Hate On Halloween

It was only a matter of time...and it's just in time for Halloween!

The Neural Correlates of _____________¹ [insert complex emotion or behavior here] is still a news- and flashy-publication-worthy title for your next neuroimaging paper! Use your imagination. How about The Neural Correlates of Procrastination? The Neural Correlates of Blogging? [NOTE: compare and contrast these two for extra credit]. The Neural Correlates of Hematophagy? The Neural Correlates of Sanguivoriphobia? The Neural Correlates of Nasophilia? Add your favorite by commenting on this post.

OK, OK, enough with Sarcasm. On to Hate. Zeki and Romaya (2008) give the following rationale for their study:

...we were interested to explore the neural correlates of hate directed against an individual. ... The hatred may be directed against a public figure or a personally known individual, for a variety of reasons. We made no attempt to distinguish between different types of personal hatred. Instead, we recruited subjects through advertisements, asking them only to volunteer if they experienced an intense enough hate for an individual, without distinguishing further between different categories of individual hate. We conformed as much as possible to our previous studies on romantic and maternal love (Bartels & Zeki 2001, 2004), asking subjects to complete a questionnaire which allowed us to correlate the declared subjective experiences with changes in the BOLD signal.

And the groundbreaking hypothesis? Love and hate might be represented by different brain states! Who knew?

We hypothesized that the pattern of activity generated by viewing the face of a hated person would be quite distinct from that produced by viewing the face of a lover.

Then they really went out on a limb:

In particular, we did not anticipate activation of the brain's reward system but believed that it would result in a different pattern of activity within the emotional brain.

[Oops, more sarcasm there.] However, there might actually be an interesting question addressed by the paper, and it's this:

Given the common association between love and hate, and the relative frequency with which one of these sentiments can transform into the other, we also hypothesized that there would be some strong correlation in the brain sites activated during the experience of these two antipodean sentiments. The results surprised us.

The study participants were 17 people (10 male, 7 female) recruited specifically because they expressed intense hatred for a particular individual. Sixteen people hated an ex-lover or a competitor at work, and one person hated a famous political figure. Two weeks before the experiment, the participants brought in photographs of their hated person and of three other people of the same sex who elicited neutral feelings. Unfortunately, the fake exemplar figure chosen for the paper consisted of four photos that were not all matched for age and race! It makes you wonder whether the authors controlled for that in the actual stimulus materials.


Figure 1. An example set of four processed face images (faces not from this study).

During the experiment, the photographs were presented for 16 sec each, followed by an inter-trial interval of 2 sec. Sometimes a blank screen was presented for 16 sec instead of a face. Each face was presented a total of 8 times.² Subjects were instructed to press a button when the picture disappeared from the screen. After the scanning session (and also during the first visit), ratings of each photographed person were obtained on the Passionate Hate Scale (File S1), designed to be similar to the Passionate Love Scale.

The questionnaire revolved around three elements of hate: (A) negation of intimacy, when an individual seeks a distance from the hated person. This is usually because the hated person arouses feeling of revulsion and disgust, exactly the opposite of the desire for greater intimacy in the context of love; (B) passion, expressing itself in intense anger at, and fear of, the hated person; and (C) devaluation of the hated person through expressions of contempt.

Example questions included (with answers rated on a 7 point scale from "strongly agree" to "strongly disagree"):

A2/ The world would be a better place if X had never existed.
A4/ I would like to interact with X.

B1/ I cannot control my hatred for X.
B3/ I have violent thoughts about X.

C1/ X is scum.

One of the key fMRI results is illustrated below.


Figure 3. Activation for the contrast Hated faces>Neutral faces.

There were 7 regions significantly more active for the Hated Face condition than for the Neutral Face condition. Fig. 3 illustrates the medial frontal gyrus [including the anterior cingulate cortex and the pre-SMA]. Fig. 4 shows the right putamen, bilateral premotor cortex [were they restraining their murderous tendencies?], the frontal pole, and bilateral insula [activated in all sorts of conditions from speech to working memory to reasoning to pain to disgust to the allure of Chanel No. 5]. I won't report on the correlation analysis that related degree of hatred to level of activation across 5,225 voxels because it used an uncorrected statistical threshold of p≤0.01.

