Posts Tagged ‘ functional networks ’

Saving Faces

A brain area that’s specialized to recognize faces has a unique structure in each of our brains – and mapping that area’s connectivity patterns can tell us how each of our brains use it, says a new study.

The fusiform gyrus - home of the Brain Face Database.

The fusiform gyrus in the temporal lobe plays a part in our recognition of words, numbers, faces, colors, and other visual specifics – but it’s becoming increasingly clear that no two people’s fusiform gyrus structure is identical. By studying this region in a larger connectomic framework, though, researchers can now predict which parts of a certain person’s fusiform gyrus are specialized for face recognition.

Since the early days of neurophysiology – way back into the 1800s – scientists have been working to pinpoint certain types of brain activity to certain structures within the brain. Simple experiments and lesion studies – many of them pretty crude by today’s standards – demonstrated that, for instance, the cerebellum is necessary for coordinating bodily movement; and that the inferior frontal gyrus (IFG) is involved in speech production.

Things get trickier, though, when we try to study more abstract mental tasks. For example, debates over the possible existence of “grandmother cells” – groups of neurons whose activity might represent complex concepts like “my grandmother” – have raged for decades, with no clear resolution in sight. The story’s similar for “mirror neurons” – networks of cells that some scientists think are responsible for our ability to understand and predict the intent of another person’s action.

All these debates reflect a far more fundamental gap in our understanding – one that many scientists seem reluctant to acknowledge: To this day, no one’s been able to demonstrate exactly what a “concept” is in neurological terms – or even if it’s a single type of “thing” at all.

This is why you’ll sometimes hear theoretical psychologists talk about “engrams” – hypothetical means by which neural networks might store memories – a bit like computer files in the brain. But the fact is, no one’s sure if the brain organizes information in a way that’s at all analogous to the way a computer does. In fact, a growing body of research points toward the idea that our memories are highly interdependent and dynamic – more like ripples in a pond than files in a computer.

This is where connectomics comes in. As researchers become increasingly aware that no two brains are quite alike, they’ve begun to focus on mapping the neural networks that connect various processing hubs to one another. As an analogy, you might say they’ve begun to study traffic patterns by mapping a country’s entire highway system, rather than just focusing on the stoplights in individual cities.

And now, as the journal Nature Neuroscience reports, a team led by MIT’s David Osher has mapped a variety of connectivity patterns linking the fusiform gyrus to other brain areas.

They accomplished this through a technique called diffusion imaging, which is based on a brain-scanning technology known as diffusion MRI (dMRI). Diffusion imaging applies a magnetic field to the brain, causing water to flow along axons – the long “tails” of neurons that connect them to other areas – allowing the MRI scan to detect which areas are sending out lots of signals to others during certain mental activities. As you can imagine, this technique has been revealing all sorts of surprising new facts about the brain’s functionality.

In this particular study, the researchers found that during face-recognition tasks, certain parts of the fusiform gyrus lit up with active connections to areas like the superior and inferior temporal cortices, which are also known to be involved in face recognition. Intriguingly, they also detected connectivity with parts of the cerebellum – an ancient brain structure involved in bodily balance and movement, which no one expected to be part of any visual recognition pathway. Sounds like a Science Mystery to me!

The team even discovered that they could use the connectivity patterns they found to predict which faces a person would recognize:

By using only structural connectivity, as measured through diffusion-weighted imaging, we were able to predict functional activation to faces in the fusiform gyrus … The structure-function relationship discovered from the initial participants was highly robust in predicting activation in a second group of participants, despite differences in acquisition parameters and stimuli.

In short, they’ve discovered patterns of functional connectivity that directly correspond to our ability to recognize a particular face.

It’s still a far cry from an engram – after all, we still don’t know exactly what information these connections encode, or how the brain encodes that data, or what other conditions might need to be met for an “a-ha!” recognition to take place – but still, network mapping appears to be a very promising starting point for investigating questions like these.

The researchers plan to use this approach to study connectivity patterns in the brains of children with severe autism, and other patients who have trouble recognizing faces. They hope it’ll also be useful for understanding how we recognize scenes and other objects – eventually, a network-oriented approach may even offer clues about how we recognize familiar ideas.

In other words, for the first time in history, we’re using our brains’ natural love of connections to understand just how our brains form those connections in the first place. For those of us who love a good mystery, it’s an exciting time to be studying our own minds!

Surprising Synchrony

Our corpus callosum is a bundle of fibers that allows our brains’ left and right hemispheres to communicate – but even in people born without these connections, the hemispheres are still somehow able to synchronize their activity, reports a new study.

Activity in a brain with a corpus callosum (left) and one without (right). See, they're twins - just like Schwarzenegger and DeVito!

The brains of people born with a condition called agenesis of the corpus callosum (AgCC) – basically, absence of a corpus callosum – show activation patterns that are essentially the same as those of people with an intact corpus callosum. It’s a Neuroscience Mystery!

For decades, the corpus callosum’s purpose seemed straightforward enough: though certain areas of our left and right hemispheres specialize in certain tasks – the left hemisphere is more adept at retrieving specific facts, for instance; while the right is better at estimation – the two hemispheres work together to achieve most goals, and show a great deal of symmetry in their activation patterns. Since the corpus callosum is composed mainly of white matterconnective neural tissue – and severing it seems to prevent epileptic activity from spreading, it seemed obvious that this structure was crucial for communication between the two hemispheres.

