08 -2014 - Brain Networks and Neuronal Communica4on [Thilo Womelsdorf]
Measures of
Neuronal Synchronization
and Brain Connectivity
- An Introduction w w w. S c i e n t i f i c A m e r i c a n . c o m
sad0410Insel4p.indd 45
SCIENTIFIC AMERICAN
45
2/17/10 5:42:09 PM
Thilo Womelsdorf
Centre for Vision Research, York University, Toronto
attentionlab.ca
08 -2014 - Brain Networks and Neuronal Communica4on [Thilo Womelsdorf]
Measures of
Neuronal Synchronization
and Brain Connectivity
- An Introduction w w w. S c i e n t i f i c A m e r i c a n . c o m
sad0410Insel4p.indd 45
SCIENTIFIC AMERICAN
45
2/17/10 5:42:09 PM
With courtesy (for some material
from Toolkits) to
Dr. Jan Matthijs Schoffelen
and Prof. Pascal Fries
Overview and
Examples: Frequency
Specific Functional
Connectivity
Coherence and
Spike-LFP
Pairwise Phase
consistency
Basic Overview:
Oscillations and
Phase Differences
al.
]
... some measures of Synchronization and
Functional Connectivity
Time
Domain
Frequency
Domain
Cross correlation
Joint Peri-stimulus Time Histogram (JPSTH)
Spike-Triggered JPSTH
Coherence coefficient
Phase lag index
Phase synchronization
Partial directed coherence
Directed transfer function
Phase locking value
Imaginary part of coherency
Pairwise phase consistency
Phase slope index
Frequency domain granger causality
al.
]
... some measures of Synchronization and
Functional Connectivity
Time
Domain
Frequency
Domain
Cross correlation
Joint Peri-stimulus Time Histogram (JPSTH)
Spike-Triggered JPSTH
Coherence coefficient
Phase lag index
Phase synchronization
Partial directed coherence
Directed transfer function
Phase locking value
Imaginary part of coherency
Pairwise phase consistency
Phase slope index
Frequency domain granger causality
... some measures of Synchronization and
Functional Connectivity
Cross correlation
Joint Peri-stimulus Time Histogram (JPSTH)
Spike-Triggered JPSTH
Spike
Rate
ion
Po
d
ali
rm
No
rm
ali
ze
d
Po
sit
sit
ion
Choice Behavior is
based on fast changes
of monosynaptic
connectivity.
No
Po
d
ze
ali
rm
No
Spike
Rate
D
ion
sit
Po
d
ze
ali
rm
No
Time (ms)
C
ion
B
Interneuron
Cell 2
ze
Pyramidal
Cell 1
sit
Time-Specific +
Choice-Selective
functional
Connection on
“choice left”
trials
Cross Corr.
Cells 1-2
Events
Time
Domain
Events
al.
]
FujisawaRate
2008 Nat Neurosci
al.
]
... some measures of Synchronization and
Functional Connectivity
Time
Domain
Frequency
Domain
Cross correlation
Joint Peri-stimulus Time Histogram (JPSTH)
Spike-Triggered JPSTH
Coherence coefficient
Phase lag index
Phase synchronization
Partial directed coherence
Directed transfer function
Phase locking value
Imaginary part of coherency
Pairwise phase consistency
Phase slope index
Frequency domain granger causality
... Joint-Peri-Stimulus Histogram (JPSTH) analysis
‘counts’ the coincidences of spikes from two
neurons at different rel. time lags:
100
STJH
Conicidences
A
0
10
Spike 3
Occurrences 0
in area B 2
1
-150
0
ounts
B
3
2
150
-100
0 0 100 150
e Time rel. to mPFC (EC)
Norm. Counts
150
-100
0
-100
Spike Occurrences
100
in area C
0
0
100
STJH
Conicidences
Cross Correlation of
perirhinal-entorhinal cell pairs.
if “Time 0” is
stimulus onset,
mPFC triggered STJH of
thenPC-EC
it is PSTH
same
cell pairs.
6
... Spike-Triggered Joint Histogram (STJH) analysis
“Paz, Bauer, Pare 2007 J Neurosci. Learning-Related Facilitation of Rhinal
Interactions by Medial Prefrontal Inputs”
100
STJH
Conicidences
A
0
10
Spike 3
Occurrences 0
in area B 2
1
-150
0
ounts
B
3
2
150
-100
0 0 100 150
e Time rel. to mPFC (EC)
Norm. Counts
150
-100
0
-100
Spike Occurrences
100
in area C
0
0
100
STJH
Conicidences
Cross Correlation of
perirhinal-entorhinal cell pairs.
