Tag Archives: neuroscience

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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How does Sports Training Restructure the Brain?

Originally published on the PLOS Neuroscience Community

The impact of regular exercise on the body is obvious. It improves cardiovascular fitness, increases strength and tones muscle. While these transformations are visible to the naked eye, changes to brain structure and function by physical activity occur behind the scenes and are therefore less understood. It’s not news that the brain is wonderfully plastic, dynamically reorganizing in response to every sensory, motor or cognitive experience. One might imagine therefore, that elite athletes–who train rigorously to perfect specialized movements–undergo robust neural adaptations that support, or reflect, their exceptional neuromuscular skills. Different sports, invoking different movements, will target unique neural substrates, but most physical activities similarly rely on regions that are key for eliciting, coordinating and controlling movement, such as the motor cortex, cerebellum and basal ganglia. In a new study published in Experimental Brain Research, Yu-Kai Chang and colleagues explored how microstructure in the basal ganglia reflects training and skill specialization of elite athletes.

Runners, martial artists and weekend warriors

The study enrolled groups of elite runners and elite martial artists, along with a control group of non-athletes who only engaged in occasional, casual exercise. Although both groups of athletes were highly trained (averaging over four hours of training daily), their uniquely specialized skills were key for determining whether basal ganglia structure varied by sport or by athletic training generally. The groups did not differ in terms of basic physical attributes, demographics or intelligence, but as expected, the athletes were more physically fit than the controls.

Measuring microstructure

The researchers focused on the basal ganglia, a set of nuclei comprising the caudate, putamen, globus pallidus, substantia nigra and subthalamic nuclei, since these structures serve critical roles in preparing for and executing movements and learning motor skills.

Structures of the basal ganglia

Structures of the basal ganglia

They used diffusion tensor imaging (DTI), which measures how water flows and diffuses within the brain. Since water diffusion is determined by neural features like axon density and myelination, it is more sensitive to finer-scale brain structure than traditional MRI approaches that measure the size or shape of brain regions. Fractional anisotropy (FA) and mean diffusivity (MD) are common metrics to assess, respectively, the directionality and amount of diffusion. Typically, higher FA and lower MD are thought to reflect higher integrity or greater organization of white matter.

Globus pallidus restructures in athletes

The basal ganglia microstructure of the athletes and controls were remarkably similar, with one exception. The internal globus pallidus showed lower FA and a trend for higher MD in the athletes than the non-athletes, but there were no differences between the runners and martial artists.

This result in intriguing for two reasons. First, it’s notable that both athletic groups showed a similar magnitude difference from non-athletes. Thus, acquiring and refining skilled movements more generally, rather than any particular movement pattern unique to running or martial arts, may restructure the globus pallidus. As study author Erik Chang explains,

“With the current results, we can only speculate that the experience of high intensity sport training, but not sport-specific factors, would trigger the localized changes in DTI indices we observed.”

This would make sense, considering the area is an important output pathway of the basal ganglia, broadly critical for learning and controlling movements. It’s likely that other regions may undergo more specialized adaptations to sport-specific training. Chang expects that future studies using a whole-brain approach with “distinctions between sport types and reasonable sample size would find cross-sectional differences or longitudinal changes in brain structure related to motor skill specialization.”

Second, although we expect athletic training to enhance regional brain structure, the reduced FA and increased MD observed in these elite athletes would commonly be considered signs of reduced white matter integrity. This is somewhat surprising in light of other studies reporting positive correlations between physical fitness and white matter integrity in non-professional athletes and children. But as Chang points out, “Professional sport experience is quite different from leisure training.” Although unexpected, this finding aligns well with similar reports that intensive training in dancersmusicians and multilinguals is associated with reduced gray or white matter volume or reduced FA. Why would this be? For starters, DTI doesn’t directly measure axonal integrity or myelination–only water diffusion. So while sports training has some clearly reorganizing effect on basal ganglia, we can’t yet infer what changes are occurring at the neuronal level. One interesting possibility is that the development of such expertise involves neuronal reorganization or pruning as circuits become more specialized and efficient. Chang cautions that their findings “could reflect the manifestation of an array of factors, including increased neural efficiency, altered cortical iron concentration in the elite athletes, or other training-specific/demographic variables.”

In the broader context, this study is a striking example of why care is warranted in interpreting neuroplasticity. Depending on the study conditions, the same intervention–here, athletic training–can apparently remodel the brain in opposing directions. This is an important reminder that although we like to assume that bigger is better in terms of brain structure, this is not always true, highlighting the need to more deeply explore exactly how and why these neural adaptations occur. Chang eagerly anticipates that future studies incorporating “HARDI (High-angular-resolution diffusion imaging) and Q-ball vector analysis, together with larger sample sizes and longitudinal design, will be very helpful in revealing finer microscopic structural differences among different types of elite athletes.”


Chang YK, Tsai JH, Wang CC and Chang EC (2015). Structural differences in basal ganglia of elite running versus martial arts athletes: a diffusion tensor imaging study. Exp Brain Res. doi: 10.1007/s00221-015-4293-x

Chaddock-Heyman L, et al. (2014). Aerobic fitness is associated with greater white matter integrity in children. Cortex. 54:179-89. doi: 10.1016/j.cortex.2014.02.014

Elmer S, Hänggi J and Jäncke L (2014). Processing demands upon cognitive, linguistic, and articulatory functions promote grey matter plasticity in the adult multilingual brain: Insights from simultaneous interpreters. Front Hum Neurosci. 8:584. doi: 10.3389/fnhum.2014.00584

Hänggi J, Koeneke S, Bezzola L and Jäncke L (2010). Structural neuroplasticity in the sensorimotor network of professional female ballet dancers. Hum Brain Mapp. 31(8):1196-206. doi:10.1002/hbm.20928

Imfeld A, et al. (2009). White matter plasticity in the corticospinal tract of musicians: a diffusion tensor imaging study. Neuroimage. 46(3):600-7. doi: 10.1016/j.neuroimage.2009.02.025

Tseng BY, et al. (2013). White matter integrity in physically fit older adults. Neuroimage. 82:510-6. doi: 10.1016/j.neuroimage.2013.06.011

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A call for acceptance of career polygamy in science

Throughout my academic career from undergrad to my current postdoc, I’ve been perplexed by my atypical relationship with science. Yes, research and I have maintained a long, passionate love affair, but an affair apparently unlike those enjoyed by my colleagues. My unconventional attitude towards my work has served as a disconcerting voice that I’m just not cut out for a serious scientific career. I’ll certainly never win a Nobel, probably won’t publish in Science and may never even hold a faculty position. This reality has never really bothered me, but my lack of bother has been a subtle source of concern.

