The authors acknowledge financial support for this research from Secretari&#769;a de Salud de Me&#769;xico (HIM/2011/058 SSA. 1009), CONACYT (SALUD-2012-01-182160), and DGAPA UNAM (PAPIIT IN109214).

This protocol has been approved by the Research, Ethics, and Safety Committee of the Hospital Infantil de Me&#769;xico &#34;Federico Go&#769;mez&#34;.
1. Behavioral Pretest
<ol>
	<li>Perform a standard, pure-tone audiometric test to confirm that all prospective participants possess normal hearing (20-dB Hearing Level (HL) over octave frequencies of -8,000 Hz). Use the Edinburgh Handedness Inventory31 to ensure that all participants are right-handed.</li>
	<li>...

<ol>
	<li>Burns, E., Deutsch, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Intervals, scales, and tuning. The psychology of music. , 215-264 (1999).</li><li>Lundin, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Toward+a+cultural+theory+of+consonance.">Toward a cultural theory of consonance.</a> J. Psychol. 23, 45-49 (1947).</li><li>Fritz, T., Jentschke, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Universal+recognition+of+three+basic+emotions+in+music.">Universal recognition of three basic emotions in music.</a> Curr. Biol. 19, 573-576 (2009).</li><li>Schellenberg, E. G., Trehub, S. 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Dev. 21 (1), 483-492 (1998).</li><li>Terhardt, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pitch,+consonance,+and+harmony.">Pitch, consonance, and harmony.</a> J. Acoust. Soc. America. 55, 1061 (1974).</li><li>Minati, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+MRI/event-related+potential+study+of+sensory+consonance+and+dissonance+in+musicians+and+nonmusicians.">Functional MRI/event-related potential study of sensory consonance and dissonance in musicians and nonmusicians.</a> Neuroreport. 20, 87-92 (2009).</li><li>Helmholtz, H. L. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> On the sensations of tone. , (1954).</li><li>McDermott, J. H., Lehr, A. J., Oxenham, A. 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This work describes a protocol in which singing is used as a means of studying brain activity during the production of consonant and dissonant intervals. Even though singing provides what is possibly the simplest method for the production of musical intervals22, it does not allow for the production of chords. However, although most physical characterizations of the notion of consonance rely, to some degree, on the superposition of simultaneous notes, a number of studies have shown that intervals c...

Certain combinations of musical pitches are generally acknowledged to be consonant, and they are typically associated with a pleasant sensation. Other combinations are generally referred to as dissonant and are associated with an unpleasant or unresolved feeling1. Although it seems sensible to assume that enculturation and training play some part in the perception of consonance2, it has been recently shown that the differences in perception of consonant and dissonant intervals and chords probably depend less on musical culture than was previously thought3 and may even derive from simple biological bases4,5,6. In order to prevent an ambiguous understanding of the term consonance, Terhardt7 introduced the notion of sensory consonance, as opposed to consonance in a musical context, where harmony, for example, may well influence the response to a given chord or interval. In the present protocol, only isolated, two-note intervals were used&#160;precisely to single out activations solely related to sensory consonance, without interference from context-dependent processing8.
Attempts to characterize consonance through purely physical means began with Helmholtz9, who attributed the perceived roughness associated with dissonant chords to the beating between adjacent frequency components. More recently, however, it has been shown that sensory consonance is not only associated with the absence of roughness, but also with harmonicity, which is to say the alignment of the partials of a given tone or chord with those of an unheard tone of a lower frequency10,11. Behavioral studies confirm that subjective consonance is indeed affected by purely physical parameters, such as frequency distance12,13, but a wider range of studies have conclusively demonstrated that physical phenomena cannot solely account for the differences between perceived consonance and dissonance14,15,16,17. All of these studies, however, report these differences when listening to a variety of intervals or chords. A variety of studies using Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI) have revealed significant differences in the cortical regions that become active when listening to either consonant or dissonant intervals and chords8,18,19,20. The purpose of the present study is to explore the differences in brain activity when producing, rather than listening to, consonant and dissonant intervals.
The study of sensory-motor control during musical production typically involves the use of musical instruments, and very often it then requires the fabrication of instruments modified specifically for their use during neuroimaging21. Singing, however, would seem to provide from the start an appropriate mechanism for the analysis of sensory-motor processes during music production, as the instrument is the human voice itself, and the vocal apparatus does not require any modification in order to be suitable during imaging22. Although the neural mechanisms associated with aspects of singing, such as pitch control23, vocal imitation24, training-induced adaptive changes25, and the integration of external feedback25,26,27,28,29, have been the subject of a number of studies over the past two decades, the neural correlates of singing consonant and dissonant intervals were only recently described30. For this purpose, the current paper describes a behavioral test designed to establish the adequate recognition of consonant and dissonant intervals by participants. This is followed by an fMRI study of participants singing a variety of consonant and dissonant intervals. The fMRI protocol is relatively straightforward, but, as with all MRI research, great care must be taken to correctly set up the experiments. In this case, it is particularly important to minimize head, mouth, and lip movement during singing tasks, making the identification of effects not directly related to the physical act of singing more straightforward. This methodology may be used to investigate the neural mechanisms associated with a variety of activities involving musical production by singing.