“Time 0” can also be
the spike times of a
mPFC triggered STJH of
thirdPC-EC
neuron
same
cell pairs.
6
Correlation of
torhinal cell pairs.
STJH
Conicidences
mPFC triggered STJH of
same PC-EC cell pairs.
... Spike-Triggered
Joint Histogram (STJH) analysis
100
1-100
0
ounts
ime (msec.)B
3
-100 0 0 100
100
-100
Spike Time rel. to
mPFC (PC)
STJH
Conicidences
Conicidences
Spike Time rel. to mPFC (EC)
100
3
0
2
100
6
medial PFC Spikes
systematically10
precedes
EC-PR synchrony
0
-100
0
0
-100
100
STJHs show more mPFC-HPC interactions
0
following onset of cue and reward
2
0
100
STJH
Conicidences
100
A
STJH
e Time rel. to mPFC (EC)
0
100
Norm. Counts
0
medial PFC Spikes
synchronize to
0
10
“Time(“join”)
0” candistant
also be
EC-PR
synchronous
-100
the
spike
times
of aof
Cross Correlation of
mPFC
triggered
STJH
assembly
0
third
neuron
perirhinal-entorhinal
cell
pairs.
same PC-EC cell pairs.
-100
0
100
6
mPFC triggered STJH of
same PC-EC cell pairs.
STJH
Conicidences
Correlation of
torhinal cell pairs.
100
0
-100
0
-100
0
100
100
100
ime (msec.)
150
C before
medialBPFC Spikes
P [spk]
6
systematically
area
B0
B before
precedes
C
EC-PR synchrony
-150
0
0
-100
0
medial PFC Spikes
synchronize to
(“join”) distant
EC-PR synchronous
assembly
10
STJH
Conicidences
0
Spike Time rel. to mPFC (EC)
... Spike-Triggered
Joint Histogram (STJH) analysis
100
-100
0
100
Spike Time rel. to
mPFC (PC)
STJHs show more mPFC-HPC interactions
following onset of cue and reward
-150
0
P [spk] area
150
C
... Spike-Triggered Joint Histogram (STJH) analysis
• allows to identify
systematic sequences
of spiking activity;
A spiking
precede B+C
C
A
B
A C B
A B C
e.g. Paz 2007 report:
that spike relations to a
conditioned stimulus
followed
1a
2
1b
P [spk]
area B
4a
4b
C B A
P [spk]
area C
medial PFC
-> Hippoc
-> peri-rhinal
B C A
A spiking
follows B+C
3
B A C
al.
]
... some measures of Synchronization and
Functional Connectivity
Time
Domain
Frequency
Domain
Cross correlation
Joint Peri-stimulus Time Histogram (JPSTH)
Spike-Triggered JPSTH
Coherence coefficient
Phase lag index
Phase synchronization
Partial directed coherence
Directed transfer function
Phase locking value
Imaginary part of coherency
Pairwise phase consistency
Phase slope index
Frequency domain granger causality
... Coherence coefficient of source localized MEG power
in humans (at the 2 Hz flicker frequency)
• IFJ flexibly couples to those visual
MEG Source
Level -
areas processing attended
information.
Baldauf & Desimone
2014 Science
... Coherence coefficient of Local Field Potentials in Prefrontal and
Parietal Cortex during Working Memory (Monkey)
working memory
task:
12-22 Hz
Coherence
PFC - Parietal
Coherence at 20 Hz
codes for object
identity
Preferred Object
Non-Preferred Object
0.2
0.6
1.0
Time (sec.)
Salazar, Dutson, Bressler, Gray, 2012, Science
1.4
1.8
... Coherence coefficient of Local Field Potentials in Prefrontal and
Parietal Cortex during Working Memory (Monkey)
Object Location
50
40
30
20
10
0
50
40
30
20
10
0
Salazar, Dutson, Bressler, Gray, 2012, Science
Coherence
Selectivity Index
Object Identity
Frequency [Hz]
working memory
task:
Percent of sign. LFP-LFP Pairs
20Hz LFP beta with object information specifically
coupled LIP - DLPFC ...
60
Coherence
Location Selective
Fronto-parietal Network Topography of
Object Identity in lat. PFC - LIP
working memory information
Object Selective
40
20
Salazar, Dutson, Bressler, Gray, 2012, Science
... Coherence coefficient of spiketrains and the Local Field Potentials
couple with high (cell-) specificity FEF and V4 during attention.