Only now, as a postdoc years into my Neuroscientific career, am I beginning to understand what makes my love affair with science so unusual. It’s by no means less genuine or less impassioned than those of colleagues madly pursuing tenure-track jobs; rather, it’s set apart by its polygamous nature. I get enthralled by new theories, overwhelmed with the excitement of shiny new data, and bore friends and family with my ecstatic ramblings about my research. I am a scientist for no other reason than I love it. However, it’s not the only object of my affection. I have never been, and probably never will be, able to suppress my love for so many other facets of life. A monogamous relationship with Neuroscience would just never suffice for me.

20131007-001611Since I was a teenager, a certain passage from Sylvia Plath’s the Bell Jar has always haunted me. She shared her predicament of being unable to choose a single fig – a life path, and as her indecision gripped her the figs wilted, leaving her starving and without a future. I’ve long been distraught by this similar fear of foregoing any one of my many dreams, wavering among so many enticing options and failing to commit to one whole-heartedly. As did Sylvia, I too considered this a flaw … a characteristic that would hold me back and prevent me from attaining my goals. As I’m finally understanding that these scattered passions or lack of focus – call it what you will – lie at the heart of my atypical approach to my work, I am also finally accepting that this is not necessarily a flaw.

“Good” scientists come in all shapes and sizes, but common to all is a sincere curiosity, a longing for answers and a rigorous devotion to unveiling them. Although these are precisely the factors that originally drew me to Neuroscience, I have always struggled with the conviction that I must not love my work quite enough – or at least not as much as the rock-stars around me, spending grueling hours in the lab, aiming for the highest impact-factor journals and power-networking with the bigwigs in their field. To a certain degree, these are crucial elements of a successful research trajectory, and I too have worked hard, held my research quality to the highest standards, and of course reveled in the rewards of grants and publications. But I have worked equally hard outside of the lab. Throughout grad school and my postdoc, I’ve allowed myself to pick several of those ripe, juicy figs and have savored every one of them. I’m not talking about the conventional concept of work-life balance that we’ve come to accept – at least superficially – is essential for job satisfaction. I’m referring more specifically to work-work balance. I indulge my writing addiction through freelance writing and editing and won’t hesitate to take on other side-projects as I’m so inspired. These endeavors are often neuro-related, but sometimes sprout from my obsession with running and fascination with sports physiology and biomechanics. These extra-neuro pursuits are as much “work” as my research, and I approach them all with the same intensity and devotion. They have not limited my productivity as a Neuroscientist, but have actually fostered it, by keeping me fresh, motivated and engaged with novel perspectives within and beyond the science community.

I’ve been blessed with both graduate and postdoc advisers who’ve been remarkably supportive of my promiscuous work habits, which has doubtlessly contributed to my own recent acceptance of my choices. Yet, I suspect my fortune is the exception rather than the rule, with the admission of this sort of behavior being met with disapproval or condemnation in many labs. In the current academic environment, time spent outside lab or even (gasp!) enjoying yourself is too often considered a sign of laziness or lack of drive. Tales of researchers working themselves to poor health or even suicide are rampant. It’s not clear how a field based on incentives so beautiful as curiosity and understanding has become so ugly, but it’s far time this trend is reversed. Outside interests or other professional pursuits should not be sources of guilt, and are not – contrary to common belief – prohibitive of a flourishing scientific career. Any culture that discourages the nurturing of broad interests can be toxic, stifling both personal growth and, ironically, professional development and productivity.

While there is certainly nothing wrong with the driven pursuit of a focused scientific career – and I strongly admire my dedicated colleagues who have chosen this path – it’s time we reject the myth that this is the only honorable or effective route to scientific success. As a first step, I’m embracing my relationship with Neuroscience, idiosyncrasies and all, and proudly proclaiming that we’ve been polygamous all along.

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This is your Brain on Wine: FMRI Signals of Alcohol Content

Originally published on the PLOS Neuroscience Community

In today’s burgeoning wine industry, winemakers are in constant search of ways to perfect their product and achieve an edge over the competition. Complicating the challenge of producing a bottle that we’re sure to select at our next fine dining experience is the variability across palates. The individuality and unpredictability of sensory experiences – which may further be manipulated by context or expectations – make predicting a wine’s appeal a daunting task. In a dream world, winemakers could peer directly into the brain to examine the biological response to a smoky syrah or a spicy zinfandel. Such a tool could theoretically empower producers to target their wine characteristics to not just the psychological, but also the physiological response to a wine. In their study recently published in PLOS ONE, Frost and colleagues sought to accomplish just this, using functional MRI to assess brain responses to a wine’s flavor attributes.

Rather than assess relatively subjective features like fruitiness, tannins or fullness, the researchers focused on alcohol content, a more objective – and therefore easier to quantify – property. Twenty-one “inexperienced” wine drinkers (they imbibed less than once per week) participated in four wine-tasting sessions while undergoing functional MRI. During each session, they alternated among sipping a tasteless solution, a low-alcohol red wine (13-13.5%) and a high-alcohol red wine (14.5-15%). A different pair of low- and high-alcohol wines, matched on flavor, was tasted in each session. A post-scan taste-test confirmed that participants could not tell the difference between the low- and high-alcohol wines of each pair, as they rated their tastes as essentially identical.

Frost and colleagues identified 30 brain regions of interest that were activated by drinking wine, regardless of alcohol content. This set of areas was then further tested for effects of alcohol. Of these regions, only the right insula and right cerebellum were differentially activated by alcohol level, demonstrating greater activity to the low- than high-alcohol wines. Surprisingly, no regions preferentially activated to more alcoholic wines.

This is your brain on wine. The right insula (left) and right cerebellum (right) were more active when participants drank low- than high-alcohol wines. Adapted from Frost et al., 2015.

This is your brain on wine. The right insula (left) and right cerebellum (right) were more active when participants drank low- than high-alcohol wines. Adapted from Frost et al., 2015.

The cerebellum is known to be involved in sensorimotor processing, which could reasonably account for its activation by subtle differences in alcohol perception. However, both the insula and cerebellum have been shown to be modulated by taste, activating to more intense flavors and feelings of satiety. Shouldn’t high-alcohol wines – which are arguably more intense– therefore more heavily engage these regions? The authors dug deeper into the literature to interpret these unexpected findings.

They propose that because these areas are involved in “cognitive modulation of sensory perception” and “coordinating the acquisition of sensory information,” the lower alcohol wines might have “induced a greater attentional orienting and exploration of the sensory attributes.”

Yet there’s one tiny hole in this explanation, at least when considering the current evidence alone. We could reasonably link activation of these regions to flavor intensity or taste perception if there were some associated behavioral indication that the wines elicit distinct sensory experiences. However, the participants in fact report no perceptible taste difference between the two classes of wines. This discrepancy between the subjective perceptual experiences and brain responses suggests that the observed insular and cerebellar effects may reflect some sensory aspect of wine-tasting that lies below conscious awareness.

