the predetermined value is -3/2
and the pre-determined value of .sigma. is a ratio of successively
propagated synchronized neuronal activity and the predetermined
value is 1; and (b) repeating step (a) one or more times (e.g. 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20,
25, 30, 35, 40, 45, 50 or more times) to continuously monitor
neuronal avalanches in a subject. The method may further comprise
(c) identifying the time when a and 6 deviate from their
predetermined values at rest.
[0063] In certain exemplary embodiments, step (a) comprises (i)
continuously recording the EEG; (ii) filtering the EEG; (iii)
detecting positive/negative threshold crossings at each EEG
electrode; (iv) clustering threshold crossings on the EEG array on
a predetermined time scale; (v) calculating the cluster size
distribution and determining the slope alpha (.alpha.); and (vi)
calculating the ratio of successive threshold crossing to obtain
sigma (.sigma.).
[0064] EEG signals can be obtained by any method known in the art,
or subsequently developed by those skilled in the art to detect
these types of signals. Sensors include but are not limited to
electrodes or magnetic sensors. Preferably, the EEG is continuously
recorded at >10 sites.
[0065] The EEG recording is characterized by amplitude, frequency
and their change over time. The frequency component of the EEG can
be utilized to infer the level of an individual's neural activity.
The frequencies are broken down into ranges which describe how
alert and conscious a person is at any given time. The delta
frequency (1-4 Hz) is associated with deep sleep. The theta
frequency (4-5 Hz to 8-9 Hz) is associated with drowsiness, and
delta activity is also common. The alpha frequency (8-13 Hz) is
associated with relaxed wakefulness, where not much brain resources
are devoted to any one thing. The beta frequency (12-20 Hz, or 30
Hz) and the gamma frequency (36-44 Hz) are associated with alert
attentiveness
[0066] In certain embodiments, the EEG is filtered between 1-100
Hz. If electrodes are used to pick up the brain wave signals, these
electrodes may be placed at one or several locations on the
subject(s)' scalp or body. The electrode(s) can be placed at
various locations on the subject(s) scalp in order to detect EEG or
brain wave signals. Common locations for the electrodes include
frontal (F), parietal (P), anterior (A), central (C) and occipital
(O). Preferably for the present invention at least one electrode is
placed in the occipital position. In order to obtain a good EEG or
brain wave signal it is desirable to have low impedances for the
electrodes. Typical EEG electrodes connections may have an
impedance in the range of 5 to 10 K ohms. It is in general
desirable to reduce such impedance levels to below 2 K ohms.
Therefore a conductive paste or gel may be applied to the electrode
to create a connection with an impedance below 2 K ohms.
Alternatively, the subject(s) skin may be mechanically abraded, the
electrode may be amplified or a dry electrode may be used. Dry
physiological recording electrodes of the type described in U.S.
patent application Ser. No. 09/949,055 are herein incorporated by
reference. Dry electrodes provide the advantage that there is no
gel to dry out, no skin to abrade or clean, and that the electrode
can be applied in hairy areas such as the scalp. Additionally if
electrodes are used as the sensor(s), preferably at least two
electrodes are used--one signal electrode and one reference
electrode; and if further EEG or brain wave signal channels are
desired the number of electrodes required will depend on whether
separate reference electrodes or a single reference electrode is
used. For the various embodiments of the present invention,
preferably an electrode is used and the placement of at least one
of the electrodes is at or near the occipital lobe of the subject's
scalp.
[0067] In certain embodiments, the time scale of performing the
method is 1 - 50 ms, for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
46, 47, 48, 49, 50 ms.
[0068] In other aspects the invention provides methods of
determining the degree of sleep deprivation in a subject or methods
of identifying subjects that are susceptible to a sleep disorder,
comprising (a) determining a deviation in avalanche exponent
(.alpha.) or branching parameter (.sigma.) from a predetermined
value at rest, wherein the pre-determined value of a is a slope of
a size distribution of the synchronized neuronal activity and the
predetermined value is -3/2 and the pre-determined value of .sigma.
is a ratio of successively propagated synchronized neuronal
activity and the predetermined value is 1; and (b) repeating step
(a) one or more times (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14,15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or more
times), wherein a change in a or 6 from the pre-determined value
indicates the degree of sleep deprivation in a subject or wherein a
change in a or 6 from the pre-determined value indicates that the
subject is susceptible to a sleep disorder.