Long-Range
Gamma
Coupling
Gregoriou et al. 2012, Neuron
al.
]
... some measures of Synchronization and
Functional Connectivity
Time
Domain
Frequency
Domain
Cross correlation
Joint Peri-stimulus Time Histogram (JPSTH)
Spike-Triggered JPSTH
Coherence coefficient
Phase lag index
Phase synchronization
Partial directed coherence
Directed transfer function
Phase locking value
Imaginary part of coherency
Pairwise phase consistency
Phase slope index
Frequency domain granger causality
... The Pairwise Phase Consistency of specific sets of spikes (e.g.
bursts) with the the Local Field Potentials shows e.g. long range
coupling during Attention states:
Attention Cue
e
ncy
Phase
Consistency
Spike-LFP Phase Consistency (PPC)
Non-Burst Spikes
Burst Spikes
x 10 -4
16
Stimulus
Baseline
8
0.06
Attentional
State
12-25 Hz
Example
0
5 10 20 30
50
70
Frequency (Hz)
Color
Baseline
90
Womelsdorf 2012 under revision
50-75 Hz
al.
]
... some measures of Synchronization and
Functional Connectivity
Time
Domain
Frequency
Domain
Cross correlation
Joint Peri-stimulus Time Histogram (JPSTH)
Spike-Triggered JPSTH
Coherence coefficient
Phase lag index
Phase synchronization
Partial directed coherence
Directed transfer function
Phase locking value
Imaginary part of coherency
Pairwise phase consistency
Phase slope index
Frequency domain granger causality
• 12-‐20 Hz Beta Synchrony
signifies feedback-‐type
interac4ons across visual cortex Bastos, Bosman, ... Fries, 2014
• Granger causal influence
Bastos, Bosman, ... Fries, 2014
coincide with beta band coherence ...
al.
]
... some measures of Synchronization and
Functional Connectivity
Time
Domain
Frequency
Domain
Cross correlation
Joint Peri-stimulus Time Histogram (JPSTH)
Spike-Triggered JPSTH
Coherence coefficient
Phase lag index
Phase synchronization
Partial directed coherence
Directed transfer function
Phase locking value
Imaginary part of coherency
Pairwise phase consistency
Phase slope index
Frequency domain granger causality
Overview and
Examples: Frequency
Specific Functional
Connectivity
Coherence and
Spike-LFP
Pairwise Phase
consistency
Basic Overview:
Oscillations and
Phase Differences
al.
]
• What constitutes an oscillation
What constitutes an oscillation? (recap)
amplitude
period
phase
t constitutes
al.
]
an oscillation? (the movie)
constitutes
an oscillation
What• What
constitutes
an oscillation?
(the movie)
Amplitude and
phi/Phase
i!"
x = Ae
x=
T=0
i!
Ae "
Vector Representation of
the oscillation at T=0
al.
]
To get to the concept of coherence, we first need the concept of
a
of a frequency
component
of the EEG/ME
Tovector
get of
to representation
the conceptwe
of
we
first
of coherence,
coherence,
weconcept
first need
need
the concept
concept of
of
To get to the concept
coherence,
first
need the
of the
a
vector
representation
of
a
frequency
component
of
the
EEG/MEG
of a frequency
component
of concept
the EEG/ME
a vector representation
frequency
of
EEG/MEG:
To get to of
thea concept
of component
coherence,
wethe
first
need the
of
To get to the concept of coherence, we first need the concept of
a vector representation of a frequency component of the EEG/MEG
a vector of
representation
of a
frequency
To get to the concept
coherence, we
first
need thecomponent
concept of of the EEG/MEG
a vector representation of a frequency component of the EEG/MEG:
... examples showing
the vector
representation of
phases of
oscillations, relative
to T=0
T=0
al.
]
Biological signals contain many frequency components
and can be decomposed into those components:
“Time domain”
“Time domain”
“Frequency
domain”
“Frequency
domain”
Frequency
Domain
cosine component
(x-component of the vector)
0
“Frequency domain”
0
0
0
0
sinus component
(y-component of the vector)
“Time domain”
cosine component
cosine component
(x-component
of the vector)
(x-component of the
vector)
Time Domain
sinus componentsinus component
of the vector)
(y-component of (y-component
the vector)
al.
]
nyq
Frequency
Frequency
0
COSINE =
x-component
of vector
Frequency
nyq
Sinus =
y-component
of vector
nyq
0
0
nyq
Frequency
Frequency
nyq
0
Frequency
nyq
al.
