#PLOS #SfN15 preview: taking an early start with the satellite meetings

As Chicago is readying itself for the arrival of some thirty thousand neuroscientists between October 17 and 21 for Neuroscience 2015 (#SfN15), the largest meeting in the field, a few hardy souls, including myself, are planning on taking an early start: dozens of satellite meetings are happening all over town on the couple of days immediately before the main event. These more specialized and more intimate meetings are a great way to get the scientific conversation started with experts about your favorite topic, be it acetylcholine receptors, rat whiskers, or comparative cognition.

Two years ago, in San Diego, I got a little bit carried away and attended two overlapping satellite events. That almost used up all my energy, leaving me exhausted just as the meeting itself was starting. This year, I’ll be more reasonable: I am planning to attend the Advances and Perspectives in Auditory Neuroscience (APAN) symposium. This one-day event will take place on Friday, October 16, at the Renaissance Blackstone Hotel in Chicago. Although I’ll be attending for the first time, this meeting is already in its 13th installment. With just over a hundred posters and a handful of talks, it’s going to be a great way for me to open the neuroscience floodgates. Further, I’ll get to present my poster and even tease it with a 3-minute, 2-slide presentation!

The keynote lecture will be given by Cynthia Moss, PhD. Dr. Moss directs the Auditory Neuroethology Lab at Johns Hopkins University in Baltimore, MD. From the lab’s awesome nickname, “Batlab”, you might guess which model organism they use: echolocating bats. As you may know, bats have evolved an extraordinary way of seeing (and flying!) in pitch-black darkness: the bat emits an auditory signal that hits objects in the environment and bounces back to the animal, whose highly refined auditory system processes these echoes into a spatial representation of the environment. Dr. Moss’s lab has developed unique methods to record the activity of multiple neurons deep in the brain of free-flying bats. Using these recordings, they have found place cells in the bats’ hippocampus, just like in rodents. (By the way, those will be the subject of a Presidential Special Lecture by Dr. May-Britt Moser, recent laureate of the Nobel Prize in Medicine or Physiology, during the main meeting. And since I’m digressing already, let me mention here that bats are not the only animals that use echolocation; in fact, humans do it too, and blind persons can even bike thanks to it!) Dr. Moss’s talk at APAN will focus on 3-D Auditory Scene Analysis by Echolocation in Bats. I’ll be live-tweeting the lecture and the entire event, so stay tuned!

What are your experiences of satellite meetings before SFN? Sound off in the Comments section!

sfm medFollow @PLOSNeuro on Twitter and track #PLOS #SfN15 for up-to-the-minute coverage of the world’s premier neuroscience meeting by our team of volunteer neuroscientists!

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#PLOS #SfN15 preview: build your itinerary with this great new tool

sfm med

Visit PLOS at SfN15!

Planning your itinerary for #SFN15? You could go through the entire program book, browsing sessions and posters one by one, day after day… Or you could turn to the very innovative and extremely user-friendly tool designed by members of Konrad Kording’s lab at Northwestern University. In a nutshell, you begin by typing anything you’re interested in–be it an author’s name or a keyword–into the research field. The tool then parses the conference’s abstract database, à la Google, showing you a list of abstracts that match your query. Now comes the good part: you can “like” or “dislike” these abstract and thus refine the search ever more finely. No excuses anymore for missing that fantastic Wednesday afternoon poster in row ZZZ!

Below is an introduction to the itinerary planning tool by Prof. Kording himself.

Screen Shot 2015-09-28 at 5.08.26 PM

My lab has a long standing interest in the science of science, we like to ask what makes scientists successful, what the signatures of good reviewers are, and how scientists should search. So it was quite natural that, after years of frustration with the process of making SFN itineraries we decided to get involved ourselves. Which can be found at:


Our criteria were:

  1. Content not person based – find great research by unknown scientists
  2. Use relevant/ irrelevant input – I can not describe what I care about, but I know it if I see it.
  3. A super lightweight design – Simplicity is king
  4. Scalability – SFN is BIG.

So a new student, Titipat Achakulvisut (@titipat_a) and an awesome postdoc Daniel Acuna (@daniel_akuna) designed a system that fulfills the criteria. We are taking all the feedback we can to make it better.

Konrad Portrait 2015

Konrad Kording is a professor at Northwestern University where he focuses on data science for neuroscience. He likes Bayesian statistics, but loves things that work. He likes stories in neuroscience but loves those that explain a lot of data.


SfN 15 attendees: be sure to check out Pierre’s new post on some interesting  “Satellite Sessions” taking place before the official start to the meeting on Oct 17th.

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That All-Nighter is not without Neuroconsequences



By Emilie Reas, PLOS Neuroscience Community Editor

As you put the finishing touches on your paper, you notice the sun rising and fantasize about crawling in bed. Your vision and hearing are beginning to distort and the words staring back at you from the monitor have lost their meaning. Your brain … well, feels like mush. We’ve all been there. That debilitating brain fog that inevitably sets in after an all-nighter prompts the obvious question: what does sleep deprivation actually do to the brain?

Neuroscientists from Norway set out to answer this question in their recent PLOS ONE study, examining how a night forgoing sleep affects brain microstructure. Among their findings, sleep deprivation induced widespread structural alterations throughout the brain. The lead author shares his thoughts on the possible biological causes of these changes, and whether they may be long-lasting.

Inducing sleep deprivation

The researchers assessed a group of 21 healthy young men over the course of a day. The participants underwent diffusion tensor imaging (DTI; a form of MRI that measures water diffusion and can be used to evaluate white matter integrity) when they first awoke, at 7:30 am. They were free to go about their day as normal before returning for a second DTI scan at 9:30 pm. They remained in the lab for monitoring until a final scan at 6:30 am the following morning, for a total period of 23 hours of continued waking. Since we’re now learning that anything and everything can influence brain structure on surprisingly short time-scales, the researchers finely controlled as many confounding factors as possible. The participants were not allowed to exercise or consume alcohol, caffeine or nicotine during the study, or to eat right before the scans. Since DTI measures water diffusion, hydration was evaluated at all sessions and accounted for in their analysis.

