Having more global brain connectivity with some regions enhances intelligence

A new study – titled “Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence” – was published just last week. In it, my co-authors and I describe our research showing that connectivity with a particular part of the prefrontal cortex can predict how intelligent someone is.

We measured intelligence using “fluid intelligence” tests, which measure your ability to solve novel visual puzzles. It turns out that scores on these tests correlate with important life outcomes like academic and job success. So, finding a neuroscientific factor underlying fluid intelligence might have some fairly important implications.

It turns out that it’s relatively unclear exactly what fluid intelligence tests actually test (what helps you solve novel puzzles, exactly?), so we also measured a more basic “cognitive control” ability thought to be related to fluid intelligence – working memory. This measures your ability to maintain and manipulate information in mind in a goal-directed manner.

Overall (i.e., global) brain connectivity with a part of left lateral prefrontal cortex (see figure above) could predict both fluid intelligence and cognitive control abilities.

What does this mean? One possibility is that this prefrontal region is a “flexible hub” that uses its extensive brain-wide connectivity to monitor and influence other brain regions in a goal-directed manner. This may sound a bit like it’s some kind of “homunculus” (little man) that single-handedly implements all brain functions, but in fact we’re suggesting it’s more like a feedback control system that is used often in engineering, that it only helps implement cognitive control (which supports fluid intelligence), and that it doesn’t do this alone.

Indeed, we found other independent factors that were important for predicting intelligence, suggesting there are several fundamental neural factors underlying intelligence. The global connectivity of this prefrontal region could account for 10% of the variability in fluid intelligence, while activity in this region accounts (independently) for 5% of the variability, and overall gray matter volume accounts (again independently) for an additional 6.7% of the variance. Together, these three factors accounted for 26% of the variance in fluid intelligence across individuals.

There are several important questions that this study raises. For instance, does this region change its connectivity depending on the task being performed, as the “flexible hub” hypothesis would suggest? Are there other regions whose global (or local) connectivity contributes substantially to intelligence and cognitive control abilities? Finally, what other factors are there in the brain that might be able to predict fluid intelligence across individuals?


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:

The Google Video link is broken, but you can see a related talk by Fuster here:


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

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The Will to be Free, Part II

 Several months ago I posted The Will to be Free, Part I. In that post I explained that memory is the key to free will. However, this insight isn’t quite satisfactory. We need three additional things to complete the picture: the ability to choose based on predictions, internal desires, and self-awareness. (A quick disclaimer: These ideas are all extremely speculative. I’ll probably test most of them at some point, but right now I’m just putting them out there to hopefully allow for refinement of these hypotheses.) First, the ability to choose based on predictions. As mentioned last time, free will comes down to decision making. Specifically it comes down to our ability to make a decision based on internal sources (or at least condoned by them), rather than external coercive forces. If we cannot predict the outcome of our decision with any certainty, then decision making is pointless. For instance, if no matter what I choose to order at dinner a random dish is served then I had no freedom to choose in the first place. Thus, our ability to predict is necessary for free will. What are these “internal sources” involved in decision making that I mentioned earlier? They are the second new idea needed to complete our picture of free will: desires.

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The Cognitive Control Network

The Cognitive Control NetworkI recently published my first primary-author research study (Cole & Schneider, 2007).

The study used functional MRI to discover a network of brain regions responsible for conscious will (i.e., cognitive control). It also revealed the network’s specialized parts, which each uniquely contribute to creating the emergent property of conscious will.

I believe this research contributes substantially to our understanding of how we control our own thoughts and actions based on current goals. Much remains a mystery, but this study clearly shows the existence of a functionally integrated yet specialized network for cognitive control.

What is cognitive control? It is the set of brain processes necessary for goal-directed thought and action. Remembering a phone number before dialing requires cognitive control. Also, anything outside routine requires cognitive control (because it’s novel and/or conflicting with what you normally do). This includes, among other things, voluntarily shifting attention and making decisions.

What brain regions are involved? A mountain of evidence is accumulating that a common set of brain regions are involved in cognitive control. We looked for these regions specifically, and verified that they were active during our experiment [see top figure]. The brain regions are spread across the cortex, from the front to the back to either side. However, it’s not the whole brain: there are distinct parts that are involved in cognitive control and not other behavioral demands.

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History’s Top Brain Computation Insights: Hippocampus binds features

Hippocampus is involved in feature binding for novel stimuli (McClelland, McNaughton, & O'Reilly – 1995, Knight – 1996, Hasselmo – 2001, Ranganath & D'Esposito – 2001)

It was demonstrated by McClelland et al.that, based on its role in episodic memory encoding, hippocampus can learn fast arbitrary association.

This was in contrast to neocortex, which they showed learns slowly in order to develop better generalizations (knowledge not tied to a single episode). This theory was able to explain why patient H.M. knew (for example) about JFK's assassination even though he lost his hippocampus in the 1950s.

Robert Knight provided evidence for a special place for novelty in hippocampal function by showing a different electrical response to novel stimuli in patients with hippocampal damage.

These two findings together suggested that the hippocampus may be important for binding the features of novel stimuli, even over short periods.

This was finally verified by Hasselmo et al. and Ranganath & D'Esposito in 2001. They used functional MRI to show that a portion of the hippocampal formation is more active during working memory delays when novel stimuli are used.

