House of Mind

"Biology gives you a brain. Life turns it into a mind" - Jeffrey Eugenides

  • 27th September
    2011
  • 27

This is your brain on stress and city living

Although city life offers many advantages and even some health benefits, meta-analyses indicate that city living is a substantial risk factor for mood and anxiety disorders. Basically, people who live in cities have a higher incidence for these disorders. Also, genetically predisposed individuals are at an even greater risk if they are brought up in cities. In schizophrenia, for example, the incidence is nearly doubled in subjects that were born, raised and currently lived in the city. And let’s not forget that, usually, with city life comes a more stressful social environment, a factor known to exacerbate many psychiatric disorders, particularly the ones mentioned above. 

So how is it that being from/living in a certain place can affect how your brain works? 

In order to understand this question, Lederborgen et al (2011) used functional magnetic resonance imaging (fMRI) to study the neural responses of subjects taking a social stressor task that consisted of solving math problems under time pressure while also receiving negative feedback from the experimenter. The subjects differed in terms of their living conditions, as they were from urban (+100,000 people), town (+10,000) or rural areas.

The task was an effective stressor as it successfully induced stress, indexed by increases in heart rate, blood pressure, and salivary cortisol (stress hormone) levels. In addition, there was significant activity in brain areas implicated in the stress response, emotion, and social behavior. Of these, 2 major areas exhibited the most robust changes: 

  • Amygdala: Current city living was associated with increased amygdala activity. Activation positively correlated with the size of the city that the individual currently lived in, with city dwellers having the highest levels of amygdala activation.
  • Anterior cingulate cortex: Activation correlated with the upbringing (or how long) a person had lived in a city. Individuals that were entirely brought up in cities showed the greatest perigenual anterior cingulate cortex (pACC) activation. This region is important due to its role in the regulation of amygdala activity during negative affect and stress. 

Moreover, the authors show evidence suggesting that there is reduced functional connectivity between the amygdala and specifically, the perigenual anterior cingulate cortex of those participants that were born and raised in cities. Considering that weakened coupling of these areas has also been linked to genetic risk for psychiatric disorders, these findings have important clinical relevance. Now let’s stretch our thinking- with urbanization increasingly becoming the way of life and the very real risk of overcrowding, what does this mean for brain development?

The authors state that the results were not explained by demographic/clinical factors or a number of other variables. They have also been able to replicate their findings in a larger and better distributed sample. However, they recognize that limitations of their work include that their study was purely correlational and they discuss the need for a larger scale study that has ways of identifying and measuring more variables that may be related to city living. 

For those of you that live (or were brought up) in cities, cheer up. There are a variety of reasons for choosing to live (and enjoy) the city life. In a way, the city has its way of forcing you into developing coping strategies- which is a good thing, right? Now here’s something to think about: psychologists have even found that one of the factors accounting for the preference of city living is the degree of control that people have (and feel they have) over their lives. 

Sources: 

Kennedy, DP & Adolphs R. 2011. Stress and the city. Comment on: Nature. 474: 452-3. doi: 10.1038/474452a

Lederborgen et al. 2011. City living and urban upbringing affect neural social stress processing in humans. Nature. 474: 498-500. oi: 10.1038/nature10190

