Pascal Grosse: Diagnostic and experimental applications of cortico-muscular and intermuscular frequency analysis |
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Klinik und Poliklinik für Neurologie
Habilitationsschrift
Diagnostic and experimental applications of cortico-muscular and intermuscular frequency analysis
zur Erlangung der Lehrbefähigung im Fach Neurologie
Charité-Universitätsmedizin Berlin, Campus Virchow-Klinikum
Pascal
Grosse
Dekan: Prof. Dr. med. Paul Martin
Gutachter:
1. Prof. Dr. med. Benecke
2. Prof. Dr. med. Deuschl
eingereicht: September 2003
Datum der Habilitation: 17.5.2004
Abstract
It can be shown in this work that distinct patterns of cortico-muscular and/or intermuscular coherence can be identfied in a variety of movement disorders (cortical myoclonus, limb dystonia, myoclonus of CBD). Additionally, it could be demonstrated that the assessment of the reticulospinal system is feasible by using intermuscular frequency analysis of homologous muscles, which might open up a new line of research of subcortical drives within the motor system.
Keywords:
frequency analysis,
myoclonus,
corticobasal degeneration,
startle,
limb dystonia
Zusammenfassung
In dieser Arbeit kann gezeigt warden, dass mit der kortiko-muskulären und intermuskulären Frequenzanalyse distinkte Koheränzmuster bei verschiedenen Bewegungsstörungen (kortikaler Myoklonus, Extremitätendystonie, Myoklonus bei kortikobasaler Degeneration) identifiziert werden können. Ferner konnte gezeigt werden, dass das retikulospinale System mit der intermuskulären Frequenzanalyse untersucht werden kann, was neue Perspektiven bei der Untersuchung subkortikaler Abschnitte des motorischen Systems ermöglicht.
Eigene Schlagworte:
Frequenzanalyse,
Myoklonus,
Extremitätendystonie,
kortiko-basalganglionäre Degeneration,
startle
Table of contents
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1.
Introduction
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1.1. Physiological drives to muscle
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1.2. Frequency analysis in pathological conditions
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2.
Methodology
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2.1. Coherence
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2.2. Phase
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2.3. Cumulant density estimate
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2.4. Surrogate measures of cortico-muscular coupling: EMG-EMG frequency analysis
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2.5. General problems of recording and interpretation
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3.
Frequency analysis in high frequency rhythmic myoclonus
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4.
EMG-EMG-frequency analysis in limb dystonia
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4.1. Patients and methods
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4.2.
Results
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4.2.1. Clinical appearance and raw EMG in the lower limb
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4.2.2.
Frequency analysis in the lower leg
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4.2.3.
EMG-EMG Coherence in the upper extremity
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4.3.
Discussion
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5.
Coherence analysis in the myoclonus of corticobasal degeneration
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6.
Bilaterally synchronous oscillatory EMG-EMG activity evoked by the acoustic startle the healthy human
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7.
Summary and perspectives
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References
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Publications of work incorporated in this thesis
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Abbreviations
Tables
Images
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Fig. 1: Hans Edmund Piper, German physiologist, born 1877, died 1915. Read biology in Kiel, Munich, Berlin and Freiburg; PhD in Freiburg in 1902. Research assistant at the Institute of Physiology in Berlin, later in Kiel. In 1908 he became head of the department for physics at the Institute of Physiology in Berlin, 1909 promotion to professor. Initially he focussed his research on embryology, his later work encompassed mostly physiological topics, in particular optics, acoustics. the physiology of muscles and nerves and a theory on electrical currents in the retina where he developed the “Piper’s law”. (From: Abeßer, Elke/Schubert, Ernst. Das Berliner Physiologische Institut der Humboldt-Universität. 100 Jahre nach seiner Gründung. Wissenschaftliche Schriftenreihe der Humboldt-Universität zu Berlin. Berlin 1977, p. 29)
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Fig. 2.1.: Schematic overview of the different methodological approaches to signal analysis in the frequency domain. Note that FFT based models can only be applied with signals assumed to be stationary whereas wavelet analysis and autoregressive models can additionally analyse non-stationary signals. For details see text
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Fig. 2.2.: Example of data processing. (A) Raw EMG from 1DI high-pass filtered at 0.53 Hz and recorded during self-paced movement at ~5 Hz. Note prominent movement artefact between EMG bursts. (B) Simultaneously recorded raw EMG high-pass filtered at 53 Hz. Movement artefact is much reduced. (C) EMG as in (B) but full-wave rectified. (D) Product of levelling signal in (C) to give a point process. (E) Power spectra corresponding to EMG in (A) and (B). Power between the two differs by a factor of ~100 (note logarithmic scale), although qualitatively the autospectra are similar. The difference in power is most marked at the tremor frequency of 5 Hz and is largely due to the presence of movement artefact with a high-pass filter of 0.53 Hz. (F) Power spectrum of rectified high-pass filtered EMG from (C). Rectification increases power and emphasises the tremor peak at 5 Hz. (G) Spectra of point processes derived from levelling rectified EMG filtered at 0.53 Hz and 53 Hz. Power spectra are almost identical, confirming that high pass filtering at 53 Hz does not diminish information about interspike intervals in the multi-unit EMG record. It is the spike timing information that is important in determining the coherence between different EMG signals. Levelling, however, diminishes the effects of low-level signals such as movement artefact or volume conduction.
