While you were sleeping: Neural reactivations promote motor learning

Originally published on the PLOS Neuroscience Community

Do you recall that moment when you first learned to ride a bike? After days of practice, it finally clicked. Almost effortlessly, your legs cycled in perfect harmony as you maneuvered gracefully around turns and maintained impeccable balance. You may have learned this skill decades ago but it will likely stick with you for the rest of your life. How did your brain accomplish this remarkable feat of transforming a series of forced and foreign actions into an automatic, fluid movement sequence? A new PLOS Biology study by Dr. Dhakshin Ramanathan and colleagues explored the neural substrates of motor memory acquisition, reporting that reactivation of task-related neural activity patterns during sleep promotes motor skill learning in mice.

Sleep enhances motor learning

To evaluate motor learning, the researchers trained mice to perform a task in which they had to reach for a food pellet. Since sleep is known to be important for memory consolidation, the mice were allowed to sleep before and again after the reach task. The mice performed the task a second time after sleeping to assess how sleep affected their performance. Accuracy on the reaching task improved over the course of the first training period, whereas the mice responded more quickly after sleeping; hence, “online learning” (during the task) improved accuracy and “offline learning” (while sleeping) improved speed. No changes occurred when the mice were sleep-deprived between task sessions, pinpointing sleep – rather than the passage of time – as the source of the performance boost.

Behavioral paradigm (Ramanathan et al., 2015)

Behavioral paradigm (Ramanathan et al., 2015)

Sleep-dependent neural changes

The researchers next explored the neurophysiological basis for this sleep-dependent learning, recording neural activity from the forelimb area of motor cortex. As co-author Dr. Karunesh Ganguly explained,

“Studies had previously studied hippocampal-based memory systems. It remained unclear specifically how the motor system (i.e., procedural memory) processes memories during sleep.”

When the mice performed the task the second time, after sleeping, the onset of neural firing (time-locked to the reach) peaked earlier, and this activity was more strongly modulated by the task. Notably, firing onset did not change after sleep deprivation, confirming that sleep was necessary for this temporal shift in the neural response.

Learning-related reactivation

Elsewhere in the cortex and hippocampus, during rest neurons will fire in a particular sequence matching the temporal pattern during a prior experience, an event known as “replay” that is thought to support formation of memory for that experience. The researchers speculated that reactivation or replay in motor cortex may similarly promote motor learning. Neural activity patterns identified from the reach task were more prevalent during sleep after the task, showing – as predicted – reactivation of task-related activity after motor learning. When mice performed the reach task on multiple days, over the course of all days the degree of reactivation during sleep correlated with reduced reaction time, linking stronger neural reactivation with behavioral improvements.

A) Reactivation of task-related neural activity after learning. B) Reactivation correlates with improvements in reaction time. (Ramanathan et al., 2015)

A) Reactivation of task-related neural activity after learning. B) Reactivation correlates with improvements in reaction time. (Ramanathan et al., 2015)

Since the authors observed neural reactivation during motor learning, they next wondered whether the temporal sequence of this reactivation may be an important element of the memory code. The neural activation pattern during sleep more closely matched the task-related activity pattern after learning than before, although some of the temporal information in the sleep sequence was lost. Although prior studies have shown a role for hippocampal or cortical replay in memory consolidation, Dr. Ganguly raises the important distinctions that here, they “did not find evidence of ‘replay’ (i.e., sequences) but ‘reactivation’ (i.e., synchronous bursts).”

Learning-related plasticity was evident even at the single neuron level. Those neurons with the highest task-related activity were most strongly reactivated during sleep, and those showing the strongest reactivation also happened to show the most dramatic shift in the onset of their response to reaching. The authors speculate that this increased temporal coupling of neural activity to the task could facilitate binding the neurons into a distributed “movement complex” that aids formation of the motor memory.

Locking to spindles and slow waves for widespread plasticity 

Burst of high-frequency activity – known as spindles – and slow wave oscillations have both been implicated in offline learning. If task-related reactivations during sleep are important for memory consolidation, the authors reasoned, they may be temporally linked to spindles or slow waves. After learning the reach task, reactivations were in fact more closely time-locked and phase-locked (i.e., occurred at a particular phase of the cycle) to fast spindles. Reactivations were also more strongly time-locked and shifted their phase-locking to slow oscillations. Thus, during sleep, neural activation patterns related to the motor task were not only more prevalent after training, but their timing was also refined to coincide with particular neural events that may facilitate memory formation. Since spindles may be involved in synchronizing long-range cortical activity, locking task-specific reactivations to spindles could tie them into neuroplastic changes throughout widespread brain networks supporting consolidation.

At a recent talk on sleep-dependent memory consolidation, the speaker compared the neural reorganization that takes place during memory formation to a house renovation. Just as it’s more comfortable and effective for us to check into a hotel while our house is renovated, learning may be more effective when the brain checks out – into the quietude of sleep – while neural reorganization occurs. This may explain why sleep is so important for learning, yet it doesn’t explain how the brain stores new memories during sleep. Past studies have identified neuronal reactivation, coordinated with spindles and slow waves, as critical for forming declarative memories. Dr. Ramanathan and colleagues’ findings suggest that these mechanisms also occur in the motor cortex to support a radically different form of memory – the kind that helped you learn to ride a bike many years ago.