]
COSINE =
x-component
of vector
0
0
sinus component
(y-component of the vector)
frequency domain for all
frequencies of the spectrum
Frequency
nyq
Sinus =
y-component
of vector
0
Frequency
nyq
Power
cosine component
(x-component of the vector)
The power spectrum gives simply
the length of the vector inThe
the power
frequency
domain
spectrum
gives simply
for all frequencies of the the
spectrum
length of the vector in the
0
0
Frequency
nyq
al.
]
• The
difference
is at the core to understand the
s look
at Phase
the phase
difference
relation of two oscillations:
rence is clustered:
x1 = A1ei!1
Phasephase
Signal signal
1
1
Phase Difference
phase difference
phase signal 2
Phase Signal 2
x2 = A2ei!2
*
i(!1-!2)
Let’s look at the phase difference
al.
]
• The
difference
is at the corei!to
understand the
s look
at Phase
the phase
difference
1
x =A e
relation of two oscillations:1
x1 = A1
1
phase signal 1
ei!1
phase difference
Phase
Phase
Signal
Difference
1
phase
signal
1
... via conj.phase
multiplication
signal 2
of complex numbers, i.e.
angle
of the
phase
difference
i!2 cross spectrum :
x2 = A2e
Phase difference is clustered:
High synchrony
rence is clustered:
signal 2i(!1-!2)
* = "A
x1xphase
#
2
1A2e
Phase Signal 2
x2 = A2ei!2
*
i(!1-!2)
al.
]
• “Conjugate multiplication” in the frequency domain is the same as
convolution in the time domain.
Convolution in the time domain is the same as
“Conjugate multiplication” in the frequency domain
Phase differences:
Convolution
Conjugate
multiplication
al.
]
• “Conjugate multiplication” in the frequency domain is the same as
convolution in the time domain.
Convolution in the time domain is the same as
“Conjugate multiplication” in the frequency domain
Phase differences:
Convolution
Conjugate
multiplication
al.
]
Measures of connectivity: coherence (the math view)
• Quantifying
Connectivity
as Coherence
- (the
Measures
of
connectivity:
Coheren
ce is compute
dcoherence
from the cross-spe
ctralmath
density, view)
which is obtained
conjugate multiplica
based
on thebyConsistency
ofcross-spectral
Phases
the
cross
spectral
tion of theusing
frequenc
y domain
representation of the
Coherence is computed
from
the
density,
which
is
obtained
signals
density:
by conjugate multiplication of the frequency
domain
x x *=A
ei!1 representation
! A e-i!2 = of the i(!1-!2)
signals
1 2
1
2
A1A2e
i!1 ! A e-i!2 = A A ei(!1-!2)
x1single
x2* trial
= Across-spe
e
ctral
1
2 density 1 2
single trial cross-spectral density
sum and normalis
sum and normalise
... with consistent phase
relations across observations
(e.g. trials), the coherence is
high
al.
]
Measures of connectivity: coherence (the math view)
• Quantifying
Connectivity
as Coherence
- (the
Measures
of
connectivity:
Coheren
ce is compute
dcoherence
from the cross-spe
ctralmath
density, view)
which is obtained
conjugate multiplica
based
on thebyConsistency
ofcross-spectral
Phases
the
cross
spectral
tion of theusing
frequenc
y domain
representation of the
Coherence is computed
from
the
density,
which
is
obtained
signals
density:
by conjugate multiplication of the frequency
domain
x x *=A
ei!1 representation
! A e-i!2 = of the i(!1-!2)
signals
1 2
1
2
A1A2e
i!1 ! A e-i!2 = A A ei(!1-!2)
x1single
x2* trial
= Across-spe
e
ctral
1
2 density 1 2
single trial cross-spectral density
sum and normalis
sum and normalise
al.
]
• Quantifying Connectivity as Coherence ... the math view on it:
Normalize cross spectra by auto (power) spectra.
Measures of connectivity: coherence & co
Coherence
PLV
=
=
1/N !A1A2ei("1-"2)
(1/N !A12)(1/N !A22)
1/N !1x1xei("1-"2)
(1/N !12)(1/N !12)
=
!ei("1-"2)
N
• The phase locking value (PLV) ignores oscillation amplitudes
completely and normalizes only by number of observations
al.
]
• Quantifying Connectivity as Coherence ... the math view on it:
Normalize cross spectra by autospectra.
