28 Aug 2018
August 28, 2018

Attention is rhythmic!


Two new, exciting papers in Neuron that “put the last nail(s) in the coffin of sustained attention.”  They present compelling evidence that sustained attention is not sustained at all but fluctuates with theta rhythms and alpha/beta rhythms. This provides yet more evidence that the brain works by rhythmic switching between representations.

Ian C. Fiebelkorn, Mark A. Pinsk, Sabine Kastner

A Dynamic Interplay within the Frontoparietal Network Underlies Rhythmic Spatial Attention
Neuron, Volume 99, Issue 4, 22 August 2018, Pages 842-853.e8

Randolph F. Helfrich, Ian C. Fiebelkorn, Sara M. Szczepanski, Jack J. Lin, Josef Parvizi, Robert T. Knight, Sabine Kastner

Neural Mechanisms of Sustained Attention Are Rhythmic
Neuron, Volume 99, Issue 4, 22 August 2018, Pages 854-865.e5

An excellent Preview by Rufin VanRullen: Attention Cycles

For further reading:
Buschman, T.J. and Miller, E.K. (2009) Serial, covert, shifts of attention during visual search are reflected by the frontal eye fields and correlated with population oscillations. Neuron, 63: 386-396. View PDF »

Buschman,T.J. and Miller, E.K. (2010) Shifting the Spotlight of Attention: Evidence for Discrete Computations in Cognition. Frontiers in Human Neuroscience. 4(194): 1-9. View PDF »

The effects of attention in the brain can be partitioned into changes in sensitivity of in the subject’s criterion.  In visual cortex, only changes in sensitivity are seen.  Here, Luo and Maunsell show that neurons in frontal cortex are sensitive to changes in sensitivity as well as criterion.

Luo, T. Z., & Maunsell, J. H. (2018). Attentional Changes in Either Criterion or Sensitivity Are Associated with Robust Modulations in Lateral Prefrontal Cortex. Neuron.

27 Feb 2018
February 27, 2018

Neuromodulation of Attention


A very nice review of how neuromodulation affects the mechanisms and circuits underlying attention.

Thiele, A., & Bellgrove, M. A. (2018). Neuromodulation of AttentionNeuron97(4), 769-785.

The authors report different effects of stimulation of the lateral prefrontal cortex.  Stimulation at or near the FEF prolonged or decreased saccade reaction time, depending on task instructions.  More rostral stimulation affected the attention weighting of saccade targets.

Schwedhelm, P., Baldauf, D., & Treue, S. (2017). Electrical stimulation of macaque lateral prefrontal cortex modulates oculomotor behavior indicative of a disruption of top-down attentionScientific reports7(1), 17715.

Due to a disruption of top-down attentional amplification.

Berkovitch, L., Dehaene, S., & Gaillard, R. (2017). Disruption of Conscious Access in SchizophreniaTrends in Cognitive Sciences.

Paper showing different, yet complementary, effects of attention and value on alpha vs gamma oscillations in posterior cortex.

Marshall, T. R., den Boer, S., Cools, R., Jensen, O., Fallon, S. J., & Zumer, J. M. (2017). Occipital Alpha and Gamma Oscillations Support Complementary Mechanisms for Processing Stimulus Value AssociationsJournal of Cognitive Neuroscience.

And they show the same independence between the visual hemifields that we saw in primates.

Balakhonov, D., & Rose, J. (2017). Crows Rival Monkeys in Cognitive CapacityScientific Reports7.

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 »

Miller, E.K. and Buschman, T.J. (2015)  Working memory capacity: Limits on the bandwidth of cognition. Daedalus, Vol. 144, No. 1, Pages 112-122. View PDF »

Kornblith, S., Buschman, T.J., and Miller, E.K. (2015)  Stimulus load and oscillatory activity in higher cortex. Cerebral Cortex. Published online August 18, 2015  doi: 10.1093/cercor/bhv182. View PDF »

Working memory for different items in a sequence is prioritized by how much attention is paid to the item at encoding.

Jafarpour, A., Penny, W., Barnes, G., Knight, R. T., & Duzel, E. (2017). Working Memory Replay Prioritizes Weakly Attended EventseNeuro4(4), ENEURO-0171.

Your eyes dart about rhythmically sampling different parts of a scene in little bites.  Your memory system papers this over to create a illusion of seamless perception.  Let Parr and Friston break it down for you:

Parr, T., & Friston, K. J. (2017). The active construction of the visual worldNeuropsychologia.