Rapid microstructural changes to waking

The researchers were interested in two main questions: How does the brain change after a normal day of wakefulness and after sleep deprivation? They focused on three DTI metrics to probe how different features of neuronal tissue may change with waking. Radial diffusivity (RD) measures how water diffuses across fibers, whereas axial diffusivity (AD) measures diffusion along the length of a tract. Fractional anisotropy (FA) is the ratio of axial to radial diffusivity and therefore measures how strongly water diffuses along a single direction.

From morning to evening, FA increased and this was driven mostly by reduced RD (Figure, left). From the evening to the next morning – after the all-nighter – FA values decreased to levels comparable to the prior morning, and this drop was coupled with a decrease in AD (Figure, right). Thus, over the course of a full day of wakefulness FA fluctuated, temporarily rising but eventually rebounding. In contrast, both RD and AD declined but at different rates, RD dropping by the end of a normal day, and AD dropping later, only after considerable sleep deprivation. These changes were non-specific, occurring throughout the brain, including in the corpus callosum, brainstem, thalamus and frontotemporal and parieto-occipital tracts.

Throughout the brain, FA values increase from morning to evening (left) and decrease from the evening to the next morning after a night without sleep (right). Elvsåshagen et al., 2015.

Throughout the brain, FA values increase from morning to evening (left) and decrease from the evening to the next morning after a night without sleep (right). Elvsåshagen et al., 2015.

How bad are the neuroconsequences of sleep deprivation?

Other studies have corroborated these reports that wakefulness alters the brain, including reduced diffusion with increasing time awake, and altered functional connectivity after sleep deprivation. How this plasticity reflects the consequences of waking on the brain, however, isn’t clear. Sleep is known to be essential to tissue repair and is particularly important for promoting lipid integrity to maintain healthy cell membranes and myelination. The question remains, therefore, how detrimental the structural reorganization from sleep deprivation really is. Does the plasticity reported here and elsewhere persist for days, weeks or longer, or can a long night of deep catch-up sleep reverse any detriment that all-nighter caused?

“My hypothesis,” says first author Dr. Torbjørn Elvsåshagen, “would be that the putative effects of one night of sleep deprivation on white matter microstructure are short term and reverse after one to a few nights of normal sleep. However, it could be hypothesized that chronic sleep insufficiency might lead to longer-lasting alterations in brain structure. Consistent with this idea, evidence for an association between impaired sleep and localized cortical thinning was found in obstructive sleep apnea syndrome, idiopathic rapid eye movement sleep behavior disorder, mild cognitive impairment and community-dwelling adults. Whether chronic sleep insufficiency can lead to longer-lasting alterations in white matter structure remains to be clarified.”

Is sleepiness really to blame?

It’s likely that multiple factors contribute to these distinct patterns of change in neuronal tissue. After sleep deprivation, the extent of AD decline correlated with subjective sleepiness ratings, suggesting that microstructural alterations may in fact be attributable to changes in alertness or arousal. This possibility is in line with the finding that changes occurred in both the thalamus and brainstem, regions important for arousal and wakefulness. However, the non-linear changes in FA suggest that some microstructural changes may be less related to sleepiness and more directly driven by circadian effects. FA increased late in the day, but – despite fatigue– dropped back after sleep deprivation to the same levels as the day prior. This rebounding may have been due to declining levels of AD and RD reaching equilibrium (reminder, FA is the ratio of AD to RD) or to neuronal features that fluctuate with our circadian rhythms, at least partially independent of our sleep habits. What’s more, other studies have found that presumably mundane activities, for example juggling or spatial learning, also induce gray and white matter changes within hours, and presumably many more as-of-yet unstudied activities also cause similarly rapid plasticity. Given that participants were free to engage in various physical and cognitive activities between the scans, it’s reasonable to assume that some of these behaviors may have also influenced brain structure. Whatever the mechanism, these effects underscore the importance of accounting for time of day in structural neuroimaging studies.

Dr. Elvsåshagen elaborates on these possible factors: “The precise neurobiological substrate for the observed DTI changes after waking remain to be clarified. We cannot rule out the possibility that both activity-independent and activity-dependent processes could contribute to DTI changes after waking. In support of potential activity-dependent white matter alterations, there is interesting evidence from in vitro studies indicating that hours of electrical activity can lead to changes in myelination. To further explore the possibility of activity-dependent white matter alterations, one could examine whether different physical or cognitive tasks lead to task-specific white matter changes.”

Sleepy outliers?

Notably, two of the 21 participants did not show the same rise in FA throughout the day as the others, and showed the smallest change in FA and AD after sleep deprivation. While variability across individuals in terms of brain structure and biological responses to the environment is expected, the remarkable consistency of the study’s other findings raises the possibility that some other factors may explain these outliers. Dr. Elvsåshagen conjectures, “These individuals were also the least tired individuals after sleep deprivation. Although highly speculative, one possible explanation for the lesser changes in these two participants might be a particular resistance to the effects of waking and sleep deprivation.” A follow-up with additional time-points and closer monitoring of activities could help more finely track how the patterns of brain microstructural change shift over periods of waking, and vary across individuals.

Linking diffusion to neurons

How sleep, fatigue, activity or circadian rhythms affect particular neuronal structural properties remains to be seen. RD and AD are thought to depend on myelin and axon integrity, respectively, but DTI metrics in general are sensitive to various other tissue features as well, including cell membrane permeability, axon diameter, tissue perfusion or glial processes. While these properties are difficult to image in living humans, insight from animal studies will help determine how waking impacts specific neuronal characteristics.

Longer-term studies are needed to answer these questions. Dr. Elvsåshagen shared that his team has since replicated their results in a larger sample, and are planning a follow-up study on the effects of waking and sleep deprivation on gray matter structure. Until these outstanding questions are answered, keeping a regular sleep schedule – and avoiding those early morning paper-writing marathons – may be better option for your brain health.

Any views expressed are those of the author, and do not necessarily reflect those of PLOS.