Although the researchers don’t directly discuss this possibility, it’s worth exploring. Since the difference in alcohol content between the wine types was notably small (just ~1.5%), it’s not surprising that the participants couldn’t detect a taste difference. It would be interesting to see whether the activations would be more robust to a wider gap in alcohol levels, or might track with a continuum of alcohol content. Furthermore, the study participants were “inexperienced” wine drinkers. Perhaps the taste differences would have been perceptible – or the brain responses stronger – in a sample of connoisseurs with more “refined palates.” As the evidence stands, we can’t conclude whether the BOLD responses indeed reflect effects of wine taste perception that were simply too subtle and hence immeasurable here, or instead relate to lower-level, unconscious sensory processes.

So what do these findings mean for the winemaker looking to neuroscience for a marketing advantage? It’s safe to assume that manipulating the alcohol content of a wine will indeed affect brain physiology (in fact, the known influence of alcohol on the BOLD signal raises concern over confounds between the wine conditions). However, it’s unclear how this brain response relates to a wine drinker’s sensory experience, let alone preference for one wine over another.

As blogger Neuroskeptic points out in his recent commentary on the study, “it’s not clear whether a brain scan is the best way to approach the question of whether high alcohol is overpowering. Surely the same thing could be demonstrated using a taste test.”

Despite these considerations, Frost and colleagues establish a solid stepping-stone to further explore the complex relationship between a wine’s flavor profile and consumers’ gustatory and neural responses. More importantly for wine-lovers everywhere, their study offers a key first step towards unraveling how and why that bold, oaky cabernet beats a merlot any day.


Bower JM et al. (1981). Principles of Organization of a Cerebro-Cerebellar Circuit. Brain Behav Evol 18:1-18. doi:10.1159/000121772

Frost R et al. (2015). What Can the Brain Teach Us about Winemaking? An fMRI Study of Alcohol Level Preferences. PLOS ONE. doi: 10.1371/journal.pone.0119220

Plassmann H et al. (2008). Marketing actions can modulate neural representations of experienced pleasantness. Proc Natl Acad Sci 105(3):1050-4. doi:10.1073/pnas.0706929105

Small DM et al. (2003). Dissociation of Neural Representation of Intensity and Affective Valuation in Human Gustation. Neuron 39(4):701-11. doi:10.1016/S0896-6273(03)00467-7

Smeets PAM et al. (2006). Effect of satiety on brain activation during chocolate tasting in men and women. Am J Clin Nutr 83(6):1297-1305.

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#PLOS #SfN14 Highlights: Exercise, Energy Intake and the Brain

Originally published on the PLOS Neuroscience Community

This Thanksgiving, many of us will be manipulating our energy balance — in one way or another. Most will be building our energy stores with a hefty dose of Turkey and pumpkin pie, while others may tap into those reserves at their local turkey trot. Either way, we’ll need to look no further than the mirror to be reminded how diet and exercise mold the body.

Less obvious, however, is how energy availability regulates brain health. Emerging research is showing that tweaking our energy use through diet and exercise elicits positive metabolic changes that promote better neuronal and mental health. In their symposium “Exercise, Energy Intake, and the Brain” at this year’s recent Society for Neuroscience conference, scientists Henriette van PraagMonika FleshnerMichael Schwartz and Mark Mattson discussed the mechanisms underlying the brain-energy relationship.


In her talk, Fleshner demonstrated the powerful effects of exercise on our response to stress. Not only does physical activity make many organisms — of all shapes and sizes — simply feel good (rats, frogs and even slugs will voluntarily run on wheels in the wild!), but it also does wonders to protect us against the hazards of stress. After only six weeks of regular running, an individual will begin to show signs of stress-robustness, including being more resilient and resistant to stress. But just how does that morning jog help us combat a stressful day at work?

Animals of all types love to run!

Animals of all types love to run!

According to Fleshner, exercise attenuates the typical stress-induced activation of the dorsal raphe nucleus — a major source of serotonergic projections. Although a logical player in this process might be the medial prefrontal cortex (mPFC) since this area regulates serotonin transmission in the dorsal raphe nucleus, the mPFC isn’t necessary for exercise-related stress resistance. Rather, six weeks of wheel-running increases levels of 5HT1A inhibitory autoreceptors and reduces stress-induced serotonin release. Thus, it appears that exercise effectively puts the breaks on the dorsal raphe nucleus-mediated serotonergic response to stress. What’s more, this stress-robustness likely involves a widespread coordinated response including exercise-induced epigenetic changes. In fact, Fleshner showed that a host of stress-related genes are differentially expressed in physically active and inactive individuals.


Mattson opened his discussion with some inspiring anecdotes on the subjective benefits of exercise and fasting. For instance, celebrated writer Joyce Carol Oates is known to do some of her best writing while running, an experience with which I — a runner and writer myself — am dearly familiar.

Running seems to allow me, ideally, an expanded consciousness in which I can envision what I’m writing as a film or a dream. — Joyce Carol Oates

Sure, it may feel like physical activity makes our thoughts flow more fluidly, but just how and why might exercise spark greater neural efficiency? Exercise promotes mitochondrial growth and development systemically, and it’s well accepted that what’s good for the body is good for brain; the benefits of exercise aren’t limited to muscle cell mitochondria, but extend to neuronal mitochondrial as well. Mattson outlined a molecular pathway by which physical activity influences mitochondrial integrity, neurogenesis and plasticity in the hippocampus. While the nitty-gritty details of the circuit are beyond the scope of this post, two key players are worth mentioning: the protein PGC1-alpha which is activated by exercise, and SIRT3, levels of which are increased in runners compared to non-runners. Notably, PGC1-alpha is necessary for mitochondrial biogenesis, including in hippocampal neurons, and activates SIRT3, which is important for normal long-term potentiation and synaptic calcium release. In short, exercise triggers a cascade of cellular processes that promote efficient mitochonondrial and neuronal function.

Mattson next highlighted some parallels between the neuroprotective effects of exercise and intermittent energy restriction (such as fasting or calorie restriction). Notably, running has been shown to up-regulate BDNF, CREB-activation and DNA-repair mechanisms which may combat the deleterious effects of aging. Energy restriction similarly elicits a host of positive neurobiological effects — for instance, increased synaptic density and neurogenesis — and even promotes longevity (30% greater lifespan in rats isn’t bad!). There’s some evidence that intermittent fasting may actually be more beneficial than calorie restriction, as it more effectively lowers heart rate, a process likely mediated by increased BDNF. Finally, Mattson pointed out that both exercise and fasting enhance production of ketones, a highly robust source of neuronal energy that have also been shown to enhance cognitive function and neural plasticity.