But now, as the Journal of Neuroscience reports, a team led by Caltech’s J. Michael Tyszka has used fMRI scans to detect communication between the hemispheres in the brains of eight adults who lack a corpus callosum:

“This was a real surprise,” says Tyszka. “We expected to see a lot less coupling between the left and right brain in this group — after all, they are missing about 200 million connections that would normally be there. How do they manage to have normal communication between the left and right sides of the brain without the corpus callosum?”

How, indeed? Well, Tyszka’s team has a few bright ideas. The most promising one hinges on functional networkspatterns of brain connectivity that rely somewhat on anatomical (structural) networks, but are much more flexible. You might think of them as mental “software” as opposed to “hardware.”

This new research suggests that functional networks may be more flexible and adaptive than many scientists suspected:

We tested two distinct possibilities: (1) functional networks arise largely from structural connectivity constraints, and generally require direct interactions between functionally coupled regions mediated by white-matter tracts; and (2) functional networks emerge flexibly with the development of normal cognition and behavior and can be realized in multiple structural architectures.

In short, the two possibilities are that 1) functional networks mostly depend on structural ones, or 2) they arise in response to brain function, and aren’t particularly dependent on anatomical structure. The surprising functional symmetry in brains that lack a corpus callosum seems to strongly imply that #2 is closer to the truth.

Like many answers in neuroscience, this one raises a lot of new, intriguing questions – the first and most obvious among them being, How the hell are these hemispheres communicating? We don’t know yet. Just how flexible are functional networks? We don’t know yet. Might there be a link to the unusual white-matter connectivity in children with autism? Probably, but we don’t yet know what it is.

And what does this suggest about the nature of consciousness? Well, let’s see – you got a comfy chair and about three hours to spare?

Hypnotized Eyes

A state of hypnosis creates detectable changes in a person’s eye movement patterns, says a new study.

"When you awake, you will experience an overwhelming desire to Make Me a Sammich!"

The “glazed” look of a person who’s been hypnotized can be linked to measurable, quantifiable changes in the patterns of that person’s reflexive eye movements – changes that non-hypnotized people aren’t able to replicate.

The exact nature – and even the actual existence – of the hypnotic state have been controversial topics since the term was first coined in the 1840s. Some have likened it to a form of sleep (the word itself comes from the ancient Greek hypnos, meaning “sleep”), while others have described it as a state of intense focus, or of heightened suggestibility. Some have claimed that hypnotized people are faking, or at least fooling themselves – but even so, the idea of hypnosis continues to fascinate many of us.

One of the first real indicators that hypnosis might be an objectively detectable state came in 1999, when a team at Belgium’s University of Liège used positron emission tomography (PET) scans to detect altered activation patterns in the brains of volunteers under hypnosis. A 2002 study at the University of Montreal lent more detail to these results; and in 2007, a team led by Andrew Fingelkurts at Finland’s Brain and Mind Technologies Research Centre attacked the problem from another angle, using electroencephalography (EEG) to detect changes in electrical activation across the scalps of hypnotized subjects.

The research seemed to suggest that hypnosis might alter the brain’s functional connectivity, preventing certain areas – such as the anterior cingulate cortex (ACC) and the dorsolateral prefrontal cortex (dlPFC), both of which are involved in goal-directed behavior – from communicating with other areas that might inhibit or otherwise modulate their activity. Interestingly, though, these changes were only detectable in “very highly hypnotizable subjects” – implying that belief and willful participation are crucial ingredients in hypnosis.

Now, as the journal PLoS ONE reports, a multidisciplinary team of researchers from Finland’s University of Turku and Aalto University, and Sweden’s University of Skövde, have found what may be even stronger indicators of a physically detectable hypnotic state – changes in various reflexive eye movements.

The team hypnotized a single volunteer (which means these results should be taken with several large grains of salt) and compared her eye movements against those of a control group of non-hypnotized subjects. The researchers found that when their subject was hypnotized, her eyes exhibited some unique behaviors:

[She] showed a markedly reduced eye-blinking rate (0.012 ± 0.04 blinks/s) as compared to [control subjects] (1.18 ± 0.63 blinks/s). Although some control subjects could mimic rather well this external feature of the “trance stare,” at the group level the changes were far less marked.

Even stranger, though, were changes in the subject’s saccades – the rapid, often involuntary eye movements that help us focus on changes in our environment or quickly scan the details of a scene:

[When hypnotized, the subject] performed only short saccades toward the target regardless of the distance from the fixation point. This “creeping” pattern of short saccades was difficult to simulate by the control group since their fixation tended to automatically gravitate to the target.

The subject’s saccades were also much slower, shorter, and fewer than any that non-hypnotized volunteers could produce. In other words, her gaze tended to shift much less, and less often, than the gazes of people in the control group.

Exactly what this data means is tough to say – and not just because it’s only been detected in one volunteer; it’s also not clear what changes in brain activity these unusual eye behaviors might reflect. The researchers have a suggestion, though: the ACC is known to be closely connected with visual brain areas, and to help coordinate saccades. Could these strange saccades reflect changes in the ACC’s functional connectivity?

It’s too soon to say for sure – but it seems that even if hypnosis does depend on willing belief, it still has some objective neurological correlates that are worth studying. It may be that we have much more control over our brains’ functional connectivity than we ever suspected.

Who knows – it could be that even now, someone is implanting subtle cues waiting for the moment when- SLEEEEP!!!