And as to The Neural Correlates of Hate, the authors say :

Our studies did indeed reveal a basic pattern. As far as we can determine, it is unique to the sentiment of hate even though individual sites within it have been shown to be active in other conditions that are related to hate. The network has components that have been considered to be important in (a) generating aggressive behavior and (b) translating this behavior into motor action through motor planning. Finally, and most intriguingly, the network involves regions of the putamen and the insula that are almost identical to the ones activated by passionate, romantic, love.

Popular media outlets, of course, really love this sort of thing, especially when your university issues this type of press release:

Brain’s ‘hate circuit’ identified

28 October 2008

People who view pictures of someone they hate display activity in distinct areas of the brain that, together, may be thought of as a ‘hate circuit’, according to new research by scientists at UCL.

See the newest neurocurmudgeon (The Neuroskeptic) for more skepticism. Zeki's flowery prose doesn't help matters:

“Hate is often considered to be an evil passion that should, in a better world, be tamed, controlled, and eradicated. Yet to the biologist, hate is a passion that is of equal interest to love. Like love, it is often seemingly irrational and can lead individuals to heroic and evil deeds. How can two opposite sentiments lead to the same behaviour?”

I must have missed the evil and heroic portion of the experiment...


Footnotes

¹ Top hits include The Neural Correlates of Consciousness, Desire (Kawabata & Zeki), Maternal and Romantic Love (Bartels & Zeki), Reward-Related Trial-and-Error Learning, Sensory Awareness, Subjective Value During Intertemporal Choice, Motor Skill Automaticity, and [my personal favorite] David Chalmers (NC/DC, the foremost Black Metal/Christian Rap band, is not to be confused with David Chalmers the philosopher).

² Not exactly an extraordinary S/N. And who knows what the subjects were actually thinking about for 16 sec?

References

Bartels A, Zeki S. (2000). The neural basis of romantic love. Neuroreport 11:3829-34.

Bartels A, Zeki S. (2004). The neural correlates of maternal and romantic love. Neuroimage 21:1155-66.

Semir Zeki, John Paul Romaya, Jan Lauwereyns (2008). Neural Correlates of Hate. PLoS ONE, 3 (10). DOI: 10.1371/journal.pone.0003556

In this work, we address an important but unexplored topic, namely the neural correlates of hate. In a block-design fMRI study, we scanned 17 normal human subjects while they viewed the face of a person they hated and also faces of acquaintances for whom they had neutral feelings. A hate score was obtained for the object of hate for each subject and this was used as a covariate in a between-subject random effects analysis. Viewing a hated face resulted in increased activity in the medial frontal gyrus, right putamen, bilaterally in premotor cortex, in the frontal pole and bilaterally in the medial insula. We also found three areas where activation correlated linearly with the declared level of hatred, the right insula, right premotor cortex and the right fronto-medial gyrus. One area of deactivation was found in the right superior frontal gyrus. The study thus shows that there is a unique pattern of activity in the brain in the context of hate. Though distinct from the pattern of activity that correlates with romantic love, this pattern nevertheless shares two areas with the latter, namely the putamen and the insula.

No-one loves you and you know it
Don't pretend that you enjoy
Or you don't care
'Cause now I wouldn't lie
Or tell you all the things you want to hear
'Cause I hate you
'Cause I hate you
'Cause I hate you
'Cause I hate you

-Green Day, Platypus (I Hate You)

Viva Hate!

Bidirectional Competition Between Striatum and Hippocampus During Learning

The "dry cleaning effect" paper was finally published online Monday Oct 27 at PNAS, a full week after the initial press release. To briefly review:

'Dry cleaning effect' explained by forgetful Yale researcher

Yale researchers have described how dueling brain systems may explain why you forget to drop off the dry cleaning and may point to ways that substance abusers and people with OCD can overcome bad habits.

In Proceedings of the National Academy of Sciences, Christopher J. Pittenger, M.D., and colleagues describe a sort of competition between areas of the brain involved in learning that results in what Pittenger calls the "dry cleaning effect."

At the time, The Neurocritic scoffed at the overreach of the authors in interpreting their data for the press, but at the same time acknowledged that the actual paper could be sound.