Unfortunately, I was only unable to attend the additional talks by van Pragg and Schwartz. But fortunately, the symposium speakers compiled a Journal of Neuroscience review highlighting their key points.

Running! If there’s any activity happier, more exhilarating, more nourishing to the imagination, I can’t think what it might be. In running the mind flies with the body; the mysterious efflorescence of language seems to pulse in the brain, in rhythm with our feet and the swinging of our arms. — Joyce Carol Oates

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@PLOSNeuro #SfN14 Highlights: Intracranial EEG and Brain Stimulation

Originally published on the PLOS Neuroscience Community

Despite their many advantages, traditional tools to study neurocognitive function in humans, such as EEG or fMRI, carry several disadvantages compared to those usable on animals. Perhaps the most significant limitation is the challenge of imaging neural activity of live human brains during mental functions, which inherently requires the application of invasive neuroimaging techniques. Recently, the cognitive neuroscientist’s tool-belt has rapidly expanded, with the growing prevalence and usability of powerful imaging methods such as intracranial EEG – or electrocorticography (ECOG) – and electrical brain stimulation, that permit direct recording or stimulation of neuronal activity in live, conscious humans.

The SfN symposium Studying Human Cognition with Intracranial EEG and Electrical Brain Stimulation (previously previewed here, including an interview with speaker Josef Parvizi) explored current advances in these evolving methods along with their applications to the human cognitive experience.


UC Berkeley’s Bob Knight opened the symposium by highlighting the unique perks of ECOG over more traditional imaging techniques — points which were later recapitulated by other speakers — including its remarkably high spatial and temporal resolution and exceptional signal to noise ratio. ECOG is in fact so precise that it can reliably measure signal down to the single trial level – a feat neither EEG nor fMRI can boast. In just his brief introduction, Knight shared some impressive clinical and cognitive applications of these electrophysiology techniques. For instance, intracranial EEG signal from the auditory cortex was effective (with 99% accuracy!) at reconstructing words, holding clear implications for patients with speech impairments. My personal favorite highlight of the session, however, was the reconstruction of Pink Floyd’s “Another brick in the wall” from intracranial auditory cortex recordings.


First up, Josef Parvizi from Stanford University presented his lab’s multimodal approach to neurocognitive assessment, incorporating fMRI, ECOG and electrical brain stimulation. Parvizi shared a series of cases illustrating the powerful – and entertaining — applications of brain stimulation. In response to stimulation of the “salience network”, which had been previously mapped using fMRI, one patient responded that he felt like he was “riding in a storm”, but “felt nothing” after sham stimulation. A second patient reported the sense that “something bad is going to happen,” confirming in both patients emotionally driven reactions to “salience network” stimulation. In a final, particularly compelling, demonstration, Parvizi showed the effects of fusiform face area stimulation: “You just turned into somebody else,” the subject reported. “That was a trip!”


Next, Rafael Malach of the Weizmann Institute discussed his lab’s use of intracranial EEG to measure spontaneous neural activity at rest. FMRI is most commonly used to study resting-state activity; however, the BOLD signal may be contaminated by non-neural signal, and — due to its poor temporal resolution — is effectively blind to rapid events. Using ECOG, which overcomes both of these hurdles, Malach demonstrated how high frequency gamma activity accurately reflects neuronal firing rate and can assess functional connectivity. Surprisingly, spontaneous activity between recording sites on opposite hemispheres is more highly correlated than between adjacent recording sites. So ECOG may be a powerful tool for measuring spontaneous activity, but this is only valuable if we can identify the signal’s associated mental processes. Using the comical and celebrated example of the entorhinal cortex “Simpsons neuron”, which selectively fired in response to images of the Simpsons or immediately before spontaneous recall of the cartoon, Malach suggested that spontaneous activity exceeding an awareness threshold might indeed represent conscious thoughts.


Jean-Philippe Lachaux, from the Lyon Research Council, took a slightly different angle on the applications of ECOG, highlighting its unique suitability for evaluating naturalistic behavior. Because of its robustness against artifacts problematic in EEG or fMRI — like motion, blinking or signal distortion — ECOG can be more flexibly used in a variety of environments. These applications can be enhanced by integrating it with other tools such as eye-tracking, to more accurately associate natural behavior with neural activity in real time. Furthermore, Lachaux illustrated the power of ECOG at unraveling the temporal dynamics of functional interactions. Lachaux presented data questioning the common assumption that inter-region communication is typically a one-way street, proposing instead that such interactions may be more akin to reciprocal “shared conversations”.


Sabine Kastner of Princeton University wrapped up the session with her lab’s comparative studies of attention in humans and monkeys. Combining human intracranial EEG with single-unit and LFP measures in monkeys during attention (Flanker task), she reported similar attention modulation in human and monkey intraparietal sulcus. Intriguingly, while attention modulated high gamma in both species, it also increased low frequency oscillations in humans. At the heart of cognitive neuroscience is the question of how neural activity translates to thoughts and behavior. To directly address this issue, Kastner is using electrophysiology to identify the optimal neural code for attention. In both humans and monkeys, she finds that spike phase better predicts behavior than spike rate, inching us one step closer to resolving the brain-cognition relationship.

Judging by the responses to my live-tweeting of this symposium, I’ll conjecture that the Neuro community is as intrigued and excited as yours-truly about the potential applications of ECOG and brain stimulation. In the words of @WiringTheBrain,

“This stuff is so COOL! And scary. But mainly COOL!”

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Sweet talking the pain way

Sure, we’ve all heard about the importance of the mind-body connection for improving our running performance, but how many of us actually give credence to the idea? Many of us have experienced the strong influence of our mental state on our physical performance, though we may not always be aware of its impact. You’re probably familiar with one of these scenarios … a bad day at work is topped off with an equally miserable run, or a celebratory run after hearing some good news sends you soaring into that runner’s high. Well, just the other day, my run highlighted exactly how powerful our mental landscape can be at assuaging or preventing injuries.

hamstring-origin-tendinopatFor the past five months (or more accurately, 17 years if you consider the repeated flare-ups since I began running) I’ve been battling chronic, relentless hamstring / glute / hip tightness and pain. Call it what you will … the various docs I’ve seen have attributed it to anything and everything, from sciatica to piriformis syndrome to hamstring tendinopathy to good old-fashioned overuse and weakness. Regardless of these meaningless diagnoses, I’ve found no relief, despite my desperate treatment attempts with massage, foam-rolling, ART and acupuncture. And despite this failed therapy, I’ve continued to run through the pain, as any typically irrational running addict would do.

