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71. Functional network properties are altered in focal epilepsies
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Society Proceedings / Clinical Neurophysiology 120 (2009) e9–e88
(B) to investigate if anodal transcranial direct current stimulation (tDCS) applied to the primary motor cortex (M1) can improve motor sequence learning. Methods: (A) Motor sequence learning of a five-digit finger sequence was tested in a group of 14 healthy elderly (56–88 years; 67.9 ± 2.3) and 14 healthy young (22–28 years; 24.5+/ 0.5). The subjects attended a training (TRAIN) composed of five blocks each three min long with a 2 min break in between. 90 min (RECALL) after TRAIN the effects were re-evaluated. The primary outcome measure was the number of correct sequences. (B) In a similar experimental setup, the effects of anodal tDCS (1 mA, 20 min, left M1) on motor sequence learning were tested in a randomised, double-blind cross-over placebo-controlled design in 10 healthy elderly (57–88 years; 68.1 ± 2.9 years) and 10 healthy young (22–31 years; 25.2 ± 2.9 years). During TRAIN, tDCS or sham stimulation (ShS) was applied. 90 min (RE-90) and 24 h (RE-24) after TRAIN the effects were re-evaluated. Results: (A) Besides a decline in general level of performance the elderly showed a lack of improvement during TRAIN (slopeelderly 0.07 ± 1.1; slopeyoung 6.39 ± 0.9) (T = 4.59, P < 0.01). This missing effect of training-induced improvement in the elderly led to a persistent decline in performance measurable at RECALL. ANOVA-RM revealed a significant TIME (TRAIN, RECALL) GROUP (elderly, young) interaction on overall (F = 15.0; P < 0.01) and correct numbers of sequences (F = 9.35; P < 0.01). (B) The lack of improvement during TRAIN in the elderly was restored under tDCS with a slope of improvement comparable to those of the young (slopetDCS 4.65 ± 0.9; slopeShS 0.48 ± 1.4) (T = 4.2, P < 0.05). The more effective training let to a persistent better performance at RE-90 and RE-24. ANOVA-RM revealed a significant TIME (Train, RE-90, RE24 h) INTERVENTION (tDCS, ShS) interaction on the number of overall sequences (F = 6.5, P < 0.01) and correct sequences (F = 10.4, P < 0.01) with significant differences between tDCS and ShS at TRAIN, RE-90 and RE-24. In the young tDCS showed no effect on TRAIN compared to ShS (slopetDCS 7.1 ± 1.0; slopeShS 6.3 ± 1.3). Conclusion: The findings support the view that apart from higher order cognitive functions normal aging also affects motor learning determined in an explicit motor learning task. Cortical stimulation can enhance the effects of motor sequence learning in the elderly and restore them to a level comparable to young subjects. This tDCS-induced functional improvement is age-related and did not occur in the young.
to characterize complex networks. We here investigated whether these network characteristics can help to distinguish between normal and disturbed brain functioning. Specifically, we address the question whether these network characteristics differ in patients with focal epilepsies and in age-matched controls. Methods: We simultaneously recorded EEG and MEG in patients suffering from mesial temporal lobe epilepsies (MTLE; 11 patients) or neocortical lesional epilepsies (NLE; 10 patients) and in 21 agematched controls during eyes-open/eyes-closed conditions (15 min each). Using a moving-window approach (duration of each window: 16 s corresponding to 4096 data points; no overlap) we calculated, for each channel combination, the mean phase coherence as a measure for phase synchronization. Phases were obtained by applying either the Hilbert-transform (broad-band signals) or the wavelet transform, which allowed us to specifically concentrate on interactions in the d-, h-, a-, b- and c-band. Using these measures we constructed, for each window, a weighted network and calculated the averaged mean path length L and the cluster coefficient C for each condition. Results: For both patients and control subjects, we could not observe a clear-cut difference in network parameters (either EEG or MEG) for the conditions eyes-open and eyes-closed. Nevertheless, the mean path lengthL and the cluster coefficient C attained significantly different values for the control and the patient group. This was most pronounced for functional networks derived from the EEG, and best discrimination could be achieved for couplings in the d- and b-band. L was smaller while C was larger for the patient group. Interestingly, the mean path length L for functional networks derived from the MEG attained significantly different values for MTLE and NLE patients, although it varied between different frequency bands. Discussion: Our findings indicate that functionally defined networks from either the EEG or the MEG reflect different aspects of normal and disturbed brain functioning. Statistical properties such as the mean path length or the cluster coefficient can be regarded as helpful in characterizing these networks, since they allow an efficient compression of the complex information content in multichannel EEG/MEG recordings. Acknowledgement This work was supported by the Deutsche Forschungsgemeinschaft (Grant Nos. SFB-TR3 sub-project A2). References
