Remote Non-invasive Stereoscopic Imaging of Blood Vessels: First In-vivo Results of a New Multispectral Contrast Enhancement Technology

We describe a contactless optical technique selectively enhancing superficial blood vessels below variously pigmented intact human skin by combining images in different spectral bands. Two CMOS-cameras, with apochromatic lenses and dual-band LED-arrays, simultaneously streamed Left (L) and Right (R) image data to a dual-processor PC. Both cameras captured color images within the visible range (VIS, 400–780 nm) and grey-scale images within the near infrared range (NIR, 910–920 nm) by sequentially switching between LED-array emission bands. Image-size-settings of 1280 × 1024 for VIS & 640 × 512 for NIR produced 12 cycles/s (1 cycle = 1 VIS L&R-pair + 1 NIR L&R-pair). Decreasing image-size-settings (640 × 512 for VIS and 320 × 256 for NIR) increased camera-speed to 25 cycles/s. Contrasts from below the tissue surface were algorithmically distinguished from surface shadows, reflections, etc. Thus blood vessels were selectively enhanced and back-projected into the stereoscopic VIS-color-image using either a 3D-display or conventional shutter glasses.
Introduction
Visually guided procedures provide instant feedback and meantime afford insights that would otherwise be difficult or even impossible to obtain.26 This is a primary reason for mankind’s continuous desire to extend vision beyond the boundaries of the human eye, which has resulted in many successful diagnostic imaging modalities like X-ray imaging, endoscopy, thermography, ultrasound scans and MRI that all found their way into the clinic.
While investigating the principal feasability of a camera for imaging blood oxygenation levels, we noticed that our multispectral images also contained information about subcutaneous vasculature, with improved contrast in the near infrared.24 Near infrared imaging of superficial blood vessels in itself is not new; soon after infrared photography2,9,25 early electronic cameras were used for experiments.13,15 Since then, numerous near infrared imaging methods have been developed using transillumination mode and/or reflection mode.5,6,10,14, 22 Our multispectral camera, however, provides the opportunity to obtain normal color images within the visible range (VIS) which are pixel-to-pixel matched with images obtained in the invisible near infrared range (NIR). By combining the image information content of both spectral bands with a processing algorithm, we developed a new technique that allows selective enhancement of superficial blood vessels with selectable pigment suppression within a normal color image.23
The underlying principle does not necessarily require stereoscopic image acquisition to derive increased vessel contrasts from the tissue. When, however, developing a new medical imaging technique, deriving image information from patient tissue is not the only issue. Especially for visually guided procedures it is also crucial to put effort in an ergonomic human interface and avoid conflicts between the proprioceptional and visual perceptions of the user. The added value of stereoscopic image recordings already was recognized and successfully used to document medical cases more than a century ago.4 We likewise reasoned that offering enhanced blood vessel contrast at the cost of depth perception would restrict the usefulness of our technique and therefore decided to realize a stereoscopic version of our vessel contrast enhancement device.
Visually guided procedures typically require a combination of well trained eyes and specific fine-motoric skills, since the coordination between eyes and hands is task dependent.16 For eye-hand coordination, brain processes for perception and action interact so closely that they cannot be separated and the influence of visual illusions (like human depth perception) to motoric tasks becomes stronger when input takes place via lower levels in the brain.3 The match between a human interface device and the visual and motoric brain processes strongly defines whether a technology can be applied intuitively or not. A new technology can be classified as intuitive if it speeds up the learning process for novices in a certain skill, without impairing the performance of persons already skilled in the existing art.20 Thus a useful increase in vascular contrast should neither imply a task-impairing decrease in depth perception (like in monoscopic techniques) nor distort spatial clues for human vision (like shadows) nor introduce false depth information (like projection parallax).
As a first reconnaissance of practical feasibility, we applied our new technique to several visual clinical procedures for which we expected that improved visualization of blood vessels would be of interest, being:blood withdrawal;vein inspection in dark skin;detection of veins through iodide;inspection of varicose veins and nevi pigmentosum.
Methods
Instrumental Setup
The instrumental hardware setup is schematically drawn in Fig. 1. Two synchronized identical single-chip CMOS-cameras (Vector Technologies Belgium, custom built) were equipped with apochromatic lenses and two identical custom-built dual-band LED-arrays (O2-View, the Netherlands). These LED-arrays were current controlled and each had two individually programmable channels for the emission of in total ±1.2 Cd visible white light with adjustable color temperature (consisting of 40 broadband white LEDs with a yellow accent and 20 broadband white LEDs with a blue accent) as well as one programmable channel for the emission of near infrared radiation (20 LEDs, 920 nm, max. 32 mW per LED). The light sources were constructed so that the geometrical beam profiles of VIS and NIR matched very closely (and thus also any resulting shadows and/or reflections). Left (L) and Right (R) image data was simultaneously acquired and streamed to a dual processor PC equipped with a stereoscopic monitor (Sharp LL-151–3D). It was also possible to connect a conventional cathode ray tube (CRT) monitor equipped with shutter glasses, for comparison of stereoscopic representation.
Figure 1.Experimental setup. Two CMOS-cameras, with apochromatic lenses and dual-band LED-arrays, simultaneously stream Left (L) and Right (R) image data to a dual processor PC. Both cameras captured color images within the visible range (VIS, 400–780 nm) and grey-scale images within the near infrared range (NIR, 910–920 nm) by sequentially switching between LED-array emission bands.
A schematical representation of image aquisition and data structure is drawn in Fig. 2. The CMOS-camera detector chips were equipped with a Bayer filter mosaic to obtain an RGB color image within the VIS. The camera pixels, however, were also sensitive to near infrared radiation because all three filter channels of the Bayer mosaic (red, green and blue) were designed as highly transparant within the NIR. Thus four NIR-pixels were acquired for each VIS RGB color pixel group. By sequentially switching between emission bands of the LED-arrays, images within the Visual range (VIS, 400–780 nm) and Near Infrared range (NIR, 910–920 nm) were acquired in an alternating fashion. VIS and NIR image-size-settings could be varied independantly. For 8-bit encoding depth, at image-size-settings of 1280 × 1024 for VIS and 640 × 512 for NIR, 12 cycles/s were obtained (1 cycle = 1 VIS L&R-pair + 1 NIR L&R-pair). At the cost of decreasing image-size-settings downto 640 × 512 for VIS and 320 × 256 for NIR, camera speed could be increased up to 25 cycles/s. Encoding depths of 10-bit and 12-bit were also available, but only used for stills (due to the lower obtainable framerate).
Figure 2.Schematic diagram of image aquisition. The sequentially acquired alternating VIS and NIR raw image frames form 3D-matrices for the Left and Right channel. The NIR image size is smaller than the VIS image size to increase framerate while maintaining an overview of the imaged area. Enlarged details illustrate the NIR transparency of the Bayer pattern RGB-filters which are applied to obtain a VIS color image. The time domain axis is expressed in image cycles. Along this image cycle axis, the control signals for NIR and VIS LEDs (synchronized with respectively NIR and VIS camera exposures) are visualized.
