Cortical activity and functional organisation during ocular pursuit is affected by concurrent upper limb movement

Cortical activity and functional organisation during ocular pursuit is affected by concurrent upper limb movement | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cortical activity and functional organisation during ocular pursuit is affected by concurrent upper limb movement Lénaïc Borot, Ruth Ogden, Simon J. Bennett This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6928711/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Tracking a moving object involves sensory-motor and cognitive processes, and is supported by a wide network of cortical areas. We investigated if cortical activity and network organisation in young adults performing a smooth pursuit task is influenced by retinal input from the moving object, and whether this is modulated by extra-retinal input from concurrent upper limb movement. As expected, we found a decrease in eye velocity, and increase in saccadic displacement, when the moving object was occluded, as well as a general facilitatory effect of oculo-manual tracking. We also found decreased activity in prefrontal (MPFC, DLPFC) and frontal (FEF) cortex during oculo-manual compared to ocular tracking when the moving object was occluded. While these effects were not influenced by a short period of training, there was an increase in activity from pre-test to post-test in prefrontal (MPFC, DLPC), parietal (IPL, SPL) and visual cortex (VC) during ocular tracking. Our findings indicate that extra-retinal input during oculo-manual tracking reduces the need for attentional and predictive processes to extrapolate and pursue the occluded object. This is an important step in better understanding impaired oculo-manual coordination (e.g., age-related decline), potentially informing the development of more effective tasks for differential diagnosis and rehabilitation. Biological sciences/Psychology Biological sciences/Psychology/Human behaviour Biological sciences/Neuroscience/Motor control Biological sciences/Neuroscience/Oculomotor system Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Smooth pursuit eye movement (SPEM) is known to involve a wide range of cortical regions [ 1 – 3 ], with activity modulated by factors that affect trajectory predictability. For example, an increased bilateral activation of prefrontal (DLPFC), frontal (medial FEF, SEF), pre-motor and parietal (SPL, IPS) cortex has been found in trials where the pursuit object was transiently occluded compared to where it remained visible [ 4 ]. It was subsequently suggested [ 5 ] that these areas were activated as part of a compensatory mechanism that attempts to maintain SPEM by predicting the occluded object trajectory. Extending upon this work, it was reported [ 6 ] that bilateral FEF activation was evident irrespective of a moving object’s visibility, whereas bilateral DLPFC activation increased when the object was occluded, as well as when trajectory predictability decreased due to the absence of additional cues (No Trace, Partial Trace, Full Trace). There were also stronger inter-hemispheric and intra-hemispheric correlations for FEF and DLPFC when the object was occluded. It was suggested that although a functional interaction exists between FEF and DLPFC whenever participants pursue a moving object, these areas make distinct contributions to oculomotor control depending on the task demands and associated requirement for higher-order cognitive processes. In our recent work [ 7 ], we showed that PFC (DLPFC, MPFC) activity and network organisation was modified when SPEM was performed with concurrent upper limb movements. We suggested that afference and/or efference from upper limb movements could have provided extra-retinal information on the occluded object trajectory (for a model and behavioural data see [ 8 ]), which modulated the predictive processes operating in PFC. However, facilitation of SPEM and the influence on PFC activity by concurrent upper limb movement was less than expected, in part due to the use of discrete, short duration externally-generated object motion. Indeed, much of the previous work showing facilitation of SPEM by concurrent upper limb movement [ 9 , 10 ] required participants to pursue cyclical object motion (i.e., triangular or sine wave) over a duration of several seconds, thereby providing greater opportunity for sharing of information between the ocular and motor control systems. In addition, we did not consider the contribution of PPC and FEF, both of which are active during smooth pursuit of a visible object [ 3 , 5 ] and play an important role in eye-hand coordination [ 11 ]. For example, these regions are part of the dorsal attention network (DAN), which is involved in overt and covert spatial attention, and thus linked with the fronto-parietal network (FPN), which is involved in cognitive processes such as working memory during goal-directed tasks [ 12 , 13 ]. In the current study, we investigated cortical activity and network organisation within a large number of cortical areas known to be involved during SPEM, as well as the modulatory effect of extra-retinal input provided by concurrent upper limb movement. To ensure that participants were provided with sufficient opportunity to adapt to the novel oculo-manual coordination, we also introduced a period of training between pre-test and post-test phases, in which participants pursued a continuously visible object with eyes and upper limb. Functional near-infrared spectroscopy (fNIRS − 24x24 optode array) was used to image regions of prefrontal (MPFC, DLPFC), frontal (FEF) and parietal (IPL, SPL) and visual cortex (VC) while participants pursued (eyes alone or eyes and upper limb) a sinusoidal object motion that was either continuously visible or transiently occluded (predictable location and duration). It was expected that smooth pursuit during occlusion would be improved by access to extra-retinal input from concurrent upper limb movement, but not to the extent that average eye velocity would match object velocity, even after a period of training. Moreover, it was expected that this oculo-manual facilitation during occlusion would offset the demand on attentional and predictive processes, which would be reflected by changes in both activation and network organisation. Results Behavioural Data As will be described in more detail below, average eye velocity and saccadic displacement differed with the availability of retinal input from the moving object and/or extraretinal input from the effectors. There was also some indication that these measures of eye data differed between pre-test and post-test, but this was more pronounced in the hand velocity data. Occlusion x Tracking As can be seen in Fig. 1, eye velocity was lower during OC and OM tracking in trials with occlusion (2.23deg/s, 2.83deg/s) than without occlusion (5.91deg/s, 6.28deg/s). Moreover, in both trials with and without occlusion, eye velocity was lower in the OC than OM tracking condition. The opposite pattern was found for saccadic displacement, which was higher during OC and OM tracking in trials with occlusion (2.285deg, 2.210deg) than without occlusion (0.951deg, 0.497deg). Also, while there was no difference between OC and OM tracking conditions in trials with occlusion, there was higher saccadic displacement in the OC than OM tracking condition in trials without occlusion. INSERT FIGURE 1 ABOUT HERE Occlusion x Test Eye velocity was lower at pre-test and post-test in trials with occlusion (2.48deg/s; 2.59deg/s) than without occlusion (6.16deg/s; 6.03deg/s). Eye velocity was also lower at post-test than pre-test in trials without occlusion. Saccadic displacement was higher at pre-test and post-test in trials with occlusion (2.302deg, 2.192deg) than without occlusion (0.693deg, 0.754deg). As can be seen in Fig. 2, hand velocity in trials with occlusion increased from pre-test (5.40deg/s) to post-test (5.56deg/s), but there was no change in trials without occlusion (5.56deg/s; 5.57deg/s). As a consequence, although hand velocity was lower at pre-test in trials with than without occlusion, there was no difference at post-test. INSERT FIGURE 2 ABOUT HERE Neuroimaging data Cortical activity Across all regions measured, but particularly in PFC through to MC, cortical activity (O 2 Hb and HHb) differed as a function of the availability of retinal input and/or the effectors used to track the moving object. As can be seen in Fig. 3 (Occlusion x Tracking) and Fig. 4 (Tracking x Test), the pattern of effects was similar in each of these regions, although pairwise comparisons did not always indicate significance. There was also some indication that cortical activity differed between pre-test and post-test, but this was influenced by the effectors used to track the moving object. Occlusion x Tracking Mean O 2 Hb in MPFC was lower during OM than OC tracking in trials with occlusion (-4.59e-10; 1.17e-07) and without occlusion (1.04e-08; 8.09e-08). In DLPFC and FEF, mean O 2 Hb was lower during OM (-2.80e-09; -5.97e-09) than OC tracking (7.30e-08; 3.65e-08), but only in trials with occlusion. In addition, mean O 2 Hb in DLPFC during OC tracking was higher in trials with than without occlusion (7.30e-08; 4.05e-08), whereas mean O 2 Hb in FEF during OM tracking was lower in trials with than without occlusion (-5.97e-09; 4.62e-08). In MC, mean O 2 Hb was higher during OM than OC tracking in trials without occlusion (5.25e-08; 2.08e-08), and lower during OM than OC tracking in trials with occlusion (means). It was also higher during OC tracking in trials with than without occlusion (5.14e-08; 2.08e-08), and lower during OM tracking in trials with than without occlusion (2.13e-08; 5.25e-08). Mean HHb in MPFC was lower during OC (-4.96e-08) than OM (-2.07e-08) tracking, but only in trials with occlusion. It was also lower during OC tracking in trials with than without occlusion (means). In DLPFC, mean HHb was higher during OM than OC tracking in trials with occlusion, but only at post-test (-2.48e-09; -3.08e-08). Mean HHb in DLPFC during OC tracking was also lower in trials with occlusion than without occlusion (-3.08e-08; -1.18e-08), but again only at post-test. Finally, mean HHb in trials with occlusion was lower at post-test than pre-test during OC tracking (means), and higher at post-test-test than pre-test during OM tracking (means). In MC, mean HHb was lower during OM than OC tracking in trials with occlusion (-4.79e-08; -2.36e-08), and lower during OM tracking in trials with than without occlusion (-4.79e-08; -2.81e-08). INSERT FIGURE 3 ABOUT HERE Tracking x Test Mean O 2 Hb in MPFC was lower at both pre-test and post-test during OM (4.67e-09; 5.23e-09) than OC tracking (6.35e-08; 1.35e-07), and increased from pre-test to post-test during OC tracking. In DLPFC, mean O 2 Hb at post-test was lower during OM than OC tracking (1.26e-08; 8.62e-08), and also increased from pre-test to post-test during OC tracking (2.73e-08; 8.62e-08). In IPL, mean O 2 Hb at pre-test was higher during OM than OC tracking (8.08e-08; 3.53e-08), and increased from pre-test to post-test during OC tracking (3.53e-08; 9.09e-08). In SPL, mean O 2 Hb at pre-test was higher during OM than OC tracking (1.00e-07; 4.91e-08). In VC, mean O 2 Hb at post-test was higher during OC than OM tracking (7.94e-08; -2.92e-08), and also increased from pre-test to post-test during OC tracking (2.02e-08; 7.94e-08). Mean HHb in MPFC was higher during OM than OC tracking condition at post-test, and increased from pre-test to post-test during OM tracking. In DLPFC, mean HHb at post-test was lower during OC than OM tracking, but only in trials with occlusion (-2.48e-09; -3.08e-08). Mean HHb in DLPFC also decreased from pre-test (-1.15e-08) to post-test (-3.08e-08) during OC tracking, but only when the object was occluded. The opposite was observed during OM tracking, which increased from pre-test (-2.15e-08) to post-test (-2.48e-09) when the object was occluded. In FEF, mean HHb during OM tracking increased from pre-test (-3.82e-08) to post-test (-8.56e-09) in trials with occlusion. It was also lower at pre-test during OM tracking in trials with than without occlusion, and higher at pre-test during OC than OM tracking in trials with occlusion. Mean HHb in MC increased from pre-test (-4.62e-08) to post-test (-2.98e-08) during OM tracking. It was also lower during OM than OC tracking at pre-test (-4.62e-08; -2.15e-08). In SPL, mean HHb was higher during OM tracking at post-test than pre-test (mean), and also higher during OM (1.52e-08) than OC (-1.39e-08) tracking at post-test. INSERT FIGURE 4 ABOUT HERE Network organisation Similar to the measures of behaviour and cortical activity, local efficiency differed between pre-test and post-test, although this was influenced by the availability of retinal input and/or the effectors used to track the moving object (see Fig. 5). Specifically, during OC tracking when the object was occluded, local efficiency was higher at pre-test (0.516) than post-test (0.494). Also, when the object was occluded, local efficiency was higher during OC (0.516) than OM (0.503) tracking at pre-test, but lower during OC (0.494) than OM (0.508) tracking at post-test. A similar pattern was