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	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:343px;">Achieva 1.5-T magnetic resonance scanner</td>
			<td style="width:297px;">Philips</td>
			<td style="width:119px;">Release 6.4</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:343px;">Audacity</td>
			<td style="width:297px;">Open source</td>
			<td style="width:119px;">2.0.5</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:343px;">Audio interface&#160;</td>
			<td style="width:297px;">Tascam</td>
			<td style="width:119px;">US-144MKII&#160;</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:343px;">Audiometer</td>
			<td style="width:297px;">Br&#252;el &#38; Kjaer</td>
			<td style="width:119px;">Type 1800</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:343px;">E-Prime Professional</td>
			<td style="width:297px;">Psychology Software Tools, Inc.</td>
			<td style="width:119px;">2.0.0.74</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:343px;">Matlab</td>
			<td style="width:297px;">Mathworks</td>
			<td style="width:119px;">R2014A</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:343px;">MRI-Compatible Insert Earphones</td>
			<td style="width:297px;">Sensimetrics</td>
			<td style="width:119px;">S14</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:343px;">Praat</td>
			<td style="width:297px;">Open source</td>
			<td style="width:119px;">5.4.12</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:343px;">Pro audio condenser microphone</td>
			<td style="width:297px;">Shure</td>
			<td style="width:119px;">SM93</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:343px;">SPSS Statistics</td>
			<td style="width:297px;">IBM</td>
			<td style="width:119px;">20</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:343px;">Statistical Parametric Mapping</td>
			<td style="width:297px;">Wellcome Trust Centre for Neuroimaging</td>
			<td style="width:119px;">8</td>
			<td></td>
		</tr>
	</tbody>
</table>

fmri mapping of brain activity associated with the vocal production of consonant and dissonant intervals

The neural correlates of consonance and dissonance perception have been widely studied, but not the neural correlates of consonance and dissonance production. The most straightforward manner of musical production is singing, but, from an imaging perspective, it still presents more challenges than listening because it involves motor activity. The accurate singing of musical intervals requires integration between auditory feedback processing and vocal motor control in order to correctly produce each note. This protocol presents a method that permits the monitoring of neural activations associated with the vocal production of consonant and dissonant intervals. Four musical intervals, two consonant and two dissonant, are used as stimuli, both for an auditory discrimination test and a task that involves first listening to and then reproducing given intervals. Participants, all female vocal students at the conservatory level, were studied using functional Magnetic Resonance Imaging (fMRI) during the performance of the singing task, with the listening task serving as a control condition. In this manner, the activity of both the motor and auditory systems was observed, and a measure of vocal accuracy during the singing task was also obtained. Thus, the protocol can also be used to track activations associated with singing different types of intervals or with singing the required notes more accurately. The results indicate that singing dissonant intervals requires greater participation of the neural mechanisms responsible for the integration of external feedback from the auditory and sensorimotor systems than does singing consonant intervals.