Measures of connectivity: coherence
& of
coconnectivity: coherence & co
Measures
Coherence
=
1/N !A1A2
Coherency
i("
1e "2 )
1/N !A1A2ei("1-"2)
=
(1/N !A12)(1/N !A22)
(1/N !A12)(1/N !A22)
Imaginary part of coherency
PLV
=
1/N !1x1xei("1-"2)
(1/N !12)(1/N !12)
=
!ei("1-"2)
N
• The phase locking value (PLV) ignores oscillation amplitudes
completely and normalizes only by number of observations
= Ce
al.
]
1/N !A1A2ei("1-"2)
i#"$
=
Ce
• Using phase differences between oscillations
(1/N !A12)(1/N !A22)
to identify time delays.
Slope of the
ive phase spectrum indicates time• The
delay
phase spectrum
indicates time relation
Schoffelen, Oostenveld, Fries, 2005
al.
]
1/N !A1A2ei("1-"2)
i#"$
=
Ce
• Using phase differences between oscillations
(1/N !A12)(1/N !A22)
to identify time delays.
Slope of the
ive phase spectrum indicates time• The
delay
phase spectrum
indicates time relation
• The Sign of the phase
spectrum indicates
which oscillation is
leading in the cycle.
Schoffelen, Oostenveld, Fries, 2005
This is suggestive of a
direction of information
flow
Overview and
Examples: Frequency
Specific Functional
Connectivity
Coherence and
Spike-LFP
Pairwise Phase
consistency
Basic Overview:
Oscillations and
Phase Differences
• Analysis of Spike-LFP phase locking, using the
pairwise phase consistency PPC
Vinck, M., van Wingerden, M., Womelsdorf, T., Fries, P., and Pennartz, C.M. (2010).
The pairwise phase consistency: A bias-free measure of rhythmic neuronal
synchronization. Neuroimage 51, 112-122.
Vinck, M., Battaglia, F.P., Womelsdorf, T., and Pennartz, C. (2012). Improved
measures of phase-coupling between spikes and the Local Field Potential.
Journal of computational neuroscience 33, 53-75.
• Analysis of Spike-LFP phase locking
• Advantage spike-LFP measures
• No volume-conduction problem (as opposed to LFP-LFP)
• Characteristics of unit (firing rate correlate, cell-type) can
be considered
• ‘Good’ Sensitivity
> LFP picks up structured rhythmic activity well
> unit-unit measures require coincident firing in time domain.
• Disadvantage spike-LFP measures
• Difficult to infer correlations between neurons.
• Coincident firing can be gauged from spike-LFP phases though.
Pairwise-phase-consistency (PPC)
Vinck, M., van Wingerden, M., Womelsdorf, T., Fries, P., and Pennartz, C.M.A. (2010).NeuroImage 51, 112–122.
θ1 θ2 θ3 θ4
LFP
filtered LFP
Spikes
θ5
θ6 θ7
θ8
θ9
θ10
al.
]
• Quantifying consistency single spike phases
m=1
LFP
Windows
Spike phases
m=2
M Trials
Time
al.
]
• PPC
d13 = 45° (phase
difference of θ1
and θ3),
θ1,2,3,4 Relative phases between two
different signals (spike-to-LFP) at a
particular frequency.
Six unit pairwise phase differences (f1,2 ,
f1,3 , f1,4, f2,3...) [1/ 2xNx(N − 1)].
f1,3 = cos(d13) = dot product of θ1 and θ3
f3,4 = cos(d34) = 0
• red dashed: a
negative dot
product, i.e., an
angular distance
greater than 90
... more consistent phases equal smaller
pairwise phase differences, equal higher
PPC.
al.
]
• Interpretation of PPC
• PPC is essentially a squared quantity. It relates to resultant
length by R^2
• PPC allows direct estimation of Effect Size (Strength of Phase
Modulation)
Peak-to-through modulation ~ (1 + 2*sqrt(PPC))/(1-2*sqrt
(PPC))
e.g., PPC = 0.01 > 1.2/0.8 = 1.5 more spikes on peak than
through.
al.
]
Lines are diff. phase
concentration
distributions
• The phase locking
value (i.e. the resultant
length) as measure of
phase coherence is
biased by the number
of observations:
al.
]
• PPC is unbiased, but variance is high for few number of spikes.
PPC can become negative then.
- Solutions:
1) Set minimum number of spikes to e.g. 50
2) Weight the contribution of a cell by its number
of spikes.
al.
]
• Weighting the PPC by spike count needs to be done
carefully:
Neurons with different properties may have different firing
rates
• Interneurons / Pyramidal cells
• Activate / Non-active neurons
The average is then dominated by a sub-class of cells
Possible solutions
• Identify a homogeneous set of cells (based on waveform
and tuning).