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Fig. 3.1: Frequency analysis in case 3. (A) Left scalp EEG and EMG from right-sided deltoid, finger extensors and APB. EMG shows myoclonic bursts at high frequency (~13-15 Hz). (B) Autospectrum of right APB and F3-C3. (C) Coherence between right APB and F3-C3 showing exaggerated EEG-EMG coherence in the range 6 - 19 Hz. The thin horizontal line is the 95% confidence level. (D) Phase between right APB and F3-C3. The thin lines either side of the phase estimate (thick line) are the 95% confidence levels. EEG precedes EMG. Regression analysis gave a time lag between the two signals of 15.7 ms (± 2.8 ms 95% confidence limits). (E) Cumulant density function showing a negative EEG deflection with a peak about 15 ms before the EMG. EMG was the input. Thin horizontal lines are 95% confidence levels.
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Fig. 3.2: Comparison of frequency content and degree of coherence between EEG-EMG and EMG-EMG for proximal (A) and distal (B) muscle pairs in case 3.
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Fig. 3.3: Areas of transformed EEG-EMG coherence, taken from above the 95% confidence level, in each patient for deltoid (A), finger extensor (B) and intrinsic hand muscles (C). Transformed coherence is plotted on a log scale; overlap is evident between individual patients and healthy subjects. (D) Transformed coherence areas averaged across patients and healthy subjects (asterixed) with 95%-confidence level of each mean.
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Fig. 3.4: Averaged transformed EEG-EMG coherence spectra in patients and normal subjects for deltoid (A), finger extensor (B) and intrinsic hand muscles (C). The level of averaged transformed coherence is higher in paients compared to healthy subjects in all three muscles (bold lines), but with overlap of the 95%-confidence limits of the mean (thin lines) between the two groups.
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Fig. 3.5: Areas of transformed EMG-EMG coherence, taken from above the 95% confidence level, in each patient for deltoid-finger extensor (A) and finger extensor-intrinsic hand muscles (B). Transformed coherence is plotted on a log scale. In comparison to respective transformed EEG-EMG coherences (fig 3) there is less overlap between patients and healthy subjects. (C) Transformed coherence areas averaged across patients and healthy subjects (asterixed) with 95%-confidence level of each mean.
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Fig. 3.6: Averaged transformed EMG-EMG coherence spectra in patients and normal subjects for deltoid-finger extensor (A) and finger extensor-intrinsic hand muscles (B). The level of averaged transformed coherence is higher in patients compared to normals, but unlike fig 4 there is no overlap of the 95%-confidence limits of the mean (thin lines) between the two groups over the 6-30 Hz band. Data were smoothed with a 3 point moving average.
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Fig. 3.7: Phase relationships in patients for proximal and distal muscles. Only patients meeting our criteria for calculation of temporal delays (see methods) are included. Horizontal lines are the 95%-confidence limit of the temporal delay in each subject. A positive sign for EEG-EMG (A, C, D) indicates that EEG leads EMG. When negative EMG leads EEG. A positive sign for EMG-EMG (B, E) indicates that the more proximal muscle leads the more distal muscle. Note that for distal muscles (A, B) the phase relation indicates a dominant efferent drive between cortex and muscle as well between muscles. For proximal muscles (C-E) phase suggests efferent and afferent drives in individual patients.
