What Can Magic Teach Us About The Brain?

http://www.sciam.com/article.cfm?id=magic-neuroscience-cognition-illusions

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http://www.npr.org/templates/story/story.php?storyId=93465269

Quick Thinking and Intelligence

http://www.npr.org/templates/story/story.php?storyId=102169531

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The Study:

The study is the first to analyze genetic and environmental factors that affect brain fiber architecture and its genetic linkage with cognitive function. It assessed white matter integrity voxelwise (voxel, is a volume element, representing a value on a regular grid in three dimensional space—analogous to a pixel) using diffusion tensor imaging at high magnetic field (4 Tesla), in 92 identical and fraternal twins. White matter integrity, quantified using fractional anisotropy (FA), was used to fit structural equation models (SEM) at each point in the brain, generating three-dimensional maps of heritability. The study visualized the anatomical profile of correlations between white matter integrity and full-scale, verbal, and performance intelligence quotients (FIQ, VIQ, and PIQ).

The quantifiable measure of white matter integrity related to cognition—fractional anisotropy (directional variability) of diffusion is higher in heavily myelinated fiber tracts, and increases with progressive myelination during development. Increases in myelination and larger axonal diameter are associated with increased neuronal conduction speed and may support better cognitive function. Fractional anisotropy correlates with intellectual performance in normal subjects and is reduced by degenerative processes that impair axonal fiber integrity.

The study was comprised of 92 twins, 23 pairs of identical (11 male pairs and 12 female pairs) and fraternal (10 male pairs and 13 female pairs). Each person was tested using the Wechsler Adult Intelligence Scale and then scanned using diffusion tensor imaging in order to create a spatially detailed map of white matter integrity.

Methods:

Using the Wechsler Adult Intelligence Scale, three verbal (information, arithmetic, and vocabulary) and two performance (spatial and object assembly) subtests were examined for the purposes of this study. Each subtests produced a raw score and verbal (VIQ), performance (PIQ) and full-scale (FIQ) intelligence quotient standardized scores were derived. In this study the IQ scores for identical and fraternal twins were not significantly different.

Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that enables the measurement of the restricted diffusion of water in tissue in order to produce neural tract images instead of using this data solely for the purpose of assigning contrast or colors to pixels in a cross sectional image. The idea of using diffusion data to aid in the production of images of neural tracts curving through the brain.

More extended diffusion tensor imaging (DTI) scans derive neural tract directional information from the data using 3D or multidimensional vector algorithms based on three, six, or more gradient directions, sufficient to compute the diffusion tensor. The diffusion model is a rather simple model of the diffusion process, assuming homogeneity and linearity of the diffusion within each image-voxel. From the diffusion tensor, diffusion anisotropy measures such as the Fractional Anisotropy (FA), can be computed. Moreover, the principal direction of the diffusion tensor can be used to infer the white-matter connectivity of the brain (i.e. tractography; trying to see which part of the brain is connected to which other part).

The principal application is in the imaging of white matter where the location, orientation, and anisotropy of the tracts can be measured. The architecture of the axons in parallel bundles, and their myelin sheaths, facilitate the diffusion of the water molecules preferentially along their main direction.


When an IQ score was significantly correlated with FA ( with FDR <0.05), intelligence as well as estimate the genetic and environmental contributions to the correlations between FA and IQ in the same set of subjects. If the correlation between the voxel value of FA in one twin and the level of IQ in the other twin is greater in identical pairs than in fraternal pairs, the excess in the identical correlation over the fraternal correlation is then assumed to be attributed to common genetic factors that mediate both white matter integrity and intelligence.

Given the correlation between IQ scores and white matter integrity in similar regions, it is plausible that overlapping sets of genes may influence IQ measures and fiber architecture. A way to determine this is to use a measure of one trait in one twin to predict the level of the other trait in the other twin. If a prediction can be made with greater precision in identical twins tan fraternal, then a common setoff genes must be involved.

Findings:

-white matter integrity under strong genetic control, with highest heritability in parietal brain regions

White matter integrity (FA) was under strong genetic control in all posterior white matter regions and was highly heritable in bilateral frontal (a2 = 0.55, p = 0.04, left; a2 = 0.74, p = 0.006, right), bilateral parietal (a2 = 0.85, p < a2 =" 0.84," a2 =" 0.76," p =" 0.003)" p =" 0.04" p =" 0.01">

-white matter integrity linked to intellectual performance, with correlations as high as 0.3-0.4 between performance IQ and white matter integrity

-using cross-trait mapping, implicated the same genes as mediating the correlation between IQ and white matter integrity—suggesting a common physiological mechanism for both.

FA and FIQ, PIQ or OBJ scores were influenced by an overlapping set of genes in the cingulum and isthmus of the corpus callosum, the cerebral peduncles, the posterior limbs of the internal capsule and the left posterior thalamic radiation/optic radiation, the right superior fronto-occipital fasciculus and the anterior, superior and posterior corona radiate bilaterally. These correlations were mediated by common genetic factors. The fiber systems whose integrity was most tightly linked with IQ include several with critical roles in visuospatial processing. FA may reflect underlying levels of axonal myelination, which may account for differences in reaction times, processing speed and intellectual performance across subjects.

Issues and Questions:

Limited by age—narrow age range, not model influence of age on heritability

This kind of DTI scan can help to detect Alzheimer’s (slow down of neural pathways) and could also help to determine whether or not new medication for Alzheimer’s is working.

The question of measuring intelligence—in order for this study to stand one must accept the use and validity of standardized intelligence tests.

Autism and Mirror Neurons

Autism Reveals Social Roots of Language

by Jon Hamilton

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Bill Cotton, Colorado State University

Temple Grandin, who teaches animal science at Colorado State University and is autistic, says it's taken her a lifetime to speak in a way that sounds natural to others.

WHAT LIES BENEATH: A multimedia interactive shows language at work on many levels.

(Download the Latest Flash Player)

Q&A with Temple Grandin

• July 9, 2006
  Grandin talks about what parents can do to help autistic children communicate better.

Weekend Edition Sunday, July 9, 2006 · People with autism often struggle to learn language, and they also struggle with personal relationships.

Scientists say that's probably not a coincidence.

There's growing evidence that language depends as much on the brain circuits that help us navigate a cocktail party as those that conjugate verbs.

One of the people who believes that evidence is Temple Grandin. She teaches animal science at Colorado State University and has written several best-selling books. She's also autistic.

Grandin says it has taken her most of her life to reach the point where she can speak with other people in a way that sounds natural. She says that's because she's had to learn language without the social abilities most people have.

Grandin didn't begin speaking until she was 3 ½ years old. Her first words referred to things, not people, she says.

"I'd point at something that I wanted, you know like a piece of candy or whatever, and say, 'there,'" Grandin says.

She wasn't using language to reach out to her parents or to other children, the way most kids do, so she didn't have the same motivation to talk.

A Tool for Information, or Attention?

When Grandin finally did become interested in words, it was because they provided a way to get information, not attention.

"When I was in third grade, I had trouble with reading, so mother taught me how to read," she says. It opened up a world full of "so many interesting things," she recalls: "I used to like to get the World Book Encyclopedia and read it."

But the encyclopedia taught her little about using language to make friends. Even when she got to high school, chit-chat and gossip meant nothing to her.

She says that made her teenage years the worst part of her life. "Kids teased me, called me tape recorder because when I talked it was kind of like just using the same phrases."

She also kept talking, without letting other people respond.

Grandin and many others with autism have no problem with the mechanics of language, says Dr. V.S. Ramachandran, a neuroscientist at University of California, San Diego. But they don't understand what's really going on in many conversations.

"That's one of the hallmarks of autism," he says, "difficulty with social interaction, manifest both in spoken language and in just lack of empathy. The ability to understand other minds would be one way of describing it."

The Role of Mind Reading

Ramachandran says it's hard to use language if you don't have any idea what someone else is thinking and feeling.

That may seem obvious. But in the past, researchers have treated language as if it were primarily a system of rules. They assumed that people spoke because every human brain came pre-wired with a "universal grammar."

Now, a growing number of researchers, including Ramachandran, argue that the social and emotional aspects of language are at least as important as the rules for stringing words together.

Emotional Neurons

Ramachandran says one reason for the new thinking is a new understanding of the human brain. He says it's become clear that babies' brains are programmed to imitate.

"You stick your tongue out at a newborn baby, very often the newborn baby will stick its tongue out," he says.

Similarly, babies return smiles and often make sounds when someone speaks to them.

A few years ago, scientists found a biological explanation for this phenomenon: specialized brain cells called mirror neurons.

These neurons fire when you do things such as sticking your tongue out. They also fire when you watch someone else stick their tongue out.

And mirror neurons can reflect emotions as well as physical actions. Experiments show that some of the same cells that fire when we feel pain also fire when we see another person in pain.

But people with autism appear to have faulty mirror neurons. That may be why they have trouble putting themselves in someone else's shoes. And Ramachandran says without that ability, a lot of what you can accomplish with language disappears.

