Skip to content

Watch ALL the neurons in a brain: Ahrens and Freeman continue their reign of terror

July 28, 2014

Okay, not quite all of them. But it looks like Misha Ahrens and Jeremy Freeman are going to continue their reign of terror, imaging the whole zebrafish brain as if it’s no big deal. Yeah they’ve got almost every neuron of a vertebrate, so what?

Besides figuring out that not shooting light at the eyes might be a good idea (I think it may have been a little more complicated than that…), they released software for analysis of these kind of big data sets. Beyond Ahrens and Freeman, I know of at least two other labs using the same type of microscope to image all of the fly and can count five labs doing the same in worms. And that’s probably both a huge undercount, as well as the tip of the iceberg that will be a coming tidal wave of massively-large neural data sets. This is something that is so important, DARPA is throwing huge amounts of money at it (or at least wants to).

Their software, called thunder, is freely available and open-sourced, and available at a really slick website. It has a really great tutorial to analyze data and make sweet figures. This kind of openness is really Science Done Right.

Seriously, look at these bad boys:

running mice make neurons go fast

Mice running make mice neurons go fast

neurons in phase space

 

Neural activity floats around in their own not-so-metaphorical dimensions.the whole brain of the zebrafish is tuned for direction

 

Neurons are tuned for motion, with different colors representing different motions.ze brafish

[via]

References

Freeman, J., Vladimirov, N., Kawashima, T., Mu, Y., Sofroniew, N., Bennett, D., Rosen, J., Yang, C., Looger, L., & Ahrens, M. (2014). Mapping brain activity at scale with cluster computing Nature Methods DOI: 10.1038/nmeth.3041

Vladimirov, N., Mu, Y., Kawashima, T., Bennett, D., Yang, C., Looger, L., Keller, P., Freeman, J., & Ahrens, M. (2014). Light-sheet functional imaging in fictively behaving zebrafish Nature Methods DOI: 10.1038/nmeth.3040

PBS series on neuroscience: Janelia, schizophrenia, creativity

July 28, 2014

PBS had three segments on neuroscience during the last week. They were:

What studying fruit flies and zebrafish can reveal about the human brain

Pinpointing genetic links to schizophrenia may open doors to better treatment

Connecting strength and vulnerability of the creative brain

The first one (on Janelia!) was especially nice, and it’s always nice to see a recognition of invertebrate neurobiology.

The series also had an article on creativity published in The Atlantic:

As I began interviewing my subjects, I soon realized that I would not be confirming my schizophrenia hypothesis. If I had paid more attention to Sylvia Plath and Robert Lowell, who both suffered from what we today call mood disorder, and less to James Joyce and Bertrand Russell, I might have foreseen this. One after another, my writer subjects came to my office and spent three or four hours pouring out the stories of their struggles with mood disorder—mostly depression, but occasionally bipolar disorder. A full 80 percent of them had had some kind of mood disturbance at some time in their lives, compared with just 30 percent of the control group—only slightly less than an age-matched group in the general population. (At first I had been surprised that nearly all the writers I approached would so eagerly agree to participate in a study with a young and unknown assistant professor—but I quickly came to understand why they were so interested in talking to a psychiatrist.)

So far, this study—which has examined 13 creative geniuses and 13 controls—has borne out a link between mental illness and creativity similar to the one I found in my Writers’ Workshop study. The creative subjects and their relatives have a higher rate of mental illness than the controls and their relatives do (though not as high a rate as I found in the first study), with the frequency being fairly even across the artists and the scientists. The most-common diagnoses include bipolar disorder, depression, anxiety or panic disorder, and alcoholism.

As in the first study, I’ve also found that creativity tends to run in families, and to take diverse forms. In this arena, nurture clearly plays a strong role. Half the subjects come from very high-achieving backgrounds, with at least one parent who has a doctoral degree. The majority grew up in an environment where learning and education were highly valued.

They also found that the ‘more creative’ are more likely to take risks, have higher activation in ‘association cortices’, and be more persistent in the face of rejection. There are some issues with the article but it’s still a nice read.