  • 29th June
    2011
  • 29
To hoard or not to hoard… But first, let’s define the term hoarding… According to An et. al (2009), compulsive hoarding is both the acquisition of and the inability to discard a large number of possessions that appear to be useless and have no value. As some of you may expect, compulsive hoarding is present in many cases of OCD, and has been usually been studied in that clinical context.  Throughout the years, neuroimaging studies have linked hoarding symptoms in OCD patients with multiple brain areas. Mataix-Cols et. al (2004) found distinct neural correlates for the different symptoms present in OCD patients such as washing, checking and hoarding. Importantly, the study found that hoarding behaviors were most significantly correlated with right orbitofrontal cortex and left precentral gyrus activation.  Other brain areas implicated in hoarding behaviors in OCD patients include the right sensorimotor cortex and the fusiform gyrus. Additionally, lesions studies of human ventromedial prefrontal cortex have resulted in the emergence of hoarding behaviors that were not present before, which combined with other findings in patients with hoarding symptoms, has led to the hypothesis of ventromedial prefrontal cortex involvement in compulsive hoarding. In order to determine the neural correlates of hoarding symptoms in OCD patients, An and colleagues recruited three subject groups: a group w/ OCD and strong hoarding symptoms, a group w/ OCD but no significant hoarding symptoms and a group of healthy controls. The image above is part of the results of this study, and shows the brain regions significantly more activated in hoarders than in non-hoarders and controls (shown in red in (a) and (b), and in healthy controls more than in hoarders and non-hoarders (shown in blue in (b) and (c) during hoarding symptom provocation. The box plots depict the percent change in blood oxygen level dependent (BOLD) responses for each group.   All three groups showed increased activation in widespread areas of the brain such as: Ventral, paralimbic, and dorsal prefrontal brain regions (including bilateral visual regions) Ventrolateral, dorsolateral, and dorsomedial prefrontal regions Cerebellum Anterior insula Temporal cortex However, some areas were only activated in specific groups. For example, only hoarders activated large clusters in the frontal pole (including a large bilateral cluster in the anterior vmPFC), extending ventrally to the anterior part of the orbitofrontal cortex and dorsally to the medial frontal gyrus. Specifically, this vmPFC cluster is spatially situated anterior to areas implicated in the decision-making circuit. The non-hoarding OCD group significantly activated the caudate putamen while the controls showed activation in the striatum, left thalamus and the vmPFC (including the orbitofrontal cortex and the anterior cingulate cortex). Note: The study also emphasized on the role of anxiety in compulsive hoarding symptoms and related brain activation. They suggest that OCD patients with compulsive hoarding symptoms have higher anxiety levels (provoked by the experimental simulation task of “choosing” which objects to discard) that correlate with greater activation in the vmPFC and other temporal lobe areas like the uncus, parahippocampal gyrus, hippocampus, amygdala, and more. Mataix-Cols et. al have also found an effect of anxiety on brain activation during a similar task. Sources: An, SK, et. al. 2009. To discard or not to discard: the neural basis of hoarding symptoms in obsessive-compulsive disorder. Molecular Psychiatry. 14 (3): 318-31. doi:10.1038/sj.mp.4002129 Mataix-Cols, D. et al. 2004. Distinct neural correlates of washing, checking, and hoarding symptom dimensions in obsessive-compulsive disorder. Arch Gen Psychiatry. 61 (6): 564-576. 

To hoard or not to hoard…

But first, let’s define the term hoarding… According to An et. al (2009), compulsive hoarding is both the acquisition of and the inability to discard a large number of possessions that appear to be useless and have no value. As some of you may expect, compulsive hoarding is present in many cases of OCD, and has been usually been studied in that clinical context. 

Throughout the years, neuroimaging studies have linked hoarding symptoms in OCD patients with multiple brain areas. Mataix-Cols et. al (2004) found distinct neural correlates for the different symptoms present in OCD patients such as washing, checking and hoarding. Importantly, the study found that hoarding behaviors were most significantly correlated with right orbitofrontal cortex and left precentral gyrus activation.  Other brain areas implicated in hoarding behaviors in OCD patients include the right sensorimotor cortex and the fusiform gyrus. Additionally, lesions studies of human ventromedial prefrontal cortex have resulted in the emergence of hoarding behaviors that were not present before, which combined with other findings in patients with hoarding symptoms, has led to the hypothesis of ventromedial prefrontal cortex involvement in compulsive hoarding.

In order to determine the neural correlates of hoarding symptoms in OCD patients, An and colleagues recruited three subject groups: a group w/ OCD and strong hoarding symptoms, a group w/ OCD but no significant hoarding symptoms and a group of healthy controls. The image above is part of the results of this study, and shows the brain regions significantly more activated in hoarders than in non-hoarders and controls (shown in red in (a) and (b), and in healthy controls more than in hoarders and non-hoarders (shown in blue in (b) and (c) during hoarding symptom provocation. The box plots depict the percent change in blood oxygen level dependent (BOLD) responses for each group.  

