Aligning the Brain in a Rhythmic World

© 2009 Schroeder
Aligning the Brain in a Rhythmic World
Charles e. Schroeder, PhD, 1,2 Peter Lakatos, PhD, 1
Chi-Ming Chen, PhD, 2 Thomas Radman, PhD, 1
and Anne-Marie Barczak 1,3
1 Cognitive Neuroscience and Schizophrenia Program
Nathan S. Kline Institute for Psychiatric Research
Orangeburg, New York
2 Department of Psychiatry, Columbia University College of Physicians and Surgeons
New York, New York
3 Department of Physiology, New York University School of Medicine
New York, New York

Introduction: Internal and
External Rhythms
Rhythm in the brain
Except under extreme conditions, such as anesthesia,
electrical recordings from the brain invariably reveal
robust oscillatory activity (fluctuations of voltage). In
pursuit of the idea that these oscillations are actually
relevant to adaptive brain functioning, increasingly
specific relationships have been proposed between
neuronal oscillations and numerous cognitive
functions. These relationships include: (1) theta
oscillations and encoding of spatial information in
the hippocampus (Maurer and McNaughten, 2007);
(2) theta oscillations and the formation of mnemonic
neuronal representations (Jensen et al., 2007);
(3) alpha oscillations and “internally directed”
cognition (Palva and Palva, 2008); and (4) gamma
oscillations and both attention/sensory selection
(Fries et al., 2002) and feature binding (Singer and
Gray, 1995). See the short course chapter Rhythms
in Cognitive Processing by C. Tallon-Baudry, for
in-depth discussion of this subject. Thus, consensus is
building that neuronal oscillations are instrumental
rather than incidental to brain function and
dysfunction (Buzsaki and Draguhn, 2004; Uhlhaas
and Singer, 2006; Buzsaki, 2007; Borgers and Kopell,
2008; Schroeder and Lakatos, 2009a).
Rhythm in the sensorium
When considering the functional significance of
neuronal oscillations, it is worth keeping in mind
just how rhythmic our sensory world is. Under
natural conditions, for example, stimulating the
hand typically involves moving the fingers across
surfaces or manipulating objects. Because the motor
routines that drive arm, hand, and finger movements
are modulated by oscillations in the delta, theta, mu,
and beta bands (McAuley et al., 1999a; Pfurtscheller
et al., 2000; Pineda, 2005; Birbaumer et al., 2006;
Wolpaw, 2007; Hatsopoulos and Donoghue, 2009),
rhythm is imposed on somatosensory inflow. (See the
chapter Rhythms in Motor Processing: Functional
Implications for Motor Behavior by N. Hatsopoulos,
in this short course.) In the visual domain, many
stimuli considered “biologically significant” stem
from observing the motion of conspecifics or other
animals; here too, because of the oscillatory patterning
mechanisms (above), the sensory inflow has a strong
rhythmic component. Although a visual scene may
be completely static, the sequence of fixations used
to encode the scene is generally rhythmic, with a rate
of ~3 Hz (McAuley et al., 1999b; Otero-Millan et
al., 2008). Even when a subject is fixated on a static
stimulus, microsaccades (also occurring at ~3 Hz)
© 2009 Schroeder
Aligning the Brain in a Rhythmic World
impose a strong rhythm on visual processing (Bosman
et al., 2009).
Interaction of neuronal oscillations
and input rhythms
The striking similarity between the brain’s oscillatory
rhythms and those in the sensorium is unlikely to
be an accident. Several other presentations in this
short course will deal with the precise physiological
mechanisms that generate neuronal oscillations
(see chapters Modeling Rhythms: from Physiology
to Function by N. Kopell, and Modeling Rhythms:
Detailed Cellular Mechanisms of In Vitro Oscillations,
with Emphasis on Very Fast Oscillations by
R. Traub). This chapter will explore the relationship
between neuronal oscillations and environmental
rhythms. We will consider the manner in which the
brain may use neuronal oscillations as instruments
of adaptive brain operations, by aligning them with
relevant environmental rhythms (Schroeder and
Lakatos, 2009a).
Oscillations Control
Neuronal Excitability
Current source density (CSD) analysis of local field
potential (LFP) distributions across cortical layers
shows that fluctuations or “oscillations” of voltage
in the extracellular medium reflect rhythmic,
synchronous alternation of inward and outward
transmembrane current flow in the local neuronal
ensembles (Lakatos et al., 2005). In agreement with
Bishop’s fundamental proposition (Bishop, 1933),
analysis of concomitant local neuronal firing (lower
trace multiunit activity [MUA]) indicates that this
current flow alternation reflects a shift between
net depolarized and hyperpolarized states in the
local neuronal ensemble (Lakatos et al., 2005).