Clarifying the neural dynamics of sleep-dependent learning holds profound implications not just for those of us hoping to learn to play a new instrument or refine our dance moves. It may also help us better understand the remarkable neuroplasticity that underlies rapid motor learning during early development, and hold potential to promote recovery from motor impairments following brain injury. Dr. Ganguly is optimistic about the possible applications of their findings:

“Motor learning is likely an essential process during rehabilitation. Surprisingly little is known about the role of sleep and replay during recovery. With further study, one could imagine using sleep and offline processing to maximize the learning during rehabilitation.”


Ji D, Wilson MA (2007). Coordinated memory replay in the visual cortex and hippocampus during sleep. Nat Neurosci. 10:100-107. doi: 10.1038/nn1825

Ramanathan DS, Gulati T, Ganguly K (2015). Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation. PLOS Biol. doi: 10.1371/journal.pbio.1002263

Stickgold R (2005). Sleep-dependent memory consolidation. Nature. 437:1272–1278. doi: 10.1038/nature04286

Image credit https://www.flickr.com/photos/echoforsberg/

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#PLOS #SfN15 Recap: Hidden variables of behavior

Originally published on the PLOS Neuroscience Community

The Society for Neuroscience meeting is unique in both is breadth and depth. There are sessions on literally everything Neuro, each delving with exquisite detail and nuance into their given topic. While this level of focus is great for those seeking comprehensive coverage of their niche, it can be daunting for those looking for a broader sampling of the field’s cutting edge. The Hidden Variables of Behavior symposium was one of the rare sessions to stray from the single-track convention to elegantly bridge seemingly disparate topics, methodologies and applications, producing a standout session with exceptionally broad appeal. It accomplished this by exploring a theme that is perhaps the unifying motivation underlying nearly all Neuroscience research: how does the brain engender behavior? How does neural activity give rise to the thoughts, interactions with our environment, and engagements with others that define our experiences? In an enthralling series of talks by Loren Frank, Mark Schnitzer, Yang Dan and Catherine Dulac, the symposium covered topics ranging from learning and memory to sleep and social behavior. This session had it all.

Rapidly alternating representations of present and past in hippocampal-cortical networks

frankLoren Frank kicked off the symposium by exploring how the hippocampus supports our ability to remember the past and plan for the future. Hippocampal cells have a remarkable ability to replay past experiences via high-frequency oscillations during sharp waves known as ripples. When an animal traverses a path its hippocampal neurons will fire in a characteristic sequence that codes its trajectory; later, at rest or while sleeping, this spiking sequence will repeat, with the sequence sped up approximately twenty times the original rate! Disrupting hippocampal ripples impairs sequence learning, indicating that they’re critical for acquiring memories. However, the mechanisms, at both regional and whole-brain levels, by which sharp wave ripples (SWRs) help to consolidate memories are unclear.


Much attention has been paid to neurons in hippocampal subregions CA1 and CA3, which are excitable by high-speed motion and positively modulated by SWRs. However, Frank’s group identified a new group of hippocampal neurons – CA2P and CA2N – that are also positively and negatively modulated by SWRs, respectively. Notably, the CA2N population has an exceptionally high level of baseline activity and preferentially fires during rest or low speed motion. Because of their distinct function, these rebellious cells may be crucial for ongoing processing of the current state while maintaining representations of the past and future.

Although some (including yours truly) may hold a hippocampo-centric view of memory, Frank reminds us that memory is “not just a hippocampal thing.” Looking to the rest of the brain, his group found that SWRs recruit 35% of prelimbic regions, including cells that are both excited and inhibited by SWRs. Similar to the distinct populations of CA2P and CA2N cells, prefrontal cortex neurons may activate during either high-speed motion or immobility. This balance of excitation and inhibition in the hippocampus and surrounding cortex may promote rapid transitions between representations of the past and future, and facilitate their integration for learning and planning.

Large-scale ensemble neural dynamics underlying learning and long-term associative memory

schnitzer_mark_107Mark Schnitzer continued with this theme, presenting intriguing findings regarding the spatiotemporal properties of neural adaptations subserving learning. However, equally impressive are the advanced imaging tools his lab is developing to explore these issues. Their techniques allow neural recordings in behaving animals at unprecedented spatial depths and extents over long time scales. For instance, their current best is recording 1202 hippocampal cells in a freely moving mouse. Someone give this man the “I-recorded-the-most-neurons” award!

Using these tools, Schnitzer has been exploring hippocampal morphological and physiological changes that contribute to learning. CA1 neurons are a likely target for spatial learning, as they show place-cell activity, preferentially responding to particular regions of an animal’s environment. Surprisingly, dendrites in subregion CA1 are remarkably stable, suggesting that dendritic plasticity is unlikely to be the critical factor underlying learning. However, CA1 spine turnover is relatively rapid – on the order of 8-10 days – in comparison to cortical spines, of which 50% are permanent over a month. Schnitzer explained that although these cells are temporally stochastic in that they sometimes take breaks from their place-coding activity, when they return to the neuronal “spatial ensemble” they always return to encode the same place. What’s more, CA1 spatial representations are refined by learning, becoming both more accurate and reliable in their coding. I’ll be eagerly following Schnitzer’s work to see how their ongoing methodological innovations and applications advance our understanding of the hippocampal dynamics supporting long-term memories.

