Corticostriatal interactions give rise to musical expectancy

Building up expectations of listeners and then either fulfilling or violating those expectations is one of the most entertaining aspects of music. For cognitive neuroscientists, this phenomenon presents with a highly interesting research question: how does the brain code musical sequences, such as those in Western classical music, in order to be able to build up harmonic expectations and thus also detect violations of harmonic expectations? Recent neuroimaging research has tentatively suggested that the motor system is involved (instead of only auditory cortical areas) in coding longer musical sequences, but there have been relatively few studies that have attempted to pinpoint the brain structures (as well as interactions between them) that support perception of harmonic expectancies.

In their recent study, Seger et al. (2013) presented 10-24 sec short Western classical melodies to 11 healthy non-musician participants during 3-Tesla functional magnetic resonance imaging. The musical pieces were manipulated so that the degree of expectancy violation varied at four steps from fulfilling of the expectancy to minor, intermediate, and large violations of the harmonic expectancy. The results disclosed multiple brain regions that respond to harmonic violations, including basal ganglia, inferior frontal gyrus, and anterior superior temporal gyrus. Granger causality mapping further revealed connectivity between the basal ganglia, inferior frontal gyrus, anterior and posterior superior temporal gyrus during music perception.

These highly exciting results shed light on the brain structures supporting harmonic expectancy. It appears that basal ganglia and the interactions of basal ganglia with inferior frontal gyrus and anterior superior temporal gyrus support the building up of harmonic expectations and perception of the violation of such expectations. Notably, instead of simplified tonal sequences, the authors utilized unaltered short pieces of music from the works of classical composers (e.g., Bach, Beethoven), thus significantly increasing the ecological validity of their findings.

Reference: Seger CA, Spiering BJ, Sares AG, Quraini SI, Alpeter C, David J, Thaut MH. Corticostriatal contributions to musical expectancy perception. Journal of Cognitive Neuroscience (2013) e-publication ahead of print. http://dx.doi.org/10.1162/jocn_a_00371

Characterizing functional fingerprint patterns of cortical areas based on thousands of datasets in BrainMap database

When browsing through cognitive neuroimaging literature, what often puzzles the beginner (and sometimes also the more advanced) cognitive neuroscientist is the relatively large number of different types of tasks and stimuli that are associated with hemodynamic/neural activity of a given cortical region. This apparent multidimensionality of functional anatomy raises the question of which cognitive functions a given brain area is most pivotal for. Databases storing neuroimaging data and results makes it increasingly possible to inspect the role(s) that a given cortical area plays in perceptual and cognitive functions over a vast number of studies, subjects, and stimulus/task conditions.

In their recent study, Anderson et al. (2013) combined the results of more than 2000 neuroimaging studies stored in the BrainMap database. They then constructed sensitivity profiles of brain regions based on 20 classes of task domains (e.g., execution-action, inhibition-action, happiness-emotion, vision-perception) associated with activity of each structure, as well as with activity of various brain networks (e.g., default mode network). The authors observed that degree of diversity varied considerably across the brain. Similarly, some brain networks consisted of relatively functionally similar areas, whereas other networks consisted of functionally more variable areas.

The study by Anderson et al. (2013) not only demonstrates the usefulness of databases, but also provides important insights into the fingerprint patterns / functional specialization exhibited by cortical areas.  The authors quite correctly point out that inferring functional specialization based on frequency of occurrence in databases are potentially subject to so-called confirmation bias present in literature; results showing activation of amygdala in emotion studies are more probable to get published than for example visual cortex activation during music processing, as the latter finding is a less intuitively obvious. Keeping these limitations pointed out by the authors in mind, the possibilities offered by the vast amounts of data stored in databases such as the BrainMap are offering wonderful opportunities for cognitive neuroscientists, and the study by Anderson et al. (2013) very nicely shows how the heterogeneity of cortical areas and functional networks can be characterized based on analysis of huge amounts of data available in a database.

Reference: Anderson ML, Kinnison J, Pessoa J. Describing functional diversity of brain regions and brain networks (2013) e-publication ahead of print. http://dx.doi.org/10.1016/j.neuroimage.2013.01.071

Contextual cues modulate via dorsolateral prefrontal cortex a network of brain regions underlying enhanced cigarette craving

Drug addictions constitute one of the most substantial societal and medical problems of our time. While early on a lot of effort was put into investigation of the mechanisms underlying drug dependency (i.e., the addicted person not being able to stop taking the drug in fear of withdrawal symptoms), it has become obvious that craving for the drug (i.e., overwhelming desire that drives one to take the drug even if one realizes it is harmful) is the mechanism that sustains the addiction by predisposing afflicted individuals to relapses even after withdrawal symptoms have subsided. Craving plays a key role in development of addiction to legal drugs such as alcohol and tobacco and, although these are not as strong as illicit drugs in their addicting effects, they share the same basic mechanisms in that prolonged use leads to both development of dependency and craving. Craving is further enhanced when the addicted person sees the drug he/she is addicted to, however, there have been few neuroimaging studies mapping brain mechanisms that underlie enhancement of craving by such contextual cues.

