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>Generation of interval sequences.
	<ol>
		<li>Produce pure tones spanning two octaves,&#160;G4-G6, using a sound-editing program. 
		NOTE: Here, the free, open-source sound editing software Audacity is described. Other packages may be used for this purpose.
		<ol>
			<li>For each tone, open a new project in the sound-editing software.</li>
			<li>Under the &#34;Generate&#34; menu, select &#34;Tone.&#34; In the window that appears, select a sine waveform, an amplitude of 0.8, and a duration of 1 s. Enter the value of the frequency that corresponds to the desired note (e.g.,&#160;440 Hz for A4). Click on the &#34;OK&#34; button.</li>
			<li>Under the &#34;File&#34; menu, select &#34;Export Audio.&#34; In the window that opens, enter the desired name for the audio file and choose WAV as the desired file type. Click &#34;Save.&#34;</li>
		</ol>
		</li>
		<li>Select two consonant and two dissonant intervals, according to Table 1, in such a way that each consonant interval is close to a dissonant interval. 
		NOTE: As an example, consider the consonant intervals of a perfect fifth and an octave and the dissonant intervals of an augmented fourth (tritone) and a major seventh. These are the intervals chosen for the study conducted by the authors.</li>
		<li>Generate all possible combinations of notes corresponding to these four intervals in the range between G4 and G6.
		<ol>
			<li>For each interval, open a new project in the sound-editing software and use &#34;Import Audio&#34; under the &#34;File&#34; menu to import the two WAV files to be concatenated.</li>
			<li>Place the cursor at any point over the second tone and click to select. Click on &#34;Select All&#34; under the &#34;Edit&#34; menu. Under the same menu, click on &#34;Copy.&#34;</li>
			<li>Place the cursor at any point over the first tone and click. Under the &#34;Edit&#34; menu click on &#34;Move Cursor to Track End&#34; and then click &#34;Paste&#34; under the same menu. Export the audio as described in step 1.2.1.3.</li>
		</ol>
		</li>
		<li>Use a random sequence generator to produce sequences consisting of 100 intervals generated pseudorandomly in such a way that each of the four different intervals occurs exactly 25 times30. To do this, use the random permutation function in the statistical analysis software (see the Table of Materials). Input the four intervals as arguments and create a loop that repeats this process 25 times.</li>
		<li>Use behavioral research software to generate two distinct runs. Load a sequence of 100 intervals in WAV format for each run and associate the identification of each interval with a single trial30. 
		NOTE: Here, E-Prime behavioral research software is used. Other equivalent behavioral research software can be used.</li>
	</ol>
	</li>
	<li>Explain to participants that they will listen to two sequences of 100 intervals each, where each sequence is associated with a different task and with its own set of instructions. Tell participants that, in both runs, the next interval will be played only when a valid key is pressed. 
	NOTE: Once the interval recognition sequence commences, it should not be interrupted so that the course of action should be as clear as possible to all participants.
	<ol>
		<li>Have the participants sit down in front of a laptop computer and wear the provided headphones. Use good-quality over-the-ear headphones. Adjust the sound level to a comfortable level for each subject.</li>
		<li>If using the behavioral research software described here, open the tasks created in step 1.2.5 with E-Run. In the window that appears, enter the session and subject number and click &#34;OK.&#34; Use the session number to distinguish between runs for each participant. 
		NOTE: The instructions for the task at hand will appear on screen, followed by the beginning of the task itself.
		<ol>
			<li>First, in a 2-alternative forced-choice task, simply have the participants identify whether the intervals they hear are consonant or dissonant. Have the participant press &#34;C&#34; on the computer keyboard for consonant and &#34;D&#34; for dissonant. 
			NOTE: Since all participants are expected to have musical training at a conservatory level, they are all expected to be able to distinguish between patently consonant and patently dissonant intervals. The first task serves, in a sense, as confirmation that this is indeed the case.</li>
			<li>Second, in a 4-alternative forced-choice task, have the participants identify the intervals themselves. Have the participants press the numerals &#34;4,&#34; &#34;5,&#34; &#34;7,&#34; and &#34;8&#34; on the computer keyboard to identify the intervals of an augmented fourth, perfect fifth, major seventh, and octave, respectively.</li>
		</ol>
		</li>
		<li>At the end of each task, press &#34;OK&#34; to automatically save the results for each participant in an individual E-DataAid 2.0 file labeled with the subject and session numbers and with the extension .edat2.</li>
		<li>Use statistical analysis software (e.g., Matlab, SPSS Statistics, or an open-source alternative) to calculate the success rates for each task (i.e.&#160;the percentage of successful answers when identifying whether the intervals were consonant or dissonant, and also when identifying the intervals themselves), both individually and as a group32.</li>
	</ol>
	</li>
</ol>
2. fMRI Experiment
<ol>
	<li>Preparation for the fMRI session.
	<ol>
		<li>Generate sequences of the same intervals as in step 1.2.3, again composed of two consecutive pure tones with a duration of 1 s each. 
		NOTE: The vocal range of the participants must now be taken into account, and all notes must fall comfortably within the singing range of each participant.
		<ol>
			<li>Use a random sequence generator to create a randomized sequence of 30 intervals for the listen-only trials30. For the singing trials, create a pseudorandomized sequence of 120 intervals for the participants to listen to a specific interval and then match this target interval with their singing voices. For the pseudorandomized sequence, use the same method as described in step 1.2.4, with the 4&#160;intervals as arguments once again, but now repeating this process 30 times.</li>
			<li>Following the same procedure as in step 1.2.5, use behavioral research software to generate three distinct runs, each consisting initially of 10 silent baseline trials, followed by 10 consecutive listen-only trials, and finally by 40 consecutive singing trials. 