• Explicitly correlate PPC with firing rate.
Overview and
Examples: Frequency
Specific Functional
Connectivity
Coherence and
Spike-LFP
Pairwise Phase
consistency
Basic Overview:
Oscillations and
Phase Differences
al.
]
• Scientific Amercian 2010
• Scientific American 2010
It is the connection, not the
population within an area
that is implicated as cause.
N CIRCUIT]
Depression sufferers have low energy and mood, and their reaction times and memory formation are inhi
though normal brain activity levels are suppressed. Yet common symptoms, such as anxiety and sleep dis
suggest certain brain areas are overactive. Imaging of the brain regions most disrupted in depression poin
source of such imbalances as a tiny brain structure called area 25, which acts as a hub for a depression cir
Depression
sufferersdirectly
have lowtoenergy
and mood,
theiramygdala,
reaction times
and memory
formation
areanxiety,
inhibited,and
as the hypothala
connects
structures
suchand
as the
which
mediates
fear and
though normal
brain
activity levels
are suppressed.
common
symptoms,
such
as anxiety
and sleep disturbances,
in stress
responses.
Those
regions, inYet
turn,
exchange
signals
with
the hippocampus,
a center of memory p
suggest and
certain
brain
areas
are
overactive.
Imaging
of
the
brain
regions
most
disrupted
in
depression
points
to normal
the
the insula, where sensory perceptions and emotions are processed. A smaller than
area 25 (re
source of such imbalances as a tiny brain structure called area 25, which acts as a hub for a depression circuit. Area 25
suspected of contributing to a higher risk of depression in people with a gene variant that inhibits seroton
ERNOR OF MOOD
• Major Depression is linked to alterations of a “hub brain
areas” (area 25) and its strongest connected brain regions
(Amygdala,
Prefrontal Cortex, Insula).
connects directly to structures such as the amygdala, which mediates fear and anxiety, and the hypothalamus, involved
in stress responses. Those regions, in turn, exchange signals with the hippocampus, a center of memory processing,
and the insula, where sensory perceptions and emotions are processed. A smaller than normal area 25 (red in inset) is
suspected of contributing to a higher risk of depression in people with a gene variant that inhibits serotonin processing.
[DEPRESSION CIRCUIT]
GOVERNOR OF MOOD
Depression sufferers have low energy and mood, and their reaction times and memory formation are inhibited, as
though normal brain activity levels are suppressed. Yet common symptoms, such as anxiety and sleep disturbances,
suggest certain brain areas are overactive. Imaging of the brain regions most disrupted in depression points to the
Prefrontal
cortex
source of such imbalances
as a tiny brain structure
called area 25, which acts as a hub for a depression circuit. Area 25
connects directly to structures such as the amygdala, which mediates fear and anxiety, and the hypothalamus, involved
in stress responses. Those regions, in turn, exchange signals with the hippocampus, a center of memory processing,
and the insula, where sensory perceptions and emotions are processed. A smaller than normal area 25 (red in inset) is
Prefrontal
cortex
suspected
of contributing
to a higher risk of depression in people with a gene variant that inhibits serotonin processing.
Prefrontal cortex
Insula
Insula
Hypothalamus
Hypothalamus
Hypothalamus
Area 25
Simplified
depression
circuit
Simplified
depression
circuit
Area 25
Hippocampus
Area 25
Amygdala
Depression offers perhaps the best example of
the rapid progress being made in understanding
the biology of mental illness. Major depressive
disorder, the official diagnostic term for depression, affects 16 percent of all Americans, poten-
man neurologist, Korbinian Brodmann, who asAmygdala
signed numbers
to various regions ofAmygdala
the cortex
in his classic 1909 atlas of the human brain. For
the past 100 years this hard-to-reach region,
which sits deep in the midline at the front of the
brain, has garnered little attention. But over the
TE-AMYGDALA
,
Stuck in Overdrive?
Hippocampus
KEITH BROFSKY Getty Images (photograph); COURTESY OF BEN J. HARRISON Mel
University of Melbourne and Institut D’alta Tecnologia-PRBB, CRC Corporació Sanit
PRECISION GRAPHICS (illustration)
NSTANT PROD
subjects, the activity of area 25 was functionally
uncoupled from that of subcortical brain regions, such as the amygdala.