Data Aquisition
Multispectral stereoscopic movies were recorded in several typical clinical settings for which the technique was considered as possibly useful. LED currents and diaphragm settings were chosen so that for each movie saturated pixels were avoided. A preview mode allowed aiming, adjustment of converging angle α and focusing of the cameras. After software triggering the stereo-camera streamed a sequence of 8-bit digitally encoded image cycles to PC-memory (using auto-incremental numbering). All images were automatically saved on a fast SATA harddisk-array. Camera and light source settings were automatically stored in a text file and located in the same directory.
All patients and volunteers gave their informed consent for filming as well as for publishing the resulting image material. No diagnosis or therapy was based upon any of our results.
Data Processing
General Aspects
Processing was performed with custom developed software (programming language C++) using the stored text file with camera and light source settings as additional input values. Figure 3 schematically represents this process.
Figure 3.Schematic diagram of image processing. The images captured within the visible range (VIS) and the images captured within the near infrared range (NIR) are combined, which reveals blood vessel patterns below the skin. The left and middle column focus on edge-enhancement and suppression of superficial artifacts, the right column serves to fill-in the blood vessel lumen. For raw VIS & NIR images as well as processed results see figures 6, 7, 8 and 9.
The processing method allowed discrimination between image information obtained from the tissue surface versus image information obtained from within the tissue. In order to achieve this, a distinction was made between shadows, reflections and absorption contrasts for both VIS and NIR.
Suppressing Shadows on the Surface
Since NIR and VIS beams were matched closely, shadows produced by irregular shapes at the tissue surface (e.g. skin structure, skin folds, nevi, hair, etc.) or by objects between the light sources and the tissue (fingers, needles, surgical tools, etc.) also matched well in both wavelength ranges. Our algorithm excluded such matching shadows from enhancement and left all useful aspects of shadows unaffected (e.g. depth clues).
Useful Effects of Shadows and Lighting Geometry
Due to the fact that the VIS and IR shadows matched very closely, the algorithm was able to selectively enhance contrast from below the surface while leaving shadows on the surface unaffected (thus not distorting these important depth clues).
Due to the shallow angle of the lightbeams, the skin texture was pronounced. Due to the lighting from two sides, objects that were brought towards the tissue surface (e.g. needles, scalpels, probes, etc.) when lighted from both sides, could produce two separate (not too heavy) shadows. These shadows met and typically formed a “V” pattern when an object touched the surface in the middle of the field of view, thus providing extra information for depth perception.
Enhancing Absorption Contrast of Blood Vessel Walls
Edge detection by a Prewitt image filter was performed on each VIS and NIR image. Pixel positions containing edge information above a certain adjustable threshold in both spectral regions were classified as surface artefacts and excluded from enhancement. The boundary regions of absorption contrasts caused by structures below the tissue surface, produced edges that were mainly present in the NIR image. Enhancement was selectively performed only for pixel positions where the NIR image contained more edge information than the corresponding VIS image. These “valid” pixels identified the vessel boundaries. The positions of these valid pixels were stored in a 1st NIR mask.
Discarding Reflections on the Surface
Shiny areas that produced reflections and/or saturated pixels also matched in both wavelength ranges. This characteristic allowed to calculate a 2nd NIR mask in which superficial reflections were also excluded from enhancement. Neighboring pixels of identified saturated pixels were excluded from enhancement. The radius of this exclusion region was programmable, but due to the favorable anti-blooming behavior of the CMOS-camera chips, suppressing direct neighbor pixels appeared sufficient.
Enhancing Absorption Contrast of Blood Vessel Lumen
The “content” of blood vessels was separately enhanced by raising pixel values of the normalized NIR image to the power of N (with N user adjustable between 0.5 and 2.5) while discriminating NIR pixels below a freely adjustable noise threshold and excluding information from identified shadows. Multiplication with the 2nd NIR mask then produced a final enhancement mask for subsequential backprojection into the VIS image by pixel-to-pixel multiplication.
Suppressing Contrasts Originating from Melanin Pigment
Superficial contrasts within the VIS, originating from melanin pigment concentrations, could either be filtered out or left unchanged. Figure 4 illustrates the normalized distribution of the intensities for the aquired separate spectral bands (R, G, B and NIR) in relation to the intensity (IVIS) of the composed visible RGB-image. Intensities were calculated using the Intel ippiRGBtoGray function.11 Four clouds of data points can be discerned, being IR/IVIS (red channel), IG/IVIS (green channel), IB/IVIS (blue channel) and INIR/IVIS (NIR channel). By calculating the ratio of (IR/IVIS )/(INIR/IVIS ) and comparing the result for each pixel with an adjustable threshold, it can be decided whether or not to apply backprojection to a pixel. This concerns the pixels located within the overlapping region of IR/IVIS and INIR/IVIS within Fig. 4. The resulting difference is clear when comparing between Fig. 9c and d with regard to the visualization of nevi pigmentosum and hair.
Figure 4.Relative intensity distribution of the different spectral bands. Dimensionless normalized distribution of the intensities for the 4 aquired red (IR), Green (IG), Blue (IB) and near infrared (INIR) individual spectral bands expressed in ratio to the normalized intensity (IVIS) of the composed RGB-image. The four data clouds (R, G and B labeled by their natural colors and NIR labeled as pink) show a generally marked separation, but especially for a number of pixels within the red and infrared the data clouds partly overlap. This indicates regions where the VIS contrast potentially is superior to the NIR contrast. By comparing an adjustable threshold with the calculated ratio of (IR/IVIS)/(INIR/IVIS) it can be decided which spectral band provides superior contrast for the pertaining pixel and thus whether or not it is used for enhanced backprojection.
Data Presentation
The images could either be displayed on an auto-stereoscopic liquid crystal display (LCD) monitor or on a conventional CRT monitor equipped with shutter glasses.
The auto-stereoscopic LCD-monitor (Sharp LL-151–3D) was equipped with software controllable switching between stereoscopic (or 3D) and normal (monoscopic or 2D) mode. Monitor resolution was 1280 × 768 in monoscopic mode (XGA). In stereoscopic mode the available pixels were split-up in two separate images (L&R) by activation of a vertical LCD parallax barrier. The principle behind this technology is illustrated in Fig. 5.
Figure 5.Principle of applied autostereoscopic LCD-monitor (reprinted with permission from Sharp). In 2D mode, only one camera-channel is displayed (either from the L or R camera) and the parallax barrier is not actuated. Both eyes of an observer therefore receive the same image and a conventional flat image with full resolution is seen. In 3D mode, both camera channels are displayed (L&R) and the parallax barrier is actuated. The left eye and right eye of an observer now receive different images, and a stereoscopic (in-depth image) with halve resolution is seen.