Unrelated to all that, 7/26 edition

July 26, 2014

The limits of animal life on Tatooine

This anecdote about filming a sci fi movie in the pre-CGI era becomes a lot more important if you’re trying to take Star Wars semi-literally, as an accounting of alien worlds and the animals and sentient beings that live there. From this perspective, there are at least 15 animal species native to desert-covered Tatooine plus another five whose origins are either otherworldy or unclear. (The two most-famous beasties — the Rancor and the Saarlac — aren’t actually natives.) Most of these animals are megafauna, big enough that a human could ride them. And you can probably guess what I’m going to say: This is scientifically unrealistic. But not necessarily because of the heat. Get too hung up on whether big animals can survive under hot and dry conditions, and you’ll miss the major reason scientists raise an eyebrow at Tatooine’s fauna.

Read more…

No, we cannot control matter with our minds: another science myth debunked #lucymovie

July 25, 2014

“I’ve accessed 28% of my cranial capacity. I can feel every living thing.”

So says Scarlett Johansson in the new movie Lucy. Unfortunately, this movie is perpetuating a common scientific myth, one that every neuroscientist reading this blog should feel offended by: that people have the power of psychokinesis.

Now, it is understandable if you are one of the many people that believes in the power to control space-time with your mind. After all, up to 47 million Americans believe that psychic powers exist and 1 in 10 people believes in psychokinesis – just go to random physics forums to see how prevalent this belief is. And you could be one of them! Yet despite the many convincing videos available on youtube, the wikihow tutorials, and this scientific-seeming experiment on weather controlpsychokinesis is a myth.

Remember, these powers have never been demonstrated scientifically! James Randi has offered a million dollars to anyone who can prove their powers to his satisfaction, but none has done so yet.

Fact #1: No part of the brain has been shown to light up and cause things to move (besides other parts of your body)

Fact #2: Telekinesis is probably inconsistent with the laws of physics

Fact #3: There is only one way to control space-time, and you probably don’t have access to it

Fact #4: I can’t believe we have to have this discussion, but there you go

So kids: when you go see Lucy – wish you should do because it looks totally sweet – remember that it is full of neurobunk: there is no such thing as telekinetic powers. It’s completely silly and the idea that someone would make a movie around it is comical.

Oh there’s also something about using a lot of the brain but I go by wikipedia article length and it seems like people care way more about psychic powers than they do about the amount of of their brain that they use.

Why the new paper by Christakis and Fowler on friendship makes me queasy

July 24, 2014

I am a neuroscientist, and as a neuroscientist I have a strange belief that most of who we are comes from our brains. My entire career is based around understanding the neural basis of behavior which, I think, is pretty justifiable.

So when I see paper looking at the genetics of behavior, I expect to see at least one or two genes that are directly involved in neural function. A dopamine receptor, probably, or maybe some calcium channels that are acting up. And in one recent paper looking at schizophrenia, that’s exactly what we find! A D2-like dopamine receptor and some glutamate genes. My world is consistent.

But then we get a paper about friendship from Christakis and Fowler who find that friends are more likely to be genetically related to you than chance. So that means that your close friend? Basically a fourth cousin. What Christakis and Fowler have found is a few sets of genes that seem like they might influence friendship. The most important is an olfactory gene which just reeks of pheromones (or possibly hygiene). But the next most important genes? They have to do with linoleic metabolism and immune processes!

Now what am I, as a neuroscientist, supposed to do with that? How do I reconcile my neural view of the world with one where metabolic processes are influencing decisions?

Perhaps I can quiet my mind a little. In a past blog post, I wrote about how social status causes changes in genes related to immune processes. So maybe I can squint and say that okay, really this is an epiphenomenon relating to social status.

But if I’m going to understand behavior – what do I have to know? Do I have to understand literally all of biology? That traits and choices are being affected by what seem to be totally non-brain factors? That my philosophical position of the extended mind is maybe true? That makes me a little queasy.

(End massively speculative rant.)