evident in the data when the object was visible throughout, but none of the pairwise comparisons were significant. There were no significant effects found for global efficiency. INSERT FIGURE 5 ABOUT HERE Discussion Studies of young, human adults pursuing a moving object with eyes alone have shown cortical activity within a number of regions, with the magnitude and correlation (interhemispheric and intrahemispheric) modulated by the availability of retinal input [ 4 , 6 ], and thus the need for attentional and predictive processes to compensate for the inevitable reduction in eye velocity [ 5 ]. Here, we examined if, and how, the availability of extra-retinal input from concurrent upper limb movement (i.e., afference and efference) influences cortical activity and network organisation when pursuing a moving object that was either continuously visible or transiently occluded with predictable timing and duration. In accord with recent discussion on how to improve the reliability and repeatability of fNIRS studies [ 14 , 15 ], here we report findings for both chromophores (O 2 Hb and HHb) as an indirect measure of cortical activity. However, we focus our discussion on the changes in O 2 Hb as they are usually of higher amplitude and less sensitive to noise than changes in HHb [ 16 ], and thus more reflective of task-dependent cortical activity. First, it is important to note that both behavioural measures of eye movement were influenced by the availability of retinal and/or extra-retinal input from the upper limb. As expected, average eye velocity decreased, and saccadic eye displacement increased, in trials where the moving object was occluded [ 17 , 18 ]. There was also clear evidence of oculo-manual facilitation, with increased average eye velocity, and decreased saccadic eye displacement, when pursuing the moving object with the eyes and upper limb compared to eyes alone. For average eye velocity, oculo-manual facilitation was evident irrespective of whether the moving object remained visible or was occluded [ 9 ]. Still, as could be expected given the need for retinal input to oculomotor control, there was little effect of the period of training between pre-test and post-test [ 19 ]. In fact, only hand velocity in trials with occlusion increased from pre-test to post-test, becoming closer to the average object velocity (see Fig. 2 ). This was only a small increase, but is consistent with the fact that the hand, unlike the eyes, can be moved voluntarily at or near object velocity in the absence of retinal input. Similar to the behavioural measures, there was evidence across all cortical regions that activity was influenced by the availability of retinal input and/or extra-retinal input. Consistent with prefrontal cortex being involved in attentional and predictive processes, there was increased activity in DLPFC during ocular tracking when the moving object was occluded compared to when it remained visible [ 4 , 5 ]. Activity in FEF during ocular tracking was similar irrespective of object visibility, replicating the findings of [ 6 ]. They can also be interpreted in line with [ 3 ], who found that FEF is involved in attentional object tracking independent of object frequency, and thus task difficulty (see also [ 20 ]). They are not, however, consistent with [ 4 ], who found increased bilateral activation of mesial FEF, as well as left lateral FEF, in trials where the pursuit object was transiently occluded compared to when it remained visible. In further analysis of the data it was found that increased activation in left FEF was negatively correlated with reduced eye velocity during occlusion [ 5 ]. We cannot be certain why there are differences reported in FEF activation, but it has been suggested that they could in part be explained by the use of different object motion characteristics and analysis procedures [ 3 ]. For example, the use of short duration, constant velocity ramps in previous work [ 4 , 5 ] may have been less predictable than longer duration sine wave motion in [ 3 ] and the current study. Still, despite these differences in cortical activity between the aforementioned studies, there is a developing consensus about DLPFC and FEF being part of a compensatory mechanism that attempts to maintain SPEM of a moving object’s trajectory during occlusion (for a behavioural model see [ 21 , 22 ]). Finally, we also found evidence of increased activity in motor cortex during ocular tracking when the moving object was occluded compared to when it remained visible. Due to the spatial resolution of NIRS and the available location options within the NIRS cap (10 − 5 coordinate system), the optodes for motor cortex also covered part of pre-motor cortex, which according to previous work [ 4 ] exhibits increased activity during pursuit of an occluded object because it is involved in the coordination of oculomotor commands with attentional and predictive processes. A further notable finding was the decreased activity in prefrontal (MPFC, DLPFC) and frontal (FEF) cortex during oculo-manual compared to ocular tracking when the object was occluded. Extra-retinal inputs from limb afference/efference would seem to have reduced the need for attentional and predictive processes (DLPFC, FEF) to extrapolate and pursue the occluded object trajectory, as well as monitoring (MPFC) the involvement of other cortical areas [ 23 , 24 ]. For motor cortex, there was decreased activity during oculo-manual compared to ocular tracking when the object was occluded, as well as decreased activity during oculo-manual tracking when the moving object was occluded compared to when it remained visible. The latter finding could be expected given the reduced need for visual guidance of upper limb movement during occlusion, and an associated reduction in the control of visual attention. We have previously shown in a similar pursuit task that there are fewer visually-based corrections in upper limb movement when the object is occluded compared to when it remains visible [ 25 ], which is consistent with the suggestion that control of the upper limb during occlusion is based on a comparison of sensorimotor signals (i.e., limb afference and efference) to an internal representation of object motion [ 26 ]. In terms of adaptation due to the period of training, a consistent finding across prefrontal (MPFC, DLPC) parietal (IPL, SPL) and visual cortex (VC) was an increase in activity from pre-test to post-test during ocular tracking. In prefrontal (MPFC, DLPFC) cortex, this resulted in higher activity at post-test during ocular than oculo-manual tracking, whereas in parietal cortex (IPL, SPL) it minimised any difference between the two tracking conditions that existed at pre-test. It has been suggested [ 11 ] that a distributed parieto-frontal network plays an important role in eye-hand coordination, and moreover that the contribution from specific cortical areas changes as a function of task demand and experience. The results of the current study could be indicative of how some of the cortical areas involved in the learned oculo-manual coupling (after a short period of oculo-manual training) are adapted when transferred to ocular tracking at post-test. There was also evidence that cortical organisation changed following the period of training. Specifically, local efficiency (measure of network segregation) decreased from pre-test to post-test during ocular tracking when the object was occluded. As a consequence, while local efficiency was higher during ocular than oculo-manual tracking at pre-test, it was lower during at post-test. It would seem, then, that the opportunity to adapt to the novel oculo-manual coordination only influenced subsequent performance of ocular tracking. Why this reorganisation was not evident in global efficiency (network integration) remains to be determined. For example, we have shown previously that oculo-manual tracking resulted in higher global efficiency in prefrontal cortex than ocular tracking [ 7 ]. Notwithstanding differences in the tasks used in these studies, it is possible that imaging a wider network may have impacted upon the global efficiency metric. For example, it could be the case that global efficiency is more likely to change within different subnetworks, such as the associative system (e.g. fronto-parietal network) and sensory-motor system (e.g. visual network, motor network), rather than across the entire network formed by 50 pairs of NIRS channels. Some evidence consistent with this position can be found in previous work [ 6 ], where the authors reported that inter-hemispheric and intra-hemispheric correlations between DLPFC and FEF were generally higher when pursuing an occluded vs continuously visible object. In future work it could be relevant to consider other approaches to analysing NIRS data from large networks. Although there were changes in both chromophores for all ROIs, these were not always significant and thus consistent with a theoretical pattern in which there is an increase in O 2 Hb and a concurrent but weaker decrease in HHb. As with other studies, it is not entirely clear why this theoretical patten is not always present, but as stated above it could in part be related to the fact that changes in O 2 Hb are usually of higher amplitude and less sensitive to noise than changes in HHb [ 16 ]. It is also important to note that we used several methods in the current study to check signal quality, as well as control measures such as a baseline correction, short-distance channels, and preprocessing steps to improve signal quality [ 27 ]. Therefore, we suggest the results for O 2 Hb can be interpreted as being more likely to represent task-evoked changes in the haemodynamic response rather than a false positive as consequence of a confounding factor. In summary, the results of the current study indicate that oculo-manual tracking influences SPEM, cortical activity and network organisation, consistent with extra-retinal input reducing the demand on attentional and predictive processes when pursuing an occluded object trajectory. These findings from young, healthy adults provide an important first step in subsequently understanding of how cortical activity and organisation during oculo-manual tracking is affected by factors such as normal aging or neurological conditions, potentially informing the development of more effective tasks for differential diagnosis and rehabilitation. Materials and Methods Participants Twenty-eight participants (16 males/ 12 females) from the University staff and student population volunteered to take part in the study (mean age of 26.54 ± 5.79 years). All participants were right-handed and self-declared with normal or corrected vision and no neurological impairment. All participants provided a written informed consent to participate in the study. The study was approved by the local ethics committee (20/SPS/014) and was conducted in accordance standards of the Declaration of Helsinki. Task and Procedure Participants came to the laboratory on a single occasion for approximately one hour. Having being given verbal and written instructions on the experimental protocol, participants were invited to sit on a height-adjustable chair at a worktop, after which the cap and optodes of the NIRS neuroimaging system (NIRSport2, NIRX) was placed on their head. To minimize potential crosstalk between the fNIRS system and the IR light from the EyeLink illuminator, a piece of black material was used to cover the optodes. Also, the lights in the laboratory were extinguished during the experiment. Next, participants were asked to place their chin and forehead on a support, which ensured their eyes were 915 mm away from a 24-inch LCD screen (ViewPixx EEG) with 1280 x 1024 pixels resolution and 100 Hz refresh rate. An EyeLink 1000 with remote optics was located beneath the lower edge of the LCD screen and used to record eye gaze at 250Hz. Participants gaze location was calibrated relative to the LCD screen using a nine-point grid prior to each block of trials. Having completed the initial set-up the experiment commenced, which comprised two test phases (pre and post), separated by a short period of training. In each, participants were asked to pursue a red circular object (0.5 degrees diameter with a black dot at its centre), which moved horizontally against a black background on the LCD screen in accord with a sine wave (20 deg amplitude and 0.1 Hz frequency) for 3.5 cycles (35s trial duration). In the pre-test and post-test, the moving object was either visible throughout the entire trial or was occluded (not during the first cycle) for 1250ms (Fig. 6 , panel 3). The occlusion was aligned to the mid-point (screen centre) of a cycle as the object moved from left to right and from right to left of the screen (i.e., 5 occlusion events per trial, see Fig. 6 ). Participants were asked to pursue the object as accurately as they could with eyes alone (ocular condition – OC) or with eyes and hand (oculo-manual condition - OM). This resulted in four conditions (OC and OM with and without object occlusion), in which three trials were performed in a randomised order, resulting in a total of 12 trials. In the training period, participants performed 10 trials in the OM condition without