Bellesi M, Pfister-Genskow M, Maret S, Keles S, Tononi G, Cirelli C (2013). Effects of sleep and wake on oligodendrocytes and their precursors. J Neurosci. 33: 14288–14300. doi: 10.1523/JNEUROSCI.5102-12.2013

Budde MD, Xie M, Cross AH, Song SK (2009). Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: a quantitative pixelwise analysis. J Neurosci. 29: 2805–2813. doi: 10.1523/JNEUROSCI.4605-08.2009

De Havas JA, Parimal S, Soon CS, Chee MW (2012). Sleep deprivation reduces default mode network connectivity and anti-correlation during rest and task performance. NeuroImage. 59: 1745–1751. doi: 10.1016/j.neuroimage.2011.08.026

Driemeyer J, Boyke J, Gaser C, Buchel C, May A (2008). Changes in gray matter induced by learning—revisited. PLOS ONE. 3: e2669. doi: 10.1371/journal.pone.0002669

Elvsåshagen T, Norbom LB, Pedersen PØ, Quraishi SH, Bjørnerud A, Malt UK (2015). Widespread Changes in White Matter Microstructure after a Day of Waking and Sleep Deprivation. PLOS ONE. 10(5): e0124859. doi: 10.1371/journal.pone.0127351

Hinard V, et al. (2012). Key electrophysiological, molecular, and metabolic signatures of sleep and wakefulness revealed in primary cortical cultures. J Neurosci. 32: 12506–12517. doi: 10.1523/JNEUROSCI.2306-12.2012

Hofstetter S, Tavor I, Tzur Moryosef S, Assaf Y (2013). Short-term learning induces white matter plasticity in the fornix. J Neurosci. 33: 12844–12850. doi: 10.1523/JNEUROSCI.4520-12.2013

Jiang C, , et al. (2014). Diurnal microstructural variations in healthy adult brain revealed by diffusion tensor imaging. PLOS ONE. 9: e84822. doi: 10.1371/journal.pone.0084822

Joo EY, et al. (2013). Brain Gray Matter Deficits in Patients with Chronic Primary Insomnia. Sleep. 36(7): 999-1007. doi: 10.5665/sleep.2796

Rayayel S, et al. (2015). Patterns of cortical thinning in idiopathic rapid eye movement sleep behavior disorder. Mov Disord. 30(5): 680–687. doi: 10.1002/mds.25820

Sanchez-Espinosa MP, Atienza M, Cantero JL (2014). Sleep deficits in mild cognitive impairment are related to increased levels of plasma amyloid-β and cortical thinning. NeuroImage. 98: 395-404. doi: 10.1016/j.neuroimage.2014.05.027

Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH (2002). Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage. 17: 1429–1436. doi: 10.1006/nimg.2002.1267

Sexton CE, et al. (2014). Accelerated changes in white matter microstructure during aging: a longitudinal diffusion tensor imaging study. J Neurosci. 34(46): 15425–15436. doi: 10.1523/JNEUROSCI.0203-14.2014

Wake H, Lee PR, Fields RD (2011). Control of Local Protein Synthesis and Initial Events in Myelination by Action Potentials. Science. 333(6049): 1647–1651. doi: 10.1126/science.1206998

Emilie Reas received her PhD in Neuroscience from UC San Diego, where she used fMRI to study memory. As a postdoc at UCSD, she currently studies how the brain changes with aging and disease. In addition to her tweets for @PLOSNeuro she is @etreas.

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One more step along the long road towards brain-to-brain interfaces

Imagine being able to communicate with others through only your thoughts. No words, no signs are exchanged: only pure information travelling directly from one brain to another. Of course, that is the stuff of dreams and science-fiction flicks: in the real world, the closest that scientists have come to establishing direct communication between brains involves an extremely convoluted apparatus and would take hours to transmit the amount of information you typically exchange in a 2-minute conversation. Nevertheless, research on these brain-to-brain interfaces, as they are called, is valuable because it might one day allow patients with brain damage who cannot speak to communicate using other means. In a recent PLOS ONE report, Andrea Stocco, Rajesh Rao and colleagues from the University of Washington, USA, expand on previous research to demonstrate that BBIs can actually be used to solve problems, albeit in the narrow sense of the experimental laboratory.

“Guess what I’m thinking about”

In the experiment, Rao and colleagues built upon previous research from their lab and others to design the brain-to-brain interface. Two participants played a game of “guess what I’m thinking about”, in which the inquirer (the one doing the guessing) asked “yes-or-no” questions to the respondent (the one doing the thinking about). In scientific experiments, the number of parameters must often be kept as low as possible, and this one was no exception: the responder had to think of one object among 8 in a predetermined category (for instance, “dog” among 7 other animals), and the inquirer, who knew the list of objects but ignored which one was selected by the respondent, could only ask three predetermined “yes-or-no” questions (e.g. “Does it fly?”). It is in the way the responder’s answers were communicated to the inquirer that the brain-to-brain interface kicked in.

From brain to brain via EEG and magnetic pulses

To indicate his or her answer, the respondent directed his or her gaze to either of two LED lights, one flashing at 13 Hz coding for “yes”, the other flashing at 12 Hz for “no”. The respondent’s brain responded to the flashing light at the corresponding frequency, and that cerebral activity could be picked up reliably and decoded in real-time by an EEG system. The “yes-or-no” answer was then transmitted to the inquirer’s brain using a transcranial magnetic stimulation (TMS) machine. TMS allows stimulating the cerebral cortex non-invasively by sending sharp magnetic pulses through the scalp and skull, which in turn briefly change the activity of neurons in a given patch of cerebral cortex. When applied to the visual cortex at the back of the head, TMS pulses trigger the perception of brief flashes of light called phosphenes. Here, Rao and colleagues simply controlled the intensity of the TMS pulses so that a “yes” answer would reliably induce the perception of a phosphene by the inquirer, whereas a “no” answer would not.

Stocco et al PLOS ONE 2015 Figure 1

The experimental setup is nicely summed up in this figure (source: Stocco et al., PLOS ONE 2015).

Not yet at the speed of thought

Again, one cannot overemphasize how clumsy the whole system was, and how slow: the respondents needed to fixate on the flashing LED for up to 20 seconds in order for the EEG decoder to pick up their answer; and the inquirers needed to be trained at detecting phosphenes reliably for 1 to 2 hours before even getting started in earnest. Compare this to the five seconds it would have taken each inquirer to guess which object the respondent was thinking of if they could have talked to each other! Nevertheless, this study established a couple of important points for future research. First, the information was transmitted reliably almost 95% of the time, which is not all that bad (and will certainly be improved upon in further work). Second, the brain-to-brain interface worked in real-time, a prerequisite for its use as a replacement to communication by standard means.