The Roots of Consciousness

The origins of subjective consciousness probably lie in an introspective brain network common to most mammals, says a new study.

What's going on in that furry little head of yours?! TALK!

When we “zone out” and let our minds wander, a functional (as opposed to structural) brain network known as the default mode network (DMN) becomes active. The DMN links our frontal lobe – an area associated with planning and abstract thought – with areas of the temporal and parietal lobes that help us associate memories with ideas and emotions. In short, this network allows us to become “lost” in thought, rather than occupied with our environment, or with a specific goal.

Since goal-directed behavior – say, hunting for food or a mate – seems to be more crucial for a species’s survival than mind-wandering is, the discovery of the DMN (which, like most new discoveries in neuroscience, has seen its share of controversy) prompted scientists to ask what purpose the DMN’s ancestors might have originally served.

Now, researchers are zoning in on the origins of zoning out, by mapping what they think is a primitive version of the DMN in rat brains. By comparing fMRI scans of rats’ brains when the animals were at rest with scans of those same rats’ brains when the animals received a mild electric shock, a team led by Yihong Yang at the US National Institute on Drug Abuse has identified a rat brain network that corresponds to non-goal-directed behavior – in short, a proto-DMN.

Though rats don’t seem to have much capacity for abstract thought, it’s likely that this network allows them to review their memories:

“[The rats could be] thinking about their past, mind wandering, and this kind of passive brain activity might be important for memory in the rat,” Yang says.

Whether rats have what we’d consider a sense of self is a more complicated question. The rat brain does include a more primitive version of our prefrontal cortex (PFC), but exactly what this region does for the rat remains an open question:

“The activity in frontal areas [could suggest] the notion of a sense of self in the rat,” says Michael Greicius of the Stanford University School of Medicine. “I’ve got to believe it’s different from humans, but it’s certainly provocative.”

Some other new findings make this question even more intriguing: a recent paper described a close analog of the DMN in monkey brains, and a 2009 study found that while the DMN is active in human patients suffering from locked-in syndrome, it seems to be disrupted in vegetative patients. But, since recent research demonstrates that vegetative patients can respond to questions by thinking certain thoughts for “yes” and others for “no,” it seems that what we call “consciousness” may be much more multi-layered than we think.

If so, it may be that rats possess some of the abilities we associate with consciousness – such as mind-wandering and memories – but that they still lack a true concept of “I.” It may be that their minds lack abstract concepts altogether, or that “abstract concepts” are a more complex phenomenon than we’re assuming. That’s the tough thing about analyzing consciousness: nothing else remotely like it seems to exist in nature, and our minds seem to be poorly adapted for understanding what exactly it is.

Still, discoveries like these are helping us inch closer to that understanding – even if it’s in tiny little mouse steps.

Consistent Networks

A new study has established that functional networks are highly consistent across the brains of many individuals.

Functional networks look delicious, don't they?

The research, published in the journal Cerebral Cortexcorrelated huge amounts of data on the connectivity of macaque visual cortices – particularly the layers known as V1, V3, and V4 – and confirmed that between one monkey’s brain and another, these functional networks follow extremely similar patterns.

The question of how consistent primate functional networks are has been a controversial issue for years. Most brains are reasonably similar to others from the same species on a structural level – but the complexity of their synaptic connections is mind blowing: a single macaque brain contains more than 100 billion neurons, which form more than 100 trillion synapses.

Thus, it’s only in the past few years that scientists have even been able to map the functional connectivity of a single brain – much less compare functional connections across a whole group of primates. It’s also led to a lot of debate about how similar such complex networks can possible be be to one another.

Well, the answer is (in technical terminology), “really really similar.”

The brain is characterized by a highly consistent, weighted network among the functional areas of the cortex, which are responsible for such functions as vision, hearing, touch, movement control and complex associations. The study revealed that such cortical networks and their properties are reproducible from individual to individual.

This network-comparison adventure began when a group of neuroanatomists in France, led by Henry Kennedy, contacted the University of Notre Dame’s Interdisciplinary Center for Network Science and Applications (iCeNSA). The French scientists commissioned the Center – known for its expertise at analyzing complex networks for fields as diverse as sociology and epidemiology – to check out the brain-to-brain consistency of a massive amount of macaque functional connectivity data they’d gathered. The iCeNSA assembled a team led by Dr. Maria Ercsey-Ravasz and Dr. Zoltan Toroczkai, two physicists who tore into the data like King Henry I at a lamprey-eating contest:

A top-down approach called functional decomposition, identifying bundles within the brain, helps overcome the sheer data volume. The macaque brain has 83 [major functional] areas; the human brain more than 120.

One of the reasons functional connectivity is so consistent from one brain to another, the teams found, is that neural connections seem to organize themselves based on a consistent set of principles across a variety of scales – in other words, they exhibit some fractal characteristics.

“It looks like there is some sort of general algorithm that is being run in this brain network,” [Toroczai] says. “The wiring is very strange.”

What these strange organizational principles are – and why primate brains have evolved to rely on them – are questions the teams plan to explore in the near future.

Even more exciting, they hope these new methods of data analysis may help them unravel one of neuroscience’s ultimate mysteries: how, exactly, brains encode information at all.

Recognition and Localization

New research has identified a wide range of brain areas that help us recognize objects we see – and it’s also revealed some surprises about how the brain distributes processing power.

Superman's brilliant disguise fools us all.

