Cortical processes associated with continuous balance control as revealed by EEG spectral power

Hülsdünker, Thorben; Mierau, Andreas; Neeb, C.; Kleinöder, Heinz; Strüder, Heiko K.

doi:10.1016/j.neulet.2015.02.049

Cited by 138 publications

(171 citation statements)

References 26 publications

Supporting

Mentioning

144

Contrasting

Order By: Relevance

“…Recent studies have also investigated theta band increases in challenging balance tasks in frontal, central, and parietal areas (Hülsdünker et al, 2015, 2016; Chang et al, 2016). Hülsdünker et al (2015) found that theta power over the fronto-central and central-parietal areas correlated with balance performance.…”

Section: Resultsmentioning

confidence: 99%

“…Hülsdünker et al (2015) found that theta power over the fronto-central and central-parietal areas correlated with balance performance. Slobounov et al (2009) found a midline theta burst during the shift from the stable stage to transition-to-instability stage which was localized to the ACC.…”

Section: Resultsmentioning

confidence: 99%

“…Hülsdünker et al (2015) and Hülsdünker et al (2016) identified and removed ocular or muscular artifacts based on the cortical mapping, frequency spectrum, and time course components using ICA. Bruijn et al (2015) described the following criteria for categorizing and removing components not associated with brain activity: Muscle artifact (50–100 Hz mean power larger than that in the beta and/or alpha bands), eye-blink artifacts (median frequency <3 Hz and the topo map corresponded to eye components), and movement artifacts (frequency spectrum at the harmonics of stride frequency).…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Neuroimaging of Human Balance Control: A Systematic Review

Wittenberg

Thompson

Nam

et al. 2017

Front. Hum. Neurosci.

116

View full text Add to dashboard Cite

This review examined 83 articles using neuroimaging modalities to investigate the neural correlates underlying static and dynamic human balance control, with aims to support future mobile neuroimaging research in the balance control domain. Furthermore, this review analyzed the mobility of the neuroimaging hardware and research paradigms as well as the analytical methodology to identify and remove movement artifact in the acquired brain signal. We found that the majority of static balance control tasks utilized mechanical perturbations to invoke feet-in-place responses (27 out of 38 studies), while cognitive dual-task conditions were commonly used to challenge balance in dynamic balance control tasks (20 out of 32 studies). While frequency analysis and event related potential characteristics supported enhanced brain activation during static balance control, that in dynamic balance control studies was supported by spatial and frequency analysis. Twenty-three of the 50 studies utilizing EEG utilized independent component analysis to remove movement artifacts from the acquired brain signals. Lastly, only eight studies used truly mobile neuroimaging hardware systems. This review provides evidence to support an increase in brain activation in balance control tasks, regardless of mechanical, cognitive, or sensory challenges. Furthermore, the current body of literature demonstrates the use of advanced signal processing methodologies to analyze brain activity during movement. However, the static nature of neuroimaging hardware and conventional balance control paradigms prevent full mobility and limit our knowledge of neural mechanisms underlying balance control.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Neuroimaging of Human Balance Control: A Systematic Review

Wittenberg

Thompson

Nam

et al. 2017

Front. Hum. Neurosci.

116

View full text Add to dashboard Cite

show abstract

“…In fact, several previous studies reported a parallel enhancement of cortical recording from the frontal cortex and supplementary motor area following posture perturbation (Mihara et al, 2008; Fujita et al, 2016). Also, the stabilometer fluctuation movements aggravated externally-induced retinal image motion, so as to hampered precise visual target location (Sipp et al, 2013; Hülsdünker et al, 2015) and then enhance mid-frontal activity for action monitoring and error processing prior to force-matching (Mihara et al, 2008). The stance-dependent increases in SL_FSM also accounted for the unexpected lack of an increase in N1 amplitude in the stabilometer condition.…”

Section: Discussionmentioning

confidence: 99%

Age-Related Differences in Reorganization of Functional Connectivity for a Dual Task with Increasing Postural Destabilization

Huang

Lin

Hwang

2017

Front. Aging Neurosci.

View full text Add to dashboard Cite

The aged brain may not make good use of central resources, so dual task performance may be degraded. From the brain connectome perspective, this study investigated dual task deficits of older adults that lead to task failure of a suprapostural motor task with increasing postural destabilization. Twelve younger (mean age: 25.3 years) and 12 older (mean age: 65.8 years) adults executed a designated force-matching task from a level-surface or a stabilometer board. Force-matching error, stance sway, and event-related potential (ERP) in the preparatory period were measured. The force-matching accuracy and the size of postural sway of the older adults tended to be more vulnerable to stance configuration than that of the young adults, although both groups consistently showed greater attentional investment on the postural task as sway regularity increased in the stabilometer condition. In terms of the synchronization likelihood (SL) of the ERP, both younger and older adults had net increases in the strengths of the functional connectivity in the whole brain and in the fronto-sensorimotor network in the stabilometer condition. Also, the SL in the fronto-sensorimotor network of the older adults was greater than that of the young adults for both stance conditions. However, unlike the young adults, the older adults did not exhibit concurrent deactivation of the functional connectivity of the left temporal-parietal-occipital network for postural-suprapostural task with increasing postural load. In addition, the older adults potentiated functional connectivity of the right prefrontal area to cope with concurrent force-matching with increasing postural load. In conclusion, despite a universal negative effect on brain volume conduction, our preliminary results showed that the older adults were still capable of increasing allocation of neural sources, particularly via compensatory recruitment of the right prefrontal loop, for concurrent force-matching under the challenging postural condition. Nevertheless, dual-task performance of the older adults tended to be more vulnerable to postural load than that of the younger adults, in relation to inferior neural economy or a slow adaptation process to stance destabilization for scant dissociation of control hubs in the temporal-parietal-occipital cortex.

show abstract

“…grand mean dDTF-TFMs were obtained by averaging over all subjects. A theta frequency sub-band (5-7 Hz) was considered, as EEGactivity in this frequency band is associated with the balance task 21,30 (see also Appendix E). The edge weight w ij (t) of the tth network W t of the resulting time-varying grand mean network W was computed by extracting the median value within the theta frequency sub-band at time t from the corresponding grand mean dDTF-TFM.…”

mentioning

confidence: 99%

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks

Schmidt

Piper

Pester

et al. 2018

Int. J. Neur. Syst.

View full text Add to dashboard Cite

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

show abstract

Cortical processes associated with continuous balance control as revealed by EEG spectral power

Cited by 138 publications

References 26 publications

Neuroimaging of Human Balance Control: A Systematic Review

Neuroimaging of Human Balance Control: A Systematic Review

Age-Related Differences in Reorganization of Functional Connectivity for a Dual Task with Increasing Postural Destabilization

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks

Contact Info

Product

Resources

About