Systematic relationships between oscillatory phase
and excitability have been substantiated for the very
low (

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24
oscillations as an instrument for enhancing auditory
processing. That is, the somatosensory input does not
appear to increase the power of ongoing oscillations;
however, it does increase phase coherence across
trials (intertrial coherence, or ITC) in the low
delta, theta, and gamma bands. The somatosensory
(modulatory) input contrasts with the auditory
(driving) input, which provokes both increase in
oscillatory power and ITC across the spectrum,
typical of an “evoked” response (Makeig et al., 2004;
Shah et al., 2004). Prestimulus-to-poststimulus
increase in phase concentration (in the absence of
an accompanying increase in power) indicates that,
in A1, somatosensory input has an effect mainly by
resetting the phase of ongoing oscillations. Thus,
somatosensory input “modulates” the dynamics of
activity in A1 but does not cause auditory neurons
to respond (Lakatos et al., 2007). Both Ghazanfar et
al. (2005) and Kayser et al. (2008) found that visual
input has similar effects on auditory processing in
primary and secondary auditory cortical areas. Thus,
it seems that this is a general mechanism whereby
nonpreferred (modulatory) stimuli can affect specific
stimulus processing.
Attentional selection
Recent findings show that when behaviorally relevant
stimuli occur in predictable (rhythmic) streams, lowfrequency
oscillations can function as instruments of
attentional selection in primary visual cortex (V1)
(Lakatos et al., 2008). For these studies, we trained
monkeys to perform an intermodal selection task in
which auditory and visual stimuli (beeps and flashes)
were delivered in jittered but rhythmic interdigitated
streams. In alternate trial blocks, the monkey
had to attend to either the visual or the auditory
stimulus stream, and make a manual response to an
infrequently presented “oddball” stimulus. Although
this paradigm is artificial, it combines the rhythmic
structure and variability characteristic of many
natural event patterns. The key finding of this
experiment is that, whereas activity in the thalamic
input (granular) layers entrains (oscillatorily phaselocks)
reliably to visual input, activity in the
extragranular layers entrains to the attended input
stream, whether visual or auditory. The difference is
most apparent in the supragranular laminae: A direct
comparison of CSD responses across the attend and
ignore conditions shows that delta oscillations in
the supragranular layers are entrained in opposite
phase in the two attention conditions. The same
comparison for the granular layers shows amplitude,
but not phase modulation by attention.
These effects share several complexities, including
concomitant theta and gamma band modulations
(Lakatos et al., 2008). However, the key factor is
the relationship between delta oscillation phase and
neuronal excitability and the modulation (shifting)
of delta phase by attention. In broad terms: (1) In
the attend visual condition, the laminar CSD profile
at stimulus onset reflects a high excitability state
that permits the transmission of inputs from granular
to extragranular laminae (and onward), whereas in
the ignore visual (attend auditory) condition, the
laminar CSD configuration at this time point reflects
a relative depression of excitability in this circuit.
(2) These differential facilitative and suppressive
states are responsible for attention’s effects on visual
response amplitudes.
Operational mode of the brain
aligns to task rhythmicity
We have proposed that, depending on task
demands, the brain dynamically shifts between
variably rhythmic operating modes (Schroeder
and Lakatos, 2009a). At the extremes are the
“continuous/vigilance” mode and the “rhythmic”
mode. The former may be best exemplified by the
classic vigilance paradigm used in Psychology. This
paradigm entails a task in which the target stimuli
occur at a completely random and unpredictable
time; an example is when a cat watches a mouse
hole, waiting for the mouse to appear. However, as
discussed above, a great deal of natural stimulation
is explicitly rhythmic and predictable. Under these
conditions, neuronal oscillations can entrain to
the structure of the stimulus stream and become
instrumental in sensory processing (Schroeder et
al., 2008) (the intermodal paradigm described above
is of this type). Rhythmic mode operation entails
entraining activity to the temporal structure of an
attended stream, resulting in the alignment of highexcitability
oscillation phases with events in the
attended stream. Rhythmic mode entrainment, in
turn, leads to systematic enhancement of responses
to attended events, and suppression of responses to
events that occur out of phase with attended events.
