Note: this is the author’s version of a work that was accepted for publication in Behavioural Brain Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Behavioural Brain Research 239, 104-114.
http://www.sciencedirect.com/science/article/pii/S0166432812007024 http://dx.doi.org/10.1016/j.bbr.2012.10.052
Prefrontal Cortex Activity, Sympatho-Vagal Reaction and Behaviour Distinguish between Situations of Feed Reward and Frustration in Dwarf Goats
Lorenz Gygaxa, Nadine Reefmannb, Martin Wolfc, Jan Langbeind*
Affiliations:
a Centre for Proper Housing of Ruminants and Pigs, Federal Veterinary Office, Agroscope Reckenholz-Tänikon Research Station ART, Tänikon, 8356 Ettenhausen, Switzerland, lorenz.gygax@art.admin.ch
b Swedish University of Agricultural Sciences, Department of Animal Environment and Health, Box 7068, 750 07 Uppsala, Sweden, nadine.reefmann@gmx.ch
c Division of Neonatology, Biomedical Optics Research Laboratory, University Hospital Zurich, 8091 Zurich, Switzerland, martin.wolf@usz.ch
d Leibniz Institute for Farm Animal Biology, Research Unit Behavioural Physiology, 18196 Dummerstorf, Germany, langbein@fbn-dummerstorf.de
*Corresponding Author:
Jan Langbein, Leibniz Institute for Farm Animal Biology, Research Unit Behavioural Physiology, D-18196 Dummerstorf, Germany, langbein@fbn-dummerstorf.de, Tel.: +49 (0)38208 68814, fax: + 49 (0)38208 68602
Abstract
Recent concepts relating to animal welfare accept that animals experience affective states.
These are notoriously difficult to measure in non-verbal species, but it is generally agreed that emotional reactions consist of well-coordinated reactions in behaviour, autonomic and brain activation. The aim of the study was to evaluate whether each or a combination of these aspects can differentiate between situations presumed to differ in emotional content. To this end, we repeatedly confronted dwarf goats at short intervals with a covered and an uncovered feed bowl (i.e. presumably frustrating and rewarding situations respectively) whilst simultaneously observing their behaviour, measuring heart-rate and heart-rate variability and haemodynamic changes in the prefrontal cortex using functional near-infrared spectroscopy.
When faced with a covered feed bowl, goats occupied themselves at locations away from the bowl and showed increases locomotion, while there was a general increase in prefrontal cortical activity. There was little indication of autonomic changes. In contrast, when feed was accessible, the goats reduced locomotion, focused their behaviour on the feed bowl, showed signs of sympathetically mediated arousal reflecting anticipation and, if any cortical activity at all was present, it was concentrated to the left hemisphere. We thus observed patterns in behaviour, sympathetic reaction and brain activity that distinguished between a situation of frustration and one of reward in dwarf goats. These patterns consisted of a well-coordinated set of reactions appropriate in respect of the emotional content of the stimuli used.
Keywords: dwarf goat, emotion, haemodynamic brain changes, fNIRS, heart rate variability, behavior
1. Introduction
The belief that animal welfare does not simply mean physical welfare but also comprises psychological welfare is nowadays commonly accepted as far as public opinion, the scientific community and legislation are concerned. Farm animals in Europe have a unique legal status as ‘sentient beings’, suggesting that they possess complex cognitive abilities and are able to experience emotions such as fear and anxiety and potentially further emotions such as anger, frustration, sadness, grief, empathy, curiosity, happiness [7, 14, 18, 20, 29, 42].
Emotions are regarded as intense, short-lived affective responses to stimuli or events accompanied by a behavioural component (e.g. movements or bodily expressions), an autonomic component (neurophysiological activation) and a cortical/subjective component (what a subject feels [3, 13]). The behavioural component is the most direct and easy to measure feedback of an animal in reaction to a pleasant or unpleasant stimulus. However, recording behaviour only does not allow for an unequivocal evaluation of the underlying emotion. The accompanying peripheral somatic and autonomic activation are central to emotions in that they are important when it comes to optimizing the body state for different types of action. The hypothalamic pituitary adrenal (HPA) and sympathetic adrenal
medullary (SAM) systems are widely known to be involved in emotional responses like fear and anxiety [7, 37, 52]. In recent years, analysing cardiovascular measurements has come to be regarded as a suitable approach for determining the activity of the autonomic nervous system in the study of emotion [16, 24, 59, 80] and cardiac vagal tone has been suggested as a psychophysiological marker of emotion regulation and of certain aspects of psychological adjustment in humans and animals [2, 40, 57, 75]. A number of brain centres like the prefrontal cortex, the cingulate cortex and the amygdala have been shown to be involved in both processing of affective states and autonomic control [71, 70]. What we need is a deeper
understanding of the link between the activity of these centres with respect to negative and positive affective states, peripheral neurophysiological changes and behaviour [4].