occlusion. Hand movement in the OM condition was recorded as participants moved a hand-held stylus on a Wacom A3 wide digitising tablet (250 Hz sampling rate). This provided real-time input on the horizontal position of the hand-held stylus, which was used to draw a grey anulus of 0.8 degrees diameter on the LCD screen (Fig. 6 , right panels). Participants were instructed to keep the anulus surrounding the moving object as accurately as they could. All trials started with 6s fixation, during which a white cross was displayed in the centre of the screen, and ended with a 30s rest period during which the screen was blank. During the last 3s second of fixation in the oculo-manual condition, the white annulus representing the hand-held stylus was displayed surrounding the fixation cross to inform participants that the next trial would involve manual tracking. Generation of the visual stimuli, recording of data from the Wacom digitising tablet and synchronisation with the EyeLink 1000 and NIRSport2 was achieved using the Cogent Toolbox in Matlab® (MATLAB R2013b, The MathWorks, USA). INSERT FIGURE 6 ABOUT HERE Changes in O 2 Hb and HHb were quantified with functional near infrared spectroscopy (NIRSport2), using two wavelengths (760nm and 850nm) at a sampling rate of 6.8 Hz. Organisation of the 24x24 optodes was made using NIRsite software based on the 10 − 5 coordinate system, and resulted in a total of 81 long distance channels and 8 short distance channels (Fig. 7 ). To define regions of interest (ROIs), Brodmann areas covered by each channel were computed using the NFRI function [ 28 ], which used the MNI (Montreal Neurological Institute) coordinates of the optode array reported by the manufacturer software. From these, we selected 7 ROIs in each hemisphere, comprised from 50 long distance channels (Fig. 7 ). INSERT FIGURE 7 ABOUT HERE Data preprocessing Eye and Hand Movement : Horizontal eye position (relative to display reference system) and eye velocity (relative to head reference system) signals were exported using the Eyelink parser software. The software also identified and labelled saccades and blinks in the horizontal eye position. The criterion for saccade identification was a velocity threshold of 30 deg/s, acceleration threshold of 8000 deg/s 2 , and a motion threshold of 0.15 deg. Blinks were identified when the pupil in the camera could not be reliably determined, such as when it was distorted by eyelid occlusion. Using routines written in Matlab® (MATLAB 2020b, The MathWorks, USA), the identified saccades and blinks were removed from the eye velocity trace, plus 5 additional data points at the beginning and end of the saccade/blink trajectory. The deleted data was replaced by a linear interpolation routine based on the smooth eye velocity before and after the saccade (5 data points). An additional pass was then made to identify and remove eye velocity greater than 15 deg/s. The desaccaded eye velocity data were then processed with a zero-phase, low-pass filter (i.e., moving average filter with a 30 frame window semi-length: nanmoving_average by Carlos Vargas). Using synchronisation signals from the stimulus generation routine (i.e., TTL), smooth eye velocity for each trial was identified. Average eye velocity during the five intervals (i.e., 1250ms) corresponding to an occlusion (i.e., same interval in trials without occlusion) was then calculated. Saccadic displacement occurring during these intervals was also calculated by summing the individual saccade amplitudes. Hand position data from the tablet was processed using custom-written routines in Matlab® (MATLAB 2020b, The MathWorks, USA). Horizontal position data were processed with a zero-phase, low-pass filter (i.e., moving average filter with a 5 frame window semi-length: nanmoving_average) after which hand velocity was derived by applying a 3-point central difference calculation to the position data. Average hand velocity in each trial was calculated over the same intervals as smooth eye velocity. Finally, any velocity data that was subsequently found to exceed the group mean ± 3SDs was replaced with NaN. Such data were deemed to be indictive of participants not performing the task as instructed, and thus classified as outliers. Neuroimaging : The first step of fNIRS preprocessing was to minimize the impact of signal noise on the subsequent data analysis. For this purpose, a consensus-based approach was applied to the raw data extracted from the Aurora software (2021.9). Three methods proposed in the literature to assess the signal quality were used. The first involved observation of the power spectrum density of the O 2 Hb signals for each channel for each participant, where the presence of a cardiac rhythm in the signal (peak around 1 Hz) indicates good contact between the scalp and optodes [ 30 ]. The second method used the coefficient of variation on O 2 Hb, with a maximal threshold of 15% used to define a channel as being of insufficient quality. The third method involved the application of QT-nirs [ 31 ] with the following parameters: window: 3s; overlap: no; qualityThreshold = 0.75; sciThreshold = 0.7; pspThreshold = 0.1. Channels not identified as being of good quality by at least 2 from 3 of the quality control methods were excluded from the subsequent analysis. Participants classified as having more than 33% excluded channels, or more than two ROIs without any good quality channels, were excluded from the fNIRS analysis (n = 7). Two channels were automatically excluded as the source-detector distance was too long. In addition, 2 participants only had data of sufficient quality at pre-test, but these were included in subsequent processing and analysis. Data were next processed using functions from the Homer2 toolbox [ 32 ]. Raw data extracted from the Aurora software (2021.9) was converted to optical density, after which the following two methods were applied to reduce possible head motion artifacts as recommended in [ 33 ]: 1) moving standard deviation and spline interpolation [ 34 ] using parameters: SDTresh = 20, AMPTresh = 0.5, tMotion = 0.5s, tMask = 2s and p = 0.99; 2) wavelet-based signal decomposition [ 35 ] with iqr = 1.5. The optical density time series were next converted into concentrations of O 2 Hb and HHb using the modified Beer-Lambert law, with a differential pathlength factor depending on the age of the participant [ 36 ]. To limit the presence of physiological artifacts in the data, a high (0.009 Hz) and low pass (0.1 Hz) Butterworth zero phase digital filter (order 4) was applied. The signal from short distance channels (n = 8) was then regressed to the long-distance channels (NB. the short distance channels were regressed to long distance channels from the closest ROI). Time series of O 2 Hb and HHb were extracted for each trial using synchronisation signals generated by the stimulus generation routine (i.e., TTL), and baseline corrected using the mean value calculated on 20s of the rest period, starting 3s before the start of the next trial. The first trial of each pre-test and post-test was excluded from further analysis. Separately for O 2 Hb and HHb, the respective time series from channels within each ROI were averaged (see Fig. 7 ), after which the average concentration was extracted from the entire 35s trial duration. For measures of efficiency, graphs metrics (see below) per participant per trial were calculated by first detrending and then calculating partial Pearson correlations between the O 2 Hb time series for all pairs of channels. The resulting 50-by-50 partial correlation matrices (channel by channel from each ROI) were next subjected to z Fisher transformation, with all negative connections then set to zero. From the weighted positive matrices, local and global efficiency were extracted using functions implemented in Brain Connectivity Toolbox [ 37 ]. Statistics Intra-participant means for each dependent measure were organised in long form according to each combination of independent variables in the factorial design. For all dependent measures except hand velocity, these were Tracking (OC; OM); Occlusion (with; without); and Test (pre-test; post-test). For hand velocity, these were: Occlusion (with; without); and Test (pre-test; post-test). The dependent measures were analysed using linear mixed modelling (lme4 package in RStudio, v). Starting with the full model that included all main and interaction effects (fixed effects), as well as a random intercept per participant (random effect), an iterative, top-down process was followed in order to find the simplest model that best fit the data. Fixed effects were sequentially removed based on their statistical significance determined using Wald Chi Squared tests (CAR package v3.1-2). Final model fit was determined using conditional R2 (piecewiseSEM v2.3.0) and AIC. Fixed effects at p ≤ 0.05 were then further analysed using a set of custom contrasts including only relevant pairwise comparisons (i.e., a change in only 1 level of a single factor while keeping levels of other factors constant), which were then subject to Bonferroni pairwise correction (EMMEANS package v1.7.2). For brevity and clarity, the presentation of results is focused on the significant interaction effects that were consistent in the behavioural and neuroimaging data. Estimated marginal means for significant pairwise comparisons (p ≤ 0.05) are also reported in the text. For additional details on the final accepted model for each dependent variable, the reader is directed to the supplementary material. Declarations Authors Contributions LB - Designed research, Performed research, Analysed data, Wrote the paper; RO - Designed research, Wrote the paper; SJB - Designed research, Performed research, Analysed data, Wrote the paper. Data Availability: The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. Conflict of Interest : All authors declare that they have no conflicts of interest. Acknowledgements: This project was funded by the Doctoral Training Alliance (DTA) Applied Biosciences for Health programme, which is supported by Horizon 2020 Marie Curie-Skłodowska Action funding. References Krauzlis, R. J. Recasting the smooth pursuit eye movement system. J Neurophysiol. , 91, 591-603 (2004). https://doi.org/10.1152/jn.00801.2003 Krauzlis, R. J. The control of voluntary eye movements: new perspectives. Neuroscientist ., 11 (2):124-137 (2005). https://doi.org/10.1177/1073858404271196 Schröder, R. et al. Functional connectivity during smooth pursuit eye movements. J Neurophysiol. , 124, 1839-1856 (2020). https://doi.org/10.1152/jn.00317.2020 Lencer, R. et al. Cortical mechanisms of smooth pursuit eye movements with target blanking. An fMRI study. Eur J Neurosci ., 19, 1430-1436 (2004). https://doi.org/10.1111/j.1460-9568.2004.03229.x Nagel, M. et al . Parametric modulation of cortical activation during smooth pursuit with and without target blanking. An fMRI study. Neuroimage , 29, 1319-1325 (2006). https://doi.org/10.1016/j.neuroimage.2005.08.050 Ding, J., Powell, D., & Jiang, Y. Dissociable frontal controls during visible and memory‐guided eye‐tracking of moving targets. Hum Brain Mapp ., 30, 3541-3552 (2009). https://doi.org/10.1002/hbm.20777 Borot L., Ogden R., & Bennett S.J. Prefrontal cortex activity and functional organisation in dual-task ocular pursuit is affected by concurrent upper limb movement. Sci Rep ., 14 (1):9996 (2024). https://doi.org/10.1038/s41598-024-57012-2 Vercher, J. L., Lazzari, S., & Gauthier, G. Manuo-ocular coordination in target tracking. II. Comparing the model with human behavior. Biol Cybern ., 77, 267-275 (1997). https://doi.org/10.1007/s004220050387 Gauthier, G. M., & Hofferer, J. M. Eye tracking of self-moved targets in the absence of vision. Exp Brain Res ., 26, 121-139 (1976). https://doi.org/10.1007/BF00238277 Gauthier, G. M., Vercher, J. L., Mussa Ivaldi, F., & Marchetti, E. Oculo-manual tracking of visual targets: control learning, coordination control and coordination model. Exp Brain Res ., 73, 127-137 (1988). https://doi.org/10.1007/BF00279667 Battaglia-Mayer, A., & Caminiti, R. Parieto-frontal networks for eye–hand coordination and movements. Handb Clin Neurol ., 151, 499-524 (2018). https://doi.org/10.1016/B978-0-444-63622-5.00026-7 Uddin, L.Q., Yeo, B.T.T., & Spreng, R.N. Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks. Brain Topogr ., 32 (6):926-942 (2019). https://doi.org/10.1007/s10548-019-00744-6 Menon, V., & D’Esposito, M. The role of PFC networks in cognitive control and executive function. Neuropsychopharmacology . 47 (1):90-103 (2022). https://doi.org/10.1038/s41386-021-01152-w Yücel, M. A. et al . Best practices for fNIRS publications. Neurophotonics , 8 (1), 012101 (2021). https://doi.org/10.1117/1.NPh.8.1.012101 Kinder, K.T. et al . Systematic review of fNIRS studies reveals inconsistent chromophore data reporting practices. Neurophotonics . 