All three groups showed increased activation in widespread areas of the brain such as:

  • Ventral, paralimbic, and dorsal prefrontal brain regions (including bilateral visual regions)
  • Ventrolateral, dorsolateral, and dorsomedial prefrontal regions
  • Cerebellum
  • Anterior insula
  • Temporal cortex

However, some areas were only activated in specific groups. For example, only hoarders activated large clusters in the frontal pole (including a large bilateral cluster in the anterior vmPFC), extending ventrally to the anterior part of the orbitofrontal cortex and dorsally to the medial frontal gyrus. Specifically, this vmPFC cluster is spatially situated anterior to areas implicated in the decision-making circuit. The non-hoarding OCD group significantly activated the caudate putamen while the controls showed activation in the striatum, left thalamus and the vmPFC (including the orbitofrontal cortex and the anterior cingulate cortex).

Note: The study also emphasized on the role of anxiety in compulsive hoarding symptoms and related brain activation. They suggest that OCD patients with compulsive hoarding symptoms have higher anxiety levels (provoked by the experimental simulation task of “choosing” which objects to discard) that correlate with greater activation in the vmPFC and other temporal lobe areas like the uncus, parahippocampal gyrus, hippocampus, amygdala, and more. Mataix-Cols et. al have also found an effect of anxiety on brain activation during a similar task.

Sources:

An, SK, et. al. 2009. To discard or not to discard: the neural basis of hoarding symptoms in obsessive-compulsive disorder. Molecular Psychiatry. 14 (3): 318-31. doi:10.1038/sj.mp.4002129

Mataix-Cols, D. et al. 2004. Distinct neural correlates of washing, checking, and hoarding symptom dimensions in obsessive-compulsive disorder. Arch Gen Psychiatry. 61 (6): 564-576. 

  • 16th June
    2011
  • 16
Dyslexia When most people think of dyslexia, they think reading disability. Early explanations of dyslexia attributed the disorder to the defects in the visual system. With time, subsequent research implicated the language system and related brain structures.Currently, dyslexia is thought to reflect a deficient processing of the most basic linguistic units (phonemes), which are the building blocks of spoken language.  Because words are made up of different combinations of phonemes, the brain must break a word down into its phonetic units before the word can be identified, understood, gain meaning or stored in memory. In spoken language, this process is thought to occur automatically. According to Noam Chomsky and Steve Pinker, language is instinctive and pretermined, which means that humans are prone towards learning and using language (see Universal Grammar Theory). In this phonological model of language, the speaker automatically assembles phonemes into words with meaning via the human speech apparatus: larynx, palate, tongue and lips. Thus, the merging of phonemes results in a unit of sound that has meaning.  Although speaking is thought to occur naturally and automatically, reading is not.In contrast to speaking, reading requires constructing invented sounds and must occur at conscious level. So what do you do when you read? Reading is basically transforming visual information into sound information by “recoding” graphemes (written letters) into phonemes. Thus, in order for a reader to understand the written words in a page, he or she must first have a conscious awareness that the letters on the page represent the sounds of spoken words as well as the structure of these words and their sequence in the page, all of which confer meaning. According to the phonological deficit hypothesis, when a child is dyslexic, a deficit at the phonological level of the language system hinders the child’s ability to separate the written words into its basic phonological components. With the development of functional magnetic resonance imaging (fMRI), scientists have been able to delve into the neurobiology of reading. Areas found to be relevant to reading and related functions include: Extrastriate cortex (in the occipital lobe): involved in the identification of letters.  Inferior frontal gyrus: involved in phonological processing. This structure’s role in phonological processing is very exciting because gender differences have been found: men primarily show unilateral activation whereas women show bilateral activation.  Middle/superior temporal gyri: implicated in meaning.  Neuroimaging in dyslexics have also found differences in other brain structures including but not limited to: Reduced size in temporo-parietal language regions Increased size in corpus callosum (splenium and isthmus) Abnormal maturation of cortical brain areas Broca’s area (left inferior frontal cortex) Weaker connectivity between anterior and posterior language regions Additionally, newer studies are suggesting possible involvement of cerebellar regions in dyslexia. So what do you guys think? Is dyslexia really a reading disability, or can it be considered a learning disorder?  References: Heim, S & Andreas Keil. 2004.Large-scale neural correlates of developmental dyslexia.European Child and Adolescent Psychiatry. 12: 125-140. DOI 10.1007/s00787-004-0361-7 Shaywitz, Sally E. 1996. Dyslexia. Scientific American.