[0069] The present invention also provides in other aspects methods
of diagnosing a sleep disorder in a subject. The methods preferably
comprise (a) determining a deviation in avalanche exponent
(.alpha.) or branching parameter (.sigma.) from a predetermined
value at rest, wherein the pre-determined value of .alpha. is a
slope of a size distribution of the synchronized neuronal activity
and the predetermined value is -3/2 and the pre-determined value of
.sigma. is a ratio of successively propagated synchronized neuronal
activity and the predetermined value is 1; and (b) repeating step
(a) one or more times (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14,15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or more
times),wherein a change in a or 6 from the pre-determined value
indicates that the subject is suffering from a sleep disorder.
[0070] In certain embodiments, the determined value a is a slope of
a size distribution that is steeper or more shallow than the
pre-determined slope. For sleep deprivation, alpha gets smaller
with wakefulness. The change in alpha can be a change in both
directions (steeper/shallower).
[0071] In other embodiments, the determined value .sigma. is a
branching ratio that is smaller or larger than 1.
[0072] In exemplary embodiments, the change in .alpha. or .sigma.
from the pre-determined value is correlated with a decrease in
behavioral performance, as reflected through an increased reaction
time in a psychomotor vigilance task.
[0073] Preferably, the subject(s) are mammal, and more preferably
human. The methods described herein can be used in subjects that
experience prolonged periods of wakefulness (e.g. the subject has
not slept for 24, 36, 48, 72 or more hours), for example, but not
limited to, subjects on duty and in patients with sleep disorders.
Typical applications may be related to many civil and military
professions. Other subjects may be those post-exercise, wherein the
methods described herein are used to identify individuals resilient
to sleep deprivation or at risk.
[0074] The subjects of the present invention may be suffering from
a sleep disorder. A sleep disorder is meant to refer to any
abnormal sleeping pattern. Examples of sleep disorders include, but
are not limited to, dyssomnia, insomnia, sleep apnea, narcolepsy,
and circadian rhythmic disorders.
[0075] In certain embodiments, the invention may include a step for
determining whether the subject maintained a normal sleeping
pattern. This step can be performed or accomplished in a number of
ways. In the simplest form, the subject can be questioned regarding
his or her previous sleep patterns. In a somewhat more complex form
the subject can be requested to fill out a questionnaire, which
then can be graded to determine whether his or her previous sleep
patterns where normal (or appeared normal). In an even more complex
form the subject might undergo all night polysomnography to
evaluate the subject's sleep architecture (e.g., obtaining
respiratory disturbance index to diagnose sleep apnea). It is clear
that there are numerous ways beyond those examples previously
mentioned of determining whether the subject being analyzed had, or
thought they had, a normal sleeping pattern, and therefore the
examples given above are included as exemplary rather than as a
limitation, and those ways of determining whether the subject
maintained or thought they were maintaining a normal sleeping
pattern known to those skilled in the art are considered to be
included in the present invention.
[0076] In any of the methods described herein, the method may
comprise a further step of gathering data from other physiological
sensors of brain activity. For example magnetoencephalography
(MEG), functional MRI (fMRI) using the BOLD signal or other related
measures, optical imaging using fluorescent dyes that track
neuronal activity such as intracellular calcium sensors, implanted
microelectrode arrays to record the local field potential (LFP) or
electrocorticogram (ECoG). In other embodiments, the method may
comprise a step of gathering data related to typical signs of
sleepiness, such as increased eye blink and/or yawning
frequency.
On-Line Evaluation
[0077] The methods of the present invention can be operational with
numerous other general purpose or special purpose computing system
environments or configurations. Examples of well known computing
systems, environments, and/or configurations that can be suitable
for use with the systems and methods comprise, but are not limited
to, personal computers, server computers, laptop devices, and
multiprocessor systems. Additional examples comprise set top boxes,
programmable consumer electronics, smart phones, network PCs,
minicomputers, mainframe computers, distributed computing
environments that comprise any of the above systems or devices, and
the like.
[0078] The methods of the present invention can be described in the
general context of computer instructions, such as program modules,
being executed by a computer. Generally, program modules comprise
routines, programs, objects, components, data structures, etc. that
perform particular tasks or implement particular abstract data
types. The systems and methods of the present invention can also be
practiced in distributed computing environments where tasks are
performed by remote processing devices that are linked through a
communications network. In a distributed computing environment,
program modules can be located in both local and remote computer
storage media including memory storage devices.
[0079] The methods of the present invention can be operational with
hardware or software to allow continuous monitoring of subjects,
for example subjects under extended wake periods. The proposed
metrics will be implemented in software or hardware and will allow
continuous monitoring of subjects under extended wake periods. In
certain embodiments, continuously recorded EEG will be evaluated on
line, and if the deviation from expected avalanche dynamics reaches
a critical threshold, an alert will be issued signaling, for
example, that the subject is at risk of underperformance, or in
post-exercise analysis to identify individuals resilient to sleep
deprivation or risk.