9 (4):040601 (2022). https://doi.org/10.1117/1.NPh.9.4.040601 Pinti, P. et al. The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Ann N Y Acad Sci ., 1464 , 5-29 (2020). https://doi.org/10.1111/nyas.13948 Becker, W., & Fuchs, A. F. Prediction in the oculomotor system: smooth pursuit during transient disappearance of a visual target. Exp Brain Res , 57, 562-575 (1985). https://doi.org/10.1007/BF00237843 Bennett, S. J., & Barnes, G. R. Human ocular pursuit during the transient disappearance of a visual target. J Neurophysiol ., 90, 2504-2520 (2003). https://doi.org/10.1152/jn.01145.2002 Madelain, L., & Krauzlis, R. J. Effects of learning on smooth pursuit during transient disappearance of a visual target. J Neurophysiol ., 90 , 972-982 (2003). https://doi.org/10.1152/jn.00869.2002 Culham, J.C., Cavanagh, P., & Kanwisher, N.G. Attention response functions: characterizing brain areas using fMRI activation during parametric variations of attentional load. Neuron . 32 (4):737-745 (2001). https://doi.org/10.1016/s0896-6273(01)00499-8 Orban de Xivry, J. J., Bennett, S. J., Lefèvre, P., & Barnes, G. R. Evidence for synergy between saccades and smooth pursuit during transient target disappearance. J Neurophysiol ., 95, 418-427 (2006). https://doi.org/10.1152/jn.00596.2005 Bennett, S. J., & Barnes, G. R. Predictive smooth ocular pursuit during the transient disappearance of a visual target. J Neurophysiol. , 92 (1), 578-590 (2004). https://doi.org/10.1152/jn.01188.2003 Mansouri, F. A., Koechlin, E., Rosa, M. G., & Buckley, M. J. Managing competing goals—a key role for the frontopolar cortex. Nat Rev Neurosci ., 18, 645-657 (2017). https://doi.org/10.1038/nrn.2017.111 Koechlin, E., Basso, G., Pietrini, P., Panzer, S., & Grafman, J. The role of the anterior prefrontal cortex in human cognition. Nature , 399, 148-151 (1999). https://doi.org/10.1038/20178 Bennett, S. J., O'Donnell, D., Hansen, S., & Barnes, G. R. Facilitation of ocular pursuit during transient occlusion of externally-generated target motion by concurrent upper limb movement. J Vis ., 12 (13), 17-17 (2012). https://doi.org/10.1167/12.13.17 Miall, R. C., Weir, D. J., Wolpert, D. M., & Stein, J. F. Is the cerebellum a smith predictor? J Mot Behav. , 25 (3), 203-216 (1993). https://doi.org/10.1080/00222895.1993.9942050 Tachtsidis, I., & Scholkmann, F. False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward. Neurophotonics , 3 , 031405; 10.1117/1.NPh.3.3.031405 (2016). https://doi.org/10.1117/1.NPh.3.3.031405 Singh, A. K., Okamoto, M., Dan, H., Jurcak, V., & Dan, I. Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI. Neuroimage , 27, 842-851 (2005). https://doi.org/10.1016/j.neuroimage.2005.05.019 Xia, M., Wang, J., & He, Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PloS one , 8 , e68910; 10.1371/journal.pone.0068910 (2013). https://doi.org/10.1371/journal.pone.0068910 Themelis, G. et al . Near-infrared spectroscopy measurement of the pulsatile component of cerebral blood flow and volume from arterial oscillations. J Biomed Opt ., 12, 014033; 10.1117/1.2710250 (2007). https://doi.org/10.1117/1.2710250 Montero-Hernandez, S., & Pollonini, L. QT-NIRS (Quality Testing of Near Infrared Scans) [Computer software]. https://github.com/lpollonini/qt-nirs Huppert, T. J., Diamond, S. G., Franceschini, M. A., & Boas, D. A. HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. Appl Opt ., 48, D280; 10.1364/AO.48.00D280 (2009). https://doi.org/10.1364/AO.48.00D280 Cooper, R. J. et al. A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy. Front Neurosci ., 6, 147; (2012). https://doi.org/10.3389/fnins.2012.00147 Scholkmann, F., Spichtig, S., Muehlemann, T., & Wolf, M. How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation. Physiol Meas ., 31, 649-662 (2010). https://doi.org/ 10.1088/0967-3334/31/5/004 Molavi, B., & Dumont, G. A. Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiol Meas ., 33, 259-270 (2012). https://doi.org/10.1088/0967-3334/33/2/259 Duncan, A. et al. Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy. Pediatr Res ., 39 , 889-894 (1996). https://doi.org/10.1203/00006450-199605000-00025 Rubinov, M., Kötter, R., Hagmann, P., & Sporns, O. Brain connectivity toolbox: a collection of complex network measurements and brain connectivity datasets. NeuroImage , 47, S169 (2009). https://doi.org/10.1016/S1053-8119(09)71822-1 Additional Declarations No competing interests reported. Supplementary Files suppTableV2.2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Dec, 2025 Reviews received at journal 29 Nov, 2025 Reviews received at journal 25 Nov, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers agreed at journal 18 Nov, 2025 Reviewers invited by journal 18 Nov, 2025 Editor assigned by journal 19 Sep, 2025 Editor invited by journal 20 Jun, 2025 Submission checks completed at journal 20 Jun, 2025 First submitted to journal 19 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6928711","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":480299824,"identity":"6d3f7b71-756c-4b19-8a3b-5d78624cf6ab","order_by":0,"name":"Lénaïc Borot","email":"","orcid":"","institution":"Liverpool John Moores University","correspondingAuthor":false,"prefix":"","firstName":"Lénaïc","middleName":"","lastName":"Borot","suffix":""},{"id":480299825,"identity":"bdca6803-8f9b-45e8-9dce-fe656bec8bda","order_by":1,"name":"Ruth Ogden","email":"","orcid":"","institution":"Liverpool John Moores University","correspondingAuthor":false,"prefix":"","firstName":"Ruth","middleName":"","lastName":"Ogden","suffix":""},{"id":480299826,"identity":"9d626dcc-2470-44b0-8ef8-f10ef8c58898","order_by":2,"name":"Simon J. Bennett","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYHCCBGYog/HBAx6Y4AFitLAxMxskEKmFAaaFTSIBLoZHi3wDwwPmgoo7efzy/ccqEmS2JTawH37AzHMGtxaDA0CHzTjzrFiyjZntRgLP7cQGnjQDZp4beLSA/MLbdjhxwzGYFoYcBmaeD3gdBtTy73DifqCWArAW/jf4tTCAHMbbALQF6H0GsBYJkC34HHaYIeHwjGOHE2ccSzaWAGoxbpN4ZnBwDh7vy7f3JD4uqDmc2N988OGHjz23Zfv5kx8+eHMMj8OYeRIOwDmMPcD4YSAYkezI8j/wqx0Fo2AUjIKRCQDV5FGI8MTaAwAAAABJRU5ErkJggg==","orcid":"","institution":"Liverpool John Moores University","correspondingAuthor":true,"prefix":"","firstName":"Simon","middleName":"J.","lastName":"Bennett","suffix":""}],"badges":[],"createdAt":"2025-06-19 07:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6928711/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6928711/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86214534,"identity":"9510ca52-b1a3-4011-bdc1-cb830b1b9a2b","added_by":"auto","created_at":"2025-07-08 05:49:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":216772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAverage eye velocity during occlusion (a: Occlusion x Tracking interaction; b: Occlusion x Test interaction). The grey dotted line corresponds to average object velocity during occlusion. Estimated marginal means (large markers) and the standard errors are shown from the accepted model.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6928711/v1/625a5ab4dc2a77abfe2a8cfb.png"},{"id":86215746,"identity":"1154fccd-bf0c-4251-882b-069e83b56a54","added_by":"auto","created_at":"2025-07-08 05:57:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103463,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAverage hand velocity during occlusion (significant Occlusion x Test interaction). The grey dotted line corresponds to average object velocity during the period where the eye velocity was calculated. Estimated marginal means (large, filled circles) and the standard errors are shown from the accepted model.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6928711/v1/6f2105d2a7c54b850df2c220.png"},{"id":86214550,"identity":"2463a788-ff8e-40a8-88ce-517e2f4335d8","added_by":"auto","created_at":"2025-07-08 05:49:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":331931,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eOcclusion x Tracking interaction for O\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eHB (red) and HHb (blue). Estimated marginal means (large, filled circles) and the standard errors are shown from the accepted model. For DLPFC and FEF in HHb, two-way interaction is represented for a comprehensive purpose, but these effects are part of a significant 3-way interaction. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6928711/v1/45a0c6429505908c5455bdac.png"},{"id":86214535,"identity":"11ae92fc-5647-4f48-9b93-0f00d3ac2293","added_by":"auto","created_at":"2025-07-08 05:49:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":325480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTracking x Test interaction for O\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eHB (red) and HHb (blue). Estimated marginal means (large, filled circles) and the standard errors are shown from the accepted model. For DLPFC and VC in HHb, two-way interaction is represented for a comprehensive purpose but these effects are part of a significant 3-way interaction. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6928711/v1/7f144c43bf7457919a1fb674.png"},{"id":86214546,"identity":"1fb23277-a3ec-44b5-ac95-e952e2162e2f","added_by":"auto","created_at":"2025-07-08 05:49:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":34776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLocal Efficiency (significant Tracking x Test x Occlusion interaction). Estimated marginal means (large, filled circles) and the standard errors are shown from the accepted models.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6928711/v1/dadd1c6e5227deccd1ee16be.png"},{"id":86214544,"identity":"a57f22c9-f7b1-4f38-9eb6-9c9485acb2f4","added_by":"auto","created_at":"2025-07-08 05:49:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":89289,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSchematic diagram showing the timeline of a trial for the ocular (left) and oculo-manual conditions (right). In the latter, a grey annulus line representing hand movement of the stylus on the tablet was drawn on the screen. Nb. White arrow depicting direction of object motion was not visible to participants. Panel 3 represents the occlusion, during which the object and anulus were not visible to participants.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6928711/v1/1a29a052071b8b58e05438de.png"},{"id":86215745,"identity":"e9b7b8d3-aab3-48f3-8024-66586814514f","added_by":"auto","created_at":"2025-07-08 05:57:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1062953,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA) Top: Representation of the 24x24 full optode organisation (emitters = light red dots; receivers = light blue dots) and channels (black edges) generated using BrainNet Viewer\u003c/em\u003e \u003cem\u003etoolbox [29]. Bottom: Representation of channels included in each ROI (one colour per ROI)\u003c/em\u003e. B) \u003cem\u003eMNI coordinates for each channel included within an ROI, as well as the Brodmann area covered by the channel identified using NFRI function [28]. In the right of the table, the channels included in an ROI can be identified by a colour assigned to each ROI.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6928711/v1/12dd5547c2b070745354a9f1.png"},{"id":86215782,"identity":"a688824b-cefd-479d-a013-94baf7492944","added_by":"auto","created_at":"2025-07-08 05:58:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2818393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6928711/v1/113465f0-0c79-40a2-9f48-b5cc0fb42b0d.pdf"},{"id":86214538,"identity":"053ef632-f884-47b4-9c7f-05d67cfc11cf","added_by":"auto","created_at":"2025-07-08 05:49:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":52990,"visible":true,"origin":"","legend":"","description":"","filename":"suppTableV2.2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6928711/v1/ef4bb68b2da765ec9f9e26d8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cortical activity and functional organisation during ocular pursuit is affected by concurrent upper limb movement","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSmooth pursuit eye movement (SPEM) is known to involve a wide range of cortical regions [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], with activity modulated by factors that affect trajectory predictability. For example, an increased bilateral activation of prefrontal (DLPFC), frontal (medial FEF, SEF), pre-motor and parietal (SPL, IPS) cortex has been found in trials where the pursuit object was transiently occluded compared to where it remained visible [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It was subsequently suggested [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] that these areas were activated as part of a compensatory mechanism that attempts to maintain SPEM by predicting the occluded object trajectory. Extending upon this work, it was reported [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] that bilateral FEF activation was evident irrespective of a moving object\u0026rsquo;s visibility, whereas bilateral DLPFC activation increased when the object was occluded, as well as when trajectory predictability decreased due to the absence of additional cues (No Trace, Partial Trace, Full Trace). There were also stronger inter-hemispheric and intra-hemispheric correlations for FEF and DLPFC when the object was occluded. It was suggested that although a functional interaction exists between FEF and DLPFC whenever participants pursue a moving object, these areas make distinct contributions to oculomotor control depending on the task demands and associated requirement for higher-order cognitive processes.