It has been shown in humans, monkeys and rats that the orbitofrontal cortex (OFC) is a trigger site in the processing of the emotional valence of external stimuli and is connected to control centres of emotional expression, including amygdala, hypothalamic and brain stem autonomic areas [1, 39, 62, 64]. Based on lesion studies in humans, the valence theory of frontal lateralization [8, 9] postulated that the right brain hemisphere is linked to avoidance behaviour and predominantly processes negative emotions, whereas the left hemisphere is linked to approach responses and the processing of positive emotions. There is some evidence from behavioural studies in animals in support of this hypothesis [5, 15, 35, 67].
Neuroimaging methods have made fundamental progress in neuroscience possible as far as research into human brain function and emotions is concerned [19, 48, 54]. However, some of these methods, such as positron emission tomography (PET) or functional magnetic resonance imaging (fMRI), are unsuitable for use in conscious or even freely moving animals, since the study subject has to be exposed to a physically constrained environment for longer time intervals per se in order to induce negative emotional states. In addition, these technologies are vulnerable to motion artefacts. Against this background, functional near- infrared spectroscopy (fNIRS) has emerged as an alternative technique for the study of the cortical component of emotions in animals due to various reasons: (1) higher tolerance of movement artefacts, (2) mobile equipment allows subjects to move about freely, (3)
measurements can be conducted in a familiar environment, (4) points (1) - (3) allow for the application of more complex emotionally challenging tasks than the simple presentation of visual or acoustical stimuli as is possible in a PET or fMRI environment [30, 34, 44]. fNIRS is a non-invasive technique that evaluates haemodynamic changes in specific brain areas. It measures the temporal changes in the concentrations of oxy-haemoglobin [O2Hb] and deoxy-
haemoglobin [HHb] relative to a baseline [32]. Although fNIRS has much higher temporal resolution than fMRI, its spatial resolution is lower. Since the near-infrared light emitted will achieve a head penetration depth of approximately 2 cm to 3 cm, only cortical areas can be measured [26].
The present study was aimed at evaluating whether each or a combination of the three aspects of an emotional reaction, i.e. behaviour, autonomic reaction and, specifically, brain activation can differentiate between situations presumed to differ in emotional content. We alternately presented dwarf goats with a plastic bowl containing feed that was either not covered or covered with a wire mesh. In so doing, we attempted to elicit a negative (frustration-like) emotion by preventing animals from feeding and a positive emotion by providing a feed reward.
2. Material and Methods 2.1 Subjects and housing
The study was conducted at the Leibniz Institute for Farm Animal Biology (FBN),
Dummerstorf, Germany. Eight female dwarf goats, aged between 12 and 18 months, were used as subjects in the study. All the goats were born and raised at the experimental goat unit of the FBN. When not participating in the experiment, they were housed in two age-related groups of ten goats in pens measuring 12 m2. The pens featured straw bedding and were equipped with an automatic waterer, a hayrack, a self-feeder for delivering commercial concentrate and a wooden rack for climbing. The goats had ad lib access to hay and were fed with a commercial concentrate at 15:00 h in the afternoon. They were previously used in group learning experiments for testing visual discrimination learning and were accustomed to being handled and fitted with technical equipment.
2.2. Treatments, habituation and experimental design
The goats were tested in a test pen measuring 2 x 2 m. A plastic bowl containing concentrate (the same as fed in the pen) was inserted into and retracted from the pen from an adjacent aisle and was either covered with a wire mesh to prevent animals from feeding (mesh aperture: 2 x 5 cm) or uncovered (feed accessible). The former situation was meant to be negative and elicit a frustration-like emotional reaction, whereas the latter was intended to represent a positive situation and elicit reward-induced reinforcement.