In their recent study, Hayashi et al. (2013) ten healthy heavy smokers underwent four fMRI scanning sessions where they were shown alternating 2-min neutral movie clips and 2-min clips depicting smoking. Behavioral ratings of craving increased when watching the smoking clips, especially when the subjects were aware that smoking would be possible immediately after the experiment, and enhanced activity in several cortical areas, most robustly medial orbitofrontal cortex, was associated with these cue-related craving effects.  Inactivation of left dorsolateral prefrontal cortex with transcranial magnetic stimulation for ~30 minutes prior to onset of the neuroimaging session eliminated the effects of knowing that the drug will be available immediately after scanning on the subjective craving, medial orbitofrontal cortex activation, and functional connectivity between dorsolateral prefrontal cortex and medial orbitofrontal cortex, ventral striatum, and anterior cingulate cortex.

These findings nicely demonstrate the role of dorsolateral prefrontal cortex in a network of brain areas that give rise to enhanced cigarette craving by contextual cues. The findings further provide a link between the areas of addiction research and decision making, as the authors successfully model their findings in the context of economic decision making theory where addiction can be viewed as steep temporal discounting (i.e., addicted individuals place greater value on immediate vs. delayed rewards) and shed light on the underlying cerebral mechanisms. This highly exciting study also demonstrates how transcranial magnetic stimulation can be utilized to produce long-lasting (up to 30 min) focal cortical inactivation, the effects of which can then be inspected using functional magnetic resonance imaging and associated behavioral measures.

Reference: Hayashi T, Koa IH, Strafella AP, Daghera A. Dorsolateral prefrontal and orbitofrontal cortex interactions during self-control of cigarette craving. Proceedings of the National Academy of Sciences USA (2013) e-publication ahead of print. http://dx.doi.org/10.1073/pnas.1212185110

Selective attention frequency-specifically enhances processing within the mirror-symmetric tonotopic areas of the human primary auditory cortex

The question of how one is able to select, out of the wealth of all possible ones, the task-relevant stimuli  for more advanced scrutiny is one of the most fundamental research questions in cognitive neuroscience. While a number of studies have stressed that selective attention involves only secondary auditory cortical areas, there are converging lines of evidence suggesting that processing of attended stimulus features could also be enhanced at the level of human primary auditory cortex. There is neuroimaging evidence showing that the mirror-symmetric tonotopic maps of primary auditory cortex can be mapped with ultra high-field functional magnetic resonance imaging, however, localization of sound-frequency specific selective attention effects to specific parts of these tonotopic fields has been  to date lacking.

Da Costa et al. (2013) addressed this question by combining, in human volunteers, 7-Tesla functional magnetic resonance imaging of the tonotopic primary auditory cortex areas with mapping of selective attention effects within these areas. The mirror-symmetric tonotopic maps were first localized on an individual basis in six healthy volunteers using multiple sound frequencies. Subsequently, the subjects were to alternate, once every 30 seconds, their focus of attention between low-frequency 250-Hz and high-frequency 4000-Hz stimulus streams. The authors observed that selective attention to the low-frequency sounds specifically enhanced responses of voxels that were most selective to the 250-Hz sounds and, vice versa, attention to high-frequency sounds specifically enhanced responses in voxels most selectively responded to 4000-Hz sounds.

The findings of this study very nicely demonstrate that selective attention indeed works at the level of primary auditory cortex, by “tuning” processing of stimuli to those occuring within the attended frequency channel, analogously to a radio being tuned on a specific frequency channel. The study also demonstrates how ultra-high field 7-Tesla functional magnetic resonance imaging can be utilized to map, and assess selective attention effects within, even the gradients of the mirror-symmetric tonotopic fields of the human auditory cortex core areas with very high accuracy.

Reference: Da Costa S, van der Zwaag W, Clarke S, Saenz M. Tuning in to sound: frequency-selective attentional filter in human primary auditory cortex. Journal of Neuroscience (2013) 33: 1858-1863. http://dx.doi.org/10.1523/JNEUROSCI.4405-12.2013