ORACLE: Options to Read in the Archive: Churchland Lab’s Experience

January 31, 2018

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Today’s commentary brought to you from (in alphabetical order): George Bekheet, Lital Chartarifsky, Anne Churchland, Ashley Juavinett, Simon Musall, Farzaneh Najafi & Sashank Pisupati. Feel free to comment, correct, express skepticism, etc. You can do so here, on Biorxiv or on Twitter (@anne_churchland). Let’s get a conversation going!

Paper #1: Discrete attractor dynamics underlying selective persistent activity in frontal cortex (Inagaki, FontolanRomani & Svoboda)

Big question: What is the deal with the persistent activity in mouse area ALM that precedes licking movements?

Take home: Using intra and extracellular recording, combined with optogenetics and network modeling (nice!), the authors conclude that attractor dynamics, and not integration, define neural activity in area ALM.
But, hmmmm: Persistent activity in advance of movements is widely observed in many critters, but its function is pretty mysterious. Other kinds of persistent activity, like memory or evidence accumulation have a clear cognitive function, but the role of motor preparatory activity is not obvious. Why does ALM need to respond to far in advance of a movement anyway?

Paper #2: Optogenetically induced low-frequency correlations impair perception (Nandy, Nassi1 & Reynolds)

Big question: While multiple groups have shown that attention reduces the correlation between neurons within the receptive field of the attended location, it has been difficult to show causation. Here, they recreate low-frequency correlations in visual cortex and ask: can one causally affect the ability of an animal to pay attention?

Take home: Using depolarizing opsin C1V1 in a lentivirus in combination with an artificial dura, the researchers created a preparation in which they could optically excite specific locations of pyramidal cells in V4. During an orientation-change detection task, they use this system to induce low frequency (4-5 Hz) as well as high frequency (20 Hz) oscillations within the receptive field of the attended region. They demonstrate that low frequency stimulation within the attended field impairs the animal’s ability to do the task, whereas low frequency stimulation in an unattended field does not. The finding was also frequency specific — high frequency stimulation does not impair performance.

But, hmmmm: These findings provide nice closure to previous skepticism that changes in correlation structure could simply be an off-target effect, but not actually causal for attention. Still, the field seemed pretty convinced that low frequency correlations were somehow involved in attention, so this result probably will not shock many researchers. In addition, the behavioral effects are not well characterized in this paper. The two sample sessions in Figure 2, Supplement 2 show very different effects on the psychometric curve — one is shifted, whereas one has a different slope, suggesting different underlying impairments. We’d love to see the researchers more closely quantify the impairments in each animal.

Paper #3: Exclusive functional subnetworks of intracortical projection neurons in primary visual cortex (Kim, Znamenskiy Iacaruso, & Mrsic-Flogel)

Big question: How do long-range projection targets constrain local connectivity of cortical neurons?

Take home: distinct populations in V1 project to higher visual areas AL and PM. These distinct populations avoid making connections with each other which is unexpected given their signal correlations (response similarity). Therefore, projection target acts independently of response similarity to constrain local cortical connectivity. The absence of recurrent connections between AL and PM potentially allows for their independent modulation by top-down signals.

But, hmmmm: Should we worry that retrograde labeling may have failed to label all projections neurons? Also is identifying double labeled neurons an error-prone task?

Paper #4 Accurate Prediction of Alzheimer’s Disease Using Multi-Modal MRI and High-Throughput Brain Phenotyping (Wang, Xu, Lee, Yaakov, Kim, Yoo, Kim & Cha)

Big Question: Does multi-modal MRI data in combination with high throughput brain phenotyping provide any utility in predicting Alzheimer’s disease?
Take home: Authors have produced a machine learning model that can discern the difference (97% accuracy) between an AD brain and one from a patient with subjective memory complaints.
 
But, hmmm…: Seeing as this is done with retrospective data, why not look at AD patients verses patients with no cognitive impairment and memory complaints? Also, we would have loved it if the authors included more information on the machine learning analytics they used.

Paper #5: Causal contribution and dynamical encoding in the striatum during evidence accumulation (Yartsev, Hanks, Yoon & Brody)

Big question: Which regions of the brain are causally involved in evidence accumulation during decision making?

