DiQuattro et al use dynamic causal modeling (DCM) of FMRI signals to show that the frontal eye fields (FEF) are more involved initiating shifts of attention than the temporoparietal junction (TPJ, another leading candidate). The FEF received sensory signals earlier than the TPJ and FEF to TPJ connectivity was modulated by appearance of a target.
Miller Lab work cited:
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 »
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 »
Kraus et al required rats to run on a treadmill during a working memory task. They dissociated distance traveled vs time spent running by requiring the rats to run for a fixed distance or a fixed amount of time. This revealed “time cells” in the hippocampus that reflect the passage of time.
Miller Lab alumnus, Andreas Nieder, finds that abstract decisions divorced from motor plans are distributed across frontal areas, even those traditionally thought of as motor areas. In fact, they are more strongly encoded in the presupplementary motor area than the prefrontal cortex.
Merten and Nieder 2013
Miller Lab work cited:
Freedman, D.J., Riesenhuber, M., Poggio, T., and Miller, E.K. (2001) Categorical representation of visual stimuli in the primate prefrontal cortex. Science, 291:312-316. View PDF »
Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24:167-202. Designated a Current Classic by Thomson Scientific as among the most cited papers in Neuroscience and Behavior. View PDF »
Miller, E.K. (2000) The prefrontal cortex and cognitive control. Nature Reviews Neuroscience, 1:59-65.
Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Single neurons in the prefrontal cortex encode abstract rules. Nature, 411:953-956. View PDF »
Nicole Rust and crew show how the perirhinal cortex can take signals from the inferior temporal cortex and sort out visual targets from distractors.
Pagan et al (2013)
Totah et al find that increased synchrony between the prelimbic cortex and anterior cingulate cortex before stimulus onset predicted behavioral choices. Further, there was a switch from beta synchrony during attention to delta synchrony before the behavioral response. This shows, among other things, that the same neurons can participate in different functional networks by virtue of how that synchronize with other neurons.
This paper is one of several recent studies providing evidence that oscillatory synchrony allows multiplexing of neural function. Here’s an excerpt from Miller and Fusi (2013) that summarizes this point:
“It (oscillatory synchrony) could allow neurons to communicate different messages to different targets depending on whom they are synchronized with (and how, e.g., phase, frequency). For example, rat hippocampal CA1 neurons preferentially synchronize to the entorhinal or CA3 neurons at different gamma frequencies and theta phases (Colgin et al, 2009). Different frequency synchronization between human cortical areas supports recollection of spatial vs temporal information (Watrous et al., 2013). Different phases of cortical oscillations preferentially signal different pictures simultaneously held in short-term memory (Siegel et al., 2009). Monkey frontal and parietal cortices synchronize more strongly at lower vs higher frequency for top-down vs bottom-up attention, respectively (Buschman and Miller, 2007). Entraining the human frontal cortex at those frequencies produces the predicted top-down vs bottom-up effects on behavior (Chanes et al., 2013). Thus, activity from the same neurons has different functional outcomes depending on their rhythmic dynamics.”
Miller, E.K. and Fusi, S. (2013) Limber neurons for a nimble mind. Neuron. 78:211-213. View PDF
Betsy Murray and crew find evidence to resolve two different views of the function of the orbitofrontal cortex (OFC). One view is that the OFC provides inhibitory control and emotion regulation. The other view is that processes the value of things. They show that damage limited to the OFC does not affect inhibitory or emotional control, but damage to nearby fiber tracts do. There you go.
Pannunzi et al propose a model of visual category learning in which bottom-up sensory inputs to the inferior temporal cortex are sculpted by top-down inputs from the prefrontal cortex (PFC). The PFC improves signal to noise by enhancing the category-relevant features of the stimuli.