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Fig 3.8: Examples of back-averaged contralateral EEG. (A) Back-averaged EEG (black line) of left finger extensor of case 3 fails to disclose a cortical correlate, although the cumulant density estimate (grey line) shows a maximal positive deflection that follows EMG onset and exceeds the 95%-confidence limits (dotted grey lines). (B) Back-average and cumulant density estimate compared in case 3 (same as Fig 1). Note that positive deflections are symmetrical and therefore the temporal difference between EEG and EMG was ambiguous with these time domain measures. In contrast, phase spectra (Fig 1 d) in the same patient clearly showed that EEG lead EMG. (C) Back-average in case 1. The peak positive deflection is ambiguous, but the oscillatory nature of the back-averaged EEG can be seen at a frequency of 14 Hz. (D) Unambiguous back-average in case 2. The peak positive deflection in the EEG precedes EMG onset in the right finger extensors by 23 ms. Note the oscillatory nature of the back-averaged EEG at a frequency of 22 Hz. In each case EMG is rectified and the same data were analysed to give the back-average and cumulant density estimate.
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Fig. 3.9: Time lag between EEG and EMG based on back-averaged EEG where a negative sign indicates that EEG leads EMG and a positive sign suggests that the EMG signal precedes the EEG. Note that results are comparable to phase estimates calculated by frequency analysis (fig. 7), although deriving partly from differrent individuals (asterixes indicate identical cases represented in fig. 7). In particular, there is a wide variation of time lags suggestive of afferent and/or efferent conduction. Except in one case, EEG always leads EMG in the hand, while there is a mixed pattern of efferent and afferent conduction between EEG and finger extensor EMG.
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Fig. 4.1: Raw EMG recordings of lower leg muscles in different patient groups. Patients sitting with the ankle actively dorsiflexed. (A) and (B) symptomatic DYT1 patients with synchronous muscle bursts of 50 (A, case 2) and 200 ms (B, case 3) duration at 4 Hz. Note that in A antagonistic muscles (GC, TA) are co-contracting. (C) Symptomatic DYT1 patient (case 11) with no involvement of the lower leg. The interference pattern is normal. (D) Patient with fixed dystonia (case 31) with a ~8 Hz bursting pattern. Patient had a fixed posture with the ankle joint plantar-flexed at ~50° and was asked to try to dorsiflex the ankle. All recordings are surface EMGs, except for GC.
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Fig. 4.2: Frequency analysis in the lower leg muscles. (A) Averaged %-power spectra of proximal and distal aspects of TA show a peak for symptomatic DYT1 patients at 4-7 Hz. (B) Averaged transformed EMG-EMG coherences for TA in the different groups. Note the distinct peak centred at ~5 Hz in the symptomatic DYT1 patients, which is absent in the other groups. (C) Individual coherence spectra in case 9 (symptomatic DYT1). Coherence at 4-7 Hz is present regardless of whether tremor was clinically evident or not. (D) Averaged transformed coherences in the 3 frequency bands. The 4-7 Hz activity was greater in symptomatic DYT1 patients (asterix = p<0.05). (E). Time delay estimates (G) between proximal and distal aspects of TA, confirming conduction delay. Bars are 95%-confidence limits. (F) Abnormal coupling between TA and GC with a distinct peak at ~4 Hz in the same patient as Fig. 1A (levelled EMG). Error bars indicate the standard error of the mean.
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Fig. 4.3: Coherence patterns in upper extremity muscles in symptomatic DYT1 patients. (A) Case 1. Inset: regular bursting pattern in TA and forearm flexors (FF). Coherence spectra confirm exaggerated EMG-EMG coherence in the 4-7 Hz range in upper and lower limb. FE = finger extensor. (B) Case 5. Inset: 7 Hz bursting pattern in TA is not reflected in upper extremity muscles. Instead alternating bursts prevail in FF and FE at ~2-3 Hz. Coherence spectra confirm exaggerated EMG-EMG coherence in the 4-7 Hz range in lower but not upper limb.