"You have to be aware of the effects that your words are having on the other person's mind," he says. Otherwise, how could we use words to manipulate other people?

Picking Up Non-Verbal Cues

Temple Grandin has learned to compensate for her difficulty.

Early in her career, she spoke to people on the phone instead of face to face. That way she didn't miss messages conveyed through eye contact or body language.

But even on the phone, people may not say what they mean. The phrase "I'm fine" sometimes means just the opposite.

So Grandin taught herself to listen very closely to a person's tone of voice.

"When I had a client that I thought might be angry with me, I'd call him up just so I could listen to his voice," she says. "If it had a certain little whine sound in it I'd go, 'Oh he's still angry with me.'"

Over time, Grandin has developed a catalogue of signals she uses to figure out what people are thinking. She checks to see if they are fidgeting during a lecture, or making eye contact during a conversation, or folding their arms during an argument -- emotional cues most of us register automatically.

"I always keep learning," Grandin says. "People ask for the single magic breakthrough. There isn't one. I keep learning every day how I think and feel is different. It's all through logic, trial and error, intellect."

Intellect can only take her so far, though. Grandin says she still has trouble with certain types of conversations.

"Just a couple of years ago I went out to dinner with some salesmen, and these people were absolutely totally social," she says. "They talked for three hours about sports-themed nothing. There was no informational content in what they were talking about. It was a lot of silly jokes about the color of medication and the color of different team mascots. It was boring for me."

Social Motivation for Language

The salesmen were using language as a way of bonding with one another -- not a way to share information. Scientists say this sort of behavior may explain how humans developed language in the first place.

Bonding is something most animals do. For example, apes bond by grooming each other. And one theory has it that early humans began to augment their grooming with affectionate gestures and sounds that eventually led to primitive language.

Ramachandran says there are some gaps in that hypothesis. Like how people got from grunts to grammar.

"The difficult part is to try to disentangle the notion that emotional empathy merely gives you motivation, a reason to talk to somebody, versus an absolutely critical role in the emergence of language," he says.

Ramachandran suspects it's the latter because empathy is what allows people to understand the intention behind an action or a phrase.

For example, he says, when we see someone reach for a peanut, empathy helps us decide if they intend to eat it, or throw it at us. And when we hear someone use a string of words, empathy tells us whether to take the words literally or figuratively.

Ramachandran says people who lack empathy also lack the ability to read another person's intentions -- whether physical or linguistic.

"Not only do they have problems understanding an action like reaching for a peanut," he says, "but also a metaphor like reaching for the stars."

Grandin doesn't use metaphors very often, even though she has mastered the mechanics of language. Grandin says she will never fully understand the social aspects of language, including other people's intentions. And that means language will never offer her more than a rough translation of what other people are trying to say.

Produced by NPR's Anna Vigran

The Study done by Ramachandran:

"EEG evidence for mirror neuron dysfunction in autism spectrum disorder"

http://cbc.ucsd.edu/ramapubs.html

2. Materials and methods
2.1. Subjects
Our original sample consisted of 11 individuals with
ASD and 13 age- and gender-matched control subjects. All
subjects in the study were male. The ASD group was
composed of ten individuals diagnosed with autism and one
individual diagnosed with Asperger ’s syndrome. One
subject with autism and two control subjects were excluded
prior to analysis due to excessive movement artifacts that
resulted in an inability to obtain sufficient EEG data. One
additional control subject was excluded prior to analysis due
to a technical malfunction in the EEG system. Therefore,
our final sample consisted of 10 individuals with ASD and
10 age- and gender-matched controls. Subjects ranged in
age from 6–47 years (ASD: M = 16.6, SD = 13.0; Control:
M = 16.5, SD = 13.6; t (18) = 0.017, P N 0.98). One
individual was left handed in the ASD group, while in the
control group 3 individuals were left-handed.
ASD subjects were recruited through the Cure Autism
Now Foundation, the San Diego Regional Center for the
Developmentally Disabled, and the Autism Research
Institute. Control subjects were recruited through the UCSD
Center for Human Development subject pool and the local
community. Individuals were included in the ASD group if
they were diagnosed with either autism or Asperger ’s
syndrome by a clinical psychologist. Subjects met DSM-
IV criteria for a diagnosis of Autistic disorder or Asperger’s
disorder [3]. In addition, subjects in the ASD group
exhibited the following diagnostic behaviors at the time of
testing, including, but not limited to, awkward use of
pragmatics, intonation, and pitch in communication, lack of
initiation of social interactions, and obsessive preoccupation
with the order and specific details of the study. All subjects
were considered high-functioning, defined as having age
appropriate verbal comprehension and production abilities
and an IQ greater than 80 as assessed by either school
assessments or psychometric evaluations from a clinician.
Subjects without age appropriate verbal comprehension and
production abilities were excluded from the study. Subjects
were given age-appropriate consent/assents (for subjects
under the age of 18). In addition, in order to ensure that
subjects understood the procedure and the tasks involved, a
picture board was created and the study was fully explained,
in age-appropriate language, prior to the subjects’ partic-
ipation. This project was reviewed and approved by the
UCSD Human Research Protections Program.

2.2. Procedure
EEG data were collected during four conditions: (1)
Moving own hand : Subjects opened and closed their right
hand with the fingers and thumb held straight, opening and
closing from the palm of the hand at a rate of approximately 1
Hz. Subjects watched their hand at a comfortable viewing
distance, the hand held at eye level. (2) Watching a video of a
moving hand : Subjects viewed a black and white video of an
experimenter opening and closing the right hand in the same
manner as subjects moved their own hand. Videos were
presented at a viewing distance of 96 cm, and the hand
subtended 58 of visual angle when open and 28 when closed.
The hand was medium gray (8. 6 cd/m2) on a black
background (3.5 cd/m2). (3) Watching a video of two
bouncing balls : two light gray balls (32.9 cd/m2) on a black
background (1.0 cd/m2) moved vertically towards each other
touched in the middle of the screen then moved apart to their
initial starting position. This motion was visually equivalent
to the trajectory taken by the tips of the fingers and thumb in
the hand video. The ball stimulus subtended 28 of visual angle
when touching in the middle of the screen and 58 at its
maximal point of separation. (4) Watching visual white noise :
full-screen television static (mean luminance 3.7 cd/m2) was
presented as a baseline condition. All videos were 80 s in
length and both the ball and hand videos moved at a rate of 1
Hz. All conditions were presented twice in order to obtain
enough clean EEG data for analyses and the order of the
conditions was counterbalanced across subjects, with the
constraint that the self-movement condition always followed
the watch condition so that the subjects had a model on which
to base their movement.
To ensure that subjects attended to the video stimuli during
the watching hand movement and bouncing balls conditions,
they were asked to engage in a continuous performance task.
Between four and six times during the 80-s video, the stimuli
stopped moving for one cycle (a period of 1 s). Subjects were
asked to count the number of times stimuli stopped moving
and report the number of stops to the experimenter at the end
of the block.

2.3. EEG data acquisition and analysis
Disk electrodes were applied to the face above and below
the eye and behind each ear (mastoids). The mastoids were
used as reference electrodes. Data were collected from 13
electrodes embedded in a cap, at the following scalp
positions: F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, T5, T6, O1,
and O2, using the international 10–20 method of electrode
placement. Following placement of the cap, electrolytic gel
was applied at each electrode site and the skin surface was
lightly abraded to reduce the impedance of the electrode-
skin contact. The impedances on all electrodes were
measured and confirmed to be less than 10 KV both before
and after testing. Once the electrodes were in place, subjects
were seated inside an acoustically and electromagnetically
shielded testing chamber.
EEG was recorded and analyzed using a Neuroscan
Synamps system (bandpass 0.1–30 Hz). Data were collected
for approximately 160 s per condition at a sampling rate of
500 Hz. EEG oscillations in the 8–13 Hz frequency
recorded over occipital cortex are influenced by states of
expectancy and awareness [31]. Since the mu frequency
band overlaps with the posterior alpha band and the
generator for posterior alpha is stronger than that for mu,
it is possible that recordings from C3, Cz, and C4 might be
affected by this posterior activity. Therefore, the first and
last 10 s of each block of data were removed from all
subjects to eliminate the possibility of attentional transients
due to initiation and termination of the stimulus. A 1-min
segment of data following the initial 10 s was obtained and
combined with the other trial of the same condition,
resulting in one 2-min segment of data per condition. Eye
blink and eye and head movements were manually
identified in the EOG recording and EEG artifacts during
these intervals were removed prior to analysis. Data were
coded in such a way that the analysis was blind to the
subjects’ diagnosis. Data were only analyzed if there was
sufficient clean data with no movement or eye blink
artifacts. For each cleaned segment, the integrated power in
the 8–13 Hz range was computed using a Fast Fourier
Transform. Data were segmented into epochs of 2 s
beginning at the start of the segment. Fast Fourier Trans-
forms were performed on the epoched data (1024 points). A
cosine window was used to control for artifacts resulting
from data splicing.
Two measures of mu suppression were calculated. First,
we calculated the ratio of the power during the observed
hand movement and self hand movement conditions relative
to the power during the baseline condition. Second, we
calculated the ratio of the power during the observed and
self hand movement conditions relative to the power in the
ball condition. A ratio was used to control for variability in
absolute mu power as a result of individual differences such
as scalp thickness and electrode impedance, as opposed to
mirror neuron activity. The ratio to the ball condition was
computed in order to control for the attention to counting or
any effects due to stimulus stopping during the continuous
performance task and processing of directional motion.
Since ratio data are inherently non-normal as a result of
lower bounding, a log transform was used for analysis. A
log ratio of less than zero indicates suppression whereas a
value of zero indicates no suppression and values greater
than zero indicate enhancement.