Dyslexia

When most people think of dyslexia, they think reading disability. Early explanations of dyslexia attributed the disorder to the defects in the visual system. With time, subsequent research implicated the language system and related brain structures.Currently, dyslexia is thought to reflect a deficient processing of the most basic linguistic units (phonemes), which are the building blocks of spoken language. 

Because words are made up of different combinations of phonemes, the brain must break a word down into its phonetic units before the word can be identified, understood, gain meaning or stored in memory. In spoken language, this process is thought to occur automatically. According to Noam Chomsky and Steve Pinker, language is instinctive and pretermined, which means that humans are prone towards learning and using language (see Universal Grammar Theory). In this phonological model of language, the speaker automatically assembles phonemes into words with meaning via the human speech apparatus: larynx, palate, tongue and lips. Thus, the merging of phonemes results in a unit of sound that has meaning. 

Although speaking is thought to occur naturally and automatically, reading is not.In contrast to speaking, reading requires constructing invented sounds and must occur at conscious level. So what do you do when you read? Reading is basically transforming visual information into sound information by “recoding” graphemes (written letters) into phonemes. Thus, in order for a reader to understand the written words in a page, he or she must first have a conscious awareness that the letters on the page represent the sounds of spoken words as well as the structure of these words and their sequence in the page, all of which confer meaning. According to the phonological deficit hypothesis, when a child is dyslexic, a deficit at the phonological level of the language system hinders the child’s ability to separate the written words into its basic phonological components.

With the development of functional magnetic resonance imaging (fMRI), scientists have been able to delve into the neurobiology of reading. Areas found to be relevant to reading and related functions include:

  • Extrastriate cortex (in the occipital lobe): involved in the identification of letters. 
  • Inferior frontal gyrus: involved in phonological processing. This structure’s role in phonological processing is very exciting because gender differences have been found: men primarily show unilateral activation whereas women show bilateral activation. 
  • Middle/superior temporal gyri: implicated in meaning. 

Neuroimaging in dyslexics have also found differences in other brain structures including but not limited to:

  • Reduced size in temporo-parietal language regions
  • Increased size in corpus callosum (splenium and isthmus)
  • Abnormal maturation of cortical brain areas
  • Broca’s area (left inferior frontal cortex)
  • Weaker connectivity between anterior and posterior language regions

Additionally, newer studies are suggesting possible involvement of cerebellar regions in dyslexia.

So what do you guys think? Is dyslexia really a reading disability, or can it be considered a learning disorder? 

References:

Heim, S & Andreas Keil. 2004.Large-scale neural correlates of developmental dyslexia.European Child and Adolescent Psychiatry. 12: 125-140. DOI 10.1007/s00787-004-0361-7

Shaywitz, Sally E. 1996. Dyslexia. Scientific American.