The CRT-monitor (iiyama vision master 21) was used at a resolution setting of 1280 × 1024 in combination with wireless shutter glasses (e-Dimensional) and thus provided stereoscopic information without sacrificing resolution.
The enhancement algorithm occurs on a pixel-to-pixel basis and does not affect resolution.
Our device offered several imaging modes for data presentation:Monocopic raw VIS preview (normal full color vision)Monoscopic raw NIR preview (greyscale)Off-line stereoscopic looped VIS view with and without enhanced blood vessel back-projection (with freely adjustable enhancement settings).Off-line stereoscopic looped raw NIR view or enhanced NIR view (with freely adjustable enhancement settings)Stereoscopic stills in all modesMonoscopic stills in all modes (freely switchable between Left and Right)
All modes offered the possibility for pause and scrolling forward or reverse frame-by-frame. By means of virtual slider controls and virtual pushbuttons the user interface allowed freely adjustable settings for shadow suppression, pigment suppression, noise threshold and vessel lumen fill-in contrast.
Results
Results for Blood Withdrawal
During routine blood withdrawal the inserted needle tip remained only slightly visible on the VIS image due to skin surface deformation (see Fig. 6a). On the raw NIR image, however, the inserted needle remained visible within the tissue for a few mm with some metallic reflection. Subcutaneous bleeding during needle removal could be detected in the raw NIR image while the needle tip was still in the tissue (see Fig. 6b). This was clearly highlighted by backprojection in the VIS image (see Fig. 6c). The processing settings used to obtain this backprojection mainly laid the accent upon enhancing the absorption contrast of blood vessel lumen, whereas edge enhancement was set to minimum. Reflections on the thumb nail did not lead to image distortion and the blood volume in the nail bed showed more contrast.
Figure 6.Routine blood withdrawal. Image pairs showing unprocessed images for VIS (a) and NIR (b) as well as the result after application of the new image processing method (c). Note the forked shadow (which is not effected by the enhancement algorithm), the clearly visualized subcutaneous bleeding and the improved visibility of the needle tip.
Results for Dark Skin
Hardly any vascular contrast is present within the VIS image (see Fig. 7a). The NIR image, however, is not affected by skin pigmentation and reveals a subcutaneous vascular pattern (see Fig. 7b). The combined information results in an enhanced image (see Fig. 7c).
Figure 7.Influence of skin pigmentation. A dark skin color (a) has no significance for the applied NIR wavelength of 920 nm. Blood vessels provide good contrasts (b) and the resulting enhanced image (c) offers an improved visualization of the vasculature.
Results for Vein Detection Through Iodide
Iodizing skin portions before surgery is a common clinical procedure which darkens the skin and lowers the visibility of blood vessels. Our method offers imaging right through iodide. To demonstrate this, we filmed a glass Petri dish, placed horizontally upon a volunteers arm while filling it up to a 3 mm thick layer of iodide solution. From the VIS image no vessels could be detected through the iodide solution (see Fig. 8b) and hardly any through the empty Petri dish (see Fig. 8a). The raw NIR image, however, clearly showed superficial blood vessels even through the Petri dish (see Fig. 8c).
Figure 8.Vein detection through iodide. Within the visible range, the superficial vasculature is only vaguely discernable (a). After filling the Petri-dish with a 3 mm thick layer of iodide solution, this fully blocks out the tissue view within the visual range (b), whereas a clear view of the vasculature remains possible at the applied NIR wavelength of 920 nm (fig 8c).
Results for Varicose Vein and Nevi Pigmentosum Inspection
The device was used to film varicosis patients during dermatological outpatient clinic. The visualization of varicose veins could be drastically improved. Even in cases where the blood vessels were covered by subcutaneous fat, they could be detected quite well (see Fig. 9). By either de-activating or activating the first NIR mask in the processing algorithm (see Fig. 3) it was also possible to choose whether pigment contrasts in the visual range were left intact or filtered out. The difference is clear when comparing between Fig. 9c and d with regard to the visualization of nevi pigmentosum and hair. By clicking a software screen button, this suppression of pigment contrast could be freely switched on and off during viewing.
Figure 9.The VIS image does not contain much information about the underlying vascular pattern (a). The NIR image, however, clearly shows what’s hiding beneath the surface (b). Note that, when building the enhanced image, the nevus which is present in the VIS image can freely either be suppressed as a surface contrast (c) or be kept visible (d).
Discussion
From the figures presented in this article, it is clear that all experiments resulted in improved visualization of superficial blood vessels. Based upon the absorption coefficients of Hb versus HbO2, arteries provide superior contrasts at 920 nm. The presented images, however, show enhanced venous contrasts, because the larger arteries are buried deeper underneath the skin. Stereoscopic movies provide a more lifely impression of the underlying, but presentation of stereoscopic movies is not possible in a printed journal. We therefore offer the possibility to download viewer software and movies via the internet http://www.erasmusmc.nl/ThoraxcenterBME/html/research/additional/bloodvesselcamera.htm
For both the autostereoscopic LCD and the CRT display plus shutter glasses, we found that the perception of depth was less for stills than for movies. This qualitative observation matches research findings on stereoscopic display techniques in X-ray technology8 and is also consistent with the differences between static and dynamic stereoacuity described by Mathias and Rudolf Sachsenweger.17 Stereoscopic movies also showed an increase in apparent image sharpness compared to stereoscopic stills, which may be explained by Shipley’s description of stereoscopic contour integration over blur.18 Compared to the autostereoscopic LCD-display, the increase in perceived sharpness was much stronger when using a CRT with shutter glasses. For monoscopic stills, however, the LCD-display (switched to monoscopic mode) was superior to the CRT. These observations are in agreement with the technology overview by Szold19 and can be explained by the sacrifice of resolution when using the LCD display in autostereoscopic mode, which price has not to be paid when using the CRT with shutter glasses.
The beneficial effect of separately adjustable enhancement paths for blood vessel contours and blood vessel lumen, which we experienced during the development of our processing algorithm, is consistent with the findings of Yin et al. that surface-features and edge processes make different contributions in determining an object’s unity and shape.27 The perceived usefulness of stereoscopic information, however, also supports the concept formulated by Tse that ‘‘mergeable’’ volumes, rather than relatable contours, are the critical elements in completion.21
With our technique we have aimed to avoid parallax errors and loss of vessel contrast by shadows which are inherent to other blood vessel contrast enhancement techniques projecting the vascular pattern via a projector onto the skin.28 We exclude parallax errors because the user truly looks beneath the tissue surface trough two camera’s. Absence of vessel contrast in shadows is avoided by lighting from two sides and by discrimination of superficial artifacts versus contrasts originating from below the surface.
