NEURAL CORRELATES OF
VOCAL PITCH REGULATION
IN SINGING
JEAN MARY ZARATE
Dept. of Neurology & Neurosurgery
McGill University
INTRODUCTION

Precise vocal pitch regulation necessary for speech
and song

Vocal pitch regulation requires integration between:




Stable vocal motor system
Auditory feedback
Interface between these two components not well-understood
Used singing to find neural substrates for audio-vocal
integration
Elicit learned vocalizations
Initiate
vocalizations
EXP 1: Experience-dependent neural substrates
involved in vocal pitch regulation (Zarate &
Zatorre, 2008)

12 non-musicians (6 ♀), 12 singers (6 ♀)

HYPOTHESES:

SIMPLE: basic network for singing (Perry et al., 1999)
IGN: ↑ attention areas, ↓ auditory cortical activity
COMP: audio-vocal integration = ACC, STG, insula?

Singers:




Singing tasks: singers > non-musicians
Experience-dependent modulation in basic network for
singing, audio-vocal integration
error (cents)
response magnitude (cents)
150
125
100
75
50
25
0
-25
-50
-75
-100
-125
-150
-175
300
275
250
225
200
175
150
125
100
75
50
25
0
-25
SIMPLE – PERC (SINGER ∩ NON-MUS)
SIMPLE
M1
NON-MUS
SINGER
*
*
b
Cbl
*
b
SMA
Th
STG/
INS
PAC/
STG
ACC
6.1
D#
F
G#
B
IGNORE
x=0
C#
!
D#
F
!
G#
!
!
B
C#
z=0
2.5
IGN – SIMP (SINGER > NON-MUS)
NON-MUS
SINGER
PT/
STG
INCORRECT
!
y = -14
STS
4.0
y = -22
2.0
COMPENSATE
NON-MUS
SINGER
CORRECT
D#
F
G#
B
C#
dPMC
3.7
z = 68
2.0
SINGER > NON-MUS
300
275
250
225
200
175
150
125
100
75
50
25
0
-25
NON-MUS > SINGER
response magnitude (cents)
COMP – SIMP (GROUP DIFF)
pSTS
RCZa
4.4
x = -10
x = 50
2.4
EXP 1: KEY FINDINGS

IGN: non-mus had pitch-shift responses



Pitch-shift response = vocal stabilization system
Training needed to suppress stabilization
Audio-vocal integration:
 Non-mus:
dPMC (sensorimotor association)
 Singers: RCZa, pSTS
EXPERIMENT 2: Neural networks involved in
voluntary and involuntary vocal pitch regulation in
experienced singers (Zarate et al., submitted)


9 singers (6 ♀)
SIMPLE; IGN/COMP 200c and 25c pitch shifts



COMP200c = voluntary vocal pitch regulation
Pitch-shift response in IGN25c = PAG?
Unable to verify role of PAG due to temporal
resolution limitations of fMRI
FUNCTIONAL CONNECTIVITY: COMP200
EFFECTIVE CONNECTIVITY:
COMP200 (vs. SIMPLE)
pSTS seed
EFFECTIVE CONNECTIVITY: IGN200 (vs. SIMPLE)
pSTS seed

EXP 1 & 2: RCZa, pSTS, anterior insula




Recruited after vocal training
Functionally connected to each other
pSTS interacts with IPS to monitor feedback
EXP 3: Training effects in non-musicians (Modulation
of functional network for singing after auditory training)



Better auditory skills = better vocal accuracy?
Better vocal accuracy  modulations in singing networks
Melodies:
 Singing tasks: 50c & 100c melodies, simple singing
 Perception: micromelody discrimination (<100c interval)
110
PRE-TRAINING
110
100
percent correct (%)
percent correct (%)
100
90
80
70
60
50
TRAINED
CONTROL
40
*
90
*
*
*
*
80
70
60
50
TRAINED
CONTROL
40
30
30
5c
10c
15c
20c
30c
40c
interval
40
*
30
20
10
0
PRE
60c
5c
10c
15c
20c
30c
40c
interval
SIMPLE PERFORMANCE
(BEHAVIORAL SESSIONS)
pitch variability (cents)
POST-TRAINING
POST
FUNC. CONNECTIVITY
(POST – PRE)
right PT seed
60c
EXP 3: CONCLUSIONS

Short-term auditory training




training effects with micromelody discrimination
no training effects on vocal production
no neural modulations specifically induced by
training-enhanced vocal production
Dissociation between perceptual and
production skills?


different time-courses of behavioral
improvement
auditory-motor training necessary