In contrast, when there is no task-relevant rhythm
to which the system can entrain, low-frequency
oscillations tend to blunt neuronal processing, because
they entail relatively long periods of low excitability
during which detection of subtle stimuli would be
impaired. Under these conditions, a continuous
(vigilance) mode of operation is implemented, lowfrequency
oscillations are suppressed, and the system
© 2009 Schroeder

is pushed as far as possible into a continuous state of
high excitability.
Several behavioral observations are consistent with
the differential operation of these two processing
modes. In continuous/vigilance mode, where the
oscillatory correlates of attention are enhanced
gamma amplitude/synchrony and lower frequency
suppression (Fries et al., 2001), variations in gamma
synchrony are predictive of reaction time variations
(Womelsdorf et al., 2006). In rhythmic mode, where
attention uses low-frequency rhythmic entrainment
(Lakatos et al., 2008), low-frequency phase predicts
reaction time.
We have suggested (Schroeder and Lakatos, 2009a)
that rhythmic mode is the preferred state of the
system for the following reasons:
(1) It is efficient because inputs that are out of phase
with the attended stream are automatically
suppressed;
(2) Gamma-band activity appears to be more
metabolically demanding than low-frequency
oscillations (Mukamel et al., 2005; Niessing et
al., 2005); and
(3) Owing to hierarchical coupling, gamma activity
is “rationed” (selectively enhanced) at critical
time points, when a high-excitability state is
most useful. In this sense, the gamma oscillation
is a “slave” rather than a “master” operator
(Schroeder and Lakatos, 2009b).
Converging Theory and Reflection
on Earlier Findings
“Dynamic attending theory” proposes to describe
many key components of rhythmic mode processing
from a psychophysical perspective (Jones and Boltz,
1989; Large and Jones, 1999; Jones et al., 2006). This
idea has also been developed in a motor framework
(Praamstra et al., 2006). The basic hypothesis put
forward by Jones and colleagues is that attending
itself can be an oscillatory process that entrains
to environmental rhythms, thereby improving
discriminative performance. In a similar vein, Nobre
and colleagues have suggested that “attention to
time” is a fundamental form of attending, controlled
by a parietocentric network of brain structures (Nobre
et al., 2007; Correa and Nobre, 2008a,b). There
is evidence (Ghose and Maunsell, 2002) that in a
temporally structured (rhythmic) task, monkeys form
an internal representation of task timing that can
guide the temporal allocation of attentional resources
in order to maximize behavioral performance. In all
© 2009 Schroeder
Aligning the Brain in a Rhythmic World
these cases, entrained neuronal oscillations, operating
in a steady-state mode and/or resetting on a trial-bytrial
basis, provide likely physiological substrates for
the effects of attention. The fact that, because of
cross-frequency coupling, oscillations can be reset to
low-excitability as well as high-excitability states on
multiple time scales (Schroeder et al., 2008) increases
the flexibility and range of the mechanism.
The distinction between rhythmic and continuous
modes of processing may be fundamental: Evidence
of rhythmic mode operation can be seen in diverse
varieties of attention, including “object attention”
(Taylor et al., 2005), whereas “intermodal attention”
(Lakatos et al., 2008) can operate in a rhythmic
mode, with excitability/gamma bursting coupled to
the rhythm of the task. Significantly, the most heavily
studied form of attention—spatial attention—
can operate either in continuous mode, with
the effect of tonic increase in excitability (Fries et
al., 2001; Womelsdorf et al., 2006), or in rhythmic
mode, with periodic variations in excitability coupled
to the temporal structure of the task (Ghose and
Maunsell, 2002).
The idea that several modes govern the operation
of attention may help resolve discrepancies between
attention effects reported by different laboratories
(Moran and Desimone, 1985; Luck et al., 1997, versus
results from McAdams and Maunsell, 1999; Treue and
Maunsell, 1999). Examining the task structures used
by these groups suggests that the former generally
examine functioning in the continuous/vigilance
mode, whereas the latter do so in the rhythmic mode.
Phase-resetting of low-frequency oscillations may help
explain cueing effects on attentional performance
and pertinent event-related potentials (ERPs).
The frontal contingent negative variation (CNV)
(Walter et al., 1964), for example, may be generated
by phase-resetting frontal low-frequency oscillations
by a warning cue, which are then averaged over trials.