All 11 participants in our experiment were female vocal students at the conservatory level, and they performed well enough in the interval recognition tasks to be selected for scanning. The success rate for the interval identification task was 65.72 &#177;21.67%, which is, as expected, lower than the success rate when identifying dissonant and consonant intervals, which was 74.82 &#177;14.15%.
In order to validate the basic desi...

Watch this Scientific Journal Video about fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals at JoVE.com

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

The neural correlates of listening to consonant and dissonant intervals have been widely studied, but the neural mechanisms associated with production of consonant and dissonant intervals are less well known. In this article, behavioral tests and fMRI are combined with interval identification and singing tasks to describe these mechanisms.

The neural correlates of consonance and dissonance perception have been widely studied, but not the neural correlates of consonance and dissonance ...

The overall goal of this methodology is to monitor neural activations associated with the vocal production of consonant and dissonant intervals, through the use of functional magnetic resonance imaging. This method can help answer key questions in the field of auditory cognitive neuroscience, such as, how does integration of external feedback from the auditory and sensory motor systems differ when singing consonant and dissonant intervals? The main advantage of this technique is that it is designed to track activity of both the motor and the auditory system, while simultaneously obtaining a measure of vocal accuracy during singing.Generally, individuals new to this method will struggle because it is difficult to record some tones faithfully from within the scanner as the entirety of the scanner is extremely noisy. Demonstrating the procedure will be Lucero Pacheco, a grad student from my laboratory. To begin producing pure tones spanning two octaves from G four to G six, open a new project folder in sound editing software.Then, under the generate menu select tone. In the window that appears, select a sine waveform, an amplitude of 0.8, and a duration of one second. Enter the value of the frequency that corresponds to the desired note and click on the okay button.Under the file menu select export. In the window that open enter the desired name for the audio file and choose wav as the desired file type. Click save.Next, generate all possible combinations of notes corresponding to two consonant and two dissonant intervals in the range between G four and G six. To do this for each interval open a new project in the sound editing software and choose import audio under the file menu to import the two wav files to be concatenated. Place the cursor at any point over the second tone and click to select.Click on select all under the edit menu. Under the same menu click on copy. Then, place the cursor at any point over the first tone and click.Under the edit menu, click on move cursor to track end and then click paste under the same menu. Finally, under the file menu select export. In the window that opens enter the desired name for the audio file and choose wav as the desired file type.Click save. Begin by having the participant sit down in front of a laptop computer and wear the provided headphones. Ensure that the sound level is comfortable for the participant.Use behavioral research software to open two previously created tasks with e-run. In the window that appears enter the session and subject Number and click okay. Then, for the two alternative force choice tasks, ask the participants to identify whether the intervals they hear are consonant or dissonant by pressing either c on the computer keyboard for consonant and d for dissonant.Next, in the four alternative force choice tasks have the participants identify the intervals themselves by pressing the numerals four, five, seven, and eight on the computer keyboard, to identify the intervals of an augmented fourth. Perfect fifth. Major seventh.And octave respectively. Finally, at the end of each task press okay to automatically save the results for each participant. Begin by connecting a non-magnetic, MR compatible headset to a laptop, and ensure the sound level is comfortable for the participant.Then, connect a small condenser microphone to an audio interface that is in turn connected to the laptop using a shielded, twisted, triplet cable. To check the microphone frequency response, start