Take home: Anterior dorsal striatum satisfies 3 major criteria for involvement, as revealed by a detailed behavioral model – necessary (pharmacological inactivation makes accumulation noisy), represents graded evidence on single trials (electrophysiology), and contributes only during accumulation (temporally-specific optogenetic inactivation) – and is hence the first known causal node in evidence accumulation.

But, hmmm…: Teasing apart the relative contributions of striatum and its upstream inputs to accumulation will require further study, as will distinguishing its contribution relative to prefrontal cortex (FOF) to subsequent aspects of the decision such as leak & lapses.

Paper #6: Confidence modulates exploration and exploitation in value-based learning (Boldt, Blundell & De Martino)

Big question: What is the link between humans’ confidence in their decisions and their uncertainty in the value of different choices? How do these quantities influence their decisions?

Take home: Belief confidence(i.e. certainty in value estimates) drives decision confidence (i.e, confidence that choices made were correct) in a two-armed bandit, and individuals with better estimates of the former also had better estimates of the latter. Moreover, the belief confidence in the higher-value option modulated the exploration-exploitation tradeoff, with participants exploring more often when they were less confident.

But, hmmm…: Relating these results to the two known forms of uncertainty-driven exploration –  one that depends on the difference in uncertainties of the two options (uncertainty bonus) and the other that depends on their sum (Thompson sampling)- will require further investigation into the effects of interaction between the belief confidences of the two options.

Paper #7: Aberrant Cortical Activity In Multiple GCaMP6-Expressing Transgenic Mouse Lines (SteinmetzBuetfering, Lecoq, Lee, PetersJacobs, CoenOllerenshawValleydeVries Garrett, Zhuang,  Groblewski Manavi Miles White Lee Griffin, LarkinRollCrossNguyenLarsenPendergraftDaigleTasicThompsonWatersOlsenMargolisZengHausserCarandiniHarris)

Big question: Transgenic animals are developing to be the standard for for measuring neural activity but potential side-effects of genetic manipulations may be overlooked. Here the authors show that several GCaMP lines show abnormal epileptiform activity that is not observed in wild-type mice. They also provide on how to avoid this issue when using affected lines.

Take-home: Epileptiform activity are short, high-amplitude bursts of activity that span large parts of cortex and occur at a rate of ~0.1-0.5 Hz. Epileptiform is clearly distinct from other neural activity (measured with ephys, 2-photon or widefield imaging) but most animals don’t show any clear behavioral impairments. The origin of epileptiform is unclear but suppressing expression of GCaMP in the first 7 weeks seems to resolve the issue even for transgenic lines that are otherwise most affected.

But, hmmmm: Presumably, there is a variety of causes for abnormal neural activity in transgenic animals. Its not clear whether suppressing GCaMP expression will prevent these issues in future lines and there might also be other problems like indicator over-expression that will cause headaches in the future. There should be more studies that describe and address potential issues.

Paper #8: Stable representation of sounds in the posterior striatum during flexible auditory decisions (GuoWalkerPonvertPenix, Jaramillo)

 Big question: What is the role of posterior striatum neurons during auditory-driven decisions in mice?

Take home message: Here, the authors show that transient pharmacological inactivation of posterior striatum (also known as “auditory striatum”) impaired performance in an auditory discrimination task, while optogenetic activation during sound presentation biased the animals’ choices. Moreover, the activity of these neurons reliably encoded stimulus features, but was only minimally influenced by the animals’ choices, suggesting that neurons in the posterior striatum provide sensory information downstream, while providing little information about behavioral choice.

But, hmmm: The activation and inactivation experiments were performed on different neuronal populations (direct-pathway medium spiny neurons vs. all posterior striatal neurons, respectively), as well as unilaterally vs. bilaterally (activation vs. inactivation, respectively). It would be interesting to know how the different populations support the behavior, as well as matching the methodology. Moreover, the pharmacological inactivation had pretty strong motor effects and it is important to make sure that the behavioral effects were not cause by motor deficits.

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Fairhall lab

Computational neuroscience at the University of Washington

Pillow Lab Blog

Neural Coding and Computation Lab @ Princeton University

Churchland lab

Perceptual decision-making at Cold Spring Harbor

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