Perspectives for the future

Rao and colleagues mention the possibility that brain-to-brain interfaces could one day be useful to patients who cannot speak following damage to the language centers of the brain (a condition known as Broca’s aphasia). I find the idea fascinating. These patients generally retain most of their intellectual faculties, and would most likely be able to associate a “yes” answer with the color green and “no” with red, for instance. A carefully thought-out classification tree of words, images, and concepts could then be navigated using these “yes-or-no” answers to yield fairly complex ideas. The same technique could potentially be applied to communicate with patients suffering from severe sensorimotor impairments such as locked-in syndrome, a devastating brainstem injury where the individual is unable to move almost every muscle, yet remains conscious. Clearly, we are just as far from reading each other’s thoughts as we were last year. In the near future, nevertheless, brain-to-brain interfaces will benefit from the fast pace of progress in the fields of neural stimulation and the decoding of neural activity from electrophysiological recordings to become incrementally faster, more reliable and more practical. What we will be able to do then might already make a significant difference.


Andrea Stocco, Chantel S. Prat, Darby M. Losey, Jeneva A. Cronin, Joseph Wu, Justin A. Abernethy, Rajesh P. N. Rao (2015) Playing 20 Questions with the Mind: Collaborative Problem Solving by Humans Using a Brain-to-Brain Interface. PLoS ONE 10(9): e0137303. doi:10.1371/journal.pone.0137303.

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#SfN15 Call for Contributors!

sfm medThe Society for Neuroscience annual meeting, to be held in Chicago from October 17-21, is just around the corner! PLOS will be attending and we’re looking for other enthusiastic community members to help us cover the conference through online and social media. If you will be attending and are an active tweeter or blogger (or would like to become more active!) we’d love for you to join our team. All are invited to contribute – researchers, students, teachers or just those with a love for neuroscience.

During last year’s meeting, our fantastic team of neuro-enthusiasts covered the conference highlights by tweeting about their favorite sessions, blogging the hottest symposia and interviewing influential speakers. We need your help to make this year’s meeting coverage as successful as last year’s. If you’d like to contribute, by tweeting or blogging as much or as little as you’d like, contact the editors and let us know a bit about your interests and why you’d make a great #PLOS #SfN15 contributor! In exchange, you’ll receive this awesome PLOS Neuroscience Community t-shirt, not to mention get to connect with other amazing members of your Neuroscience community, both throughout the conference and at a PLOS-hosted social (details coming soon).

The awesome PLOS Neuroscience Community T-shirt The awesome PLOS Neuroscience Community T-shirt

See you in Chicago!

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The stuff of screams

Luc Arnal, a post-doctoral scientist in David Poeppel’s laboratory at New York University, was having his brain hijacked. No, this isn’t a report of futuristic brain implants and neurohacking: rather, Arnal was experiencing the joys of fatherhood, together with the unavoidable alarm at hearing his newborn baby scream. Ever the scientist, he decided to explore what made those screams such an irresistible alarm signal. The answer fits in one word: roughness. Arnal applied an innovative approach to unpack the acoustic properties of screams along the two dimensions that characterize all sounds: time (how a sound evolves through time) and frequency (the pitch of a sound, among others, depends on its frequency). He found that alarm signals exploit a portion of the acoustic space that other sounds, such as normal voices, do not use: this part of the spectrum corresponds to our perception of roughness (think of how a heavily distorted guitar sounds, for instance, as opposed to a pure piano note). He then went on to characterize how our brains respond to alarm signals, and discovered that rough sounds selectively activate the amygdala, one of the structures in the brain that process emotions. Arnal and colleagues’ findings were recently published in Current Biology. Here, he went out of his way to answer questions, serious and otherwise, about his research.

I scream, you scream, we all scream... Arnal and colleagues' findings help illuminate what makes screams such powerful alarm signals and how they trigger specific responses in our brains.

I scream, you scream, we all scream… Arnal and colleagues’ findings reveal what makes screams such powerful alarm signals and how they trigger specific responses in our brains. (source: The Scream, by Edvard Munch. Retrieved on Wikipedia)

Where did you find that idea of studying screams? Who among the authors is the horror movie buff? Did you create a “scream scale” to rate the most famous screams in Hollywood history by roughness?

I started being interested in screams because my newborn’s screams were literally hijacking my brain and I wondered what makes screams so efficient as an alarm signal.

Interestingly, screams are highly relevant biologically: screaming is innate in humans and it constitutes a primordial vocalization that is possibly shared by various animals.

So I decided to characterize the acoustics of human screams. I wanted to analyze good scream recordings. Before recording volunteer screamers on a roller-coaster or in the lab I needed some preliminary evidence supporting my theory. I started working on excerpts from horror movies that were available on Youtube. But to be honest I’m not really a horror movie buff and it’s been really brutal and depressing for me to listen and edit so many screams overnight. But in the end, a few nightmares were totally worth the findings and the potential applications. A scream scale? We haven’t thought about that, but great idea to cast more credible victims for movies!

More seriously, you show that roughness is the defining characteristic of “alarm” signals, and that adding roughness to normal words makes them more fearsome, whereas filtering out the roughness makes screams more benign. Could you imagine a way that this filter would work in real time, in order to remove roughness in cases where it is unwanted (e.g. in economy class aboard planes)?

Well, filtering out rough modulations from the acoustic spectrum is a rather tricky manipulation especially in real time; we’re not there yet but I agree that ‘roughLess’ earplugs would be pretty amazing (although you’d probably miss the alarm signals if there is a real problem during your flight).

Also, you tested a series of sounds from musical instruments that were all considered “non-alarm” sounds. But did you try a very distorted guitar, like in hard rock or heavy metal (Black Sabbath comes to mind)?

We have compared alarm sounds with sounds from musical instruments played using Garage Band, but effects like distortion were not tested. On the other hand, we found that dissonant intervals sound rough (as do distorted guitars). It may sound counterintuitive that modern music (such as jazz and metal) uses these rough sounds that presumably trigger unpleasant, fearful responses in the brain.