a new project in the sound editing software and select the condenser microphone as the input device. Then, generate a 440 hertz test tone with a duration of 10 seconds.After the tone is generated, use sound reproduction software to press play to send the test tone through the headphones to two locations. Inside on top of the head rest and outside of the scanner in the control room with the microphone placed between the sides of the headset in each case. Next, press record in the sound editing software to record approximately one second of the test tone at each location.Then, select plot spectrum from the analyze menu for each case and compare the response of the microphone to the test tone, both inside and outside the scanner by checking that the fundamental frequency of the signal received at each location is 440 hertz. Finally, tape the condenser microphone to the participant's neck just below the larynx, and have them wear the headset. Then, place the participant in the magnetic resonance scanner.Begin by opening the magnetic resonance user interface MRUI software package. Within the MRUI software select the patient option from the onscreen menu. Enter the participant's name, age and weight.Then, click on the exam button. First, choose head and then brain from the available options. Next, select 3D and then T1 isometric with the following values for the relevant parameters:repetition time equals 10.2 milliseconds, echo time equals 4.2 milliseconds, flip angle equals 90 degrees, and voxel size equals one by one by one millimeters cubed.Then, click on program and begin the acquisition of the functional T2 star weighted whole head scan with the values of the relevant parameters as follows:TE equals 40 milliseconds, TR equals three seconds, field of view equals 256 millimeters squared, flip angle equals 90 degrees, matrix dimensions equal 64 by 64, delay in TR equals seven seconds. Note that in this case TR is the total time during which the acquisition takes place. Use the dummy option to acquire five volumes while entering a value of 55 for the total number of volumes.Click copy to make a copy of this sequence. Place the cursor at the bottom of the list of sequences and then click paste twice to set up three consecutive sparse sampling sequences. Then, click start to begin the T1 weighted anatomical volume acquisition.Finally, present three runs to the participant. Synchronize the start of the runs with the acquisition by the scanner using the scanner trigger box and differentiate between each run using the session number. Then, save the results of each run.This study identified neural activity during singing in the regions known to constitute the singing network, which match those reported in prior literature. Results from a two way repeated measures analysis of variance indicated that for the contrast resulting from singing dissonant as opposed to consonant intervals, increased activations were observed in the right primary somatosensory cortex, right primary auditory cortex, left midbrain, right posterior insula, left amygdala, and left putamen. Once mastered, this technique can be done in four hours per subject.If it is performed properly, taking into account, the behaviors test, the fMRI session, and the data analysis. While attempting this procedure, it is important to remember to check that the recording equipment is functioning correctly and to instruct the participants clearly that they should sing by humming, moving their heads as little as possible to avoid artifacts. Following this procedure, which is used to track differences in brain activation between binary conditions, a modified version of the method can be performed in order to study, for example, differences between singing wide and narrow intervals.After each development the fMRI technique paves the way for researchers in the field of auditory neuroscience to explore neural mechanisms associated with audition and vocal production. After watching this video, you should have a good understanding of how to use fMRI to monitor the activity of both the motor and auditory systems during singing, in order to study the neural correlates of vocal production. Don't forget that working with a magnetic resonance scanner can be extremely hazardous and great care should always be taken to keep metallic objects away from the scanner.

fmri-mapping-brain-activity-associated-with-vocal-production

Research

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Behavior

Functional Magnetic Resonance Imaging (fMRI) with Auditory Stimulation in Songbirds