[0080] One skilled in the art will appreciate that the methods
disclosed herein can be implemented via a general-purpose computing
device in the form of a computer. The components of the computer
can comprise, but are not limited to, one or more processors or
processing units, a system memory, and a system bus that couples
various system components including the processor to the system
memory. Further, the methods of the present invention can be
operational with numerous general purpose or special purpose
computing system environments or configurations. Examples of
well-known computing systems, environments, and/or configurations
that can be suitable for use with the systems and methods comprise,
but are not limited to, personal computers, server computers,
laptop devices, smartphones, and multiprocessor systems. Additional
examples comprise set top boxes, programmable consumer electronics,
network PCs, minicomputers, mainframe computers, distributed
computing environments that comprise any of the above systems or
devices, and the like.
[0081] The methods of the present invention can be described in the
general context of computer instructions, such as program modules,
being executed by a computer. Generally, program modules comprise
routines, programs, objects, components, data structures, etc. that
perform particular tasks or implement particular abstract data
types. The systems and methods of the present invention can also be
practiced in distributed computing environments where tasks are
performed by remote processing devices that are linked through a
communications network. In a distributed computing environment,
program modules can be located in both local and remote computer
storage media including memory storage devices.
[0082] One skilled in the art will appreciate that the systems and
methods disclosed herein can be implemented via a general-purpose
computing device in the form of a computer. The components of the
computer can comprise, but are not limited to, one or more
processors or processing units, a system memory, and a system bus
that couples various system components including the processor to
the system memory.
[0083] The system bus represents one or more of several possible
types of bus structures, including a memory bus or memory
controller, a peripheral bus, an accelerated graphics port, and a
processor or local bus using any of a variety of bus architectures.
By way of example, such architectures can comprise an Industry
Standard Architecture (USA) bus, a Micro Channel Architecture (MCA)
bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards
Association (VESA) local bus, an Accelerated Graphics Port (AGP)
bus, and a Peripheral Component Interconnects (PCI) bus also known
as a Mezzanine bus. The bus, and all buses specified in this
description can also be implemented over a wired or wireless
network connection and each of the subsystems, including the
processor, a mass storage device, an operating system, NAS
software, neuronal data, a network adapter, system memory, an
Input/Output Interface, a display adapter, a display device, and a
human machine interface, can be contained within one or more remote
computing devices at physically separate locations, connected
through buses of this form, in effect implementing a fully
distributed system.
[0084] The computer typically comprises a variety of computer
readable media. Exemplary readable media can be any available media
that is accessible by the computer and comprises, for example and
not meant to be limiting, both volatile and non-volatile media,
removable and non-removable media. The system memory comprises
computer readable media in the form of volatile memory, such as
random access memory (RAM), and/or non-volatile memory, such as
read only memory (ROM). The system memory typically contains data
such as neuronal data and/or program modules such as operating
system and NAS software that are immediately accessible to and/or
are presently operated on by the processing unit.
[0085] The computer can also comprise other
removable/non-removable, volatile/non-volatile computer storage
media. For example, and not meant to be limiting, a mass storage
device can be a hard disk, a removable magnetic disk, a removable
optical disk, magnetic cassettes or other magnetic storage devices,
flash memory cards, CD-ROM, digital versatile disks (DVD) or other
optical storage, random access memories (RAM), read only memories
(ROM), electrically erasable programmable read-only memory
(EEPROM), and the like.
[0086] Optionally, any number of program modules can be stored on
the mass storage device, including by way of example, an operating
system and NAS software. Each of the operating system and NAS
software (or some combination thereof) can comprise elements of the
programming and the NAS software. Neuronal data can also be stored
on the mass storage device. Neuronal data can be stored in any of
one or more databases known in the art. Examples of such databases
comprise, DB2, MICROSOFT Access, MICROSOFT SQL Server, ORACLE,
mySQL, PostgreSQL, and the like. The databases can be centralized
or distributed across multiple systems.
[0087] The user can enter commands and information into the
computer via an input device (not shown). Examples of such input
devices comprise, but are not limited to, a keyboard, pointing
device (e.g., a "mouse"), a microphone, a joystick, a scanner, and
the like. These and other input devices can be connected to the
processing unit via a human machine interface that is coupled to
the system bus, but can be, connected by other interface and bus
structures, such as a parallel port, game port, an IEEE 1394. Port
(also known as a Firewire port), a serial port, or a universal
serial bus (USB).