This ability to selectively enhance contrasts from beneath the surface, while preserving the natural depth clues of shadows on the surface, supports both static and dynamic transmittance anchoring of the visual system which is crucial for depth perception.1 Preservation of natural shadows is furthermore important for the correct interpretation of depth clues by occlusion (e.g. from a hand or an instrument positioned between observer and tissue) thus precluding errors in depth perception.12
Since only carefully balanced white light is projected on the skin, there is no impairment of color perception. It is up to the user to freely switch between normal full color vision, color vision plus superimposed blood vessel backprojection and stereoscopic near infrared greyscale vision (with or without enhancement features).
The embodiment described in this article still has some drawbacks:Due to the fact that the pertaining configuration aquires VIS and NIR images sequentially, motion artifacts can lead to a backprojection shift.The deeper the vessels lie under the surface, the less we can visualize them. In the pertaining configuration, the depth range is limited to about 1 mm. Based upon the absorption coefficients of Hb and HbO2, arteries provide superior contrasts at 920 nm. The presented images, however, show enhanced venous contrasts, because the larger arteries are buried deeper underneath the skin.
The above mentioned drawbacks require further development. Fortunately, many technological improvents still can be added. Allthough the device worked quite well under normal ambient lighting conditions (100–200 Lux), the use of a switchable filter might further improve spectral separation. Furthermore, known techniques like application of crossed polarizers, in combination with more powerful light sources, can offer a considerably larger penetration depth.7
Conclusions
Compared to inspection with the naked eye under normal lighting conditions, the tested stereoscopic blood vessel contrast enhancer offered improved visualization in all investigated settings, providing the best stereoscopic image quality when using the CRT monitor with shutter glasses and the best monoscopic image quality when using the LCD set to monoscopic mode.
Our technique supports perception of depth, 3-dimensional motion and discrimination between tissue surface and underlying structures. It also has potential as an educational tool by offering the possibility to look and record trough the eyes of an experienced specialist. Further improvements on penetration depth, frame-rate and focus depth-of-field form targets for momentary ongoing further research.

contactless enhanced viewing of superficial vasculature

reconnaissance of feasibility for various clinical applications


Regularity of center-of-pressure trajectories depends on the amount of attention invested in postural control

The influence of attention on the dynamical structure of postural sway was examined in 30 healthy young adults by manipulating the focus of attention. In line with the proposed direct relation between the amount of attention invested in postural control and regularity of center-of-pressure (COP) time series, we hypothesized that: (1) increasing cognitive involvement in postural control (i.e., creating an internal focus by increasing task difficulty through visual deprivation) increases COP regularity, and (2) withdrawing attention from postural control (i.e., creating an external focus by performing a cognitive dual task) decreases COP regularity. We quantified COP dynamics in terms of sample entropy (regularity), standard deviation (variability), sway-path length of the normalized posturogram (curviness), largest Lyapunov exponent (local stability), correlation dimension (dimensionality) and scaling exponent (scaling behavior). Consistent with hypothesis 1, standing with eyes closed significantly increased COP regularity. Furthermore, variability increased and local stability decreased, implying ineffective postural control. Conversely, and in line with hypothesis 2, performing a cognitive dual task while standing with eyes closed led to greater irregularity and smaller variability, suggesting an increase in the “efficiency, or “automaticity” of postural control”. In conclusion, these findings not only indicate that regularity of COP trajectories is positively related to the amount of attention invested in postural control, but also substantiate that in certain situations an increased internal focus may in fact be detrimental to postural control.
Introduction
By now, it is well established that maintaining and controlling an upright posture requires a certain amount of attention (for a review see Woollacott and Shumway-Cook 2002). The relation between attentional resources and the processing of information from somatosensory, visual, and vestibular systems is readily apparent in cases of reduced or conflicting sensory information (e.g., Redfern et al. 2001, 2004; Shumway-Cook and Woollacott 2000; Teasdale and Simoneau 2001). On the one hand, the degree of attention, or cognitive involvement, required for controlling posture increases with task difficulty. This has been (indirectly) illustrated by, for example, Lajoie et al. (1993) and Vuillerme and Nougier (2004), who both found that the reaction time of a verbal response to an auditory stimulus increased with the difficulty of the postural task. On the other hand, the amount of attention required to perform a secondary suprapostural task is known to influence posture (e.g., Balasubramaniam et al. 2000; Huxhold et al. 2006; Pellecchia 2003; Riley et al. 2003). The dependency of posture on attention is even more prominent in the presence of pathology or aging, when both peripheral and central changes occur that decrease the (physical) capability needed to maintain balance during standing or walking (e.g., Brown et al. 1999; Lindenberger et al. 2000; Marchese et al. 2003; Rankin et al. 2000; Redfern et al. 2004). In balance-impaired elderly individuals, for example, the performance of a secondary cognitive task (i.e., dual-task paradigm) may promote postural instability and even falls (Barra et al. 2006; Brauer et al. 2001). Hence, the amount of attention required for maintaining and controlling upright posture is indicative of the degree of “automaticity” of postural control and, for that reason, has been advocated and used as an important tool in clinically oriented studies (cf., Geurts et al. 1991; Melzer et al. 2001).
Center-of-pressure (COP) fluctuations measured while standing on a force platform provide a complex output signal of the postural control system in which various pertinent cognitive, perceptual, and motor processes are reflected. Recently, COP measures pertaining to the dynamical structure of COP fluctuations have helped to understand the inherent complexity of the postural control system and its constituent processes (e.g., Baratto et al. 2002; Collins and De Luca 1993; Newell et al. 1993; Pascolo et al. 2005; Peterka 2000; Yamada 1995a; cf., Riley and Turvey 2002 for a review). Inspired by this development in modern posturography, Roerdink et al. (2006)—in a study on the functional recovery of posture in stroke patients—proposed a direct relation between the amount of attention invested in postural control and the regularity of COP fluctuations. COP trajectories were more regular (as indexed by reduced sample entropy) in stroke patients than in healthy elderly and became less regular when performing a secondary cognitive task while standing. These results were interpreted to imply that postural sway regularity is positively correlated with the degree of cognitive involvement in postural control. Interestingly, the regularity of the COP fluctuations decreased with rehabilitation, whereas postural stability (as indexed by the largest Lyapunov exponent) increased, suggesting that the required degree of cognitive involvement in postural control decreased during the course of rehabilitation. Hence, these progressively more irregular COP fluctuations (as indexed by an increase in sample entropy) may be interpreted as an increase in the efficiency or “automaticity” of postural control. This interpretation is in line with physiological studies showing that a decrease in “complexity” or “irregularity” of a physiological time-series is indicative of a decrease in healthiness or effectiveness of the physiological control system (cf., Goldberger et al. 2002), a phenomenon known as “dynamical diseases” (cf. Belair et al. 1995). In other words, increased COP regularity may be explained as an indication of an increasingly ineffective postural control strategy.