One possible explanation is that, in the same way that people enjoy being frightened and stimulating their amygdala when watching a horror movie, it is possible that roughness in music may induce slightly unpleasant and fearful responses in the brain of the listener, and maybe people who like hard rock music like it because it’s slightly aggressive and stimulating.

Even more seriously, your neuroimaging results suggest some specialization for the processing of screams and alarm sounds. Do we know of neurological disorders where the capacity to recognize danger through sounds is altered or abolished? Do the anatomical substrates of these disorders coincide with your fMRI findings?

Well yes, there are well-known cases of patients with bilateral lesions of the amygdala who have impaired perception of vocal affect, in particular of the expression of fear and anger. To our knowledge, however, no other work had found any acoustic specificity of the amygdalar response.

You show that alarm sounds activate the amygdala as well as the auditory cortex to a greater degree than non-danger sounds. Could you speculate on the neuronal pathways involved, especially with respect to the timing of their activation? In other words, would you think that the amygdala gets activated by alarm signals directly from subcortical structures, and then influences the amplitude of activity in the auditory cortex? Or, on the contrary, is the auditory cortex passing on information to the amygdala that then feeds the alarm detection signal back to auditory cortex?

This is a great question but we can only speculate about that since our fMRI data do not really allow investigating the timing of brain responses. Whether the recruitment of the amygdala by rough aversive sounds results from a direct routing from subcortical auditory nuclei to the amygdala or an indirect routing through the auditory cortex remains an open question.

However, we think that our finding might support the view that fast temporally modulated (rough) sounds would be directly routed from subcortical auditory nuclei to the amygdala. The fact that the amygdala is activated by roughness regardless of context (vocal, musical) is consistent with this view.

The fast recruitment of the amygdala might in turn cause sensory unpleasantness, increased attention or arousal, and speed up the reaction to the signaled danger. Importantly, this hypothesis does not rule out subsequent interactions with other cortical areas involved in the processing of more complex information (pertaining to the context or valence of the stimulus).


Arnal LH, Flinker A, Kleinschmidt A, Giraud AL, Poeppel D. Human Screams Occupy a Privileged Niche in the Communication Soundscape. Curr Biol. 2015 Aug 3;25(15):2051-6. doi: 10.1016/j.cub.2015.06.043.

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Astrocytes may Hold the Key to Exercise-Induced Cognitive Enhancement

By Emilie Reas, PLOS Neuroscience Community Editor

Forget expensive pills or exotic miracle supplements. Exercise may be the most effective – not to mention free and accessible – cognitive enhancer on the market. Research in humans has shown that physical activity can improve cognitive function and may help stave off dementia, yet the biological mechanisms underlying these benefits aren’t fully understood. Animal studies have made substantial progress on this front, demonstrating such positive responses to running as enhanced neurogenesis and elevated levels of neural growth factors. However, much of this research has been relatively narrowly focused, with particular attention devoted to neuronal changes and one notable brain region – the hippocampus. The hippocampus is selectively important for certain functions like learning and episodic memory, but exercise improves a range of cognitive processes, many of which depend on other, non-hippocampal brain regions. Therefore, researchers from Princeton University looked beyond the hippocampus and neurons to more thoroughly characterize the neural events that impart cognitive protection from physical activity. In their study recently published in PLOS ONE, Adam Brockett and colleagues report that running enhances performance on various cognitive tasks, improvements which may be mediated by changes in astrocytes, the lesser appreciated brain cells.

Running selectively boosts some cognitive functions

To manipulate levels of physical activity, rats were divided into a group of runners who were allowed free access to running wheels for 12 days, and another group of sedentary controls. Prior studies have shown that running improves performance on tasks requiring the hippocampus, like learning and memory. Here, the runners and non-runners were subjected to three tests to determine how exercise affects cognitive functions that are not dependent on the hippocampus. An object-in-place task, which tests how well rats remember the location of previously encountered objects, relies on the medial prefrontal cortex, hippocampus and perirhinal cortex. A novel object task, in which rats distinguish novel from familiar objects, selectively depends on the perirhinal cortex. Lastly, a set-shifting task, supported by the orbitofrontal and medial prefrontal cortices, measures attention and cognitive flexibility.

Compared to their non-runner companions, the runners performed better on the object-in-place test and on several measures of the set-shifting task. However, there were no differences between runners and non-runners in performance on the novel object recognition test. Of course, the cognitive benefits of running don’t end here, since many cognitive domains were not assessed in this test battery. But these findings highlight a striking selectivity of the brain-boosting powers of exercise. In particular, they suggest that running may enhance functions that specifically depend on the medial prefrontal and orbitofrontal cortices, along with the hippocampus, but it does not appear to modulate perirhinal-dependent functions.

Cognitive enhancement is linked to astrocytes

Although behavioral changes provide a window into the underlying neural events, they do not tell the complete mechanistic story. To directly examine how running affects the brain, the researchers assessed changes to both neuronal and non-neuronal brain cells. Running induced widespread neuronal changes, including higher levels of pre- and postsynaptic markers throughout the brain (including in the hippocampus and orbitofrontal, medial prefrontal and perirhinal cortices), and increased density and length of dendritic spines in the medial prefrontal cortex. While these effects suggest that exercise elicits generalized synaptic changes, they do not explain why particular cognitive functions are selectively boosted over others.

The researchers therefore looked for this crucial link to behavior in astrocytes. As Brockett explains, “We hypothesized that all cells likely change as a function of experience. We chose to focus on astrocytes because there is lots of evidence suggesting that astrocytes could be implicated in cognitive behavior. Loss of astrocytes correlate with impairment on a cognitive task and astrocytes connect the majority of neurons to blood vessels. They extend numerous processes that envelop nearby synapses, and gliotransmitters have been implicated directly in LTP-induction.”

Confirming their suspicions, in runners, astrocytes increased in size (Figure, A-B) and showed more contacts with blood vessels (Figure, C-D). But these changes only occurred in the hippocampus, medial prefrontal cortex and orbitofrontal cortex – critically, all regions that support the tasks showing running-related improvement. In contrast, running did not alter astrocytes in the perirhinal cortex, a region necessary for novel object recognition, which did not benefit from running. Thus, while running modified both neurons and astrocytes, the pattern of selective cognitive enhancements corresponded only with changes to astrocytes.

