Cingulate Cortex and the Evolution of Human Uniqueness

Figuring out how the brain decides between two options is difficult. This is especially true for the human brain, whose activity is typically accessible only via the small and occasionally distorted window provided by new imaging technologies (such as functional MRI (fMRI)).

In contrast, it is typically more accurate to observe monkey brains since the skull can be opened and brain activity recorded directly.

Despite this, if you were to look just at the human research, you would consider it a fact that the anterior cingulate cortex (ACC) increases its activity during response conflict. The thought is that this brain region detects that you are having trouble making decisions, and signals other brain regions to pay more attention.

If you were to only look at research with monkeys, however, you would think otherwise. No research with macaque monkeys (the ‘non-human primate’ typically used in neuroscience research) has found conflict activity in ACC.

My most recent publication looks at two possible explanations for this discrepancy: 1) Differences in methods used to study these two species, and 2) Fundamental evolutionary differences between the species.

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Joaquin Fuster on Cortical Dynamics

I recently watched this talk (below) by Joaquin Fuster. His theories provide a good integration of cortical functions and distributed processing in working and long-term memory. He also has some cool videos of likely network interactions across cortex (in real time) in his talk.

Here is a diagram of Dr. Fuster’s view of cortical hierarchies:

Joaquin Fuster’s talk:

Link to Joaquin Fuster’s talk [Google Video]

Here is an excerpt from Dr. Fuster’s amazing biography:

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Measuring Innate Functional Brain Connectivity

 Functional magnetic resonance imaging (fMRI), a method for safely measuring brain activity, has been around for about 15 years. Within the last 10 of those years a revolutionary, if mysterious, method has been developing using the technology. This method, resting state functional connectivity (rs-fcMRI), has recently gained popularity for its putative ability to measure how brain regions interact innately (outside of any particular task context).

Being able to measuring innate functional brain connectivity would allow us to know if a set of regions active during a particular task is, in fact, well connected enough generally to be considered a network. We could then predict what brain regions are likely to be active together in the future. This could, in turn, motivate us to look deeper at the nature of each brain region and how it contributes to the neuronal networks underlying our behavior.

Rs-fcMRI uses correlations of very slow fluctuations in fMRI signals (< 0.1 Hz) when participants are at rest to determine how regions are connected. The origin of these slow fluctuations has been unclear.

Some have argued that the thoughts and day dreams of participants “at rest” may explain the strong correlations typically found between brain regions. Recently, Vincent et al., 2007 sought to address this possibility using fMRI with anesthetized monkeys.

The idea is that if unconscious monkey brains show low-frequency correlated activity across known brain networks, then such findings in humans at conscious rest are likely not due to spurious thoughts, but something more innate.

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Redefining Mirror Neurons

Monkey imitating humanIn 1992 Rizzolatti and his colleagues found a special kind of neuron in the premotor cortex of monkeys (Di Pellegrino et al., 1992).

These neurons, which respond to perceiving an action whether it's performed by the observed monkey or a different monkey (or person) it's watching, are called mirror neurons.

Many neuroscientists, such as V. S. Ramachandran, have seized upon mirror neurons as a potential explanatory 'holy grail' of human capabilities such as imitation, empathy, and language. However, to date there are no adequate models explaining exactly how such neurons would provide such amazing capabilities.

Perhaps related to the lack of any clear functional model, mirror neurons have another major problem: Their functional definition is too broad.

Typically, mirror neurons are defined as cells that respond selectively to an action both when the subject performs it and when that subject observes another performing it. A basic assumption is that any such neuron reflects a correspondence between self and other, and that such a correspondence can turn an observation into imitation (or empathy, or language).

However, there are several other reasons a neuron might respond both when an action is performed and observed.

First, there may be an abstract concept (e.g., open hand), which is involved in but not necessary for the action, the observation of the action, or any potential imitation of the action.

Next, there may be a purely sensory representation (e.g., of hands / objects opening) which becomes involved independently of action by an agent.

Finally, a neuron may respond to another subject's action not because it is performing a mapping between self and other but because the other's action is a cue to load up the same action plan. In this case the 'mirror' mapping is performed by another set of neurons, and this neuron is simply reflecting the action plan, regardless of where the idea to load that plan originated. For instance, a tasty piece of food may cause that neuron to fire because the same motor plan is loaded in anticipation of grasping it.

It is clear that mirror neurons, of the type first described by Rizzolati et al., exist (how else could imitation occur?). However, the practical definition for these neurons is too broad.

How might we improve the definition of mirror neurons? Possibly by verifying that a given cell (or population of cells) responds only while observing a given action and while carrying out that same action.

Alternatively, subtractive methods may be more effective at defining mirror neurons than response properties. For instance, removing a mirror neuron population should make imitation less accurate or impossible. Using this kind of method avoids the possibility that a neuron could respond like a mirror neuron but not actually contribute to behavior thought to depend on mirror neurons.

Of course, the best approach would involve both observing response properties and using controlled lesions. Even better would be to do this with human mirror neurons using less invasive techniques (e.g., fMRI, MEG, TMS), since we are ultimately interested in how mirror neurons contribute to higher-level behaviors most developed in homo sapiens, such as imitation, empathy, and language.


Image from The Phineas Gage Fan Club (originally from Ferrari et al. (2003)).

Grand Challenges of Neuroscience: Day 3

Topic 3: Spatial Knowledgeskaggs96figure3.png

Animal studies have shown that the hippocampus contains special cells called "place cells".  These place cells are interesting because their activity seems to indicate not what the animal sees, but rather where the animal is in space as it runs around in a box or in a maze. (See the four cells in the image to the right.)

Further, when the animal goes to sleep, those cells tend to reactivate in the same order they did during wakefulness.  This apparent retracing of the paths during sleep has been termed "hippocampal replay".

