How accurate is it to say that a brain area is the ‘seat of’ any particular facet of human experience?

A question from Quora:

In  neuroscience, how accurate is it to say that a brain area (or circuit)  ‘mediates’, ‘evaluates’ or is the ‘seat of’ any particular facet of  human experience?

My answer:

A very interesting and important question! Here’s a capsule version of my answer:

Understanding the brain is like understanding the shape of a very complex, dynamic and multifaceted object by looking at the shadows projected by it on a wall. The color of the light source and its orientation with respect to the object (and the wall) will change the nature of the shadow. Different scientific techniques are like different light sources. Different experimental protocols are like different angles. We have to look at the sequence of shadows and imagine what the object’s actual form is — it is not possible to view this object simultaneously from all angles and with all illumination sources. So there is a degree of creativity and freedom in each person’s own conception of the true nature of the object. Nevertheless, the conception that scientists eventually agree on is likely to be one that tempers this freedom with rationality and responsibility.

(Picture from Wikipedia.)

In general, cautious scientists try to avoid stating explicitly that a particular brain region or circuit is solely responsible for a particular subjective experience. Instead, we use phrases like “dopamine seems to be implicated in the processing of discovering novelty” or “emotional processes may be mediated in part by the amygdala”. Having to qualify all our statements with ‘maybe’, ‘perhaps’, and ‘seems’ is part of the reason academic papers can be a drag for both readers and writers. By the time scientific findings enter the popular press, they are simplified for mass consumption, and come to seem more certain.

When we say a region is implicated in, say, emotion, we generally use several converging lines of evidence. So brain scanning techniques like fMRI — which have several widely publicized problems — are not the only ways to infer function. The earliest studies to implicate the temporal lobe in emotion used very coarse lesions. In the 1800s the entire temporal lobe (including the amygdala) was removed in monkeys, and several mood/emotion related disorders ensued. Progressively more precise lesions were conducted, allowing scientists to discover that the amygdala was the key to what is now known as Klüver–Bucy syndrome. Symptoms include docility, hypersexuality, and hyperorality — overeating or inappropriately exploring things orally.

So long before fMRI was invented, people were starting to piece together a picture of brain functioning based on studies in monkeys and rodents.

Every technique has some confound or drawback, so specific claims about functions require integrating a variety of experimental findings. Lesions, for instance, lead to compensatory mechanisms during post-surgical recovery, so one must be careful about attributing a change in behavior to the lesion per se, rather than to the recovery process. The same goes for post-mortem studies of human brains.

A complementary technique that helps us understand the flow of signals in the brain is Neuroanatomy. By tracing how axons travel from region to region, anatomists can infer how signals from the body — sense organs, muscles, glands, viscera etc — travel to the brain and percolate through the brain. The confound here is that form does not completely constrain function — given the complexity of the brain’s connections, multiple theoretical models can be constructed using a common circuit.

Brain scanning techniques like fMRI are part of a broader field known as Electrophysiology — the study of electrical signals in the cells and tissues. Older techniques in this lineage include EEG, neuronal recordings, and PET. Neuronal firing patterns can be correlated with external stimuli or with bodily processes, so the neural dynamics that occur in parallel with a particular cognitive, emotional or unconscious/autonomic process can be inferred.

So to sum it up, sweeping statements about brain function should be taken with a pinch of salt, but the size of the pinch should be proportional to the newness of the claim. Some ideas about the hypothalamus, for example, have been corroborated by many of the techniques I have mentioned. The intro or review section of a neuroscience paper typically tries to link new findings with older ones.

None of this addresses the far more difficult philosophical questions surrounding neuroscience such as

  1. How do we best integrate information from various species?
  2. How similar are observable animal behaviors to subjective human feelings?
  3. How do we understand the idea of a neural correlate of some mental phenomenon?

This integration and interpretation problem is sometimes described as being underdetermined. But hopefully, the scientific community will work to ensure that their (always provisional!) descriptions of the brain/mind interface harmonize the data with logic and practical usefulness.

View Answer on Quora

What does fMRI measure, anyway?