In the hippocampus, medial prefrontal cortex and orbitofrontal cortex, astrocytes were larger and made more contacts with blood vessels for rats who ran than those who did not. Brockett et al., 2015

In the hippocampus, medial prefrontal cortex and orbitofrontal cortex, astrocytes were larger and made more contacts with blood vessels for rats who ran than those who did not. Brockett et al., 2015

Implications for the active human

Although the varied and widespread cognitive benefits of exercise have long been appreciated, this study provides some of the first insight into the remarkable selectivity of these enhancements. Follow-up studies will help elucidate why, from both biological and evolutionary perspectives, running would demonstrate such selectivity. Might, for example, attention or task-switching abilities have been more important than object recognition for the efficiency of both animals and our persistence-hunting, distance-runner ancestors? Does running more heavily recruit certain brain regions over others, making them more susceptible to remodeling?

Given the cognitive and neurobiological differences between rats and humans, future research will be important to help extrapolate beyond rodents. Currently it’s unclear how different forms of exercise enjoyed by humans – for instance, swimming, yoga or strength training – uniquely influence distinct cognitive functions. According to Brockett,

“There is a lot of evidence that running has numerous beneficial effects on rodent and human cognitive functioning, but it is likely that aerobic exercise in general is responsible for these effects rather than running per se.”

Perhaps most notably, these findings add to the growing pool of studies underscoring the importance of astrocytes in neural processes that support cognition, and reveal a novel role for these cells in experience-dependent plasticity. As Brockett explains:

“Astrocytes are a unique cell type that haven’t been explored as much as neurons by the field of Neuroscience at large. Few studies have directly examined the role of astrocytes in complex behavior, and this was our first attempt at investigating this question.”

Any views expressed are those of the author, and do not necessarily reflect those of PLOS.


Alaei H, et al (2007). Daily running promotes spatial learning and memory in rats. Pathophysiology. 14:105–8. doi:10.1016/j.pathophys.2007.07.001

Brockett AT, LaMarca EA, Gould E (2015) Physical Exercise Enhances Cognitive Flexibility as Well as Astrocytic and Synaptic Markers in the Medial Prefrontal Cortex. PLoS ONE 10(5): e0124859. doi:10.1371/journal.pone.0124859

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Emilie Reas received her PhD in Neuroscience from UC San Diego, where she used fMRI to study memory. As a postdoc at UCSD, she currently studies how the brain changes with aging and disease. In addition to her tweets for @PLOSNeuro she is @etreas.

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The brain’s ebb and flow cares not for distance

Over the past decade, functional neuroimaging has revealed that our brains go through ever-changing patterns of activity, whether we are active or at rest, healthy or sick, under legal medication or high on illegal drugs. Yet this dynamic activity takes place over the comparatively fixed anatomical grid of neuronal connections; the functional weights of those connections therefore must be changing over time. Two competing hypotheses have been put forth regarding the strength and malleability of neuronal connections: on the one hand, local neuronal connections could be more stable than long-distance ones, because neighboring regions of the cerebral cortex tend to take part in the same functions. On the other one, the flexibility of connections might not depend on their length, thus promoting equilibrium between local specialization and widespread integration. Bratislav Misic, Marc G. Berman and their colleagues, from the Rotman Research Institute in Toronto and the University of Chicago, among others, set out to find evidence for either of these hypotheses by analyzing an extremely diverse bunch of data, lumping together different functional neuroimaging modalities (what the participants were tested with), clinical populations (who the participants were) and task parameters (what they were asked to do). Notwithstanding this immense heterogeneity, they were able to show that the spatial distance between regions does not impact the stability of their functional connections. Their results, published in PLOS ONE, support the notion that the brain’s functional connectivity transits seamlessly between local specialized processing and global integration.

A cocktail of studies

The authors re-analyzed the data from six studies: four used functional MRI, one magnetoencephalography and one positron emission tomography. The participants were either healthy volunteers or patients suffering from depression or breast cancer. Finally, the experimental conditions consisted of either just resting in the scanner on 2 separate occasions, ruminating autobiographical memories versus rest, or performing various tasks of sensory perception or learning and memory. The authors selected such a diverse group of studies precisely so that they would be able to assess connectivity changes across a wide spectrum of situations, regardless of the methodological details of each study.

In each study and for each participant, the authors first grouped measurements of brain activity into regions of interest and then correlated cerebral activity in each region of interest to that of all the others, yielding matrices of functional connectivity within each experimental condition. They then measured the distance between all the regions of interest and computed the correlation between distance and functional connectivity. Others had previously found that regions of the brain that were closer to each other tended to have higher functional connectivity, and that is also what the authors observed here. This probably reflects the fact that neighboring brain regions tend to carry out the same functions.

No correlation between anatomical distance and changes in functional connectivity

The authors then undertook to compare how distance between brain regions correlated with changes in functional connectivity across experimental conditions. They computed two indexes of connectivity changes: salience values derived from a partial least-squares analysis, as well as simply subtracting the connectivity values from different experimental conditions. Overall, they found no correlation between the distance between two brain regions and changes in their connectivity across experimental conditions.

This held true regardless of whether the connectivity increased or decreased as a result of the experimental condition, whether only those brain regions that displayed strong connectivity changes were taken into account, whether the regions were part of the same functional brain networks, or even whether they were in the same or the opposite cerebral hemisphere. These results thus argue against the notion that there is a relationship between anatomical distance and changes in functional connectivity. Importantly, it made no difference which neuroimaging technique was used to look at brain function, since the magnetoencephalography and positron emission studies yielded essentially the same observations as the ones that used functional MRI.

Intriguingly, homotopic regions (i.e. the same region on both cerebral hemispheres) had the lowest tendency to see their functional connectivity change across experimental conditions, suggesting that interhemispheric connections between homotopic regions are among the most stable.

When negative evidence yields positive results

In this study, the authors provide mostly negative evidence, which means that they looked for a systematic tendency of functional connectivity changes as a function of anatomical distance and failed to find it. Because absence of evidence is not the same as evidence of absence, does that mean that the conclusions of the article are unwarranted? Most likely not: the fact that there was no correlation across such a diverse group of participant populations, task parameters, and even neuroimaging modalities argues strongly for the hypothesis that, indeed, anatomical distance plays no role in determining the stability or flexibility of functional connections.