References

Christakis NA, & Fowler JH (2014). Friendship and natural selection. Proceedings of the National Academy of Sciences of the United States of America, 111 (Supplement 3), 10796-10801 PMID: 25024208

Ripke, S., Neale, B., Corvin, A., Walters, J., Farh, K., Holmans, P., Lee, P., Bulik-Sullivan, B., Collier, D., Huang, H., Pers, T., Agartz, I., Agerbo, E., Albus, M., Alexander, M., Amin, F., Bacanu, S., Begemann, M., Belliveau Jr, R., Bene, J., Bergen, S., Bevilacqua, E., Bigdeli, T., Black, D., Bruggeman, R., Buccola, N., Buckner, R., Byerley, W., Cahn, W., Cai, G., Campion, D., Cantor, R., Carr, V., Carrera, N., Catts, S., Chambert, K., Chan, R., Chen, R., Chen, E., Cheng, W., Cheung, E., Ann Chong, S., Robert Cloninger, C., Cohen, D., Cohen, N., Cormican, P., Craddock, N., Crowley, J., Curtis, D., Davidson, M., Davis, K., Degenhardt, F., Del Favero, J., Demontis, D., Dikeos, D., Dinan, T., Djurovic, S., Donohoe, G., Drapeau, E., Duan, J., Dudbridge, F., Durmishi, N., Eichhammer, P., Eriksson, J., Escott-Price, V., Essioux, L., Fanous, A., Farrell, M., Frank, J., Franke, L., Freedman, R., Freimer, N., Friedl, M., Friedman, J., Fromer, M., Genovese, G., Georgieva, L., Giegling, I., Giusti-Rodríguez, P., Godard, S., Goldstein, J., Golimbet, V., Gopal, S., Gratten, J., de Haan, L., Hammer, C., Hamshere, M., Hansen, M., Hansen, T., Haroutunian, V., Hartmann, A., Henskens, F., Herms, S., Hirschhorn, J., Hoffmann, P., Hofman, A., Hollegaard, M., Hougaard, D., Ikeda, M., Joa, I., Julià, A., Kahn, R., Kalaydjieva, L., Karachanak-Yankova, S., Karjalainen, J., Kavanagh, D., Keller, M., Kennedy, J., Khrunin, A., Kim, Y., Klovins, J., Knowles, J., Konte, B., Kucinskas, V., Ausrele Kucinskiene, Z., Kuzelova-Ptackova, H., Kähler, A., Laurent, C., Lee Chee Keong, J., Hong Lee, S., Legge, S., Lerer, B., Li, M., Li, T., Liang, K., Lieberman, J., Limborska, S., Loughland, C., Lubinski, J., Lönnqvist, J., Macek Jr, M., Magnusson, P., Maher, B., Maier, W., Mallet, J., Marsal, S., Mattheisen, M., Mattingsdal, M., McCarley, R., McDonald, C., McIntosh, A., Meier, S., Meijer, C., Melegh, B., Melle, I., Mesholam-Gately, R., Metspalu, A., Michie, P., Milani, L., Milanova, V., Mokrab, Y., Morris, D., Mors, O., Murphy, K., Murray, R., Myin-Germeys, I., Müller-Myhsok, B., Nelis, M., Nenadic, I., Nertney, D., Nestadt, G., Nicodemus, K., Nikitina-Zake, L., Nisenbaum, L., Nordin, A., O’Callaghan, E., O’Dushlaine, C., O’Neill, F., Oh, S., Olincy, A., Olsen, L., Van Os, J., Endophenotypes International Consortium, P., Pantelis, C., Papadimitriou, G., Papiol, S., Parkhomenko, E., Pato, M., Paunio, T., Pejovic-Milovancevic, M., Perkins, D., Pietiläinen, O., Pimm, J., Pocklington, A., Powell, J., Price, A., Pulver, A., Purcell, S., Quested, D., Rasmussen, H., Reichenberg, A., Reimers, M., Richards, A., Roffman, J., Roussos, P., Ruderfer, D., Salomaa, V., Sanders, A., Schall, U., Schubert, C., Schulze, T., Schwab, S., Scolnick, E., Scott, R., Seidman, L., Shi, J., Sigurdsson, E., Silagadze, T., Silverman, J., Sim, K., Slominsky, P., Smoller, J., So, H., Spencer, C., Stahl, E., Stefansson, H., Steinberg, S., Stogmann, E., Straub, R., Strengman, E., Strohmaier, J., Scott Stroup, T., Subramaniam, M., Suvisaari, J., Svrakic, D., Szatkiewicz, J., Söderman, E., Thirumalai, S., Toncheva, D., Tosato, S., Veijola, J., Waddington, J., Walsh, D., Wang, D., Wang, Q., Webb, B., Weiser, M., Wildenauer, D., Williams, N., Williams, S., Witt, S., Wolen, A., Wong, E., Wormley, B., Simon Xi, H., Zai, C., Zheng, X., Zimprich, F., Wray, N., Stefansson, K., Visscher, P., Trust Case-Control Consortium, W., Adolfsson, R., Andreassen, O., Blackwood, D., Bramon, E., Buxbaum, J., Børglum, A., Cichon, S., Darvasi, A., Domenici, E., Ehrenreich, H., Esko, T., Gejman, P., Gill, M., Gurling, H., Hultman, C., Iwata, N., Jablensky, A., Jönsson, E., Kendler, K., Kirov, G., Knight, J., Lencz, T., Levinson, D., Li, Q., Liu, J., Malhotra, A., McCarroll, S., McQuillin, A., Moran, J., Mortensen, P., Mowry, B., Nöthen, M., Ophoff, R., Owen, M., Palotie, A., Pato, C., Petryshen, T., Posthuma, D., Rietschel, M., Riley, B., Rujescu, D., Sham, P., Sklar, P., St Clair, D., Weinberger, D., Wendland, J., Werge, T., Daly, M., Sullivan, P., & O’Donovan, M. (2014). Biological insights from 108 schizophrenia-associated genetic loci Nature, 511 (7510), 421-427 DOI: 10.1038/nature13595