  • 27th May
    2011
  • 27
Attention-Deficit/Hyperactivity Disorder In the US, ADHD has a prevalence of around 5-12% during childhood. Approx. 5 million children and adolescents in the US have ADHD, while only 2 million are being treated (mostly through psychostimulants). ADHD has long been characterized by its well-known pervasive behavioral symptoms: hyperactivity, impulsivity and inattention, which begin in childhood. There are 2 types of ADHD according to the DSM-IV: Hyperactive/Impulsive ADHD and Inattention ADHD. Hyperactive/Impulsive ADHD Symptoms: ADHD 1 Fidgeting/squirming Inability to remain seated Restless Loud and noisy (difficulty playing quietly) Excessive talking Impulsive Intrusive  “Always on the go” Inattention Symptoms: ADHD2 Careless errors Inattention to detail Sustains attention poorly Appears to not be listening Disorganized Trouble following through with directions/obligations Loses needed objects Dislikes sustained mental effort Easily distracted  Forgetful In order for criteria to be met, +6 of the symptoms mentioned above (according to ADHD type) need to be present for 6 months or more and cause impairments in more than 1 setting (social, academic, occupational). These symptoms must also not be attributable to any other condition (i.e. depression, anxiety, substance use, etc)  and can cause impairment in children by the age of 7. Other characteristics that are important in the understanding and diagnosis of ADHD patients include: age, sex, comorbidity with other psychiatric disorders, intelligence, prematurity, exposure to toxins during early life, locomotor hyperactivity, differences in delay aversion, reward salience, motor inhibition tasks, error processing and working memory compared to controls. Moreover, ADHD is a disorder that’s characterized by high intra-subject variability (which is thought to be mediated by competition among functional neural networks).  Although the underlying cause of ADHD is currently unknown, there is a belief that both genetic (mostly dopaminergic and noradrenergic genes) and environmental factors (i.e. parental smoking, brain injury) play a role in ADHD. Moreover, some have suggested that gene-environment interactions account for about 70-80% of ADHD cases.  ADHD has strongly been linked to developmental, volumetric and functional differences in several brain structures/areas. For example, brain imaging studies of children with ADHD have found smaller sizes in the corpus callosum, caudate nucleus, and right frontal cortex. Overall, the brains of children with ADHD are significantly smaller and that the brain volume reduction in ADHD is widespread and also affects the cerebrum and cerebellum. Other studies from different disciplines have implicated disruption of the frontostriatal pathway and other circuitry in diverse areas like the prefrontal cortex, the basal ganglia and the cerebellum. Additionally, other studies have found delayed cortical maturation in children with ADHD-meaning that they take longer and are slower to develop compared to normal brains. More recently, disruptions in other brain networks and their relation to ADHD are starting to be explored. The diagram above is taken from Castellanos et. al (2008). Dr. Castellanos is an NYU clinician who employs neuroimaging techniques like fMRI to study differences in brain circuitry and wiring in patients with ADHD. In the ADHD brain, the precuneus (red part towards the posterior end of the brain), which is involved in high-level integration of posterior association processes with anterior executive function, appears to be enlarged. ADHD related differences in brain regions are shown at the right. The authors suggest that functional circuits linking the anterior cingulate cortex to the precuneus and posterior cingulate cortex and their long range connections should be considered as dysfunctional center in the ADHD brain.  Sources: Castellanos et. al. 2008. Cingulate-precuneus interactions: a new locus of dysfunction in adult attention-deficit/hyperactivity disorder. Biological Psychiatry. 63 (3): 332-7.  Castellanos and Tannock. 2002. Neuroscience of attention-deficit/hyperactivity disorder: The search for endophenotypes. Nature Reviews Neuroscience. 3: 617-626.  Castellanos, XF. 2011. The Restless Brain: Spontaneous Brain Fluctuations and Variability in ADHD. Disorders of the Nervous System Lecture.  Kieling et. al. 2008. Neurobiology of attention deficit hyperactivity disorder. Child and Adolescent Psychiatric Clin N America. 17: 285-307. 

Attention-Deficit/Hyperactivity Disorder

In the US, ADHD has a prevalence of around 5-12% during childhood. Approx. 5 million children and adolescents in the US have ADHD, while only 2 million are being treated (mostly through psychostimulants). ADHD has long been characterized by its well-known pervasive behavioral symptoms: hyperactivity, impulsivity and inattention, which begin in childhood. There are 2 types of ADHD according to the DSM-IV: Hyperactive/Impulsive ADHD and Inattention ADHD.