The neurobiology of birdsong, as a model for human speech, is a pronounced area of research in behavioral neuroscience. Whereas electrophysiology and molecular approaches allow the investigation of either different stimuli on few neurons, or one stimulus in large parts of the brain, blood oxygenation level dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) allows combining both advantages, i.e. compare the neural activation induced by different stimuli in the entire brain at once. fMRI in songbirds is challenging because of the small size of their brains and because their bones and especially their skull comprise numerous air cavities, inducing important susceptibility artifacts. Gradient-echo (GE) BOLD fMRI has been successfully applied to songbirds 1-5 (for a review, see 6). These studies focused on the primary and secondary auditory brain areas, which are regions free of susceptibility artifacts. However, because processes of interest may occur beyond these regions, whole brain BOLD fMRI is required using an MRI sequence less susceptible to these artifacts. This can be achieved by using spin-echo (SE) BOLD fMRI 7,8 . In this article, we describe how to use this technique in zebra finches (Taeniopygia guttata), which are small songbirds with a bodyweight of 15-25 g extensively studied in behavioral neurosciences of birdsong. The main topic of fMRI studies on songbirds is song perception and song learning. The auditory nature of the stimuli combined with the weak BOLD sensitivity of SE (compared to GE) based fMRI sequences makes the implementation of this technique very challenging.

Functional Magnetic Resonance Imaging fMRI with Auditory Stimulation in Songbirds

This article shows an optimized procedure for imaging of the neural substrates of auditory stimulation in the songbird brain using functional Magnetic Resonance Imaging (fMRI). It describes the preparation of the sound stimuli, the positioning of the subject and the acquisition and subsequent analysis of the fMRI data.

The neurobiology of birdsong, as a model for human speech, is a pronounced area of research in behavioral neuroscience. Whereas electrophysiology and ...

How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging

In trace fear conditioning a conditional stimulus (CS) predicts the occurrence of the unconditional stimulus (UCS), which is presented after a brief stimulus free period (trace interval)1. Because the CS and UCS do not co-occur temporally, the subject must maintain a representation of that CS during the trace interval. In humans, this type of learning requires awareness of the stimulus contingencies in order to bridge the trace interval2-4. However when a face is used as a CS, subjects can implicitly learn to fear the face even in the absence of explicit awareness*. This suggests that there may be additional neural mechanisms capable of maintaining certain types of "biologically-relevant" stimuli during a brief trace interval. Given that the amygdala is involved in trace conditioning, and is sensitive to faces, it is possible that this structure can maintain a representation of a face CS during a brief trace interval.
It is challenging to understand how the brain can associate an unperceived face with an aversive outcome, even though the two stimuli are separated in time. Furthermore investigations of this phenomenon are made difficult by two specific challenges. First, it is difficult to manipulate the subject's awareness of the visual stimuli. One common way to manipulate visual awareness is to use backward masking. In backward masking, a target stimulus is briefly presented (&lt; 30 msec) and immediately followed by a presentation of an overlapping masking stimulus5. The presentation of the mask renders the target invisible6-8. Second, masking requires very rapid and precise timing making it difficult to investigate neural responses evoked by masked stimuli using many common approaches. Blood-oxygenation level dependent (BOLD) responses resolve at a timescale too slow for this type of methodology, and real time recording techniques like electroencephalography (EEG) and magnetoencephalography (MEG) have difficulties recovering signal from deep sources.
However, there have been recent advances in the methods used to localize the neural sources of the MEG signal9-11. By collecting high-resolution MRI images of the subject's brain, it is possible to create a source model based on individual neural anatomy. Using this model to "image" the sources of the MEG signal, it is possible to recover signal from deep subcortical structures, like the amygdala and the hippocampus*.

This article describes how to record amygdala activity with magnetoencephalography (MEG). In addition this article will describe how to conduct trace fear conditioning without awareness, a task that activates the amygdala. It will cover 3 topics: 1) Designing a trace conditioning paradigm using backward masking to manipulate awareness. 2) Recording brain activity during the task using magnetoencephalography. 3) Using source imaging to recover signal from subcortical structures.

In trace fear conditioning a conditional  stimulus (CS) predicts the occurrence of the unconditional stimulus (UCS),  which is presented after a brief ...