Call me humanocentric, but I think distractibility (and the frontal lobes) might have a role in this sort of absentmindedness. Nevertheless, the study itself could be perfectly reasonable, and the results sound interesting...

But what does the paper actually say? What were the experimental tasks and manipulations? To begin with, the authors (Lee et al., 2008) adopted the multiple memory systems perspective (e.g., Squire 2004) that views different types of learning and memory (e.g., spatial learning and stimulus-response learning) as subserved by dissociable brain circuits (which include the hippocampus and the dorsal striatum, respectively). That's nothing new. In rodents, these two types of learning and memory are differentially affected by damage to the hippocampus and striatum in the fashion of a "double dissociation"¹ -- lesions to the hippocampus impair spatial learning but not S-R learning, while lesions to the dorsal striatum impair S-R learning but not spatial learning. This is true in humans as well, where (more broadly speaking) hippocampal damage impairs declarative memory but not procedural memory, and striatal damage impairs procedural memory but not declarative memory.

So what is new here? The way these two systems interact with each other is not fully understood. However (as cited by the authors), neuroimaging studies in humans indicate that hippocampal and striatal activity during spatial navigation (for example) shows an inverse relationship (Hartley et al., 2003),² raising the possibility that the two systems compete with each other.³ And as far as competition goes, it's quite well-known that habit learning is suppressed by other learning mechanisms, even in flies (Brembs et al., submitted). Brembs goes on to state in his comment on the dry cleaning post:

What I thought was surprising was that apparently the habit learning striatum also inhibited spatial learning, which is something I have never heard about.

Or as Lee et al. put it in their Introduction:

In rodents, hippocampal lesions can enhance acquisition of a striatum-dependent win-stay behavioral strategy in a radial arm maze task, perhaps by removing competitive interference from spatial information.

To date, however, we are aware of no studies that have provided clear evidence for interference by striatum-dependent processes on hippocampus-dependent learning; as a result, it remains unclear whether there is true bi-directional competition between these learning systems.

Their methods? Cued and spatial learning were assessed in mice with a modified water-maze task described in great detail in Fig S1 (which has a 588 word legend, not reproduced below).


Fig S1 (Lee et al. 2008).

From the main Methods section:

Briefly, animals learned to escape a pool of opaque water (similar to that used in the Morris water maze) by swimming to one of two visually distinct cues. ... Three distinct visible cues were used [plastic cylinders painted either solid gray or with black-and-white stripes...]

The first 5 days consisted of shaping to the task. On day -5, animals were placed on the platform four times (20-min inter-trial interval). On days -4 through -1, the escape platform was marked with the uniform gray cue; animals were placed in the pool and allowed 120 seconds to swim to it.

Following shaping, animals were trained in the two-cue task for 5 or 7 days; each animal was trained in either the cued or spatial task, never in both. All experiments consisted of four trials per day with a 20-min inter-trial interval. In the cued task, the escape platform was moved on each trial but was reliably marked by one of the two cues... In the spatial task, the escape platform was always in the same location but was variably associated with the two striped cues. In both tasks, the second visible cue (i.e., the lure) was present in a quadrant adjacent to the escape platform and its associated cue (i.e., the goal) on a stand that held it at an identical height in the water but did not permit escape. [NOTE: mean!]...

Learning was assayed by using a probe trial, administered in place of the fourth training trial after 3, 5, and/or 7 days of training... In the probe trial, both goal and lure cues were placed on stands that did not allow escape; the animal’s search was monitored by an overhead camera over 60 seconds. Extra-maze cues were identical to those present in a training trial. In both the cued and the spatial task, a systematic bias toward the goal cue relative to the lure cue (i.e., toward the location where the platform would have been on a regular training trial) was interpreted as evidence of learning.

Hopefully, it was not as difficult for the mice to learn the task as it is for the reader to understand what was done. On that note, to minimize the reader's cognitive load, the major results of the study are conveyed via the authors' paragraph subheadings.

Dorsal Striatal Lesions Impair Cued Learning and Enhance Spatial Learning.

Disruption of Striatal Synaptic Plasticity in Transgenic Mice Also Impairs Cued Learning and Enhances Spatial Learning.

KCREB Transgenic Mice Continue to Show Accelerated Learning upon Spatial Reversal.