\u003c/p\u003e\u003cp\u003eIn our recent work [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], we showed that PFC (DLPFC, MPFC) activity and network organisation was modified when SPEM was performed with concurrent upper limb movements. We suggested that afference and/or efference from upper limb movements could have provided extra-retinal information on the occluded object trajectory (for a model and behavioural data see [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]), which modulated the predictive processes operating in PFC. However, facilitation of SPEM and the influence on PFC activity by concurrent upper limb movement was less than expected, in part due to the use of discrete, short duration externally-generated object motion. Indeed, much of the previous work showing facilitation of SPEM by concurrent upper limb movement [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] required participants to pursue cyclical object motion (i.e., triangular or sine wave) over a duration of several seconds, thereby providing greater opportunity for sharing of information between the ocular and motor control systems. In addition, we did not consider the contribution of PPC and FEF, both of which are active during smooth pursuit of a visible object [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and play an important role in eye-hand coordination [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For example, these regions are part of the dorsal attention network (DAN), which is involved in overt and covert spatial attention, and thus linked with the fronto-parietal network (FPN), which is involved in cognitive processes such as working memory during goal-directed tasks [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the current study, we investigated cortical activity and network organisation within a large number of cortical areas known to be involved during SPEM, as well as the modulatory effect of extra-retinal input provided by concurrent upper limb movement. To ensure that participants were provided with sufficient opportunity to adapt to the novel oculo-manual coordination, we also introduced a period of training between pre-test and post-test phases, in which participants pursued a continuously visible object with eyes and upper limb. Functional near-infrared spectroscopy (fNIRS \u0026minus;\u0026thinsp;24x24 optode array) was used to image regions of prefrontal (MPFC, DLPFC), frontal (FEF) and parietal (IPL, SPL) and visual cortex (VC) while participants pursued (eyes alone or eyes and upper limb) a sinusoidal object motion that was either continuously visible or transiently occluded (predictable location and duration). It was expected that smooth pursuit during occlusion would be improved by access to extra-retinal input from concurrent upper limb movement, but not to the extent that average eye velocity would match object velocity, even after a period of training. Moreover, it was expected that this oculo-manual facilitation during occlusion would offset the demand on attentional and predictive processes, which would be reflected by changes in both activation and network organisation.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eBehavioural Data\u003c/h2\u003e\n \u003cp\u003eAs will be described in more detail below, average eye velocity and saccadic displacement differed with the availability of retinal input from the moving object and/or extraretinal input from the effectors. There was also some indication that these measures of eye data differed between pre-test and post-test, but this was more pronounced in the hand velocity data.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eOcclusion x Tracking\u003c/h3\u003e\n\u003cp\u003eAs can be seen in Fig.\u0026nbsp;1, eye velocity was lower during OC and OM tracking in trials with occlusion (2.23deg/s, 2.83deg/s) than without occlusion (5.91deg/s, 6.28deg/s). Moreover, in both trials with and without occlusion, eye velocity was lower in the OC than OM tracking condition. The opposite pattern was found for saccadic displacement, which was higher during OC and OM tracking in trials with occlusion (2.285deg, 2.210deg) than without occlusion (0.951deg, 0.497deg). Also, while there was no difference between OC and OM tracking conditions in trials with occlusion, there was higher saccadic displacement in the OC than OM tracking condition in trials without occlusion.\u003c/p\u003e\n\u003ch3\u003eINSERT FIGURE 1 ABOUT HERE\u003c/h3\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e\u003cem\u003eOcclusion x Test\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eEye velocity was lower at pre-test and post-test in trials with occlusion (2.48deg/s; 2.59deg/s) than without occlusion (6.16deg/s; 6.03deg/s). Eye velocity was also lower at post-test than pre-test in trials without occlusion. Saccadic displacement was higher at pre-test and post-test in trials with occlusion (2.302deg, 2.192deg) than without occlusion (0.693deg, 0.754deg). As can be seen in Fig.\u0026nbsp;2, hand velocity in trials with occlusion increased from pre-test (5.40deg/s) to post-test (5.56deg/s), but there was no change in trials without occlusion (5.56deg/s; 5.57deg/s). As a consequence, although hand velocity was lower at pre-test in trials with than without occlusion, there was no difference at post-test.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eINSERT FIGURE 2 ABOUT HERE\u003c/h3\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eNeuroimaging data\u003c/h2\u003e\n \u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eCortical activity\u003c/h2\u003e\n \u003cp\u003eAcross all regions measured, but particularly in PFC through to MC, cortical activity (O\u003csub\u003e2\u003c/sub\u003eHb and HHb) differed as a function of the availability of retinal input and/or the effectors used to track the moving object. As can be seen in Fig.\u0026nbsp;3 (Occlusion x Tracking) and Fig.\u0026nbsp;4 (Tracking x Test), the pattern of effects was similar in each of these regions, although pairwise comparisons did not always indicate significance. There was also some indication that cortical activity differed between pre-test and post-test, but this was influenced by the effectors used to track the moving object.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eOcclusion x Tracking\u003c/h3\u003e\n\u003cp\u003eMean O\u003csub\u003e2\u003c/sub\u003eHb in MPFC was lower during OM than OC tracking in trials with occlusion (-4.59e-10; 1.17e-07) and without occlusion (1.04e-08; 8.09e-08). In DLPFC and FEF, mean O\u003csub\u003e2\u003c/sub\u003eHb was lower during OM (-2.80e-09; -5.97e-09) than OC tracking (7.30e-08; 3.65e-08), but only in trials with occlusion. In addition, mean O\u003csub\u003e2\u003c/sub\u003eHb in DLPFC during OC tracking was higher in trials with than without occlusion (7.30e-08; 4.05e-08), whereas mean O\u003csub\u003e2\u003c/sub\u003eHb in FEF during OM tracking was lower in trials with than without occlusion (-5.97e-09; 4.62e-08). In MC, mean O\u003csub\u003e2\u003c/sub\u003eHb was higher during OM than OC tracking in trials without occlusion (5.25e-08; 2.08e-08), and lower during OM than OC tracking in trials with occlusion (means). It was also higher during OC tracking in trials with than without occlusion (5.14e-08; 2.08e-08), and lower during OM tracking in trials with than without occlusion (2.13e-08; 5.25e-08).\u003c/p\u003e\n\u003cp\u003eMean HHb in MPFC was lower during OC (-4.96e-08) than OM (-2.07e-08) tracking, but only in trials with occlusion. It was also lower during OC tracking in trials with than without occlusion (means). In DLPFC, mean HHb was higher during OM than OC tracking in trials with occlusion, but only at post-test (-2.48e-09; -3.08e-08). Mean HHb in DLPFC during OC tracking was also lower in trials with occlusion than without occlusion (-3.08e-08; -1.18e-08), but again only at post-test. Finally, mean HHb in trials with occlusion was lower at post-test than pre-test during OC tracking (means), and higher at post-test-test than pre-test during OM tracking (means). In MC, mean HHb was lower during OM than OC tracking in trials with occlusion (-4.79e-08; -2.36e-08), and lower during OM tracking in trials with than without occlusion (-4.79e-08; -2.81e-08).\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eINSERT FIGURE 3 ABOUT HERE\u003c/h2\u003e\n \u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eTracking x Test\u003c/h2\u003e\n \u003cp\u003eMean O\u003csub\u003e2\u003c/sub\u003eHb in MPFC was lower at both pre-test and post-test during OM (4.67e-09; 5.23e-09) than OC tracking (6.35e-08; 1.35e-07), and increased from pre-test to post-test during OC tracking. In DLPFC, mean O\u003csub\u003e2\u003c/sub\u003eHb at post-test was lower during OM than OC tracking (1.26e-08; 8.62e-08), and also increased from pre-test to post-test during OC tracking (2.73e-08; 8.62e-08). In IPL, mean O\u003csub\u003e2\u003c/sub\u003eHb at pre-test was higher during OM than OC tracking (8.08e-08; 3.53e-08), and increased from pre-test to post-test during OC tracking (3.53e-08; 9.09e-08). In SPL, mean O\u003csub\u003e2\u003c/sub\u003eHb at pre-test was higher during OM than OC tracking (1.00e-07; 4.91e-08). In VC, mean O\u003csub\u003e2\u003c/sub\u003eHb at post-test was higher during OC than OM tracking (7.94e-08; -2.92e-08), and also increased from pre-test to post-test during OC tracking (2.02e-08; 7.94e-08).\u003c/p\u003e\n \u003cp\u003eMean HHb in MPFC was higher during OM than OC tracking condition at post-test, and increased from pre-test to post-test during OM tracking. In DLPFC, mean HHb at post-test was lower during OC than OM tracking, but only in trials with occlusion (-2.48e-09; -3.08e-08). Mean HHb in DLPFC also decreased from pre-test (-1.15e-08) to post-test (-3.08e-08) during OC tracking, but only when the object was occluded. The opposite was observed during OM tracking, which increased from pre-test (-2.15e-08) to post-test (-2.48e-09) when the object was occluded. In FEF, mean HHb during OM tracking increased from pre-test (-3.82e-08) to post-test (-8.56e-09) in trials with occlusion. It was also lower at pre-test during OM tracking in trials with than without occlusion, and higher at pre-test during OC than OM tracking in trials with occlusion. Mean HHb in MC increased from pre-test (-4.62e-08) to post-test (-2.98e-08) during OM tracking. It was also lower during OM than OC tracking at pre-test (-4.62e-08; -2.15e-08). In SPL, mean HHb was higher during OM tracking at post-test than pre-test (mean), and also higher during OM (1.52e-08) than OC (-1.39e-08) tracking at post-test.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eINSERT FIGURE 4 ABOUT HERE\u003c/h2\u003e\n \u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eNetwork organisation\u003c/h2\u003e\n \u003cp\u003eSimilar to the measures of behaviour and cortical activity, local efficiency differed between pre-test and post-test, although this was influenced by the availability of retinal input and/or the effectors used to track the moving object (see Fig.\u0026nbsp;5). Specifically, during OC tracking when the object was occluded, local efficiency was higher at pre-test (0.516) than post-test (0.494). Also, when the object was occluded, local efficiency was higher during OC (0.516) than OM (0.503) tracking at pre-test, but lower during OC (0.494) than OM (0.508) tracking at post-test. A similar pattern was evident in the data when the object was visible throughout, but none of the pairwise comparisons were significant. There were no significant effects found for global efficiency.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eINSERT FIGURE 5 ABOUT HERE\u003c/h2\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eStudies of young, human adults pursuing a moving object with eyes alone have shown cortical activity within a number of regions, with the magnitude and correlation (interhemispheric and intrahemispheric) modulated by the availability of retinal input [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and thus the need for attentional and predictive processes to compensate for the inevitable reduction in eye velocity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Here, we examined if, and how, the availability of extra-retinal input from concurrent upper limb movement (i.e., afference and efference) influences cortical activity and network organisation when pursuing a moving object that was either continuously visible or transiently occluded with predictable timing and duration. In accord with recent discussion on how to improve the reliability and repeatability of fNIRS studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], here we report findings for both chromophores (O\u003csub\u003e2\u003c/sub\u003eHb and HHb) as an indirect measure of cortical activity. However, we focus our discussion on the changes in O\u003csub\u003e2\u003c/sub\u003eHb as they are usually of higher amplitude and less sensitive to noise than changes in HHb [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and thus more reflective of task-dependent cortical activity.