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Simultaneous EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the high spatial accuracy of fMRI. The data from these two modalities can be combined in a number of ways, but all rely on the acquisition of high quality EEG and fMRI data. EEG data acquired during simultaneous fMRI are affected by several artifacts, including the gradient artefact (due to the changing magnetic field gradients required for fMRI), the pulse artefact (linked to the cardiac cycle) and movement artifacts (resulting from movements in the strong magnetic field of the scanner, and muscle activity). Post-processing methods for successfully correcting the gradient and pulse artifacts require a number of criteria to be satisfied during data acquisition. Minimizing head motion during EEG-fMRI is also imperative for limiting the generation of artifacts.
Interactions between the radio frequency (RF) pulses required for MRI and the EEG hardware may occur and can cause heating. This is only a significant risk if safety guidelines are not satisfied. Hardware design and set-up, as well as careful selection of which MR sequences are run with the EEG hardware present must therefore be considered.
The above issues highlight the importance of the choice of the experimental protocol employed when performing a simultaneous EEG-fMRI experiment. Based on previous research we describe an optimal experimental set-up. This provides high quality EEG data during simultaneous fMRI when using commercial EEG and fMRI systems, with safety risks to the subject minimized. We demonstrate this set-up in an EEG-fMRI experiment using a simple visual stimulus. However, much more complex stimuli can be used. Here we show the EEG-fMRI set-up using a Brain Products GmbH (Gilching, Germany) MRplus, 32 channel EEG system in conjunction with a Philips Achieva (Best, Netherlands) 3T MR scanner, although many of the techniques are transferable to other systems.

Simultaneous electroencephalography (EEG) and functional Magnetic Resonance imaging (fMRI) is a powerful neuroimaging tool. However, the inside of an MRI scanner forms a difficult environment for EEG data recording and safety must be considered whenever operating EEG equipment inside a scanner. Here, we present an optimised EEG-fMRI data acquisition protocol.

Simultaneous  EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the  high spatial accuracy of fMRI. The data  from these two ...

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

The ability to adjust behavior to sudden changes in the environment develops gradually in childhood and adolescence. For example, in the Dimensional Change Card Sort task, participants switch from sorting cards one way, such as shape, to sorting them a different way, such as color. Adjusting behavior in this way exacts a small performance cost, or switch cost, such that responses are typically slower and more error-prone on switch trials in which the sorting rule changes as compared to repeat trials in which the sorting rule remains the same. The ability to flexibly adjust behavior is often said to develop gradually, in part because behavioral costs such as switch costs typically decrease with increasing age. Why aspects of higher-order cognition, such as behavioral flexibility, develop so gradually remains an open question. One hypothesis is that these changes occur in association with functional changes in broad-scale cognitive control networks. On this view, complex mental operations, such as switching, involve rapid interactions between several distributed brain regions, including those that update and maintain task rules, re-orient attention, and select behaviors. With development, functional connections between these regions strengthen, leading to faster and more efficient switching operations. The current video describes a method of testing this hypothesis through the collection and multivariate analysis of fMRI data from participants of different ages.

This video presents a method of examining age-related changes in functional connectivity of cognitive control networks engaged by targeted tasks/processes. The technique is based on multi-variate analysis of fMRI data.

The ability to adjust behavior to sudden changes in the environment develops gradually in childhood and adolescence. For example, in the Dimensional ...

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with schizophrenia who are at familial high risk (FHR) may show similar cognitive and emotional changes. However, the neurological changes underlying the emergence of these symptoms remain unclear. This study sought to identify differences in frontal, striatal, and limbic regions in children and adolescents with FHR using functional magnetic resonance imaging. Groups of 21 children and adolescents at FHR and 21 healthy controls completed an emotional oddball task that relied on selective attention and the suppression of task-irrelevant emotional information. The standard oddball task was modified to include aversive and neutral distractors in order to examine potential group differences in both emotional and executive processing. This task was designed specifically to allow for children and adolescents to complete by keeping the difficulty and emotional image content age-appropriate. Furthermore, we demonstrate a technique for suitable fMRI registration for children and adolescent participants. This paradigm may also be applied in future studies to measure changes in neural activity in other populations with hypothesized developmental changes in executive and emotional processing.