Can we predict evolution?

July 24, 2014

Is evolution random, or predictable?

But Gould had a deeper question in mind as he wrote his book. If you knew everything about life on Earth half a billion years ago, could you predict that humans would eventually evolve?

Gould thought not. He even doubted that scientists could safely predict that any vertebrates would still be on the planet today. How could they, he argued, when life is constantly buffeted by random evolutionary gusts? Natural selection depends on unpredictable mutations, and once a species emerges, its fate can be influenced by all sorts of forces, from viral outbreaks to continental drift, volcanic eruptions and asteroid impacts. Our continued existence, Gould wrote, is the result of a thousand happy accidents.

If Gould were right, the pattern of evolution on each island would look nothing like the pattern on the other islands. If evolution were more predictable, however, the lizards would tend to repeat the same patterns…For the most part, though, lizard evolution followed predictable patterns. Each time lizards colonized an island, they evolved into many of the same forms. On each island, some lizards adapted to living high in trees, evolving pads on their feet for gripping surfaces, along with long legs and a stocky body. Other lizards adapted to life among the thin branches lower down on the trees, evolving short legs that help them hug their narrow perches. Still other lizards adapted to living in grass and shrubs, evolving long tails and slender trunks. On island after island, the same kinds of lizards have evolved.

The article also discusses Lenski’s work with the evolution of E. coli. He has a fantastic blog that you should be reading if you care about evolution at all.

A big theme in behavior right now is prediction – how well can we guess what an animal will do based on what it’s done in the past, and what it’s experienced? It turns out on an individual level, you can do a lot better than you’d think.

As a butterfly flaps its wings in Tokyo, a neuron in your head veers slightly heavenward…

July 23, 2014

When you look at the edge of a table, there is a neuron in your head that goes from silence to pop pop pop. As you extend your arm, a nerve commanding the muscle does the same thing. Your retina has neurons whose firing rate goes up or down depending on whether it detects a light spot or a dark spot. The traditional view of the nervous system descends from experiments that have supported this view of neural activity. And perhaps it is true at the outer edges of the nervous system, near the sensory inputs and the motor outputs. But things get murkier once you get inside.