The aim of the present study was to examine the role of attention in the regulation of posture. To this end, the amount of attention invested in postural control was manipulated experimentally in a large group of young healthy adults. In particular, we increased the difficulty of the postural task through visual deprivation under the assumption that an increase in task difficulty is associated with an increase in cognitive involvement in postural control, creating a so-called internal attentional focus (cf., Andersson et al. 1998; Teasdale et al. 1993; Teasdale and Simoneau 2001). In contrast, we used a concurrent attention demanding cognitive task in order to withdraw attention from postural control, creating an external focus of attention (cf., Huxhold et al. 2006). These manipulations of attention allowed us to examine the proposed direct relation between COP regularity and the amount of attention invested in postural control (Roerdink et al. 2006). In line with this relation, we hypothesized that: (1) increasing postural task difficulty (i.e., by standing with eyes closed, creating an internal focus) increases the cognitive involvement in postural control and hence the regularity of COP fluctuations, and (2) reduced attention to postural control (i.e., by performing a cognitive dual task, creating an external focus) decreases the regularity of COP fluctuations. In addition, based on the suggestion that posture is mainly controlled in the direction of the largest postural sway (Roerdink et al. 2006) and the common finding that in healthy young adults postural sway is largest in the sagittal plane relative to the frontal plane (e.g., Gatev et al. 1999; Winter et al. 1998), we expected COP regularity to be largest in the sagittal plane.
Regularity of COP trajectories was quantified by the sample entropy (Richman and Moorman 2000; Roerdink et al. 2006). In order to examine the structure of COP fluctuations in more detail, we further used a combination of more traditional (i.e., based on summary statistics) and dynamical measures that are all defined operationally in terms of readily interpretable features of motor control (see also Table 1): standard deviation of COP time-series (indexing variability or the amount of postural sway), sway-path length of the normalized posturogram (indexing the amount of twisting and turning of the COP trajectory), largest Lyapunov exponent (Rosenstein et al. 1993; indexing local stability), correlation dimension (Grassberger and Procaccia 1983; indexing the number of active, dynamical degrees of freedom involved in postural control and hence its dimensionality, e.g., Kay 1988) and the scaling exponent (e.g., Peng et al. 1995; indexing long-range correlations in COP time-series). Based on previous research, we expected visual deprivation to decrease local stability and to increase variability of COP time-series (cf., Roerdink et al. 2006; Woollacott and Shumway-Cook 2002; Yardley et al. 1999a). Moreover, we anticipated the attention manipulations to induce adjustments in the dynamical structure of postural control leading to changes in scaling exponent, sway-path length and dimensionality of COP fluctuations.
Table 1Abbreviations and meaning of interest of the calculated COP measuresVariableMeaning of interestSample entropy, SEnNegatively related with the regularity of COP trajectoryStandard deviation, σ (mm) Positively related with the variability of COP trajectorySway-path length, SPn (s−1)Positively related with the curviness of COP trajectoryLargest Lyapunov exponent, λmaxNegatively related with the local stability of COP trajectoryDimensionality, D2Positively related with the number of active control variablesScaling exponent, αLong-range correlations: α = 0.5 for uncorrelated data (i.e., white noise),α = 1.5 for “Brown noise”, the integration of white noise
Methods
Participants and procedures
A total of 30 healthy young adults (10 males, 20 females; mean age = 24 years, range = 19–30 years), without known motor impairments or movement-related disorders, volunteered to participate in the experiment. Participants stood barefoot on a 1 × 1 m custom-made strain gauge force plate1 with their arms hanging relaxed alongside their body. The medial sides of the heels were separated by about 8 cm and each foot was placed with the toes outward at a 10° angle from the sagittal midline (i.e., standard Romberg position). In order to examine the role of attention in the regulation of posture we carried out two manipulations. On the one hand, we increased the postural task difficulty by inviting participants to stand with eyes closed, while on the other hand, we withdrew attention from postural control by inviting them to perform a cognitive dual task. In particular, the participants were invited to stand upright with (1) eyes open (EO-ST), (2) eyes closed (EC-ST), (3) eyes open while performing a dual task (EO-DT) and (4) eyes closed while performing a dual task (EC-DT). The dual task consisted of uttering backwards names read out aloud by the investigator (e.g., “Simon” had to be repeated as “nomis”). The sole aim of this cognitive dual task was to withdraw attention from the postural task. Therefore, the participants were instructed to perform the task to the best of their ability. No feedback on the accuracy with which they performed this task was provided. Each participant performed the four task conditions in random order and once in reverse order, resulting in a total of eight recordings. COP trajectories were collected for 35 s at a sampling rate of 100 Hz.
After the local ethics committee had approved the study, all participants gave their informed consent prior to their participation.
Data analysis
In order to examine whether posture is actively controlled in the direction of largest postural sway (Roerdink et al. 2006), we analyzed both the registered x (mediolateral ML) and y (anterioposterior AP) COP time-series. After omitting the first 5 s of each recording, leaving 30 s of data for further analyses (i.e., 3,000 samples), the time-series were bi-directionally filtered (second-order low-pass Butterworth filter, cut-off frequency of 12.5 Hz) to eliminate low amplitude measurement noise.2 First, we calculated the conventional standard deviation σ of x and y COP trajectories to quantify the variability, or amount, of postural sway. Subsequently, we normalized the x and y trajectories to unit variance (i.e., by dividing the time-series in question by their respective standard deviation) and calculated the sway path defined as the length of the COP trajectory traveled per second. The applied normalization procedure enabled us to determine the sway path in the normalized posturogram, abbreviated as SPn, providing a scale-independent measure of the amount of twisting and turning of the COP trajectory.
Apart from these summary statistics of postural sway (i.e., SPn and σ), which by definition ignore the temporal structure of the COP time-series, we assessed COP dynamics by means of sample entropy, largest Lyapunov exponent, correlation dimension and scaling exponent, which will be briefly explained in the following (for a more extensive description we refer to Roerdink et al. (2006) and references therein). Table 1 presents the six different measures and their interpretation.
Sample entropy, SEn
Healthy physiological systems are often characterized by an irregular and complex type of variability, whereas disease or aging is often associated with greater regularity and less complexity (cf., Goldberger 1996; Goldberger et al. 2002; Pincus et al. 1991). A method to quantify the regularity of time-series is the sample entropy analysis (Lake et al. 2002; Richman and Moorman 2000). Sample entropy indexes the regularity of a given time-series, and is used to analyze complex stochastic systems that (by definition) include both deterministic and random processes (Pincus 1991). Specifically, sample entropy calculates the probability that a sequence of data points, having repeated itself within a tolerance r for a window length M, will also repeat itself for M + 1 points, without allowing self-matches (see Lake et al. 2002; Richman and Moorman 2000).3 Smaller sample entropy values are associated with greater regularity. In the present study, a decrease in sample entropy (i.e., more regular sway fluctuations) was interpreted as a decrease in the effectiveness of postural control.