A twitter discussion, following a particularly traumatic (to the hamstring) 12-miler, got me thinking. @skorarunning pointed out “I’ve even read that rolling could cause tightness, as it’s a stress to the muscles & they could tighten as a safety mechanism”. @rickmerriam corroborated “Muscles tighten up to prevent joints from going into positions of vulnerability. #BuiltInProtectiveMechanism”.

As I started my run the next day and my hammie/glute/hip immediately tightened up (per usual), I thought back to these comments. Why was it cramping? What was it trying to protect itself against? For whatever reason, it was vulnerable, and – sensing the need to shield itself against some mysterious stressor – locked up in defense. The vision of an anxious child came to mind: unnecessarily frightened of a harmless, imaginary threat. ‘If only I could just convince my hamstring that the threat is not real … there’s no reason to ‘fear’ the run,’ I wished. And so I did. I had a chat with my leg and encouraged it to clam the heck down. To stop overreacting. There was no real danger. It was safe and strong and protected. At the slightest hint of tension, I sweet-talked the muscle into soft, loose submission. And to my complete astonishment, the muscle listened, sending me sailing comfortably and strongly through 8 pain-free miles.

Was it merely a coincidence? Would my hamstring have behaved had I not whispered soothing lullabies into into its, um, hammie-ears? This was but another experiment of one, and I will never know. But I do know our muscles activate in a beautifully orchestrated neuromuscular symphony, which is intimately connected with our central nervous system. It would not surprise me if the the cognitive superstar of the human nervous system – the brain – is charismatic enough to use its mental coercion to sway its fellow motor neurons into passive compliance.

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What does Work-Related Burnout do to the Brain?

Originally published on the PLOS Neuroscience Community

We’ve all experienced it – the fatigue, stress and irritability after a long day of work. For most, these feelings are fleeting, and are nothing a good night’s sleep or a cup of tea over a good book can’t remedy. But for others, the daily stress extends into weeks and months, and eventually into long-term burnout. The physical toll on the over-worked can be so extreme that occupational burnout is being increasingly recognized as a serious medical condition. While the behavioral symptoms – including problems with memory or concentration, mood imbalances, insomnia and body aches – are well documented, the consequences of chronic burnout on brain function, and how such neural changes give rise to emotional dysregulation, have been inadequately examined. A recent PLOS One study, by Amita Golkar and colleagues from the Karolinska Institute, sought to better understand how chronic work-related stress alters brain function and emotional processing. While their findings confirm that impaired emotional regulation has neurobiological roots, another expert in the field has raised the question of whether stress may affect additional neural circuits undetected here.

Assessing stress

Thirty-two individuals with chronic burnout and 61 healthy controls participated. The patients worked 60-70 hours per week, manifested symptoms including sleeplessness, fatigue, irritability, cognitive impairments or impaired working ability for at least a year, and had lost at least six months of work to sickness. Each participant completed two test sessions, including a startle response task to measure emotional regulation, and resting-state functional MRI to evaluate functional brain connectivity.

During the behavioral task, a series of neutral and negative pictures was shown, with each picture flashed before and after an instruction cue (Figure 1). For negative pictures, subjects were told to either up-regulate, down-regulate or maintain their emotional response to the image (i.e., to experience the second presentation as more, less or similarly emotionally charged as the first presentation). Neutral pictures were always paired with the instruction to maintain their emotional response. To assess how the cues affected participants’ physiological responses to the images, during each picture presentation the researchers administered an acoustic startle and measured eye-blink responses using electromyography. This allowed them to compare stress responses to an identical stimulus, differing only in how the participants manipulated their emotional reactions.

Figure 1. Startle responses were measured before and after an emotional regulation cue to the same picture. doi: 10.1371/journal.pone.0104550

Figure 1. Startle responses were measured before and after an emotional regulation cue to the same picture. doi: 10.1371/journal.pone.0104550

Burnout impairs emotional regulation

When they were told to maintain or up-regulate their emotional responses, the burnout and control groups showed similar startle responses (response to the post-cue picture – response to the pre-cue picture). But critically, during the down-regulate condition the burnout group not only exhibited a greater stress response than controls (Figure 2), but also reported less success at implementing the emotional regulation instructions to the negative images. Just from these behavioral findings, it’s clear that chronic stress can dramatically alter how we process negative emotions. In particular, the burnt-out workers demonstrated less control over their reactions to negative experiences, showing signs of elevated distress that they were unable to dampen.

Figure 2. Patients showed an exaggerated response to negative images when instructed to down-regulate their emotions. doi: 10.1371/journal.pone.0104550

Figure 2. Patients showed an exaggerated response to negative images when instructed to down-regulate their emotions. doi: 10.1371/journal.pone.0104550

Burnout alters limbic function

Given this strong evidence that something was awry in these patients’ emotional regulation circuitry, Golkar and colleagues next asked whether altered neural function might underlie their symptoms. Naturally, they looked to the limbic system, a brain network involved in processing emotion. They focused particularly on the amygdala, which is known to be critical for evoking fear and anxiety, and is enlarged in people with occupational stress. Here, functional connectivity during rest between the amygdala and several brain regions was altered in patients; most notably, connections were weaker with the prefrontal cortex and stronger with the insula. What’s more, the stronger the correlation of the amygdala with the insula or a thalamic/hypothalamic region, the higher the individual’s perceived stress. Finally, connectivity between the amygdala and the anterior cingulate correlated with participants’ ability to down-regulate their emotional response from the startle-response task.

Figure 3. Differences in functional connectivity with the amygdala between patients and controls. doi: 10.1371/journal.pone.0104550

Figure 3. Differences in functional connectivity with the amygdala between patients and controls. doi: 10.1371/journal.pone.0104550

The findings of Golkar and colleagues help to establish a concrete understanding of the cognitive and neural changes underlying a too-often overlooked serious health condition. These findings add credence to the subjective feeling of being overly sensitive to negativity, or unable to control emotions, when burnt out. Perhaps more importantly, they confirm that such emotional impairments indeed have neurobiological underpinnings – changes that fit in beautifully with our knowledge of how the brain processes emotion. A stress-related disconnect between the amygdala and the prefrontal cortex and anterior cingulate – even at rest – builds upon earlier studies showing reduced volume and altered task-evoked responses in these areas associated with stress. And chronic stress was further related to amygdala hyperconnectivity with the insula and thalamus/hypothalamus, key regions for eliciting a stress response.

Dissociating the neural effects of stress

However, this study leaves several questions unanswered and raises a few more. Given the complexity of the patients’ psychological conditions, there were most certainly numerous other physical and psychological differences between the groups that went undocumented and uncontrolled. In the future, closer examination of these possible confounds will help identify their unique neural and behavioral effects. Furthermore, in addition to functional changes in several expected regions, altered resting connectivity also occurred in two unexpected regions – the cerebellum and motor cortex. Whether these were false positives, or whether occupational stress may have additional underappreciated motor or cognitive consequence, remains to be seen.