doi:10.1016/j.clinph.2008.07.069
71. Functional network properties are altered in focal epilepsies— M.-T. Horstmann 1,2,3, N. Noennig 4, H. Hinrichs 4, K. Lehnertz 1,2,3 (1 University of Bonn, Department of Epileptology, Bonn, Germany, 2 University of Bonn, Helmholtz-Institute for Radiation and Nuclear Physics, Bonn, Germany, 3 University of Bonn, Interdisciplinary Center for Complex Systems, Bonn, Germany, 4 University of Magdeburg, Department of Neurology II, Magdeburg, Germany) Introduction: There is currently an increasing interest in the developing refined analysis methods that can help to understand the relationship between structure and function in complex systems (Boccaletti, 2006). Recent studies show that new insights into normal and disturbed brain functioning can be achieved by considering the brain as a complex network of interacting dynamical systems (Reijneveld, 2007). A number of analysis methods already allow one to characterize statistical and spectral properties of functionally (e.g. via EEG or MEG recordings) defined networks, and the mean path length L and the cluster coefficient C have been widely used
Boccaletti S et al. Complex networks: structure and dynamics. Phys. Rep. 2006;424:175–308. Reijneveld JC et al. The application of graph theoretical analysis to complex networks in the brain. Clin. Neurophysiol. 2007;118:2317–31. doi:10.1016/j.clinph.2008.07.070
72. Bilateral synchronous motor responses over the M. biceps brachii after stimulation of the sural nerve—M. Kornhuber, M. Theis, S. Zierz (Universität Halle-Wittenberg, Halle/Saale, Germany) Introduction: Long latency reflexes (LLRs) are poorly understood motor responses (MRs) that occur after mechanical or electrical sensory stimuli. The presented data are in line with the concept that LLR evoked by remote stimuli may reflect non-thalamically transmitted arousal responses. Methods: MRs were recorded over the tonically active biceps brachii (BB) muscle on either side of 11 healthy volunteers after supramaximal stimulation of the left sural nerve (1 Hz single stimuli or trains of 2, 3, 4, or 5 stimuli with an interstimulus-interval of 3 ms).
Society Proceedings / Clinical Neurophysiology 120 (2009) e9–e88
(B) to investigate if anodal transcranial direct current stimulation (tDCS) applied to the primary motor cortex (M1) can improve motor sequence learning. Methods: (A) Motor sequence learning of a five-digit finger sequence was tested in a group of 14 healthy elderly (56–88 years; 67.9 ± 2.3) and 14 healthy young (22–28 years; 24.5+/ 0.5). The subjects attended a training (TRAIN) composed of five blocks each three min long with a 2 min break in between. 90 min (RECALL) after TRAIN the effects were re-evaluated. The primary outcome measure was the number of correct sequences. (B) In a similar experimental setup, the effects of anodal tDCS (1 mA, 20 min, left M1) on motor sequence learning were tested in a randomised, double-blind cross-over placebo-controlled design in 10 healthy elderly (57–88 years; 68.1 ± 2.9 years) and 10 healthy young (22–31 years; 25.2 ± 2.9 years). During TRAIN, tDCS or sham stimulation (ShS) was applied. 90 min (RE-90) and 24 h (RE-24) after TRAIN the effects were re-evaluated. Results: (A) Besides a decline in general level of performance the elderly showed a lack of improvement during TRAIN (slopeelderly 0.07 ± 1.1; slopeyoung 6.39 ± 0.9) (T = 4.59, P < 0.01). This missing effect of training-induced improvement in the elderly led to a persistent decline in performance measurable at RECALL. ANOVA-RM revealed a significant TIME (TRAIN, RECALL) GROUP (elderly, young) interaction on overall (F = 15.0; P < 0.01) and correct numbers of sequences (F = 9.35; P < 0.01). (B) The lack of improvement during TRAIN in the elderly was restored under tDCS with a slope of improvement comparable to those of the young (slopetDCS 4.65 ± 0.9; slopeShS 0.48 ± 1.4) (T = 4.2, P < 0.05). The more effective training let to a persistent better performance at RE-90 and RE-24. ANOVA-RM revealed a significant TIME (Train, RE-90, RE24 h) INTERVENTION (tDCS, ShS) interaction on the number of overall sequences (F = 6.5, P < 0.01) and correct sequences (F = 10.4, P < 0.01) with significant differences between tDCS and ShS at TRAIN, RE-90 and RE-24. In the young tDCS showed no effect on TRAIN compared to ShS (slopetDCS 7.1 ± 1.0; slopeShS 6.3 ± 1.3). Conclusion: The findings support the view that apart from higher order cognitive functions normal aging also affects motor learning determined in an explicit motor learning task. Cortical stimulation can enhance the effects of motor sequence learning in the elderly and restore them to a level comparable to young subjects. This tDCS-induced functional improvement is age-related and did not occur in the young.