3. Results
3.1. Behavioral performance
To ensure that the subjects were attending to the stimuli,
during the hand and ball conditions, they were asked to
count the number of times the stimuli stopped moving.
Since all subjects performed with 100% accuracy on this
continuous performance task, we infer that any differences
found in mu suppression are not due to differences in
attending to the stimuli.

3.2. Mu suppression
Power in the mu frequency at scalp locations correspond-
ing to sensorimotor cortex (C3, Cz, and C4) during the self-
initiated action and watching action conditions was com-
pared to power during the baseline (visual white noise)
condition by forming the log ratio of the power in these
conditions for both groups (Figs. 1A, B). Although data
were obtained from electrodes across the scalp, mu rhythm
is defined as oscillations measured over sensorimotor
cortex, thus only data from C3, Cz, and C4 are presented.
The control group (Fig. 1A) showed significant suppres-
sion from baseline in mu oscillations at each electrode during
both the self-initiated hand movement condition (C3 t (9) =
3.97, P b 0.002; Cz t (9) = 2.85, P b 0.01; C4 t (9) =
4.00, P b 0.002) and observed hand movement condition
(C3 t (9) = 3.99, P b 0.002; Cz t (9) = 3.21, P b 0.005; C4
t (9) = 2.78, P b 0.01). The ASD group (Fig. 1B) also
showed significant mu suppression during the self-initiated
hand movement condition (C3 t (9) = 2.27, P b 0.03; Cz
t (9) = 1.91, P b 0.05; C4 t (9) = 2.50, P b 0.02). Unlike
controls, the ASD group did not show significant suppres-
sion during the observed hand movement condition (C3
t (9) = 0.64, P N 0.25; Cz t (9) = 0.98, P N 0.15; C4
t (9) = 0.74, P N 0.20). The failure to find suppression in
the ASD group was not due to differences in baseline mu
power (C3 t (9) = 0.99, P N 0.30; Cz t (9) = 0.69, P N
0.50; C4 t (9) = 0.47, P N 0.50). Lastly, neither group
showed significant suppression from baseline during the
non-biological motion (bouncing balls) condition (ASD:
C3 t (9) = 0.73, P N 0.20; Cz t (9) = 0.49, P N 0.65; C4
t (9) = .25, P N 0.40; Control: C3 t (9) = 1.45, P N 0.08;
Cz t (9) = 0.54, P N 0.30; C4 t (9) = 0.00, P N 0.50).

Early descriptions of dreams and papers on the nature of dreaming suggest that color was commonly present in dreams before the 20th century (Schwitzgebel, 2003). Before the development of scientific psychology, even scholars such as Aristotle and Descartes, who were at some point interested in dreams, generally acknowledged or assumed that dreams were in color. However, studies from 1915 through to the 1950s suggested that the vast majority of dreams are in black and white, which many people attribute to the rise of black and white media. On the other hand, studies from the 60s and later (when colored media was prominent) suggested that up to 83% of dreams contain some color (Robson, 2008).

The Study:

Eva Murzyn (2007) focused on whether the influence of black and white media had any affect on the reported color of dreams. The subjects (30 females, 30 males; half under 25, the other half over 55) were asked:

· the age at which the participant was first exposed regularly to black and white media and/or colored media (on a following scale: 0–3 years old, 4–6, 7–10, 11–14, 15 years and over, never)

· the number of hours spent currently watching TV

· the percentage of programs watched in black and white.

For 10 days, the participants were asked to record the number of dreams they remembered when they woke up and answered six yes-no questions about the color type of each dream. The answers to these questions allowed Murzyn to classify the dream into on of four main categories: color, grayscale, mixed (containing both colored and grayscale elements) and neither.

The Results:

Out of the 30 participants under the age of 25;

• All had frequent access to color television and film by the age of 6, most of them having such access before the age of 4.
• Their results indicated that 21 participants had colored dreams, 6 had dreams with a mixture of both color and grayscale and 3 were unsure.

Out of the 30 participants over the age of 55;

· None of the participants had access to color media before the age of 7, and 22 participants indicated they had gained access to black and white media before colored media.

· Their results showed that only 8 people indicated they dream in color, 4 said they only have grayscale dreams and 12 mentioned having both types.

The overall results revealed that people who were exposed to black and white media before color media experienced more grayscale dreams than people with no such exposure.

Even in the group with black and white media experience, the average percentage of grayscale dreams experienced in this experiment was much lower than the proportions typically reported in the 1940’s and 1950’s. This difference is most likely due to the influence from color media, which has been the dominant media type for at least the last 40 years. Without black and white film media, scholars such as Aristotle and Descartes might not have thought that something colored may perhaps be represented in the mind as black and white. It would be natural to assume that since the things dreamed about are colored in real life, they should be colored in dreams. However, with the rise black and white media in the early twentieth century, people recognized that the images in their dreams resembled the images in black and white media.

Controversy:

Murzyn (2007) implies that the older group, now are over 55 years of age, has retained at least some of their grayscale dreaming patterns despite a long and intense color media exposure. However, the older participants recalled dreams less often than the younger group. Their dream reports also contained significantly less visual imagery which could lead to mislabeling dreams as grayscale. Because each participant was asked to record the color type of their dream the moment they wake up, many results could be also skewed due to memory errors.

Every day a person with the ability to see observes the world around them in full color. Because of this, Schwitzgebel (2003) suggests that it seems odd to suppose that what an individual sees in a movie or on the television screen would affect whether they dream about them in color. Despite their cultural significance, it seems unlikely that film and television would transform the dreams we have of the colored world around us into dreams of black and white. He believes that it may be more reasonable to assume, that the reporting of dreams that changed rather than their content.

Resources:

Murzyn, E. (2008). Do we only dream in colour? A comparison of reported dream colour in younger and older adults with different experiences of black and white media. Consciousness and Cognition, 17(4), 1228-1237.

O'Connor, A. (2008). The claim: Some people dream only in black and white. Retrieved March 1, 2008, from http://www.nytimes.com/2008/12/02/health/02real.html?_r=2&ref=health

Okada, H. (2005). Individual differences in the range of sensory modalities experienced in dreams. Dreaming, 15(2), 106-115.

Robson, D. (2008, ). ^{It's black and white: TV influences your dreams}. Message posted to http://www.newscientist.com/article/dn14959-its-black-and-white-tv-influences-your-dreams.html

Schwitzgebel, E. (2001). Why did we think we dreamed in black and white? Studies in History and Philosophy of Science, 33(4), 649-660.

Schwitzgebel, E. (2006). Do we dream in color? cultural variations and skepticism. Dreaming, 16(1), 36-42.

Jazz Improvisation Deactivates Brain!

Jazz Improvisation on the Brain: an fMRI study.

Background
This NPR story talked about a recent study (published in February of 2008) dealing with Jazz improvisation on the brain; the description on the NPR page, however, is a little misleading as far as the content of the program and the content of the experiment goes.

The Study
Six jazz pianists were studied in an fMRI machine. All were right-handed, healthy, “normal” hearing males, 21-50 years old (mean 34.2 yrs); all were full-time professional musicians. They were accompanied by a pre-recorded quartet that played straight into their headphones, along with the sounds from their specially-designed fMRI keyboards that are not and cannot be induced to be magnetic (non-ferromagnetic) and only outputs MIDI information. These keyboards were full-sized and specially designed, played a high-quality piano sample. Subjects played with their right hands, looking through a series of mirrors to see the propped-up keyboard on their legs. This study used no mechanical restraints.

Two block-designed test paradigms. First paradigm: Scales. Designed to test brain during a highly constrained situation but of low technicality. Right-hand only. Control task: play scale. Improv task: Improvise, but only use quarter notes and only notes in the scale. Number of notes, note range, key, and technical requirements all accounted for and remaining unchanged. 1 minute each, 30 second rest. 6 blocks total (3 scale, 3 improv)

Second Paradigm: Jazz. All were given a 12-bar musical piece to memorize. In the control condition, they played the memorized piece. In the Jazz condition, they were asked to improv to the chord progression of the pre-recorded accompaniment. The condition for the improvisation was that it should follow the style of the melody, and the “improvisation should be consistent with one another.” (in order to minimize variations in number of notes played, rhythmic complexity, or stylistic approach). Each block 1 minute, 5 control blocks, 5 improv blocks, and 9 non-performance auditory blocks (for use in a separate manuscript), with 20-second rest between each.