Largest Lyapunov exponent, λmax
The largest Lyapunov exponent4 provides a measure of the local stability of a dynamical system (e.g., Abarbanel et al. 1996), i.e., the system’s sensitivity to initial conditions or its resistance to small internal perturbations, such as the natural fluctuations that occur while maintaining an upright stance. It quantifies the exponential divergence or convergence of initially nearby trajectories in state space as time progresses (e.g., Rosenstein et al. 1993). If nearby points diverge, they produce instability. The exponent λmax indexes this instability: positive values of λmax indicate either the presence of deterministic chaos (i.e., a form of variability that is brought about by an underlying lawful nonlinear dynamical structure) or complete randomness (i.e., noise), implying that nearby points diverge rapidly, reflecting local instability and lack of predictability. In order to distinguish a deterministic component from genuine randomness, it is necessary to validate results against surrogate data (cf., Theiler et al. 1992).
Dimensionality, D2
The dimensionality of all COP time-series was calculated using the correlation dimension algorithm of Grassberger and Procaccia (1983).5 The correlation dimension provides an index of the number of independent degrees of freedom (equations of motion) that are required to reproduce the time evolutionary properties of the COP time-series. Note that this analysis of the dynamical degrees of freedom is different from the analysis of the (mechanical) degrees of freedom of the joints as commonly applied in the study of motor control, and that no straightforward or uniform relation exists between the number of component degrees of freedom in motion and the dimension of the organizational dynamic in controlling those components (Newell and Vaillancourt 2001).
Scaling exponent, α
In order to determine whether the measured COP time-series were characterized by the presence of long-range correlations, we applied a fractal analysis method for biological time-series called detrended fluctuation (DFA) analysis (Peng et al. 1995).6 The scaling exponent α as determined by this method indicates the presence or absence of (long-range) correlations in the COP trajectories, as defined by Peng et al. (1995). For uncorrelated data, (e.g. white noise) α = 0.5. An α greater than 0.5 and less than or equal to 1.0 indicates persistent long-range power-law correlations. In contrast, 0 < α < 0.5 indicates a different type of power-law correlation such that large and small values of the time series are likely to alternate. For α > 1 correlations exist but cease to be of a power-law form; α = 1.5 indicates Brown noise, i.e., integrated white noise. The scaling exponent α can also be viewed as an indicator that describes the roughness of the time series: the larger the value of α, the “smoother” the time series (Peng et al. 1995).
Surrogate analysis
To test for spurious effects and to distinguish between deterministic features and randomness, the scaling exponents, dimension estimates, Lyapunov exponents and entropy values were also computed for surrogate data (Theiler et al. 1992). In particular, we generated both time- and phase-randomized surrogate data of the filtered COP time-series (Fig. 1). In time-randomized surrogate data, the distribution of the original data is being preserved (i.e., mean, variance, etc. are unaltered), whereas the temporal correlations in the COP time-series are destroyed. The absence of temporal correlations will result in a scaling exponent α close to 0.5 and very large values for dimension and sample entropy. Phase-randomized surrogate data are obtained by randomizing the data’s Fourier phases. In contrast to time-randomization, this procedure does not alter the spectral power distribution and preserves the data’s auto-correlation function. Consequently, scaling exponents of phase-randomized and original data should match, whereas estimates of correlation dimension and sample entropy should be largely increased in the surrogate data.Fig. 1An example of the surrogate analysis, as applied to all time-series. Surrogate data of a measured COP trajectory (upper panel) were constructed by randomizing the Fourier phase (middle panel) and the temporal order (lower panel)
Statistical analysis
For all dependent variables, the first and second trials were averaged. To test for differences between AP and ML COP measures and to examine the effect of the different experimental tasks, we used a repeated measures analysis of variance (ANOVA) design with within-subject factors vision (2 levels: standing with eyes open EO, and standing with eyes closed EC), dual task (2 levels: standing without cognitive dual task performance, i.e., single task, ST, and standing with cognitive dual task performance, DT), and plane (2 levels: sagittal and frontal plane).7 Subsequently, we tested for differences between surrogate and original data using a design involving a within-subject factor surrogate (3 levels: original data and time- and phase-randomized surrogate data). To assess the strength of the (main and interaction) effects, we determined the eta squared (η2), a commonly used measure of effect size in AVOVAs, reflecting the proportion of variance in the dependent variable that is attributable to each effect. Subsequently, η2 was converted into Cohen’s f according to:  An effect size (f) of > 0.4 was considered to reflect a strong effect (Cohen 1988). The analyses were performed using SPSS (SPSS, Inc., Chicago, IL, USA).
Results
The result section is organized as follows. We first report possible differences in the dependent variables between the eyes open and eyes closed conditions (i.e., main effect of vision, hypothesis 1). Second, we describe the effect of experimentally withdrawing attention from postural control by comparing single task and dual task conditions (i.e., main effect of dual task, hypothesis 2). Third, we report whether significant vision × dual task interaction effects were present. Finally, we present the effects of plane, which may reveal possible directional differences in control. In this context, we also report the effects of randomization on the dependent variables to ensure that the observed changes in the dynamics of COP trajectories were genuine effects.
Table 2 presents the results of the vision × dual task ANOVA for the six dependent posturographic measures (i.e., interindividual means corresponding to the main effects of vision and dual task, collapsed over x and y time-series, as well as F, P and f values for main and interaction effects). Significant vision × dual task interaction effects are presented in Fig. 2.
Table 2Main and interaction effects of vision and dual task (i.e., collapsed over x and y time-series) of sample entropy (SEn), standard deviation (σ), sway-path length of the normalized (by the standard deviation) posturogram (SPn), largest Lyapunov exponent (λmax) and scaling exponent (α) of COP time-series for 30 healthy individualsConditionMeanVision (EO vs. EC)ConditionMeanDual task (ST vs. DT)Vision × dual taskaF(1, 29)PƒF(1, 29)PƒF(1, 29)PƒSEnEO0.723.83=0.060*0.36ST0.701.45ns0.256.72<0.050.48EC0.70DT0.72σEO3.5211.82<0.0050.64ST3.892.45ns0.293.18=0.0850.33EC4.01DT3.64SPnEO4.275.28<0.050.43ST4.1313.57<0.0050.686.98<0.050.49EC4.52DT4.66λmaxEO1.5636.23<0.0011.12ST1.710.10ns0.064.26<0.050.38EC1.88DT1.73D2EO2.2323.58<0.0010.90ST2.2045.70<0.0011.266.15<0.050.46EC2.48DT2.51αEO1.3913.70<0.0010.69ST1.3924.57<0.0010.921.80ns0.25EC1.34DT1.35* Significant vision × plane interaction (F(1, 29) = 5.48, P < 0.05, ƒ = 0.44), which was caused by the fact that, in contrast to that in the frontal plane, the effect of vision was significant in the sagittal plane (F(1, 29) = 6.47, P < 0.05, ƒ = 0.47)a  See Fig. 2 for mean values of the conditions EO-ST, EC-ST, EO-DT and EC-DTFig. 2Interindividual averages, collapsed over x and y time-series, of sample entropy (SEn), standard deviation (σ), sway-path length of the normalized (by the standard deviation) posturogram (SPn), local stability (λmax) and dimensionality (D2) for the four experimental conditions: standing with eyes open (EO-ST), eyes closed (EC-ST), eyes open while performing a cognitive dual task (EO-DT) and eyes closed while performing a cognitive dual task (EC-DT). The asterisks indicate significant (P < 0.05) differences between conditions
Increased postural task difficulty (EO vs. EC)
As is apparent from the significant main effects of vision in Table 2, standing with eyes closed resulted in more regular sway fluctuations, as indexed by a decrease in SEn. In addition, sway variability (σ), dimensionality (D2) and sway-path length (SPn) were increased, whereas α and local stability (as indexed by an increase in λmax) decreased.