Hyperactive/Impulsive ADHD Symptoms: ADHD 1

  • Fidgeting/squirming
  • Inability to remain seated
  • Restless
  • Loud and noisy (difficulty playing quietly)
  • Excessive talking
  • Impulsive
  • Intrusive 
  • “Always on the go”

Inattention Symptoms: ADHD2

  • Careless errors
  • Inattention to detail
  • Sustains attention poorly
  • Appears to not be listening
  • Disorganized
  • Trouble following through with directions/obligations
  • Loses needed objects
  • Dislikes sustained mental effort
  • Easily distracted 
  • Forgetful

In order for criteria to be met, +6 of the symptoms mentioned above (according to ADHD type) need to be present for 6 months or more and cause impairments in more than 1 setting (social, academic, occupational). These symptoms must also not be attributable to any other condition (i.e. depression, anxiety, substance use, etc)  and can cause impairment in children by the age of 7. Other characteristics that are important in the understanding and diagnosis of ADHD patients include: age, sex, comorbidity with other psychiatric disorders, intelligence, prematurity, exposure to toxins during early life, locomotor hyperactivity, differences in delay aversion, reward salience, motor inhibition tasks, error processing and working memory compared to controls. Moreover, ADHD is a disorder that’s characterized by high intra-subject variability (which is thought to be mediated by competition among functional neural networks). 

Although the underlying cause of ADHD is currently unknown, there is a belief that both genetic (mostly dopaminergic and noradrenergic genes) and environmental factors (i.e. parental smoking, brain injury) play a role in ADHD. Moreover, some have suggested that gene-environment interactions account for about 70-80% of ADHD cases. 

ADHD has strongly been linked to developmental, volumetric and functional differences in several brain structures/areas. For example, brain imaging studies of children with ADHD have found smaller sizes in the corpus callosum, caudate nucleus, and right frontal cortex. Overall, the brains of children with ADHD are significantly smaller and that the brain volume reduction in ADHD is widespread and also affects the cerebrum and cerebellum. Other studies from different disciplines have implicated disruption of the frontostriatal pathway and other circuitry in diverse areas like the prefrontal cortex, the basal ganglia and the cerebellum. Additionally, other studies have found delayed cortical maturation in children with ADHD-meaning that they take longer and are slower to develop compared to normal brains.

More recently, disruptions in other brain networks and their relation to ADHD are starting to be explored. The diagram above is taken from Castellanos et. al (2008). Dr. Castellanos is an NYU clinician who employs neuroimaging techniques like fMRI to study differences in brain circuitry and wiring in patients with ADHD. In the ADHD brain, the precuneus (red part towards the posterior end of the brain), which is involved in high-level integration of posterior association processes with anterior executive function, appears to be enlarged. ADHD related differences in brain regions are shown at the right. The authors suggest that functional circuits linking the anterior cingulate cortex to the precuneus and posterior cingulate cortex and their long range connections should be considered as dysfunctional center in the ADHD brain. 

Sources:

Castellanos et. al. 2008. Cingulate-precuneus interactions: a new locus of dysfunction in adult attention-deficit/hyperactivity disorder. Biological Psychiatry. 63 (3): 332-7. 

Castellanos and Tannock. 2002. Neuroscience of attention-deficit/hyperactivity disorder: The search for endophenotypes. Nature Reviews Neuroscience. 3: 617-626. 

Castellanos, XF. 2011. The Restless Brain: Spontaneous Brain Fluctuations and Variability in ADHD. Disorders of the Nervous System Lecture. 

Kieling et. al. 2008. Neurobiology of attention deficit hyperactivity disorder. Child and Adolescent Psychiatric Clin N America. 17: 285-307. 