The Findings

fMRI analysis: “Significant” activation was considered those brain functions that were both greater than control and resting baseline. Significant deactivation was that with lower activation in comparison to these two conditions. Both paradigms (Jazz and Scale) have “strikingly similar results…a highly congruous pattern of activations and deactivations in prefrontal cortex, sensorimotor and limbic regions of the brain." Activation during improv was generally coupled with deactivation in control, and vice-versa.

The Brain Areas:
Prefrontal cortex (playing a role of sense of self?)—widespread deactivation. lateral prefrontal regions: LOFC (making sure your behaviors fit in with society?) & DLPFC (planning and step-by-step implementations). Almost all of lateral prefrontal cortices, but focal activation of frontal polar portion of the medial prefrontal cortex (keeping a goal while doing various things)

Neocortical Areas (mediate organization and execution of performance)—broad increase of activity: sensory areas: anterior portions of superior and middle temporal gyri (STG and MTG), including anterior portions of the superior temporal sulcus (STS), inferior temporal, fusiform and lateral occipital gyri, as well as inferior and superior parietal lobules and the intervening intraparietal sulci.

premotor and motor areas: selective activation: ventral and dorsal lateral premotor areas, supplementary motor area and portions of the primary motor cortex. The anterior cingulate cortex, cingulate motor area, right lateral cerebellar hemisphere, and vermis were activated as well extensive deactivation of dorsolateral prefrontal and lateral orbital regions with focal activation of the medial prefrontal (frontal polar) cortex.
Limbic and Paralimbic Regions —widespread activation during improvisation. Selective deactivations (motivation, emotional tone):  amygdala, entorhinal cortex, temporal pole, posterior cigulate cortex, parahippocapal gyri, hippocampus and hypothalamus.

Hypotheses: this unique pattern may have insights into the cognitive workings of the creative process. 

Differences? Other studies?

Seems as though it may be like hypnosis and REM sleep with its lateral prefrontal deactivation.

Classically trained pianists: Bengtsson et al. found activations in right dorsolateral prefrontal cortex (as well as premotor and auditory areas) during improvisation. They were not looking at deactivations, however. The two studies used different masking strategies and therefore results would be expected to be different. Also jazz is different than classical. Jazz is characterized by improvisation. The argument is that current findings are based on a more “natural” habitat for improvisation.
Requiring Further Study, and Notes
More extensive deactivation of various limbic systems was observed in this study. This is on top of the expected deactivation of the amygdala and hippocampus, consistent with previous studies of music perception (harmonious and “intensely pleasing” music show these deactivated areas).

Have their findings “characterized a higher qualitative level of musical output (as opposed to that which might be produced by less skilled performers)”? But, despite music simplicity, findings suggest that this is a more generalized neural mechanism than to the highly-trained alone.

Compiling this information with others that actually look at the various studies done on activations of the brain during various tasks (though for activities such as soccer and dance, this fMRI technology is out. It’s one thing to say “put a piano on your lap and see it through these mirrors”—I imagine these pianists really did not have a need to see the piano, in any case—and try to kick this ball without moving your upper body). But, for instance, what NPR seems to have done, albeit without citing any sources, is to find comparisons to other studies. I imagine there may be some major differences between dreaming and jazz improvisation, for instance.

As far as the efficacy of the project, it is always important to question a researcher’s means of obtaining results. More so than the awkward positioning, I think, would be the level of constraint on the improvisation. The point of the study, however, was to observe the differences between the two conditions. Yes, there were certainly elements that were not present in ordinary improvisational conditions (an MRI machine, being horizontal, only using one’s right hand…), however the results did happen and were there. There are differences between the two circumstances, and significant ones, in the brain. Other studies could do with other creative (relatively nonmoving) arts such as painting or writing, to see how those differ in the brain.

My concern had to do more with the one-handedness of the study. The right hand connects directly to the left brain. Granted, the right and left brain of these individuals could communicate with each other, this seems to be something important that was left out for ease, lack of funds, and consistency’s sake.


This post was originally written on March 2, 2009 as a part of a neuroscience course.

Interested in learning more?
Limb CJ, Braun AR (2008) Neural Substrates of Spontaneous Musical Performance: An fMRI Study of Jazz Improvisation. PLoS ONE 3(2): e1679. doi:10.1371/journal.pone.0001679

Study: Jazz Improv Cranks Up Brain's Creativity. http://www.npr.org/templates/story/story.php?storyId=88827029

The affect of media on the color of our dreams

http://www.newscientist.com/article/dn14959-its-black-and-white-tv-influences-your-dreams.html

This is Your Brain on Dope (amine).

http://www.mcmanweb.com/love_lust.html

A crazy article going every which way, but I'd like to focus on what dopamine does to the brain in times of love and times of not.

The Colbert ReportMon - Thurs 11:30pm / 10:30c

Helen Fisher

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Dopamine Explained


J Neurophysiol 94: 327-337, 2005. First published May 31, 2005; doi:10.1152/jn.00838.2004
0022-3077/05 $8.00

	ABSTRACT

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS ACKNOWLEDGMENTS REFERENCES

Early-stage romantic love can induce euphoria, is a cross-cultural phenomenon, and is possibly a developed form of a mammalian drive to pursue preferred mates. It has an important influence on social behaviors that have reproductive and genetic consequences. To determine which reward and motivation systems may be involved, we used functional magnetic resonance imaging and studied 10 women and 7 men who were intensely "in love" from 1 to 17 mo. Participants alternately viewed a photograph of their beloved and a photograph of a familiar individual, interspersed with a distraction-attention task. Group activation specific to the beloved under the two control conditions occurred in dopamine-rich areas associated with mammalian reward and motivation, namely the right ventral tegmental area and the right postero-dorsal body and medial caudate nucleus. Activation in the left ventral tegmental area was correlated with facial attractiveness scores. Activation in the right anteromedial caudate was correlated with questionnaire scores that quantified intensity of romantic passion. In the left insula-putamen-globus pallidus, activation correlated with trait affect intensity. The results suggest that romantic love uses subcortical reward and motivation systems to focus on a specific individual, that limbic cortical regions process individual emotion factors, and that there is localization heterogeneity for reward functions in the human brain.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS ACKNOWLEDGMENTS REFERENCES

Intense romantic love is a cross-culturally universal phenomenon. In a survey of 166 contemporary societies, Jankowiak and Fischer (1992) found evidence of romantic love in 147 of them; they noted that the 19 remaining cases were examples of ethnographic oversight—the anthropologists failed to ask the appropriate questions; they found no negative evidence. They concluded that romantic love constitutes a "human universal...or near universal" (Jankowiak and Fischer 1992). Romantic love is also associated, particularly in early stages, with specific physiological, psychological, and behavioral indices that have been described and quantified by psychologists and others (Fisher 1998; Gonzaga et al. 2001; Harris and Christenfeld 1996; Hatfield and Sprecher 1986; Hatfield et al. 1988; Shaver et al. 1987; Tennov 1979). These include emotional responses such as euphoria, intense focused attention on a preferred individual, obsessive thinking about him or her, emotional dependency on and craving for emotional union with this beloved, and increased energy. Tennov (1979) coined the term "limerance" for this special state, and Hatfield and Sprecher (1986) developed a questionnaire scale to measure it. The universality, euphoria, and focused attention of romantic love suggest that reward and motivation systems in the human brain could be involved (Fisher 1998; Liebowitz 1983).

In addition, cross-cultural descriptions of romantic love regularly include reward-related images and suggest strong motivation to win a specific mating partner. For example, the oldest love poem from Summeria, "Inanna and Dumuzi," dating 4,000 yr ago and found on cuneiform tablets in the Uruk language is translated, "My beloved, the delight of my eyes..." (Wolkstein and Kramer 1983). From the Song of Songs, the Hebrew 10th century poem comes, "...your love is more wonderful than wine ...the sound of your name is perfume ... . I sought the one my soul loves..." (Wolkstein and Kramer 1983). Furthermore, among the ethnographies canvassed in the review of Jankowiak and Fischer (1992) is one by Harris (1995) who cited evidence of the yearning for love and the motivation to win the beloved among the peoples of Mangaia, Cook Islands, Polynesia. These people have a word for "dying for love." They translate it as, "You don’t want anything else; you die for love, but you don’t mind if you die; you don’t feel ashamed about loving that person to death. If you really love someone nothing will stop you." Worldwide, romantic love plays a key role in courtship, suggesting that it evolved as a primary aspect of the human mating system (Fisher 1998). Its ubiquity and strong, measurable properties make it an excellent candidate for understanding the human neural systems associated with reward, positive emotion, and attention, as well as the neurobiology of an important phase in human reproductive relationships, which have genetic consequences.