[0088] A display device can also be connected to the system bus via
an interface, such as a display adapter. It is contemplated that
the computer can have more than one display adapter and the
computer can have more than one display device. For example, a
display device can be a monitor, an LCD (Liquid Crystal Display),
or a projector. In addition to the display device, other output
peripheral devices can comprise components such as speakers (not
shown) and a printer (not shown) which can be connected to the
computer via Input/Output Interface.
[0089] A neuronal activity detector can communicate with the
computer via Input/Output Interface or across a local or remote
network. In one aspect, users utilize a neuronal activity detector
that is capable of collecting neuronal data. It will be appreciated
that the neuronal activity detector can be any type of neuronal
activity detector, for example and not meant to be limiting, a
microelectrode array (to record LFPs and single/multi-unit
activity), a surface electrode system (to record the EEG or ECoG),
a charge-coupled device camera (CCD) or photodiode array (to record
activity-dependent fluorescence changes), a magnetometer type SQUID
(superconducting quantum interference device) sensor (to record the
MEG), a functional magnetic resonance imaging (fMRI) device to
measure the activity related blood oxgen-level dependent signal
(BOLD), and the like. In another aspect, the neuronal activity
detector can be an independent stand alone device, or can be
integrated into another device. Optionally, the communication with
computer via Input/Output Interface can be via a wired or wireless
connection.
[0090] The computer can operate in, a networked environment using
logical connections to one or more remote computing devices. By way
of example, a remote computing device can be a personal computer,
portable computer, a server, a router, a network computer, a peer
device or other common network node, and so on. Logical connections
between the computer and a remote computing device can be made via
a local area network (LAN) and a general wide area network (WAN).
Such network connections can be through a network adapter. A
network adapter can be implemented in both wired and wireless
environments. Such networking environments are conventional and
commonplace in offices, enterprise-wide computer networks,
intranets, and the Internet.
[0091] An implementation of NAS software can be stored on or
transmitted across some form of computer readable media. Computer
readable media can be any available media that can be accessed by a
computer. By way of example and not meant to be limiting, computer
readable media can comprise "computer storage media" and
"communications media." "Computer storage media" comprise volatile
and non-volatile, removable and non-removable media implemented in
any method or technology for storage of information such as
computer readable instructions, data structures, program modules,
or other data. Exemplary computer storage media comprises, but is
not limited to, RAM, ROM, EEPROM, flash memory or other memory
technology, CD-ROM, digital versatile disks (DVD) or other optical
storage, magnetic cassettes, magnetic tape, magnetic disk storage
or other magnetic storage devices, or any other medium which can be
used to store the desired information and which can be accessed by
a computer.
[0092] The methods can employ Artificial Intelligence techniques
such as machine learning and iterative learning. Examples of such
techniques include, but are not limited to, expert systems, case
based reasoning, Bayesian networks, behavior based AI, neural
networks, fuzzy systems, evolutionary computation (e.g. genetic
algorithms), swarm intelligence (e.g. ant algorithms), and hybrid
intelligent systems (e.g. Expert inference rules generated through
a neural network or production rules from statistical
learning).
[0093] The processing of the disclosed systems and methods of the
present invention can be performed by software components. The
disclosed systems and methods can be described in the general
context of computer-executable instructions, such as program
modules, being executed by one or more computers or other devices.
Generally, program modules comprise computer code, routines,
programs, objects, components, data structures, etc. that perform
particular tasks or implement particular abstract data types. The
disclosed methods can also be practiced in grid-based and
distributed computing environments where tasks are performed by
remote processing devices that are linked through a communications
network. In a distributed computing environment, program modules
can be located in both local and remote computer storage media
including memory storage devices.
[0094] The NAS Software allows for the study of neuronal avalanches
and includes many analysis features. NAS Software allows for the
calculation of alpha (.alpha.), the slope of the avalanche size
distribution, and sigma (.sigma.), the branching parameter at the
correct temporal resolution (.DELTA.t.sub.avg). Avalanche
calculation controls set the parameters for concatenating neuronal
events into avalanches. A multi-function control window contains
functions that extract the avalanche parameters .alpha. and .sigma.
at corresponding .DELTA.t.sub.avg. For visual control, avalanche
size distributions in log-log coordinates can be generated and
displayed. Cross-correlation plots used to calculate
.DELTA.t.sub.avg can also be displayed at various temporal
resolutions. Additional features relate to the identification and
labeling of recording locations to superficial cortical layers in
which avalanches occur. For example, the NAS Software allows for
the topological identification of electrode positions on a
microelectrode array with respect to brain region and cortical
layer. NAS Software allows for the storage of spatial information,
e.g. images, and miscellaneous data specific to an experimental
configuration.