  • 3rd May
    2011
  • 03
Anatomically Distinct Dopamine Release During Anticipation and Experience of Peak Emotion to Music Music has long been recognized as both an abstract and rewarding stimulus that produces feelings of euphoria and pleasure in many listeners.  Music may also elicit emotional responses from listeners and alter affective states. While music has been present across multiple cultures and societies throughout time, the experience of pleasure while listening to music is highly specific, personal and subjective. In a study featured in Nature Neuroscience last February, Salimpoor and others set out to study what goes on in the brain of individuals while they listened to enjoyable/pleasurable music.  For the study, subjects were asked to bring their own pleasurable music, and the other subjects’ music was used as neutral music for comparison. Dopamine release while listening to music was estimated indirectly by using ligand-based positron emission tomography (PET) scan in which 11C raclopride, a radioactively labeled ligand, competes with endogenous dopamine for D2 receptor binding. The assumption is that if brain areas are experiencing surges of dopamine release, they binding capacity of 11C raclopride will decrease in these areas. The experience of feeling chills, a marker of peak emotional responses to music, was self-reported by the subjects. In addition, psychophysiological measurements (i.e. respiration rate, heart rate, skin conductance, temperature) were also conducted while the subjects listened to music while undergoing PET scanning.  PET scanning revealed changes in 11C raclopride binding in the striatum, specifically in the right caudate and the right nucleus accumbens. There was also a significant positive correlation between reports of chills and feelings of overall pleasure, perhaps indicating that chills may serve as an objective measure of pleasure while listening to music. The experience of overall greater pleasure while music listening was also correlated with greater autonomic nervous system arousal, as indexed by changes in psychophysiological measurements.  To assess the temporal dynamics in dopamine release, the group employed functional magnetic resonance imaging (fMRI) while subjects listened to neutral or pleasurable music. Subjects were asked to press a button whenever they felt chills (typically during pleasurable moments), and the 15s prior to the pressing of the button, which indicated chills + pleasure, were denoted as the anticipation window. Thus, dopamine release was studied in two different time periods: anticipation period (15s before reported pleasure and chills), and peak response (chills/pleasure). When the fMRI scans were conjoined with the PET masks, the group was able to identify a temporally mediated BOLD response in the right side of dorsal (caudate) and ventral (nucleus accumbens) striatum that corresponded with anticipation epochs and peak experience, respectively. Moreover, as demonstrated above, behavioral measures like the number of reported chills were more correlated with 11C raclopride binding changes in the right caudate while intensity of chills and overall degree of reported pleasure were more significantly correlated with changes in 11C raclopride binding potential in the right nucleus accumbens.  In summary, the experience of pleasure while listening to music acts on the brain similarly to other rewards like food, sex and drugs. Listening to pleasurable music targets striatal areas associated with mesolimbic reward circuitry and dopaminergic neurotransmission.  Source: Salimpoor, et al. 2011. Anatomically Distinct Dopamine Release During Anticipation and Experience of Peak Emotion to Music. Nature Neuroscience. doi:10.1038/nn.2726

Anatomically Distinct Dopamine Release During Anticipation and Experience of Peak Emotion to Music

Music has long been recognized as both an abstract and rewarding stimulus that produces feelings of euphoria and pleasure in many listeners.  Music may also elicit emotional responses from listeners and alter affective states. While music has been present across multiple cultures and societies throughout time, the experience of pleasure while listening to music is highly specific, personal and subjective. In a study featured in Nature Neuroscience last February, Salimpoor and others set out to study what goes on in the brain of individuals while they listened to enjoyable/pleasurable music. 

For the study, subjects were asked to bring their own pleasurable music, and the other subjects’ music was used as neutral music for comparison. Dopamine release while listening to music was estimated indirectly by using ligand-based positron emission tomography (PET) scan in which 11C raclopride, a radioactively labeled ligand, competes with endogenous dopamine for D2 receptor binding. The assumption is that if brain areas are experiencing surges of dopamine release, they binding capacity of 11C raclopride will decrease in these areas. The experience of feeling chills, a marker of peak emotional responses to music, was self-reported by the subjects. In addition, psychophysiological measurements (i.e. respiration rate, heart rate, skin conductance, temperature) were also conducted while the subjects listened to music while undergoing PET scanning. 