Consolidated after adequate audiovocal training
Short-term auditory training does not
engage or consolidate network
ACKNOWLEDGMENTS
Robert J. Zatorre
Advisory Committee:
D. Louis Collins
Alan Evans
David Ostry
Université de Montréal / BRAMS / CIRMMT:
James Bergstra
Douglas Eck
Sean Wood
McGill / MNI:
Pierre Ahad
Patrick Bermudez
Marc Bouffard
André Cormier
Karine Delhommeau
Michael Ferreira
Nicholas Foster
Talya Grumberg
Funding:

Canadian Institutes of Health Research
(CIHR)

Eileen Peters McGill Majors Fellowship

Centre for Interdisciplinary Research in
Music Media and Technology (CIRMMT)
New York:
Henry McDonagh III
Members of the Z-Lab
FUTURE DIRECTIONS

A-V network specific to vocal pitch?




MEG, EEG/ERP: pitch-shift response
Auditory training  vocal accuracy



manipulate other features (e.g., formants)
training effects: foreign language students
more testing sessions of vocal production
longer auditory training
Similar network with other perturbations?

somatosensory feedback
EXP 1:
Audio-vocal integration
SINGERS & NON-MUS
EXP 2:
Voluntary/involuntary
vocal pitch regulation
SINGERS
EXP 3:
Vocal pitch regulation
after auditory training
NON-MUS


SIMPLE: Sing back single note
PITCH-SHIFTED TASKS:
ignore/compensate for ± 200c-shift
Deviation from target note (cents)
Deviation from target n
125
100 100
75 75
TARGET
OWN FEEDBACK
50 50
25 25
0
0
-25 -25
-50 -50
-75 -75
-100-100
-1.0
-125
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
IGNORE
TIME(s)
-150
-175
-200
-225
-250
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
2.0
2.5
3.0
3.5
4.0
TIME(s)
250
225
Deviation from target note (cents)
200
175
150
125
100
75
50
25
0
-25
-0.5
0.0
0.5
1.0
1.5
-50
-75
-100
-125
-150
-175
-200
-225
-250
TIME(s)
TARGET
OWN FEEDBACK
COMPENSATE
error (cents)
150
125
100
75
50
25
0
-25
-50
-75
-100
-125
-150
-175
SIMPLE
NON-MUS
SINGER
D#
F
*
*
b
*
b
G#
B
C#
SIMPLE – PERC
(SINGER ∩ NON-MUS)
M1
STG/
INS
SMA
Cbl
Th
PAC/
STG
ACC
6.1
2.5
x=0
y = -14
z=0
response magnitude (cents)
300
275
250
225
200
175
150
125
100
75
50
25
0
-25
IGNORE
NON-MUS
SINGER
INCORRECT
!
!
D#
F
!
G#
!
!
B
C#
IPS
SMG
4.2
2.0
y = -50
SINGER > NON-MUS
CONJUNCTION
IGNORE - SIMPLE
STS
PT/
STG
4.0
2.0
y = -22
response magnitude (cents)
300
275
250
225
200
175
150
125
100
75
50
25
0
-25
COMPENSATE
NON-MUS
SINGER
CORRECT
D#
F
G#
B
C#
COMPENSATE – SIMPLE
ACC
IPS
3.9
SMG
2.0
y = -44
dPMC
3.7
z = 68
2.0
SINGER > NON-MUS
x=4
NON-MUS > SINGER
CONJUNCTION
rACC
x = -10
pSTS
4.4
2.4
x = 50
EXP 1: Experience-dependent neural substrates
involved in vocal pitch regulation (Zarate &
Zatorre, 2008)

Behavioral tasks:





SIMPLE, IGN: singers > non-mus
COMP: both groups successful
Programmed to stabilize systems against disturbances
Training needed to suppress stabilization mechanisms
fMRI results



SIMPLE: singers ≈ non-musicians
COMP/IGN: ↑ auditory activity in singers
Audio-vocal integration:
 Non-mus: dPMC

Singers: rACC, pSTS
Deviation from target note (cents)
Deviation from target note (cents)
200
175
150
IGNORE
125
100
100
75
75
TARGET
OWN FEEDBACK
50
50
25
25
0
0
-25
-25
-50
-50
-75
-75
-100
-100
-1.0
-125
-0.5
0.0
0.5
1.0
pitch-shift
1.5
2.0
response
TIME(s)
2.5
3.0
3.5
4.0
2.5
3.0
3.5
4.0
-150
-175
-200
-225
-250
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
TIME(s)
Responses to long pitch shifts (>500ms):
– Early: ~100-150ms, more automatic
– Late: ~300ms, may be subject to voluntary control
EXPERIMENT 2: Neural networks involved in
voluntary and involuntary vocal pitch regulation in
experienced singers (Zarate et al., submitted)