In the previous post, I began discussing functional magnetic resonance imaging (fMRI), a popular but controversial experimental technique that allows researchers to investigate brain activity in humans and animals in a relatively safe and  non-invasive way. But I found myself commenting more on the problems associated with statistical methods. While these problems are important to acknowledge and deal with, they are not specific to fMRI. In this post I hope to leave stats aside, and examine some of the biophysical assumptions underlying interpretations of fMRI studies. For some years I had been ignoring fMRI papers because of the myriad problems of interpretation, but a recent paper from Aniruddha Das’s group at Columbia University (Cardoso et al., 2012) rekindled my curiosity, and spurred me to survey the literature on the links between neural activity, blood flow, and metabolism in the brain.

“It’s a long way from behaviour to BOLD.” From Singh (2012). Click to view full size.

Despite its popularity, it is still not entirely clear what fMRI is measuring. Many people know that the technique measures a signal called BOLD: the blood-oxygen-level-dependent contrast. But I imagine fewer people know that the  relationship between blood flow and neural activity is still under active investigation. This is vividly illustrated in the figure above, taken from Singh (2012).

When people point to an fMRI heatmap and say that some brain region or the other “lights up”, what they mean is that oxygenated blood is flowing into this region at a rate that is higher than some baseline. (See my previous post for a discussion of baselines and subtractions.) Interpreting blood flow as neural activity rests on a key assumption: that neural activity is coupled with increased blood flow. Blood flow, in turn, is assumed to correlate with metabolic activity.  The idea rests on a line of thinking that is intuitively straightforward [but see notes]. Neural firing activity and synaptic change both require energy in the form of glucose. The brain does not store glucose –it produces it locally as needed, with the help of oxygen delivered through haemoglobin in the blood. So we would expect increased neural firing to necessitate increased flow of oxygenated blood. In summary, the simplest description of fMRI is that it measures oxygenated blood flow, which changes as a result of oxidative metabolic processes that drive neural firing. But none of the steps in this purported chain of causation (Neural Activity > Metabolism > Blood Flow) has been fully understood, so as usual, the devil is in the details.

To understand the typical view of fMRI, an analogy might help. Let us imagine a modern city, most of whose inhabitants require electricity to do their work. Imagine that the city planners wish to know how many of the inhabitants work at night. They can’t go from house to house to check, so they decide to monitor electricity usage, for which they have accurate meter readings. They assume that no one works in the dark, no one sleeps with the lights on, and that lights and machines cannot be turned on automatically. They also assume that the engineers in charge of the power plants do not actively change or redirect electricity flows. If these assumptions are correct, then the meter readings can be aggregated to create a heatmap of the city’s nocturnal work habits. The more people work at night, the more electricity they draw from the grid. Thus electrical power usage becomes a useful stand-in for human activity.

Researchers who use blood flow as a proxy for brain activity are like the city planners who use electricity usage as a proxy for nocturnal work activity. But in the labyrinthine metropolis that is the brain, perhaps some inhabitants don’t need that much power? Perhaps some need a lot? Perhaps the engineers in charge of the power plants, by virtue of a holistic, long-term, and wide-angled view of the needs of the city, can anticipate which neighborhoods will need the most power at any given time, and preemptively allocate it?

A recent study from Aniruddha Das’s group (Cardoso et al., 2012) adds to a growing body of work that should encourage us to take that last possibility seriously. They looked at the correlation between blood flow and neural activity, and found that the blood flow signal was not just coupled with neural firing — it had more to say! The research team examined neural activity and blood flow simultaneously — but instead of using fMRI, which is a coarse measure, they used a technique called optical imaging. Since the technique is invasive, it cannot be done in humans, so it was conducted with rhesus macaques. They investigated the responses of cells in primary visual cortex (V1) to periodic sensory tasks. The monkeys had to fixate on a visual stimulus, and if they succeeded, they were rewarded with juice. Monkeys love juice.

Cardoso, Das and colleagues found that the blood flow signal could be decomposed into two separate signals — one that was linearly correlated with stimulus-triggered local neural firing, and another than had no connection with local electrical activity or the visual stimuli being presented, but instead was related to the task the monkey was performingPrior work (Sirotin and Das, 2009) from the same group showed that this non-sensory component of the blood flow signal is anticipatory, and can preempt the behavior of the monkey. Cardoso et al. suggest that the non-sensory component of the BOLD signal might reflect neuronal processes that have nothing to do with stimulus-driven spiking activity. The BOLD signal might reflect neural modulation of blood flow via top-down executive control or via a timing signal from the brain stem.

