P values and scientific communication: a (very) small step

Nearly everyone recognizes the shortcomings of p values and the associated null hypothesis significance testing framework. Of course, much has been written about it, including potential alternatives. While change is needed, it is hard — and it will take time.

At present, I would advocate that the scientific community adopt the suggestion by John Carlin to remove the s-word:

  • “Eliminate the term ‘statistical significance’ from the scientific discourse”

In the past few years, I have been using something similar, but I think a stronger stance is needed by all of us (see also Carlin’s suggestions). In the research from my lab, we use more and more expressions such as “an interaction effect was detected (F value, p value)”.

As important, I suggest avoiding (like the plague) sentences saying that “there was no difference between conditions 1 and 2” — not to mention statements to the effect that conditions 1 and 2 are equivalent. A much better way of expressing this is to say that differences between conditions 1 and 2 were not detected (t value, p value)”.

This is a tiny step. By itself it won’t do much, but in conjunction with educating a new generation of researchers in alternative methods, it will help start changing scientific reporting — an hopefully improve science.

Brain structure and its origins

I just finished reading most of this wonderful new book by Gerald Schneider from MIT (who was already describing two visual systems in 1969): Brain Structure and Its Origins: in Development and in Evolution of Behavior and the Mind.

9780262026734Comparative neuroscience is seldom ever touched in standard textbooks, which is really unfortunate because so much of how we think about the brain has to do with some “folk neuroscience” ways of thinking. This can hardly be ignored when we start studying emotion, motivation, reward, all those good things that gained so much traction in neuroscience in the past two decades. And now are mainstream.

No ones’ work, or book, is perfect of course. My main problem — perhaps not surprisingly — with the book is its treatment of the “limbic system”. Although it is grounded in comparative neuroanatomy and thus much better than other treatments of the purported system, the treatment is problematic for lots of reasons. The presentation is miles better than what would be found in a medical neuroscience textbook, but discussing an emotion circuit of Papez is just unfortunate.

But otherwise, what a great book. I wish I had learned about the brain from a book like this, where was it all along?!

Reward prediction error and fMRI: do they go together?

New paper: Bissonette GB, Gentry RN, Padmala S, Pessoa L, Roesch MR. Impact of appetitive and aversive outcomes on brain responses: linking the animal and human literatures. Front Syst Neurosci. 2014 Mar 4;8:24. eCollection 2014.

With Matt Roesch and folks in his lab, we have a new paper reviewing the animal and animal literatures on appetitive and aversive processing. But what I want to discuss here is one issue that became apparent when we started collecting some pilot fMRI data. Whereas work in the rat has shown prediction errors in a few places in the brain, some fMRI work has shown quite widespread signals. This might be true but one possible confound that we find in many papers in the literature is that regions that simply respond to the US stimulus could be incorrectly described as showing prediction errors because of the way the fMRI analysis is done. This is explained in Fig. 6 of our paper and in the figure below (thanks to Brenton McMenamin).

The problem is that when a regressor modeling the US (say, reward) is also introduced in the model in addition to the prediction error regressor, it can absorb variance in a way that what is left is actually how a prediction error looks like. In that way, the region will appear to show a prediction error simply because it responds to reward itself.

I have no idea how prevalent this problem is, and it is at times even unclear how the data were modeled. (In fact, as an aside, it is at times very surprising how people don’t explain what they do in their papers.) Some people have talked about this problem but again I’m not sure how much people are taking this into account.

fig6_v2 (1)

Understanding brain networks and brain organization: new paper

New paper to appear in Physics of Life that will come with commentary (including by Dani Bassett, Barry Horwitz, Claus Hilgetag, Vince Calhoun, Michael Anderson, Evan Thompson, Franco Cauda, among others).