9 singers (6 ♀)
SIMPLE; IGN/COMP 200c and 25c pitch shifts
HYPOTHESES:




Resp. magnitude: COMP200c > IGN 200c
Singers cannot suppress pitch-shift responses to
small shifts: COMP25c = IGN25c
IGN/COMP200c networks similar to exp1
PAG  pitch-shift response in IGN/COMP25c?
200
*
IGN 200c INCORRECT
COMP 200c CORRECT
0
response magnitude (cents)
150
125
100
75
50
25
!
IGN 25c INCORRECT
COMP 25c CORRECT
-25
IGN 25c
COMP 200c
IGN 25c INCORRECT
COMP 25c CORRECT
IGN 200c
IGN 25c
COMP 200c
-75
-100
-125
-150
COMP 25c
-200
-25
150
140
130
120
110
FULL VOLUNTARY CORRECTION (COMP)
100 FULL INVOLUNTARY CORRECTION (IGN)
90
80
70
60
50
40
30
20
10
0
IGN 200c
IGN 25c
COMP 200c
IGN 200c INCORRECT
COMP 200c CORRECT
!
#
#
*
COMP 25c
COMP 25c
+
-50
-175
0
percent response magnitude (%)
response magnitude (cents)
175
IGN 200c
*
EXP 2: CONCLUSIONS

Pitch-shift responses to IGN25c under less voluntary
control than IGN200c


Role of PAG in pitch-shift response: not verified



part of stabilization system
occurs in milliseconds, fMRI temporal resolution in seconds
MEG, EEG/ERP: temporal interaction during A-V integration
Voluntary vocal corrections: same network for different
magnitudes: rACC, pSTS, anterior insula


functionally connected to each other
pSTS interacts with IPS to monitor shifted feedback
EXPERIMENT 3: Modulation of functional
network for singing after auditory training
(Zarate et al., in prep)
HYPOTHESES:

Auditory training with pure tones
 ↑ micromelody discrimination (pure- and vocal-tone)
 ↑ vocal accuracy

Melodic singing requires audio-vocal integration:
 similar regions seen in Exp 1, 2
 auditory working memory (e.g., inf. frontal)

Modulation of regions after training:
 singing network
 audio-vocal integration
• Perception:
– 2 micromelodies: same/different?
– Trained/tested with micromelodies (pure & vocal tones)
• Production: simple singing & 5-note melodies
– Middle note ≈ 250 Hz
– Intervals: 50 and 100 cents
EXP 3: ORDER OF TASKS
beh
pre
Trained
9 subj
(6 ♀)
Production:
Simple
Melodies
Perception:
Micromelody
discrimination
Control
10 subj
(6 ♀)
Production:
Simple
Melodies
Perception:
Micromelody
discrimination
fMRI
pre
Production:
Simple
Melodies
TRAINING
(2 weeks)
YES
NO
fMRI
post
Production:
Simple
Melodies
beh
post
Perception:
Micromelody
discrimination
Production:
Simple
Melodies
Perception:
Micromelody
discrimination
Production:
Simple
Melodies
SIMPLE – PERC (PRE)
SMA
PAC / STG / PT
INS
sensorimotor
(mouth)
ACC
7.8
2.5
x=2
y = -16
z = 10
MEL(50+100) – SIMPLE (PRE)
PT/STG
6.6
2.6
y = -12
z=4
POST – PRE
SCANNER PARAMETERS
Exp 1 & 3: 1.5 Tesla
 TR = 10s, TE = 85ms
 voxel = 5 mm3
 25 slices (whole head)
 Matrix: 64x64
Exp 2: 3 Tesla, cardiac gating
 TR = 10.3s, TE = 60ms
 voxel = 3.5 mm3
 40 slices (whole head)
 Matrix: 64x64
DUAL-STREAM MODEL OF
AUDITORY PROCESSING

Rauschecker/Tian 2000:


Ventral: “what” – auditory object info
Dorsal: “where” – auditory spatial info

Belin/Zatorre 2000: Dorsal = “how”
Warren et al. 2005: Dorsal = “do”

Updated model: Dorsal = how / do

SINGING NETWORKS IN OTHER STUDIES
Hickok et al. 2003: covert
speech vs. covert humming
Schultz et al. 2005: voiced vs. whispered speech
Toyomura et al. 2007: COMP
EXP 1: PUTAMINAL ACTIVITY
IGNORE - SIMPLE
COMP - SIMPLE
Put
pSTS
Put
4.0
4.4
2.0
z = 10
2.4
z=2