			NOTE: During the baseline trials, the four intervals appear in random order, while during the singing trials, the four intervals appear in pseudorandomized order, in such a manner that each interval is eventually presented exactly 10 times. The duration of each trial is 10 s, so one whole run lasts 10 min. Since each subject goes through 3 experimental runs, the total duration of the experiment is 30 min. However, allowing for the participants to enter and exit the scanner, for time to set up and test the microphone, for time to obtain the anatomical scan, and for time between functional runs, approximately 1 h of scanner time should be allotted to each participant.</li>
		</ol>
		</li>
		<li>Explain to the participants the sequences of trials to be presented, as described in step 2.1.1.2, and respond to any doubts they might have. Instruct the participants to hum the notes without opening their mouths during the singing trials, keeping the lips still while producing an &#34;m&#34; sound.</li>
		<li>Connect a non-magnetic, MR-compatible headset to a laptop. Adjust the sound level to a comfortable level for each subject.</li>
		<li>Connect a small condenser microphone to an audio interface that is in turn connected to the laptop using a shielded twisted-triplet cable. 
		NOTE: The microphone power supply, the audio interface, and the laptop should all be located outside the room housing the scanner.</li>
		<li>Check the microphone frequency response. 
		NOTE: The purpose of this test is to confirm that the microphone behaves as expected inside the scanner.
		<ol>
			<li>Start a new project in the sound-editing software and select the condenser microphone as the input device.</li>
			<li>Generate a 440 Hz test tone with a duration of 10 s, as described in section 1.2.1, with the appropriate values for frequency and duration.</li>
			<li>Using the default sound reproduction software on the laptop, press &#34;Play&#34; to send the test tone through the headphones at locations inside (on top of the headrest) and outside (in the control room) the scanner, with the microphone placed between the sides of the headset in each case.</li>
			<li>Press &#34;Record&#34; in the sound-editing software to record approximately 1 s of the test tone at each location.</li>
			<li>Select &#34;Plot Spectrum&#34; from the &#34;Analyze&#34; 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 Hz.</li>
		</ol>
		</li>
		<li>Tape the condenser microphone to the participant&#39;s neck, just below the larynx.</li>
		<li>Have the participant wear the headset. Place the participant in a magnetic resonance (MR) scanner.</li>
	</ol>
	</li>
	<li>fMRI session.
	<ol>
		<li>At the beginning of the session, open the magnetic resonance user interface (MRUI) software package. Use the MRUI to program the acquisition paradigm. 
		NOTE: Some variation in the interface is to be expected between different models.
		<ol>
			<li>Select the &#34;Patient&#34; option from the onscreen menu. Enter the participant&#39;s name, age, and weight.</li>
			<li>Click on the &#34;Exam&#34; button. First, choose &#34;Head&#34; and then &#34;Brain&#34; from the available options.</li>
			<li>Select &#34;3D&#34; and then &#34;T1 isometric,&#34; with the following values for the relevant parameters: Repetition Time (TR) = 10.2 ms, Echo Time (TE) = 4.2 ms, Flip Angle = 90&#176;, and Voxel Size = 1 x 1 x 1 mm3. 
			NOTE: For each participant, a T1-weighted anatomical volume will be acquired using a gradient echo pulse sequence for anatomical reference.</li>
			<li>Click on &#34;Program&#34; and select EchoPlanaImage_diff_perf_bold (T2*), with the values of the relevant parameters as follows: TE = 40 ms, TR = 10 s, Acquisition Time (TA) = 3 s, Delay in TR = 7 s, Flip Angle = 90&#176;, Field of View (FOV) = 256 mm2, and Matrix Dimensions = 64 x 64. Use the &#34;Dummy&#34; option to acquire 5 volumes while entering a value of &#34;55&#34; for the total number of volumes. 
			NOTE: These values permit the acquisition of functional T2*-weighted whole-head scans according to the sparse sampling paradigm illustrated in Figure 1, where an echo-planar imaging (EPI) &#34;dummy&#34; scan is acquired and discarded to allow for T1 saturation effects. Note that in some MRUIs, the value of TR should be 3 s, as it is taken to be the total time during which the acquisition takes place.</li>
			<li>Click &#34;Copy&#34; to make a copy of this sequence. Place the cursor at the bottom of the list of sequences and then click &#34;Paste&#34; twice to set up three consecutive sparse sampling sequences.</li>
			<li>Click &#34;Start&#34; to begin the T1-weighted anatomical volume acquisition.</li>
			<li>Present three runs to the participant, with the runs as described in step 2.1.1.2. Synchronize the start of the runs with the acquisition by the scanner using the scanner trigger-box.
			<ol>
				<li>Follow the same procedure as described in section 1.3.2 to begin each one of the three runs, differentiating between runs using the session number. Save the results of three complete runs using the same procedure described in step 1.3.3. 
				NOTE: The timing of the trial presentations is systematically jittered by &#177;500 ms.</li>
			</ol>
			</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" src="/files/ftp_upload/55419/55419fig1.jpg" /> 
Figure 1: Sparse-sampling Design. (A) Timeline of events within a trial involving only listening to a two-tone interval (2 s), without subsequent overt reproduction. (B) Timeline of events within a trial involving listening and singing tasks. <a href="https://cloudfront-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55419/55419fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. Data Analysis
<ol>
	<li>Preprocess the functional data using software designed for the analysis of brain imaging data sequences following standard procedures33. 
	NOTE: All of the data processing is done using the same software.
	<ol>
		<li>Use the provided menu option to realign the images to the first volume, resampled and spatially normalized (final voxel size: 2 x 2 x 2 mm3) to standard Montreal Neurological Institute (MNI) stereotactic space34.</li>
		<li>Use the provided menu option to smooth the image using an isotropic, 8 mm, Full Width at Half Maximum (FWHM) Gaussian kernel.</li>
		<li>To model the BOLD response, select a single-bin Finite Impulse Response (FIR) as a basis function (order 1) or boxcar function, spanning the time of volume acquisition (3 s)28. 
		NOTE: Sparse-sampling protocols, such as this one, do not generally require the FIR to be convolved with the hemodynamic response function, as is commonly the case for event-related fMRI.</li>
		<li>Apply a high-pass filter to the BOLD response for each event (1,000 s for the &#34;singing network&#34; and 360 s elsewhere). 
		NOTE: Modelling all singing tasks together will amount to a block of 400 s35.</li>
	</ol>
	</li>
</ol>