This line of research is exciting to me for two reasons. Firstly, it points to a potential source of ambiguity in interpreting fMRI studies that assume the BOLD signal is a perfect stand-in for local firing rate or local field potentials. If these results hold up, it means that the BOLD signal carries at least two pieces of information — one about local neuronal activity, and the other about anticipatory or preparatory task-related activity. Researchers must be careful about interpreting a BOLD signal as neural firing — perhaps in some situations only task-related blood flow is being modulated. In such situations a brain region might “light up” even when its neurons are not electrically active. If researchers are interested in localizing neural firing, they must be sure to subtract out all irrelevant aspects of the signal [but see note 5].

The second source of excitement relates to the anticipatory component of blood flow. Most of us are not interested in the electrical activity of the brain for its own sake — we assume it is the most important physiological phenomenon in the brain, underlying adaptive human and animal behavior. We typically assume that  cognition, emotion, memory and control over the body are subserved primarily by neurons. But what if anticipatory blood flow is itself an adaptive process? If this implies too radical a paradigm shift, consider this possibility: neural activity in some brain regions may play a role in modulating the blood flow elsewhere. This could be an extension of the brain’s role in monitoring and controlling bodily states. Perhaps there are metabolic coordination centers in the brain that can influence blood flow? Perhaps active resource allocation can improve speed and efficiency? Perhaps some parts of the brain assess contexts and goal states to infer which brain regions will require additional metabolic resources in the future. There is evidence that the locus coeruleus may participate in the active modulation of blood flow (Bekar et al, 2010).

To return to the city analogy, perhaps the engineers running the power stations can anticipate when and where power usage will be particularly high — say, during wartime, or in periods of creative expansion and neighborhood renewal — and redirect power actively, as peers of the city planners, rather than as passive subordinates.

Studies such as these serve as an important reminder that the brain is not simply a computer manipulating abstract information — it is also like a city with a bustling economy that requires energy and raw materials for its growth and maintenance. Resource allocation via blood flow and other mechanisms may therefore be an active, adaptive process, and therefore as important to neuroscientists as neuronal signaling.

~

If you spot errors, desire clarification, or have comments / additional citations, feel free to let me know in the comments section!

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Notes:

  1. The concept of neural activity can be a bit fuzzy in these contexts. As KD Singh (2012) says: “the very phrase neural activity is in itself a rather poorly specified and ultimately meaningless term. In most people’s minds the term is probably a surrogate for the firing of action potentials. However, within the cortex there are multiple neural signals, at different oscillatory frequencies, that might all contribute to the metabolic demand that then drives the BOLD signal. Furthermore, it’s not clear which of these neural signatures are most relevant to each aspect of perception and cognition.”
  2. “[F]ocal, stimulus-induced increases in brain blood flow are driven by local metabolic demand“. This hypothesis has been around since at least 1890, and has since been almost universally accepted, despite some important critiques by researchers such as Peter T. Fox, whose group has carried out experiments that indicate that blood flow and metabolism are not very closely coupled. For example, in a study from 2010, his group concluded that the cerebral blood flow response is “mediated by factors other than oxygen demand”.
  3. A complete picture how neural activity, blood oxygen, and cellular metabolism depend upon each other must also incorporate astrocytes and other non-neuronal brain cells — cells that were until recently assumed to serve custodial and infrastructural purposes, but are now believed to play active roles in signal transduction and memory.
  4. The review by Heeger and Ress (2002) is also worth looking at. The topic of neural activity and metabolism is far too complicated for a single blog post — maybe to fully understand we must “solve the brain” (!?) — so it’s important to glance at the primary literature from time to time.
  5. A somewhat bipolar commentary on this paper by Karl Friston (2012)  seems to suggest the fMRI community was well aware of the task-related component of the BOLD signal. He says “they have chosen to use their set-up to largely confirm things that have been known for over a decade”. And yet he goes on to say that the “implications of their findings are far-reaching”. Not sure what to make of this.