[0095] NAS Software can analyze the similarity in spatiotemporal
organization between neuronal avalanches. The spatiotemporal
organization of avalanche is highly diverse and the diversity can
be used to further evaluate the quality of the data and to quantify
the critical state in the cortical network. More specifically, the
size distribution of significant avalanche families reveals a
heavy-tail in family sizes that forms a power law with slope gamma.
Shuffle and cluster controls allow for the generation of shuffled
data sets and statistical evaluation of family significance and
calculation of gamma. Several additional features allow for a
detailed examination of avalanche similarity on which the family
size distribution is based. An avalanche generation tree can be
generated that represents the generational relationship between
avalanches based on similarity. An avalanche similarity matrix can
be generated that contains the similarity index for all possible
pair wise comparisons between avalanches. A multi-function control
window for cluster analysis contains functions for studying the
spatiotemporal organization of avalanches, for example, displaying
a family frequency distribution plot to derive gamma.
[0096] NAS Software allows for visualization and editing of the
temporal organization of neuronal avalanches. This can play a role
when judging the quality of recorded data. An overview plot of LFP
activity can be generated that displays the occurrence of LFPs
during an experiment. A zoom view can display LFP occurrence for
the temporal duration indicated by a colored rectangle in an
overview plot. This allows for a detailed examination of avalanches
and can be used to clean data sets from spurious noise. It also
allows for the indication of avalanche extent and precise labeling
of individual avalanches within a data set with respect to rank of
occurrence in time, corresponding family and order within a family
For evoked activity, this feature can display identified stimuli
and corresponding evoked avalanches Family controls can display the
type and occurrence of families over time. The family control and
avalanche zoom view can be aligned in time for precise
comparison.
Apparatus
[0097] According to various embodiments, an EEG headset is provided
to subjects for use at home, recreational, at work, as well as in
laboratory environments. In particular embodiments, the EEG headset
includes multiple dry electrodes individually isolated and
amplified. Data from individual electrodes may be processed prior
to continuous transmission to a data analyzer. The continuously
recorded EEG can be evaluated online as described herein and in US
20090036791, incorporated by reference in its entirety herein. For
example, the methods described herein are used by consumers to
monitor sleep deprivation in real time, e.g., on a smart phone.
[0098] Typical applications of the methods described herein are
related to many civil and military professions, although not
limited as such. A subject may wear the portable neuro-response
data collection mechanism during a variety of activities in
non-laboratory settings. This allows collection of data from a
variety of sources while a subject is in a natural state. For
example, dry EEG electrodes can be easily integrated into helmets
of pilots and soldiers to monitor the EEG. The present inventors
have demonstrated that the avalanche metric is evident even when
using a relatively small set of sensors. The continuously recorded
EEG can be evaluated online, and if the deviation from expected
avalanche dynamics reaches a critical threshold, an alert will be
issued signaling that the subject wearing the helmet is at risk of
underperformance.
[0099] In certain aspects, EEGs are recorded by a wireless EEG
headset.
[0100] In certain aspects, the invention features an integrated
program that includes the methods described herein performed with
an EEG headset, for example a wireless headset. The methods can be
performed in the comfort of the subject's home or workplace. The
data are reviewed by a specialist after upload and can be used for
diagnosis or intervention, and can be made through an integrated
web-portal. The portal allows for on-going clinician monitoring of
progress.
EXAMPLES
[0101] It should be appreciated that the invention should not be
construed to be limited to the examples that are now described;
rather, the invention should be construed to include any and all
applications provided herein and all equivalent variations within
the skill of the ordinary artisan.
Example 1: Neuronal Avalanches and Sleep Deprivation
[0102] The present invention is based, in part, on the hypothesis
that neuronal avalanche dynamics may be used to assess the effect
of sleep deprivation in single subjects by following changes in
metrics such as the avalanche exponent, .alpha., and the branching
parameter, .sigma., over time. This hypothesis was tested with high
density EEG (hd-EEG) recordings from human subjects during 36 hrs
of sleep deprivation. It was found that indeed both avalanche
metrics correlated with sleep deprivation and corresponding
decrease in behavioral performance
[0103] First, neuronal avalanches were looked for in the baseline
recordings. FIG. 1A depicts the distribution of avalanche sizes
from a single subject (solid black curve; .DELTA.t=6 ms), showing a
clear power law behavior (broken black line represents a reference
power law with an exponent of -3/2). A maximum likelihood based
analysis (see Materials and Methods; [21,22]) demonstrates a
significantly better fit to a power law compared to an exponential
function (p<10.sup.-5 for all subjects). The power law reflects
long-range spatiotemporal correlations among sensors. Accordingly,
destroying the correlations among sensors by shuffling the phases
of different frequency components in each sensor (while maintaining
the power spectrum) destroys the power law behavior (FIG. 1A,
broken red curve).