More recently, studies in humans — who have deep microelectrodes implanted to help detect the origin of epileptic seizures — have shown place-responsive cells.  Place cells in these studies were found not only in the human hippocampus but also in nearby brain regions.

The computation which converts sequences of visual and other cues into a sense of "place" is a very interesting one that has not yet been fully explained.  However, there do exist neural network models of the hippocampus that, when presented with sequences, exhibit place-cell like activity in some neurons.

The notion of place cell might also extend beyond physical space.  It has been speculated that computations occur to convert sequences events and situations into a distinct sense of "now".  And indeed, damage to the hippocampus has been found not only to impair spatial memory but also "episodic" memory, the psychological term for memory for distinct events.


How can we understand the ways in which we understand space? Understanding spatial knowledge seems more tangible than understanding the previous two topics in this series. It seems that researchers are already using some of the most effective methods to tackle the problem.

First, the use of microelectrodes throughout the brain while human participants play virtual taxi games and perform problem solving tasks promises insight into this question.  Second, computational modeling of regions (e.g., the hippocampus) containing place cells should help us understand their properties and how they emerge.  Finally, continued animal research and possibly manipulation of place cells in animals to influence decision making (e.g., in a T-maze task) may provide an understanding of how spatial knowledge is used on-line. 


History’s Top Brain Computation Insights: Day 23

23) Motor cortex is organized by movement direction (Schwartz  & Georgopoulos – 1986, Schwartz – 2001)

Penfield had shown that motor cortex is organized in a somatotopic map. However, it was unclear how individual neurons are organized. What does each neuron's activity represent? The final location of a movement, or the direction of that movement?

Shwartz & Georgopoulos found that movement direction correlated best with neural activity. Further, they discovered that the neurons use a population code to specify a given movement. Thus, as illustrated above, a single neuron responds to a variety of movement direction but has one preferred direction (PD).

The preferred direction code across a large population of neurons thus sums to specify each movement.

Schwartz has since demonstrated how these population vectors can be interpreted by a computer to control a prosthetic arm. He has used this to imbue monkeys with Jedi powers; able to move a prosthetic limb in another room (or attached) with only a thought. Using this technology the Schwartz team hopes to allow amputee humans to control artificial limbs as they once did their own.

A general computational insight one might take from the Schwartz & Georgopoulos work is the possibility of population coding across cortex. It appears that all perception, semantics, and action may be coded as distributed population vectors.

Representational specificity within these vectors likely arises from conjunctions of receptive fields, and are dominated by those receptive fields most specific to each representation. 

Implication: The mind, largely governed by reward-seeking behavior on a continuum between controlled and automatic processing, is implemented in an electro-chemical organ with distributed and modular function consisting of excitatory and inhibitory neurons communicating via ion-induced action potentials over convergent and divergent synaptic connections altered by timing-dependent correlated activity often driven by expectation errors. The cortex, a part of that organ organized via local competition and composed of functional column units whose spatial dedication determines representational resolution, is composed of many specialized regions involved in perception (e.g., touch: parietal, vision: occipital), action (e.g., frontal), and memory (e.g., short-term: prefrontal, long-term: temporal), which depend on inter-regional connectivity for functional integration, population vector summation for representational specificity, and recurrent connectivity for sequential learning.

[This post is part of a series chronicling history's top brain computation insights (see the first of the series for a detailed description). See the history category archive to see all of the entries thus far.]


Human Versus Non-Human Neuroscience

Most neuroscientists don't use human subjects, and many tend to forget this important point: 
All neuroscience with non-human subjects is theoretical.

If the brain of a mouse is understood in exquisite detail, it is only relevant (outside veterinary medicine) in so far as it is relevant to human brains.

Similarly, if a computational model can illustrate an algorithm for storing knowledge in distributed units, it is only as relevant as it is similar to how humans store knowledge.

It follows from this point that there is a certain amount of uncertainty involved in any non-human research. An experiment can be brilliantly executed, but does it apply to humans?

Circumventing this uncertainty problem by looking directly at humans, another issue arises:  Only non-invasive techniques can be used with humans, and those techniques tend to involve the most uncertainty.

For instance, fMRI is a non-invasive technique that can be used to measure brain processes in humans. However, it measures the oxygenation levels, which is only indirectly related to neural activity. Thus, unlike with animal models, measures of neuronal activity are surrounded by an extra layer of uncertainty in humans.

So, if you're a neuroscientist you have to "choose your poison": Either deal with the uncertainty of relevance to humans, or deal with the uncertainty of the processes underlying the measurable signals in humans.

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Eliminating Common Misconceptions About fMRI

Most researchers in neuroscience use animal models.

Though most neuroscientists are interested in understanding the human brain, they can use more invasive techniques with animal brains. In exchange for these invasive abilities they must assume that other animals are similar enough to humans that they can actually learn something about humans in the process.

Functional magnetic resonance imaging (fMRI) is a non-invasive technique for measuring changes in local blood flow (which are significantly correlated with changes in neural activity) in the brain. fMRI measures what is called the blood oxygen level-dependent (BOLD) signal. Because it is non-invasive it can be used with human subjects.

Researchers like ourselves recognize the value of animal research, especially when the behavior being investigated is similar between the studied species and humans.

However, there is at least one fundamental cognitive difference between humans and all other animals, and likely many more given the dominant position of our species.
For researchers like ourselves it is much more interesting to learn something about the human brain (the item of interest) rather than, say, the rat brain.

Why do some neuroscientists think that using fMRI to study the neural basis of cognition in humans is of little value?

Many have heard that there are issues with fMRI as a technique. There are (like any technique), but not as many as most believe.

Here are some common misconceptions about fMRI:

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