References

ResearchBlogging.org

Cardoso MM, Sirotin YB, Lima B, Glushenkova E, & Das A (2012). The neuroimaging signal is a linear sum of neurally distinct stimulus- and task-related components. Nature neuroscience, 15 (9), 1298-306 PMID: 22842146

Singh, K. D.  (2012). Which “neural activity” do you mean? fMRI, MEG, oscillations and neurotransmitters. NeuroImage Volume 62, Issue 2, 15 August 2012, Pages 1121–1130. dx.doi.org/10.1016/j.neuroimage.2012.01.028

Sirotin, Y. B. & Das, A. (2009). Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity Nature, 457 (7228), 475-479 DOI: 10.1038/nature07664

Bekar, L. K., Wei, H. S., & Nedergaard, M. (2012). The locus coeruleus-norepinephrine network optimizes coupling of cerebral blood volume with oxygen demand. Journal of Cerebral Blood Flow & Metabolism. doi:10.1038/jcbfm.2012.115

Fox, P. T. (2012). The coupling controversy. NeuroImage Volume 62, Issue 2, 15 August 2012, Pages 594–601. dx.doi.org/10.1016/j.neuroimage.2012.01.103

Heeger, D. J., & Ress, D. (2002). What does fMRI tell us about neuronal activity?. Nature Reviews Neuroscience, 3(2), 142-151. doi:10.1038/nrn730

Friston, K. J. (2012). What does functional MRI measure? Two complementary perspectives. Trends in Cognitive Sciences. dx.doi.org/10.1016/j.tics.2012.08.005

Brains, Boats & Baseball bats — some thoughts on fMRI

I wanted to write a post on a new fMRI paper that looks really interesting. But in attempting to do so I felt the need to condense some of my own cloudy thoughts on fMRI. Think of this as one part explanation, one part rant, and one part thinking aloud.

Functional magnetic resonance imaging (fMRI) has become a popular tool for human neuroscience and experimental psychology. But its popularity masks several major issues of interpretation that call into question many of the generalizations fMRI researchers would like to make. These generalizations often lead to overzealous and premature brain-centric redefinitions of high-level concepts such as thinking, emotion, love, and pleasure.

Sensationalist elements in the media run with these redefinitions, taking advantage of secular society’s respect for science in order to promote an unfounded reductionist attitude towards psychological and cultural phenomena. A reductionist attitude feeds off two often contradictory human desires: one for simple, intuitive explanations, and the other for controversial, novel solutions. This biased transfer of ideas from the laboratory to the newspaper, the blog and the self-help book is responsible for a rash of fallacious oversimplifications, such as the use of dopamine as a synonym for “pleasure”. (It correlates with non-so-pleasurable events too.)

Neuroscientific redefinitions of high-level phenomena, even when inspired by an accurate scientific picture, often fall prey to the “mereological fallacy“, i.e.,  the conflating of parts of a phenomenon for the whole. Bits of muscle don’t dance, people do. And a free-floating brain doesn’t think or feel, a whole organism does.

Wikipedia saves lives.

what is this i dont even

But before dealing with the complex philosophical, sociological and semantic issues posed by neuroscience, we must be sure that we understand what the experimental techniques are actually telling us. fMRI experiments are usually interpreted as indicating which parts of the brain “light up” during a particular, task, event, or subjective experience. For instance, a recent news headline informs us that “Speed-Dating Lights Up Key Brain Areas“. The intuitive simplicity of such statements masks a hornets’ nest of interpretation problems.

What does an fMRI picture represent? fMRI results only reach statistical significance if the studies are carried out in a group of at least 5-10 people, so the “lighting up” is reliable only after pooling data [But see the edit below]. And in addition to averaging the data from multiple subjects, usually the experimenter must also average multiple trials for each subject. So  the fMRI heat-map is an average of averages. Far from being a snapshot of the brain activity as it evolves through time, an fMRI heat-map is like a blurry composite photograph produced by superimposing several long-exposures of similar, non-identical things.

As if this were not problematic enough, the results of an fMRI scan of a single person must be subtracted from a baseline before further analysis. The brain is never quiescent, so to find out about activity in a particular region during a particular event or task, the experimenter must design a control that is identical to the task except with respect to the phenomenon of interest. Imagine a boat with three people on it, sailing on choppy seas. If an observer watching calmly from the shore wants to understand how each person is moving on the boat, she must first factor out the overall motion of the boat caused by the waves. Only then can the observer determine, say,  that one person on the boat is jumping up and down, the other is swaying from side to side, and the third is trying to be as still as possible. The subtractions used in fMRI studies are like the factoring out of the motion of the boat — they allow the experimenter to zoom in on the activities of interest and ignore the choppy sea that is the brain’s baseline activity.