Another perspective

Because of the study’s justifiable focus on connectivity with the amygdala, it’s unclear how specific or broad the neural changes associated with chronic stress may be. Tom Liu, a researcher studying resting-state brain connectivity at UC San Diego, who was not involved in this study, explains,

“This begs the question of what other connections might be different between the two groups or perhaps show even better correlation with the stress scores. The issue there is that because of the large number of potential connections, a researcher is very quickly faced with a large multiple comparisons problem – this is an open issue in the field.”

Further work will help clarify whether stress – or other differences between the groups – predominantly affects limbic circuitry or might also contribute to global brain changes. Liu points out,

“One aspect that would have been interesting to look at is whether there were any global differences between the two groups that could have accounted for the differences, as the authors did not perform global signal regression.”

For instance, two recent studies report altered global signal associated with schizophrenia and variance in vigilance.

Golkar et al. help to bridge the gap between the emotional dysregulation of workplace burnout and its long-term impact on brain function. Such work is a valuable step towards not only better understanding the brain’s response to stress, but also better equipping us to manage our emotional and brain health – even after a long day of work.


Blix E, Perski A, Berglund H and Savic I (2013). Long-Term Occupational Stress Is Associated with Regional Reductions in Brain Tissue Volumes. PLOS One 8(6): e64065. doi:10.1371/journal.pone.0064065

Davis M (1992). The role of the amygdala in fear and anxiety. Annu Rev Neurosci 15:353-75. doi: 10.1146/annurev.ne.15.030192.002033

Flynn FG, Benson DF and Ardila, A (1999). Anatomy of the insula functional and clinical correlates. Aphasiology 13(1): 55-78. http://dx.doi.org/10.1080/026870399402325

Herman JP and Cullinan WE (1997). Neurocircuitry of stress: central control of the hypothalamo-pituitary-adrenocortical axis. Trends Neurosci 20(2):78-84. doi: 10.1016/S0166-2236(96)10069-2

Golkar A, Johansson E, Kasahara M, Osika W, Perski A and Ivanka S (2014). The influence of work-related chroinic stress on the regulation of emotion and on functional connectivity in the brain. PLOS One 9(9): e104550. doi: 10.1371/journal.pone.0104550

Jovanovic H, Perski A, Berglund H amd Savic I (2011). Chronic stress is linked to 5-HT(1A) receptor changes and functional disintegration of the limbic networks. Neuroimage 55(3):1178-88. doi: 10.1016/j.neuroimage.2010.12.060

LeDoux JE (2000). Emotion Circuits in the Brain. Annu Rev Neurosci 23: 155-84. doi: 10.1146/annurev.neuro.23.1.155

Savic I (2013). Structural Changes of the Brain in Relation to Occupational Stress. Cereb Cortex. doi: 10.1093/cercor/bht348

Schutte N, Toppinen S, Kalimo R and Schaufeli W (2000). The factorial validity of the Maslach Burnout Inventory—General Survey (MBI—GS) across occupational groups and nations. J Occup Organ Psych, 73(1), 53-66. http://dx.doi.org/10.1348/096317900166877

Wong CW, Olafsson V, Tal O, Liu TT (2013). The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures. Neuroimge, 83, 983-90. doi: 10.1016/j.neuroimage.2013.07.057

Yang GJ, Murray JD, Repovs G, Cole MW, Savic A, Glasser MF, Pittenger C, Krystal JH, Wang XJ, Pearlson GD, Glahn DC, Anticevic A (2014). Altered global brain signal in schizophrenia. PNAS, 111(20), 7438-43. doi: 10.1073/pnas.1405289111

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Brain Connectivity Patterns of Shifting Memory Processes

Originally published on the PLOS Neuroscience Community

At a recent dinner party, the memories flood your mind as you reminisce with an old friend. A woman approaches and your friend introduces you: “I’d like you to meet my wife, Margaret.” Your attention shifts from the past to this present moment, as you focus on making a new association between “Margaret” and the tall, dark-haired woman before you.

As during a dinner party with old friends and new acquaintances, the dynamically shifting stimulus landscape around us may trigger the retrieval of old memories or the formation of novel ones, often in overlap or rapid succession. What’s more, memory does not simply involve compartmentalized processes of the birth or reactivation of memories in isolation. Rather, successful execution of these processes also relies on support from non-mnemonic processing, such as evaluating a recalled memory or paying attention to new information. Although there is some overlap in the brain regions involved in laying down new memories and recovering old ones, the complex coordination of the many sub-processes of encoding and retrieval naturally requires cross-talk across distinct neural systems.

The brain’s medial temporal lobe is commonly considered the seat of memory – with the hippocampus lying at its heart – as encoding and retrieval rely critically on these regions. However, just as memory involves the coordination of many cognitive functions, so does it require the coordination of widespread brain networks. Both small-scale circuits across hippocampal subregions, and long-range brain systems, work together to integrate sensory information, control attention and filter relevant details in support of memory. A recent study from Katherine Duncan, Alexa Tompary, and Lila Davachi at NYU demonstrated just how the hippocampus shifts its communication with the surrounding brain to support its remarkable ability to rapidly switch between memory processes.

The researchers conducted fMRI while participants performed alternating blocks during which they encoded pairs of objects and then recalled those object pairs. A day later, participants returned for an unscanned long-term memory test, in which they reported whether they recognized the objects, and rated how confidently they recalled the pairs. This delayed memory test was used to measure how well the object associations had been encoded the day prior.

A standard analysis confirmed that across all hippocampal subregions (CA1, DG/CA3, subiculum) activity increased for both successful encoding and retrieval. Notably, the retrieval effect was strongest in DG/CA3, in line with past studies suggesting that this region might function as an auto-associative network that serves to reactivate stored memory traces. Now, we’ve long known that the hippocampus is engaged during these processes; but less certain is how the region interacts with the surrounding brain.

The researchers focused on hippocampal subregion CA1, an important hub along the bidirectional cortex-hippocampus highway, as it both receives input from the medial temporal lobe (via the dentate gyrus and CA3), and also provides output back to the cortex. Connectivity between DG/CA3 and CA1 was stronger during the retrieval than the encoding block, whereas connectivity with CA1 didn’t differ between memory blocks for any of the other medial temporal lobe or midbrain regions they investigated (Figure 1). Thus, not only was DG/CA3 highly activated, but it was also more strongly connected with its downstream hippocampal target, during retrieval.

Figure 1. Connectivity between CA1 and DG/CA3 is stronger during retrieval than encoding. Adapted from Duncan et al., 2014.

Figure 1. Connectivity between CA1 and DG/CA3 is stronger during retrieval than encoding. Adapted from Duncan et al., 2014.