Neural circuits for sleep control

YIR_RH_DanYang Dan turned from this fast-paced discussion of rapid neural plasticity, spatial navigation and learning to examine neural regulation of sleep. Historically, neurons that trigger alertness and waking have been easy to identify, but researchers have struggled to track down those “sleep neurons.” Past lesion and c-fos studies have shown that hypothalamic – particularly preoptic – neurons are important for inducing sleep.

Combining optogenetics with electrophysiology, Dan’s lab has expanded upon these findings to pinpoint both the responsible cell types and their specific sleep-inducing effects. In particular, activating GABAergic preoptic cells projecting to the tuberomammillary nucleus (also of the hypothalamus) promotes non-REM sleep initially, and REM sleep later. The midbrain’s ventrolateral periaqueductal gray also promotes sleep, but only the non-REM type. Dan’s findings together suggest that mutual inhibition across these key hypothalamic and brainstem regions regulates transitions across three general brain states of waking, REM sleep and non-REM sleep.

Long-term changes in the representation of social information in the mouse medial amygdala

DulacAfter all this talk about sleep, my hypothalamic sleep neurons had begun batting the morning’s adenosine antagonists. Fortunately, Catherine Dulac’s captivating talk exploring the bases of social interactions and sex-specific behavior kept me alert and engaged. Two key circuits working in concert to process social information, she explained, are the olfactory and vomeronasal systems. This latter system in particular may act as a switch to promote appropriate (and suppress inappropriate) sex-specific behavior.

Dulac’s research, fusing molecular, genetic and electrophysiological techniques, has identified the medial amygdala as a critical stop along the vomeronasal circuit for mediating sex-specific social signaling in mice. Medial amygdalar encoding of social cues is not only sexually dimorphic, but is also regulated by salient social experiences including mating and co-housing. Furthermore, the efficiency of medial amygdalar signaling also changes after mating in a sex-specific manner, increasing in males but decreasing in females. Together, Dulac’s work has pinpointed the medial amygdala as an indispensible hub within an extensive neural circuit that regulates social behavior and in turn, is modulated by sexual and social experience.

Every SfN has at least one session that reminds me why I love the brain and re-ignites my passion for Neuroscience. This year, the Hidden Variables of Behavior symposium was it! It may be a year away, but I’m eagerly awaiting #SfN16 for similarly inspiring talks.

For an abbreviated play-by-play, visit my Storified live-tweeting of the symposium’s highlights.


Anderson EB, Grossrubatscher I, Frank L (2015). Dynamic Hippocampal Circuits Support Learning- and Memory-Guided Behaviors. Cold Spring Harb Symp Quant Biol. 79:51–58. doi: 10.1101/sqb.2014.79.024760

Attardo A, Fitzgerald JE, Schnitzer MJ (2015). Impermanence of dendritic spines in live adult CA1 hippocampus. Nature. 523:592–596. doi: 10.1038/nature14467

Bergan JF, Ben-Shaul Y, Dulac C (2014). Sex-specific processing of social cues in the medial amygdala. eLife. 3:e02743. doi: http://dx.doi.org/10.7554/eLife.02743

Brennan PA (2001). The vomeronasal system. Cell Mol Life Sci. 58(4):546–555.

Ego-Stengel V, Wilson MA (2010). Disruption of ripple-associated hippocampal activity during rest impairs spatial learning in the rat. Hippocampus. 20(1):1–10. doi: 10.1002/hipo.20707

Weber F, Chung S, Beier KT, Xu M, Luo L, Dan Y (2015). Control of REM sleep by ventral medulla GABAergic neurons. Nature. 526:435–438. doi: 10.1038/nature14979

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Embracing sadness

Years ago, I was a chronically injured runner – stress fractures, torn tendons, you name it, I had it. This came to a blissful end about a year ago when I bid adieu to my most frustrating injury, a chronic hamstring tendinopathy that’s haunted me nearly my entire running career. Since then I’ve enjoyed remarkable growth as a runner, witnessing improvements in my strength, endurance and technique. I’ve returned to racing after a several-year hiatus and recently registered for what would be my eleventh marathon and first barefoot marathon!

My mirage of invincibility disintegrated suddenly just 10 days ago. Having completed a particularly hard week including a great 18-mile barefoot run just a few days prior, I was fatigued and sore. Just five miles into an “easy recovery run”, my hamstring seized up and left me limping home. My heart sank, recognizing that all-too-familiar pattern of pain that I’d battled since high school track and cross-country. I had re-strained my hamstring.

Over the years of incessant injuries I developed resilience and adaptability as I learned the invaluable benefits of a positive attitude for healing (and sanity!). This optimism has kept me on the fast track to healing, nurturing my health for optimal rehabilitation. During past injuries I would attack cross-training and strength work, diligently adhere to my physical therapy, and target my diet to heal as efficiently as possible. I have always been convinced that this is the best way – the right way – to approach recovery. The athletes I most admire would never let injury get them down, but attack it head-on with hard work and determination. The first few days after re-injuring my hamstring I was similarly optimistic. This is a minor bump in the road … just some fleeting soreness that a little massage, active release and acupuncture will nip in the bud, I convinced myself.

Yet 10 days of essentially no running (excepting a few very painful failed efforts) later, I can no longer feign positivity. I am in mourning and I am embracing it. This is a sadness that only a runner could understand. I am sad to have lost a defining piece of myself. A source of inspiration, energy, passion and power. My source of life. This admission comes with a heavy dose of embarrassment and guilt for such distress over what is truly a trivial matter. Rationally I’m deeply grateful for my remarkable fortune for my otherwise great health, a job I love, and the most wonderful friends and family. I have tried to deny this melancholy, to convince myself that this sadness is no match for my optimism. But that is a lie. Pretending that I don’t miss those hours alone on the road, that I don’t fear another long struggle with injury, is perhaps even more toxic than the negativity itself.

