The Cognitive-Emotional Brain

It’s out! Finally, The Cognitive-Emotional Brain: From Interactions to Integration, from MIT Press, is out. I remember a few years ago when talking to Olaf Sporns about writing a book and him encouraging me… (he was just finishing his first one on networks for MIT Press as well). It’s a long journey, but I found it really rewarding and in a way refreshingly unlike the cycle of paper-reject/grant-reject that many of us are used to… Who knows, maybe it’s time to start a new one! I hope that some of you will enjoy it…

Amygdala evolution and cortical-subcortical integration

I finally had a chance to take a more careful look at this paper by

Chareyron, L. J., Banta Lavenex, P., Amaral, D. G., & Lavenex, P. (2011). Stereological analysis of the rat and monkey amygdala. Journal of Comparative Neurology, 519(16), 3218-3239.
I think the figure here summarizes a major point of the paper. Although the scale bar is not the same for the 3 species, it is evident that the lateral amygdala (red) is disproportionately represented in the human case. To the contrary, the central nucleus is less represented. The basolateral amygdala has extensive connectivity with cortex, whereas the central nucleus is more “autonomic”. One can speculate that the increase in relative size of the basolateral amygdala paralleled increases in cortical representations. In fact, this could be an example of the proposal by Harvey and Barton that brain structures with major anatomical and functional links evolve together (independently of evolutionary changes in other unrelated structures).
I completely agree with the paper’s suggestion that their results are consistent with the “hypothesis of a higher convergence and integration of information in the primate amygdala.”
On the other hand, I don’t agree with their conclusion that “although the fundamental function of the amygdala, to regulate fear and emotional learning, is conserved across species, amygdala function might be under greater influence of cortical activity in primates, and therefore integrate additional contextual information that influences the regulation of more complex behaviors such as social interactions.” I believe the statement is still too attached to the traditional view of the amygdala as a simple “alarm system”. Such view neglects the amygdala’s sophisticated involvement in a host of perceptual and cognitive functions (see this paper) and underestimates the potential for altered connectivity to change the functional repertoire of the amygdala.
Left: Rat (top), Macaque (middle), and Human (bottom) amygdala. Right: schematic illustration of cortical-subcortical connectivity.

Left: Rat (top), Macaque (middle), and Human (bottom) amygdala. Right: schematic illustration of cortical-subcortical connectivity with the amygdala. From Chareyron et al. (2011).

 

Amygdala modulation of visual cortex

Further evidence that the amygdala modulates visual cortex. Unfortunately, it is not unit recording, it is actually an optical imaging study. The study was performed in the cat under anesthesia, not ideal either.

Y. Chen, H. Li, Z. Jin, T. Shou, H. Yu (2013). Feedback of the amygdala globally modulates visual response of primary visual cortex in the cat. Neuroimage, in press.

From Brain Networks to Cognitive Function

Olaf Sporns was asked to organize a symposium on networks for the annual meeting of the Association for Psychological Science 2013 and invited four of us to present our work: Paul Laurienti, Barry Horwitz, Randy McInstosh, and I. Lots of parallel sessions, so the room was somewhat small, but I believe it went well.

I described the work that I’ve done with Michael Anderson and more recently with Lucina Uddin (together with Josh Kinnison from my lab). Here are the slides:APS 2013

One part that I like is the possibility of characterizing the multidimensional functional fingerprint of a brain network — not a region.

network fingerprint

Fingerprint of the “attention network”.

Down with “centers” (= brain areas)

Great Opinion paper by Marlene Berhmann and David Plaut on “face” and “word” processing. The upshot is that we should understand these as distributes circuits, not in terms of circumscribed centers as they state in the title.

Behrmann, M., & Plaut, D. C. (2013). Distributed circuits, not circumscribed centers, mediate visual recognition. Trends in cognitive sciences, 17(5), 210-219.

Carl G. Lange: First proponent of the “low” vs.”high” road?

I finally got to reading Lange’s The Emotions: A Psychophysiological Study from 1885 (English 1922 translation of the German version) and found the most amazing thing: a dual-pathway, “low” and “high” road model of emotional processing:

From Lange (1885)

From Lange (1885). The “low road” goes from the eye to CO’ to CV — all subcortically. The “high road” goes up via cortex before coming down to CV.

The pathway from the eye to CO’ leads to CV rather directly. CV is a motor stage somewhere unspecified but in the brainstem. This applies to “simple” cases. But when a “mental process” is involved, cortical stages of vision and taste must be engaged, namely CO” and CG”. Now, the “impulses” get to CV from cortex!

I didn’t know about this passage, which I found rather stunning!

Here’s what Lange says himself: “… those emotions which are due to a simple sense impression, a loud noise, a beautiful color combination, etc., the path to the vasomotor center must be quite direct, and the cerebral mechanism but slightly complicated… The matter becomes somewhat more complicated when those affections are involved which are produced not by a simple impression upon some sense-organ, but by some ‘mental process,’ some memory or association of ideas, even if the latter be due to
sense-impression.”

Do you know of earlier formulations of this type of dual-route model? If so, let me know!

Understanding the function of brain regions and brain networks

Michael Anderson, Josh Kinnison and I have just published a paper in Neuroimage describing a framework for capturing a brain region’s functional fingerprint, in addition to the fingerprint an entire network of brain regions. These are computed based on studies published in the BrainMap database and provide an estimate of the functional repertoire of a given brain region/network. For example, here’s the fingerprint of the dorsal ACC:

Functional fingerprint of the dorsal anterior cingulate cortex. The red and blue lines represent the uncertainty range of our estimate).

Functional fingerprint of the dorsal anterior cingulate cortex. The red and blue lines represent the uncertainty range of our estimate.

The framework allows the quantification of functional diversity (captured via Shannon’s entropy) in a voxelwise manner (by moving a 10-mm radius “spotlight” around). We found that most brain regions had diverse functional profiles, though diversity varied considerably across the brain (below, the zones in red are the most diverse):

Voxelwise functional diversity.

Voxelwise functional diversity.

The functional profiles of brain networks can also be characterized, such as the one of the “ventral attention network”:

Functional fingerprint of the ventral attention network.

Functional fingerprint of the ventral attention network.

Our study thus allowed us to characterize the contributions of individual brain regions and networks of brain regions without using singular task- or role-bound functional attributions.

Here’s the reference in case you have problems with the PDF link:

Anderson, M. L., Kinnison, J., & Pessoa, L. (2013). Describing functional diversity of brain regions and brain networks. NeuroImage, 73, 50–58.