Historically, people began thinking about the brain in terms of how single neurons represent the physical world. The framework they settled on had neurons responding to a specific set of things out in the world, with the activity of those neurons increasing when they saw those specific things and decreasing when they saw their opposite. As time flowed by, this neural picture became jumbled up with questions about whether overall activity level or specific timing of an individual spike was what was important.

When it comes to multiple neurons, a similar view has generally prevailed: activity levels go up or down. Perhaps each neuron has some (noisy) preference for something in the world; now just think of the population as the conjunction of each of their activity. Then the combination of all of the neurons is less noisy than any individual. But still: it’s all about activity going up or down. Our current generation of tools for manipulating neural activity unconsciously echoes this idea of how the nervous system functions. Optogenetics cranks the activity of cells – though often specific subpopulations of cells – to move their activity up or down in aggregate.

An alternate view which I has been pushed primarily by Krishna Shenoy and Mark Churchland takes a dynamic perspective of neural activity, and I think comes from taking a premotor view of the nervous system. Generally, nervous  activity is designed to control our physical behavior: moving, shouting, breathing, looking, remaining silent. But that is a lot to have to control, and selection of the correct set of behaviors has to take a huge numbers of factors into account and has a lot to prepare for. What have I seen? How much do I like that? What am I afraid of? How hungry am I? This means that premotor cortical activity is probably representing many things simultaneously in order to choose among them.

The problem can be approached by looking at the population of activity and asking how many different things it could represent, without necessarily knowing what those are. Perhaps the population is considering six different things at the same time (a noted mark of genius)! Now that’s a slightly different perspective: it’s not about the up or down of overall activity, but how that activity flows through possibilities on the level of the whole population.

These streams of possible action must converge into a river somewhere. There are many possible options for how this could happen. They could be lying in wait, just below threshold, building up until they overcome the dam holding their behavior at bay. They could also be gated off, allowed to turn on when some other part of the system decides to allow movement.

But when we stop and consider the dynamics required in movement, in behavior, another possibility emerges. Perhaps there is just a dynamical system churning away, evolving to produce some reaching or jumping. Then these streams of preparatory activity could be pushing the state of the dynamical system in one direction or another to guide its later evolution. Its movement, its decision.

Churchland and Shenoy have worked on providing evidence for this happening in motor cortex as well as prefrontal cortex: neurons there may be tuned to move their activity in some large space, where only the joint activity of all the neurons is meaningful. In this context, we cannot think usefully about the individual neuron but instead must consider the whole population simultaneously. It is not the cog that matters, but the machine.

References

Kaufman MT, Churchland MM, Ryu SI, & Shenoy KV (2014). Cortical activity in the null space: permitting preparation without movement. Nature neuroscience, 17 (3), 440-8 PMID: 24487233

Mante V, Sussillo D, Shenoy KV, & Newsome WT (2013). Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature, 503 (7474), 78-84 PMID: 24201281

Churchland, M., Cunningham, J., Kaufman, M., Foster, J., Nuyujukian, P., Ryu, S., & Shenoy, K. (2012). Neural population dynamics during reaching Nature DOI: 10.1038/nature11129

Shenoy KV, Sahani M, & Churchland MM (2013). Cortical control of arm movements: a dynamical systems perspective. Annual review of neuroscience, 36, 337-59 PMID: 23725001

Ames KC, Ryu SI, & Shenoy KV (2014). Neural dynamics of reaching following incorrect or absent motor preparation. Neuron, 81 (2), 438-51 PMID: 24462104

Churchland, M., Cunningham, J., Kaufman, M., Ryu, S., & Shenoy, K. (2010). Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine? Neuron, 68 (3), 387-400 DOI: 10.1016/j.neuron.2010.09.015

Follow

Get every new post delivered to your Inbox.

Join 990 other followers

%d bloggers like this: