Age-independent activation in areas of the mirror neuron system during action observation and action imagery. A fMRI study

Nedelko, Violetta; Hassa, Thomas; Hamzei, Farsin; Weiller, Cornelius; Binkofski, Ferdinand; Schoenfeld, Mircea Ariel; Tüscher, Oliver; Dettmers, Christian

doi:10.3233/rnn-2010-0542

Cited by 52 publications

(49 citation statements)

References 42 publications

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“…However, the sub-cortical sources were only identified for the MEG and the combined approach (MEG+EEG). These same network of sources have been previously described in EEG [24], [53], [54], MEG [21]–[23], [55], functional magnetic resonance imaging (fMRI) [56], [57] and in a positron-emission tomography (PET) study [23]. In a combined EEG and fMRI study, similar sources were identified for the voluntary movements [54].…”

Section: Discussionsupporting

confidence: 74%

Beamformer Source Analysis and Connectivity on Concurrent EEG and MEG Data during Voluntary Movements

et al. 2014

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Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2–4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.

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Section: Discussionsupporting

confidence: 74%

Beamformer Source Analysis and Connectivity on Concurrent EEG and MEG Data during Voluntary Movements

et al. 2014

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“…With respect to the AON in particular, we did not find age-related activation differences that reached cluster-corrected significance as in Nedelko et al [58]. However, the comparison between the action categories within each age group indicated that AON activity was mainly right lateralized in younger adults and bilaterally active in older adults during the prediction of movement exercises compared to figure skating elements.…”

Section: Discussioncontrasting

confidence: 47%

“…By using transcranial magnetic stimulation (TMS), Léonard and Tremblay [57] showed that corticomotor facilitation in relevant muscles is less specialized in older compared to younger adults during action observation, imitation, and imagery. In addition, Nedelko et al [58] did not report age-related activation differences in the premotor and inferior parietal regions of the AON during observation and imagery of simple goal directed actions. The authors concluded that activity in these regions is age-independent.…”

Section: Introductionmentioning

confidence: 95%

Action Prediction in Younger versus Older Adults: Neural Correlates of Motor Familiarity

et al. 2013

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Generating predictions during action observation is essential for efficient navigation through our social environment. With age, the sensitivity in action prediction declines. In younger adults, the action observation network (AON), consisting of premotor, parietal and occipitotemporal cortices, has been implicated in transforming executed and observed actions into a common code. Much less is known about age-related changes in the neural representation of observed actions. Using fMRI, the present study measured brain activity in younger and older adults during the prediction of temporarily occluded actions (figure skating elements and simple movement exercises). All participants were highly familiar with the movement exercises whereas only some participants were experienced figure skaters. With respect to the AON, the results confirm that this network was preferentially engaged for the more familiar movement exercises. Compared to younger adults, older adults recruited visual regions to perform the task and, additionally, the hippocampus and caudate when the observed actions were familiar to them. Thus, instead of effectively exploiting the sensorimotor matching properties of the AON, older adults seemed to rely predominantly on the visual dynamics of the observed actions to perform the task. Our data further suggest that the caudate played an important role during the prediction of the less familiar figure skating elements in better-performing groups. Together, these findings show that action prediction engages a distributed network in the brain, which is modulated by the content of the observed actions and the age and experience of the observer.

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“…Increasingly bilateral motor cortex activation is correlated with contralateral ventral premotor cortex activation. Studies have found increased multisensory activation including the somatosensory cortices (Zwergal et al, 2012) visual cortical areas (Zwergal et al, 2012) (Nedelko et al, 2010) and the superior parietal lobule (Nedelko et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

Effects of healthy ageing on activation pattern within the brain during movement and motor imagery: an fMRI study

Budhdeo

Baron

Sharma

2019

Preprint

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Introduction: Motor imagery (MI) has potential as an intervention to improve performance in neurological disease affecting the motor system and to modulate brain computer interfaces (BCI). We hypothesized that the shared networks of MI and executed movement (EM) would be affected by age. Understanding these changes is important in application of MI in neurological disorders.Methods: Using tensor-independent component analysis (TICA), we mapped the neural networks involved during MI and EM in 31 healthy volunteers (ages 20-72), who were recruited and screened for their ability to perform imagery. We used an fMRI block-design with MI & rest and EM & rest.Results: TICA defined 37 independent components (ICs). Eight remained after excluding ICs representing artifacts. These ICs accounted for 35% of variance. While all ICs had greater activation in EM than MI. Two ICs increased with greater age for EM only. These ICs contained a bilateral network of brain areas, including primary motor cortex and cerebellum.Conclusion: This study demonstrates the prominence of shared cerebral networks between MI and EM. There are age-dependent changes to EM activation, while MI activation appeared age independent. This strengthens the rationale for using MI to access the motor networks independent of age. 4

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Age-independent activation in areas of the mirror neuron system during action observation and action imagery. A fMRI study

Cited by 52 publications

References 42 publications

Beamformer Source Analysis and Connectivity on Concurrent EEG and MEG Data during Voluntary Movements

Beamformer Source Analysis and Connectivity on Concurrent EEG and MEG Data during Voluntary Movements

Action Prediction in Younger versus Older Adults: Neural Correlates of Motor Familiarity

Effects of healthy ageing on activation pattern within the brain during movement and motor imagery: an fMRI study

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