We used functional MRI (fMRI) methods to test two predictions about the neural systems involved in romantic love. First, romantic love would specifically involve subcortical regions that mediate reward, such as the ventral tegmental area (VTA) and ventral striatum/nucleus accumbens (Esposito et al. 1984; Hollerman et al. 2000; McBride et al. 1999; Porrino et al. 1984; Robbins and Everitt 1996; Schultz 2000; Wise and Hoffman 1992). Several of the behavioral aspects of romantic love suggest that it can be like cocaine-reward producing exhilaration, excessive energy, sleeplessness, and loss of appetite (Fisher 1998). Consistent with animal studies of cocaine addiction (David et al. 2004; Kalivas and Duffy 1998; McBride et al. 1999; Wise and Hoffman 1992), acute cocaine injection has been shown to activate the VTA in fMRI studies of humans (Breiter et al. 1997). In addition, fMRI studies have shown that secondary rewards like money activated the nucleus accumbens/subcallosal region and VTA (Breiter et al. 2001; Delgado et al. 2000; Elliott et al. 2000, 2003, 2004; Knutson et al. 2001). Furthermore, chocolate, acting as a food reward, activated the VTA and subcallosal region (Small et al. 2001). These regions decreased their metabolic activity with decreasing desire for more chocolate. More data implicating reward regions and dopamine as an important neurotransmitter for the feelings and behaviors of romantic love have been summarized previously (Fisher 1998).

In addition to basic reward functions, further evidence from studies of monogamous prairie voles suggests that the nucleus accumbens, striatum, and dopamine could be involved in human romantic love. When a female prairie vole is mated with a male, she forms a distinct preference for this partner; however, when a dopamine agonist is infused into the nucleus accumbens, she begins to prefer a male present at the time of infusion, even if she has not mated with this male (Gingrich et al. 2000; Liu and Wang 2003). Furthermore, a striatal output region through the ventral pallidum is strongly implicated as critical to male prairie vole mate preference behaviors (Lim et al. 2004). Also, electrochemical studies in male rats have shown increased dopamine release in the dorsal and ventral striatum in response to the presence of a receptive female rat, more so even than during copulation (Montague et al. 2004; Robinson et al. 2002). These data suggest that the nucleus accumbens and dopamine release are major factors underlying rodent mate preference. Thus several lines of evidence from both human fMRI and animal studies support the prediction that multiple reward regions using dopamine could be activated during feelings of romantic love.

Our second prediction about the neural systems involved in early-stage romantic love was that it would be associated with other goal and reward systems, such as the anterior caudate nucleus. The caudate plays a role in reward detection and expectation, the representation of goals, and the integration of sensory inputs to prepare for action (e.g., Lauwereyns et al. 2002; Martin-Soelch et al. 2001; O’Doherty et al. 2004; e.g., Schultz 2000). While some investigators view romantic love largely as a specific emotion (Gonzaga et al. 2001; Shaver et al. 1987, 1996), others have proposed that romantic love is a goal-directed state that leads to varied emotions (Aron and Aron 1991). It tends to be hard to control, is not associated with any specific facial expression, and is focused on a specific reward. The caudate nucleus is a brain region that could represent rewards and goals in a complex behavioral state like romantic love because it has widespread afferents from all of the cortex except V1 (Eblen and Graybiel 1995; Flaherty and Graybiel 1995; Kemp and Powell 1970; Saint-Cyr et al. 1990; Selemon and Goldman-Rakic 1985) and is organized to integrate diverse sensory, motor, and limbic functions (Brown 1992; Brown et al. 1998; Eblen and Graybiel 1995; Haber 2003; Parent and Hazrati 1995; Parent et al. 1995; Parthasarathy et al. 1992).

One previous fMRI study (Bartels and Zeki 2000) used methods similar to ours, but investigated romantic love in a later stage. Participants in that study had been in love substantially longer than those in our study [28.8 vs. 7.4 mo; t(32) = 4.28, P < 0.001]. Also, participants in that study were less extremely in love, based on the same standard questionnaire [scores of 7.55 vs. 8.54, t(31) = 3.91, P <> is the first fMRI study of early stage romantic love. We also show novel effects in the human VTA that may be associated with different aspects of reward, novel time-dependent effects in the cingulate and insular cortex, and novel brain activation correlations with quantified self-reports of passion intensity and trait affect intensity.

	METHODS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS ACKNOWLEDGMENTS REFERENCES

Participants

Ten women and seven men were recruited from the State University of New York at Stony Brook community, the Rutgers University community, and the New York City area by word of mouth and with flyers seeking individuals who were currently intensely in love. All participants preferred their right hand (Edinburgh Handedness Inventory, Oldfield 1971) and were not taking antidepressant medications. The age range was 18–26 yr (mean = 20.6 yr; median = 21 yr). The reported duration of "being in love" was 1–17 mo (mean = 7.4 mo; median = 7 mo). All participants gave informed written consent and each received $50 for his or her participation. The institutional review boards at Stony Brook and Rutgers approved all procedures.

Interviews and questionnaires

A few days in advance of the scanning session, one of us (H.E.F.) orally interviewed each participant in a semistructured format to establish the duration, intensity, and range of his or her feelings of romantic love. Just prior to the scanning session, each participant also completed two self-report questionnaires: 1) the passionate love scale (PLS) (Hatfield 1986; example items: "I want ___ physically, emotionally, and mentally"; "Sometimes I can’t control my thoughts; they are obsessively on ___") (Cronbach’s for questionnaire reliability in this study = 0.81; Cronbach 1951) and 2) the affect intensity measure (AIM) (Larsen et al. 1987; Cronbach’s in this study = 0.85; example items: "I get overly enthusiastic"; "Sad movies deeply touch me"), which assesses the general tendency to experience emotions intensely. After the scanning session, two of us (H.E.F. and D.J.M.) conducted exit interviews to determine whether the participants followed the instructions and what they thought about. Also, we tested whether any of the questionnaire data correlated significantly with sex, relationship length, or the other questionnaire (or whether any of these variables correlated with each other); they did not. That is, there were no significant correlations among AIM scores, PLS scores, relationship length, and sex.

Stimuli

The stimuli and length of presentation we used were based on a preliminary investigation that identified a photograph of the beloved as better than other stimuli (e.g., touch, voice) for eliciting feelings of intense romantic love (Mashek et al. 2000). Also, Mashek et al. (2000) found that the intensity of feeling tended to diminish after about 30 s of exposure to the photograph. Before the scanning session, each participant provided a photograph of the beloved (positive stimulus) and a similar photograph of a familiar, emotionally neutral acquaintance of the same age and sex as the beloved (neutral stimulus). Photographs were digitized and sized to show the head only. An angled mirror was mounted on the RF coil, enabling the participant to view each image, which was projected on a screen placed directly outside the MRI tube, subtending a visual angle of 17°. To be sure that the quality of the photos provided by participants was not a factor, we had a group of individuals rate picture quality. The quality of the positive and neutral photos did not differ significantly (P = 0.88). Also, picture quality did not correlate significantly with PLS, AIM, relationship length, sex, or separately rated attractiveness of the face (all Ps > 0.14).

Because it is difficult to quell intense feelings of romantic love, we devised a protocol to decrease the carryover effect after the participant viewed the positive stimulus. We interspersed the positive stimulus and neutral stimulus with a distraction, serial countback task. This task involved viewing a number such as 8,421 on the screen and mentally counting backward in increments of seven beginning with this number. A randomly selected different starting number was presented each time the task was given. Pilot testing established that 40 s of the countback task effectively erased feelings associated with the previous positive stimulus in most individuals. To provide a similar distraction after the neutral stimulus (but reduce experiment duration), participants did the countback task for 20 s. The different lengths of the countback task preceding the positive and neutral stimulus presentations was a possible confound. However, the length of the stimulus presentation block was likely great enough to reduce any carryover effects from the countback task. Indeed, inspection of the data showed that the positive and neutral conditions began at the same response magnitude rather than different response magnitudes, which would be indicative of carryover effects from the previous block.

Instructions to participants and exit interviews

In preliminary studies, participants reported that in addition to a photo, thinking about specific events relating to their beloved was the best circumstance to elicit intense romantic love during a 30-s time period (Mashek et al. 2000). Thus the instructions to the participants were to think about events that occurred with the beloved that were especially pleasurable, but not sexual, while they viewed the positive picture. To control for event recall, instructions for the neutral picture were to think about events with the person in that picture also. During the interview before the experiment, the interviewer established pleasurable events that the participant might think about while looking at the beloved, and neutral events, like watching television, while viewing the neutral stimulus. These events were discussed also just before the fMRI session. During exit interviews, the participants reported that they had, indeed, thought about specific events when they looked at the stimuli. For example with regard to the positive stimulus: "I thought about the time we both woke up at 3 AM and walked back from the 7/11 store, it was fun walking back and kissing." With fewer sexual overtones, one said, "I felt I could really rely on her, I could open up to her, I felt protected by her." One felt a rush of euphoria; one felt "happy" and "comfortable." These are descriptions of positive, rewarding feelings. Regarding the neutral stimulus, participants reported that they thought about the specified events with the neutral person that were discussed before the experiment, and that by comparison, one felt "bored." All reported that they did the countback task, although we had no behavioral verification. Several reported that it was hard to do the countback task after the positive stimulus but not after the neutral stimulus, which is additional evidence that they carried out the task.