For further reading:

Buschman,T.J. and Miller, E.K. (2010) Shifting the Spotlight of Attention: Evidence for Discrete Computations in Cognition. Frontiers in Human Neuroscience. 4(194): 1-9. View PDF »

Buschman, T.J. and Miller, E.K. (2009) Serial, covert, shifts of attention during visual search are reflected by the frontal eye fields and correlated with population oscillations. Neuron, 63: 386-396. View PDF 

MEG study in humans shows the functional significance of high alpha-band synchrony for visual attention.

Lobier, M., Palva, J. M., & Palva, S. (2017). High-alpha band synchronization across frontal, parietal and visual cortex mediates behavioral and neuronal effects of visuospatial attention. bioRxiv, 165563.


Neurons in LIP contribute to two distinct stages of processing during a search task.  The working memory for the sought-after feature and then the focusing of attention on target location.

Levichkina, E., Saalmann, Y. B., & Vidyasagar, T. R. (2017). Coding of spatial attention priorities and object features in the macaque lateral intraparietal cortex. Physiological Reports, 5(5), e13136.

An excellent and comprehensive review of the brain basis of visual attention.  Worthy of yours.

Moore, T., & Zirnsak, M. (2017). Neural Mechanisms of Selective Visual Attention. Annual Review of Psychology, 68, 47-72.

New Miller Lab paper:
Jia, N., Brincat, S.L., Salazar-Gomez, A., Panko, M., Guenther, F. and Miller, E.K. (2017) Decoding of intended saccade direction in an oculomotor brain-computer interface. Journal of Neural Engineering, 2017. https://doi.org/10.1088/1741-2552/aa5a3e

Objective. To date, invasive brain-computer interface (BCI) research has largely focused on replacing lost limb functions using signals from of hand/arm areas of motor cortex. However, the oculomotor system may be better suited to BCI applications involving rapid serial selection from spatial targets, such as choosing from a set of possible words displayed on a computer screen in an augmentative and alternative communication (AAC) application. Here we aimed to demonstrate the feasibility of a BCI utilizing the oculomotor system. Approach. We developed a chronic intracortical BCI in monkeys to decode intended saccadic eye movement direction using activity from multiple frontal cortical areas. Main results. Intended saccade direction could be decoded in real time with high accuracy, particularly at contralateral locations. Accurate decoding was evident even at the beginning of the BCI session; no extensive BCI experience was necessary. High-frequency (80-500 Hz) local field potential magnitude provided the best performance, even over spiking activity, thus simplifying future BCI applications. Most of the information came from the frontal and supplementary eye fields, with relatively little contribution from dorsolateral prefrontal cortex. Significance. Our results support the feasibility of high-accuracy intracortical oculomotor BCIs that require little or no practice to operate and may be ideally suited for point and click computer operation as used in most current AAC systems.

The title says it all.  A comprehensive review on how salience is determined and used by the brain.

Veale, R., Hafed, Z. M., & Yoshida, M. (2017). How is visual salience computed in the brain? Insights from behaviour, neurobiology and modelling. Phil. Trans. R. Soc. B, 372(1714), 20160113.

Ester et al addressed the dichotomy of source vs site in visual attention.  The frontoparietal cortex has long been thought to be the “source” of top-down attention signals that enhance activity at “sites” in posterior (sensory) cortex that represent visual stimuli.  They used fMRI, a roving searchlight analysis, and an inverted encoding model to show that stimulus representations are all over the cortex and enhanced by attention.  This calls the dichotomy between source and site into question.

Ester, Edward F., et al. “Feature-selective attentional modulations in human frontoparietal cortex.” The Journal of Neuroscience 36.31 (2016): 8188-8199.

Ibos and Freedman show that spatial and feature-based attention independently modulate activity in area LIP and that they added together. This suggests a common function of gating task-relevant features, whether they are spatial or non-spatial.

Ibos, Guilhem, and David J. Freedman. “Interaction between Spatial and Feature Attention in Posterior Parietal Cortex.” Neuron (2016).



Michael Halassa, Guoping Feng, and colleagues show that a genetic deficit  found in patients with ADHD produces (in mice) deficit in the thalamic reticular nucleus.  This adds to Halassa’s recent work (also in Nature) suggesting that  attention is focused when the prefrontal cortex acts on sensory cortex via the thalamus.  It adds a link to potential path to treatment.  Cool.