\u003c/p\u003e\u003cp\u003eFirst, it is important to note that both behavioural measures of eye movement were influenced by the availability of retinal and/or extra-retinal input from the upper limb. As expected, average eye velocity decreased, and saccadic eye displacement increased, in trials where the moving object was occluded [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. There was also clear evidence of oculo-manual facilitation, with increased average eye velocity, and decreased saccadic eye displacement, when pursuing the moving object with the eyes and upper limb compared to eyes alone. For average eye velocity, oculo-manual facilitation was evident irrespective of whether the moving object remained visible or was occluded [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Still, as could be expected given the need for retinal input to oculomotor control, there was little effect of the period of training between pre-test and post-test [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In fact, only hand velocity in trials with occlusion increased from pre-test to post-test, becoming closer to the average object velocity (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This was only a small increase, but is consistent with the fact that the hand, unlike the eyes, can be moved voluntarily at or near object velocity in the absence of retinal input.\u003c/p\u003e\u003cp\u003eSimilar to the behavioural measures, there was evidence across all cortical regions that activity was influenced by the availability of retinal input and/or extra-retinal input. Consistent with prefrontal cortex being involved in attentional and predictive processes, there was increased activity in DLPFC during ocular tracking when the moving object was occluded compared to when it remained visible [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Activity in FEF during ocular tracking was similar irrespective of object visibility, replicating the findings of [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. They can also be interpreted in line with [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], who found that FEF is involved in attentional object tracking independent of object frequency, and thus task difficulty (see also [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]). They are not, however, consistent with [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], who found increased bilateral activation of mesial FEF, as well as left lateral FEF, in trials where the pursuit object was transiently occluded compared to when it remained visible. In further analysis of the data it was found that increased activation in left FEF was negatively correlated with reduced eye velocity during occlusion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. We cannot be certain why there are differences reported in FEF activation, but it has been suggested that they could in part be explained by the use of different object motion characteristics and analysis procedures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For example, the use of short duration, constant velocity ramps in previous work [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] may have been less predictable than longer duration sine wave motion in [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and the current study. Still, despite these differences in cortical activity between the aforementioned studies, there is a developing consensus about DLPFC and FEF being part of a compensatory mechanism that attempts to maintain SPEM of a moving object\u0026rsquo;s trajectory during occlusion (for a behavioural model see [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]). Finally, we also found evidence of increased activity in motor cortex during ocular tracking when the moving object was occluded compared to when it remained visible. Due to the spatial resolution of NIRS and the available location options within the NIRS cap (10\u0026thinsp;\u0026minus;\u0026thinsp;5 coordinate system), the optodes for motor cortex also covered part of pre-motor cortex, which according to previous work [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] exhibits increased activity during pursuit of an occluded object because it is involved in the coordination of oculomotor commands with attentional and predictive processes.\u003c/p\u003e\u003cp\u003eA further notable finding was the decreased activity in prefrontal (MPFC, DLPFC) and frontal (FEF) cortex during oculo-manual compared to ocular tracking when the object was occluded. Extra-retinal inputs from limb afference/efference would seem to have reduced the need for attentional and predictive processes (DLPFC, FEF) to extrapolate and pursue the occluded object trajectory, as well as monitoring (MPFC) the involvement of other cortical areas [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For motor cortex, there was decreased activity during oculo-manual compared to ocular tracking when the object was occluded, as well as decreased activity during oculo-manual tracking when the moving object was occluded compared to when it remained visible. The latter finding could be expected given the reduced need for visual guidance of upper limb movement during occlusion, and an associated reduction in the control of visual attention. We have previously shown in a similar pursuit task that there are fewer visually-based corrections in upper limb movement when the object is occluded compared to when it remains visible [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which is consistent with the suggestion that control of the upper limb during occlusion is based on a comparison of sensorimotor signals (i.e., limb afference and efference) to an internal representation of object motion [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn terms of adaptation due to the period of training, a consistent finding across prefrontal (MPFC, DLPC) parietal (IPL, SPL) and visual cortex (VC) was an increase in activity from pre-test to post-test during ocular tracking. In prefrontal (MPFC, DLPFC) cortex, this resulted in higher activity at post-test during ocular than oculo-manual tracking, whereas in parietal cortex (IPL, SPL) it minimised any difference between the two tracking conditions that existed at pre-test. It has been suggested [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] that a distributed parieto-frontal network plays an important role in eye-hand coordination, and moreover that the contribution from specific cortical areas changes as a function of task demand and experience. The results of the current study could be indicative of how some of the cortical areas involved in the learned oculo-manual coupling (after a short period of oculo-manual training) are adapted when transferred to ocular tracking at post-test. There was also evidence that cortical organisation changed following the period of training. Specifically, local efficiency (measure of network segregation) decreased from pre-test to post-test during ocular tracking when the object was occluded. As a consequence, while local efficiency was higher during ocular than oculo-manual tracking at pre-test, it was lower during at post-test. It would seem, then, that the opportunity to adapt to the novel oculo-manual coordination only influenced subsequent performance of ocular tracking. Why this reorganisation was not evident in global efficiency (network integration) remains to be determined. For example, we have shown previously that oculo-manual tracking resulted in higher global efficiency in prefrontal cortex than ocular tracking [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Notwithstanding differences in the tasks used in these studies, it is possible that imaging a wider network may have impacted upon the global efficiency metric. For example, it could be the case that global efficiency is more likely to change within different subnetworks, such as the associative system (e.g. fronto-parietal network) and sensory-motor system (e.g. visual network, motor network), rather than across the entire network formed by 50 pairs of NIRS channels. Some evidence consistent with this position can be found in previous work [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], where the authors reported that inter-hemispheric and intra-hemispheric correlations between DLPFC and FEF were generally higher when pursuing an occluded vs continuously visible object. In future work it could be relevant to consider other approaches to analysing NIRS data from large networks.\u003c/p\u003e\u003cp\u003eAlthough there were changes in both chromophores for all ROIs, these were not always significant and thus consistent with a theoretical pattern in which there is an increase in O\u003csub\u003e2\u003c/sub\u003eHb and a concurrent but weaker decrease in HHb. As with other studies, it is not entirely clear why this theoretical patten is not always present, but as stated above it could in part be related to the fact that changes in O\u003csub\u003e2\u003c/sub\u003eHb are usually of higher amplitude and less sensitive to noise than changes in HHb [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It is also important to note that we used several methods in the current study to check signal quality, as well as control measures such as a baseline correction, short-distance channels, and preprocessing steps to improve signal quality [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, we suggest the results for O\u003csub\u003e2\u003c/sub\u003eHb can be interpreted as being more likely to represent task-evoked changes in the haemodynamic response rather than a false positive as consequence of a confounding factor.\u003c/p\u003e\u003cp\u003eIn summary, the results of the current study indicate that oculo-manual tracking influences SPEM, cortical activity and network organisation, consistent with extra-retinal input reducing the demand on attentional and predictive processes when pursuing an occluded object trajectory. These findings from young, healthy adults provide an important first step in subsequently understanding of how cortical activity and organisation during oculo-manual tracking is affected by factors such as normal aging or neurological conditions, potentially informing the development of more effective tasks for differential diagnosis and rehabilitation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eTwenty-eight participants (16 males/ 12 females) from the University staff and student population volunteered to take part in the study (mean age of 26.54\u0026thinsp;\u0026plusmn;\u0026thinsp;5.79 years). All participants were right-handed and self-declared with normal or corrected vision and no neurological impairment. All participants provided a written informed consent to participate in the study. The study was approved by the local ethics committee (20/SPS/014) and was conducted in accordance standards of the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eTask and Procedure\u003c/h2\u003e\u003cp\u003eParticipants came to the laboratory on a single occasion for approximately one hour. Having being given verbal and written instructions on the experimental protocol, participants were invited to sit on a height-adjustable chair at a worktop, after which the cap and optodes of the NIRS neuroimaging system (NIRSport2, NIRX) was placed on their head. To minimize potential crosstalk between the fNIRS system and the IR light from the EyeLink illuminator, a piece of black material was used to cover the optodes. Also, the lights in the laboratory were extinguished during the experiment. Next, participants were asked to place their chin and forehead on a support, which ensured their eyes were 915 mm away from a 24-inch LCD screen (ViewPixx EEG) with 1280 x 1024 pixels resolution and 100 Hz refresh rate. An EyeLink 1000 with remote optics was located beneath the lower edge of the LCD screen and used to record eye gaze at 250Hz. Participants gaze location was calibrated relative to the LCD screen using a nine-point grid prior to each block of trials.\u003c/p\u003e\u003cp\u003eHaving completed the initial set-up the experiment commenced, which comprised two test phases (pre and post), separated by a short period of training. In each, participants were asked to pursue a red circular object (0.5 degrees diameter with a black dot at its centre), which moved horizontally against a black background on the LCD screen in accord with a sine wave (20 deg amplitude and 0.1 Hz frequency) for 3.5 cycles (35s trial duration). In the pre-test and post-test, the moving object was either visible throughout the entire trial or was occluded (not during the first cycle) for 1250ms (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, panel 3). The occlusion was aligned to the mid-point (screen centre) of a cycle as the object moved from left to right and from right to left of the screen (i.e., 5 occlusion events per trial, see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Participants were asked to pursue the object as accurately as they could with eyes alone (ocular condition \u0026ndash; OC) or with eyes and hand (oculo-manual condition - OM). This resulted in four conditions (OC and OM with and without object occlusion), in which three trials were performed in a randomised order, resulting in a total of 12 trials. In the training period, participants performed 10 trials in the OM condition without occlusion. Hand movement in the OM condition was recorded as participants moved a hand-held stylus on a Wacom A3 wide digitising tablet (250 Hz sampling rate). This provided real-time input on the horizontal position of the hand-held stylus, which was used to draw a grey anulus of 0.8 degrees diameter on the LCD screen (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, right panels). Participants were instructed to keep the anulus surrounding the moving object as accurately as they could.