This paper describes how to use the emotional oddball task and fMRI to measure brain activation in children and adolescents at familial high risk for schizophrenia (FHR).&#160;FMRI was used to measure differences in fronto-striato-limbic regions during an emotional oddball task. Children with FHR exhibited abnormal functional activation during adolescence.

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with ...

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm (the visual oddball task) with a long history of behavioral and electroencephalography (EEG) experiments to a functional magnetic resonance imaging (fMRI) experiment is considered. The aims of this paper are to briefly describe fMRI and when its use is appropriate in cognitive neuroscience; illustrate how task design can influence the results of an fMRI experiment, particularly when that task is borrowed from another imaging modality; explain the practical aspects of performing an fMRI experiment. It is demonstrated that manipulating the task demands in the visual oddball task results in different patterns of blood oxygen level dependent (BOLD) activation. The nature of the fMRI BOLD measure means that many brain regions are found to be active in a particular task. Determining the functions of these areas of activation is very much dependent on task design and analysis. The complex nature of many fMRI tasks means that the details of the task and its requirements need careful consideration when interpreting data. The data show that this is particularly important in those tasks relying on a motor response as well as cognitive elements and that covert and overt responses should be considered where possible. Furthermore, the data show that transferring an EEG paradigm to an fMRI experiment needs careful consideration and it cannot be assumed that the same paradigm will work equally well across imaging modalities. It is therefore recommended that the design of an fMRI study is pilot tested behaviorally to establish the effects of interest and then pilot tested in the fMRI environment to ensure appropriate design, implementation and analysis for the effects of interest.

Transferring a paradigm with a history of use in EEG experiments to an fMRI experiment is considered. It is demonstrated that manipulating the task demands in the visual oddball task resulted in different patterns of BOLD activation and illustrated how task design is crucial in fMRI experiments.

As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm ...

Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression

Ruminative brooding is associated with increased vulnerability to major depression. Individuals who regularly ruminate will often try to reduce the frequency of their negative thoughts by actively suppressing them. We aim to identify the neural correlates underlying thought suppression in at-risk and depressed individuals. Three groups of women were studied; a major depressive disorder group, an at-risk group (having a first degree relative with depression) and controls. Participants performed a mixed block-event fMRI paradigm involving thought suppression, free thought and motor control periods. Participants identified the re-emergence of &#8220;to-be-suppressed&#8221; thoughts (&#8220;popping&#8221; back into conscious awareness) with a button press. During thought suppression the control group showed the greatest activation of the dorsolateral prefrontal cortex, followed by the at-risk, then depressed group. During the re-emergence of intrusive thoughts compared to successful re-suppression of those thoughts, the control group showed the greatest activation of the anterior cingulate cortices, followed by the at-risk, then depressed group. At-risk participants displayed anomalies in the neural regulation of thought suppression resembling the dysregulation found in depressed individuals. The predictive value of these changes in the onset of depression remains to be determined.

We aim to identify the neural correlates underlying sustained and transient thought suppression, and thought re-emergence in controls, at-risk and depressed individuals. Activation was greatest for controls compared to the at-risk and the depressed group in the dorsolateral prefrontal cortex during thought suppression and anterior cingulate cortex during thought re-emergence.

Ruminative brooding is associated with increased vulnerability to major depression. Individuals who regularly ruminate will often try to reduce the ...