A recent study published in the journal Neuron focuses on a patient – known as “SM” – who suffered a lesion in the right lateral fusiform gyrus (LFG), an area known to be involved in recognition of objects and faces. This has created a disorder known as “visual agnosia,” in which the patient can see just fine, but has serious trouble identifying objects. Decades of research on similar cases have shown that this isn’t a language difficulty – it’s a difficulty associating familiar objects with their names.

Visual agnosia is a pretty fascinating disorder, but it’s actually not the big news here – the unexpected part is that this new study has revealed two surprises about how object recognition works. First, it turns out that many more brain regions than scientists expected are involved in object recognition. And second,  it seems that the recognition process involves symmetrical activation patterns across both brain hemispheres, one of which can “fill in” when its partner suffers damage.

By correlating fMRI scans with behavioral observations, a team led by Princeton psychologist Dr. Christina Konen discovered that the functional connections involved in object recognition extend throughout the temporal and visual cortices of both the left and right hemispheres:

Visual responses, object-related, and -selective responses were reduced in regions immediately surrounding the lesion in the right hemisphere, and also, surprisingly, in corresponding locations in the structurally intact left hemisphere. In contrast, hV4 of the right hemisphere showed expanded response properties.

Carnegie Mellon University’s Dr. Marlene Behrmann, one of the study’s other leaders, was pretty stunned by this new data. As she puts it:

These results will force us in the field to step back a little and rethink the way we understand the relationship between brain and behavior. We now need to take into account that there are multiple parts of the brain that underlie object recognition, and damage to any one of those parts can essentially impair or decrease the ability to normally recognize objects.

It’ll probably help to back up a bit, and explain exactly why neuroscientists are so amazed by this information.

See, even though the two hemispheres of our brain might look symmetrical, there are also plenty of asymmetries between them – both in structure and in function. One of the most obvious examples is handedness: the majority of people are right-handed, even though there doesn’t seem to be any particular anatomical reason why this should be so – and in fact, plenty of other animals have about a 50/50 distribution between right- and left-handedness.

And then there’s language: it definitely seems to be localized in the left hemisphere. This is fairly easy to verify – the Wada Test involves injecting an anesthetic into one hemisphere of a patient whose corpus callosum, which acts as a communication bridge between the two hemispheres, has been severed. When the two hemispheres can’t communicate, anesthetizing the left hemisphere seriously impairs speech and language comprehension.

In short, the idea that certain functions are localized in one hemisphere is largely accepted today. Even so, fMRI scans show that plenty of tasks activate our brains in symmetrical patterns. What exactly this means is still up for debate – some neuroscientists think it might reflect a “safety net” of functional redundancy, while others say the two hemispheres may complement each others’ processing.

Whatever the case, experiments like the one above demonstrate that even when one hemisphere is damaged, the brain does its best to work around the problem:

There … appeared to be some functional reorganization in intact regions of SM’s damaged right hemisphere, suggesting that neural plasticity is possible even when the brain is damaged in adulthood.

Now, this ties into a much more exciting set of implications about our brains. For example, what about children who’ve had one of their brain hemispheres surgically removed, yet go on to complete college and lead productive lives? Or people who – as fMRI scans have confirmed – learn to literally see via sound or touch?

As long as the brain in question is young enough, or is given enough time to adapt, many areas can take over the functions of others. Perhaps our functional processing is distributed more widely in childhood, and “solidifies” more as we age – but even grown-ups can teach an old brain region new tricks.

This is why it’s not very accurate to say there’s “a” human connectome – each of our brains is wired in a unique way, and is constantly rewiring itself every second. New neurons are being born all the time – and existing ones are always forming new connections and being co-opted for new tasks.

In the unfortunate case of “SM,” not all the IFG’s functionality could be preserved; but even so, the case is a striking example of how versatile our brains can be – and how much we still have to learn about the ways they process information.

Measuring Maturity

New data is enabling neuroscientists to make accurate predictions about a young connectome’s future development.

A groovy-looking chart of functional connectivity modifications - green pathways weaken with maturity; orange ones grow stronger.

By comparing the resting-state functional networks in pre-adolescent brains with connectivity’ patterns found in adult brains, neuroscientists have developed a brain maturity growth curve that charts functional connectivity changes as the brain matures.

A report published in the journal Science explains that nodes in these networks are a bit like high-schoolers, because they join, branch, and and rejoin in a series of predictable “cliques” as an individual ages. Many of these cliques involve brain areas that influence a person’s ability to sustain attention, and to quickly come up with a reasonable response to a novel situation.

A team led by Dr. Bradley Schlaggar at the Washington State University School of Medicine in St. Louis began the study by asking whether fMRI scans could provide enough data to predict certain aspects of a person’s brain development. They discovered that, despite individual variations, these functional changes follow a regular pattern:

The researchers used functional connectivity data to determine measure the subject’s “brain age,” and to chart the maturation process from birth to full adulthood (age 30). This type of data visualization allows researchers to characterize the typical trajectory of maturation as a biological growth curve.

By correlating this data with maps of the actual functional networks, the researchers were able to predict which specific changes were likely to occur at various stages of a person’s climb toward maturity. In particular, this study focused on resting-state functional connectivity – the connective networks that take shape when the mind is “idling.”