<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. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Frequency+ratios+and+the+discrimination+of+pure+tone+sequences.">Frequency ratios and the discrimination of pure tone sequences.</a> Percept. Psychophys. 56, 472-478 (1994).</li><li>Trainor, L. J., Heinmiller, B. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+development+of+evaluative+responses+to+music.">The development of evaluative responses to music.</a> Infant Behav. Dev. 21 (1), 77-88 (1998).</li><li>Zentner, M. R., Kagan, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Infants'+perception+of+consonance+and+dissonance+in+music.">Infants' perception of consonance and dissonance in music.</a> Infant Behav. 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. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Individual+differences+reveal+the+basis+of+consonance.">Individual differences reveal the basis of consonance.</a> Curr. Biol. 20, 1035-1041 (2010).</li><li>Cousineau, M., McDermott, J. H., Peretz, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+basis+of+musical+consonance+as+revealed+by+congenital+amusia.">The basis of musical consonance as revealed by congenital amusia.</a> Proc. Natl. Acad. Sci. USA. 109, 19858-19863 (2012).</li><li>Plomp, R., Levelt, W. J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tonal+Consonance+and+Critical+Bandwidth.">Tonal Consonance and Critical Bandwidth.</a> J. Acoust. Soc. Am. 38, 548-560 (1965).</li><li>Kameoka, A., Kuriyagawa, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Consonance+theory+part+I:+Consonance+of+dyads.">Consonance theory part I: Consonance of dyads.</a> J. Acoust. Soc. Am. 45, 1451-1459 (1969).</li><li>Tramo, M. J., Bharucha, J. J., Musiek, F. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Music+perception+and+cognition+following+bilateral+lesions+of+auditory+cortex.">Music perception and cognition following bilateral lesions of auditory cortex.</a> J. Cogn. Neurosci. 2, 195-212 (1990).</li><li>Schellenberg, E. G., Trehub, S. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Children's+discrimination+of+melodic+intervals.">Children's discrimination of melodic intervals.</a> Dev. Psychol. 32 (6), 1039-1050 (1996).</li><li>Peretz, I., Blood, A. J., Penhune, V., Zatorre, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+deafness+to+dissonance.">Cortical deafness to dissonance.</a> Brain. 124, 928-940 (2001).</li><li>Mcdermott, J. H., Schultz, A. F., Undurraga, E. A., Godoy, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Indifference+to+dissonance+in+native+Amazonians+reveals+cultural+variation+in+music+perception.">Indifference to dissonance in native Amazonians reveals cultural variation in music perception.</a> Nature. 535, 547-550 (2016).</li><li>Blood, A. J., Zatorre, R. J., Bermudez, P., Evans, A. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Emotional+responses+to+pleasant+and+unpleasant+music+correlate+with+activity+in+paralimbic+brain+regions.">Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions.</a> Nat. Neurosci. 2, 382-387 (1999).</li><li>Pallesen, K. J., 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=Emotion+processing+of+major,+minor,+and+dissonant+chords:+A+functional+magnetic+resonance+imaging+study.">Emotion processing of major, minor, and dissonant chords: A functional magnetic resonance imaging study.</a> Ann. N. Y. Acad. Sci. 1060, 450-453 (2005).</li><li>Foss, A. H., Altschuler, E. L., James, K. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+correlates+of+the+Pythagorean+ratio+rules.">Neural correlates of the Pythagorean ratio rules.</a> Neuroreport. 18, 1521-1525 (2007).</li><li>Limb, C. J., Braun, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+substrates+of+spontaneous+musical+performance:+An+fMRI+study+of+jazz+improvisation.">Neural substrates of spontaneous musical performance: An fMRI study of jazz improvisation.</a> PLoS ONE. 3, (2008).</li><li>Zarate, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+neural+control+of+singing.">The neural control of singing.</a> Front. Hum. Neurosci. 7, 237 (2013).</li><li>Larson, C. R., Altman, K. W., Liu, H., Hain, T. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interactions+between+auditory+and+somatosensory+feedback+for+voice+F0+control.">Interactions between auditory and somatosensory feedback for voice F0 control.</a> Exp. Brain Res. 187, 613-621 (2008).</li><li>Belyk, M., Pfordresher, P. Q., Liotti, M., Brown, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+neural+basis+of+vocal+pitch+imitation+in+humans.">The neural basis of vocal pitch imitation in humans.</a> J. Cogn. Neurosci. 28, 621-635 (2016).</li><li>Kleber, B., Veit, R., Birbaumer, N., Gruzelier, J., Lotze, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+brain+of+opera+singers:+Experience-dependent+changes+in+functional+activation.">The brain of opera singers: Experience-dependent changes in functional activation.</a> Cereb. Cortex. 20, 1144-1152 (2010).</li><li>J&#252;rgens, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+pathways+underlying+vocal+control.">Neural pathways underlying vocal control.</a> Neurosci. Biobehav. Rev. 26, 235-258 (2002).</li><li>Kleber, B., Birbaumer, N., Veit, R., Trevorrow, T., Lotze, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Overt+and+imagined+singing+of+an+Italian+aria.">Overt and imagined singing of an Italian aria.</a> Neuroimage. 36, 889-900 (2007).</li><li>Kleber, B., Zeitouni, A. G., Friberg, A., Zatorre, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experience-dependent+modulation+of+feedback+integration+during+singing:+role+of+the+right+anterior+insula.">Experience-dependent modulation of feedback integration during singing: role of the right anterior insula.</a> J. Neurosci. 33, 6070-6080 (2013).</li><li>Zarate, J. M., Zatorre, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experience-dependent+neural+substrates+involved+in+vocal+pitch+regulation+during+singing.">Experience-dependent neural substrates involved in vocal pitch regulation during singing.</a> Neuroimage. 40, 1871-1887 (2008).</li><li>Gonz&#225;lez-Garc&#237;a, N., Gonz&#225;lez, M. A., Rend&#243;n, P. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+activity+related+to+discrimination+and+vocal+production+of+consonant+and+dissonant+musical+intervals.">Neural activity related to discrimination and vocal production of consonant and dissonant musical intervals.</a> Brain Res. 1643, 59-69 (2016).</li><li>Oldfield, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+assessment+and+analysis+of+handedness:+The+Edinburgh+inventory.">The assessment and analysis of handedness: The Edinburgh inventory.</a> Neuropsychologia. 9, 97-113 (1971).</li><li>Samuels, M. L., Witmer, J. A., Schaffner, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Statistics for the Life Sciences. , (2015).</li><li>Eickhoff, S. B., 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=A+new+SPM+toolbox+for+combining+probabilistic+cytoarchitectonic+maps+and+functional+imaging+data.">A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data.</a> NeuroImage. 25, 1325-1335 (2005).</li><li>Evans, A. C., Kamber, M., Collins, D. L., MacDonald, D., Shorvon, S. D., Fish, D. R., Andermann, F., Bydder, G. M., Stefan, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+MRI-based+probabilistic+atlas+of+neuroanatomy.">An MRI-based probabilistic atlas of neuroanatomy.</a> Magnetic Resonance Scanning and Epilepsy. 264, 263-274 (1994).</li><li>Ashburner, J., 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=.">.</a> SPM8 Manual. , (2013).</li><li>&#214;zdemir, E., Norton, A., Schlaug, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Shared+and+distinct+neural+correlates+of+singing+and+speaking.">Shared and distinct neural correlates of singing and speaking.</a> Neuroimage. 33, 628-635 (2006).</li><li>Brown, S., Ngan, E., Liotti, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+larynx+area+in+the+human+motor+cortex.">A larynx area in the human motor cortex.</a> Cereb. Cortex. 18, 837-845 (2008).</li><li>Worsley, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Statistical+analysis+of+activation+images.">Statistical analysis of activation images.</a> Functional MRI: An introduction to methods. , 251-270 (2001).</li><li>. FSL Atlases Available from: <a target="_blank" href="https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases">https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases</a> (2015)</li><li>Bidelman, G. M., Krishnan, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+correlates+of+consonance,+dissonance,+and+the+hierarchy+of+musical+pitch+in+the+human+brainstem.">Neural correlates of consonance, dissonance, and the hierarchy of musical pitch in the human brainstem.</a> J. Neurosci. 29, 13165-13171 (2009).</li><li>McLachlan, N., Marco, D., Light, M., Wilson, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Consonance+and+pitch.">Consonance and pitch.</a> J. Exp. Psychol. – Gen. 142, 1142-1158 (2013).</li><li>Thompson, W. F., 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=Intervals+and+scales.">Intervals and scales.</a> The psychology of music. , 107-140 (1999).</li><li>Hurwitz, R., Lane, S. R., Bell, R. A., Brant-Zawadzki, M. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Acoustic+analysis+of+gradient-coil+noise+in+MR+imaging.">Acoustic analysis of gradient-coil noise in MR imaging.</a> Radiology. 173, 545-548 (1989).</li><li>Ravicz, M. E., Melcher, J. R., Kiang, N. Y. -. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Acoustic+noise+during+functional+magnetic+resonance+imaging.">Acoustic noise during functional magnetic resonance imaging.</a> J Acoust. Soc. Am. 108, 1683-1696 (2000).</li><li>Cho, Z. H., 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=Analysis+of+acoustic+noise+in+MRI.">Analysis of acoustic noise in MRI.</a> Magn. Reson. Imaging. 15, 815-822 (1997).</li><li>Belin, P., Zatorre, R. J., Hoge, R., Evans, A. C., Pike, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Event-related+fMRI+of+the+auditory+cortex.">Event-related fMRI of the auditory cortex.</a> Neuroimage. 429, 417-429 (1999).</li><li>Hall, D. A., 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=&amp;#34;Sparse&amp;#34;+temporal+sampling+in+auditory+fMRI.">&amp;#34;Sparse&amp;#34; temporal sampling in auditory fMRI.</a> Hum. Brain Mapp. 7, 213-223 (1999).</li><li>Ternstr&#246;m, S., Sundberg, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Acoustical+factors+related+to+pitch+precision+in+choir+singing.">Acoustical factors related to pitch precision in choir singing.</a> Speech Music Hear. Q. Prog. Status Rep. 23, 76-90 (1982).</li><li>Ternstr&#246;m, S., Sundberg, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intonation+precision+of+choir+singers.">Intonation precision of choir singers.</a> J. Acoust. Soc. Am. 84, 59-69 (1988).</li></ol>