A lot has been written about brain networks, especially say after 2005. In Chapter 8 of my book The Cognitive Emotional Brain I wrote about this from the perspective of understanding structure-function mappings (what do regions do? what do networks do?). In a paper in press in Physics of Life, I update some of my evolving thoughts on this question. Some of the newer points are:

  • Is brain architecture really small world?  Cortical connectivity seems too dense.  But an important ingredient of small-world organization — the existence of non-local connections, especially long-range ones – is clearly present. Although they appear to be relatively weak, long-range connections play a major role in the cortical network.
  • The mapping from network (as a set of regions) to function is not one-to-one. For instance: Menon, Uddin, and colleagues suggest that a salience network involving the anterior insula and the anterior cingulate cortex “mediates attention to the external and internal worlds”. They note, however, that “to determine whether this network indeed specifically performs this function will require testing and validation of a sequence of putative network mechanisms…” I argue that a network’s operation will depend on several more global variables, namely an extended context that includes the state of several “neurotransmitter systems”, arousal, slow wave potentials, etc. In other words, a network that is solely defined as a “collection of regions” is insufficient to eliminate the one-to-many problem observed with brain regions (such as the amygdala being involved in several functions).
  • Cortical myopia (echoing points by Parvizi, 2009). Large-scale analyses and descriptions of brain architecture suggest principles of organization that become apparent when information is combined across many individual studies. Unfortunately, most of these “meta” studies are cortico-centric – they pay little or no attention to subcortical connectivity. This paints a rather skewed view of brain organization. For example: if one considers “signal communication” as proposed by Sherman (see figure), cortical communication might go via thalamus (including pulvinar), flipping the traditional view.

    Scheme by Sherman SM. The thalamus is more than just a relay. Current Opinions in Neurobiology. 2007;17:417-22.

    Scheme by Sherman SM. The thalamus is more than just a relay. Current Opinions in Neurobiology. 2007;17:417-22.

  • Evolution. Related to the previous point, I suggest that to understand the contributions of subcortical connectivity, we need to consider the evolution of the brain. For example: a cortico-centric framework is one in which the “newer” cortex controls subcortical regions, which are typically assumed to be relatively unchanged throughout evolution. Instead, I suggest that cortex and subcortex change in a coordinated fashion.
  • The importance of weak connections. I critique a central component of the “standard” network view, which goes something like this: “network states depend on strong structural connections; conversely, weak connections have a relatively minor impact on brain states.” My contention is that weak connections are much more important.


Reference: Parvizi J. Corticocentric myopia: old bias in new cognitive sciences. Trends in Cognitive Sciences. 2009;13:354-9.

Dopamine: reward or a lot more? We knew it was a lot more…

I recently took the time to read this paper, something that I should have done a while back… Bromberg-Martin, Matsumoto, and Hikosaka (2010) provide this great perspective on the multi-dimensional nature of dopamine neurons and signaling. I’m not going to summarize it, the authors have done a better job . It’s worth reading the whole quote (and paper) (pp. 827-828; emphasis added):

Scheme by Bromberg, Matsumoto, and Hikosaka.

Scheme by Bromberg-Martin, Matsumoto, and Hikosaka (2010).

“An influential concept of midbrain DA [dopamine] neurons has been that they transmit a uniform motivational signal to all downstream structures. Here we have reviewed evidence that DA signals are more diverse than commonly thought. Rather than encoding a uniform signal, DA neurons come in multiple types that send distinct motivational messages about rewarding and nonrewarding events. Even single DA neurons do not appear to transmit single motivational signals. Instead, DA neurons transmit mixtures of multiple signals generated by distinct neural processes. Some reflect detailed predictions about rewarding and aversive experiences, while others reflect fast responses to events of high potential importance…

Many previous theories have attempted to identify DA neurons with a single motivational process such as seeking valued goals, engaging motivationally salient situations, or reacting to alerting changes in the environment. In our view, DA neurons receive signals related to all three of these processes. Yet rather than distilling these signals into a uniform message, we have proposed that DA neurons transmit these signals to distinct brain structures in order to support distinct neural systems for motivated cognition and behavior. Some DA neurons support brain systems that assign motivational value, promoting actions to seek rewarding events, avoid aversive events, and ensure that alerting events can be predicted and prepared for in advance. Other DA neurons support brain systems that are engaged by motivational salience, including orienting to detect potentially important events, cognitive processing to choose a response and to remember its consequences, and motivation to persist in pursuit of an optimal outcome. We hope that this proposal helps lead us to a more refined understanding of DA functions in the brain, in which DA neurons tailor their signals to support multiple neural networks with distinct roles in motivational control.”

