Womeldorf et al observed bursts of firing in the anterior cingulate and prefrontal cortex during shifts of attention. These bursts (but not non-burst firing) synchronized over long distances (between the AC and PFC) to local field field potentials at beta and gamma frequencies. These bursts were proceeded by bursts of inhibitory neurons. The authors propose burst firing mechanisms help form functional networks to coordinate shifts of attention.
Kopell et al provide an excellent review of the role of neural rhythms in brain function and argue that we need to know more than anatomy, no matter how detailed. We also need to connect it to an understanding of brain dynamics. They review our current knowledge of brain rhythms and identify (many) open questions.
Our lab and others (e.g., Buschman and Miller, 2007; Bastos et al 2012) has suggested that top-down (feedback) vs bottom-up (feedforward) cortical processing is mediated by synchrony between cortical areas at different frequencies: lower (e.g., beta band) for top-down vs higher (e.g., gamma band) for bottom-up. These two different frequency bands allow top-down vs bottom-signals to multiplex through the same circuits, much as different FM radio stations multiplex through the airwaves. They may also allow cortical microcircuits to engage in helpful things like predictive coding (Bastos et al., 2012).
Schmiedt et al (2014) provide new evidence for this. They recorded neural activity in visual area V4 after damage to primary visual area V1. V4 is higher in the cortical hierarchy, so V1 has a bottom-up influence on V4. They found that damage to V1 decreased the gamma in V4 that follows appearance of a visual stimulus. That is consistent with gamma carrying bottom-up or feedforward signals, lost after V1 damage. By contrast, V4 beta activity was minimally affected, reflecting the unaffected top-down influence on V4 Normally there is beta suppression during visual stimulation, presumably because the bottom-up inputs overwhelm or suppress beta-mediated top-down processing. After V1 damage, this suppression of top-down beta rhythms was diminished, presumably because it was no longer suppressed by bottom-up influences from V1.
For further reading:
Buschman, T.J. and Miller, E.K. (2007) Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science. 315: 1860-1862 The Scientist’s “Hot Paper” for October 2009. View PDF »
Bastos AM, Usrey WM, Adams RA, Mangun GR, Fries P, Friston KJ. Canonical microcircuits for predictive coding. Neuron. 2012 Nov 21;76(4):695-711. doi:
10.1016/j.neuron.2012.10.038. Review.
At this risk of kvelling, in 2011 we published a paper (Buschman et al., 2011) showing independent visual working memory capacities in the right vs left visual hemifields. We were told “no way” and “that’s impossible”. Since then, a bunch of papers have supported this. Here’s another one.
Wang et al used FMRI and found that brain networks primarily interact with ipsilateral, not contralateral networks. Thus, the brain emphasizes processing within each hemisphere (visual hemifield) and minimizes across-hemisphere processing.
Also see:
Buschman,T.J., Siegel, M., Roy, J.E. and Miller, E.K. (2011) Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences. 108(27):11252-5. View PDF »
Ibos and Freedman show that area LIP is more than just space and spatial attention. They trained monkeys to make decisions based on conjunctions of motion and color. LIP neurons integrated color and motion when it was task-relevant.
More evidence (this time in humans) that top-down vs bottom-up cortical processing depends on synchrony in different frequency bands, lower frequencies (beta) for top-down and higher frequencies (gamma) for bottom-up. There was cross-frequency coupling such that gamma power in auditory cortex was modulated by phase of beta in the anterior cingulate (but not the other way around). Top-down and bottom-up processing alternattively dominated. Thus, the brain uses both frequency- and time-multiplexing to optimize directional flow of information.
The contribution of frequency-specific activity to hierarchical information processing in the human auditory cortex.
L. Fontolan, B. Morillon, C. Liegeois-Chauvel & Anne-Lise Giraud
There is increasing evidence and much discussion about the role of synchronized oscillations in fostering communication in neural networks. The flip side is that anti-synchronization (i.e., out of phase) should decrease or prevent neural communication. Stetson and Andersen find evidence for this between the parietal cortex and premotor cortex. During movement planning these areas oscillate at similar frequencies but out of phase of one another. This suggests decreased communication between them.
Botvinick and Cohen provide a very nice overview of where computational modeling of executive control has been and where it is going.
Zhang et al optogenetically activated the mouse cingulate region and found that it enhanced activity in primary visual cortex (V1), improved visual discrimination and increased center-surround effects. This modulation was mediated by long-range projections that activated GABAergic (inhibitory) circuits in V1. Thus, long-range projection from the frontal lobe may modulate sensory cortex via excitatory action on local inhibitory circuits.
According to the press release, Kay made a “list of exceptionally talented technologists whose work has great potential to transform the world.” It won’t be through her poker play, I’ll tell you that much.
Congrats Kay! Well deserved.
Bob Desimone and crew find that removal of the prefrontal cortex (PFC) reduces (but, notably, does not eliminate) the effects of attention on neurons in visual cortical area V4. The modulation of attention on firing rates was weaker and onset was delayed relative to the hemisphere with an intact PFC and there was a reduction of gamma power and synchrony. Thus, PFC is an important, but not the only, source of top-down modulation on visual cortex.