to characterize complex networks. We here investigated whether these network characteristics can help to distinguish between normal and disturbed brain functioning. Specifically, we address the question whether these network characteristics differ in patients with focal epilepsies and in age-matched controls. Methods: We simultaneously recorded EEG and MEG in patients suffering from mesial temporal lobe epilepsies (MTLE; 11 patients) or neocortical lesional epilepsies (NLE; 10 patients) and in 21 agematched controls during eyes-open/eyes-closed conditions (15 min each). Using a moving-window approach (duration of each window: 16 s corresponding to 4096 data points; no overlap) we calculated, for each channel combination, the mean phase coherence as a measure for phase synchronization. Phases were obtained by applying either the Hilbert-transform (broad-band signals) or the wavelet transform, which allowed us to specifically concentrate on interactions in the d-, h-, a-, b- and c-band. Using these measures we constructed, for each window, a weighted network and calculated the averaged mean path length L and the cluster coefficient C for each condition. Results: For both patients and control subjects, we could not observe a clear-cut difference in network parameters (either EEG or MEG) for the conditions eyes-open and eyes-closed. Nevertheless, the mean path lengthL and the cluster coefficient C attained significantly different values for the control and the patient group. This was most pronounced for functional networks derived from the EEG, and best discrimination could be achieved for couplings in the d- and b-band. L was smaller while C was larger for the patient group. Interestingly, the mean path length L for functional networks derived from the MEG attained significantly different values for MTLE and NLE patients, although it varied between different frequency bands. Discussion: Our findings indicate that functionally defined networks from either the EEG or the MEG reflect different aspects of normal and disturbed brain functioning. Statistical properties such as the mean path length or the cluster coefficient can be regarded as helpful in characterizing these networks, since they allow an efficient compression of the complex information content in multichannel EEG/MEG recordings. Acknowledgement This work was supported by the Deutsche Forschungsgemeinschaft (Grant Nos. SFB-TR3 sub-project A2). References
doi:10.1016/j.clinph.2008.07.069
71. Functional network properties are altered in focal epilepsies— M.-T. Horstmann 1,2,3, N. Noennig 4, H. Hinrichs 4, K. Lehnertz 1,2,3 (1 University of Bonn, Department of Epileptology, Bonn, Germany, 2 University of Bonn, Helmholtz-Institute for Radiation and Nuclear Physics, Bonn, Germany, 3 University of Bonn, Interdisciplinary Center for Complex Systems, Bonn, Germany, 4 University of Magdeburg, Department of Neurology II, Magdeburg, Germany) Introduction: There is currently an increasing interest in the developing refined analysis methods that can help to understand the relationship between structure and function in complex systems (Boccaletti, 2006). Recent studies show that new insights into normal and disturbed brain functioning can be achieved by considering the brain as a complex network of interacting dynamical systems (Reijneveld, 2007). A number of analysis methods already allow one to characterize statistical and spectral properties of functionally (e.g. via EEG or MEG recordings) defined networks, and the mean path length L and the cluster coefficient C have been widely used
Boccaletti S et al. Complex networks: structure and dynamics. Phys. Rep. 2006;424:175–308. Reijneveld JC et al. The application of graph theoretical analysis to complex networks in the brain. Clin. Neurophysiol. 2007;118:2317–31. doi:10.1016/j.clinph.2008.07.070
72. Bilateral synchronous motor responses over the M. biceps brachii after stimulation of the sural nerve—M. Kornhuber, M. Theis, S. Zierz (Universität Halle-Wittenberg, Halle/Saale, Germany) Introduction: Long latency reflexes (LLRs) are poorly understood motor responses (MRs) that occur after mechanical or electrical sensory stimuli. The presented data are in line with the concept that LLR evoked by remote stimuli may reflect non-thalamically transmitted arousal responses. Methods: MRs were recorded over the tonically active biceps brachii (BB) muscle on either side of 11 healthy volunteers after supramaximal stimulation of the left sural nerve (1 Hz single stimuli or trains of 2, 3, 4, or 5 stimuli with an interstimulus-interval of 3 ms).