PET scanning revealed changes in 11C raclopride binding in the striatum, specifically in the right caudate and the right nucleus accumbens. There was also a significant positive correlation between reports of chills and feelings of overall pleasure, perhaps indicating that chills may serve as an objective measure of pleasure while listening to music. The experience of overall greater pleasure while music listening was also correlated with greater autonomic nervous system arousal, as indexed by changes in psychophysiological measurements. 

To assess the temporal dynamics in dopamine release, the group employed functional magnetic resonance imaging (fMRI) while subjects listened to neutral or pleasurable music. Subjects were asked to press a button whenever they felt chills (typically during pleasurable moments), and the 15s prior to the pressing of the button, which indicated chills + pleasure, were denoted as the anticipation window. Thus, dopamine release was studied in two different time periods: anticipation period (15s before reported pleasure and chills), and peak response (chills/pleasure).

When the fMRI scans were conjoined with the PET masks, the group was able to identify a temporally mediated BOLD response in the right side of dorsal (caudate) and ventral (nucleus accumbens) striatum that corresponded with anticipation epochs and peak experience, respectively. Moreover, as demonstrated above, behavioral measures like the number of reported chills were more correlated with 11C raclopride binding changes in the right caudate while intensity of chills and overall degree of reported pleasure were more significantly correlated with changes in 11C raclopride binding potential in the right nucleus accumbens. 

In summary, the experience of pleasure while listening to music acts on the brain similarly to other rewards like food, sex and drugs. Listening to pleasurable music targets striatal areas associated with mesolimbic reward circuitry and dopaminergic neurotransmission. 

Source:

Salimpoor, et al. 2011. Anatomically Distinct Dopamine Release During Anticipation and Experience of Peak Emotion to Music. Nature Neuroscience. doi:10.1038/nn.2726


  • 23rd November
    2010
  • 23

Want vs. Should: Neural and behavioral effects of affect and cognition on decisions about material goods (Preston)

What brain areas are involved in deciding what we want to keep, what we can discard, what has meaning for us, and what has monetary value? 

A study carried out by Preston set out to explore the neuroanatomical substrates for these decisions by performing an fMRI task in which subjects made force-decisions between everyday goods. 

Before that, let’s familiarize ourselves with the term acquisitiveness, which is characterized by a strong desire to possess, gain or retain. Acquisitiveness is a trait that may be beneficial for heatlh, quality of life, and the environment. 

Findings

Personal decisions: According to Preston, “personal decisions activate the self-referential, default midline systems more while decisions for financial benefit activate more lateral, cognitive control regions associated with comparing and calculating the value of items.” Because these decisions are of an intuitive and affective nature, they engage affective and self-relevant regions like the orbitofrontal cortex, right angular gyrus, right middle temporal gyrus (MTG) and others. These decision are marked by increased activity in the medial prefrontal cortex (mPFC) and the anterior cingulate cortex (ACC), a self-processing area. 

Monetary decisions: are controlled and economical. They involve the right insula (value-judgement area), the dorsolateral prefrontal cortex (dLPFC), an evaluative area, and Broca’s area (analysis and planning).

Acquisition decisions: act like a hedonic (pleasure) signal are marked by an increase in desirability and engage the medial orbitofrontal cortex (mOFC) and nucleus accumbens (reward area) engagement. Decisions in which reward may be acquired are also related to associated with mesolimbocortical (MLC) regions. 

Discarding decisions: have elements of additional control and evaluation. Individuals usually think in utilitarian terms to establish preference and possessiveness has been shown to increase an object’s desirability. These are taken in basis of monetary value and involves the insula and the ACC.

  • All decision types (personal, monetary, acquire, discard) engage the mPFC. 
  • Acquisitiveness involves both acquisition and failure to discard.
  • Acquisitiveness entails incentive salience (motivational, wanting attribute due to the brain’s prediction for reward) for even mundane, utilitarian items. 
  • Hoarding behaviors are associated with the OFC.