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Fig. 5.1: (A): Raw EEG and EMG of case 1 exhibiting irrregular short myoclonic bursts at an average frequency of ~12 Hz during a postural contraction. (B) Normalised autospectra of EEG over FC3-C3 and EMG from finger extensor and 1DI. (C) Intermuscular coherence (thick line) between finger extensor and 1DI discloses exaggerated coherence up to 58 Hz. Note that the partial coherence between the two muscles (thin line) with the EEG as predictor is only slightly lower from 6 to 12 Hz.
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Fig. 5.2: Pooled results in the 5 patients compared to age-matched healthy subjects. Normalised power for EEG (A), finger extensor (B) and 1DI (C). Pooled transformed coherence for finger extensor and 1DI is inflated in the range up to 60 Hz with a peak centered around 15 Hz (D). Note that EMG-EMG coherence for patients with established high frequency cortical myoclonus is less exaggerated and occupies a narrower frequency band. (E) In CBD patients coherence is significantly different from both normals and patients with cortical myoclonus across 8-30 Hz and 31-60 Hz, while for patients with cortical myoclonus only the 8-30 Hz band is statistically different from normals. Error bars indicate standard error of the mean. (*=p<0.05). (F) Time delays between the two muscles for patients with CBD, patients with cortical myoclonus and healthy controls showing an appropriate delay between 1DI and finger extensors, thereby indicating that high levels of coherence were not due to volume conduction.
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Fig. 5.3: (A) Transformed coherence between affected side (FC3/C3-right 1DI) and unaffected side (FC4/C4-left 1DI) in case 1 showing exaggerated coherence on the affected side up to 18 Hz with a distinct peak at 10 Hz which is neither present on the unaffected side nor in averaged coherence in normals. Compare this narrow EEG-EMG coherence with the broad band of EMG-EMG coherence in the same patient shown in Fig 1C. (B) Phase spectrum on the affected side discloses that right 1DI EMG leads EEG by 28.4 ± 7.7ms.
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Fig. 6.1. EMG record of a typical reflex startle (A) and voluntary sham startle (B) in the same healthy subject. Note phasic discharges repeating every 70-80 ms in deltoid during the ASR.
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Fig. 6.2. Averaged spectra of the percentage total EMG power in ASR, voluntary sham startles and tonic voluntary contraction in deltoid (A), biceps (B) and 1DI (C). Homologous muscles from the two sides of the body have been pooled in 15 subjects to give 390 data blocks. Note the peak centred around 14 Hz during reflex startles in deltoid (arrowed).
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Fig. 6.3. Coherence spectra between right and left deltoid (A), biceps (C) and 1DI (E) during ASR, voluntary sham startles and tonic voluntary contraction and cumulant density estimates for the same muscles during the ASR (B, D and F). Only the spectra from the ASR in deltoid and biceps have a discrete peak in coherence around 14 Hz (arrows). The coherence spectrum for levelled deltoid-deltoid EMG pooled over 15 subjects is shown in the inset to (A). Note the peak at around 14 Hz (arrow) in the point process coherence in the ASR but not sham startles or voluntary contraction. There is also considerable coherence <10 Hz. This was diminished by detrending the data (not shown), although the latter did not affect the coherence in the 10-20 Hz band. The cumulant density estimate (black line) for deltoid (B) has a broad central peak with side-lobes every 70 ms during the ASR. Side-lobes are less distinct in biceps (D) and absent in 1DI (F). Cross-correlograms (grey lines in B, D and F) match the cumulant density estimates in deltoid and biceps (black lines in B, D, F). Peak-to-peak r-value in deltoid is 0.16; same scaling for biceps and 1DI. Dottes lines indicate 95% confidence limit of the cumlant density estimate. 100 s of data drawn from 15 subjects.
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Fig. 6.4. Five blocks of 20 s of data have been analysed, the power normalised, the coherence transformed and averaged for the 10-20 and 20-30 Hz bands in deltoid and 1DI. (A) Normalised power. (B) Transformed coherences. Bars indicate standard error of the means. Asterixes indicate statistically significant differences between conditions (p<0.05).
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Fig. 6.5. Power and coherence spectra (insets) in reflex and voluntary sham startles in two subjects. A. 15 s concatenated data. B. 13s concatenated data. Note the spectral peaks at about 14 Hz (arrowed) in proximal muscle pairs in the ASR.
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