Introduction
This document outlines two functional magnetic resonance imaging experiments and the
theory behind and expected results of these experiments. At this time, only the first
experiment is described in detail. Both experiments are designed to look at specific
aspects of speech motor planning, in particular the vocalization of memory-guided
sequences of syllables. Very generally, the first experiment deals with identifying the
neural substrates involved in speech sequence performance, and considers issues of the
representation of sequence items. The second experiment deals with issues of timing,
including rhythm, syllable stress, and speaking rate.
Background
Fluent speech requires the complex coordination of multiple movements at appropriate
times and in the appropriate temporal order. Currently in the DIVA model, the issue of
sequencing multiple speech movements is not realistically addressed. Instead, individual
phonemes are treated as targets, and once a target is reached, a next target is loaded
algorithmically from a list. The neural mechanisms responsible for representing and
executing a sequence of sounds, including temporal characteristics such as rhythm, stress,
and speaking rate need to be addressed in a more serious manner with a focus on
understanding the true neural systems responsible for sequence regulation.
Summary of Data
Still working on this… coming soon.
Experiment I: Neural Correlates of Speech Sequences
Stimuli
Stimuli consist of sequences of five syllables that vary in sequential complexity and in
syllabic complexity. Here, sequential complexity refers to the intricacy of the sequence
across syllables. That is, a sequence in which the same syllable (irrespective of the
syllable content itself) is repeated five times is the least complex sequence; a sequence
consisting of five different syllables is the most complex sequence. Syllabic complexity
refers to the intricacy of the articulations specified by an individual syllable. For
example, the syllable ‘ba’, which requires only a bilabial closure prior to the steady state
vowel /a/ has less syllabic complexity than the syllable ‘bla’, which requires an additional
tongue movement to reach /l/ between the lip closure and the vowel.
Note: Sequences that vary in complexity have been used successfully in functional
imaging of finger movement sequences (e.g. Boecker et al, 1998) with the idea that
increasing complexity requires additional resources in areas used in sequence execution.
These results are discussed in the Expectations section below.
The four stimulus types to be used as well as a control condition are summarized in the
table below:
1
Low syllabic complexity,
Low sequential complexity
2
High syllabic complexity,
Low sequential complexity
3
Low syllabic complexity,
High sequential complexity
4
High syllabic complexity,
High sequential complexity
5
Presentation of nonsense
characters arranged similar
to 1-4
Syllables are simply CV’s, and
each syllable is simply
repeated 5 times with each
syllable of equal duration and
without stress
stra-stra-stra-straSyllables have more complex
stra
phonemic content (CCCV’s),
with each syllable repeated 5
times.
ba-di-ta-ro-gu
Syllables are CV’s, but each
syllable is a different CV, such
that sequence content is richer.
stra-fla-bro-kli-shro Phonemic content of each
syllable is more complicated
than CV’s, each syllable is
different.
 Simple control – should help
cancel out visual cue
information, provide a
baseline. Nonsense characters
should prevent formation of a
speech-like sequence.
ba-ba-ba-ba-ba
In each case, the subject will be presented visually (for a short duration (~2 seconds))
with an example of the sequence that they are to produce (using the notation discussed
above). The visual cue will then be replaced by a white fixation point for np
milliseconds. Subjects are instructed to memorize the sequential and temporal content of
the sequence, and to prepare to repeat the utterance. Thus, the period during which
subjects see the white fixation point can be thought of as a preparation phase. It is
important that the sequence information is not available externally to the subject when
the utterance is initiated, as externally or memory-guided sequence execution may use a
separate neural circuit (see Goldberg 1985, Passingham 1994 for instance).
Following the brief (approximately 1-2 seconds chosen randomly) preparation phase, the
subject will either be presented with a “go” signal (the fixation point changes color), or
nothing will change. In the case in which the “go” signal is received, the subject will
then utter the sequence of syllables as they were trained (prior to the experiment subjects
will be given examples and will practice for a short time under supervision of the
experimenter to ensure that syllables are presented at a normal speaking rate, with
approximately even duration and without stress on any particular syllable). In the case
where nothing changes, the subject will remain in the preparation phase (as nothing will
have cued them to switch to execution, nor will they be able to anticipate a change in the
experimental phase since the length of the preparation phase is random).
Note: In order to save time, and to collect more data for each condition, we could limit
the ‘no go’ case to one or two of stimulus types 1-4. Since the go type is really designed
to look at motor execution, I would not expect to see different areas show up in the go/nogo contrasts for different stimulus types.