fMRI Validation of fNIRS Measurements During a Naturalistic Task

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced version of the task using fMRI (functional magnetic resonance imaging). Recently, it has been shown that fNIRS can accurately record functional brain activities equivalent to those concurrently recorded with functional magnetic resonance imaging for classic psychophysical tasks and simple finger tapping paradigms. However, an often quoted benefit of fNIRS is that the technique allows for studying neural mechanisms of complex, naturalistic behaviors that are not possible using the constrained environment of fMRI. Our goal was to extend the findings of previous studies that have shown high correlation between concurrently recorded fNIRS and fMRI signals to compare neural recordings obtained in fMRI procedures to those separately obtained in naturalistic fNIRS experiments. Specifically, we developed a modified version of the dance video game Dance Dance Revolution (DDR) to be compatible with both fMRI and fNIRS imaging procedures. In this methodology we explain the modifications to the software and hardware for compatibility with each technique as well as the scanning and calibration procedures used to obtain representative results. The results of the study show a task-related increase in oxyhemoglobin in both modalities and demonstrate that it is possible to replicate the findings of fMRI using fNIRS in a naturalistic task. This technique represents a methodology to compare fMRI imaging paradigms which utilize a reduced-world environment to fNIRS in closer approximation to naturalistic, full-body activities and behaviors. Further development of this technique may apply to neurodegenerative diseases, such as Parkinson&#8217;s disease, late states of dementia, or those with magnetic susceptibility which are contraindicated for fMRI scanning.

We present a method to compare functional brain activity recorded during a naturalistic task using fNIRS with activity recorded during fMRI.

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced ...

A View of Their Own: Capturing the Egocentric View of Infants and Toddlers with Head-Mounted Cameras

Infants and toddlers view the world, at a basic sensory level, in a fundamentally different way from their parents. This is largely due to biological constraints: infants possess different body proportions than their parents and the ability to control their own head movements is less developed. Such constraints limit the visual input available. This protocol aims to provide guiding principles for researchers using head-mounted cameras to understand the changing visual input experienced by the developing infant. Successful use of this protocol will allow researchers to design and execute studies of the developing child&#39;s visual environment set in the home or laboratory. From this method, researchers can compile an aggregate view of all the possible items in a child&#39;s field of view. This method does not directly measure exactly what the child is looking at. By combining this approach with machine learning, computer vision algorithms, and hand-coding, researchers can produce a high-density dataset to illustrate the changing visual ecology of the developing infant.

Infants and toddlers view the world in a fundamentally different way from their parents. Head-mounted cameras provide a tractable mechanism to understand the infant visual environment. This protocol provides guiding principles for experiments in the home or laboratory to capture the egocentric view of toddlers and infants.

Infants and toddlers view the world, at a basic sensory level, in a fundamentally different way from their parents. This is largely due to biological ...

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

A real-time locating system (RTLS) can be used to track the walking activity of institutionalized older adults in long-term care who are at risk for wandering behaviors. The benefits of a RTLS are objective and continuous measurements of activity. Self-report methods of activity, especially wandering, by health care staff are vulnerable to floor effects and recall bias, and continuous clinical or research observation over the long-term can be time-consuming and expensive. Health care staff also fail to recognize the onset and/or duration of wandering behaviors, which are associated with a variety of adverse health outcomes in this population but amenable to intervention. RTLS technologies can measure the walking activity of institutionalized residents with cognitive impairment over time with a high degree of accuracy. This is particularly useful for the study of wandering, defined as walking for at least 60 seconds with few (if any) breaks in activity. Wandering is associated with disease progression, hospitalizations, falls and death. Previous work suggests older adults with poor balance ability and high sustained walking activity may be particularly susceptible to poor health outcomes. RTLS&#39;s are used to assess cognitive impairment and factors associated with gait and balance; however, supplemental paper and pencil gait/balance tools may be used to further refine risk profiles. This project discusses the use of a RTLS to measure walking activity and also gait quality and balance ability measures on this population.

This paper discusses the use of a continuous and objective real-time locating system to measure walking activity associated with wandering behaviors, focusing on older adults with cognitive impairment. Walking activity is measured by walking distance, sustained walking distance, and sustained gait speed. Also assessed are gait quality and balance ability.

A real-time locating system (RTLS) can be used to track the walking activity of institutionalized older adults in long-term care who are at risk for ...