Lesions of prefrontal cortex reduce attentional modulation of neuronal responses and synchrony in V4
Georgia G Gregoriou, Andrew F Rossi, Leslie G Ungerleider & Robert Desimone
Nature Neuroscience 17, 1003–1011 doi:10.1038/nn.3742
Chan et al show that the prefrontal cortex (PFC) may exert top-down influences on the superior colliculus (SC) via oscillatory synchrony. Animals performed both pro- and anti-saccade trials. Anti-saccades are highly dependent on the PFC because they involve inhibiting a highly prepotent response (a pro-saccade). Bilateral deactivation of the PFC attenuated beta and gamma power in the SC around the time the animals were preparing to respond. The gamma power was correlated with spiking activity whereas beta was tonic (and reduced after PFC deactivation) and may facilitate communication between the PFC and SC.
The evidence is mounting that the primate brain has separate, independent attentional/working memory capacities in the right and left visual hemifields. In this study, Matushima and Tanaka trained monkeys to track single or multiple objects across both visual hemifields. Neural activity to a given object was only degraded when another object was in the same hemifield, not when another object was in the opposite hemifield. This could not be explained by distance between objects; there was no difference between upper and lower visual fields, for example. This suggests that the anatomical separation of the right and left visual hemifields into the left vs right cerebral hemispheres results in separate cognitive capacities for the right vs left sides of vision. Buschman et al (2011) found similar effects for object identity.
For further reading:
Buschman,T.J., Siegel, M., Roy, J.E. and Miller, E.K. (2011) Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences. 108(27):11252-5. View PDF »
We (Antoulatos and Miller) show increased beta-band synchrony between (but not within) the prefrontal cortex and striatum during category learning. By the time the categories were fully learned, the beta synchrony became category-specific. That is, different patterns of prefrontal cortex-striatum recording sites showed increased beta synchrony for one category or the other. Thus, category learning may depend on formation of oscillatory synchrony-aided functional circuits between the prefrontal cortex and striatum. Further, causality analysis suggested that the striatum exerted a greater influence on the prefrontal cortex than the other way around. This supports models positing that the basal ganglia “train” the prefrontal cortex (Pasupathy and Miller, 2005; Seger and Miller, 2010).
Antzoulatos, E.G. and Miller, E.K. (2014) “Increases in functional connectivity between the prefrontal cortex and striatum during category learning.” Neuron, 83:216-225 DOI: http://dx.doi.org/10.1016/j.neuron.2014.05.005 View PDF
For further reading:
Pasupathy, A. and Miller, E.K. (2005) Different time courses for learning-related activity in the prefrontal cortex and striatum. Nature, 433:873-876. View PDF »
Antzoulatos,E.G. and Miller, E.K. (2011) Differences between neural activity in prefrontal cortex and striatum during learning of novel, abstract categories. Neuron. 71(2): 243-249. View PDF »
Seger, C.A. and Miller, E.K. (2010) Category learning in the brain. Annual Review of Neuroscience, Vol. 33: 203-219. View PDF »
“Frequency sliding” (temporal fluctuations in peak oscillatory frequency is consider as a neural mechanism. Neuron models are used to show how it can change spiking threshold and coincidence detection, and used to identify networks from EEG activity.
Fluctuations in Oscillation Frequency Control Spike Timing and Coordinate Neural Networks
Michael X Cohen
The Journal of Neuroscience, 2 July 2014, 34(27): 8988-8998; doi: 10.1523/JNEUROSCI.0261-14.2014
IFLScience: Brain Waves Synchronize for Faster Learning
Summary:
As our thoughts dart from this to that, our brains absorb and analyze new information at a rapid pace. According to a new study, these quickly changing brain states may be encoded by the synchronization of brain waves across different brain regions. Waves originating from two areas involved in learning couple to form new communication circuits when monkeys learn to categorize different patterns of dots.
Read more here
A (very brief) mention of the new paper by Antzoulatos and Miller (2014) on National Public Radio.
The paper:
Antzoulatos, E.G. and Miller, E.K. (in press) “Increases in functional connectivity between the prefrontal cortex and striatum during category learning.” Neuron. View PDF
Huffington Post article about the evils of multitasking.
You’re Not Busy, You Just Think You Are: 7 Ways To Find More Time The Huffington Post UK | By Georgia James Posted: 13/06/2014 15:00 BST
(with quotes from Earl Miller)
New Miller Lab paper in press and online at Neuron:
Antzoulatos EG and Miller EK (in press) Increases in Functional Connectivity between Prefrontal Cortex and Striatum during Category Learning. Neuron, in press.
DOI: http://dx.doi.org/10.1016/j.neuron.2014.05.005
Animals were trained to learn new category groupings by trial and error. Once they started to “get” the categories, there was an increase in beta-band synchrony between the prefrontal cortex and striatum, two brain areas critical for learning. By the time the categories were well-learned, the beta synchrony between the areas became category-specific, that is, unique sets of sites in the prefrontal cortex and striatum showed increased beta synchrony for the two different categories. This suggests that synchronization of brain rhythms can quickly establish new functional brain circuits and thus support cognitive flexibility, a hallmark of intelligence.