Experimental design and procedures

The protocol consisted of four tasks presented in an alternating block design. 1) For 30 s, the participant viewed the positive stimulus; 2) for the following 40 s, the participant performed the countback distraction task; 3) for the following 30 s, the participant viewed the neutral stimulus; and 4) for the following 20 s, the participant performed the countback task. The starting image was either the neutral stimulus or positive stimulus and was counterbalanced across participants. The four-part sequence was repeated six times; the total stimulus protocol was 720 s (12 min).

Image acquisition and analysis

Data were acquired using a 1.5-T Marconi (Phillips) Edge MRI system. We measured the blood oxygen level–dependent (BOLD) response and took in-plane anatomical data for each participant. The images were 1) anatomical, axial T1-weighted Spin-Echo Scans: 14-ms TE, 600-ms TR, 90° flip angle, 24-cm FOV, 4-mm slice thickness, 0-mm gap, 256 x 256 matrix size, 20 slices; and 2) functional, T2-weighted Gradient-Echo EPI scans: 70-ms TE (not optimal), 5,000-ms TR, 90° flip angle, 24-cm FOV, 4-mm slice thickness, 0-mm gap, 64 x 94 matrix size (0 filled into 128 x 128 before FFT and the resulting 128 x 128 images were averaged into 64 x 64 before analysis), 20 slices. Voxel size for the functional images was 3.75 x 3.75 x 4.00 mm.

The fMRI data analyses were performed using Statistical Parametric Mapping software (SPM 99 Wellcome Department of Imaging Neuroscience, London, UK; Friston et al. 1995). Functional images were realigned, smoothed with a Gaussian kernel of 8 mm, and normalized to the SPM EPI template brain (19 participants were recruited, but 2 were dropped from the study because they moved >2 mm). We treated each of the stimulus types (positive, neutral, countback1, countback2) as a separate regressor, modeled as a boxcar function convolved with the canonical hemodynamic response; we applied a high-pass filter with a cut-off of 240 s to remove low-frequency signal components. We created contrast images for each comparison for each participant. We then analyzed the contrast images across participants using a mixed-effects general linear model, treating participants as a random effect and conditions as a fixed effect. Interpretation of the group analyses was facilitated by inspection of individual results. Time-course data are reported as "response," a calculation by SPM99 based on raw data that uses the mean of all the conditions as the baseline.

For planned comparisons (hypothesis-driven analyses), we applied small volume corrections with a sphere as a region of interest (P 0.05, corrected for multiple comparisons). The coordinates for the centers of the regions of interest were based on a review of 15 fMRI articles that had studied reward or romantic love (Table 1). Rewards in the previous studies included acute cocaine injection, receipt of money, and eating chocolate.

View this table: [in this window] [in a new window] TABLE 1. Predicted regions of change

To investigate unpredicted regions of activation, we thresholded the images at P <> There were no significant differences found.

Using SPM99, we performed correlations between participant questionnaire scores and brain responses for the PLS and AIM. Also, because another study showed that there are specific BOLD responses in humans to faces rated as beautiful compared with faces rated as average (Aharon et al. 2001), we had five men and five women (nonparticipants) rate the images for overall "attractiveness." We correlated the attractiveness score difference between positive and neutral for each participant with their neural response (for the positive-minus-neutral contrast). In addition, because we thought that differences between our data and the findings of the study of longer-term romantic love (Bartels and Zeki 2000) might be caused by the difference in relationship length, we correlated brain responses and months the participants reported having been in love.

We tested for differences between men and women; however, none met the criterion of P <>

Anatomical localization

To aid our identification of regions affected, we used the atlas of Duvernoy (1999) and the Talairach Daemon Client (Version 1.1, Research Imaging Center, University of Texas Health Science Center, San Antonio, TX). Data were analyzed for individuals on their T1 images, on the mean T1 image for the group, and on the average 305 T1 MNI template in SPM99. To display some of the data, we chose the SPM99 Single Subject T1 (scanned multiple times) dataset because major landmarks are more visible than in the other renderings. The calculated error for SPM99 anatomic normalization within a group is 8 mm between sulci, and there is 94% overlap among the same gray matter regions (Hellier et al. 2001, 2002). Thus the Talairach descriptions of the locations of cortical changes that include Brodmann’s areas (BAs) are an approximation only; we include them in the report because they are useful to compare with other studies. In addition, the data were smoothed with a Gaussian kernel of 8 mm so that any single localization point reported in the tables should be considered within an area of 8 mm. Technical considerations limited us to 20 slices that did not cover the entire brain. Parts of the dorsal neocortex (1–3 cm from the superior surface) were not sampled in some participants, whereas the ventral temporal lobe was not included in others. Thus we analyzed separately the amygdala and ventral hippocampus in the nine participants (6 women and 3 men) who had data in those regions.

Localization of activation to the medial caudate appeared to be partially in the ventricle in the normalized group images. To confirm that it was caudate activation, we examined unnormalized individual data. We calculated the distance (in mm) from the anterior commissure to the caudate peak activation in each individual and plotted it on a horizontal section from the Montreal Neurological Institute average brain template based on scans from 305 individuals (Fig. 1).

View larger version (43K): [in this window] [in a new window] FIG. 1. Caudate nucleus activation, positive-minus-neutral contrast. A: an enlargement of an axial section through the caudate nucleus from the MNI T1 template that averaged 305 subjects. Black dots show peak activation points for each participant in the present study. Activation points were near the medial edge of the caudate in the vicinity of Talairach coordinates 12, 11, 14 (dark gray areas are lateral ventricles). B: a sagittal section from an individual participant shows the extent of the posterior dorsal caudate activation (arrow). Images in this and all following figures are presented in radiologic convention (participants’ left on the right side of the image). C, caudate.

	RESULTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS ACKNOWLEDGMENTS REFERENCES

Positive-minus-neutral stimulus contrast (activations)

Predicted, small volume measurements showed significant differences in the right medial caudate (Fig. 1A; Table 2), in the right antero-dorsal caudate body (Fig. 1B, Table 2), in another region of the right dorsal caudate body, and in the right BA30/retrosplenial cortex (Table 2). Significant bilateral caudate activations were in the antero-dorsal region (Table 2).

View this table: [in this window] [in a new window] TABLE 2. Regional activations specific to the picture of the beloved compared to a picture of a familiar, neutral acquaintance

In the ventral midbrain, significant activation was localized in the region of the VTA/A10 dopamine cells (Fig. 2, A and B). Also, plots of the time-course of the BOLD response show that neural activation increased in response to the positive image relative to the other conditions, whereas there was a decrease in the BOLD signal for each control task relative to the positive stimulus (Fig. 2C). No caudate region showed a similar time-course.

View larger version (67K): [in this window] [in a new window] FIG. 2. Group mean data and an individual subject show the localized ventral midbrain effect. A: positive-minus-neutral contrast. B: positive-minus-countback contrast. Activity in the right VTA region (arrows) specifically increased in response to the positive image compared with both control conditions. The regional activation is highly localized to the medial A10 dopamine cell region with little inclusion of the medial substantia nigra. C: time-course of the BOLD response (means ± SE, 0 = mean of all conditions) for a voxel in the right VTA shows that the signal increased to the positive image (solid line) relative to the others; the signal during control stimuli presentations decreased relative to the positive image, especially for the countback task (short-dash line; 40-s countback task shown). Long-dash line, neutral stimulus. D: in a single subject, a sagittal view shows the anteroposterior extent of the right VTA activation (arrow). E: in the same subject, a coronal view of the right VTA activation (arrow) shows how it is limited to the medial midbrain. Locations of responses shown in the graph are given in Talairach coordinates. L, left side; VTA, ventral tegmental area.

Positive-minus-countback

We had included the countback task to provide a distraction between positive and neutral stimuli. However, we reasoned that it might serve as a supplementary control condition. That is, areas showing strong activation for the positive-minus-neutral subtraction that also showed activations for a positive-minus-countback subtraction may be additional evidence that these represent areas associated with intense romantic love. The positive-minus-countback subtraction yielded activations overlapping with the positive-minus-neutral subtraction for the right ventral midbrain and the right postero-dorsal caudate (Table 2), providing more evidence that activation in these regions is specific to the image of the beloved.

Self-report of degree of passionate love

Focusing again on the positive-minus-neutral contrast, we conducted a between-subject random effects analysis correlating degree of the BOLD response and participants’ scores on the PLS. (Recall that there were no significant correlations among PLS scores, AIM scores, relationship length, and sex.) As shown in Fig. 3 and Table 2, PLS scores had high positive correlations with activation in two of the regions that were significant for the contrast by itself, the right antero-medial caudate body (r = 0.60; P = 0.012, Fig. 3C) and the septum-fornix region (r = 0.54; P <> levels of romantic love than others also showed greater activation than others in this region of the caudate and septum when viewing their beloved. As noted by Kosslyn et al. (Kosslyn et al. 2002), the consistency of a between-subject correlation with a subtraction result provides particularly strong triangulating evidence for the link of a function with an activated area.