The authors declare no conflicts of interest.

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 constructed with notes that correspond to consonant or dissonant chords are still perceived as consonant or dissonant, respectively4,6,15,41,42.
The behavioral interval perception task is used to establish, before participants can go through to the scanning session, if they are able to adequately distinguish the intervals. Thus, they perform well once inside the magnetic resonator. Any participants unable to meet a predetermined threshold when performing these identification tasks should not proceed to the fMRI experiment. The main purpose of this selection process is to ensure that differences in performance between the participants are not due to deficient perceptual abilities. Chosen participants should have similar degrees of vocal and musical training, and also, if possible, similar tessituras. If they have vocal ranges that vary significantly, the range of intervals presented during the singing tasks they will need to perform must be personalized.
The microphone setup is critical for the acquisition to be reliable and artifact-free. The type of microphone itself is very important, and although it is possible to use optical28 or specially designed, MR-compatible29 microphones, it has been shown that the sensitivity of condenser microphones is not affected by the presence of the intense magnetic fields in the imaging environment43. Indeed, a small Lavalier condenser microphone may be used in this context, provided a shielded twisted-triplet cable is used to connect the microphone to the preamplifier, which must be placed outside the room where the MR scanner is housed. This arrangement will prevent the appearance of imaging artifacts44, but researchers should also ensure that the scanner does not interfere with the performance of the microphone. To this end, a test tone can be sent through the MR-compatible headphones to the microphone placed inside the MR scanner, and the signal obtained in this manner can then be compared to that obtained by sending the same tone to the microphone now placed outside the scanner. The sound pressure levels inside the MR scanner can be extremely high45, so the microphone must be placed as close as possible to the source. By asking participants to hum rather than openly sing notes, the movement in and around the mouth area can be minimized. By placing the microphone just below the larynx, covered by tape, it is possible to obtain a faithful recording of the singer&#39;s voice. The recording will naturally be very noisy &#8211; this cannot be avoided &#8211; but if researchers are mainly interested in pitch and not in the articulation or enunciation of words, a variety of software packages can be used to clean the signal enough for the detection of the fundamental frequency of each sung note. A standard method would be to use audio-editing software to filter the time signals through a Hamming window and then to use the autocorrelation algorithms built into certain speech and phonetics software packages to identify the sung fundamentals. Vocal accuracy can then be calculated for each participant. Potential applications of the data obtained from the recordings include correlating pitch or rhythmic accuracy with either degree of training or interval distances.
Functional images are acquired using a sparse sampling design so as to minimize BOLD or auditory masking due to scanning noise25,28,29,46,47. Every subject undergoes 3 experimental runs, each one lasting 10 min. During each run, subjects are first asked to lay in silence during 10 silent baseline trials, then to listen passively to a block of 10 intervals, and finally to listen to and sing back another block of 40 intervals. One purpose of keeping individual runs as short as possible is to avoid participant fatigue. Nonetheless, it has since been concluded that it might be better in the future to include the same number of listen-only and singing trials, which can then be presented in alternating blocks. This would have the effect of increasing statistical power. As an example, a run could consist of 2 blocks of 5 silent baseline trials, 4 blocks of 5 listen-only trials, and 4 blocks of singing trials. The blocks would then be presented to participants in alternation, with a total duration of 500 s per run.
The main reason for having participants listen passively inside the resonator is to have a means of subtracting auditory activity from motor activity. Thus, a favorable comparison of singing activations against the &#34;singing network&#34;25,27,28,29,36,37 is indispensable for the proper validation of the study. Note that &#34;singing network&#34; activations are very robust and well-established and are usually detected by means of one-sample t-tests and a corrected cluster significance threshold of p = 0.05. Activations corresponding to the contrast between singing dissonant/consonant and consonant/dissonant intervals are typically identified by means of 2-way repeated factors analyses of variance (ANOVA) according to the significance voxel threshold p &#60; 0.001, uncorrected for multiple comparisons28,29. It is expected that participants will find singing dissonant intervals more challenging than singing consonant intervals48,49; thus, different activations for each of the two contrasts described above are anticipated. Results indicate that singing dissonant intervals involves a reprogramming of the neural mechanisms recruited for the production of consonant intervals. During singing, produced sound is compared to intended sound, and any necessary adjustment is then achieved through the integration of external and internal feedback from auditory and somatosensory pathways. A detailed discussion of these results and the conclusions drawn from them is included in the article by Gonz&#225;lez-Garc&#237;a, Gonz&#225;lez, and Rend&#243;n30.
This protocol provides a reasonably straightforward method for the study of the neural correlates of musical production and for monitoring the activity of both the motor and auditory systems. It can be used to track differences in brain activation between binary conditions, such as singing consonant or dissonant intervals, and singing narrow or wide intervals30. It is also well-suited to study the effect of training on a variety of tasks associated with singing specific frequencies. On the other hand, because of the very large amount of noise contained in recordings of the sung voice obtained during the scan, it would be difficult to employ this protocol to analyze tasks concerned with quality of tone or timbre, especially because these are qualities that cannot be gauged correctly while humming.