As a result of this study and others, neuroscientists now think of depression as a circuitry disorder involving abnormal activity in area 25 that
disrupts its vast connected network, including
the hypothalamus and brain stem, which influence changes in appetite, sleep and energy; the
amygdala and insula, which affect anxiety and
mood; the hippocampus, which is critical to
memory processing and attention; and parts of
the frontal cortex, which mediate insight and
self-esteem.
The brain, after all, is an information-pro-
If this conception is correct, resetting the firing
of area 25 should moderate each of these downstream centers, thereby lessening the symptoms
of depression. Indeed, Mayberg has demonstrated that direct electrical stimulation near area 25
reduces the activity of this node and can lead to
recovery in people with depression who did not
respond to standard therapies.
If area 25 can cause the brain, like a computer, to get stuck in a loop of abnormal activity,
then the goal of treatment might be akin to
“rebooting” a computer that has become frozen. The same principle can be applied to other
mental disorders, particularly OCD, which appears even to a casual observer as though the suf-
People with obsessive-compulsive disorder (OCD) liken their intrusive thoughts and powerful urges to perfo
action repeatedly to uncontrollable tics. A connection does exist: involuntary movements such as those seen
Huntington’s disease originate in the basal ganglia, a group of structures involved in initiating and coordina
basic motor actions. The caudate nucleus of the basal ganglia is also part of the brain circuit that drives OCD
with the orbitofrontal cortex, a region critical to decision making and moral judgment, and the thalamus, wh
relays and integrates sensory information. In OCD sufferers (left inset), overactivity is evident in parts of the
cortex and basal ganglia, and firing of those regions is more synchronized than in normal subjects (right inse
• Obsessive-Compulsive Disorder is linked to alterations
of basal ganglia - to - motor cortex circuit ‘loops’, and the
orbitofrontal cortex.
[OCD CIRCUIT]
A CONSTANT PROD
People with obsessive-compulsive disorder (OCD) liken their intrusive thoughts and powerful urges to perform an
Huntington’s
circuit
action repeatedly to uncontrollable
tics. A connection does
exist: involuntary movements such as those seen in
Huntington’s disease originate in the basal ganglia, a group of structures involved in initiating and coordinating
basic motor actions. The caudate nucleus of the basal ganglia is also part of the brain circuit that drives OCD, along
with the orbitofrontal cortex, a region critical to decision making and moral judgment, and the thalamus, which
relays and integrates sensory
information.cortex
In OCD sufferers (left inset), overactivity is evident in parts of the frontal
Prefrontal
cortex and basal ganglia, and firing of those regions is more synchronized than in normal subjects (right inset).
Huntington’s circuit
Motor cortex
Prefrontal cortex
Caudate
nucleus
Basal ganglia
(blue)
Caudate nucleus
OCD circuit
Thalamus
OCD circuit
Orbitofrontal cortex
Orbitofrontal cortex
w w w. S c i e n t i f i c A m e r i c a n . c o m
sad0410Insel4p.indd 47
SCIENTIFIC AMERICAN
47
2/17/10 5:42:40 PM
Motor co
Ba
actual by
tics
as well as obsessive thoughts. Most necting the orbitofrontal cortex from the caupsychic conflict, and ideal for treatment by dramatically in Huntington’s disease reflect ab-
become frozen.
psychoanalysis. People with OCD suffer from normal activity in this circuit, usually originatintrusive, repetitive thoughts (obsessions) and ing in the basal ganglia. Neuroimaging studies
may be impaired by the need to perform stereo- of patients with OCD have discovered abnormal
typic, repetitive rituals (compulsions). Some peo- activity in an adjacent loop that includes the orple may feel they are contaminated and will wash bitofrontal cortex, which is involved in complex
repetitively, to the point of abrading their skin. tasks such as decision making, the ventral cauOthers have a nagging sense of having failed to date nucleus within the basal ganglia, and the
In post-traumatic
stress disorder (PTSD), cues that evoke a traumatic experience induce fear reactions long after the
carry out some responsibility and will need to thalamus, which relays and integrates sensory
information.
check the stove or the
or thestructure
doorknobs called
event. Malfunctioning
offaucets
a brain
the ventromedial prefrontal cortex (vmPFC) is thought to increase
Evidence for overactivity in this circuit in
repeatedly before leaving the house. While peovulnerability
tothisthe
condition
modulates
the
amygdala,
driver of fear and anxiety. Normally recovery after
OCD comes
from
more than justaneuroimaging
ple with
condition
generallybecause
realize that it
their
research.