What is the functional consequence of this? According to the authors, the fact that short- and long-distance connections have an equal propensity to change might favor a subtle balance between local, presumably specialized processing of information by the brain and the integration of this processing with that of distant modules within distributed networks. The authors suggest that the stable interhemispheric connections between homotopic regions might serve as anchors within this dynamic connectivity landscape.


Mišić B, Fatima Z, Askren MK, Buschkuehl M, Churchill N, Cimprich B, Deldin PJ, Jaeggi S, Jung M, Korostil M, Kross E, Krpan KM, Peltier S, Reuter-Lorenz PA, Strother SC, Jonides J, McIntosh AR, Berman MG (2014) The Functional Connectivity Landscape of the Human Brain. PLoS ONE 9(10): e111007. doi: 10.1371/journal.pone.0111007

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Pinpointing the neural pieces of obesity

Obesity is a nationwide epidemic, with more than one-third of adults in the United States qualifying as obese (about 78.6 million people) (Ogden et al., 2014). While there are strategies to prevent obesity, we are in need of better treatment for those struggling with the disease. If we understood how obesity developed, perhaps then we could design interventions to help combat it.

obesity heat map

The prevalence of adult obesity in the USA, broken down by state. (source: Centers for Disease Control and Prevention, 2013).

Although obesity is characterized by body mass (that is, a Body Mass Index of 30 or greater is considered obese), how the body expends energy and gains or loses weight is under strict control by the brain (for review, see Zeltser et al., 2012). A brain region of historical and present-day interest is one of the many nuclei that make-up the hypothalamus, namely the paraventricular nucleus (PVH). Sets of PVH neurons defined by the genes they express are known for regulating appetite and energy expenditure, including locomotion, heat production (aka thermogenesis) and oxygen consumption. For example, neurons expressing the melanocortin-4 receptor affect appetite (Garfield et al., 2014), while those expressing oxytocin play a role in energy expenditure (Wu et al., 2012; Sutton et al., 2014).

Now, a new study published in Cell Metabolism, from the lab of Baoji Xu at The Scripps Research Institute in Jupiter, Florida, reports on a formerly unknown population of neurons responsible for energy balance, and whose disruption in mice ultimately leads to obesity.

PVH neurons expressing brain-derived neurotrophic factor (BDNF)

Brain-derived neurotrophic factor (BDNF), a molecule involved in everything from neural circuit development to fear processing, has also been associated with human obesity (see below; Cohen-Cory et al., 2010; Penzo et al., 2015). Hence, the experimenters chose to examine its expression within the PVH. By genetically labeling neurons expressing the Bdnf gene, they found BDNF neurons throughout the PVH and that they were excitatory (i.e. glutamatergic). However, they did not seem to express other genes that mark neurons previously implicated in appetite and energy balance (see Figure). “The population we identified is different from the neuronal populations described in previous studies,” said Xu. But are these neurons players in energy balance?

Losing BDNF leads to overeating and obesity

To tackle this question, the experiments performed two complementary genetic manipulations in mice – deleting BDNF from neurons expressing a transcription factor called SIM1, which encompasses but is not limited to PVH neurons, and by injecting a virus into the PVH to locally rid the region of the molecule. Although BDNF neurons were still likely signaling via glutamate, losing only BDNF eventually caused overeating, lower energy expenditure (less oxygen consumption, locomotion, and thermogenesis in brown fat), and obesity. Interestingly, removing BDNF from the anterior PVH (versus its posterior portion) led to much more severe overeating, suggesting that BDNF neurons in this subregion are more involved in appetite.

Since BDNF seems to be playing such a large role, by what mechanism(s) is it doing so? “It is unclear what it is doing and where it is acting,” said Brad Lowell, a leader in the field whose lab recently showed that PVH neurons expressing the melanocortin-4 receptor are key in appetite control. “For example, retrograde BDNF affects synaptic plasticity while terminal release could affect downstream neurons.”

From brain to fat

A clue to the BDNF’s mechanism may lie in the polysynaptic circuitry of these PVH neurons.  Circuit mapping and labeling experiments pointed to PVH BDNF neurons indirectly increasing brown fat thermogenesis via sympathetic (cholinergic) neurons of the spinal cord that express the receptor for BDNF – TrkB. In BDNF-lacking mice however, these sympathetic neurons were found to be substantially smaller, suggesting that BDNF may be released presynaptically to affect downstream sympathetic neurons. However, these mice had BDNF loss since birth, so its signaling from PVH neurons to the spinal cord may just be needed during development and not necessarily in adulthood.

PVH BDNF Neuro Community Figure

Another possibility is that BDNF signals in a retrograde fashion – from PVH neurons to those of another nucleus of the hypothalamus, the arcuate nucleus (ARC). The ARC has received much attention recently for housing potential “hunger neurons” (Palmiter, 2015). Polysynaptic circuit mapping indeed showed that the PVH BDNF neurons likely receive input from ARC neurons, although gene expression there did not appear to change.

BDNF connects mice to humans

The obesity resulting from loss of PVH BDNF is artificial. So is BDNF involved in human obesity? “It’s possible,” stated Xu. “Certainly it is a really interesting and important question.” The possibility stems from the fact that BDNF-TrkB mutations are linked to the disease (Han et al., 2008; Yeo et al., 2004). But whether BDNF is recruited in obesity induced by diet, arguably the most common form, remains to be shown. “We’re working on it,” added Xu.