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.

<table><tbody><tr><td>Achieva 1.5-T magnetic resonance scanner</td><td>Philips</td><td>Release 6.4</td><td></td></tr><tr><td>Audacity</td><td>Open source</td><td>2.0.5</td><td></td></tr><tr><td>Audio interface</td><td>Tascam</td><td>US-144MKII</td><td></td></tr><tr><td>Audiometer</td><td>Br&amp;uuml;el &amp;amp; Kjaer</td><td>Type 1800</td><td></td></tr><tr><td>E-Prime Professional</td><td>Psychology Software Tools, Inc.</td><td>2.0.0.74</td><td></td></tr><tr><td>Matlab</td><td>Mathworks</td><td>R2014A</td><td></td></tr><tr><td>MRI-Compatible Insert Earphones</td><td>Sensimetrics</td><td>S14</td><td></td></tr><tr><td>Praat</td><td>Open source</td><td>5.4.12</td><td></td></tr><tr><td>Pro-audio condenser microphone</td><td>Shure</td><td>SM93</td><td></td></tr><tr><td>SPSS Statistics</td><td>IBM</td><td>20</td><td></td></tr><tr><td>Statistical Parametric Mapping</td><td>Wellcome Trust Centre for Neuroimaging</td><td>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 design of the study, we hoped to identify neural activity during singing in the regions known to constitute the &#34;singing network,&#34; as defined in a number of previous studies25,26,27,28,29,30,31,32,33,34,35,36,37. The effect of singing is observed by means of the first-level linear contrast of interest, which corresponds to singing as opposed to listening. One-sample t-tests were used with clusters determined by Z &#62;3 and a cluster significance threshold of p = 0.05, Family-Wise Error (FWE) corrected38. For anatomic labeling, the SPM Anatomy Toolbox33 and the Harvard-Oxford cortical and subcortical structural atlas were used39. The brain regions where significant activation was observed were the primary somatosensory cortex (S1), the secondary somatosensory cortex (S2), the primary motor cortex (M1), the supplementary motor area (SMA), the premotor cortex (PM), Brodmann area 44 (BA 44), the primary auditory cortex (PAC), the superior temporal gyrus (STG), the temporal pole, the anterior insula, the putamen, the thalamus, and the cerebellum. These activations match those reported in the studies cited above regarding the &#34;singing network,&#34; and they are illustrated in Figure 2. Note that in Figures 2 and 3 both, the x-coordinate is perpendicular to the sagittal plane, the y-coordinate is perpendicular to the coronal plane, and the z-coordinate is perpendicular to the transverse, or horizontal plane.
Once the basic design has been validated, two further first-level linear contrasts were calculated for each participant, corresponding to singing dissonant as opposed to consonant intervals and to singing consonant as opposed to dissonant intervals. These linear contrasts were then taken to a second-level random-effects model involving a set of 2-way repeated-measures analyses of variance (ANOVA), with the factors consonance and dissonance. In this manner, the activated or deactivated areas were examined for possible interactions, with activations of interest determined according to the significance voxel threshold, p &#60;0.001, uncorrected for multiple comparisons28,29. For the contrast resulting from singing dissonant as opposed to consonant intervals, increased activations were observed in the right S1, right PAC, left midbrain, right posterior insula, left amygdala, and left putamen. These activations are shown in Figure 3. Regarding the complementary contrast, no significant changes in activation were detected during the singing of consonant intervals.
<img alt="Figure 2" src="/files/ftp_upload/55419/55419fig2.jpg" /> 
Figure 2: Activation in Regions that Constitute the &#34;Singing Network.&#34; Activation maps are presented with a cluster significance threshold of p = 0.05, family-wise error (FWE) corrected. BOLD responses are reported in arbitrary units. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55419/55419fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/55419/55419fig3.jpg" /> 
Figure 3: Contrast between the Singing of Dissonant and Consonant Intervals. Activation maps are presented, uncorrected for multiple comparisons, with a cluster significance threshold of p = 0.001. BOLD responses are reported in arbitrary units. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55419/55419fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Interval</td>
			<td>Number of semitones</td>
			<td>Ratio of fundamentals</td>
		</tr>
		<tr>
			<td>Unison</td>
			<td>0</td>
			<td>1:1</td>
		</tr>
		<tr>
			<td>Minor second</td>
			<td>1</td>
			<td>16:15</td>
		</tr>
		<tr>
			<td>Major second</td>
			<td>2</td>
			<td>9:8</td>
		</tr>
		<tr>
			<td>Minor third</td>
			<td>3</td>
			<td>6:5</td>
		</tr>
		<tr>
			<td>Major third</td>
			<td>4</td>
			<td>5:4</td>
		</tr>
		<tr>
			<td>Perfect fourth</td>
			<td>5</td>
			<td>4:3</td>
		</tr>
		<tr>
			<td>Tritone</td>
			<td>6</td>
			<td>45:32</td>
		</tr>
		<tr>
			<td>Perfect fifth</td>
			<td>7</td>
			<td>3:2</td>
		</tr>
		<tr>
			<td>Minor sixth</td>
			<td>8</td>
			<td>8:5</td>
		</tr>
		<tr>
			<td>Major sixth</td>
			<td>9</td>
			<td>5:3</td>
		</tr>
		<tr>
			<td>Minor seventh</td>
			<td>10</td>
			<td>16:9</td>
		</tr>
		<tr>
			<td>Major seventh</td>
			<td>11</td>
			<td>15:8</td>
		</tr>
		<tr>
			<td>Octave</td>
			<td>12</td>
			<td>2:1</td>
		</tr>
	</tbody>
</table>
Table 1: Consonant and Dissonant Musical Intervals. Consonant intervals appear in boldface, while dissonant intervals appear in italics. Observe that the more consonant an interval, the smaller the integers that appear in the frequency ratio used to represent it. For an in-depth discussion of consonance and dissonance as a function of frequency ratios, see Bidelman &#38; Krishnan40.