Lesions of Dorsal Hippocampus Impair Spatial Learning and Potentiate Cued Learning.

And the conclusion? It's in the title: A double dissociation revealing bidirectional competition between striatum and hippocampus during learning.


Footnotes

¹ It must be noted here that not all researchers agree on the significance of a double dissociation. Although many take it as strong evidence of independent brain (or cognitive) systems, others disagree. For instance, Berry et al. (2008) state:

Do dissociations imply independent systems? In the memory field, the view that there are independent implicit and explicit memory systems has been predominantly supported by dissociation evidence. Here, we argue that many of these dissociations do not necessarily imply distinct memory systems. We review recent work with a single-system computational model that extends signal-detection theory (SDT) to implicit memory. SDT has had a major influence on research in a variety of domains. The current work shows that it can be broadened even further in its range of application. Indeed, the single-system model that we present does surprisingly well in accounting for some key dissociations that have been taken as evidence for independent implicit and explicit memory systems.

² I have to point out here that even this spatial navigation study in humans is not analogous to the true "dry cleaning effect" where distractibility, absentmindedness, and lapses of attention are key culprits.

³ It seems to be a somewhat different story for reversal learning (Shohamy et al., 2008).

References

Berry CJ, Shanks DR, Henson RN. (2008). A unitary signal-detection model of implicit and explicit memory. Trends Cog Sci. 12:367-73.

Hartley T, Maguire EA, Spiers HJ, Burgess N. (2003). The well-worn route and the path less traveled: distinct neural bases of route following and wayfinding in humans. Neuron 37:877-88.

A. S. Lee, R. S. Duman, C. Pittenger (2008). A double dissociation revealing bidirectional competition between striatum and hippocampus during learning Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.0807749105

The multiple memory systems framework proposes that distinct circuits process and store different sorts of information; for example, spatial information is processed by a circuit that includes the hippocampus, whereas certain forms of instrumental conditioning depend on the striatum. Disruption of hippocampal function can enhance striatum-dependent learning in some paradigms, which has been interpreted as evidence that these systems can compete with one another in an intact animal. However, it remains unclear whether such competition can occur in the opposite direction, as suggested by the multiple memory systems framework, or is unidirectional. We addressed this question using lesions and genetic manipulations in mice. Impairment of dorsal striatal function with either excitotoxic lesions or transgenic inhibition of the transcription factor cAMP response element-binding protein, which disrupts striatal synaptic plasticity, impaired striatum-dependent cued learning but enhanced hippocampus-dependent spatial learning. Conversely, excitotoxic lesions of the dorsal hippocampus disrupted spatial learning and enhanced cued learning. This double dissociation demonstrates bidirectional competition that constitutes strong evidence for the parallel operation of distinct memory systems.

Shohamy D, Myers CE, Hopkins RO, Sage J, Gluck MA. (2008). Distinct Hippocampal and Basal Ganglia Contributions to Probabilistic Learning and Reversal. J Cog Neurosci. Sep 29. [Epub ahead of print].

The hippocampus and the basal ganglia are thought to play fundamental and distinct roles in learning and memory, supporting two dissociable memory systems. Interestingly, however, the hippocampus and the basal ganglia have each, separately, been implicated as necessary for reversal learning-the ability to adaptively change a response when previously learned stimulus-outcome contingencies are reversed. Here, we compared the contribution of the hippocampus and the basal ganglia to distinct aspects of learning and reversal. Amnesic subjects with selective hippocampal damage, Parkinson's subjects with disrupted basal ganglia function, and healthy controls were tested on a novel probabilistic learning and reversal paradigm. In this task, reversal can be achieved in two ways: Subjects can reverse a previously learned response, or, they can select a new cue during the reversal phase, effectively "opting out" of the reversal. We found that both patient groups were intact at initial learning, but differed in their ability to reverse. Amnesic subjects failed to reverse, and continued to use the same cue and response learned before the reversal. Parkinson's subjects, by contrast, opted out of the reversal by learning a new cue-outcome association. These results suggest that both the hippocampus and the basal ganglia support reversal learning, but in different ways. The basal ganglia are necessary for learning a new response when a previously learned response is no longer rewarding. The failure of the amnesic subjects to reverse their response or to learn a new cue is consistent with a more general role for the hippocampus in configural learning, and suggests it may also support the ability to respond to changes in cue-outcome contingencies.