One day I will heal. One day I will run again. I know this. But for now, I am sad.

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That All-Nighter is not without Neuroconsequences

Originally published on the PLOS Neuroscience Community

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.


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

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Astrocytes may Hold the Key to Exercise-Induced Cognitive Enhancement

Originally published on the PLOS Neuroscience Community

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.”


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

Gibbs ME, O’Dowd BS, Hertz E, Hertz L (2006) Astrocytic energy metabolism consolidates memory in young chicks. Neuroscience 141(1): 9-13. doi:10.1016/j.neuroscience.2006.04.038

Henneberger C, Papouin T, Oliet SH, Rusakov DA (2010). Long-term potentiation depends on release of D-serine from astrocytes. Nature. 463:232-6. doi:10.1038/nature08673

Kramer AF, Erickson KI (2007). Capitalizing on cortical plasticity: influence of physical activity on cognition and brain function. Trends Cogn Sci. 11: 342–8. doi:10.1016/j.tics.2007.06.009

Marlatt MW, Potter MC, Lucassen PJ, van Praag H (2012). Running throughout middle-age improves memory function, hippocampal neurogenesis, and BDNF levels in female C57BL/6J mice. Dev Neurobiol. 72:943–52. doi:10.1002/dneu.22009

van Praag H, Kempermann G, Gage FH (1999). Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus. Nat Neurosci. 2:266–70. doi:10.1038/6368

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Barefoot Running Workshop 3: Hills, Speed & Precautions

Many thanks to all who attended the final session of our Barefoot Running Workshops! In today’s workshop we built on the fundamentals of running mechanics covered in the first and second workshops. We tweaked our speed and hill running techniques, addressed safety issues unique to barefooting and took running video selfies for gait analysis. Here are some of the highlights of the day’s fun …

Happy, dirty feet, post-hills and sprints!

Happy, dirty feet, post-hills and sprints!



When running downhill, the impact on the body increases due to acceleration from gravity. When you drop a ball, it will fall faster when it hits the ground if dropped from 10 feet than 5 feet; similarly, your body will actually descend faster towards the ground when plummeting downhill than climbing up. The secret to effective downhill running lies in using that acceleration to your advantage, rather than letting gravity get the better of you.

Minimize bouncing. With that extra distance between you and the earth, downhill running comes with additional vertical motion. Try to minimize any unnecessary upwards motions, like jumping or bouncing, that will only exacerbate the stress from the downward fall. Aim to stay low to the ground and level on the horizontal plane.

Bend the knees. The knees serve as shock absorbers, so bent knees can greatly counter the added stress from downhill running. This also facilitates a low, steady stride, making it even easier to avoid bouncing and pounding.

Avoid breaking. Embrace gravity, don’t resist it. Steep inclines will automatically increase your pace, and a faster than normal clip can feel uncomfortable. A natural – often subconscious – response is to put on the breaks, stiffening the joints to counter the impact. This defense mechanism is far from beneficial, creating unnecessary tension as we clench in resistance, which only opens the door to injury. Take advantage of the acceleration and allow yourself to float. Once you release and embrace the descent, the ride will feel more like flying than a downward crash.

Don’t over-stride. Over-striding is always dangerous, but exceptionally so when running downhill. What’s worse, downhills actually encourage over-striding, as they entice us to extend the leg out in front as a protective mechanism. This only forces you into a heel strike and increases stress on the shins and knees – a dangerous combination when coupled with an already elevated impact from the incline!


In contrast to downhills, uphill running requires us to fight against gravity. Maintaining proper form will keep you strong to efficiently conquer these demands.

Lean into hill. Exaggerate your forward lean to counteract the incline. But take care to lean not at the waist, but with the entire body. Collapsing forward will only increase your workload and make that hill feel extra torturous!

Stay tall. Since the goal is upward movement, aim to lengthen the body upward. This is where good posture is key, keeping the back tall and long, head high and looking forward.

Steady effort. Powering up a daunting hill may not be the best tactic to smoke your competition. Your strong sprint could easily backfire, leaving you exhausted by the time you summit. Rather than keeping a steady pace, aim to maintain a steady effort. This of course, means slowing it down on those inclines. To track your effort, monitor your breathing rate; regular breathing means regular effort and is a good indication you’re not over-exerting yourself.


Increase forward lean. To run faster, we need to increase the amount of forward motion per step. This extra ground coverage can be achieved relatively easily be simply leaning forward.

Light feet and high cadence. Faster speed does not in fact require higher cadence (leg turnover rate). You should strive for the same high cadence as always (at least 180 steps per minute). However, when sprinting this high cadence will even further work to your advantage. Speed can be more strenuous on bare feet, encouraging shearing and friction. Keeping your foot-strike light and cadence high can minimize these effects by reducing your ground contact time.

Open stride. Don’t be afraid to open up your stride. Barefoot running often encourages a shorter stride, but a longer stride can help support speed for any runner. Allow your hips to open a bit more and your leg to lift a touch higher than usual, but remain fluid and never force a gait change.