View larger version (50K): [in this window] [in a new window] FIG. 3. Activation in the anteromedial caudate body was correlated with the passionate love scale (PLS) scores of participants. A: caudate location for the correlation (arrow). B: correlation of activity in the caudate with PLS scores. Location of responses shown in graph are given in Talairach coordinates. C, caudate; L, left side.

Attractiveness effects of the positive and neutral faces

The correlation between the BOLD response and independently rated attractiveness for the positive image minus the attractiveness of the neutral image was significant for voxels in the left VTA (r = 0.74, P = 0.009; Fig. 4). This is a different location from activation for the positive-minus-neutral contrast, which was in the right VTA (Fig. 2).

View larger version (50K): [in this window] [in a new window] FIG. 4. Activity in the VTA for the positive-minus-neutral contrast was correlated with the independently rated attractiveness of the positive minus the attractiveness of the neutral faces. A: activation is on the left and on the midline (arrow) and different from the localization of activation in Fig. 2, A and B. B: neural activity in response to positive images was greater when the positive face was more attractive than the neutral face.

Neutral-minus-positive stimulus (deactivations)

The only predicted region of change to show a deactivation was the amygdala (coordinates: 20, –3, –15, P <>

Length of time in love

Because our participants were in love for a shorter amount of time than those in the study done by Bartels and Zeki (2000), we correlated degree of activation and participant’s reported length of time in love. The correlation was done for the positive-minus-neutral contrast. As shown in Fig. 5 and Table 3, several regions of special interest showed changes as the relationship lengthened: the right mid-insular cortex; the right anterior and posterior cingulate cortex; and the right posterior cingulate/retrosplenial cortex. Scatter plots of the correlation in the anterior cingulate cortex and retrosplenial cortex suggest that participants in longer relationships (8–17 mo) were different from those engaged in relatively short relationships (1–7 mo; Fig. 5). Thus it appears that length of time in love is a major factor for neural activity in the insula and cingulate/retrosplenial cortex when looking at an image of a romantic partner.

View larger version (75K): [in this window] [in a new window] FIG. 5. Length of time in love correlated with activation in specific regions. Regions are indicated on axial (A) and sagittal (C) sections. A: cluster location for the retrosplenial cortex correlation (arrow) is similar to a region correlated with satiation while eating chocolate (Small et al. 2001). B: correlation for the peak voxel in the cluster labeled R in A. C: location of voxel clusters correlated with relationship length in the AC and PC (arrows). D: graph of the correlation between the BOLD response and months in love for the AC. AC, anterior cingulate cortex; PC, posterior cingulate cortex; R, posterior cingulate/BA30 retrosplenial cortex.

View this table: [in this window] [in a new window] TABLE 3. Regional changes in brain activity that were correlated with the length of the relationship

One brain region showed greater activation the shorter the length of time in love: the left posterior cingulate cortex/retrosplenial cortex region (Table 3).

Self-report of general tendency for emotional intensity

Once again using the positive-minus-neutral contrast, we correlated degree of activation and participants’ scores on the AIM (the AIM was not significantly correlated with the PLS). The AIM is a self-assessment of general affect tendencies, and the correlation with the BOLD response tested for a potentially important trait difference among participants in a study that may involve emotion. The left mid-insular cortex (Talairach coordinates –42,–6,0) was correlated with AIM score (r = 0.58; P <> and Zeki (2000) reported activation in their study. Thus a left insular cortical region was affected by the positive stimulus, but the response varied depending on an individual’s self-report of how strongly the person experiences affect in general.

	DISCUSSION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS ACKNOWLEDGMENTS REFERENCES

Several results support our two predictions that 1) early stage, intense romantic love is associated with subcortical reward regions that are also dopamine-rich (e.g., Fisher 1998) and 2) romantic love engages a motivation system involving neural systems associated with motivation to acquire a reward rather than romantic love being a particular emotion in its own right (Aron and Aron 1991). Foremost, when our participants looked at a beloved, specific activation occurred in the right ventral midbrain around the VTA, dorsal caudate body, and caudate tail. These regions were significant compared with two control conditions, providing strong evidence that they are associated with specific aspects of romantic love.

The VTA contains dopaminergic cells (A10) that send projections to several brain regions (Gerfen et al. 1987; Oades and Halliday 1987; Williams and Goldman-Rakic 1998), including the medial caudate where we found specific activations. In addition, both the VTA and caudate regions activated in this study receive visual afferents and respond to visual stimuli (Caan et al. 1984; Horvitz et al. 1997; Saint-Cyr et al. 1990). Although fMRI is limited to measurements of location and relatively long-term neural responses, and cannot determine neurotransmitters used, other electrophysiological, lesion, fMRI, voltammetry, drug infusion, and self-stimulation studies have established that the VTA, dopamine, and the caudate nucleus play major roles in reward and motivation in the mammalian brain (Delgado et al. 2003; Kawagoe et al. 1998; Martin-Soelch et al. 2001; Phillips et al. 2003; Salinas and White 1998; Schultz 2000; White and Hiroi 1998; Wise 1996). Most importantly, Zald et al. (2004) found that predictable monetary reward presentation caused dopamine release in the medial caudate body where we found activation. The combined findings implicate reward and motivation functions as likely aspects of early-stage romantic love and dopamine as one of the candidate transmitters involved.

Cortical areas associated with emotion were involved, also, such as the insular and cingulate cortex. However, as expected for a goal-directed state with diverse outcomes, activation of emotion-associated areas varied among individuals depending on their general affect intensity, passion score, and the length of the relationship. Two subcortical areas associated with emotion were affected: the amygdala and the septum. Amygdala activity was decreased relative to the neutral stimulus, whereas septal activity was correlated with the PLS score. Deactivation was seen in the amygdala by Bartels and Zeki (2000, 2004), also. They suggested that love reduces fearful responses. However, amygdala activity plays a role in the recognition of faces in general, and it is not clear why it was specifically involved in this study (Kosaka et al. 2003). The septum was one of the first regions found to be rewarding during electrical self-stimulation (Olds and Milner 1954). It is activated by VTA self-stimulation reward and medial forebrain bundle self-stimulation reward, and it is involved in several emotional responses in animals, including relief from aversive emotional states (Esposito et al. 1984; Porrino et al. 1984, 1990; Yadin and Thomas 1996). Thus it is consistent with a large body of data that the septum would be active during a reward state. Finally, the lateral septum has also been implicated in pair-bonding in prairie voles (Liu et al. 2001).

Regional heterogeneity of ventral tegmental area reward functions

To establish whether the VTA activation occurred because our participants were feeling romantic passion or were stimulated by an esthetically pleasing face, we correlated facial attractiveness (as rated by others) with neural activation. This correlation showed that those with more esthetically pleasing partners compared with the neutral stimulus showed greater neural activity in the left VTA than those with less attractive partners compared with the neutral stimulus. Several fMRI studies indicate that the right VTA, where we found activation for our basic positive-minus-neutral contrast, is associated with rewards and/or working for rewards (Aharon et al. 2001; Breiter et al. 2001; Elliott et al. 2000, 2004; Small et al. 2001); others showed bilateral activation of the VTA (Breiter et al. 1997; O’Doherty et al. 2002). Importantly, Aharon et al. (2001) showed that the left VTA activation was specifically associated with a face deemed esthetically pleasing (liking), whereas right VTA activation increased during presentation of a face that participants would work to see longer (wanting). Thus several fMRI studies corroborate reward effects in the human VTA (Table 1), but this is the second fMRI study to show a localization effect within the VTA for two aspects of reward, wanting and liking (Berridge and Robinson 2003) and to show that this effect is lateralized. Caudate effects for the positive-minus-neutral contrast were on the right, providing more evidence that the lateralized VTA effect is not spurious.

The localization of the VTA activation appears to be quite specific in the figures, given the size of the original voxels (3.75 x 3.75 x 4.0 mm) and the size of the smoothing filter (8 mm). The specificity appearance is enhanced by the normalized images (2 x 2 x 2 mm). However, the human VTA is 8 mm across, from anterior to posterior, and 4 mm dorsoventral, well within the area covered by several voxels. In addition, by smoothing the data, which tends to enhance effects in large regions of cortex, we probably diluted the observed effect in this small region. The VTA is a small region, however, and given the variability inherent in the brains of subjects and the normalization procedures, we cannot be sure that the activation does not include other surrounding areas.