These network maps might look pretty tangled, but their changes follow a logical pattern. In early childhood, the “fast/adaptive” network, which is centered in the frontal and parietal lobes and allows us to rapidly adapt to new situations, works closely with the “slow/maintenance” network, which is centered in the cingulate cortex and the operculum and helps us sustain mental activity – but that close partnership gradually changes:

In children there is marked connectivity [between these networks]. In fact, the two networks, which have not yet differentiated, are in active conversation as a single amalgam of regions. In adolescents, the brain regions are in an intermediate state.

The scientists speculate childhood integration between these two networks influences the short attention span of children, and that the gradual separation may allow our maturing brains to control our mental focus more precisely. By our mid-20s, these networks have gradually separated from each other while strengthening connections within themselves – further improving our ability to mentally shift gears.

The next step in this maturity research, Schlaggar says, is to study the brains of children with diseases like Tourette’s syndrome and ADHD, in an effort to understand just where this functional connectivity maturation process goes awry.

The better we’re able to map the details of these dynamic connectivity maps, the more we’ll be able to create effective therapies that target specific aspects of cognitive development. In fact, the latest evidence points toward the idea that these maps continue to reshape themselves well into adulthood – which is a hopeful sign not just for therapists, but also for those of us who love to hack our own connectomes.

Signs and Visions

A.k.a. Form Constants in Connectomic Context: Abnormal Visuocortical Activation May Reflect Large-Scale Connectivity Alterations in Brain Functional Networks

A rendering of a form constant.

In a widely cited paper, Ermentrout, Cowan, et al. (1978) discussed visuocortical correlates for the geometric patterns sometimes hallucinated (or pseudohallucinated) by patients experiencing simple partial (“petit mal“) seizures, migraines, and several other conditions.

The authors correlated these perceived patterns with fluctuations in resting-state electrophysiological activity in the visual cortex. However, the human visual pathway has been mapped with increasing precision over the past two decades, and it is now well understood that an interlaced sequence of structures and functional networks in the brain is associated with the processing of visuocortical activation patterns, each element of every network playing a crucial part in the analysis of and response to these signals. For example, the discovery of visuotopic (and possibly even directly retinotopic) maps in the temporal and parietal cortices – as well as the thalamic lateral geniculate nucleus (LGN) – lends weight to the idea that even visual/spatial perception itself is a multilobar process.

A logical correlate of these discoveries is that certain neurophysiological pathologies underlying visual form constants would also likely result in multimodal processing changes – and thus, perceptive changes – throughout the brain’s visual, somatosensory, associative, and cognitive processing centers.

Nevertheless, many previous studies of form constants have focused on activation patterns in the visual cortex alone. While visuocortical mapping can provide useful neural correlates for frequently observed visual patterns, the current state of research in the field of multi-scale cerebral functional networks makes it clear that an analysis of functional connectivity alterations associated with form constants is crucial to understanding the correlates and linkages of these patterns – as well as other hallucinatory and pseudohallucinatory percepts – at multiple functional and nodal scales within the human connectome as a whole. A tentative framework for such an integrative model is proposed here.

The layers of the visual cortex: V1 through V4.

To begin, an analysis of the neural correlates for visual form constants will prove useful for comparison with other abnormal percepts.

The visual cortex, the frontal eye fields (FEF), and several other regions – including areas of the parietal and temporal lobes, as demonstrated by Slotnic (2009, 2010) and others – have been shown to render 2-dimensional visuotopic maps. However, the most directly retinotopic of these maps are found in the lower layers of the visual cortex, especially V1 (striate cortex).

As Ermentrout et al. showed, the correspondence between retinal position and visuocortical position can be expressed with some degree of accuracy as a complex logarithm function:

In other words, straight lines in the lower visuocortical layers are mapped to lines following logarithmic spiral curves along the retina – and vice versa. By applying this function to observed patterns of visuocortical “noise,” shapes corresponding to the reported form constants can be produced.

Although the Ermentrout logarithm function predicts an isotropic map, evidence suggests that some anisotropies exist – in particular, more neuronal resources appear to be associated with vertical coordinates than horizontal ones. Nevertheless, evidence for visual forms similar to those predicted is widespread.

Ermentrout and his coauthors also discussed a related and relevant idea: that although these geometric forms themselves were likely reliant on visuocortical activity (as opposed to activation patterns in the more rostral lobes) more complex associations and expansions on the forms would involve many other cerebral areas. Models of cerebral functional connectivity at the time of the study were largely focused on modular network analysis, but more recent research has begun to contextualize form constants – and the processing centers associated with them – within a more multimodal connectomic framework.

It is to these multimodal percept alterations that we now turn. A brief explanation of larger-scale multilobar resting-state connectivity patterns will provide a useful introduction for an examination of possible neurophysiological correlates for such abnormal perceptions.

As He, Chen, et al. (2007) and others have demonstrated, cerebral structural networks display a high degree of modularity, with processing subgroups acting as nodes within the overall connectome.

Also, as has been demonstrated by Sepulcre, Liu, et al. (2010), neurons in the retinotopic visual areas display a significant resting-state preference for local, rather than distant, functional coupling. (These functional couplings are to be distinguished from the anatomical structural networks mentioned above.) This local functional tendency is similar to preferentially local resting-state connectivity patterns found in the somatosensory, auditory and motor areas. In contrast, association cortices in the parietal, temporal and frontal lobes display a resting-state preference for distant functional coupling, in keeping with their functionality as correlative processing centers. Areas such as the posterior cingulate (PCC) and medial prefrontal (MPFC) cortices, which deal with emotional processing and self-referential cognition (respectively), display high levels of resting-state functional coupling at both local and distant ranges.

