Tag Archives: functional connectivity

@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.

Knight

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.

Parvizi

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!”

Malach

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.

Lachaux

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”.

Kastner

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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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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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.

References

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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