Most
people
with
OCD
report
prothoughts
are
senseless,
they
cannot
control
either
trauma, known as extinction, replaces a fear response with a neutral oneathrough
a learning process that engages the
the obsessions or compulsions, and in severe cas- found reduction in symptoms with treatment,
hippocampus
andbecome
the dorsolateral
prefrontalwhether
cortex.
The therapy
vmPFCor ismedication,
believedandto serve as the critical link between the
behavior
es they may
completely disabled.
this
symptom
improvement
consistently
goes the amygdala.
Patients
with
OCD
often
describe
their
sympdorsolateral PFC and the amygdala, allowing such extinction learning to quiet
toms as “mental tics,” as though they were phys- along with a decrease in orbitofrontal cortical
ical movements that are not under voluntary activity. In patients who do not respond to medcontrol. Indeed, many people with OCD have ication or behavior therapy, actually disconactual tics as well as obsessive thoughts. Most necting the orbitofrontal cortex from the cauDAVID LEESON Dallas Morning News/Corbis Sygma (photograph); PRECISION GRAPHICS (illustration)
T]
ETUATOR
OF FEAR
• Post-traumatic
Stress Disorder (PTSD) involves
(uncontrollable) fear memories. PTSD is associated with
overactive amygdala and ventromedial prefrontal cortex
(vmPFC), and altered activation of dorsolateral PFC.
[PTSD CIRCUIT]
PERPETUATOR OF FEAR
Prefrontal cortex
In post-traumatic stress disorder (PTSD), cues that evoke a traumatic experience induce fear reactions long after the
event. Malfunctioning of a brain structure called the ventromedial prefrontal cortex (vmPFC) is thought to increase
vulnerability to the condition because it modulates the amygdala, a driver of fear and anxiety. Normally recovery after
trauma, known as extinction, replaces a fear response with a neutral one through a learning process that engages the
Dorsolateral
hippocampus
and thePFC
dorsolateral prefrontal cortex. The vmPFC is believed to serve as the critical link between the
dorsolateral PFC and the amygdala, allowing such extinction learning to quiet the amygdala.
Prefrontal cortex
PTSD circuit
Dorsolateral PFC
Hippocampus
PTSD circuit
Hippocampus
vmPFC
vmPFC
Amygdala
Amygdala
48
SCIENTIFIC AMERICAN
A p r i l 2 0 10
• The organization of brain networks is investigated with
methods from “network theory”. Network theory distinguishes
two core elements of the brain:
1. Nodes
(e.g. brain areas)
2. Edges
(connections)
• Connectivity Measures
• The organization of brain networks is investigated with
methods from “network theory”. Network theory distinguishes
two core elements of the brain:
1. Nodes
(e.g. brain areas)
2. Edges
(connections)
• Edges
(Connections)
• In brain networks, edges (connections) are variously taken as:
1.
Anatomical connections
2.
Effective connections
3.
Functional connections
• There are different ways how brain areas can connect in the
brain.
Brain areas could be connected like a lattice,
i.e. highly regular. When each node in the
network has only 2 neighbours, few
connections are needed, i.e. wiring costs are
low. But the information flow would be
rather inefficient. There is no global
integration !
Lattice like
Topology
• There are different ways how brain areas can connect in the
brain.
Brain areas could also be connected randomly,
i.e. highly irregular. Random connectivity
would be good for global integration. But
here, many and many long connections exist
which is extremely costly.
Random
Topology
• Few connections
needed, i.e. the wiring
costs are low.
• But: lack of global
integrations, i.e.
information flow is very
inefficient.
• Many and long connections in a
random network. This is excellent
for global integration where each
area can speak to each other brain
area quickly.
• But: many long connections are
“expensive” as it takes much energy
to build and sustain connections.
• The best trade-off between cost and
efficiency of wiring between brain areas
is to have some “modules” with lattice
like connections, and few longer
distance connections.
• These principles are realized in the
human (and other species’s) brains.
Conclusion:
“Brain networks can be said
to negotiate an economical
trade-off between minimizing
physical connection cost and
maximizing topological
value”
• The “complex organization” of brain areas achieves a balance between
functionally highly specialized and efficient computations AND an
integration of different, highly specialized processes. These two aspects
are illustrated here:
Brain
area
Functional segregation
(within red circles) allows for
efficient, specialized
computations
Functional integration (via
blue edges) ensures that
special computations are
integrated
Functional segregation
in communities
Functional integration
via hubs
... this scheme is strongly supported by network analysis
of the human brain.
Network (Graph) Theory
quantifies segregation
and integration, e.g. as
“communities” (reflecting
segregated processing) or
“hubs” (for integration)
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