Squire LR. (2004). Memory systems of the brain: a brief history and current perspective. Neurobiol Learn Mem. 82:171-7.

The Trouble With Tephritidae

A True Fruit Fly - Tephritidae (via Myrmecos Blog)

Bjoern Brembs has written extensively about the latest anti-science commentary by VP candidate Sarah Palin, who said...

Where does a lot of that earmark money end up anyway? […] You've heard about some of these pet projects they really don't make a whole lot of sense and sometimes these dollars go to projects that have little or nothing to do with the public good. Things like fruit fly research in Paris, France. I kid you not.

...in a series of blog posts, including this one:

Who needs to know about bears, planetariums or fruit flies anyway? To hell with science!

In the beginning, there was bear DNA. Then the projector for the quintessential planetarium experience. Last Friday it was research on fruit flies. Which research project a semi-educated Republican politician doesn't understand will be next?

Myrmecos Blog informs us that Drosophila is not a Fruit Fly:

Fruit flies are a family, Tephritidae, containing about 5,000 species of often strikingly colored insects. As the name implies, these flies are frugivores. Many, such as the mediterranean fruit fly, are agricultural pests.

Drosophila melanogaster, the insect that has been so important in genetic research, is not a true fruit fly. Drosophila is a member of the Drosophilidae, the vinegar or pomace flies. They are mostly fungivores, and their association with fruit is indirect: they eat the fungus that lives in rotting fruit...

I bring this up because the confusion between fruit flies and vinegar flies entered into U.S. presidential politics this week when Sarah Palin attacked Fruit Fly spending as wasteful...

. . .

Palin was referring to a project to fund studies of the olive fruit fly, a true tephritid and a major threat to California’s olive industry.

I guess California's olive industry has little or nothing to do with the public good...


via Bjoern Brembs

...because the public good is better served by astronomical deficits and defense spending.

The Trouble With Tina

Tiny Fey's trouble is with the ratings for her NBC comedy show, 30 Rock. But she hopes her recent appearances on SNL will provide a boost:

"I hope this ends up helping 30 Rock," allows Fey, referring to her Sarah Palin sideline the past few weeks on Saturday Night Live. But she's keeping her expectations modest. "I would like the audience to go up just enough so that people don't have to refer to it as 'the ratings-challenged 30 Rock' anymore."

Tina and her 30 Rock character (Liz Lemon) look a lot like Liz Spikol, who writes The Trouble With Spikol, a blog about mental health. TTWS was featured in Psych Central's Top Ten Bipolar Blogs 2008.

In January 2007, Tina Fey was asked, “Do you ever get confused with Philadelphia Weekly columnist Liz Spikol?” You can see her answer this question (and others) in Tina Fey Apologizes for Looking Like Me.

So will Liz Spikol go as Sarah Palin for Halloween?

Correlations between Slow Cortical Potentials and Spontaneous Fluctuations of the BOLD Signal

How do hemodynamic and electrophysiological¹ measures of brain activity relate to each other? A cool new open access article in PNAS (He et al., 2008) reports on the similarities between spontaneous fluctuations in the BOLD (blood-oxygen-level-dependent) signal in fMRI and ultraslow brain waves recorded directly from the cortex of patients undergoing surgical monitoring for epilepsy. The figure below shows the subdural grid electrodes implanted over sensorimotor cortex in one patient (Fig. 1A), along with the somatotopic/somatomotor mapping of hand and face regions (Fig 1B).


Fig. 1 (He et al., 2008). Spatial topography of electrode coverage and sensorimotor network in Patient 1. (A) Radiograph showing electrode placements. (B) Three-dimensional rendering of anatomical MRI and projection of electrode locations onto the three-dimensional surface. Clinical mapping of the sensorimotor cortex is indicated by color patches. Red indicates hand motor area based on median nerve somatosensory evoked potential (SSEP); yellow indicates hand sensory area based on SSEP; blue indicates facial twitching in response to cortical stimulation; and green indicates hand grasp in response to cortical stimulation.