[0104] Finite-size scaling was demonstrated by dividing the
original sensor array into sub-arrays of different sizes and
recalculating the avalanche distribution for each size. The
resulting distributions are shown in FIG. 1B (see color key for the
number of sensors used in each case). A clear power law behavior
was obtained in all cases with the cutoff proportional to the
number of sensors used in line with the expectation for neuronal
avalanches. The recording was made with eyes open and similar power
law behavior was obtained with eyes closed.
[0105] The values of .sigma. and .alpha. monotonically increase
with the time bin used to identify avalanches. FIG. 2 demonstrates
this increase in .alpha. and .sigma. for time scales from 2 ms to
10 ms. Importantly, at the time scale of 6 ms, the branching
parameter, .sigma., is close to 1 and the avalanche exponent,
.alpha., is close to -3/2, in line with predictions from the theory
of critical branching processes. The time scale of 6 ms is also the
one used in FIG. 1.
[0106] Next, the avalanche analysis was applied to EEG recordings
collected every 3 hrs during an extended wake period of 36 hours
(see Material and Methods and [9]). FIGS. 3A and B depict the mean
branching parameter and the mean avalanche exponent as a function
of time awake (mean value at each time point was calculated from 5
different datasets). Both branching parameter and avalanche
exponent increased with time awake (solid lines represent linear
regression and the corresponding p-values are shown in each panel).
They also had a similar envelope and were clearly correlated (FIG.
3D). The red dot in each plot marks the results after recovery
sleep of at least 8 hours, indicating a strong recovery effect,
which led to values even lower than the baseline.
[0107] In addition to EEG, subjects were evaluated using a 10-min
psychomotor vigilance task (see Materials and Methods). The mean
reaction time (RT) in this task followed a very similar pattern to
that of .alpha. and .sigma. (FIG. 3C) and was highly correlated
with .sigma. (FIG. 3E). Importantly, even a transient improvement
in reaction time is accompanied with a corresponding, transient
approach of the avalanche measures of the optimal point of
(.alpha., .sigma.)=(-3/2, 1) (see FIG. 3A-C, 4.sup.th time point=12
hrs of wakefulness). This further strengthens the claim for a
direct relationship between avalanche dynamics and behavioral
performance that is beyond a simple monotonic correlation with
wakefulness.
[0108] As noted above, the values of .sigma. and .alpha. depend on
the analysis time scale, which in turn is linked to the use of an
electrode array with a particular interelectrode distance, which is
linked to the propagation velocity of neuronal activity. Thus, the
changes observed in avalanche parameters with increase in
wakefulness, could have resulted from changes in the effective time
scale of activity propagation in the brain and not a change in the
avalanche organization. As a control, the inverse of the
propagation velocity, i.e. time between successive events on the
array, was estimated using the mean time interval between
consecutive events on the sensor array [11]. FIG. 3F depicts the
mean IEI (inter-event-interval) as a function of time awake. It
remains approximately constant around 5 ms, consistent with the
estimate obtained from the plot of .alpha. vs. .sigma. (FIG. 2; 6
ms). Thus, the observed effects are indeed more likely to be
directly related to deviations of the brain activity from neuronal
avalanche dynamics, as can be induced by changes in the balance of
excitation and inhibition.
[0109] The dependence of neuronal avalanche metrics on wake time
was analyzed using EEG recordings of human subjects during an
extended wake period of 36 hrs. The branching parameter and
avalanche exponent increased with time awake, reflecting increased
dominance of excitatory forces in the underlying network dynamics.
This indication of excitation-inhibition imbalance is consistent
with the prediction of the synaptic homeostasis hypothesis [7]. A
strong correlation was also found between avalanche metrics and
behavior as measured through reaction times, demonstrating that
these metrics reflect meaningful features of brain dynamics and can
be useful for predicting a subject's performance. Sleep deprivation
has different effects on different subjects. Whereas some subjects
show mild effects, others exhibit more dramatic effects. Continuous
monitoring of the changes in avalanche metrics during wake time may
be useful for identifying subjects that are more susceptible to the
side-effects of sleep deprivation as well as for providing a
warning message when a relevant threshold is crossed.
[0110] The findings of the present invention may be used to monitor
the degree of potential dysfunction in subjects that experience
prolonged periods of wakefulness on duty and in patients with sleep
disorders. Traditional EEG technology relies on electrodes that are
attached to the scalp using conducting gel, limiting its use in
many practical applications. However, in recent years there were
major advances in the new technology of dry EEG electrodes and
there are by now FDA-approved commercially available headsets.