The perceptual effects known as “attentional blink”
(Raymond et al., 1992) and “inhibition of return,” or
IOR (Klein, 2000), result when stimuli are delivered
during the low-excitability phase of a low-frequency
oscillation that has been reset by the appearance of
either a salient target or a cue to attend.
Outstanding questions
Is rhythmic mode processing
generalizable?
A number of empirical predictions need to
be evaluated. Sensory selection in a typical
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spatial attention paradigm, for example, could
be accomplished by entraining low-frequency
oscillations in the neuronal representations of the
“relevant” locations to the basic rhythm of stimulus
presentation. The representations of all other
locations would be left in random phase, thereby
passively degrading the processing of irrelevant
event streams. Alternatively, activity at sites
representing irrelevant locations could be aligned in
counterphase to activity in the site representing the
relevant location, which would produce active (and
stronger) degradation of processing. This technique
would be particularly useful for dealing with locations
containing distractor events. Similar predictions
could be applied to feature-based and object-
based selection.
What is the role of corollary eye
movement/fixation signals in
predictive phase alignment?
Primates, including humans, actively examine the
visual world by rapidly shifting gaze (fixation) over
the elements in a scene. Our earlier findings (Rajkai
et al., 2007) indicate that a brief period of increase in
cortical excitability follows each fixation. Just after
fixation onset: (1) The neuronal oscillation phase
transitions from random to a highly organized state;
(2) This phase concentration is accompanied by
increased spectral power in several frequency bands;
and (3) Visual response amplitude is enhanced at the
specific oscillatory phase associated with fixation.
Based on these findings, we have hypothesized
that the brain uses nonvisual “corollary” signals to
increase cortical excitability at fixation onset, thus
predictively “priming” the system for new visual
inputs generated at fixation. Several other research
groups have endorsed this view (MacEvoy et al.,
2008; Melloni et al., 2009), and thus it appears to
be gaining currency. Because these effects have been
identified in only a subset of the system, it will be
important to determine how widespread they are. It
will also be important to determine whether, as we
suspect, fixation-related phase reset also functions to
increase the synchrony of oscillations across layers
within areas, as well as across areas; this determination
would clearly contribute to amplification of the
neuronal representation of the object that forms the
target of fixation.
Going Forward
Natural stimulation acquired through one’s own
motor behavior, or produced by that of another
animal, is generally rhythmic. When intrinsic brain
oscillations can entrain to relevant stimulus rhythms,
the brain operates in a rhythmic mode, that is,
ambient neuronal oscillations amplify the perception
of important inputs and suppress perception of
irrelevant ones. When relevant stimuli lack rhythm,
attention operates in a continuous mode, maximizing
the sensitivity of the system by suppressing lowerfrequency
oscillations and exploiting the advantages
of extended continuous gamma-band oscillations
(Borgers and Kopell, 2008).
Although questions about rhythmic mode processing
remain, several tentative conclusions are gathering
weight. Most importantly, prominent rhythm can
be found in many surprising places. Because of the
rhythmic influences of both microsaccades and
macrosaccades (Martinez-Conde et al., 2004; Rajkai
et al., 2008; Bosman et al., 2009) used to sample a visual
scene, even for static scenes, processing is rhythmic.
The essential rhythmicity of saccades (including
microsaccades) has prompted the suggestion that
saccadic behavior reflects “an optimal sampling
strategy by which the brain discretely acquires visual
information” (Otero-Millan et al., 2008b). This
idea offers strong support to that of a characteristic
“attentional sampling rate” (VanRullen et al., 2007).
Both of these notions reinforce the theory that lowfrequency
rhythms are instrumental rather than
incidental to brain operations.
A strong interdependence between brain and
environmental rhythms, during both evolution
and ontogeny, is suggested by the fact that certain
frequencies dominate the spectra of spontaneous
oscillations in sensory cortices (Lakatos et al., 2005)
as well as by the remarkable match between these
oscillatory bands and the temporal structure of
biologically relevant sensory inputs (Lou and Poeppel,
2007; Schroeder et al., 2008). Widespread crossfrequency,
oscillatory coupling offers the potential
for improving the matching of brain oscillations with
complex natural input patterns that cover different
time scales (Schroeder et al., 2008), as well as more
subtle computational benefits (Krupa et al., 2008).
The modulation of low-frequency phase, and of
cross-frequency coupling by attention (Lakatos et al.,
2008) molds sensory processing to the current goals
of an observer.
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