Tag Archives: brain

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.

References

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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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.

References

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.
4. Gillund G, Shiffrin RM. 1984. A retrieval model for both recognition and recall. Psychol Rev 91:1-67.
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.
9. Xue G, Dong Q, Chen C, Lu Z, Mumford JA, Poldrack RA. 2010. Greater neural pattern similarity across repetitions is associated with better memory. Science 330:97-101.
10. Yassa MA, Stark CE. 2011. Pattern separation in the hippocampus. Trends Neurosci. 34:515-25.

ResearchBlogging.org
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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The hippocampus: Going the distance beyond space and time

Anyone who’s spent a day exploring the streets of New York City will understand the importance of integrating accurate representations of space, time and distance. That hipster dive-bar you’re dying to check out is twelve streets from tonight’s dinner spot, whereas that hot new rooftop bar is just four avenues away. Which do you choose to save the most time and energy? A tiring day of shopping has taught you that a Manhattan block is not just a block; those four avenues are in fact equidistant to the twelve streets. But distance and time don’t always perfectly correlate; the tourists and congestion near the rooftop bar will certainly cost you a few minutes … dive-bar it is.

Place cells. Dots represent hippocampal spiking, color coded by neuron. Each cell has a preferred place field.

Place cells. Dots represent hippocampal spiking, color coded by neuron. Each cell has a preferred place field.

How do our brains represent and bind together the temporal and spatial features of our experiences? Since the discovery of place cells in the 1970’s we’ve known that the hippocampus is important for signaling where an animal is in its environment (O’Keefe, 1976). These hippocampal neurons possess distinct place fields, regions of space in which the cell preferentially fires with maximum frequency.

Neuroscientists have recently discovered that the hippocampus not only codes spatial information, but also represents information about when particular events occur. These neurons, aptly named “time cells”, selectively fire during particular moments of an experience. For example, when a rat runs on a treadmill, neuron A might fire one minute into its jog, whereas neuron B will remain silent until ten minutes in. But in such paradigms, time and distance are correlated, such that the longer a rat runs, the further he’s gone. This confound between time and distance makes it difficult to know whether a purported time cell indeed codes temporal information, or instead might be tuned to distance, or the integration of both spatial and temporal features.

Benjamin Kraus and colleagues set out to disentangle these confounding effects of time and distance in their study recently published in Neuron (Kraus et al., 2013). As in prior experiments, they trained rats to run on a treadmill while they recorded from hippocampal pyramidal cells, the region’s primary excitatory neurons. Many of the neurons (43%) were active when the rats ran, with most showing time cell-like behavior, demonstrating peak firing at particular moments during the run.

The activity of time cells (A and B) is locked to time running, and the acitivty of distance cells (C and D) is tuned to distance run.

The activity of time cells (A and B) is locked to time running, and the activity of distance cells (C and D) is tuned to distance run.
From Kraus et al., 2013.

Critically, to dissociate spiking locked to time versus distance they next varied the rats’ running speed, and examined hippocampal activity referenced either to running time or running distance. Thus, a neuron that consistently fired after five minutes of running, regardless of distance covered, would be labeled a time cell. Likewise, a neuron that fired after running two meters would be considered a distance cell. They found cells of both types, indicating that the hippocampus not only represents elapsed time, but also distance covered. Next, they measured how much space, time or distance influenced a given cell’s response. Although they found a small portion of cells that were only tuned to time or distance, most wore many hats, simultaneously performing the roles of time cell, distance cell and place cell.

So maybe the hippocampus isn’t simply a neural map, clock or ruler, but serves more as the brain’s GPS. Its integrated spatial and temporal code could serve the obvious function of helping us navigate the space-time continuum in which our world is embedded. But does its role end there, or might it serve a higher purpose? The hippocampus was once thought to have a Jekyll-and-Hyde personality, with alter-egos subserving both spatial navigation and memory for past experiences. But scientists are now unraveling how these distinct roles interactively support one another. Episodic memories aren’t one-dimensional snapshots, but rather, integrate details about events with the spatial and temporal context in which they’re experienced. While there’s strong evidence that the hippocampus subserves memory by binding such contextual features together, the neural computations by which it accomplishes this feat are unclear. Kraus et al’s findings contribute a critical piece to this puzzle, identifying hippocampal neurons that simultaneously represent the “what”, “where” and “when” of an experience, whose coordinated activity just might hold the code for a rich, multi-dimensional memory landscape.

References

Kraus BJ et al. 2013. Hippocampal “time cells”: Time versus path integration. Neuron. 78:1090-101.

O’Keefe J. 1976. Place units in the hippocampus of the freely moving rat. Exp Neurol. 51:78-109

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ResearchBlogging.org
Kraus BJ, Robinson RJ 2nd, White JA, Eichenbaum H, & Hasselmo ME (2013). Hippocampal “Time Cells”: Time versus Path Integration. Neuron, 78 (6), 1090-101 PMID: 23707613

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Active-state functional connectivity

Task-positive and task-negative brain networks (Fox et al, 2005)

I’m currently preparing for a talk on a recent paper (Powers et al, 2011) using resting-state functional connectivity MRI to study large-scale networks in the human brain. (Okay, actually I’m procrastinating working on the talk by writing this post.) Different brain regions are anatomically connected via projections from one area to the next, allowing long-range communication across the brain. This functional connectivity can be observed as synchronized BOLD signal in connected regions while a person is at rest or performing a task, and such correlated areas have been found to correspond with functional sub-networks within the brain. For instance, an extensive “task-positive” network (shown in warm colors at right, from Fox et al, 2005) is thought to comprise multiple sub-networks including, for example, a task control system composed of the dorsal anterior cingulate and frontal operculum, and a dorsal attention system involving areas of superior frontal and parietal cortex.