These headsets can be directly used in many civil and military
applications and they can also be easily integrated into helmets of
soldiers and pilots. One limitation of these headsets is that they
typically contain a small number of sensors. However, as
demonstrated in FIG. 1, the power law behavior is evident even with
smaller sets of sensors and does not require the full hd-EEG sensor
set. The continuously recorded EEG can be evaluated online. If the
deviation from expected avalanche dynamics reaches a critical
threshold, an alert will be issued signaling that the subject is at
risk of underperformance. Other applications include post-exercise
analysis to identify individuals resilient to sleep deprivation or
at risk.
[0111] Further, the avalanche metrics can be integrated with
additional sources of information, such as the theta power or data
from other physiological sensors, to make the decision even more
accurate and less prone to artifacts.
Materials and Methods
[0112] The experiments described herein were carried out with, but
are not limited to, the following materials and methods.
Experimental Design
[0113] The experimental design and data collection have been
described in detail in [9] and are incorporated by reference
herein. The experiment involved sixteen healthy participants
(age=19-26) from the University of Wisconsin-Madison campus. All
participants completed two experiments separated by at least 2
weeks, the order of which was randomly assigned and
counter-balanced across subjects. In each experiment participants
were asked to stay awake for at least 24 hrs and up to 36 hrs,
during which they were engaged in a language task or a visuo-motor
task (see [9] for more details). Here we focused on 5 datasets in
which 3 subjects stayed awake for 36 hours.
[0114] The night before the experiment, participants were asked to
go to bed at their usual bedtime, wake up at .about.7:00, and
arrive in the lab at 8:30 to prepare for hd-EEG recordings (256
electrodes, Electrical Geodesics Inc.). Participants completed a
baseline EEG recording session at 10:00, followed by 11 EEG
recording sessions in intervals of 3 hours. Between recording
sessions, subjects completed 2-h periods of experimental tasks.
After 36 hours they went to sleep and were woken up after .about.8
hrs. A final testing session was performed 30 min after they woke
up (to reduce the influence of sleep inertia).
[0115] Each EEG session consisted of two 2-min eyes-open periods,
interleaved by two 2-min eyes-closed periods (order counterbalanced
across participants). At the beginning and at the end of each
session, sleepiness was evaluated using self-rating questionnaires
(Stanford Sleepiness Scale; [17]) and a 10-min psychomotor
vigilance task (PVT; [18]). Participants performed the PVT (adapted
version of [19]) for .about.10 min while fixating a cross placed at
the center of a computer screen. They were instructed to respond as
quickly as possible to stop a millisecond counter that started to
scroll at random intervals between 2 and 12 sec.
Measuring Neuronal Avalanches using EEG
[0116] Neuronal avalanches can be identified in human subjects
using the technologies of EEG and MEG [13,20]. Here we follow the
same steps as in [13]. The data were sampled at 500 Hz and bandpass
filtered (1-150 Hz). Four channels that contained artifacts were
removed. For each sensor, positive and negative excursions beyond a
threshold of 3SD were identified. A single event was identified per
excursion, at the most extreme value (maximum for positive
excursions and minimum for negative excursions). The resulting time
series of events was individually discretized with time bins of
duration .DELTA.t. The timescale of the analysis, .DELTA.t, was
explored systematically in multiples of .DELTA.t.sub.min=2 ms,
which was the inverse of the data acquisition sampling rate (500
Hz). A cascade was defined as a continuous sequence of time bins in
which there was an event on any sensor, ending with a time bin with
no events on any sensor. The number of events on all sensors in a
cascade was defined as the cascade size. The avalanche size
distribution's fit to a power law was obtained using the methods
described in [21,22].
[0117] The branching parameter, .sigma., was estimated by
calculating the ratio of the number of events in the second time
bin of a cascade to that in the first time bin. This ratio was
averaged over all cascades for each subject with no exclusion
criteria,
.sigma. = 1 N a v k = l N a v n e v e n t s ( 2 nd bin of k ' th
avalanche ) n e v e n t s ( 1 st bin of k ' th avalanche ) ( 1 )
##EQU00010##
where N.sub.av is the total number of avalanches in the dataset and
n.sub.events represents the number of events in a particular bin.
Note that for single bin cascades the second bin is an empty bin
and therefore the corresponding ratio is 0.
Statistical Analysis
[0118] Correlations between avalanche metrics, time awake and
reaction times were evaluated using linear regression and the
corresponding p-values were calculated in each case.
Example 2: Changes in Neuronal Avalanches During Sustained
Wakefulness in Humans and Rodents
[0119] Described herein is a rat model of sleep deprivation. Using
invasive recording technology and behavioral paradigms, it was
demonstrated in the rat model that avalanche metrics change as a
function of time awake as reported in humans.
