NEURAL CORRELATES OF VOCAL PITCH REGULATION IN SINGING J EAN M ARY Z ARATE Dept. of Neurology &...
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NEURAL CORRELATES OF VOCAL PITCH REGULATION IN SINGING JEAN MARY ZARATE Dept. of Neurology & Neurosurgery McGill University
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Transcript of NEURAL CORRELATES OF VOCAL PITCH REGULATION IN SINGING J EAN M ARY Z ARATE Dept. of Neurology &...
NEURAL CORRELATES OFVOCAL PITCH REGULATIONIN SINGING
JEAN MARY ZARATE
Dept. of Neurology & NeurosurgeryMcGill 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
Initiatevocalizations
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
x = 0
ACC
SMATh
Cbl
y = -14
M1
PAC/STG
z = 0
STG/INS
6.1
2.5
SIMPLE – PERC (SINGER ∩ NON-MUS)SIMPLE
-175-150
-125-100
-75
-50-25
025
50
75100
125
150
NON-MUSSINGER
D# C#BG#F
* *b *b
erro
r (c
ents
)
IGNORE
-250
255075
100125150175200225250275300
D# C#BG#F
INCORRECT
! !!!!
re
sp
on
se
ma
gn
itu
de
(ce
nts
)
NON-MUSSINGER
4.0
2.0y = -22
PT/STG
STS
IGN – SIMP (SINGER > NON-MUS)
COMPENSATE
-250
255075
100125150175200225250275300
D# C#BG#F
CORRECT
re
sp
on
se
ma
gn
itu
de
(ce
nts
)
NON-MUSSINGER
4.4
2.4SIN
GE
R >
NO
N-M
US
x = -10
RCZa
x = 50
pSTS
3.7
2.0NO
N-M
US
> S
ING
ER
z = 68
dPMC
COMP – SIMP (GROUP DIFF)
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
EXP 1: KEY FINDINGS
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
EFFECTIVE CONNECTIVITY: IGN200 (vs. SIMPLE)pSTS seed
EFFECTIVE CONNECTIVITY:COMP200 (vs. SIMPLE)
pSTS seed
FUNCTIONAL CONNECTIVITY: COMP200
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)
PRE-TRAINING
30
40
50
60
70
80
90
100
110
TRAINEDCONTROL
5c 60c20c15c10c 40c30c
interval
per
cen
t co
rrec
t (%
)
POST-TRAINING
30
40
50
60
70
80
90
100
110
TRAINEDCONTROL
15c5c 60c20c10c 40c30c
interval
per
cen
t co
rrec
t (%
)
*****
SIMPLE PERFORMANCE(BEHAVIORAL SESSIONS)
0
10
20
30
40
PRE POST
*
pit
ch
va
ria
bil
ity
(c
en
ts)
FUNC. CONNECTIVITY(POST – PRE)right PT seed
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 audio-vocal training
Short-term auditory training does not engage or consolidate network
ACKNOWLEDGMENTSRobert J. Zatorre
Advisory Committee:D. Louis CollinsAlan EvansDavid Ostry
McGill / MNI:Pierre AhadPatrick BermudezMarc BouffardAndré CormierKarine DelhommeauMichael FerreiraNicholas FosterTalya Grumberg
New York:Henry McDonagh III
Université de Montréal / BRAMS / CIRMMT:James BergstraDouglas EckSean Wood
Funding: Canadian Institutes of Health Research
(CIHR) Eileen Peters McGill Majors Fellowship Centre for Interdisciplinary Research in
Music Media and Technology (CIRMMT)
Members of the Z-Lab
FUTURE DIRECTIONS
A-V network specific to vocal pitch? manipulate other features (e.g., formants) training effects: foreign language students
MEG, EEG/ERP: pitch-shift response Auditory training vocal accuracy
more testing sessions of vocal production longer auditory training
Similar network with other perturbations? somatosensory feedback
EXP 1:Audio-vocal integrationSINGERS & NON-MUS
EXP 2:Voluntary/involuntary vocal pitch regulation
SINGERS
EXP 3:Vocal pitch regulationafter auditory training
NON-MUS
SIMPLE: Sing back single note PITCH-SHIFTED TASKS:
ignore/compensate for ± 200c-shift
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0-250
-225
-200
-175
-150
-125
-100
-75
-50
-25
0
25
50
75
100TARGETOWN FEEDBACK
TIME(s)
De
via
tio
n f
rom
ta
rge
t n
ote
(c
en
ts)
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0-100
-75
-50
-25
0
25
50
75
100
125
150
175
200
225
250 NON-MUSICIAN
TIME(s)
De
via
tio
n f
rom
ta
rge
t n
ote
(c
en
ts)
IGNORE
-0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
-250
-225
-200
-175
-150
-125
-100
-75
-50
-25
0
25
50
75
100
125
150
175
200
225
250
TARGETOWN FEEDBACKTIME(s)
De
via
tio
n f
rom
ta
rge
t n
ote
(c
en
ts)
COMPENSATE
x = 0
ACC
SMA
ThCbl
y = -14
M1
PAC/STG
z = 0
STG/INS
6.1
2.5
SIMPLE – PERC (SINGER ∩ NON-MUS)
SIMPLE
-175-150-125-100-75-50-25
0255075
100125150
NON-MUSSINGER
D# C#BG#F
* *b *b
erro
r (c
ents
)
IGNORE - SIMPLE
4.2
2.0y = -50
CO
NJU
NC
TIO
N
IPS
SMG
IGNORE
-250
255075
100125150175200225250275300
D# C#BG#F
INCORRECT
! !!!!
re
sp
on
se
ma
gn
itu
de
(ce
nts
)
NON-MUSSINGER
4.0
2.0
SIN
GE
R >
NO
N-M
US
y = -22
PT/STG
STS
COMPENSATE
-250
255075
100125150175200225250275300
D# C#BG#F
CORRECT
re
sp
on
se
ma
gn
itu
de
(ce
nts
)
NON-MUSSINGER
COMPENSATE – SIMPLE
3.9
2.0
y = -44
x = 4
CO
NJU
NC
TIO
N
IPS
SMG
ACC
3.7
2.0
z = 68
NO
N-M
US
> S
ING
ER
dPMC
4.4
2.4
x = 50
x = -10
SIN
GE
R >
NO
N-M
US
rACC
pSTS
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
EXP 1: Experience-dependent neural substrates involved in vocal pitch regulation (Zarate & Zatorre, 2008)
IGNORE
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0-250
-225
-200
-175
-150
-125
-100
-75
-50
-25
0
25
50
75
100TARGETOWN FEEDBACK
TIME(s)
De
via
tio
n f
rom
ta
rge
t n
ote
(c
en
ts)
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0-100
-75
-50
-25
0
25
50
75
100
125
150
175
200
225
250 NON-MUSICIAN
TIME(s)
De
via
tio
n f
rom
ta
rge
t n
ote
(c
en
ts)
pitch-shift response
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?
-25
0
25
50
75
100
125
150
175
200COMP 200c CORRECTIGN 200c INCORRECT
COMP 25c CORRECTIGN 25c INCORRECT
IGN 200c IGN 25c COMP 200c COMP 25c
!
*re
sp
on
se
ma
gn
itu
de
(c
en
ts)
-200
-175
-150
-125
-100
-75
-50
-25
0
+
*
IGN 200c IGN 25c COMP 200c COMP 25c
COMP 200c CORRECTIGN 200c INCORRECT
COMP 25c CORRECTIGN 25c INCORRECT
res
po
ns
e m
ag
nit
ud
e (
ce
nts
)
IGN 200c IGN 25c COMP 200c COMP 25c0
102030405060708090
100110120130140150
*
FULL VOLUNTARY CORRECTION (COMP)FULL INVOLUNTARY CORRECTION (IGN)
!
#
#
pe
rce
nt
res
po
ns
e m
ag
nit
ud
e (
%)
EXP 2: CONCLUSIONS
Pitch-shift responses to IGN25c under less voluntary control than IGN200c part of stabilization system
Role of PAG in pitch-shift response: not verified 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
fMRI
pre
TRAINING
(2 weeks)
fMRI
post
beh
post
Trained
9 subj
(6 ♀)
Production:SimpleMelodies
Perception:Micromelody discrimination
Production:SimpleMelodies
YESProduction:SimpleMelodies
Perception:Micromelody discrimination
Production:SimpleMelodies
Control
10 subj
(6 ♀)
Production:SimpleMelodies
Perception:Micromelody discrimination
NO
Perception:Micromelody discrimination
Production:SimpleMelodies
SIMPLE – PERC (PRE)
7.8
2.5
x = 2 y = -16 z = 10
SMA
ACC
sensorimotor(mouth)
PAC / STG / PTINS
MEL(50+100) – SIMPLE (PRE)
6.6
2.6
PT/STG
z = 4y = -12
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 OFAUDITORY 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
Schultz et al. 2005: voiced vs. whispered speech
Hickok et al. 2003: covert speech vs. covert humming
Toyomura et al. 2007: COMP
z = 10
Put
EXP 1: PUTAMINAL ACTIVITY
IGNORE - SIMPLE
4.0
2.0
4.4
2.4
z = 2
Put
pSTS
COMP - SIMPLE