This suggests that hippocampus is not just important for long term memory. Instead, it is important for short term memory and perhaps novel perceptual binding in general.

Some recent evidence suggests that hippocampus may be important for imagining the future, possibly because binding of novel features is necessary to create a world that does not yet exist (for review see Schacter et al. 2007).


History’s Top Brain Computation Insights: Day 24

Cognitive control network (Cole & Schneider, 2007)24) Cognitive control processes are distributed within a network of distinct regions (Goldman-Rakic – 1988, Posner – 1990, Wager & Smith 2004, Cole & Schneider – 2007)

Researchers investigating eye movements and attention recorded from different parts of the primate brain and found several regions showing very similar neural activity. Goldman-Rakic proposed the existence of a specialized network for the control of attention.

This cortical system consists of the lateral frontal cortex (fronto-polar, dorsolateral, frontal eye fields), medial frontal cortex (anterior cingulate, pre-SMA, supplementary eye fields), and posterior parietal cortex. Subcortically, dorsomedial thalamus and superior colliculus are involved, among others.

Many computational modelers emphasize the emergence of attention from the local organization of sensory cortex (e.g., local competition). However, when a shift in attention is task-driven (i.e., top-down) then it appears that a specialized system for attentional control drives activity in sensory cortex. Many properties of attention likely arise from the organization of sensory cortex, but empirical data indicate that this is not sufficient.

With the advent of neuroimaging in humans (PET and fMRI), Posner et al. found very similar regions as those reported by Goldman-Rakic. He found that some regions are related more to orienting to stimuli, while others are related more to cognitive control (i.e., controlled processing).

After many fMRI studies of cognitive control were published, Wager et al. performed a meta-analysis looking at most of this research. They found a set of cortical regions active in nearly all cognitive control tasks.

My own work with Schneider (in press) indicates that these regions form an innate network, which is better connected than the rest of cortex on average. We used resting state correlations of fMRI BOLD activity to determine this. This cognitive control network is involved in controlled processing in that it has greater activity early in practice relative to late in practice, and has greater activity for conflicting responses (e.g., the Stroop task).

Though these regions have similar responses, they are not redundant. Our study showed that lateral prefrontal cortex is involved in maintaining relevant task information, while medial prefrontal cortex is involved in preparing and making response decisions. In most cases these two cognitive demands are invoked at the same time; only by separating them in time were we able to show specialization within the cognitive control network. We expect that other regional specializations will be found with more work.

I’ll be covering my latest study in more detail once it is published (it has been accepted for publication at NeuroImage and should be published soon). The above figure is from that publication. It lists the six regions within the human cognitive control network. These regions include dorsolateral prefrontal cortex (DLPFC), inferior frontal junction (IFJ), dorsal pre-motor cortex (dPMC), anterior cingulate / pre-supplementary motor area (ACC/pSMA), anterior insula cortex (AIC), and posterior parietal cortex (PPC).

A general computational insight arising from this work (starting with Goldman-Rakic) is that cortex is composed of specialized regions that form specialized networks. This new paradigm for viewing brain function weds the old warring concepts of localized specialization and distributed function.

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 forming specialized networks 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.]


History’s Top Brain Computation Insights: Day 17

17) Reverbatory activity in lateral prefrontal cortex maintains memories and attention over short periods (Fuster – 1971, Jacobsen – 1936, Goldman-Rakic – 2000)

Patient H.M., with his lack of long term memory but largely intact working (short-term) memory, illustrated a dissociation between these two forms of memory. While long-term memory seemed to rely on hippocampus and the neocortical temporal lobes, in the 1960s it was not clear how working memory might be maintained.

Hebb had postulated a distributed and dynamic mechanism for working memory that was quite hard to test. However, a more testable hypothesis had emerged from observations of patients with prefrontal cortex damage. Such patients had trouble making and carrying out plans over time, possibly due to a working memory deficit. Previous work by Jacobsen lesioning primate prefrontal cortex supported this theory, but this work was far from conclusive.

In 1970 Joaquin Fuster cooled the monkey prefrontal cortex, showing a reversible deficit in working memory. The following year he recorded from monkey prefrontal cortex neurons and found cells that maintained activity over delay periods (‘memory cells’). These neurons respond not just to the stimulus presentation and the response, but also maintain activity between the two events (see figure for illustration).

A decade later Fuster found visual memory cells in inferior temporal cortex. Subsequent research has suggested that the prefrontal memory cells drive the temporal cortex memory cells to maintain their activity.

Patricia Goldman-Rakic, another monkey neurophysiologist, was instrumental in elucidating the network properties of working memory function. She showed in 2000 that lateral prefrontal and posterior parietal cortices mutually support the sustained working memory activity discovered by Fuster. She also showed the importance of the dopamine system and thalamus in working memory function.

Since the advent of PET and functional MRI in the 1990s a number of researchers have extended the primate working memory findings to humans. Some of these researchers include Jonathan Cohen, Mark D’Esposito, Michael Petrides, Julie Fiez, and John Jonides.

Implication: The mind, largely governed by reward-seeking behavior, 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 strengthened by correlated activity. The cortex, a part of that organ composed of functional column units whose spatial dedication (determined via local competition) 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 communication for functional integration.
[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.]