But how might memory-specific communication across regions subserve the brain’s changing cognitive goals? To test whether connectivity patterns in fact support memory success, the researchers correlated functional connectivity measures with encoding and retrieval performance. Supporting their prior findings, CA1-DG/CA3 connectivity correlated with immediate retrieval accuracy, but not with long-term memory (i.e., day 2 retrieval) (Figure 2, left). Conversely, connectivity between CA1 and the ventral tegmentum correlated with long-term memory, but not immediate retrieval accuracy (Figure 2, right). As Davachi explains, “This suggests that whatever this signal represents, it is explaining long-term – not short-term – memory, which arguably suggests that across subject variability in CA1-ventral tegmentum connectivity is related to the consolidation of memories, not just their initial encoding.”

Figure 2. CA1-DG/CA3 connectivity correlates with immediate retrieval, whereas CA1-ventral tegmentum connectivity correlates with memory consolidation. Adapted from Duncan et al., 2014.

Figure 2. CA1-DG/CA3 connectivity correlates with immediate retrieval, whereas CA1-ventral tegmentum connectivity correlates with memory consolidation. Adapted from Duncan et al., 2014.

Notably, these connectivity patterns emerged when examining activity across each encoding or retrieval block, but disappeared when isolating the trial-evoked responses. It therefore seems possible that these increases in connectivity strength may not directly support isolated moments of memory formation or reactivation, but instead, auxiliary processes that evolve gradually over time. However, Davachi cautions “These null effects do not necessarily imply that there are not important trial-evoked changes in connectivity, but rather, that the trial-evoked data are simply swamped with the incoming perceptual and task signals.”

While the role of DG/CA3 and its connectivity to CA1 in associative retrieval has been well documented, the encoding-specific link between CA1 and the ventral tegmentum is less expected. Regions such as the medial temporal lobe and the prefrontal cortex are traditionally considered the major players in memory encoding; yet, recent research has hinted at a more important role for the ventral tegmentum than previously thought. Furthermore, this finding aligns well with animal studies showing that input to CA1 from the ventral tegmentum is required for synaptic plasticity, and that long-term potentiation – key to long-term memory formation – is dopamine-dependent. But as Davachi emphasizes, “You can never know if the BOLD response is related to long-term potentiation. All we show is that the coordinated activation between the ventral tegmentum and CA1 is related to successful encoding (and not retrieval) but what this represents is unclear.” Indeed, the ventral tegmentum is involved in a host of other, non-memory functions as well, such as novelty detection and motivation, both of which would be critical for the encoding task used here – or when making a new acquaintance at a dinner party. What remains to be determined is how, if at all, hippocampal connectivity with the ventral tegmentum supports memory consolidation, or rather, these adjunct processes that might be important for establishing new memories.

Although this study demonstrated unique hippocampal interactions during encoding and retrieval, it can’t speak to the direction of information flow. For instance, since the hippocampal-ventral tegmentum connection is reciprocal, signaling could feasibly proceed in either direction. Furthermore, their findings don’t show that encoding and retrieval are exclusively associated with CA1-ventral tegmentum and CA1-DG/CA3 connectivity, respectively – only that the strength of these interactions differs depending on the memory manipulation.

While further studies, especially those which more directly measure neural activity, will help clarify questions concerning directionality and causality, these findings build significantly upon our knowledge of human memory. In particular, Duncan and colleagues’ techniques enable the assessment of communication with and across hippocampal subregions while directly evaluating memory, which has been challenging in animals. Their findings not only raise several important questions for follow-up, but critically, also bridge the gap between human and animal studies to help unify our understanding of the brain systems supporting encoding and retrieval.


Duncan K, Tompary A and Davachi L (2014). Associative encoding and retrieval are predicted by functional connectivity in distinct hippocampal area CA1 pathways. J Neurosci 34(34): 11188-98. doi: 10.1523/jneurosci.0521-14.2014

Murty VP and Adcock RA (2014). Enriched encoding: Reward motivation organizes cortical networks for hippocampal detection of unexpected events. Cereb Cortex 24(8):2160-8. doi: 10.1093/cercor/bht063

Treves A and Rolls ET (2004). Computational constraints suggest the need for two distinct input systems to the hippocampal CA3 network. Hippocampus 2(2):189-99. doi: 10.1002/hipo.450020209

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Global similarity signals of recognition strength

The below article was recently rejected from the Journal of Neuroscience as a ‘Journal Club’ commentary on Davis et al., 2014, ‘Global neural pattern similarity as a common basis for categorization and recognition memory’. Hoping that my efforts will not go to waste, I’d like to give the piece an alternate home here. Please read, comment and share, all free of paywalls!

Theories of long-term memory have linked an item’s memory strength to its “global similarity” (Clark and Gronlund, 1996). The greater the conceptual overlap between a target item and other items stored in memory, the more familiar the item will seem. While psychological models have consistently supported the theory that across-item similarity contributes to recognition memory, it is unclear how neural computations give rise to this relationship between representational similarity and memory strength. Neuroimaging studies have shown that activity in the brain’s medial temporal lobe tracks memory strength as well as the representational overlap between stimuli in memory, establishing this region as a likely host for a global similarity signal that confers accuracy and confidence to recognition judgments.

Model of the multivoxel pattern similarity analysis. A) The multivoxel activation pattern within a region is extracted for each stimulus (S1, S2, etc.). B) The correlation between the activation pattern for each stimulus and that of all other stimuli is computed. Across-item correlations are expected to be higher for stimuli that are strongly remembered than those that are poorly remembered. Adapted from Xue et al., 2010.

Model of the multivoxel pattern similarity analysis. A) The multivoxel activation pattern within a region is extracted for each stimulus (S1, S2, etc.). B) The correlation between the activation pattern for each stimulus and that of all other stimuli is computed. Across-item correlations are expected to be higher for stimuli that are strongly remembered than those that are poorly remembered. Adapted from Xue et al., 2010.

In their study recently published in the Journal of Neuroscience, Davis and colleagues (2014) tested whether the similarity between blood oxygen level dependent (BOLD) activation patterns elicited by an item and other encoded items predicted how confidently the item would later be recognized (see figure). Participants underwent functional magnetic resonance imaging while performing an incidental encoding task of repeated Chinese words. They were later administered a surprise recall task, in which they freely reported any recalled words from the encoding task, as well as a surprise recognition task, in which they reported their confidence in having previously seen a word. The authors then computed a neural similarity score for each word, which measured the correlation between the multivoxel activity pattern for that word and the activity pattern for all other words (Davis et al., Figure 1). This global similarity metric was compared to recognition confidence ratings to assess the relationship between neural representational overlap and memory strength.