During task-positive states, however, functional connectivity networks in classification and sequencing areas – such as the inferior frontal gyrus (IFG), the inferior parietal lobule (IPL), the lateral temporal cortex (LTC), and the dorsal anterior cingulate cortex (ACC) – display an increased tendency toward local functional coupling; as do retinotopic areas of the frontal cortex. The visual cortex, by contrast, displays a heightened preference for distant functional coupling during task-positive states. This seems to imply that during task engagement, the visual cortex becomes increasingly communicative with the parietal and temporal association and orientation hubs. These areas exhibit increased associative and correlative activity, and the FEF exhibit a heightened tendency toward local pattern analysis.

These functional connectivity patterns invite comparison with connectivity abnormalities observed in several pathologies associated with form constants. Because visual form constants are known to constitute only one result of multi-scale disruptions in various functional networks, the neurophysiological nature of these disruptions bears closer examination if their full range of effects is to be understood.

An artistic representation of visual migraine aura.

Hadjikhani et al. (2001) have demonstrated that during migraines, cortical spreading depression (CSD) creates disruptions in the electrophysiological activity of the visual cortex, sometimes resulting in migraine aura: altered visual perceptions – often of geometric shapes – which may be accompanied by other sensory distortions, linguistic impairment, spatiotemporal fluctuations (altered senses of body, time, and space) and even high-level cognitive changes.

Previous research has demonstrated that while the visual and auditory disruption types reflect functional connectivity alterations in and among their respective cortices, these and other hallucinatory and pseudohallucinatory percepts are ultimately etiology-independent, and rather represent the responses of specific sensory networks (and thus, of a subjectively perceived sensory modality) to the same underlying CSD process.

For example, imaging studies of cerebral areas known to be affected by CSD activity indicate that as functional connectivity is disrupted throughout the somatosensory area, as well many areas of the temporal and parietal lobes, these areas display preferences for local functional coupling. In short, it seems likely that the form constants associated with migraine aura are but one manifestation of a multimodal hallucinatory (and/or pseudohallucinatory) pattern reflecting functional alterations across a variety of cerebral regions. However, the exact natures of the altered perceptions produced by these alterations – and the relationships of those perceptual alterations to CSD itself – present promising avenues for further research.

Functional connectivity alterations during epileptic activity have also been mapped. Temporal lobe epilepsy (TLE) is known to be associated with simple partial seizures, which often constitute preludes to seizures of the partial-complex or tonic-clonic types – all of which involve excessively expanded functional connectivity of networks throughout the temporal, parietal, and frontal lobes. Simple partial seizures are distinguished from other types primarily on the indication that the flow of consciousness is uninterrupted. Patients often report a sense of déjà vu, as well as auditory, olfactory, and visual hallucinations – including those of geometric form constants – during these seizures.

As Liao, Zhang, et al. (2010) have demonstrated, in interictal periods, the brains of patients with mesial temporal lobe epilepsy (mTLE) manifest decreased functional connectivity in the frontal and parietal lobes, and along the frontoparietal border. These regions are strongly implicated in the default mode network (DMN) and the dorsal attention network (respectively), implying that disruptions of both resting-state and task-specific connectivity during larger-scale TLE seizures may contribute to long-term functional connectivity alterations. This is consistent with the findings of Laufs, Hamandi, et al. (2007), who demonstrated that even resting-state default-mode activity was frequently interrupted and localized by small interictal epileptic discharges in TLE patients.

EEG and fMRI studies of complex-partial TLE seizures, and interictal activity in mTLE patients, provide the most thoroughly studied evidence of functional disruptions in the DMN during epileptic events. However, further research into these network fluctuations is likely to provide more precise insight into some of TLE’s more cognitive symptoms – such as hyperreligiosity and hypergraphia - and their relationship to the altered visual perceptions with which they may share a related etiology.

These pathologies provide several examples - but by no means a full range – of conditions under which alterations in functional connectivity networks may produce not only visual form constants, but multimodal hallucinations (and/or pseudohallucinations). While research has been successful in mapping neural and functional correlates for some of these percept types (especially visual and auditory aura), these findings also imply a need for further research into the relationships between pathological alterations in functional networks’ overall connectivity and the diverse range of altered somatosensory, spatial, temporal, and cognitive percepts reported by patients. Integration of this data into functional connectivity models will likely yield a more complete connectomic context for the neural processes underlying visual form constants, as well as other types of alterations in subjective perception.

__________

References

1. Ermentrout GB, Cowan JD. (1978) A mathematical theory of visual hallucination patterns. Biol Cybern. 1979 Oct;34(3):137-50.

2. Bressloff PC, Cowan JD, Golubitsky M, Thomas PJ, Wiener MC. (2002) What geometric visual hallucinations tell us about the visual cortex. Neural Comput. 2002 Mar;14(3):473-91.

3. Slotnick SD. (2009) Rapid retinotopic reactivation during spatial memory. Brain Res. 2009 May 1;1268:97-111.

4. Slotnick SD. (2010) Synchronous retinotopic frontal-temporal activity during long-term memory for spatial location. Brain Res. 2010 May 12;1330:89-100.

5. Greenlee MW. (2000) Human cortical areas underlying the perception of optic flow: brain imaging studies. Int Rev Neurobiol. 2000;44:269-92.

6. Maunsell JH, Nealey TA, DePriest DD. (1990) Magnocellular and parvocellular contributions to responses in the middle temporal visual area (MT) of the macaque monkey. J Neurosci. 1990 Oct;10(10):3323-34.