MIT Press release:
Synchronized brain waves enable rapid learning
MIT study finds neurons that hum together encode new information.
A well-known correlate of working memory is sustained neural activity that bridges short gaps in time. It is well-established in the primate brain, but what about birds? They have working memory. (In fact, there is a lot of classic work that detailed the behavioral characteristics of working memory in pigeons).
Miller Lab alumnus Andreas Nieder and crew trained crows to perform a working memory task and found sustained activity in the nidopallium caudolaterale (NCL). This is presumably a neural correlate of the crow’s visual working memory.
Now if crows could only pass that causality test.
Anderson et al used scalp EEG recordings to decode the content of working memory and its quality. Subjects performed a orientation working memory task. Anderson et al found that the spatial distribution of alpha band power could be used to determine what orientation the subject was remembering and how precisely they were remembering it. Cool.
Matsushima and Tanaka compared neural correlates of spatial working memory for locations within the same hemifield or across hemifields. When the two remembered locations were in the same hemifield (right or left side of vision), the neural response in the prefrontal cortex was intermediate to the two cues presented alone. When the cues were across hemifields, the neural response was the same as the preferred cue presented alone. In other words, remembered locations within a hemifield seemed to be in competition with each other whereas locations across the hemifields seemed to be have no interaction at all. In yet other words, it was as if the (intact) monkeys had their brains split down the middle. The authors concluded local inhibitory interactions between cues within, but not across, hemifields.
This confirms Buschman et al (2011) who found that independent capacities for visual working memory in the right and left hemifields.
Further reading:
Buschman,T.J., Siegel, M., Roy, J.E. and Miller, E.K. (2011) Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences. 108(27):11252-5. View PDF »
Gamma band oscillations are seen throughout the cortex and subcortex. Do they have a single or different functions? Bosman et al review the literature and conclude the latter but nonetheless point out that gamma likely rises from a cortical motif involving interactions between excitatory and inhibitory neurons. So, just as activity of individual neurons means different things in different brain areas so does gamma rhythms.
Volume 26, Issue 6 – June 2014
Special Issue Honoring Charles G. Gross
PREFACE
“Charlie’s Lab”
Charles G. Gross
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1195–1195.
RESEARCH ARTICLES
Facial Expressions and the Evolution of the Speech Rhythm
Asif A. Ghazanfar, Daniel Y. Takahashi
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1196–1207.
Effect of Microstimulation of the Superior Colliculus on Visual Space Attention
Ricardo Gattass, Robert Desimone
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1208–1219.
Subcortical Projections of Area V2 in the Macaque
Leslie G. Ungerleider, Thelma W. Galkin, Robert Desimone, Ricardo Gattass
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1220–1233.
S-cone Visual Stimuli Activate Superior Colliculus Neurons in Old World Monkeys: Implications for Understanding Blindsight
Nathan Hall, Carol Colby
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1234–1256.
Interpersonal Competence in Young Adulthood and Right Laterality in White Matter
Nicola De Pisapia, Mauro Serra, Paola Rigo, Justin Jager, Nico Papinutto, Gianluca Esposito, Paola Venuti, Marc H. Bornstein
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1257–1265.
Reorganization of Retinotopic Maps after Occipital Lobe Infarction
Lucia M. Vaina, Sergei Soloviev, Finnegan J. Calabro, Ferdinando Buonanno, Richard Passingham, Alan Cowey
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1266–1282.
PFC Neurons Reflect Categorical Decisions about Ambiguous Stimuli
Jefferson E. Roy, Timothy J. Buschman, Earl K. Miller
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1283–1291.
Persistent Spatial Information in the FEF during Object-based Short-term Memory Does Not Contribute to Task Performance
Kelsey L. Clark, Behrad Noudoost, Tirin Moore
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1292–1299.
ESSAYS
Speculations on the Evolution of Awareness
Michael S. A. Graziano
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1300–1304.
Travels with Charlie
Thomas D. Albright
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1305–1323.
“It Is Hardly News that Women Are Oppressed”: Sexism, Activism, and Charlie
Rhoda K. Unger
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1324–1326.
POEMS
A Charliad
Michael E. Goldberg
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1327–1327.
Travels with Charlie: Part II
Suzanne Corkin
Journal of Cognitive Neuroscience June 2014, Vol. 26, No. 6: 1328–1329.
Does the prefrontal cortex (PFC) maintain the contents of working memory or does it direct the focus of attention? Lara and Wallis asked this question by training monkeys to perform a multi-color change detection task. Few PFC neurons encoded the color of the stimuli. Instead, the dominant signals were the spatial location of the item and the location of focal attention. This suggests that the PFC is more involved in directing attention than retaining information in working memory. Supporting this was increased power in alpha and theta power in the PFC, frequency bands associated with long-range neural communication.