Following an approximately 2-3 second execution phase (when the ‘go’ signal is not
received, this phase can be effectively removed from the design) where the scanner will
remain silent, the stimulus presentation software will trigger the scanner to collect three
full volumes of functional data. (see experimental protocol below). Following this
acquisition, there will be an approximately 10 second delay until the next stimulus
presentation in order to allow for the return of the BOLD signal to steady state.
Time
Visual presentation
of sequence to be
uttered (ns ms fixed)
Preparation phase
– white fixation
point (np ms
pseudo-random)
Execution phase – Data
red fixation point (ne acquisition
ms random jitter) phase (3 TR’s
+ delay)
Figure 1: Timeline for the presentation of a single stimulus
In total, the inter-stimulus interval is approximately 20-25 seconds.
Experimental Protocol
The experiment is event-related, using the triggering mechanism we have developed and
used in past studies. This will allow the subjects to speak in relative silence, which keeps
the production task as close to speech under natural conditions as possible inside the
magnet (see Munhall, 2001). The scanner will be triggered following the execution
phase (see Figure 1), and three full volumes will be collected. The acquisition parameters
will be typical of those used in our previous experiments (echo planar imaging, 30 slices
covering the entire cortex and cerebellum aligned to the AC-PC line, 5 mm slice
thickness, 0 mm gap between slices, flip angle = 90) with the exception that the TR will
be reduced to a minimal or near minimal duration for the magnet used (approximately 2s
in the Siemens 3T Trio whole-body system present at MGH Bay 4).
Subjects
Subjects will consist or right-handed (to reduce probability of right-lateralized speech
processing) men and women whose first language was American English.
Expected Results
By contrasting each of conditions 1-4 with condition 5, we should elicit the neural
circuits involved in representing and executing sequences of speech sounds. I expect to
see activations in SMA, pre-SMA, basal ganglia, cerebellum, primary motor cortex,
ventral pre-motor cortex and anterior cingulate, as well as in auditory areas responding to
the sound of one’s own voice.
Go / No Go Contrasts
Because in some cases, the subject will receive no ‘go’ signal, we can contrast each of
conditions 1-4 with ‘go’ signal with the corresponding ‘no go’ case. Since the sequential
information remains stored (and presumably prepared and ready for use) in the ‘no go’
case, we should find areas related to sequence selection and initiation, rather than
sequence representation by subtracting the ‘no go’ cases from the ‘go’ cases. I would
expect to see increased activation in basal ganglia and SMA proper (but not pre-SMA) as
well as motor cortex in these subtractions.
2 vs 1 (and also 4 vs 3) – These contrasts only change the phonemic (articulatory)
composition of each syllable in the sequence. Some researchers (e.g. MacNeilage, 1998)
have suggested that the “frame” and “content” for speech sequences may be somewhat
independent of one another. In these cases, the “frame” – meant here as the sequential
content across syllables – is constant, but the “content” of each syllable is varied.
Additionally, at least one case study (Ziegler et al, 1997) suggests that SMA may be
“blind to the segmental content of each syllable.” If there is no significant difference
between the simple and complex syllable content tasks proposed herein (in SMA and preSMA), this may lend support to these ideas. If there is a significant difference in
activation, we might consider that sequential representations in SMA/pre-SMA have
access to sub-syllabic information.
3 vs. 1 (and 4 vs. 2) – The difference between these conditions is the level of sequential
complexity. Presumably, the representation of a heterogeneous sequence requires more
resources in the brain region(s) responsible for that representation than does a
homogeneous sequence. This positive correlation between sequence complexity and
BOLD response has been seen in non-speech sequencing tasks. For example, Boecker et
al (1998) showed positive correlations between rCBF and key-press sequence complexity
in rostral SMA (pre-SMA), and the associated pallido-thalamic loop as well as in parietal
area 7 and primary motor cortex using PET. This supports the notion that pre-SMA is
primarily responsible for sequence representation, and I would expect to see a similar
positive correlation between sequence complexity in speech tasks. Since I am
hypothesizing that SMA proper is responsible for initiation of the motor plan, I would not
expect to see such a correlation between complexity and BOLD response here.)
References
Ackermann, H., Konczak, J., Hertrich, I. (1997). The temporal control of repetitive
articulatory movements in parkinson’s disease. Brain and Language, 56, 312-319.
Alexander, G.E., Crutcher, M.D. (1990). Functional architecture of basal ganglia circuits:
neural substrates of parallel processing. Trends in Neuroscience, 13(7), 266-271.
Boecker, H., Dagher, A., Ceballos-Baumann, A.O., Passingham, R.E., Samuel, M.,
Friston, K.J., Poline, J.-B., Dettmers, C., Conrad, B., Brooks, D.J. (1998). Role of the
human rostral supplementary motor area and the basal ganglia in motor sequence control:
Investigations with H215O PET. Journal of Neurophysiology, 79, 1070-1080.