Miller Lab work cited:
Freedman, D.J., Riesenhuber, M., Poggio, T., and Miller, E.K. (2001) Categorical representation of visual stimuli in the primate prefrontal cortex. Science, 291:312-316. View PDF »
Freedman, D.J., Riesenhuber, M., Poggio, T., and Miller, E.K (2003) A comparison of primate prefrontal and inferior temporal cortices during visual categorization. Journal of Neuroscience, 23(12):5235-5246. View PDF »
Meyers, E.M., Freedman, D.J., Kreiman, G., Miller, E.K., and Poggio, T. (2008) Dynamic population coding of category information in the inferior temporal cortex and prefrontal cortex. Journal of Neurophysiology. 100:1407-1419. View PDF »
Muhammad, R., Wallis, J.D., and Miller, E.K. (2006) A comparison of abstract rules in the prefrontal cortex, premotor cortex, the inferior temporal cortex and the striatum. Journal of Cognitive Neuroscience, 18: 974-989. 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 »
Ekstrom and Watrous review the role of low frequency oscillatory coupling in cognition. The propose that different resonant frequencies within the same networks support movement vs memory related functions. They provide further evidence and argument for a role for oscillatory coupling in multiplexing of function. In other words, different frequency coupling can allow the same networks to have different roles by allowing them to communicate different messages to different targets.
Miller Lab work on oscillatory coupling and multiplexing:
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 View PDF »
Miller, E.K. and Buschman, T.J. (2013) Cortical circuits for the control of attention. Current Opinion in Neurobiology. 23:216–222 View PDF »
Miller Lab alumnus, Andreas Nieder, shows number tuned neurons in pefrontal and parietal cortices of naive (untrained) subjects.
Viswanathan and Nieder 2013
Andreas Nieder’s Miller Lab work on the neural substrates for numerosity:
Nieder, A., Freedman, D.J., and Miller, E.K. (2002) Representation of the quantity of visual items in the primate prefrontal cortex. Science. 297:1708-1711. View PDF »
Nieder, A. and Miller, E.K. (2003) Coding of cognitive magnitude: Compressed scaling of numerical information in the primate prefrontal cortex. Neuron. 37:149-157. View PDF »
Nieder, A. and Miller, E.K. (2004) A parieto-frontal network for visual numerical information in the monkey. Proceedings of the National Academy of Sciences, 101:7457-7462. View PDF »
Nieder, A. and Miller, E.K. (2004) Analog numerical representations in rhesus monkeys: Evidence for parallel processing. Journal of Cognitive Neuroscience. 16:889-901. View PDF »
DIY attempts at electrical brain stimulation to improve cognition are to get easier. Nature editorial.
Kometer et al show that psilocybin decreased spontaneous alpha oscillations which precluded the usual decrease in alpha when a visual stimulus is presented. Psilocybin may result in a brain state in which normal stimulus-driven cortical excitation is overwhelmed by spontaneous neuronal excitation resulting in altered perception and hallucinations.
We recently found evidence that alpha oscillations are useful for clearing out unwanted thoughts (neural ensembles) that could interfere with the current cognitive demands:
- Buschman, T.J., Denovellis, E.L., Diogo, C., Bullock, D. and Miller, E.K. (2012) Synchronous oscillatory neural ensembles for rules in the prefrontal cortex. Neuron, 76: 838-846. View PDF
Soltani et al (2013) explored the role of D1 and D2 dopamine receptors in saccade target selection. They find evidence that D1 receptors modulate the strength of inputs to the frontal eye fields and recurrent connectivity whereas D2 may modulate the output of the FEF. This may be because D1 seems to reduce LTP and LTD, which is consistent with observations that D1 receptors contribute to associative learning (Puig and Miller, 2012). Like Puig and Miller (2012), they also found that D1 blockade increases response perseveration.
Further reading:
Puig, M.V. and Miller, E.K. (2012) The role of prefrontal dopamine D1 receptors in the neural mechanisms of associative learning. Neuron. 74: 874-886. View PDF »
Matt Chafee and crew show that monkeys under the influence of ketamine show similar deficits as human schizophrenia patients on a test of context processing.