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 ...

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

Nanyang Technological University

Research

JoVE Journal

Behavior

7.2K Views.  Hospital Infantil de México "Federico Gómez".  De neurale correlaten van het luisteren naar consonante en dissonante intervallen zijn in grote mate bestudeerd, maar de neurale mechanismen die verband houden met de productie van consonante en dissonante intervallen zijn minder bekend. In dit artikel worden gedragstesten en fMRI gecombineerd met intervalidentificatie en zangtaken om deze mechanismen te beschrijven. 

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

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 ...

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

The ability to gauge social interactions is crucial in the assessment of others&#x2019; intentions. Factors such as facial expressions and body language affect our decisions in personal and professional life alike 1. These "friend or foe" judgements are often based on first impressions, which in turn may affect our decisions to "approach or avoid". Previous studies investigating the neural correlates of social cognition tended to use static facial stimuli 2. Here, we illustrate an experimental design in which whole-body animated characters were used in conjunction with functional magnetic resonance imaging (fMRI) recordings. Fifteen participants were presented with short movie-clips of guest-host interactions in a business setting, while fMRI data were recorded; at the end of each movie, participants also provided ratings of the host behaviour. This design mimics more closely real-life situations, and hence may contribute to better understanding of the neural mechanisms of social interactions in healthy behaviour, and to gaining insight into possible causes of deficits in social behaviour in such clinical conditions as social anxiety and autism 3.

This article demonstrates an experimental design in which whole-body animated characters are used in conjunction with functional magnetic resonance imaging (fMRI) to investigate the neural correlates of observing virtual social interactions.

The ability to gauge social interactions is crucial in the assessment of others&#x2019; intentions. Factors such as facial expressions and body language ...

Neuroscience

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

The aim of this methods paper is to describe how to implement a neuroimaging technique to examine complementary brain processes engaged by two similar tasks. Participants' behavior during task performance in an fMRI scanner can then be correlated to the brain activity using the blood-oxygen-level-dependent signal. We measure behavior to be able to sort correct trials, where the subject performed the task correctly and then be able to examine the brain signals related to correct performance. Conversely, if subjects do not perform the task correctly, and these trials are included in the same analysis with the correct trials we would introduce trials that were not only for correct performance. Thus, in many cases these errors can be used themselves to then correlate brain activity to them. We describe two complementary tasks that are used in our lab to examine the brain during suppression of an automatic responses: the stroop1 and anti-saccade tasks. The emotional stroop paradigm instructs participants to either report the superimposed emotional 'word' across the affective faces or the facial 'expressions' of the face stimuli1,2. When the word and the facial expression refer to different emotions, a conflict between what must be said and what is automatically read occurs. The participant has to resolve the conflict between two simultaneously competing processes of word reading and facial expression. Our urge to read out a word leads to strong 'stimulus-response (SR)' associations; hence inhibiting these strong SR's is difficult and participants are prone to making errors. Overcoming this conflict and directing attention away from the face or the word requires the subject to inhibit bottom up processes which typically directs attention to the more salient stimulus. Similarly, in the anti-saccade task3,4,5,6, where an instruction cue is used to direct only attention to a peripheral stimulus location but then the eye movement is made to the mirror opposite position. Yet again we measure behavior by recording the eye movements of participants which allows for the sorting of the behavioral responses into correct and error trials7 which then can be correlated to brain activity. Neuroimaging now allows researchers to measure different behaviors of correct and error trials that are indicative of different cognitive processes and pinpoint the different neural networks involved.