Clower, W.T., Alexander, G.E. (1998). Movement sequence-related activity reflecting
numerical order of components in supplementary and presupplementary motor areas.
Journal of Neurophysiology, 80, 1562-1566.
Crosson, B., Sadek, J.R., Maron, L., Gökcay, D., Mohr, C.M., Auerbach, E.J., Freeman,
A.J., Leonard, C.M., Briggs, R.W. (2001). Journal of Cognitive Neuroscience, 13(2),
272-283.
Goldberg, G. (1985). Supplementary motor area structure and function: review and
hypotheses. Behavioral and Brain Sciences, 8, 567-616.
Hikosaka, O., Nakahara, H., Rand, M.K., Sakai, K., Lu, X., Nakamura, K., Miyachi, S.,
Doya, K. (1999). Parallel neural networks for learning sequential procedures. Trends in
Neuroscience, 22(10), 464-471.
Hlustik, P., Solodkin, A., Gullapalli, R.P., Noll, D.C., Small, S.L. (2002). Functional
lateralization of the human premotor cortex during sequential movements. Brain and
Cognition, 49, 54-62.
Ho, A.K., Bradshaw, J.L., Cunnington, R., Phillips, J.G., Iansek, R. (1998). Sequence
heterogeneity in parkinsonian speech. Brain and Language, 64, 122-145.
Ivry, R.B. (1996). The representation of temporal information in perception and motor
control. Current Opinion in Neurobiology, 6, 851-857.
Jonas, S. (1987). The supplementary motor region and speech. In: The Frontal Lobes
Revisited. New York: IRBN Press, 241-250.
Keller E. 1990. Speech motor timing. In Hardcastle W. J. and Marchal A. (eds) Speech
Production and Speech Modelling, 343-364. Dordrecht: Kluwer Academic Publishers.
Lieberman, P. (2001). Human language and our reptilian brain: the subcortical bases of
speech, syntax, and thought. Perspectives in Biology and Medicine, 44(1), 32-51.
Macar, F., Lejeune, H., Bonnet, M., Ferrara, A., Pouthas, V., Vidal, F., Maquet, P.
(2002). Activation of the supplementary motor area and of attentional networks during
temporal processing. Experimental Brain Research, 142, 475-485.
MacNeilage, P.F. (1998). The frame/content theory of evolution of speech production.
Behavioral and Brain Sciences, 21, 499-546.
Munhall, K.G. (2001). Functional imaging during speech production. Acta Psychologia,
107(1-3), 95-117.
Pai, M.C. (1999). Supplementary motor area aphasia: a case report. Clinical Neurology
and Neurosurgery, 101, 29-32.
Picard, N., Strick, P.L. (2001). Imaging the premotor areas. Current Opinion in
Neurobiology, 11, 663-672.
Pickett, E.R., Kuniholm, E., Protopapas, A., Friedman, J., Lieberman, P. (1998).
Selective speech motor, syntax and cognitive deficits associated with bilateral damage to
the putamen and the head of the caudate nucleus: a case study. Neuropsychologia, 36(2),
173-188.
Riecker, A., Ackermann, H., Wildgruber, D., Meyer, J., Dogil, G., Haider, H., Grodd, W.
(2000). Articulatory/Phonetic sequencing at the level of the anterior perisylvian cortex: A
functional magnetic resonance imaging (fMRI) study. Brain and Language, 75, 259-276.
Schubotz, R.I., von Cramon, D.Y. (2001). Interval and ordinal properties of sequences
are associated with distinct premotor areas. Cerebral Cortex, 11, 210-222.
Tanji, J. (2001). Sequential organization of multiple movements: involvement of cortical
motor areas. Annual Reviews of Neuroscience, 24, 631-651.
Tanji, K., Suzuki, K., Yamadori, A., Tabuchi, M., Endo, K., Fujii, T., Itoyama, Y. (2001).
Pure anarthria with predominantly sequencing errors in phoneme articulation: a case
report. Cortex, 37, 671-678.
Verwey, W.B., Lammens, R., van Honk, J. (2002). On the role of the SMA in the discrete
sequence production task: a TMS study. Neuropsychologia, 40, 1268-1276.
Wildgruber, D., Kischka, U., Ackermann, H., Klose, U., Grodd, W. (1999). Dynamic
pattern of brain activation during sequencing of word strings evaluated by fMRI.
Cognitive Brain Research, 7, 285-294.
Ziegler, W., Kilian, B., Deger, K. (1997). The role of the left mesial frontal cortex in
fluent speech: Evidence from a case of left supplementary motor area hemorrhage.
Neuropsychologia, 35(9), 1197-1208.