The signal fluctuations of interest are of the ultraslow variety, occurring over tens of seconds. As explained in the accompanying commentary (Balduzzi et al., 2008):

These [EEG] fluctuations can be very slow (infraslow oscillations, less than 0.1 Hz; slow oscillations, less than 1 Hz; and slow waves or delta waves, 1–4 Hz), intermediate (theta, 4–8 Hz; alpha, 8–12 Hz; and beta, 13–20 Hz), and fast (gamma, greater than 30 Hz). Moreover, slower fluctuations appear to group and modulate faster ones. The BOLD signal underlying functional magnetic resonance imaging (fMRI) also exhibits spontaneous fluctuations at the timescale of tens of seconds (infraslow, less than 0.1 Hz) which occurs at all times, during task-performance as well as during quiet wakefulness, rapid eye movement (REM) sleep, and non-REM sleep (NREM).

If these infraslow BOLD fluctuations occur all the time, across different behavioral states, what is their significance? What sort of information are they conveying? Turns out that no one knows yet. It isn't even discussed by He et al. So let's turn to the Balduzzi et al. commentary again:

Although infraslow fluctuations are a prominent, constant feature of both BOLD and EEG signals, we still do not know where they originate. One possibility is that they reflect diffuse input from a subcortical source that is fluctuating in the infraslow range. For example, the firing of neurons in the midbrain reticular formation waxes and wanes with a period of ~10 sec during both wakefulness and sleep. Alternatively, neurons may slowly modulate their level of activity because of intrinsic changes in excitability, in the activity of ionic pumps, of neurotransmitter transporters, and of glial cells. Last, modeling the overall organization of corticocortical connectivity suggests that infraslow, system-level fluctuations in activity may be an emergent network property, not reducible to properties of individual neurons.

Nevertheless, one [somewhat obvious] principle is the inverted Hebbian dictum, "what wires together, fires together."

...both BOLD and ECoG fluctuations display a pattern of regional correlations, or functional connectivity, which closely reflects those regions’ anatomical connectivity.

We see an anatomical example of that in the figure below, which illustrates the sensorimotor correlation map in one of the patients.


Fig. 1C (He et al., 2008). BOLD sensorimotor correlation map and electrode locations overlaid on the pial surface reconstructed from anatomical MRI. Four sensorimotor ROIs (delineated by magenta contours) and four control ROIs (blue contours) were determined in this patient. The cross-hatching indicates the epileptogenic zone that was subsequently resected.

Again (not surprisingly),

BOLD correlations between sensorimotor ROIs should be, by definition, higher than correlations between a sensorimotor ROI [region of interest] and a control ROI (see Fig 2B below). The question was whether some type of electrophysiological activity may similarly differentiate sensorimotor-sensorimotor from sensorimotor-control correlations. To pursue this question, ECoG signals filtered in eight different frequency bands were used to compute the lagged correlation functions...

ECoG activity in the two slowest frequency bands (less than 0.5-Hz and 1–4-Hz bands) distinguished sensorimotor-sensorimotor from sensorimotor-control ROI pairs: the sensorimotor-sensorimotor ROI pairs were positively correlated, whereas the sensorimotor-control ROI pairs were negatively or not correlated (Fig 2C).

Fig. 2 (He et al., 2008). ROI-pair cross-correlations computed using BOLD signals. (B) Patient 1: BOLD lagged cross-correlation functions were averaged separately for SM-SM (red) and SM-C (blue) ROI pairs. (C) Combining data over all patients: peak ECoG cross-correlation values (within + 500 ms lag) as a function of ROI-pair type (SM-SM vs. SM-C) and arousal state (awake, SWS, and REM). Two-way ANOVA yielded a highly significant main effect of ROI-pair type. Neither the effect of arousal state nor the interaction of ROI-pair type x arousal state was significant.

This effect in the electrocorticogram held whether the patients were awake, in SWS, or in REM sleep. Comparing the slowest two frequency bands in Fig. 2A below to the BOLD signal in Fig. 2B above, one can see the similarity between the correlation structures of the BOLD signal and the very slow ECoG activity. This was demonstrated quantitatively by a number of additional analyses shown in the main paper and in the supporting information.