7. Chen ZJ, He Y, Rosa-Neto P, Germann J, Evans AC. (2008) Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cereb Cortex. 2008 Oct;18(10):2374-81.

8. Ringach DL. (2007) On the origin of the functional architecture of the cortex. PLoS One. 2007 Feb 28;2(2):e251.

9. Sepulcre J, Liu H, Talukdar T, Martincorena I, Yeo BT, Buckner RL. (2010) The organization of local and distant functional connectivity in the human brain. PLoS Comput Biol. 2010 Jun 10;6(6):e1000808.

10. Yan C, Liu D, He Y, Zou Q, Zhu C, Zuo X, Long X, Zang Y. (2009) Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load. PLoS One. 2009 May 29;4(5):e5743.

11. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005 Jul 5;102(27):9673-8.

12. Honey CJ, Kötter R, Breakspear M, Sporns O. (2007) Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci U S A. 2007 Jun 12;104(24):10240-5.

13. Cordes D, Haughton VM, Arfanakis K, Carew JD, Turski PA, Moritz CH, Quigley MA, Meyerand ME. (2001) Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR Am J Neuroradiol. 2001 Aug;22(7):1326-33.

14. Fair DA, Cohen AL, Power JD, Dosenbach NU, Church JA, Miezin FM, Schlaggar BL, Petersen SE. (2009) Functional brain networks develop from a “local to distributed” organization. PLoS Comput Biol. 2009 May;5(5):e1000381.

15. Hadjikhani N, Sanchez Del Rio M, Wu O, Schwartz D, Bakker D, Fischl B, Kwong KK, Cutrer FM, Rosen BR, Tootell RB,Sorensen AG, Moskowitz MA. (2001) Mechanisms of migraine aura revealed by functional MRI in human visual cortex. Proc Natl Acad Sci U S A. 2001 Apr 10;98(8):4687-92.

16. Dahlem MA, Hadjikhani N. (2009) Migraine aura: retracting particle-like waves in weakly susceptible cortex. PLoS One. 2009;4(4):e5007.

17. Van Paesschen W, King MD, Duncan JS, Connelly A. (2001) The amygdala and temporal lobe simple partial seizures: a prospective and quantitative MRI study. Epilepsia. 2001 Jul;42(7):857-62.

18. Liao W, Zhang Z, Pan Z, Mantini D, Ding J, Duan X, Luo C, Lu G, Chen H. (2010) Altered functional connectivity and small-world in mesial temporal lobe epilepsy. PLoS One. 2010 Jan 8;5(1):e8525.

19. Laufs H, Hamandi K, Salek-Haddadi A, Kleinschmidt AK, Duncan JS, Lemieux L. (2007) Temporal lobe interictal epileptic discharges affect cerebral activity in “default mode” brain regions. Hum Brain Mapp. 2007 Oct;28(10):1023-32.

Hermes and Aphrodite

It’s time for another edition of Comics by People Cleverer Than Me. Enjoy the following introductory XCKD. Click it to embiggen!

Today, I’m going to talk about what exactly it is that we call “me.”

But first – let’s talk about feelings.

Have you ever wondered why the English language has hundreds (if not thousands) of words for precise shades of colors, and comparatively few terms for precise emotions?

Why are we often limited to vague words like “rapport” and “energy” when we talk about the emotion of a conversation? I mean, they’re fine for ordinary conversation, but they don’t tell us much about exactly how it felt. It’s easy enough to say something like, “his lighthearted tone made us all laugh,” but where’s the laconic precision of terms like “dark magenta” and burnt umber?”

Authors often convey emotions with metaphors - “A cold knife twisted in his gut as he heard the dreadful news,” but these kinds of descriptions are more meant to convey a sensation the reader can empathize with – they’re not intended as precise terms for cataloged, objective emotions. I’m not even talking about scientific precision here, necessarily – just enough to provide the listener with a clearly defined idea of what emotion the speaker was feeling.

It seems that emotions often exceed and transcend words. That’s because they affect and involve many more areas of the brain. In fact, they may involve a functional network that’s much older than the one that thinks in words. But I’m getting ahead of myself. First, I’ve got to explain what I mean by “networks.”

While the left and right hemispheres of the brain are physically divided, they’re connected by a bridge of neural fibers known as the corpus callosum (Latin for “tough body”), which allows signals to pass between them. Scientific evidence from functional magnetic resonance imaging (fMRI) research shows that the left and right hemispheres don’t work as separate processors, but as one overall functional unit composed of many subunits heavily dependent on one hemisphere or the other.

The fissures and sulci (ridges) of the brain exhibit some intriguing anatomical non-symmetries between the left and right-hand sides; and some neurophysiological similarities – and differences – have also been identified. Which means there are no exclusively left-brained or right-brained people. However, each hemisphere does seem to specialize in a few particular types of processing.

In an earlier post on connectome hacking, I talked about switching between your mind’s two basic modes of processing: rational vs. experiential; sequential vs. timeless. One of these modes is that of subjective perception of the present moment – full of feelings, sight, and sound; but devoid of interpretation or meaning. The second mode, which can be run in addition to the first, is that of associations - dividing the subjective experience into parts, and placing those parts in a sequence of causes and effects, attaching words to them, and calling up memories to compare them to. By choosing to inhibit the sequential, verbally-oriented components, we can just experience existence.