Decreased attention to posture (ST vs. DT)
Collapsed over x and y time-series and visual conditions, no significant main effects involving dual task were found for σ, SEn or local stability. In contrast, a main effect of dual task was found for both SPn and D2 in that performing a cognitive dual task brought about an increase in both variables as compared to the single task condition (Table 2). Moreover, as shown in Table 2, performing a dual task resulted in a significant decrease of α indicating that dual task performance brought about changes in the time-varying structure of sway fluctuations.
Vision × dual task interaction effects
Significant dual task × vision interaction effects (Table 2) revealed that the effects of visual deprivation on SEn, σ and SPn were different for single and dual task conditions (see Fig. 2). In particular, the observed effect of visual deprivation on σ and SEn was significant only for the single task condition, whereas the observed increase in SPn only existed for dual task performance (see Fig. 2). On the other hand, as can be appreciated from both Table 2 and Fig. 2, the effects of introducing a cognitive dual task on SEn, σ and SPn depended on eye closure. Although no significant main effect of dual task was found for SEn, there was a significant dual task × vision interaction. This effect occurred because SEn increased significantly from 0.68 to 0.72 through the introduction of a dual task when standing with eyes closed, whereas such an increase was absent when standing with eyes open (0.72 for both single and dual task conditions). Similarly, when standing with eyes open, dual task performance had no significant effect on σ, whereas dual task performance resulted in a decrease in σ when standing with eyes closed (see Fig. 2).
The observed significant dual task × vision interaction effects for λmax were brought about by the fact that the increase (i.e., decrease in local stability) when standing with eyes closed was stronger for the single task condition than for the dual task condition. A similar asymmetric effect was found for D2, in that the increase when standing with eyes closed was stronger for the dual task condition than for the single task condition. Moreover, as can be observed in Fig. 2, the effect of performing a dual task (i.e., increase in D2) was larger when standing with eyes closed than with eyes open.
Effects of plane and randomization
Collapsed over conditions, significant differences between sagittal and frontal plane were found for all variables, except α. In particular, sway variability, σ, and local stability, λmax, were significantly larger in the sagittal than in the frontal plane (F(1, 29) = 20.94, P < 0.001, f = 0.82 and F(1, 29) = 15.19, P < 0.005, f = 0.72, respectively). In contrast, sample entropy, SEn, and dimensionality, D2, were significantly lower in the sagittal than in the frontal plane (F(1, 29) = 145.38, P < 0.001, f = 2.31 and F(1, 29) = 16.01, P < 0.001, f = 0.74, respectively). A significant vision × plane interaction was found for SEn (F(1, 29) = 5.48, P < 0.05, f = 0.44), which was caused by the fact that SEn was smaller when standing with eyes closed (EC-ST, see Fig. 2) for COP fluctuations in the sagittal plane, whereas this was not the case for COP fluctuations in the frontal plane.
Figure 3 shows the results of the interindividual means of the surrogate analyses. Sample entropy measures of both phase- and time-randomized surrogate data were significantly higher compared to the original COP time-series (F(2, 58) = 10985.1, P < 0.001, f = 22.34). Whereas scaling exponents of phase-randomized surrogate data and original COP time-series did not differ, correlations were completely absent when the data were time-randomized as evidenced by α values around 0.5 (F(2, 58) = 5436.7, P < 0.001, f = 14.12). As a result of very high-dimensional noise in the time-randomized data, no embedding dimension could be estimated and hence no dimensionality estimates and Lyapunov exponents could be determined for the time-randomized surrogate data.8 Randomizing the phases of original COP data significantly increased the dimensionality (F(1, 29) = 50.07, P < 0.001, f = 1.31). In addition, the Lyapunov exponent of the phase-randomized surrogate data was significantly higher than that of the original time-series (F(1, 29) = 53.44, P < 0.001, f = 1.36).
Fig. 3Grand means, collapsed over all conditions, planes and participants, of sample entropy, scaling exponent, dimensionality and local stability for the original (OR) COP time-series and their phase-randomized (PHASE) and time-randomized (TIME) surrogate counterparts. The error bars represent the interindividual standard deviations. The asterisks represent significant (P < 0.05) differences between the surrogate data and the original time-series
Discussion
The present experiment was conducted to investigate the role of attention in the regulation of posture. Specifically, we examined whether an increase in postural sway regularity (i.e., as indexed by a decrease in SEn) is representative of an increase in cognitive investment in postural control. We hypothesized that COP trajectories become more regular (i.e., SEn decreases) when task difficulty is increased (EC vs. EO) and, conversely, become less regular (i.e., SEn increases) when an attention-demanding cognitive dual task is introduced (DT vs. ST). We further expected that these changes in regularity of COP fluctuations would be accompanied by changes in variability, local stability, sway-path length, dimensionality and scaling exponent reflecting functional modifications of postural control. For the proper interpretation of the present findings, however, it was necessary to ascertain that the observed structure (and changes herein) of the COP fluctuations did not result from noise, but was indeed brought about by deterministic processes. Therefore, we will first discuss the results of the surrogate analyses before discussing the respective effects of vision, dual task and plane on the dynamical structure of postural sway.
Surrogate analyses
Although nonlinear estimates of dynamical structure are not readily interpretable in an absolute sense, they can be meaningfully interpreted by comparing them across conditions, (see e.g., Newell et al. 1993), as well as with surrogate data. For example, entirely random data are characterized by large (theoretically infinite) dimensionality and large λmax values, whereas chaotic/deterministic data have smaller dimensionality and smaller λmax values. In the present analysis, the surrogate data had greater dimensionality and larger λmax values than the original data, implying that the latter had considerable deterministic structure (cf., Theiler et al. 1992). Moreover, both phase- and time-randomized surrogate data showed increased sample entropy values. Hence, the original COP fluctuations clearly had a deterministic component, which was evidenced further by the fact that the scaling exponents became 0.5 after time-randomization (resulting from a loss of temporal correlations in the shuffled time-series), but remained unaffected by phase-randomization (i.e., preserving temporal correlations). These findings are consistent with those of previous studies suggesting that COP fluctuations are (largely) of deterministic origin (e.g., Doyle et al. 2004; Riley et al. 1999; Yamada 1995b), and testify to the relevance and need of including dynamical measures in posturography.