Fig. 2 (He et al., 2008). ROI-pair cross-correlations computed using ECoG signals. (A) Patient 1: lagged cross-correlation functions were computed by using ECoG signal filtered in eight frequency bands for all possible SM-SM and SM-C ROI pairs. Red hues indicate SM-SM, and blue/green hues indicate SM-C.

What does it all mean? What is the role of all this slow "spontaneous" activity? We don't know yet.

...spontaneous slow cortical potentials (SCP) and the correlated BOLD signal fluctuations likely reflect endogenous fluctuations of cortical excitability within functional systems.

OK, that's pretty vague, so the authors also suggest that

...spontaneous fMRI BOLD signals and SCPs both reflect a fundamental stratum of the brain’s intrinsic organization that transcends levels of consciousness.

Well then. Not exactly settled yet, is it? I'll let Balduzzi et al. offer this final speculation (and metaphor):

...spontaneous activity may be important for the brain’s trillions of synapses, perhaps by keeping them exercised or consolidating and renormalizing their strength. Another notion is that spontaneous activity may be necessary to maintain a fluid state of readiness that allows the cortex to rapidly enter any of a number of available states or firing patterns—a kind of metastability. Theoretical work suggests that the repertoire of available states is maximal under moderate spontaneous activity, and shrinks dramatically with either complete inactivity or hyperactivity. But what kind of neural states? One possibility is that the cortex is like a sea undulating gently, and that evoked or task-related responses would be like small ripples on its surface. This possibility is consistent with fMRI studies, because spontaneous slow fluctuations in BOLD are as large or larger than those evoked by stimuli.

Footnote

¹ Specifically, recordings of local field potentials that comprise the electroencephalogram recorded from the scalp or the electrocorticogram recorded from the cortical surface.

References

Balduzzi D, Riedner BA, Tononi G. (2008). A BOLD window into brain waves. Proc Natl Acad Sci. 105:15641-2.

B. J. He, A. Z. Snyder, J. M. Zempel, M. D. Smyth, M. E. Raichle (2008). Electrophysiological correlates of the brain's intrinsic large-scale functional architecture. Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.0807010105

Spontaneous fluctuations in the blood-oxygen-level-dependent (BOLD) signals demonstrate consistent temporal correlations within large-scale brain networks associated with different functions. The neurophysiological correlates of this phenomenon remain elusive. Here, we show in humans that the slow cortical potentials recorded by electrocorticography demonstrate a correlation structure similar to that of spontaneous BOLD fluctuations across wakefulness, slow-wave sleep, and rapid-eye-movement sleep. Gamma frequency power also showed a similar correlation structure but only during wakefulness and rapid-eye-movement sleep. Our results provide an important bridge between the large-scale brain networks readily revealed by spontaneous BOLD signals and their underlying neurophysiology.

The "Dry Cleaning Effect" in Mice

In the ridiculous extrapolation of the day, we have a paper that purports to solve the problem of why we forget to drop off our dry cleaning...based on results from a study conducted in mice. I don't know about you, but I don't remember ever seeing a mouse drop off his clothes at the dry cleaner. And as an added bonus, the authors may have cured substance abuse and OCD!

Here's the press release from Yale:

'Dry cleaning effect' explained by forgetful Yale researcher

Yale researchers have described how dueling brain systems may explain why you forget to drop off the dry cleaning and may point to ways that substance abusers and people with obsessive compulsive disorder can overcome bad habits.

In Proceedings of the National Academy of Sciences, Christopher J. Pittenger, M.D., and colleagues describe a sort of competition between areas of the brain involved in learning that results in what Pittenger calls the "dry cleaning effect."

Unfortunately (and in their pertetually annoying fashion), PNAS has not yet made the article available to us non-press plebians, so we'll have to rely on EurekAlert! for now.

One area of the brain called the striatum helps record cues or landmarks that lead to a familiar destination. It is the area of the commuter's brain that goes on autopilot and allows people to get to work, often with little memory of the trip.

But when driving to an unfamiliar place, the brain recruits a second area called the hippocampus, which is involved in a more flexible system called spatial learning. The commuter must employ this system if he or she wants to run an errand before work.