The goal of our research is to correlate behavior to brain activity. Accurate behavioral measures and imaging techniques allow us to elucidate brain-behavior relationships.

The aim of this methods paper is to describe how to implement a neuroimaging technique to examine complementary brain processes engaged by two similar ...

Functional Imaging of Auditory Cortex in Adult Cats using High-field fMRI

Current knowledge of sensory processing in the mammalian auditory system is mainly derived from electrophysiological studies in a variety of animal models, including monkeys, ferrets, bats, rodents, and cats. In order to draw suitable parallels between human and animal models of auditory function, it is important to establish a bridge between human functional imaging studies and animal electrophysiological studies. Functional magnetic resonance imaging (fMRI) is an established, minimally invasive method of measuring broad patterns of hemodynamic activity across different regions of the cerebral cortex. This technique is widely used to probe sensory function in the human brain, is a useful tool in linking studies of auditory processing in both humans and animals and has been successfully used to investigate auditory function in monkeys and rodents. The following protocol describes an experimental procedure for investigating auditory function in anesthetized adult cats by measuring stimulus-evoked hemodynamic changes in auditory cortex using fMRI. This method facilitates comparison of the hemodynamic responses across different models of auditory function thus leading to a better understanding of species-independent features of the mammalian auditory cortex.

Functional studies of the auditory system in mammals have traditionally been conducted using spatially-focused techniques such as electrophysiological recordings. The following protocol describes a method of visualizing large-scale patterns of evoked hemodynamic activity in the cat auditory cortex using functional magnetic resonance imaging.

Current knowledge of sensory processing in the mammalian auditory system is mainly derived from electrophysiological studies in a variety of animal ...

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain activity, even for conditions with similar levels of univariate activation. Multi-voxel pattern (MVP) analysis has been used to decode this information with great success. FMRI investigators also often seek to understand how brain regions interact in interconnected networks, and use functional connectivity (FC) to identify regions that have correlated responses over time. Just as univariate analyses can be insensitive to information in MVPs, FC may not fully characterize the brain networks that process conditions with characteristic MVP signatures. The method described here, informational connectivity (IC), can identify regions with correlated changes in MVP-discriminability across time, revealing connectivity that is not accessible to FC. The method can be exploratory, using searchlights to identify seed-connected areas, or planned, between pre-selected regions-of-interest. The results can elucidate networks of regions that process MVP-related conditions, can breakdown MVPA searchlight maps into separate networks, or can be compared across tasks and patient groups.

Informational connectivity measures the correspondence between time courses of multi-voxel information across different brain regions. Multi-voxel pattern discriminability time series are extracted from regions and compared, revealing networks that are not identified in a typical functional connectivity approach.

It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain ...

EEG Study on Recognizing Familiar Speakers in Expressive Speech

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Recognizing familiar speakers from vocal streams is a fundamental aspect of human verbal communication. However, it remains unclear how listeners can still discern the speaker's identity in expressive speech. This study develops a memorization-based individual speaker identity recognition approach and an accompanying electroencephalogram (EEG) data analysis pipeline, which monitors how listeners recognize familiar speakers and tell unfamiliar ones apart. EEG data captures online cognitive processes during new versus old speaker distinction based on voice, offering a real-time measure of brain activity, overcoming limits of reaction times and accuracy measurements. The paradigm comprises three steps: listeners establish associations between three voices and their names (training); listeners indicate the name corresponding to a voice from three candidates (checking); listeners distinguish between three old and three new speaker voices in a two-alternative forced-choice task (testing). The speech prosody in testing was either confident or doubtful. EEG data were collected using a 64-channel EEG system, followed by preprocessing and imported into RStudio for ERP and statistical analysis and MATLAB for brain topography. Results showed an enlarged late positive component (LPC) was elicited in the old-talker compared to the new-talker condition in the 400-850 ms window in the Pz and other wider range of electrodes in both prosodies. Yet, the old/new effect was robust in central and posterior electrodes for doubtful prosody perception, whereas the anterior, central, and posterior electrodes are for confident prosody condition. This study proposes that this experiment design can serve as a reference for investigating speaker-specific cue-binding effects in various scenarios (e.g., anaphoric expression) and pathologies in patients like phonagnosia.

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

The study introduces a training-testing paradigm to investigate old/new effects of event-related potentials in confident and doubtful prosodic scenarios. Data reveals an enhanced late positive component between 400-850 ms at Pz and other electrodes. This pipeline can explore factors beyond speech prosody and their influence on cue-binding target identification.

Recognizing familiar speakers from vocal streams is a fundamental aspect of human verbal communication. However, it remains unclear how listeners can ...

Mapping the After-effects of Theta Burst Stimulation on the Human Auditory Cortex with Functional Imaging