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll trials started with 6s fixation, during which a white cross was displayed in the centre of the screen, and ended with a 30s rest period during which the screen was blank. During the last 3s second of fixation in the oculo-manual condition, the white annulus representing the hand-held stylus was displayed surrounding the fixation cross to inform participants that the next trial would involve manual tracking. Generation of the visual stimuli, recording of data from the Wacom digitising tablet and synchronisation with the EyeLink 1000 and NIRSport2 was achieved using the Cogent Toolbox in Matlab\u0026reg; (MATLAB R2013b, The MathWorks, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eINSERT FIGURE \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e ABOUT HERE\u003c/h2\u003e\u003cp\u003eChanges in O\u003csub\u003e2\u003c/sub\u003eHb and HHb were quantified with functional near infrared spectroscopy (NIRSport2), using two wavelengths (760nm and 850nm) at a sampling rate of 6.8 Hz. Organisation of the 24x24 optodes was made using NIRsite software based on the 10\u0026thinsp;\u0026minus;\u0026thinsp;5 coordinate system, and resulted in a total of 81 long distance channels and 8 short distance channels (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). To define regions of interest (ROIs), Brodmann areas covered by each channel were computed using the NFRI function [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], which used the MNI (Montreal Neurological Institute) coordinates of the optode array reported by the manufacturer software. From these, we selected 7 ROIs in each hemisphere, comprised from 50 long distance channels (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eINSERT FIGURE \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e ABOUT HERE\u003c/h2\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003eData preprocessing\u003c/h2\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eEye and Hand Movement\u003c/span\u003e: Horizontal eye position (relative to display reference system) and eye velocity (relative to head reference system) signals were exported using the Eyelink parser software. The software also identified and labelled saccades and blinks in the horizontal eye position. The criterion for saccade identification was a velocity threshold of 30 deg/s, acceleration threshold of 8000 deg/s\u003csup\u003e2\u003c/sup\u003e, and a motion threshold of 0.15 deg. Blinks were identified when the pupil in the camera could not be reliably determined, such as when it was distorted by eyelid occlusion. Using routines written in Matlab\u0026reg; (MATLAB 2020b, The MathWorks, USA), the identified saccades and blinks were removed from the eye velocity trace, plus 5 additional data points at the beginning and end of the saccade/blink trajectory. The deleted data was replaced by a linear interpolation routine based on the smooth eye velocity before and after the saccade (5 data points). An additional pass was then made to identify and remove eye velocity greater than 15 deg/s. The desaccaded eye velocity data were then processed with a zero-phase, low-pass filter (i.e., moving average filter with a 30 frame window semi-length: nanmoving_average by Carlos Vargas). Using synchronisation signals from the stimulus generation routine (i.e., TTL), smooth eye velocity for each trial was identified. Average eye velocity during the five intervals (i.e., 1250ms) corresponding to an occlusion (i.e., same interval in trials without occlusion) was then calculated. Saccadic displacement occurring during these intervals was also calculated by summing the individual saccade amplitudes.\u003c/p\u003e\u003cp\u003eHand position data from the tablet was processed using custom-written routines in Matlab\u0026reg; (MATLAB 2020b, The MathWorks, USA). Horizontal position data were processed with a zero-phase, low-pass filter (i.e., moving average filter with a 5 frame window semi-length: nanmoving_average) after which hand velocity was derived by applying a 3-point central difference calculation to the position data. Average hand velocity in each trial was calculated over the same intervals as smooth eye velocity. Finally, any velocity data that was subsequently found to exceed the group mean\u0026thinsp;\u0026plusmn;\u0026thinsp;3SDs was replaced with NaN. Such data were deemed to be indictive of participants not performing the task as instructed, and thus classified as outliers.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eNeuroimaging\u003c/span\u003e: The first step of fNIRS preprocessing was to minimize the impact of signal noise on the subsequent data analysis. For this purpose, a consensus-based approach was applied to the raw data extracted from the Aurora software (2021.9). Three methods proposed in the literature to assess the signal quality were used. The first involved observation of the power spectrum density of the O\u003csub\u003e2\u003c/sub\u003eHb signals for each channel for each participant, where the presence of a cardiac rhythm in the signal (peak around 1 Hz) indicates good contact between the scalp and optodes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The second method used the coefficient of variation on O\u003csub\u003e2\u003c/sub\u003eHb, with a maximal threshold of 15% used to define a channel as being of insufficient quality. The third method involved the application of QT-nirs [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] with the following parameters: window: 3s; overlap: no; qualityThreshold\u0026thinsp;=\u0026thinsp;0.75; sciThreshold\u0026thinsp;=\u0026thinsp;0.7; pspThreshold\u0026thinsp;=\u0026thinsp;0.1. Channels not identified as being of good quality by at least 2 from 3 of the quality control methods were excluded from the subsequent analysis. Participants classified as having more than 33% excluded channels, or more than two ROIs without any good quality channels, were excluded from the fNIRS analysis (n\u0026thinsp;=\u0026thinsp;7). Two channels were automatically excluded as the source-detector distance was too long. In addition, 2 participants only had data of sufficient quality at pre-test, but these were included in subsequent processing and analysis.\u003c/p\u003e\u003cp\u003eData were next processed using functions from the Homer2 toolbox [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Raw data extracted from the Aurora software (2021.9) was converted to optical density, after which the following two methods were applied to reduce possible head motion artifacts as recommended in [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]: 1) moving standard deviation and spline interpolation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] using parameters: SDTresh\u0026thinsp;=\u0026thinsp;20, AMPTresh\u0026thinsp;=\u0026thinsp;0.5, tMotion\u0026thinsp;=\u0026thinsp;0.5s, tMask\u0026thinsp;=\u0026thinsp;2s and p\u0026thinsp;=\u0026thinsp;0.99; 2) wavelet-based signal decomposition [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] with iqr\u0026thinsp;=\u0026thinsp;1.5. The optical density time series were next converted into concentrations of O\u003csub\u003e2\u003c/sub\u003eHb and HHb using the modified Beer-Lambert law, with a differential pathlength factor depending on the age of the participant [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To limit the presence of physiological artifacts in the data, a high (0.009 Hz) and low pass (0.1 Hz) Butterworth zero phase digital filter (order 4) was applied. The signal from short distance channels (n\u0026thinsp;=\u0026thinsp;8) was then regressed to the long-distance channels (NB. the short distance channels were regressed to long distance channels from the closest ROI). Time series of O\u003csub\u003e2\u003c/sub\u003eHb and HHb were extracted for each trial using synchronisation signals generated by the stimulus generation routine (i.e., TTL), and baseline corrected using the mean value calculated on 20s of the rest period, starting 3s before the start of the next trial. The first trial of each pre-test and post-test was excluded from further analysis. Separately for O\u003csub\u003e2\u003c/sub\u003eHb and HHb, the respective time series from channels within each ROI were averaged (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), after which the average concentration was extracted from the entire 35s trial duration. For measures of efficiency, graphs metrics (see below) per participant per trial were calculated by first detrending and then calculating partial Pearson correlations between the O\u003csub\u003e2\u003c/sub\u003eHb time series for all pairs of channels. The resulting 50-by-50 partial correlation matrices (channel by channel from each ROI) were next subjected to z Fisher transformation, with all negative connections then set to zero. From the weighted positive matrices, local and global efficiency were extracted using functions implemented in Brain Connectivity Toolbox [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eStatistics\u003c/h2\u003e\u003cp\u003eIntra-participant means for each dependent measure were organised in long form according to each combination of independent variables in the factorial design. For all dependent measures except hand velocity, these were Tracking (OC; OM); Occlusion (with; without); and Test (pre-test; post-test). For hand velocity, these were: Occlusion (with; without); and Test (pre-test; post-test). The dependent measures were analysed using linear mixed modelling (lme4 package in RStudio, v). Starting with the full model that included all main and interaction effects (fixed effects), as well as a random intercept per participant (random effect), an iterative, top-down process was followed in order to find the simplest model that best fit the data. Fixed effects were sequentially removed based on their statistical significance determined using Wald Chi Squared tests (CAR package v3.1-2). Final model fit was determined using conditional R2 (piecewiseSEM v2.3.0) and AIC. Fixed effects at p\u0026thinsp;\u0026le;\u0026thinsp;0.05 were then further analysed using a set of custom contrasts including only relevant pairwise comparisons (i.e., a change in only 1 level of a single factor while keeping levels of other factors constant), which were then subject to Bonferroni pairwise correction (EMMEANS package v1.7.2). For brevity and clarity, the presentation of results is focused on the significant interaction effects that were consistent in the behavioural and neuroimaging data. Estimated marginal means for significant pairwise comparisons (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) are also reported in the text. For additional details on the final accepted model for each dependent variable, the reader is directed to the supplementary material.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLB - Designed research, Performed research, Analysed data, Wrote the paper; RO - Designed research, Wrote the paper; SJB - Designed research, Performed research, Analysed data, Wrote the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e: All authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eThis project was funded by the Doctoral Training Alliance (DTA)\u0026nbsp;\u003c/em\u003eApplied Biosciences for Health programme, which is supported by Horizon 2020 Marie Curie-Skłodowska Action funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKrauzlis, R. J. Recasting the smooth pursuit eye movement system. \u003cem\u003eJ Neurophysiol.\u003c/em\u003e, \u003cstrong\u003e91,\u003c/strong\u003e 591-603 (2004). https://doi.org/10.1152/jn.00801.2003\u003c/li\u003e\n\u003cli\u003eKrauzlis, R. J. The control of voluntary eye movements: new perspectives. \u003cem\u003eNeuroscientist\u003c/em\u003e., \u003cstrong\u003e11\u003c/strong\u003e(2):124-137 (2005). https://doi.org/10.1177/1073858404271196\u003c/li\u003e\n\u003cli\u003eSchr\u0026ouml;der, R. \u003cem\u003eet al.\u003c/em\u003e Functional connectivity during smooth pursuit eye movements. \u003cem\u003eJ Neurophysiol.\u003c/em\u003e, \u003cstrong\u003e124,\u003c/strong\u003e 1839-1856 (2020). https://doi.org/10.1152/jn.00317.2020\u003c/li\u003e\n\u003cli\u003eLencer, R. \u003cem\u003eet al.