Blisters & Abrasions

Blisters and raw skin are relatively common for novice barefoot runners. While unpleasant, these can be valuable training tools as they’re telltale signs of sub-optimal biomechanics. Use their appearance and location to pinpoint your weaknesses. Blisters on your toes? You may be pushing off or gripping excessively. Calluses on your heel? You may be striking too far back on the rearfoot. Abrasion on the ball of your foot? Try not to scrape, shuffle or shear the foot on landing, but lightly place and lift instead.

Dangerous” debris

The greatest concern for the new barefoot runner is cutting or bruising their feet on all the glass, rocks and dirty needles littering our earth. In truth, such dangers aren’t prevalent and are relatively innocuous to the conditioned bare foot. That said, there are of course certain encounters that are best avoided by even the most experienced barefooters. 

Urban debris. Most obvious are artificial hazards such as shards of glass or rusty nails. Large dangers are easily avoided by scanning the ground, and smaller ones may not even penetrate the thick, tough skin of the foot’s plantar surface. Of course, in the unlikely case you sustain a bad cut or puncture, seek medical attention!

Natural debris. More likely to take down a barefoot runner are hazards lurking naturally in the trails and grass. Thorns and burs love attaching to feet and although painful, are easily pulled out. Landing hard on a stone can bruise, but the feet will become resilient to even the most daunting rocks and pebbles as the feet strengthen with experience. A less often considered risk, but one that’s taken down yours truly on countless occasions, are bees. Depending on your reaction to bee stings, you may want to seriously reconsider running in grass, especially during the spring and summer, when bees love frolicking through the grass as much as we humans do.


Heat: Depending on your foot conditioning and tolerance, hot ground can pose unique challenges to the barefoot runner. But because of the reduced foot-contact time when running compared to walking, it’s surprisingly easier on your feet to run on hot terrain. Some surfaces heat more readily than others, so stick to concrete or dirt over pavement. The painted white lines on roads can offer some refuge, as long as you’re careful to avoid cars!

Cold. In some aspects, the cold can be more hazardous to the barefooter than the heat. In extreme cases, the feet can go numb, which reduces sensory feedback and encourages poor biomechanics (not to mention posing a risk of frostbite!). Feet often warm up after just a few minutes of running, but if you do lose sensation, stay smart and stop or put on some protection. Just a pair of socks will often suffice to keep the feet warm while retaining a mostly barefoot feel.

Wet: Running through the rain, mud and puddles can be one of the most exhilarating barefoot experiences. But stay cautious of smooth surfaces, which can become dangerously slick when wet. Water can also soften the skin, making it more likely to rub raw on rough terrain or long runs.

As both a student and teacher, these workshops have been a far more rewarding and educational experience than I originally anticipated. More importantly, they’ve also served as a fantastic tool to connect with the small but passionate community of barefoot runners in San Diego. Given how fun and successful this “pilot” series has been, there will definitely be more! Please don’t hesitate to get in touch with suggestions for what you would like featured in upcoming workshops, and stay tuned details about future events.

Happy barefoot trails!

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

Originally published on the PLOS Neuroscience Community

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.


Crouch PJ et al (2005). Copper-dependent inhibition of human cytochrome c oxidase by a dimeric conformer of amyloid-beta1-42. J Neurosci. 25(3):672-9. doi: ​10.​1523/​JNEUROSCI.​4276-04.​2005

Debette S et al. (2009). Association of parental dementia with cognitive and brain MRI measures in middle-aged adults. Neurology. 73(24):2071-8. doi: 10.1212/WNL.0b013e3181c67833

Edland SD et al (1996). Increased risk of dementia in mothers of Alzheimer’s disease cases: evidence for maternal inheritance. Neurology. 47:254–6. doi: 10.​1212/​WNL.​47.​1.​254

Hirai K et al. (2001). Mitochondrial abnormalities in Alzheimer’s disease. J Neurosci. 21(9):3017-23

Hogliner GU et al. (2005). The mitochondrial complex I inhibitor rotenone triggers a cerebral tauopathy. J Neurochem. 95(4):930-9. doi: 10.1111/j.1471-4159.2005.03493

Honea RA et al. (2010). Reduced gray matter volume in normal adults with a maternal family history of Alzheimer disease. Neurology. 74(2):113-20. doi: 10.1212/WNL.0b013e3181c918cb

Kish SJ et al. (1992). Brain cytochrome oxidase in Alzheimer’s disease. J Neurochem. 59(2):776-9. doi: 10.1111/j.1471-4159.1992.tb09439

Mosconi L et al (2007). Maternal family history of Alzheimer’s disease predisposes to reduced brain glucose metabolism. Proc Natl Acad Sci. 104(48):19067-72. doi: 10.1073/pnas.0705036104

Mosconi L et al. (2010). Increased fibrillar amyloid-{beta} burden in normal individuals with a family history of late-onset Alzheimer’s. Proc Natl Acad Sci. 107(13):5949-54. doi: 10.1073/pnas.0914141107

Mosconi L et al. (2011). Reduced Mitochondria Cytochrome Oxidase Activity in Adult Children of Mothers with Alzheimer’s Disease. J Alzheimers Dis. 27(3): 483–490. doi: 10.3233/JAD-2011-110866

Roses AD et al (2010). A TOMM40 variable-length polymorphism predicts the age of late-onset Alzheimer’s disease. Pharmacogenomics J. 10(5): 375–84. doi: 10.1038/tpj.2009.69