Blackman et al 2013
Attentional blink is decreased attention to a second stimulus if it quickly follows (200-500 ms) another stimulus. Maloney et al find that neural information in area LIP tracks attentional blink.
More evidence for a role for beta coherence in cognition.
Lipsman et al find that an increase in beta coherence in human VM prefrontal cortex just before humans subjectively evaluated faces as “sad” but not before “happy” judgments, especially true when the faces were more ambiguous and thus more difficult to judge.
Miller Lab work on beta coherence and cognition:
- Miller, E.K. and Buschman, T.J. (2013) Cortical circuits for the control of attention. Current Opinion in Neurobiology. 23:216–222 View PDF »
- Buschman, T.J., Denovellis, E.L., Diogo, C., Bullock, D. and Miller, E.K. (2012) Synchronous oscillatory neural ensembles for rules in the prefrontal cortex. Neuron, 76: 838-846. 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 »
- 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 View PDF »
Charlie Schroeder shows us the laminar profile of oscillations in cortex. Different strengths for different frequency bands in different cortical layers. Attention phase-synchronizes oscillations across layers facilitating communication between them. See Lakatos et al (2005) J. Neurophys.
Circuits from different thalamic nuclei to cortex, one broad and modulatory, the other narrow and specific, may regulate oscillatory entrainment.
New Neuron paper shows cortical entrainment that matches periodic sensory inputs; phase depended on the attended frequency content., enhancing attended representations. Lakatos et al 2013
Entrainment may explain cocktail party effect. Low frequency phase and high gamma power track attended speech. Zion Golumbic et al
Zara Bergstrom, Jon Simons and crew show that people can beat EEG tests of guilt detection by suppressing the guilty memories. Research calls into question reliability of such tests.
http://www.cam.ac.uk/research/news/people-can-beat-guilt-detection-tests-by-suppressing-incriminating-memories
If you are interested in cognition, brain rhythms, and, especially, brain rhythms and cognition, this is the place to be.
http://cogrhythms.bu.edu/conference.htm
The Rhythmic Dynamics and Cognition Conference is a two-day event sponsored by the Cognitive Rhythms Collaborative (CRC). The program will be held at the Brain Building (Building 46) on the MIT campus (Room 3002) and will include lectures, a reception, and a poster session.
Speakers include:
- Pascal Fries, (Ernst Strungmann Institute (ESI), Frankfurt)
- Elizabeth Buffalo (Emery University)
- Charlie Schroeder (Nathan Kline Institute)
- Peter Brown (University College London)
- Fiona Le Beau (Newcastle University)
- Earl Miller (MIT)
- Charlie Wilson (University of Texas, San Antonio)
- Peter Uhlhaas (University of Glasgow)
- Christa van Dort (Mass. General Hospital)
- Markus Siegal (University of Tubingen)
- Robert Knight (UC Berkely)
It was recently reported that low-voltage, non-invasive brain stimulation improves mathematical abilities. Does it? Here’s a cautionary discussion:
Does Brain Stimulation Make You Better at Maths?
Visual attention modulates several aspects of neural coding. There is an increase in firing rate and changes in temporal dynamics: a reduction of neural variance and noise correlation as well as changes in oscillatory synchronization. The authors used glutamatergic receptor activation, combined with neurophysiological recording to show that the NMDA receptor is responsible for attention -related changes in neural temporal dynamics but not for increases in firing rate. Thus, different neurophysiological mechanisms that underlie attention can be dissociated at the receptor level. This supports the hypothesis that attention is mediated in part by the temporal dynamics of neural activity, not merely changes in the firing rate of neurons, and that the changes temporal dynamics are not simply a byproduct of changes in firing rate.
