Full Length ArticleCortical activation during balancing on a balance board
Introduction
Maintaining and recovering of balance are complex processes which require the coordination of multiple joints and muscles. To do so, information from different sensory systems (e.g. vestibular, tactile, visual) has to be processed and integrated in order to perform motor adjustments. The vestibular system for example is important for the perception of self-motion and provides, in turn, vital sensory information for neuromotor control of postural balance (Brandt and Dieterich, 1999, Cullen, 2012, Day and Fitzpatrick, 2005, Dieterich and Brandt, 2015, Horak, 2010). Furthermore, the vestibular system has strong reciprocal inhibitory connections with the visual system (Bense et al., 2001, Brandt et al., 1998, Brandt and Dieterich, 1999, Deutschlander et al., 2002) and presumably compensates for the decline of visual processing capabilities during aging (Faraldo-Garcia, Santos-Perez, Crujeiras-Casais, Labella-Caballero, & Soto-Varela, 2012). A widespread network of cortical structures (e.g. parieto-insular vestibular cortex (PIVC); superior temporal gyrus (STG); insular cortex) has been identified as crucial for the processing of vestibular and multisensory information in the brain (Dieterich & Brandt, 2008). These findings are based on functional magnetic resonance imaging (fMRI) studies using caloric (Fasold et al., 2002) or galvanic vestibular stimulation (Stephan et al., 2005). Similar results underlining the role of parieto-temporal areas in neuromotor control of postural balance, have been observed in studies employing fNIRS (Karim et al., 2013, Karim et al., 2012, Takakura et al., 2015). fNIRS which measures the relative changes in hemoglobin concentration by means of near infrared light attenuation allows the quantification of cortical activity changes during the execution of movements (Leff et al., 2011). So far, fNIRS was used for studying cortical activity during walking (for review see Hamacher, Herold, Wiegel, Hamacher, & Schega, 2015), turning (Maidan et al., 2015) and balancing (Fujimoto et al., 2014, Karim et al., 2012, Karim et al., 2013, Mihara et al., 2008, Takakura et al., 2015). However, most of this research has used either virtual reality paradigms (Basso Moro et al., 2014, Ferrari et al., 2014) or focused on neurological diseases (Fujimoto et al., 2014, Mahoney et al., 2015, Mihara et al., 2012). In contrast, balance training in therapeutic settings (e.g. rehabilitation) is usually conducted on unstable surfaces like foam pads or balance boards. Furthermore, prior fNIRS studies mainly evaluated the activation in prefrontal (Basso Moro et al., 2014, Ferrari et al., 2014, Mahoney et al., 2015, Mihara et al., 2008) or parieto-temporal brain areas (Karim et al., 2012, Karim et al., 2013). However, recent fMRI studies have provided evidence that motor areas like SMA and M1 play a considerable role in neuromotor balance control, too (Ferraye et al., 2014, Taube et al., 2015). This evidence is supported by a number of studies examining structural changes after training of a challenging whole-body balancing task (Taubert et al., 2016, Taubert et al., 2012, Taubert et al., 2010). Taken together, the contribution of sensorimotor areas during the execution of a demanding balance task on unstable surfaces has been poorly studied so far. As a consequence, our understanding of neuromotor balance control is still limited (Bolton, 2015). Therefore, the first goal of this exploratory study was to evaluate the effect of balancing on a balance board (also known as wobble board) on cortical activity in sensorimotor areas as assessed by fNIRS.
Furthermore, given the importance of higher cortical processes for postural balance control (Hülsdünker et al., 2015, Jacobs and Horak, 2007) and the reported significant correlation of balance scores and brain activity in the SMA of stroke patients (Fujimoto et al., 2014, Mihara et al., 2012) we assumed a correlation between sway parameters and brain activity. Consequently, the second aim of this study was to identify possible relations between sway parameters and hemodynamic responses in sensorimotor brain areas.
Section snippets
Subjects
Ten healthy adults participated in the study (median age = 25 years, range 21–47 years; mean height = 1.77 ± 0.5 m; mean body weight = 76.10 ± 9.46 kg). No participant had a self-reported history of musculoskeletal diseases, balance problems or neurological impairments. Furthermore, all participants had normal or corrected vision. The participants had no prior experience in experimental and similar balance tasks and they did not practice special balance-requiring sports (e.g. gymnastics, slacklining). All
fNIRS data
The median of oxyHb-values and the corresponding interquartile ranges are presented in Table 1. The oxyHb-values increased considerably from standing to balancing in SMA (Z (10) = −2.803; p = 0.005), PrG (Z (10) = −2.803; p = 0.005) and PoG (Z (10) = −2.497; p = 0.013). The analyses of deoxyHb-values revealed no significant differences between conditions.
Kinematic data
In Table 2, the median of RMS values and their interquartile ranges are shown. The RMS values of pelvis in ML (T (10) = 7.674; p = 0.000) and AP direction (Z
Discussion
Previous studies have suggested that balance training can modulate the amount of cortical influence on balance control (Beck et al., 2007, Taube, 2013, Taube et al., 2008). However, only limited knowledge is available regarding the online sensorimotor control of balance (Bolton, 2015). Hence, the first aim of this study was to evaluate the impact of a challenging balance task on activity in sensorimotor brain areas. The second aim was to examine possible relations between brain activation and
Limitations
Some limitations of our study have to be considered. Firstly, as we focused on specific brain areas we are not able to draw conclusions about activation changes in other brain regions which are known to be relevant for balance control such as prefrontal cortex (Ferrari et al., 2014, Ferraye et al., 2014, Mihara et al., 2008), cerebellum, mesencephalic locomotor region and basal ganglia (Ferraye et al., 2014). Secondly, the small and unrepresentative sample clearly limits the generalizability of
Conclusion
In conclusion, the higher activation of SMA, PrG and PoG during balancing in comparison to still standing emphasizes the importance of higher cortical processes for postural balance control (Hülsdünker et al., 2015, Jacobs and Horak, 2007). Notably, the SMA seems to be involved in the online cortical control of sway in ML direction.
Conflict of interest
The authors declare that they have no conflict of interest.
Acknowledgments
We thank all volunteers who have participated in our study. Moreover, we thank Christin Ruβ and Irmgard Griessbach for their technical support.
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