Fantastic! Above is a picture of their scheme (Fig. 7).

Reference: Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010). Dopamine in motivational control: rewarding, aversive, and alerting. Neuron, 68(5), 815-834.

Of snakes, the pulvinar, and fear

A new paper in PNAS suggests that “Pulvinar neurons reveal neurobiological evidence of past selection for rapid detection of snakes” (from the title). I’m happy that more research is being done on the functions of the pulvinar, a structure that is fascinating. There are many interesting findings in the paper, and it’s certainly worth reading.


The problem, as usual, is not with the results but with their interpretation.Establishing selectivity to visual stimuli is challenging at best (cf. all the disputes re. faces in ventral visual cortex). Some puzzling (and to me telling) aspects of the data that the authors barely discuss are:

  • Good responses were observed to high spatial frequency stimuli (!), not just low pass images. In fact, the effect of low vs. high pass had a small effect size (given a p value that was only < .1)
  • Latency to snake pictures was fast (around 55 ms on average) but how much faster than other stimuli it was not clear (but maybe I missed this).
  • The authors suggest that they recorded from the medial pulvinar (the “associational” sector). Talking to colleagues who are familiar with the intricacies of pulvinar anatomy in several species, the  figure shown by the authors does not make this point convincingly. The authors really need to demonstrate that this is not visual pulvinar (that is, from what is shown it is not clear that they were in the medial pulvinar as described in the literature).

These are issues that can be resolved with further research. My main concern is the evolutionary conclusion of the paper.  As phrased by the authors: “Our data provide unique neuronal evidence supporting the hypothesis that snakes provided a novel selective pressure that contributed to the evolution of the primate order by way of visual modification”. This is unfortunate; I’m not a comparative neuroscientist, but without studying multiple extant species, a claim like this is clearly over-reaching.

Reference: Van Le, Q., Isbell, L. A., Matsumoto, J., Nguyen, M., Hori, E., Maior, R. S., … & Nishijo, H. (2013). Pulvinar neurons reveal neurobiological evidence of past selection for rapid detection of snakes. Proceedings of the National Academy of Sciences, 110(47), 19000-19005.

Brain evolution: amygdala bigger than PFC??

This year I attended the pre-SFN meeting on Evolutionary Neuroscience by the J.B. Johnston Club. I enjoyed the meeting a lot (though was somewhat baffled by their obsession with isometric lines with slope 1…) and ended up bumping into a couple of comparative papers on the amygdala (that I should have known about).

Although fairly crude, one can gain insight into brain evolution by measuring volume or counting cells across brain regions and species. This has led to much debate, for instance, regarding the PFC and its possible “enlarged status” in humans. If you do that for different amygdala nuclei, you find that “the human amygdala is evolutionarily reorganized in relation to great ape amygdala”.

This quote is also quite revealing: “Neuron numbers in the human lateral nucleus were nearly 60% greater than predicted by allometric trends, a degree of magnitude rarely seen in comparative analyses of human brain evolution (Sherwood et al., 2012). For example, the volume of the human neocortex is 24% larger than expected for a primate of our brain size (Rilling and Insel, 1999), whereas the human frontal lobe, long assumed to be enlarged, is approximately the size expected for an ape of human brain size (Semendeferi et al., 2002; Semendeferi and Damasio, 2000).”

So much for such a highly conserved structure… Interesting also that the authors discuss “evolutionary specializations” of the amygdala in terms of the social brain, not “fear processing” (as for instance described in this previous post).

Reference: Barger, N., Stefanacci, L., Schumann, C. M., Sherwood, C. C., Annese, J., Allman, J. M., … & Semendeferi, K. (2012). Neuronal populations in the basolateral nuclei of the amygdala are differentially increased in humans compared with apes: a stereological study. Journal of Comparative Neurology, 520(13), 3035-3054.

The other reference is also interesting: Barger, N., Stefanacci, L., & Semendeferi, K. (2007). A comparative volumetric analysis of the amygdaloid complex and basolateral division in the human and ape brain. American journal of physical anthropology, 134(3), 392-403.


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