\u003c/em\u003e Cortical mechanisms of smooth pursuit eye movements with target blanking. An fMRI study. \u003cem\u003eEur J Neurosci\u003c/em\u003e., \u003cstrong\u003e19,\u003c/strong\u003e 1430-1436 (2004). https://doi.org/10.1111/j.1460-9568.2004.03229.x\u003c/li\u003e\n\u003cli\u003eNagel, M. \u003cem\u003eet al\u003c/em\u003e. Parametric modulation of cortical activation during smooth pursuit with and without target blanking. An fMRI study. \u003cem\u003eNeuroimage\u003c/em\u003e, \u003cstrong\u003e29,\u003c/strong\u003e 1319-1325 (2006). https://doi.org/10.1016/j.neuroimage.2005.08.050\u003c/li\u003e\n\u003cli\u003eDing, J., Powell, D., \u0026amp; Jiang, Y. Dissociable frontal controls during visible and memory‐guided eye‐tracking of moving targets. \u003cem\u003eHum Brain Mapp\u003c/em\u003e., \u003cstrong\u003e30,\u003c/strong\u003e 3541-3552 (2009). https://doi.org/10.1002/hbm.20777\u003c/li\u003e\n\u003cli\u003eBorot L., Ogden R., \u0026amp; Bennett S.J. Prefrontal cortex activity and functional organisation in dual-task ocular pursuit is affected by concurrent upper limb movement. \u003cem\u003eSci Rep\u003c/em\u003e., \u003cstrong\u003e14\u003c/strong\u003e(1):9996 (2024). https://doi.org/10.1038/s41598-024-57012-2\u003c/li\u003e\n\u003cli\u003eVercher, J. L., Lazzari, S., \u0026amp; Gauthier, G. Manuo-ocular coordination in target tracking. II. Comparing the model with human behavior. \u003cem\u003eBiol Cybern\u003c/em\u003e., \u003cstrong\u003e77,\u003c/strong\u003e 267-275 (1997). https://doi.org/10.1007/s004220050387\u003c/li\u003e\n\u003cli\u003eGauthier, G. M., \u0026amp; Hofferer, J. M. Eye tracking of self-moved targets in the absence of vision. \u003cem\u003eExp Brain Res\u003c/em\u003e., \u003cstrong\u003e26,\u003c/strong\u003e 121-139 (1976). https://doi.org/10.1007/BF00238277\u003c/li\u003e\n\u003cli\u003eGauthier, G. M., Vercher, J. L., Mussa Ivaldi, F., \u0026amp; Marchetti, E. Oculo-manual tracking of visual targets: control learning, coordination control and coordination model. \u003cem\u003eExp Brain Res\u003c/em\u003e., \u003cstrong\u003e73,\u003c/strong\u003e 127-137 (1988). https://doi.org/10.1007/BF00279667\u003c/li\u003e\n\u003cli\u003eBattaglia-Mayer, A., \u0026amp; Caminiti, R. Parieto-frontal networks for eye\u0026ndash;hand coordination and movements. \u003cem\u003eHandb Clin Neurol\u003c/em\u003e., \u003cstrong\u003e151,\u003c/strong\u003e 499-524 (2018). https://doi.org/10.1016/B978-0-444-63622-5.00026-7\u003c/li\u003e\n\u003cli\u003eUddin, L.Q., Yeo, B.T.T., \u0026amp; Spreng, R.N. Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks. \u003cem\u003eBrain Topogr\u003c/em\u003e., \u003cstrong\u003e32\u003c/strong\u003e(6):926-942 (2019). https://doi.org/10.1007/s10548-019-00744-6\u003c/li\u003e\n\u003cli\u003eMenon, V., \u0026amp; D\u0026rsquo;Esposito, M. The role of PFC networks in cognitive control and executive function. \u003cem\u003eNeuropsychopharmacology\u003c/em\u003e. \u003cstrong\u003e47\u003c/strong\u003e(1):90-103 (2022). https://doi.org/10.1038/s41386-021-01152-w\u003c/li\u003e\n\u003cli\u003eY\u0026uuml;cel, M. A. \u003cem\u003eet al\u003c/em\u003e. Best practices for fNIRS publications. \u003cem\u003eNeurophotonics\u003c/em\u003e, \u003cstrong\u003e\u003cem\u003e8\u003c/em\u003e\u003c/strong\u003e(1), 012101 (2021). https://doi.org/10.1117/1.NPh.8.1.012101\u003c/li\u003e\n\u003cli\u003eKinder, K.T. \u003cem\u003eet al\u003c/em\u003e. Systematic review of fNIRS studies reveals inconsistent chromophore data reporting practices. \u003cem\u003eNeurophotonics\u003c/em\u003e. \u003cstrong\u003e9\u003c/strong\u003e(4):040601 (2022). https://doi.org/10.1117/1.NPh.9.4.040601\u003c/li\u003e\n\u003cli\u003ePinti, P. \u003cem\u003eet al.\u003c/em\u003e The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. \u003cem\u003eAnn N Y Acad Sci\u003c/em\u003e., \u003cstrong\u003e1464\u003c/strong\u003e, 5-29 (2020). https://doi.org/10.1111/nyas.13948\u003c/li\u003e\n\u003cli\u003eBecker, W., \u0026amp; Fuchs, A. F. Prediction in the oculomotor system: smooth pursuit during transient disappearance of a visual target. \u003cem\u003eExp Brain Res\u003c/em\u003e, \u003cstrong\u003e57,\u003c/strong\u003e 562-575 (1985). https://doi.org/10.1007/BF00237843\u003c/li\u003e\n\u003cli\u003eBennett, S. J., \u0026amp; Barnes, G. R. Human ocular pursuit during the transient disappearance of a visual target. \u003cem\u003eJ Neurophysiol\u003c/em\u003e., \u003cstrong\u003e90,\u003c/strong\u003e 2504-2520 (2003). https://doi.org/10.1152/jn.01145.2002\u003c/li\u003e\n\u003cli\u003eMadelain, L., \u0026amp; Krauzlis, R. J. Effects of learning on smooth pursuit during transient disappearance of a visual target. \u003cem\u003eJ Neurophysiol\u003c/em\u003e., \u003cstrong\u003e90\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e 972-982 (2003). https://doi.org/10.1152/jn.00869.2002\u003c/li\u003e\n\u003cli\u003eCulham, J.C., Cavanagh, P., \u0026amp; Kanwisher, N.G. Attention response functions: characterizing brain areas using fMRI activation during parametric variations of attentional load. \u003cem\u003eNeuron\u003c/em\u003e. \u003cstrong\u003e32\u003c/strong\u003e(4):737-745 (2001). https://doi.org/10.1016/s0896-6273(01)00499-8\u003c/li\u003e\n\u003cli\u003eOrban de Xivry, J. J., Bennett, S. J., Lef\u0026egrave;vre, P., \u0026amp; Barnes, G. R. Evidence for synergy between saccades and smooth pursuit during transient target disappearance. \u003cem\u003eJ Neurophysiol\u003c/em\u003e., \u003cstrong\u003e95,\u003c/strong\u003e 418-427 (2006). https://doi.org/10.1152/jn.00596.2005\u003c/li\u003e\n\u003cli\u003eBennett, S. J., \u0026amp; Barnes, G. R. Predictive smooth ocular pursuit during the transient disappearance of a visual target. \u003cem\u003eJ Neurophysiol.\u003c/em\u003e, \u003cstrong\u003e92\u003c/strong\u003e(1), 578-590 (2004). https://doi.org/10.1152/jn.01188.2003\u003c/li\u003e\n\u003cli\u003eMansouri, F. A., Koechlin, E., Rosa, M. G., \u0026amp; Buckley, M. J. Managing competing goals\u0026mdash;a key role for the frontopolar cortex. \u003cem\u003eNat Rev Neurosci\u003c/em\u003e., \u003cstrong\u003e18,\u003c/strong\u003e 645-657 (2017). https://doi.org/10.1038/nrn.2017.111\u003c/li\u003e\n\u003cli\u003eKoechlin, E., Basso, G., Pietrini, P., Panzer, S., \u0026amp; Grafman, J. The role of the anterior prefrontal cortex in human cognition. \u003cem\u003eNature\u003c/em\u003e, \u003cstrong\u003e399,\u003c/strong\u003e 148-151 (1999). https://doi.org/10.1038/20178\u003c/li\u003e\n\u003cli\u003eBennett, S. J., O\u0026apos;Donnell, D., Hansen, S., \u0026amp; Barnes, G. R. Facilitation of ocular pursuit during transient occlusion of externally-generated target motion by concurrent upper limb movement. \u003cem\u003eJ Vis\u003c/em\u003e., \u003cstrong\u003e12\u003c/strong\u003e(13), 17-17 (2012). https://doi.org/10.1167/12.13.17\u003c/li\u003e\n\u003cli\u003eMiall, R. C., Weir, D. J., Wolpert, D. M., \u0026amp; Stein, J. F. Is the cerebellum a smith predictor? \u003cem\u003eJ Mot Behav.\u003c/em\u003e, \u003cstrong\u003e25\u003c/strong\u003e(3), 203-216 (1993). https://doi.org/10.1080/00222895.1993.9942050\u003c/li\u003e\n\u003cli\u003eTachtsidis, I., \u0026amp; Scholkmann, F. False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward. \u003cem\u003eNeurophotonics\u003c/em\u003e, \u003cstrong\u003e3\u003c/strong\u003e, 031405; 10.1117/1.NPh.3.3.031405 (2016). https://doi.org/10.1117/1.NPh.3.3.031405\u003c/li\u003e\n\u003cli\u003eSingh, A. K., Okamoto, M., Dan, H., Jurcak, V., \u0026amp; Dan, I. Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI. \u003cem\u003eNeuroimage\u003c/em\u003e, \u003cstrong\u003e27,\u003c/strong\u003e 842-851 (2005). https://doi.org/10.1016/j.neuroimage.2005.05.019 \u003c/li\u003e\n\u003cli\u003eXia, M., Wang, J., \u0026amp; He, Y. BrainNet Viewer: a network visualization tool for human brain connectomics. \u003cem\u003ePloS one\u003c/em\u003e, \u003cstrong\u003e8\u003c/strong\u003e, e68910; 10.1371/journal.pone.0068910 (2013). https://doi.org/10.1371/journal.pone.0068910\u003c/li\u003e\n\u003cli\u003eThemelis, G. \u003cem\u003eet al\u003c/em\u003e. Near-infrared spectroscopy measurement of the pulsatile component of cerebral blood flow and volume from arterial oscillations. \u003cem\u003eJ Biomed Opt\u003c/em\u003e., \u003cstrong\u003e12,\u003c/strong\u003e 014033; 10.1117/1.2710250 (2007). https://doi.org/10.1117/1.2710250\u003c/li\u003e\n\u003cli\u003eMontero-Hernandez, S., \u0026amp; Pollonini, L. QT-NIRS (Quality Testing of Near Infrared Scans) [Computer software]. https://github.com/lpollonini/qt-nirs\u003c/li\u003e\n\u003cli\u003eHuppert, T. J., Diamond, S. G., Franceschini, M. A., \u0026amp; Boas, D. A. HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. \u003cem\u003eAppl Opt\u003c/em\u003e., \u003cstrong\u003e48,\u003c/strong\u003e D280; 10.1364/AO.48.00D280 (2009). https://doi.org/10.1364/AO.48.00D280\u003c/li\u003e\n\u003cli\u003eCooper, R. J. \u003cem\u003eet al.\u003c/em\u003e A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy. \u003cem\u003eFront Neurosci\u003c/em\u003e., \u003cstrong\u003e6,\u003c/strong\u003e 147; (2012). https://doi.org/10.3389/fnins.2012.00147\u003c/li\u003e\n\u003cli\u003eScholkmann, F., Spichtig, S., Muehlemann, T., \u0026amp; Wolf, M. How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation. \u003cem\u003ePhysiol Meas\u003c/em\u003e., \u003cstrong\u003e31,\u003c/strong\u003e 649-662 (2010). https://doi.org/ 10.1088/0967-3334/31/5/004\u003c/li\u003e\n\u003cli\u003eMolavi, B., \u0026amp; Dumont, G. A. Wavelet-based motion artifact removal for functional near-infrared spectroscopy. \u003cem\u003ePhysiol Meas\u003c/em\u003e., \u003cstrong\u003e33,\u003c/strong\u003e 259-270 (2012). https://doi.org/10.1088/0967-3334/33/2/259 \u003c/li\u003e\n\u003cli\u003eDuncan, A. \u003cem\u003eet al.\u003c/em\u003e Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy. \u003cem\u003ePediatr Res\u003c/em\u003e., \u003cstrong\u003e39\u003c/strong\u003e, 889-894 (1996). https://doi.org/10.1203/00006450-199605000-00025\u003c/li\u003e\n\u003cli\u003eRubinov, M., K\u0026ouml;tter, R., Hagmann, P., \u0026amp; Sporns, O. Brain connectivity toolbox: a collection of complex network measurements and brain connectivity datasets. \u003cem\u003eNeuroImage\u003c/em\u003e, \u003cstrong\u003e47,\u003c/strong\u003e S169 (2009). https://doi.org/10.1016/S1053-8119(09)71822-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6928711/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6928711/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTracking a moving object involves sensory-motor and cognitive processes, and is supported by a wide network of cortical areas. We investigated if cortical activity and network organisation in young adults performing a smooth pursuit task is influenced by retinal input from the moving object, and whether this is modulated by extra-retinal input from concurrent upper limb movement. As expected, we found a decrease in eye velocity, and increase in saccadic displacement, when the moving object was occluded, as well as a general facilitatory effect of oculo-manual tracking. We also found decreased activity in prefrontal (MPFC, DLPFC) and frontal (FEF) cortex during oculo-manual compared to ocular tracking when the moving object was occluded. While these effects were not influenced by a short period of training, there was an increase in activity from pre-test to post-test in prefrontal (MPFC, DLPC), parietal (IPL, SPL) and visual cortex (VC) during ocular tracking. Our findings indicate that extra-retinal input during oculo-manual tracking reduces the need for attentional and predictive processes to extrapolate and pursue the occluded object. This is an important step in better understanding impaired oculo-manual coordination (e.g., age-related decline), potentially informing the development of more effective tasks for differential diagnosis and rehabilitation.\u003c/p\u003e","manuscriptTitle":"Cortical activity and functional organisation during ocular pursuit is affected by concurrent upper limb movement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 05:49:49","doi":"10.21203/rs.3.rs-6928711/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-02T14:02:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-29T22:56:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-25T10:45:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117738997333650452406040688495120002356","date":"2025-11-20T09:16:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291727364836690473364165939562903796350","date":"2025-11-19T07:24:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246673664573075410555637782671997873405","date":"2025-11-18T16:40:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284309397465401573668124171513753017752","date":"2025-11-18T11:31:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-18T07:20:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-19T12:46:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-20T10:14:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-20T04:55:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-19T07:37:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89d81020-7be9-4400-b0a5-17c3c9533179","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51007642,"name":"Biological sciences/Psychology"},{"id":51007643,"name":"Biological sciences/Psychology/Human behaviour"},{"id":51007644,"name":"Biological sciences/Neuroscience/Motor control"},{"id":51007645,"name":"Biological sciences/Neuroscience/Oculomotor system"}],"tags":[],"updatedAt":"2026-05-04T07:39:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 05:49:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6928711","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6928711","identity":"rs-6928711","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}