Cohen-Cory, S et al. (2010) Brain-derived neurotrophic factor and the development of structural neuronal connectivity. Dev Neurobiol 70(5):271-88. doi: 10.1002/dneu.20774

Garfield, AS et al. (2015) A neural basis for melanocortin-4 receptor-regulated appetite. Nat Neurosci 18(6):863-71. doi: 10.1038/nn.4011

Han, JC et al. (2008) Brain-derived neurotrophic factor and obesity in the WAGR syndrome. N Engl J Med 359(9):918-27. doi: 10.1056/NEJMoa0801119

Juan, JA et al. (2015) Discrete BDNF neurons in the paraventricular hypothalamus control feeding and energy expenditure. Cell Metab 22(1):175-88. doi: 10.1016/j.cmet.2015.05.008

Ogden, CL et al. (2014) Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 311(8):806-14. doi: 10.1001/jama.2014.732

Palmiter, RD. (2015) Hunger logic. Nat Neurosci 18(6):789-91. doi: 10.1038/nn.4032

Penzo, MA et al. (2015) The paraventricular thalamus controls a central amygdala fear circuit. Nature 519(7544):455-9. doi: 10.1038/nature13978

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Yeo, GS et al. (2004) A de novo mutation affecting human TrkB associated with severe obesity and developmental delay. Nat Neurosci 7(11):1187-89. doi: 10.1038/nn1336

Zeltser, LM et al. (2012) Synaptic plasticity in neuronal circuits regulating energy balance. Nat Neurosci 15(10):1336-42. doi: 10.1038/nn.3219

Any views expressed are those of the author, and do not necessarily reflect those of PLOS.

Matthew Soleiman

Matthew Soleiman is currently finishing his graduate work at the University of Washington on the cell types and circuitry of the central amygdala. In parallel, he is working as a freelance science writer. You can find him on Twitter as @MatthewSoleiman, or contact him at mtsoleiman@gmail.com.

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The Mitochondrial Hypothesis: Is Alzheimer’s a Metabolic Disease?

By Emilie Reas, PLOS Neuroscience Community Editor

Despite decades of research devoted to understanding its origins, Alzheimer’s disease remains a daunting and devastating neurological mystery, ranking as the sixth leading killer of Americans. Countless therapeutic attempts, each designed with fresh anticipation, have repeatedly failed. A common thread across many of these drugs is their targeting the defining marker of the disease, amyloid plaques – those nasty extracellular deposits of beta-amyloid protein that invariably present in the Alzheimer’s brain and are thought to be toxic to neurons. Given the frustrating loss of research money, time and effort, many scientists agree it’s time to stop running circles around the amyloid hypothesis and begin seriously considering alternative explanations. One such theory showing increasing promise is the “mitochondrial hypothesis”. Its proponents posit that mitochondrial dysfunction lies at the heart of neural degeneration, driven by metabolic abnormalities which lead to classic Alzheimer’s pathology.

Steps by which mitochondrial function may lead to Alzheimer's. Based on the model outlined in Swerdlow et al (2010).

Steps by which mitochondrial function may lead to Alzheimer’s. Based on the model outlined in Swerdlow et al (2010).

Thank mom for your genetic risk

The first hints at this possibility arose from epidemiological observations about the genetic patterns of Alzheimer’s prevalence. These findings suggest that genetic influences may include more nuanced interactions than the better-known contributions from genes such as ApoE and TOMM40. Although both parents determine genetic risk, your likelihood of getting Alzheimer’s is much higher if the affected parent was your mother. This argues strongly that some maternal element underlies the association. Mitochondrial DNA is a logical target, as this subset of DNA is solely passed down from the mother. Many features of Alzheimer’s show this same maternal-dominant inheritance; those whose mother (but not father) had the disease also show reduced glucose metabolism and cognitive function, as well as elevated PIB uptake (a marker of amyloid) and brain atrophy.

Are metabolic enzymes the pathological trigger?

So if mitochondrial dysfunction initiates the Alzheimer’s cascade, what are the steps leading from metabolic disruption to neurodegeneration and ultimately, dementia? Studies point to cytochrome oxidase – a key enzyme for mitochondrial metabolism that’s encoded by both mitochondrial and nuclear DNA – as a likely trigger for early pathological events. Studies suggest that the enzyme is dysfunctional in the earliest disease stages; its activity is reduced not just in those with Alzheimer’s, but even in asymptomatic individuals who are at genetic risk for the disease or had a mother with Alzheimer’s. Furthermore, this stunted activity is linked directly to mitochondrial (or maternal) genetic contributions. By simply replacing the mitochondrial portion of the cytochrome oxidase DNA with DNA from Alzheimer’s patients, an otherwise normal cell will now have reduced cytochrome oxidase activity.

Bridging metabolism to Alzheimer’s pathology

For the mitochondrial theory to hold water, it must critically account for the classic pathological markers that define Alzheimer’s and have shaped traditional disease models – namely, amyloid plaques, tau tangles and brain atrophy. Indeed, growing evidence is elegantly bridging altered mitochondrial function to these key markers. For instance, disrupting mitochondrial electron transport chain activity (if you’ve forgotten your basic biochemistry, this is essential to cell metabolism) increases phosphorylated tau. What’s more, inhibiting cytochrome oxidase promotes a host of neurotoxic downstream effects including increased oxidative stress, apoptosis and amyloid production. Conversely, there’s also evidence that amyloid disrupts electron transport chain and cytochrome oxidase function, posing a chicken-or-egg conundrum. Amyloid has been found to buddy-up to mitochondria, but which comes first, the amyloid or the mitochondrial dysfunction, isn’t entirely clear. Both events occur early in the disease process, even before individuals show any symptoms of cognitive impairment. Whatever the mechanism, neurons from Alzheimer’s patients show signs of increased mitochondrial degradation. And when a neuron’s “powerhouse” begins to degrade, it cannot possibly support normal cognitive function.

A promising path for progress

It remains to be seen whether metabolic dysfunction is the key to unlocking the mechanisms of Alzheimer’s, and to ultimately developing effective therapeutics. While the current evidence is quite promising, many of the issues underlying the failure of other theories (poor translation of animal findings to humans, the challenge of identifying causal mechanistic pathways, etc.) similarly apply to the mitochondrial hypothesis. But at the very least, the proposal lays new ground for neuroscientists to continue progressing forward after a recent history of frustrating dead-ends. Even if mitochondria don’t hold the answer researchers have been seeking, understanding its contributions to Alzheimer’s pathology can only bring us closer to solving the mystery of this devastating disease.

Any views expressed are those of the author, and do not necessarily reflect those of PLOS.


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Emilie Reas received her PhD in Neuroscience from UC San Diego, where she used fMRI to study memory. As a postdoc at UCSD, she currently studies how the brain changes with aging and disease. In addition to her tweets for @PLOSNeuro she is @etreas.

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