Swerdlow RH et al. (1997). Cybrids in Alzheimer’s disease: a cellular model of the disease? Neurology. 49(4):918-25. doi: ​10.​1212/​WNL.​49.​4.​918

Swerdlow RH, Burns JM and Khan SM (2010). The Alzheimer’s Disease Mitochondrial Cascade Hypothesis. J Alzheimers Dis. 20 Suppl 2:S265-79. doi: 10.3233/JAD-2010-100339

Swerdlow RH. (2012). Mitochondria and cell bioenergetics: increasingly recognized components and a possible etiologic cause of Alzheimer’s disease. Antioxid Redox Signal. 16(12):1434-55. doi: 10.1089/ars.2011.4149

Valla J et al. (2010). Reduced posterior cingulate mitochondrial activity in expired young adult carriers of the APOE ε4 allele, the major late-onset Alzheimer’s susceptibility gene. J Alzheimers Dis. 22(1):307-13. doi: 10.3233/JAD-2010-100129

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The Vancouver Half, a victorious defeat

When life gives you lemons … suck it up. Isn’t that how the saying goes? Well, at the Scotiabank Vancouver Half Marathon last weekend – my second barefoot half – I sucked it up and it was sour.

The saga began a few weeks prior, when I was spontaneously struck with debilitating chest pain. It gripped me intensely, leaving me barely able to breath and fearing a heart attack. An X-ray showed a healthy heart, lungs and ribcage, yet the pain persisted for weeks. Massage, active release and chiropractic adjustments brought some temporary relief, and although I’ll never know for sure, I now suspect it was a strained pec or intercostal muscle. Many days, running was impossible. On good days I could eke out a short, slow, uncomfortable trot. To make matters worse, the stress and tension in my chest and back trickled down to knock the rest of my body out of wack. My opposite leg felt weak and limp, as if it were dragging powerless behind me … as if it belonged to someone else, completely out of my neuromuscular control. As race day neared, I began to abandon my hopes of running at all, mentally preparing for a restful vacation exploring a new city.

Come race morning, I convinced myself anything was possible and knew I would regret not at least trying. The gun went off and to my great surprise, my chest quickly loosened up and my breathing was fluid. My right leg, on the other hand, forgot how to move. For the first seven miles, it took every ounce of mental focus to coerce my muscles into lifting and propelling forward my dead leg. The sun blazed as the pack of runners hugged every smidgen of shade to escape the 80 degree heat. My battle to maintain a semblance of a functional stride intensified as I pranced precariously over nasty stretches of gravel. Eight miles in, a tiny stone sent a zinger through my toe and I pulled to the side for several minutes waiting for the ache to subside. I fought the discouraged voices rationalizing an early finish and pushed ahead. The toe pain gradually dissipated and I even enjoyed a brief surge of strength and fluidity.

But by that point, it was too late and the damage from my wonky gait coupled with the hot, rough and canted roads, had been done. My right heel began to burn and I felt an escalating squish as my bare foot struck the pavement with each step. I refused to inspect my foot and acknowledge that a monstrous blood blister had developed, with four miles still remaining. I refused to focus on the distance ahead, allowing myself to think only of the present moment. “Just take one more step. One step is nothing. Then, just take one more.” I convinced myself that the pain was illusory – that it only existed if I gave it life – and somehow, this denial empowered me through, single squishy step by squishy step. As I sprinted to the finish, a huge smile was plastered on my face and a flood of endorphins masked the havoc I had wreaked on my body. And just like Cinderella at midnight, as I crossed the finish line and broke that invisible endorphin wall, my ecstatic sprint transformed into an awkward hobble over to the medical tent.


As I saw my finish time, I was surprisingly unfazed by learning I had raced my slowest half ever. Those 13.1 miles were more painful than any I had raced before, but they hurt far less than a DNF or worse – a DNS. Despite the physical pain and frustration, I genuinely enjoyed almost every moment. There is a reason runners return again and again to race, through heat, injury and fatigue … the energy of the running community, the intoxication of the journey, and the discoveries along the way entice us back as addictive rewards.

Several years ago this race would have devastated me. Indeed, by dwelling on insignificant matters of time and speed, racing can destroy a runner and quench the very passion that fuels us to run. But by embracing each experience as a novel opportunity for growth and self-discovery, we can only evolve into better runners – and better human beings. For me, the aggregate challenges of my years of running have reinforced one invaluable lesson. We runners are so much stronger, and our bodies capable of so much more, than we’re aware. Our power is only bounded by the limits of our mind and the integrity of our spirit. To paraphrase a particularly accomplished marathoner, my fastest days may be behind me, but my best running days lay ahead.

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Barefoot Running Workshop 2: Lower and Upper Biomechanics

In the first of our Barefoot Running Workshops, we explored facts and fiction of barefoot running, sensory awareness and mechanics of the foot. In our second workshop today, we introduced basic running kinematics before moving north from the foot to cover mechanics of the lower and upper body. Here’s a recap of the workshop highlights for those who missed it.


Before diving into the nitty-gritty of leg, hip and torso function, it’s essential to understand how one gets from zero to running in the first place. Running has been described in a multitude of ways, from a controlled fall to alternating one-legged hops to a springy, aerial variant of walking. Given this confusing jumble of terminology, what then are the essential movements that convert a stationary body to a running body? The basic motion is far simpler than most runners would imagine. There’s no jumping, bouncing or flying required! In essence, running is nothing more than marching while moving forward.