While out for a run yesterday I was struck by the remarkable parallel between such integrated brain systems and our non-brain “networks” of muscle, fascia, tendons and ligaments that coordinate distinct yet complementary functions while we run. Unfortunately but necessarily, this appreciation was triggered by the heightened bodily awareness that accompanies injury. About a month ago during a particularly intense and hilly run, I noticed a nagging tension in both hip flexors. I followed this the next day with a short, easy barefoot run during which a dull ache appeared on the top of my foot. Simply a classic case of the novice barefooter’s too-much-too-soon, right? Over the subsequent weeks, I’ve dealt with recurring minor flare-ups of both these top-of-foot and hip issues and assumed they were unrelated and exacerbated by distinct factors – my continued increase in barefoot / minimalist mileage and hill running, respectively.

But it wasn’t until my monthly sports massage this week that I learned just how connected – possibly causally related – these problems were. As my therapist applied pressure just lateral to my hip flexor a subtle burning appeared in my extensor tendon along the shin and foot. Release of hip pressure … relief of extensor ache. Application of hip pressure … return of extensor pain. And so on. I was astounded by the consistency of this pattern to the point that I even questioned my own sensations. There was an undeniable connection between my hip flexor and extensor, such that tension in one translated into pain and impaired function in the other. Although I’m not happy to report that my “anterior leg network” hasn’t fully recovered, its continued dysfunction has further highlighted the strong connectivity I suspected during the massage. While running a moderate uphill climb yesterday, my hip flexor predictably tightened up. Moments later, an ache appeared along the top of my foot and ankle. Then – just to mix things up, my knee began to burn as I felt my knee cap riding out of alignment. While certainly not a pleasant finale to the run, this sudden cascade of pains clearly demonstrated a deeply integrated anterior chain, from foot to knee to hip – and likely beyond.

Tom Meyers' Anatomy Trains

A clear picture of functional brain organization requires understanding not only the role of single units – a neuron or isolated region, but also the critical interactions between such elements. Similarly, effective communication within and across our musculoskeletal sub-systems, along with an integration of mind with body, is essential to properly function as a runner. The springy tendons of the foot cannot propel us along without power from our quads and gluts, stability from our core, and motor commands from and sensory feedback to the brain, together coordinating a smooth, fluid ride.

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Yet another running blog

Note: This post was transferred from a now extinct prior blog entitled “run think smile”. Hence, the reference to these words in the blog title.

While I’ve been contemplating resuming my blogging days for a while now, the prospect of actually writing is surprisingly daunting. I suppose a logical starting place is who I am and why this blog, starting with the title.

I initially set out to write about my experiences as a runner, but soon realized these rants would inevitably lead to tangential commentaries on human cognition and endorphin-driven euphoria. For you runner readers these connections may be obvious. For those who consider running the ultimate expression of sadomasochism, let me elaborate. These three elements – running, thinking and smiling – are inextricably, reciprocally, related. I do my best thinking while running, and have composed countless essays, designed new research paradigms and analyses while on the road. And while running sustains my brain, my brain also sustains my running addiction. Each run takes me on a new, unpredictable mental journey that entertains me for miles on end and keeps me eager to return for more the next day. Running can be the most powerful antidepressant, evidenced by the cheese-ball smile I often discover plastered to my face mid-run.

I’ve been running for over 14 years now, starting with high school track. Over the years the distances have grown longer and the sport has evolved from a simple past-time to a way of life. Those times when sickness, travel or the simple craziness of life has kept me off the roads, it’s felt like life has been put on hold. My energy levels, mental clarity and mood plummet.

While this all may sound a bit extremist, it’s a well-documented natural response to something humans and animal were “born” to do (excuse the McDougall reference). A part of my Neuroscience graduate program is a hypothetical research proposal outside of my primary research focus. While I spend most of my time grappling the mysteries of human memory retrieval, I’ve devoted this side project to understanding the neurobiological effects of running. Much more on this to come later I’m sure, but briefly, running does more than keep you healthy and feeling good. It actually releases similar neurotransmitters and activates similar neural circuits that go haywire in response to chronic drug use … hence the euphoria and addictive nature of running. It causes a host of other fantastic neurobiological changes, including increasing levels of proteins and transcription factors that promote neuronal growth, survival and function (for example, BDNF, delta-FosB and LTP are all increased by running). Remarkably, running also increases the birth and maturation of new brain cells in the hippocampus, an area critical to learning and memory.

I’ve always been a strong believer that we have a fantastic ability to self-treat, if only we listen closely to our body. It’s not all that surprising then that people continually return to running to keep our bodies and minds happy and healthy.

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