Consistent with their prediction, the extent of global similarity between the multivoxel activation pattern of a word and that of all other words correlated with the word’s subsequent recognition confidence ratings (Davis et al., Figure 3A). Within their medial temporal lobe region of interest, the effect was localized to clusters in both the parahippocampal gyrus and hippocampus. This link between neural global similarity and recognition strength held even after controlling for within-item similarity, which the authors previously showed to correlate with memory strength (Xue et al., 2010). Furthermore, medial temporal lobe pattern similarity also correlated with the semantic relatedness between words (Davis et al., Figure 6). This demonstrated, for the first time, that medial temporal lobe substrates of between-item similarity mirror psychological metrics of memory strength and semantic relatedness. The stronger an item is represented in memory, the more highly its semantic content, and its representation in the medial temporal lobe, overlaps with that of other stimuli.

While these initial results speak to the mechanisms by which an item is perceived as familiar, considerable debate exists over whether recognition is mediated by a single neural system. For instance, some neuroimaging and lesion studies have reported functional segregation of familiarity and recollection signals within the medial temporal lobe (Eichenbaum et al., 2007), while others indicate that the medial temporal lobe collectively supports all forms of recognition memory (Squire et al., 2007). Furthermore, there is evidence that successful recall can be mediated by global similarity (Gillund and Shiffrin, 1984), but also by pattern separation of an item from other items (O’Reilly and Norman, 2002). Thus, to dissociate between effects of global similarity on recognition strength and on recall, pattern similarity analyses were additionally performed on non-recalled words alone, and also on recalled versus non-recalled words. Global similarity of medial temporal lobe activation patterns both correlated with the memory strength of non-recalled words and predicted recall success. Thus, both recognition and recall may rely on the degree of neural representational overlap across items in memory.

Finally, the authors investigated whether the association between memory strength and neural similarity extends beyond long-term memory. Given prior evidence that learning categorical rules increases the psychological similarity of learned items and activates the medial temporal lobe, they tested whether global similarity of medial temporal lobe representations reflected category learning. Indeed, global similarity of medial temporal lobe activity patterns correlated with the psychological similarity between an item and others in its category (Davis et al., Figure 3B, C). Notably, these regions overlapped considerably with those from the long-term memory pattern similarity analysis (Davis et al., Figure 4). Thus, global similarity computations in the medial temporal lobe may not selectively subserve episodic memory formation, but might support a range of learning processes.
These findings suggest a universal mechanism of coding memory strength within the medial temporal lobe that generalizes across domains, beyond just recognition memory. This bridge across cognitive domains aligns well with our understanding that acquiring both episodic memories and categorization rules involves learning new information, a process fundamentally supported by memory encoding. However, the breadth of such a medial temporal lobe code, which extends beyond recognition to encompass recall and categorization, raises important considerations regarding the heterogeneity versus homogeneity of medial temporal lobe memory functions.

Some theories of medial temporal lobe function propose distinct roles for the hippocampus and parahippocampal gyrus in long-term memory. A recent study examined whether these regions also functionally dissociate according to memory-related global similarity computations. LaRocque et al. (2013) reported a correlation between across-item neural similarity and recognition strength in the parahippocampal gyrus, but an inverse correlation in the hippocampus. This dissociation contrasts with the parallel representations in the hippocampus and parahippocampal gyrus observed by Davis et al. (2014). Thus, hippocampal representations of global similarity and distinctiveness may both contribute to recognition memory. These seemingly contradictory findings in fact align with computational models of hippocampal function suggesting that the structure performs both pattern completion and separation in the service of long-term memory (Yassa and Stark, 2011). These operations are likely computed by separate hippocampal subregions and support distinct memory functions. Specifically, pattern separation may be mediated by the dentate gyrus and promote discriminative processes that aid encoding and recollection, whereas pattern completion may be mediated by CA3 and generalize across inputs to signal familiarity. It is therefore possible that hippocampal signals of representational overlap and distinctiveness coexist in complex tasks like those employed in these studies (LaRocque et al., 2013; Davis et al., 2014), which may dynamically engage concurrent memory encoding and retrieval processes. Follow-up studies will help to resolve why a pattern completion or separation signal would dominate depending on the task condition or memory manipulation.

Furthermore, given the inherent ambiguity of multivoxel signal content, it is unclear what particular information is carried in overlapping activation patters. Here, BOLD patterns correlated with both memory strength and semantic content; yet, multiple additional variables may covary with these cognitive measures and hence contribute to the similarity across multivoxel space. As the authors acknowledge, an infinite number of factors, which can be challenging to detect or control, may increase the similarity between BOLD activation patterns (Todd et al., 2013). Further research will be important to more completely characterize how variance in factors such as stimulus features, cognitive sub-processes, BOLD dynamics, or analysis procedures, may additionally drive the overlap in BOLD patterns of neural representations.

The findings of Davis and colleagues provide novel insight into medial temporal lobe coding mechanisms of memory strength, linking computational models that implicate psychological similarity in recognition strength with representational similarity of memory-related brain activation patterns. Together, these results solidify a base upon which to more thoroughly examine the breadth of this medial temporal lobe similarity signal across cognitive processes. Such findings will serve as critical steps towards clarifying the extent to which overlapping neural representations in the hippocampus and parahippocampal gyrus contribute to a range of learning processes – including both those within and beyond the domain of episodic memory.


1. Clark SE, Gronlund SD. 1996. Global matching models of recognition memory: How the models match the data. Psychon Bull Rev 3:37-60.
2. Davis T, Xue G, Love BC, Preston AR, Poldrack RA. 2014. Global neural pattern similarity as a common basis for categorization and recognition memory. J Neurosci 34:7472-84.
3. Eichenbaum H, Yonelinas AP, Ranganath C. 2007. The medial temporal lobe and recognition memory. Annu Rev Neurosci 30:123-52.
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5. LaRocque KF, Smith ME, Carr VA, Witthoft N, Grill-Spector K, Wagner AD. 2013. Global similarity and pattern separation in the human medial temporal lobe predict subsequent memory. J Neurosci 33:5466-74.
6. O’Reilly RC, Norman KA. 2002. Hippocampal and neocortical contributions to memory: advances in the complementary learning systems framework. Trends Cogn Sci 6:505-10.
7. Squire LR, Wixted JT, Clark RE. 2007. Recognition memory and the medial temporal lobe: a new perspective. Nat Rev Neurosci 8:872-83.
8. Todd MT, Nystrom LE, Cohen JD. 2013. Confounds in multivariate pattern analysis: Theory and rule representation case study. Neuroimage 77:157-65.
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Davis T, Xue G, Love BC, Preston AR, & Poldrack RA (2014). Global neural pattern similarity as a common basis for categorization and recognition memory. The Journal of neuroscience : the official journal of the Society for Neuroscience, 34 (22), 7472-84 PMID: 24872552

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