Thalamic reticular impairment underlies attention deficit in Ptchd1Y/− mice
Michael F. Wells, Ralf D. Wimmer, L. Ian Schmitt, Guoping Feng & Michael M. Halassa

Increases in beta power associated with top-down attention.  Beta seemed unite visual cortex.  There was a more homogeneous pattern of beta correlation across the cortex during top-down vs bottom-up attention.

Bekisz, M., Bogdan, W., Ghazaryan, A., Waleszczyk, W. J., Kublik, E., & Wróbel, A. (2016). The Primary Visual Cortex Is Differentially Modulated by Stimulus-Driven and Top-Down Attention. PloS one, 11(1), e0145379.

Oscillatory synchrony of  prefrontal parvalbumin plays a role in top-down control of attention.

Kim, H., Ährlund-Richter, S., Wang, X., Deisseroth, K., & Carlén, M. (2016). Prefrontal Parvalbumin Neurons in Control of Attention. Cell, 164(1), 208-218.

Bichot et al find that a particular part of the prefrontal cortex is the source of information about an object when we search for it.  In other words, when you look for your missing keys, this is the part of the brain that reminds you what they look like.

Bichot, Narcisse P., et al. “A Source for Feature-Based Attention in the Prefrontal Cortex.” Neuron (2015).

Excellent review of how wide-spread brain areas use synchronized rhythms form networks for focusing attention.  Very comprehensive and thorough on both a maco and micro-circuit level.

Womelsdorf, Thilo, and Stefan Everling. “Long-range attention networks: circuit motifs underlying endogenously controlled stimulus selection.” Trends in Neurosciences 38.11 (2015): 682-700.

It is widely thought that the volitional focusing of attention on a sensory input depends on top-down influences from the prefrontal cortex (PFC) acting on sensory cortex.  However, much of the evidence for this is circumstantial.  Halassa et al now provide direct evidence using optogenetic manipulation in mice.  When they temporarily disrupted the PFC, mice had trouble focusing on a visual input in the face of an auditory distraction and vice-versa.  Moreover, they went on to show that the PFC acts on sensory cortex, not directly but, through the thalamic reticular nucleus (TRN).  Manipulation of thalamocortical circuits showed that behavior depended on PFC interactions with the thalamus, not on PFC interactions with sensory cortex.  Further, thalamic activity was correlated with behavioral performance and its manipulation was causal to performance.  This all suggests that attention is focused when the PFC acts on sensory cortex via the thalamus.

Wimmer, R. D., Schmitt, L. I., Davidson, T. J., Nakajima, M., Deisseroth, K., & Halassa, M. M. (2015). Thalamic control of sensory selection in divided attention. Nature.

Pascal Fries walks us through the latest in the communication through coherence theory.

Fries, Pascal. “Rhythms for Cognition: Communication through Coherence.”Neuron 88.1 (2015): 220-235.

Tim Buschman and Sabine Kastner review work on visual attention and propose a new theory that ties together a wide range of observations.  Here’s an outline of the theory in their own words:

  1. Attention can either be (a) automatically grabbed by salient stimuli or (b) guided by task representations in frontal and parietal regions to specific spatial locations or features.
  2. The pattern-completion nature of sensory cortex sharpens the broad top-down attentional bias, restricting it to perceptually relevant representations. Interactions with bottom-up sensory drive will emphasize specific objects.
  3. Interneuron-mediated lateral inhibition normalizes activity and, thus, suppresses competing stimuli. This results in increased sensitivity and decreased noise correlations.
  4. Lateral inhibition also leads to the generation of high-frequency synchronous oscillations within a cortical region. Inter-areal synchronization follows as these local oscillations synchronize along with the propagation of a bottom-up sensory drive. Both forms of synchrony act to further boost selected representations.
  5. Further buildup of inhibition acts to “reset” the network, thereby restarting the process. This reset allows the network to avoid being captured by a single stimulus and allows a positive-only selection mechanism to move over time.

Makes a lot of sense.
Buschman, Timothy J., and Sabine Kastner. “From Behavior to Neural Dynamics: An Integrated Theory of Attention.” Neuron 88.1 (2015): 127-144.

Nice demo showing that cues that automatically draw attention can modulate activity in primary visual cortex.

Wang, Feng, et al. “Modulation of Neuronal Responses by Exogenous Attention in Macaque Primary Visual Cortex.” The Journal of Neuroscience 35.39 (2015): 13419-13429.