Auditory cortex pertains to the processing of sound, which is at the basis of speech or music-related processing1. However, despite considerable recent progress, the functional properties and lateralization of the human auditory cortex are far from being fully understood. Transcranial Magnetic Stimulation (TMS) is a non-invasive technique that can transiently or lastingly modulate cortical excitability via the application of localized magnetic field pulses, and represents a unique method of exploring plasticity and connectivity. It has only recently begun to be applied to understand auditory cortical function 2. 
An important issue in using TMS is that the physiological consequences of the stimulation are difficult to establish. Although many TMS studies make the implicit assumption that the area targeted by the coil is the area affected, this need not be the case, particularly for complex cognitive functions which depend on interactions across many brain regions 3. One solution to this problem is to combine TMS with functional Magnetic resonance imaging (fMRI). The idea here is that fMRI will provide an index of changes in brain activity associated with TMS. Thus, fMRI would give an independent means of assessing which areas are affected by TMS and how they are modulated 4. In addition, fMRI allows the assessment of functional connectivity, which represents a measure of the temporal coupling between distant regions. It can thus be useful not only to measure the net activity modulation induced by TMS in given locations, but also the degree to which the network properties are affected by TMS, via any observed changes in functional connectivity.
Different approaches exist to combine TMS and functional imaging according to the temporal order of the methods. Functional MRI can be applied before, during, after, or both before and after TMS. Recently, some studies interleaved TMS and fMRI in order to provide online mapping of the functional changes induced by TMS 5-7. However, this online combination has many technical problems, including the static artifacts resulting from the presence of the TMS coil in the scanner room, or the effects of TMS pulses on the process of MR image formation. But more importantly, the loud acoustic noise induced by TMS (increased compared with standard use because of the resonance of the scanner bore) and the increased TMS coil vibrations (caused by the strong mechanical forces due to the static magnetic field of the MR scanner) constitute a crucial problem when studying auditory processing. 
This is one reason why fMRI was carried out before and after TMS in the present study. Similar approaches have been used to target the motor cortex 8,9, premotor cortex 10, primary somatosensory cortex 11,12 and language-related areas 13, but so far no combined TMS-fMRI study has investigated the auditory cortex. The purpose of this article is to provide details concerning the protocol and considerations necessary to successfully combine these two neuroscientific tools to investigate auditory processing. 
Previously we showed that repetitive TMS (rTMS) at high and low frequencies (resp. 10 Hz and 1 Hz) applied over the auditory cortex modulated response time (RT) in a melody discrimination task 2. We also showed that RT modulation was correlated with functional connectivity in the auditory network assessed using fMRI: the higher the functional connectivity between left and right auditory cortices during task performance, the higher the facilitatory effect (i.e. decreased RT) observed with rTMS. However those findings were mainly correlational, as fMRI was performed before rTMS. Here, fMRI was carried out before and immediately after TMS to provide direct measures of the functional organization of the auditory cortex, and more specifically of the plastic reorganization of the auditory neural network occurring after the neural intervention provided by TMS. 
Combined fMRI and TMS applied over the auditory cortex should enable a better understanding of brain mechanisms of auditory processing, providing physiological information about functional effects of TMS. This knowledge could be useful for many cognitive neuroscience applications, as well as for optimizing therapeutic applications of TMS, particularly in auditory-related disorders.

Auditory processing is the basis of speech and music-related processing. Transcranial Magnetic Stimulation (TMS) has been used successfully to study cognitive, sensory and motor systems but has rarely been applied to audition. Here we investigated TMS combined with functional Magnetic Resonance Imaging to understand the functional organization of auditory cortex.

Auditory cortex pertains to the processing of sound, which is at the basis of speech or music-related processing1. However, despite considerable recent ...

Biology

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

The Translational Brain Mapping Program at the University of Rochester is an interdisciplinary effort that integrates cognitive science, neurophysiology, neuroanesthesia, and neurosurgery. Patients who have tumors or epileptogenic tissue in eloquent brain areas are studied preoperatively with functional and structural MRI, and intraoperatively with direct electrical stimulation mapping. Post-operative neural and cognitive outcome measures fuel basic science studies about the factors that mediate good versus poor outcome after surgery, and how brain mapping can be further optimized to ensure the best outcome for future patients. In this article, we describe the interdisciplinary workflow that allows our team to meet the synergistic goals of optimizing patient outcome and advancing scientific understanding of the human brain.

This article provides an overview of a multi-modal brain mapping program designed to identify regions of the brain that support critical cognitive functions in individual neurosurgery patients.

The Translational Brain Mapping Program at the University of Rochester is an interdisciplinary effort that integrates cognitive science, neurophysiology, ...

Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging

Functional ultrasound (fUS) imaging is a novel brain imaging modality that relies on the high-sensitivity measure of the cerebral blood volume achieved by ultrafast doppler angiography. As brain perfusion is strongly linked to local neuronal activity, this technique allows the whole-brain 3D mapping of task-induced regional activation as well as resting-state functional connectivity, non-invasively, with unmatched spatio-temporal resolution and operational simplicity. In comparison with fMRI (functional magnetic resonance imaging), a main advantage of fUS imaging consists in enabling a complete compatibility with awake and behaving animal experiments. Moreover, fMRI brain mapping in mice, the most used preclinical model in Neuroscience, remains technically challenging due to the small size of the brain and the difficulty to maintain stable physiological conditions. Here we present a simple, reliable and robust protocol for whole-brain fUS imaging in anesthetized and awake mice using an off-the-shelf commercial fUS system with a motorized linear transducer, yielding significant cortical activation following sensory stimulation as well as reproducible 3D functional connectivity pattern for network identification.

This protocol describes the quantification of volumetric cerebral hemodynamic variations in the mouse brain using functional ultrasound (fUS). Procedures for 3D functional activation map following sensory stimulation as well as resting-state functional connectivity are provided as illustrative examples, in anesthetized and awake mice.

Functional ultrasound (fUS) imaging is a novel brain imaging modality that relies on the high-sensitivity measure of the cerebral blood volume achieved by ...

Noninvasive EEG for Comparative Studies of Vocal Perception in Marmosets

Electroencephalography Measurements in Awake Marmosets Listening to Conspecific Vocalizations

Vocal communication plays a crucial role in the social interactions of primates, particularly in survival and social organization. Humans have developed a unique and advanced vocal communication strategy in the form of language. To study the evolution of human language, it is necessary to investigate the neural mechanisms underlying vocal processing in humans, as well as to understand how brain mechanisms have evolved by comparing them with those in nonhuman primates. Herein, we developed a method to noninvasively measure the electroencephalography (EEG) of awake nonhuman primates. This recording method allows for long-term studies without harming the animals, and, importantly, allows us to directly compare nonhuman primate EEG data with human data, providing insights into the evolution of human language. In the current study, we used the scalp EEG recording method to investigate brain activity in response to species-specific vocalizations in marmosets. This study provides novel insights by using scalp EEG to capture widespread neural representations in marmosets during vocal perception, filling gaps in existing knowledge.

Author Spotlight: Investigating Vocal Information Representation in Small Primates and Its Alteration by Psychiatric Disorders Using Noninvasive EEG

To study the evolution of language, comparing brain mechanisms in humans with those in nonhuman primates is important. We developed a method to noninvasively measure the electroencephalography (EEG) of awake animals. It allows us to directly compare EEG data between humans and animals for the long term without harming them.

Vocal communication plays a crucial role in the social interactions of primates, particularly in survival and social organization. Humans have developed a ...

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

Samenvatting

Meer video's verkennen