"When you have driven the same route many times and are doing it on autopilot, it can be really difficult to change," said Pittenger, assistant professor of psychiatry at Yale and senior author the paper. "This is why I cannot, for the life of me, remember to drop off my dry cleaning on the way to work. If I'm not paying enough attention right at that moment, if I am thinking about something else, I just sail right on by."

Call me humanocentric, but I think distractibility (and the frontal lobes) might have a role in this sort of absentmindedness. Nevertheless, the study itself could be perfectly reasonable, and the results sound interesting:

Pittenger and Yale colleagues Anni S. Lee and Ronald S. Duman developed a way to study how these two modes of learning might be interconnected in mice.

In one group, they disrupted areas of the striatum in mice and discovered that their ability to complete landmark navigation tasks was impaired. However, these mice actually improved on tasks that involved spatial learning.

Conversely, when the researchers disrupted an area of the hippocampus involved in spatial learning, the animals could no longer navigate spatially but learned landmark tasks more quickly.

But then we see the concluding extrapolation, and it involves mice in cognitive-behavioral therapy:

Pittenger speculates that the interactions between these two systems may be important for understanding certain mental illnesses in which patients have destructive, habit-like patterns of behavior or thought. Obsessive-compulsive disorder, Tourette syndrome, and drug addiction involve abnormal function of the striatum and may also involve disruption of the interactions between the two learning systems, which may make habits stronger and less flexible.

"This is part of what we are doing in cognitive-behavioral therapy when we teach patients to recognize their destructive habits, to take a step back, and to learn to do things differently," Pittenger said. "What we're really asking them to do is to use one of these systems to overcome and, ultimately, to re-train the other."

I understand the plight of basic researchers these days, how essential it is emphasize the clinical relevance of one's work in order to obtain NIH funding, but sometimes it can be a bit of a stretch...

Hershey-Funded Study Finds Chocolate May Be Good For You

In the tradition of industry-sponsored studies (i.e., Wrigley-Funded Study Finds Chewing Gum May Help Reduce Stress), a press release from the Hershey Company informs us that:

Resveratrol, red wine compound linked to health, also found in dark chocolate and cocoa

Hershey's Center for Health and Nutrition announced the publication of a study that shows resveratrol, the compound often associated with the health benefits of red wine, is also found in cocoa and dark chocolate products. In the September 24 edition of the Journal of Agricultural and Food Chemistry, scientists report that cocoa powder, baking chocolate and dark chocolate all have significant levels of resveratrol, a naturally occurring antioxidant.

"This study shows that the levels of resveratrol found in cocoa and chocolate products is second to red wine among known sources of resveratrol and forms yet another important link between the antioxidants found in cocoa and dark chocolate to other foods," says David Stuart, PhD, Director of Natural Product Science at The Hershey Company who partnered with Planta Analytica to conduct this study.

In the study, top selling retail products from six categories were tested for the level of resveratrol and its sister compound, piceid. The six product categories included cocoa powder, baking chocolate, dark chocolate, semi-sweet baking chips, milk chocolate and chocolate syrup. Gram for gram, cocoa powder had the highest average amount of resveratrol and piceid, followed by baking chocolates, dark chocolates, semi-sweet chips, milk chocolate and then chocolate syrup...

When the cocoa and chocolate levels were compared to published values for a serving of red wine, roasted peanuts and peanut butter, resveratrol levels of cocoa powders, baking chocolates and dark chocolate all exceeded the levels for roasted peanuts and peanut butter per serving, but were less than California red wine.

So keep drinking that red wine, cocoa powder isn't enough. The PR continues:

According to a review article published this month in Nutrition Reviews, resveratrol, a naturally occurring antioxidant, was shown to improve insulin sensitivity, blood cholesterol levels and have neuroprotective actions in animal studies. Further, the article states, studies in mice indicate that diets high in resveratrol were associated with increased longevity.

At any rate, if one has the money to splurge, why not take a step up from Special Dark to Scharffen Berger?¹

Footnote

¹ Oh, wait, Hershey's owns Scharffen Berger:

About The Hershey Company

...Artisan Confections Company, a wholly owned subsidiary of The Hershey Company, markets such premium chocolate offerings as Scharffen Berger, known for its high-cacao dark chocolate products, Joseph Schmidt, recognized for its fine, handcrafted chocolate gifts, and Dagoba, known for its high-quality.