In my beloved TED video, Dr. Jill Bolte Taylor describes the left hemisphere as a serial processor (one that processes tasks sequentially) and the right hemisphere as a parallel processor (one that processes multiple tasks at the same time). Now, I doubt that Dr. Taylor would be a fan of any neurophysiological theory that treated the left and right hemispheres as independent systems. Still, she does make a convincing case for a somewhat similar idea – that the two hemispheres are semi-independent components of a single system.

When Dr. Taylor suffered a stroke in her left frontal cortex, words, sequential reasoning, and finally even her perception of her own body gave way to an experience detached from physical reality. As she describes it, her subjective consciousness was freed entirely from associations, from sequential planning, and even from subject/object relativity, and simply bathed in awareness itself. It seems she was self-conscious, and yet not conscious of any abstract “self” beyond the present moment and place.

Her journey reveals some intriguing features of consciousness, which is clearly composed of more than just a single self-referential loop of some kind. We can start by looking at the break with external reality that took place in Dr. Taylor’s brain. Between the two types of processes she describes – raw subjective experience, and thoughts about subjective experience – we have a possible (very simplified) model for the basic structure of consciousness. But within this fundamental functional structure, a more complex interplay of communication circuits is at work.

Some scientists are discussing the idea that the brains of human infants come pre-wired with at least five intrinsic connectivity networks (ICNs), each of which utilizes neurocircuitry across both brain hemispheres.

The five networks are specialized, and activity in each one corresponds to a certain type of processing:

1. Sensory (Visual cortex /occipital lobe)
2. Motor (somatosensory / motor cortex)
3. Memory (temporal cortex / auditory)
4. Language/Spatial (depending on lateralization and hemisphere)  (sup. parietal , cerebellum)
5. Cognition (frontal)

Though language processing does involve both sides of the brain, precise speech and verbal “mental chatter” seem to be heavily dependent on the left hemisphere (the right hemisphere is active in language processing, but seems to deal more with the tone and rhythm of speech than the words themselves). Sensory and cognitive processes involve neural networks distributed throughout both hemispheres, and emotions are rooted in even older structures in the lower parts of the brain, like the amygdala and the cingulate cortex. In other words, language is a newer and much more specialized phenomenon than emotions are.

There’s another important difference between emotions and language: we’re able to use precise words for colors because their shades can be measured and quantified fairly easily – they correspond to wavelengths of light. But emotions are more like symphonies – they’re formed through a complex interaction of connectome networks over time, and they can’t be directly perceived (i.e., experienced) by an external observer the way colors in the physical world can. Thus, we describe emotions with words the listener can empathize with, rather than trying to quantify them in any objective sense.

In fact, the more we focus cognitive attention on understanding an emotion rationally, the less attention and processing power is available for feeling that emotion. This is similar to the principle of doing math to stop a panic attack. In fact, it’s a technique used in many meditation schools for dealing with negative feelings.

Now, another intriguing thing about ICNs is that as we age, they continue to grow, change, and interact in new ways. By adulthood, entirely new networks have formed:

  1. Visual – occipital
  2. Sensorimotor -pre-post central gyrus
  3. Auditory/memory – auditory/temporal cortex
  4. Language/spatial – fronto-parietal, strongly lateralized in two hemispheres
  5. SALience (also Known as SAL) Anterior Insula+ anterior cingulate
  6. Balance and co-ordination – cerebellum
  7. Default Mode Network - Medial frontal, posterior cingulate, Angular gyrus
  8. Executive Control Network – dorsolateral, prefrontal + superior parietal

Even as the interactions between our ICNs become more complex, our connectomes still manage to sustain a continued sense of self (“ego”) – or at least the subjective illusion of one. The underlying idea here seems to be that the subjective consciousness – that pure awareness that draws heavily on right-hemisphere resources – can focus a “spotlight” or “active grip” of attention on one or more of these networks at any given time, thus causing the subjective self to experience that network’s activity. This is how my subjective self can go from “drifting” in a daydream to being “absorbed” in a conversation to being “focused” on a sequential task.

Meanwhile, all these ICNs are independently active, to some degree or another, during most of our waking hours – but the subjective self’s spotlight can only focus on a certain amount of neural activity at a time. This, the (easy permeable) division between perceived reality, memory, and feelings, and those that are said to be processed by the “subconscious” – i.e., the majority of non-spotlighted nervous processing taking place at any given moment. It’s even possible that some of the networks and sub-networks constitute independent “selves” of their own.

I suspect that some budding neuropshychologists of the ancient world may have had a sense of this idea of specialized networks. In Greek mythology, for instance, Hermes was the gods’ messenger – the god who conveyed information from the gods on Mount Olympus to listeners throughout the world. He was also the god of words, and of symbols. He was known for being a charmer and a schemer – the “silver-tongued” and the “many-cloaked.” Aphrodite was the goddess of love, beauty, eroticism, and sensuality.

These two archetypes provide an intriguing look at the way ancient thinkers classified aspects of the mind – because, after all, what are gods if not reflections and personifications of aspects of ourselves? Now, I’m not suggesting that these two gods each represent a hemisphere of the brain, or a particular ICN. Rather, I think they each reflect certain abilities and attributes of certain functional networks within the brain – not unlike software that can be run on a particular type of hardware.

I’ll get into more of this in future posts. For now, I’ll sign off with this: when words turn back from your thoughts, chances are you’ve stumbled on something worth thinking about.

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