Herrero et al (2013) Neuron
For a further discussion of the role of temporal dynamics in attention see:
Miller, E.K. and Buschman, T.J. (2013) Cortical circuits for the control of attention. Current Opinion in Neurobiology. 23:216–222 View PDF »
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 . 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 »
This paper uses EEG to examine the timecourse of synchronization patterns across the brain during a simple cognitive task. First, there was low frequency (delta) synchrony, which may reflect global, long-range synchronization and may help organize the higher frequency synchrony that followed. Then, there was higher frequency (gamma) synchrony, which may reflect reorganization of local circuits for bottom-up processing of sensory inputs. Finally, there was beta synchrony, which may reflect the final stage of top-down processing in the task. Gamma and beta synchronization has been shown to be correlated with bottom-up vs top-down cortical processing (Buschman and Miller, 2007; Chanes et al, 2013; Ibos et al, 2013). This study identifies and confirms some of the proposed mechanisms of global information integration in the brain.
Brazdil et al (2013)
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 »
Chanes et al (2013) Journal of Neuroscience
Ibos et al (2013) Journal of Neuroscience
In this week’s NY Times, Susana Martinez-Conde reminds us that our visual system works by detecting change.
http://www.nytimes.com/2013/05/19/opinion/sunday/vision-is-all-about-change.html
Rhythmic synchrony between neurons has been suggested as a mechanism for establishing communication channels between neurons. However, this hypothesis has been criticized because of observations that the exact frequency of gamma oscillations bounces around too much to provide a stable communication channel. (BTW, it doesn’t seem to bother anyone that single neuron activity also bounces around).
In this study, Roberts et al record from V1 and V2 simultaneously while presenting gratings of varying contrast. Even though the gamma frequencies changed with stimulus contrast and fluctuated over time, coherence remained stable between V1 and V2. Thus, rhythmic synchrony can provide a stable channel for neural communication.
http://www.cell.com/neuron/abstract/S0896-6273(13)00227-4?utm_source=feedly
For further reading on the role of rhythmic synchrony in neural communication see:
Miller, E.K. and Buschman, T.J. (2013) Cortical circuits for the control of attention. Current Opinion in Neurobiology. 23:216–222 View PDF »
Buschman, T.J., Denovellis, E.L., Diogo, C., Bullock, D. and Miller, E.K. (2012) Synchronous oscillatory neural ensembles for rules in the prefrontal cortex. Neuron, 76: 838-846. View PDF
Ibos et al examined the relative roles of the frontal eye fields (FEF) and lateral intraparietal area (LIP) in bottom-up vs top-down selection. They found that intrinsic salience (bottom-up) was signaled in LIP before the FEF whereas extrinsic salience (top-down) was signaled in FEF before LIP. The authors conclude that bottom-up vs top-down control of attention predominates in the parietal vs frontal cortex, respectively.
http://www.jneurosci.org/content/33/19/8359.abstract
As noted by the authors, this is highly consistent with our lab’s observations that attention signals for bottom-up capture by stimulus salience (pop-out) vs top-down search originate from parietal vs frontal cortex, respectively.
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 View PDF »
We also showed that rhythmicity of frontal cortical top-down signals may control the periodic shifts of attention during visual search that leads to eventual selection of a target:
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 »
Our very limited ability to hold multiple thoughts in mind is apparent to anyone who has tried to talk on the phone and email at the same time. Traditionally, this is thought of as a limitation in the capacity of working memory, the “mental scratchpad” used to keep important information “online” after it is no longer available. However, Ed Vogel and crew show that the bottleneck is not in working memory per se but instead present during processing of visual stimuli while they are still visible. Thus, the bottleneck is not (just) in memory but also in the processing of sensory inputs. In other words, capacity limitations seem to be a fundamental limit in neural processing related to consciousness in general, not a unique byproduct of working memory.
http://www.jneurosci.org/content/33/19/8257.abstract
As noted by the authors, this is consistent with our finding that when capacity is exceeded, information is lost in a bottom-up fashion during initial processing of visual stimuli:
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 »