Regional caudate effects implicate emotion and visual attention functions

Participants who scored higher than others on the PLS showed greater activation in the right antero-medial caudate body. This region is rich in limbic-associated membrane protein, calbindin, and medial cortical afferents, each of which is associated with higher-order cognitive and emotional functions (Parent et al. 1995). The specific region where activation correlated with the PLS in our study was activated during anticipation of a monetary reward (Knutson et al. 2001), reward-based stochastic learning (Haruno et al. 2004), attention (Zink et al. 2003), a spatial and temporal somatosensory discrimination task (Pastor et al. 2004), and simple passive visual processing (Bleicher et al. 2003). Recently, this same right-sided region showed increased dopamine release for a predictable money reward (Zald et al. 2004). In addition, our countback task (when compared with the neutral stimulus) activated this region. Thus this area of the antero-medial body of the caudate is most likely associated with rewarding, visual, and perhaps attentional aspects of romantic love. Intense, focused attention on an individual is one of the cardinal behavioral signs of romantic love (Fisher 1998; Hatfield and Sprecher 1986). Visual attention, reward, motivation, and motor planning are often related to caudate function (Dagher et al. 1999; Hikosaka et al. 2000; Lauwereyns et al. 2002; Zink et al. 2003). In addition, there is one report of electrical stimulation of the caudate through a chronically indwelling electrode in a human epilepsy patient (Delgado et al. 1973). The patient’s words expressing love toward the investigator were stimulus-bound (J. Delgado and E. E. Coons, personal communication), providing evidence that caudate activation could be causally related to feelings of romantic love.

Months in love: cortical and subcortical effects

One of the most interesting findings of this study is regional effects related to the number of months in love. Notably, several limbic cortical regions showed a correlation with the length of the relationship: anterior and posterior cingulate, mid-insula, and retrosplenial cortex; but also, parietal, inferior frontal, and middle temporal cortex. One small region of the left ventral putamen and pallidum was associated with time in love. The ventral pallidum has been implicated in attachment in prairie voles (Lim and Young 2004; Lim et al. 2004). In their fMRI study of longer-term romantic love, Bartels and Zeki (2000) found activation in the anterior cingulate and mid-insula. Thus we confirm the results of Bartels and Zeki (2000) that these brain regions are involved; but in addition, our results suggest that the activation is dependent on time factors. The time-related activations may be related to memory, familiarity, motivation, and attention functions (Velanova et al. 2003; Yamasaki et al. 2002) or an emotional internal state factor such as heart rate (Critchley et al. 2003; Porro et al. 2003). The correlation in the anterior cingulate is notable because it is implicated in a cardinal trait of romantic love: obsessive thinking; it is also involved in cognition and emotion (Bush et al. 2000; Rauch et al. 2001; Ursu et al. 2003). The right retrosplenial cortex correlation with length of relationship is of special interest because metabolic activity there increased during satiation for chocolate (Small et al. 2001) and was correlated with level of thirst (Denton et al. 1999). In any case, these results highlight the importance of these cortical regions for processing stimulus/internal state change, and the importance of taking time factors into account in future studies of human relationships. At the same time, these results must be interpreted cautiously because they are cross-sectional, so that, for example, it is possible they represent differences in the kinds of people that remain intensely in love over a longer period rather than changes over time.

Evolution of romantic love and its distinction from the sex drive

Studies of prairie voles show that D2 dopamine and oxytocin receptor stimulation in the nucleus accumbens is associated with mate preference in females (Gingrich et al. 2000; Liu and Wang 2003), and recent studies of male voles focus on the ventral striatum/pallidum and the distribution of vasopressin and oxytocin receptors; however, oxytocin receptors are found throughout the striatum as well as in the accumbens in both males and females (Lim and Young 2004; Lim et al. 2004). A comparison with our findings leads us to speculate about the evolution of romantic love: with the development of the human cerebral cortex, ancestral hominids employed the phylogenetically newer cortex and dorsal caudate to initiate partner preference. Romantic love may be a developed form of a general mammalian courtship system, which evolved to stimulate mate choice, thereby conserving courtship time and energy (Fisher 1998). Also, previous fMRI studies of human sexual arousal show regional activation largely different from the pattern we saw for our participants (Arnow et al. 2002; Redoute et al. 2000), consistent with romantic love being distinct from the sex drive (Aron and Aron 1991; Fisher 1998).

Comparison with a study of longer-term romantic love

Bartels and Zeki (2000) reported findings of an fMRI study on the neural correlates of romantic love and reanalyzed their data in relation to another study they carried out on maternal love (Bartels and Zeki 2004). As previously stated, their participants were in love longer and were not as intensely in love as in this study [28.8 vs. 7.3 mo; t(32) = 4.28, P <> scores of 7.55 vs. 8.54; t(31) = 3.91, P <> used 17 participants and a photograph of the beloved as the positive stimulus. In this study, we used a familiar acquaintance as a control, whereas Bartels and Zeki used photographs of friends. Also, we used a distraction task that served as a second control condition, the countback task. Many of the basic results are remarkably similar. The ventral midbrain region/VTA and dorsal caudate nucleus were activated by romantic love in both studies (Bartels and Zeki 2000, 2004); the amygdaloid region was deactivated in both studies; the mid-insula, anterior, and posterior cingulate were affected in both studies, but in this study, activation in the cortex was found to be correlated with relationship length. The fact that all of the above regions were affected in both studies strongly suggests that they are involved in romantic love in important ways and that reward and motivation systems are critical.

In addition, there were differences between the two studies for the positive-minus-neutral contrast. Among them, this study showed effects in several regions of the caudate, in the septum, and the retrosplenial cortex, and no effect in the dorsal hippocampus or putamen, as was seen by Bartels and Zeki (2000). These differences may be caused by the difference between early-stage and longer-term romantic love that could not be assessed with the limited time range in our study, but could also be caused by individual characteristic responses that differed between the two samples or by the differences in experimental methods.

Technical considerations and assumptions

The BOLD responses that we measured were relatively sustained during the presentation of the stimuli and also limited by the hemodynamic response function model that we used. If we had employed other models of response, we might have detected other areas that could be involved. For example, Moritz et al. (2000) reported that caudate-putamen BOLD responses to finger-tapping had a short duration, quite different from the cortex. Also, we base our interpretation of the data on the assumption that a BOLD response largely reflects axon terminal activity and field potentials rather than cell body activity, although the two can be correlated (Arthurs et al. 2004; Logothetis et al. 2001; Mata et al. 1980; Sokoloff 1999, 1993). In an animal study using metabolic mapping of natural somatosensory corticostriate activity, increased metabolism was associated with corticostriate axon terminal fields (Brown et al. 2002). Thus we interpret the measured activation in the caudate to be largely the result of afferent activity from the cortex, VTA, and intrinsic caudate cell axon collaterals. Likewise, the BOLD response in the VTA may reflect afferent activity from the caudate or accumbens or other region, not necessarily activity of local cells.

The BOLD response reflects venous blood flow and volume (e.g., Arthurs and Boniface 2002). Thus activation from the medial caudate and tail that appeared to be in or lining the ventricle may be a signal change in the large draining veins on the surface of the caudate that forms the ventricular borders (Netter 1983). Such a signal still reflects nearby parenchymal activity, however. Other studies have seen lateral "ventricular activation" that follows the curved surface of the caudate (Bleicher et al. 2003; Haruno et al. 2004). We ascribe this activation to the caudate because we expect activity there based on known cortical projection patterns, the activation is localized and on one side, the caudate protrudes into the ventricle and could produce a partial volume effect, and ventricular activation that might be caused by an artifact such as movement is not seen in other ventricular regions.

The 30-s trial period to look at faces was relatively unconstrained, although instructions were given. Also, differences between the positive and neutral conditions may have been caused by different eye movements or different habituation effects. If number of eye movements had been a major factor, we expect that the frontal eye fields would have shown an effect. It is possible that a difference in habituation is an inextricable factor.

In conclusion, the results lead us to suggest that early-stage, intense romantic love is associated with reward and goal representation regions, and that rather than being a specific emotion, romantic love is better characterized as a motivation or goal-oriented state that leads to various specific emotions such as euphoria or anxiety. With this new view of romantic love as a motivation state, it becomes clearer why the lover expresses an imperative to be with a preferred individual (the beloved) and to protect the relationship. Moreover, our results suggest to us that romantic love does not use a functionally specialized brain system. Romantic love may be produced, instead, by a constellation of neural systems that converge onto widespread regions of the caudate where there is a flexible combinatorial map representing motivating stimuli and memories dependent on the individual and the context (Brown 1992; Brown et al. 1998; Lidsky and Brown 1999). As such, it would be an example of how a complex human behavioral state that includes emotions is processed. Taken together, our results and those of Bartels and Zeki (2000, 2004) with longer-term in love participants show similar cortical, VTA, and caudate localization, suggesting that these regions are consistently and critically involved in this aspect of human reproduction and social behavior, romantic love. Further experiments will be needed to determine whether a circumscribed caudate region and specific afferents are necessary to the experience and behaviors of romantic love. Importantly, we found potential regional heterogeneity for different aspects of reward in the VTA and identified some cortical regions whose neural activation was different for individuals who had been in love over a time scale of months or who showed affect trait differences.