[0120] The rat model was utilized to demonstrate that cortical
dynamics in rats become supercritical, i.e., move away from
avalanche dynamics, with prolonged sleep deprivation, which is
congruent with the results presented for humans in FIG. 3A-FIG.
3F.
[0121] Sleep has been suggested as a homeostatic regulatory process
that rebalances cortical excitability increased during wakefulnes's
(Huber, R. et al. 2013 Cereb Cortex, 23: 332-338; Tononi, G. &
Cirelli, C. 2003 Brain research bulletin, 62: 143-150). The
dependency of avalanche dynamics on the E-I balance (Shew et al.,
2009 J. Neurosci., 29: 15595-15600; Shew et al., 2011 J. Neurosci.;
5: 55-63) predicts a progressive deviation towards a supercritical
state as a function of time awake. Indeed, a recently observed
alteration in critical brain dynamics during sustained wakefulness
in humans suggests a pivotal role of sleep to maintain brain
dynamics at a critical state (Meisel et al., 2013 J. Neurosci., 33:
17363-17372).
[0122] Described herein is a positive correlation between time
awake, increase in branching parameter, and increase in reaction
time in sleep deprived humans (see also, FIG. 3A-FIG. 3F). It was
examined if similar to humans, rodents also undergo significant
deviations from avalanche dynamics with sustained wakefulness,
which are reset by sleep. This was examined by taking advantage of
the insight into neuronal avalanches in the rodent brain using
high-resolution LFP measured with MEAs.
Design and Data Analysis
[0123] 4.times.8 MEAs (Neuronexus) were implanted into superficial
layers of prefrontal cortex for chronic recordings in the awake,
behaving rat. To monitor vigilance state, EEG and electromyogram
(EMG) were simultaneously recorded. Rats were sleep deprived by the
commonly used method of `gentle handling` for up to 6 h (equivalent
to up to 18 h time awake) during which MEA activity, EEG and EMG
were recorded (Blackrock Systems) for off-line analysis. The LFP
(1-100 Hz; sampling rate 500 Hz) was z-normalized, subjected to
thresholding, and spatiotemporal clusters were identified for
various temporal resolutions.
Results and Interpretation
[0124] Neuronal avalanche sizes exhibited a systematic shift
towards larger avalanches leading to progressive distortion of the
underlying power law in size distribution measured by kappa,
.kappa., (Shew et al., 2009 J Neurosci, 29: 15595-15600) and
increasing branching parameter .sigma. as a function of time awake
(FIG. 4A and FIG. 4B). The observed changes were reversed by
consecutive sleep. These results suggest that sleep functions to
tune cortical networks in the brain to a critical state where
information processing is optimized.
Neuronal Avalanche Dynamics and Sleep
[0125] As demonstrated in Meisel (Meisel et al., 2013 The Journal
of Neuroscience, 33(44):17363-17372, incorporated herein by
reference), cortical dynamics progressively becomes supercritical
with sleep deprivation. Specifically, Meisel demonstrated that the
precise power-laws governing the cascading activity of neuronal
avalanches and the distribution of phase-lock intervals in human
electroencephalographic recordings are increasingly disarranged
during sustained wakefulness. The results presented in FIG. 5A-FIG.
5D, which were obtained from humans, correlate with the results
obtained from rats presented herein (FIG. 4A and FIG. 4B).
Moreover, FIG. 6A-FIG. 6B demonstrate that the deviation from sigma
(one of the critical avalanche measures) shows a high correlation
with self-rated alertness in humans.
[0126] The results described herein in humans and rats demonstrate
that .sigma. and size distributions change with sleep
deprivation.
Other Embodiments
[0127] While the invention has been described in conjunction with
the detailed description thereof, the foregoing description is
intended to illustrate and not limit the scope of the invention,
which is defined by the scope of the appended claims. Other
aspects, advantages, and modifications are within the scope of the
following claims.
[0128] The patent and scientific literature referred to herein
establishes the knowledge that is available to those with skill in
the art. All United States patents and published or unpublished
United States patent applications cited herein are incorporated by
reference. All published foreign patents and patent applications
cited herein are hereby incorporated by reference. Genbank and NCBI
submissions indicated by accession number cited herein are hereby
incorporated by reference. All other published references,
documents, manuscripts and scientific literature cited herein are
hereby incorporated by reference.
[0129] While this invention has been particularly shown and
described with references to preferred embodiments thereof, it will
be understood by those skilled in the art that various changes in
form and details may be made therein without departing from the
scope of the invention encompassed by the appended claims.
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