Here’s another analogy that might help (me) understand what’s happening with fMRI averaging and subtracting. Let’s say you want to understand the technique of baseball batters that allows them to successfully hit a particular kind of pitch. You take videos of 20 batters hitting, say, a fastball. The fastball is the “task”, and each attempt to hit the ball is a “trial”. Suppose each batter makes 100 swings, and around 50 connect with the ball. The rest are strikes. So there are 50 hits and 50 misses. You take the videos for the 50 hits and average them, so you get a composite or superimposed video for each batter. Then you take the videos of the 50 strikes, and average those. This is the control. Now you “subtract” these two averaged videos, for each batter, getting a video that would presumably show a series of ghostly images of floating, morphing body parts — highlighting only what was different about the batter’s technique when he made contact with the ball versus when he didn’t.  In other words, if the batter’s torso moves in exactly the same way whether he hits or misses, then in the video the torso will be subtracted out, and only the head, arms and legs will be visible. Finally, you pool together the subtracted videos for all 20 batters and average them. Now you have a single video that shows the average difference in batting technique between successful hits and misses. If you’ve done everything right, you have some idea of which batting techniques tend on average to work against fastballs.

But consider what may be misleading about this video. Perhaps there are two different techniques or strategies for hitting a fastball. The averaged video will only show a kind of midway point between them. Basically, individual differences can get blurred out by averaging. So sometimes the batting technique that seems to be suggested by the average doesn’t really exist — it  can be an artifact of averaging, rather than a picture of an actual trend. Good statistical practices help experimenters avoid artifacts, but as the task and the stats become more complicated, the scope for misunderstanding and misuse expand. In other words, every mathematical angel is shadowed by its very own demon.

Another interpretation issue has to do with what the subtraction means. In the case of the missing torso, you can assert that the difference between success and failure at hitting a fastball does not depend on the torso’s movement, since it’s the same regardless of what the batter does. But this does not mean, however, that the torso has nothing to do with batting. After all, we know the torso is what links up everything else and provides the crucial central services to the arms and legs. So if a brain region doesn’t light up in an fMRI study, this doesn’t mean that it has no role to play in the task being studied. It may in fact be as central to the task as the torso is!

But the problems associated with averaging and subtraction crop up in all forms of data analysis, so they’re among the inevitable hazards that go with  experimental science. The central question that plagues fMRI interpretation is not mathematical or philosophical, it’s physiological. What neural phenomenon does fMRI measure, exactly? It seems a partial answer may have been found, which I’ll touch on, hopefully, in the next post.

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Notes:

  • William Uttal’s book The New Phrenology (which I have only read a chapter or so of) describes how the “localization of function” thread that runs through fMRI and other neuroscientific approaches may be a misguided return to that notorious Victorian pseudoscience. Here is a precis of the book.
  • This New Yorker piece deals with many of the issues with fMRI, and also links to related resources including a New York Times article.
  • Neuroscience may not be as misleading to the public as was originally thought. Adding fMRI pictures or neurobabble was thought to make people surrender their logical faculties, but a recent study suggests that the earlier one may have been flawed.
  • For a vivid illustration of the potential effects of averaging, check out this art project. The artist averaged every Playboy centerfold, grouping them by decade, producing one blurry image each for the 60, 70s, 80s, and 90s. Don’t worry, it’s very SFW.
  • You can also play around with averaging faces here.

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EDIT: In the comment section Kayle made some very important corrections and clarifications:

“You say that fMRI results only reach statistical significance if the studies are carried out in groups. This is not quite right. Almost all fMRI studies begin with a “first-level analysis,” which are single-subject statistics. This way you can contrast different conditions for a single subject. With large differences, small variablity, and enough trials, robust maps can be created. This is done for surgical planning when doctors are considering how much brain they can resect surrounding a tumor without endangering someone’s ability to move or talk. When examining mean differnces between groups, however, you need to examine results from multiple people (“second-level analysis”). Again, this is not specific to fMRI. The rule of thumb goes something like this: Most people are interested in being able to detect differences with effect sizes of about 1 SD or above. To do this with some confidence (Type II p < 0.20) you need about 10 to 30 observations per group.”

also

” Every fMRI result you’ve seen includes single-person single-session results that generally are not reported because most people aren’t interested.”