Increasing postural task difficulty (EO vs. EC)
In the present study, standing with eyes closed brought about an increase of sway variability, which was accompanied by an increase in dimensionality and λmax, implying that local stability decreased. Possibly, the observed increase in dimensionality may serve as a mechanism to enrich information so as to facilitate the control of standing and to cope with the reduced (local) stability (cf., Riley and Clark 2003; van Emmerik and van Wegen 2002). These observations are in line with the common notion that visual deprivation increases the task difficulty of postural control, and, consequently, requires cognitive monitoring of postural control. We expected that this increase in cognitive investment would be accompanied by a decrease in SEn (i.e., an increase in regularity), which was indeed the case (see Fig. 2, compare EO-ST with EC-ST), thus confirming hypothesis 1. These findings are consistent with the results of other studies showing that COP variability tends to increase as experimental task conditions become increasingly difficult, whereas the temporal structure of postural sway tends to become increasingly regular (Riley and Clark 2003; Roerdink et al. 2006; Thurner et al. 2002).
In the present study, visual deprivation resulted in qualitatively similar changes in the structure of COP trajectories as reported in the study of Roerdink et al. (2006) for stroke patients (i.e., increased σ, λmax, D2, and decreased SEn and α with respect to healthy elderly adults). In healthy young adults, the qualitatively similar changes in COP dynamics with visual deprivation as compared to standing with eyes open may, likewise, be interpreted to indicate that postural control is performed less automatically and effectively. Whereas in stroke patients such modifications in postural control may be due to a defect or slowing down of the central processing of sensory information (cf., Teasdale et al. 1991; Woollacott et al. 1986), in young healthy adults a more regular sway, resulting from standing with eyes closed, implies increased “active” monitoring of postural control with increasing task difficulty (cf., Andersson et al. 1998; Nashner and McCollum 1985; Redfern et al. 2001; Teasdale et al. 1993; Teasdale and Simoneau 2001). If the proposed relation between cognitive investment in postural control and postural sway regularity does indeed exist, then the performance of a cognitive dual task should result in less regular COP trajectories. That this was indeed the case will be discussed in the next two subsections.
Withdrawing attention from posture
The performance of a concurrent dual task led to changes in scaling exponents and an increase in dimensionality, reflecting cognition-invoked adjustments of postural control. These changes under dual task performance may have served to enrich the information captured in sway fluctuations without increasing the amount of sway (i.e., variability remained unaltered). This interpretation is amplified by the observation that the sway-path length of the normalized posturogram increased, indicating more twisting and turning in the COP trajectories. Interestingly, despite the fact that attention was withdrawn experimentally from postural control, local stability remained unaltered. In contrast to what we expected, no main effect of dual task was found for SEn. However, this finding does not necessarily militate against the proposed relation between the regularity of COP fluctuations and the amount of attention directed to postural control, as will be argued in the following subsection.
Vision × dual task interaction
Especially noteworthy in this context and in view of our expectations is that while standing with eyes closed postural sway regularity decreased (i.e., sample entropy increased) when performing a cognitive dual task (compare EC-DT and EC-ST in Table 2 and Fig. 2). This finding is consistent with hypothesis 2 and supports the proposed positive correlation between COP regularity and the degree of attention involved in postural control. Apparently, the fact that regularity remained unaltered (i.e., 0.72) when performing a dual task while standing with eyes open (EO-DT) implies that, for young healthy adults, standing with eyes open is not very attention demanding. Conversely, during the more challenging task of standing with eyes closed (EC-ST) COP fluctuations became more regular (i.e., 0.68; Fig. 2). However, the finding that sample entropy again increased to its “normal” (EO-ST) level when withdrawing the focus of attention from the postural task (EC-DT) indicates that the increased cognitive monitoring of posture during EC-ST had a detrimental effect. Visual deprivation increased the awareness of the postural task (i.e., creating an internal focus), resulting in efforts to actively (consciously) control posture and, as such, preventing the postural control system to work in a relatively automatic and efficient manner (viz., Andersson et al. 2002; Hunter and Hoffman 2001; McNevin and Wulf 2002; Milton et al. 2004).
Similarly, when standing with eyes open the attention-demanding dual task had no significant effect on σ, whereas when standing with eyes closed the dual task did result in a decrease in σ (see Fig. 2), corroborating the findings of e.g., Andersson et al. (2002), McNevin and Wulf (2002) and Morioka et al. (2005). In contrast, many authors have found an increase in postural sway variability when performing a cognitive dual task (see Shumway-Cook and Woollacott 2000). It has been suggested that this effect of dual task on the amount of sway may, in part, result from articulation (Dault et al. 2003; Yardley et al. 1999b). In this context, it is important to note that the present finding that variability actually decreased with the introduction of a cognitive dual task indicates that articulation played no significant role in the present study.
Sagittal versus frontal plane
The increased variability of the COP trajectories in the sagittal plane (as compared to the frontal plane) was accompanied by reduced local stability and greater regularity (i.e., a decrease in sample entropy). These observations are consistent with the findings of Roerdink et al. (2006), which showed that in healthy elderly adults local stability was reduced in the sagittal plane, whereas regularity and variability were elevated in this plane. Based on these results, they suggested that posture is mainly controlled in the direction of largest postural sway (i.e., sagittal plane), which required a certain amount of attention as reflected by the regularity findings. The present observation that during standing with eyes closed (EC-ST) regularity increased significantly in the sagittal plane, whereas no effect was found for the frontal plane, is in line with this suggestion: young healthy adults mainly control posture in the sagittal plane, which becomes particularly attention demanding when task difficulty is increased (see also Pellecchia 2003; Riley et al. 2003).
Conclusion
The present study showed that the amount of attention invested in postural control is positively correlated with sway regularity. Specifically, the present study showed that increasing postural task difficulty by means of visual deprivation (EC-ST) not only resulted in an increase of COP variability and a decrease in local stability, but also in more regular COP trajectories. These findings could be taken to imply that the participants actively monitored their posture in order to cope with the increased postural task difficulty. However, when the amount of attention invested in postural control was experimentally reduced by introducing a cognitive dual task (EC-DT), both regularity and variability of sway fluctuations returned to values observed when standing with eyes open (EO-ST). This finding suggests that during standing with eyes closed (EC-ST) the increase in monitoring posture was due to an increase in awareness of the postural task (i.e., internal attentional focus), preventing postural control from working in an automatic and efficient manner (e.g., Hunter and Hoffman 2001). All in all, it is fair to conclude that the methodological and analytical approach adopted in the present study allows for disentangling whether or not there is an increase in cognitive involvement and to what extent an increase in cognitive involvement has a detrimental or beneficial effect.

regularity
center-of-pressure
attention
postural control