March. Simply pick up the feet, the ankle gliding parallel to the shin up to the knee. Return the foot to its starting position and repeat with the other leg. That’s it. The 100-ups are a great exercise to reinforce this motor pattern.

Lean. Once you’ve mastered marching in place, it’s time to transform this into forward motion. This too is simpler than it sounds. To move forward, the body must lean forward. This lean should NOT come from bending at the waist; “sitting” or folding forward will cause a host of problems from the back to the hips to the knees. Instead, the lean should originate at the ankles, the entire body leaning angled together along the same plane. By simply adopting a slight lean from the ankles, you will fall forward and be propelled from stationary marching into forward travel. March, lean, and BAM … you’re magically running!


Lift the legs. A constant upward motion should be maintained throughout the gait cycle. This is especially important after striking, when the legs should immediately lift up. The feet should land directly under the hips, neither reaching forward nor crossing over the midline. Both overstriding and a cross-over gait can lead to various injuries. The Gait Guys offer an excellent series of videos on correcting a cross-over gait (part 1, 2 and 3).

Bend the knees. To facilitate a smooth ride, bend and relax the knees. The knees can serve as shock absorbers when allowed to flex, so the greater the bend, the less impact will be sustained upon landing. This is especially helpful when running downhill.

Stable hips. The shin bone’s connected to the thigh bone … the thigh bone’s connected to the hip bone … Yes, it’s all connected, and these chains are particularly notable in the context of how the legs move in response to the hips. The hips are indeed the powerhouse and main driver of a strong running stride. Strong, stable hips are essential, and muscular imbalances or poor hip mechanics are the source of many leg and foot injuries. Don’t let the hips sink or drop, but keep them level on the horizontal plane. The hips serve as the body’s steering wheel, so be sure to keep them facing forward and aligned with the shoulders.


Core rotation. Some rotation is key to balancing the body’s left-right movements, but excessive rotation, or from the wrong place, can be problematic. Most of the rotation should originate in the core. Imagine the pelvis as a chandelier, the torso as its suspension cable and your head as the ceiling. The pelvis should dangle, relaxed, and rotate freely from the waist, supported by the strength of the strong, elongated core. As the right legs swings back, the right pelvis rotates back. It’s not forced or pulled, but swings naturally, allowing greater leg extension without over-stressing the hips. (The chandelier example was adapted from this excellent article.)

Shoulders and arms. Keep the shoulders low and relaxed, but don’t slouch. Some shoulder motion is fine, but be careful not to dip them or overly rotate the chest. After the hips, the shoulders serve as a second steering wheel, so they should remain stable and facing forward. Keep the arms close to your sides, elbows at a 90 degree angle and swinging forward and backward rather than across the chest. The rhythm of your arms directly affects hip and leg motion; a rapid arm punp can encourage faster leg turnover, and fluid forward-backward swinging will minimize inefficient lateral movements.

Head and posture. Your head leads and guides its body below. Keep your head up and neck stretched tall and long. Unlike owls, humans are blessed with eyes that move independently from the head, so you can still look at the ground without titling the head down. The entire body – from the ankles up to the tip of the head – should form a strong, continuous line, without kinks from poor posture or bending at the waist. Imagine being lifted upwards, suspended by a bird or plane (or pick your favorite flying power-creature) directly above your head.


Now it’s time to integrate these elements into your perfect running form! This video from the Natural Running Center is a beautiful example of a strong, efficient stride. Revisit this video and try to mimic Mark’s fluid, light motion whenever you need a refresher.

The final key to optimizing your stride is forgetting everything you just read and just run. Yes, I am (mostly) serious. Sometimes less can be more in terms of tapping into your optimal gait. Running is one of the most natural movements for humans, and a strong, healthy body will readily fall into it’s own unique running stride. Obsessing about every component of your form will not only take the joy out of running, but can also backfire, inducing unnecessary tension or forced, inefficient motor patterns. If you find this occurring while tweaking your running mechanics, abandon the effort and simply allow your body move fluidly and aimlessly. You might find that your muscles were one step ahead of your mind, and knew the route to efficient running all along.

Join us for the final session of our Barefoot Running Workshop series Sunday, July 12 at 3 pm. As usual, we’ll meet at the Founder’s Statue at the northwest corner of Balboa and El Prado in Balboa Park. In this final session, we’ll wrap up with how best to run hills and do speed work, as well as safety and practical considerations of running barefoot. More details and RSVP here.

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Task Shifting may Shift our Understanding of the Default Network

Originally published on the PLOS Neuroscience Community

Over the past two decades, one of the most impactful discoveries to come from the surge in functional MRI (fMRI) research has been the existence of the brain’s “default network”. Countless studies have found that that this system, mainly comprising medial frontal, parietal and temporal, and lateral parietal regions, is most active during rest or passive tasks such as mind-wanderingimagining or self-reflection. A new study, recently published in eLife by Ben Crittenden, Daniel Mitchell and John Duncan, presents a striking finding that may flip our understanding of the role of the default network on its head.

Task-switching: the common thread?

Many of the experiments evoking default network activity compare relatively unconstrained states conducive to rest or mind-wandering against rigid task conditions with targeted cognitive demands. Thus, while these studies contrast active and passive conditions, they also incidentally contrast states of sustained attentional focus with unrestricted, dynamically changing mental landscapes. Crittenden and colleagues argue that these shifting cognitive contexts may be the common thread to default network activity and thus explain its promiscuous involvement across such heterogeneous conditions. First author Crittenden explains how their seemingly radical diversion from classic theories came about through a serendipitous pilot experiment: “I developed an initial version of the current experiment to test the idea of which regions may be involved in orchestrating large switches, and the default network came out as really strong at the individual subject level. If these results held out we could be onto something quite interesting. We tweaked the task a bit and fortunately it followed the pilot data really nicely!”

To test their new hypothesis, the researchers conducted fMRI while participants performed three levels of task switching–make a major cognitive switch, a minor switch or no switch. For example, if they were previously asked whether two geometric figures were the same shape, a minor change would be determining if two figures were the same height, whereas a major change would be determining if a dolphin is living or non-living. The minor-switch condition is similar in cognitive load to other tasks that have not shown reliable default network activation. If context changes are driving the default network, then radical task switches should more effectively engage it.

Task conditions. A switch from the red-box to the blue-box tasks would be a minor switch, whereas a switch from the red-box to the green-box task would be a major switch. Adapted from Crittenden et al., 2015

Task conditions. A switch from the red-box to the blue-box tasks would be a minor switch, whereas a switch from the red-box to the green-box task would be a major switch. Adapted from Crittenden et al., 2015

Major task switches recruit the default network

Past studies have found that the default network does not function as a whole, but roughly dissociates into three subnetworks – “core,” medial temporal lobe (MTL) and dorsomedial prefrontal cortex (DMPFC) networks. Suspecting that these subnetworks are not equally involved in switching, they analyzed each subnetwork separately.

Compared to repeating the same task, major task switches activated the core and MTL networks. Small task switches did not activate any of the subnetworks. Using multivoxel pattern analysis, they further showed that the pattern of activity (versus the overall activation level) in all three subnetworks distinguished between the highly dissimilar tasks, but only the DMPFC network discriminated similar tasks. Thus, although both the overall magnitude and pattern of activity signaled contextual shifts, Crittenden raises some caution over interpreting the source of the pattern discrimination. “I imagine that a considerable amount of the classification accuracy between dissimilar tasks will be driven by lower-level visual features. However, it is still interesting that the default network is reliably representing this task information, which given the usual definition of the default network as task-negative, one may not have predicted.”

Activity for regions of the core (yellow), MTL (green) and DMPFC (blue) subnetworks for major (light colors) and minor (dark colors) task switches. Major switches activate many regions of the core and MTL subnetworks. Adapted from Crittenden et al., 2015

Activity for regions of the core (yellow), MTL (green) and DMPFC (blue) subnetworks for major (light colors) and minor (dark colors) task switches. Major switches activate many regions of the core and MTL subnetworks. Adapted from Crittenden et al., 2015

A shifting theory

If this finding is replicated, it could be the beginning of a major shift in our understanding of default network function. In contrast to the wealth of prior studies implicating the default network as “task-negative” – shutting down during demanding task conditions – here the default network was maximally engaged during dramatic contextual changes. These large task switches were objectively more challenging (participants responded more slowly) than the small-switch or no-switch conditions, in striking opposition to the notion that task difficulty suppresses the network. This implies that cognitive control or effort aren’t the key factors modulating these regions, but rather changing contextual states.

But does this model fit with the other mental states that reliability recruit the default network? Although it’s not yet clear what aspects of task shifting drive the observed response, the authors convincingly argue that indeed, many common default network activations can be accounted for by changes in cognitive context. At rest, during mind-wandering, imagining or reflecting on one’s past experiences, the mind is relatively free to jump between cognitive states. This contrasts with the constrained task conditions used in most fMRI studies that typically deactivate the default network. This relative cognitive liberty may give rise to radical mental shifts, for example, from thinking about the loud banging of the MRI scanner to planning your afternoon errands. Whether these spontaneous contextual changes are frequent enough to ramp up default network activity as observed remains to seen. Alternatively, the key factor may not be adoption of a new task, but the attentional release to do so. When switching from one task to another, the brain must let go of its attention to the first task before focusing on the next. In passive cognitive states, attention is relaxed, liberating the mind to focus on various tasks at will.

Until their findings are replicated and expanded, Crittenden explains that these possibilities are yet speculation. “I think that switches could be a contributing factor to the signal, however, by its nature the signal that we are envisioning is likely to be quite transient. More sustained activation such as during reminiscing/prospection/navigation etc. is likely to be a strong driver of default network activity. As we all like to say – more experiments are needed!”


Addis DR, Wong AT and Schacter DL (2007). Remembering the past and imagining the future: common and distinct neural substrates during event construction and elaboration. Neuropsychologia. 45(7):1363-77. doi: 10.1016/j.neuropsychologia.2006.10.016

Buckner RL (2012). The serendipitous discovery of the brain’s default network. Neuroimage. 62(2):1137-45. doi: 10.1016/j.neuroimage.2011.10.035

Crittenden BM, Mitchell DJ and Duncan J (2015). Recruitment of the default mode network during a demanding act of executive control. eLife. 4:e06481. doi: 10.7554/eLife.06481.001

Mason MF et al. (2007). Wandering Minds: The Default Network and Stimulus-Independent Thought. Science. 315(5810):393-5. doi: 10.1126/science.1131295

Gusnard DA, Akbudak E, Shulman GL and Raichle ME (2001). Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function. PNAS. 98(7):4259-64. doi: 10.1073/pnas.071043098

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