Over a period of ten days the goats were habituated to the test pen, the technical equipment and the experimental design. First of all, on five consecutive days the test subjects from each group were transferred to a waiting area (2 x 2 m) adjacent to the test pen, and separated from it by a metal gate covered with acrylic glass. Individual goats were then moved to the test pen and were allowed to walk around for ten minutes, during which period they had visual, acoustic and olfactory contact to the group mates in the waiting area to minimise the negative impact of isolation. Over the next five days, the animals were additionally equipped with the chest belt for heart rate measurement and the fNIRS sensor (Fig. 1c) before they were released into the test pen. On the last three of these days, the goats were given additional training using both treatments (ten times per treatment) i.e. alternate presentation of the bowl containing freely available concentrate or covered by a wire mesh for the periods applied as in the trials when measurements were taken. The start and end of the stimulus presentation was preceded in each case by counting down from 3 to 1 to coordinate the presentation and withdrawal of the feed bowl and the tracking of stimulus onset and cessation in the fNIRS software.
Experimental trials were carried out between 8:00 h and 14:00 h on three consecutive days.
The goats were again subjected to both treatments alternately, with half the goats starting with the positive and the other half of the goats starting with the negative treatment. An
amount of 500 g concentrate was put into the bowl at the beginning of each experimental trial and some feed always remained at the end of a trial. The experimental trial started and ended with a 45 s pre- and post-trial phase. In between, the goats were alternately faced with the covered bowl for 15 s, followed by a post-stimulus phase of 45 s, and the uncovered bowl for 10 s and a post-stimulus phase of 50 s (each stimulus ten times). Presentation of the
accessible feed was curtailed because pilot trials had shown that goats would continue chewing for approximately five seconds after the bowl was withdrawn. Given this time schedule, both the periods involving eating and feed frustration were of comparable duration (see also the results section) and were followed by a similar amount of time without bowl- or feed-related behaviour. In the sense that the covered bowl is expected to induce frustration in the goats and that the uncovered bowl is expected to be rewarding, the two experimental treatments correspond to situations of negative and positive valence respectively.
In principle, all the goats should have undergone the experimental measurements once. Due to technical problems, each goat was subjected to 2-3 measurement trials and we ended up with complete data on at least one trial covering seven goats. For these seven goats, we used the first trial yielding data from all measurement channels in our evaluation. In one of these seven goats only eight instead of ten single stimuli were available.
2.3. Measurements and data processing 2.3.1. fNIRS
We applied functional near-infrared spectroscopy (fNIRS) as described in Muehlemann et al.
([44], Fig. 1). We exposed our goats to external stimuli with presumed emotional content and thus deliberately elicited neuronal activity. This activity resulted in changes in [O2Hb] and [HHb] at the sites of the cortical area responsible for processing the stimulus [32]. [O2Hb]
and [HHb] were calculated on the basis of the raw attenuation data [25]. The instrumental
noise of fNIRS primarily depends on the amount of photons taken into account. In general, the instrumental noise of the fNIRS instrument is far lower than the physiological noise, i.e.
physiological fluctuations (such as heart beat, breathing, Mayer waves) and movement
artefacts. Therefore [O2Hb] and [HHb], measured in µmol/l, were rounded off to four decimal places.
We used a mobile, purpose-built miniaturised 8-channel wireless NIRS sensor on the unrestrained goats [43]. The layout of the sensor included two detectors and four light sources with two wavelengths each (LED at 760 and 870 nm peak emission wavelength;
source–detector distances of 14 and 22 mm). A silicon PIN photo diode was used to detect the intensity of the light after transmission through tissue and the signal was digitised with a sampling rate of 100 Hz. The data was transmitted wirelessly to the host computer for storage and later processing.
The sensor was located on the goats' front so as to cover the frontal area of the cortex (Fig.
1b). In that region of the head, a sinus is situated between the skull and the brain (Fig. 1d) potentially interfering with the light paths. Haeussinger et al. [26] have shown that a frontal sinus may weaken the signal but still allows for the detection of functional responses in humans. This fact may complicate the comparison between individuals since their sinuses vary in size. This is irrelevant for the current study because the main comparison is within subjects, thus allowing for such individual idiosyncrasies.
Data were filtered resulting in values for [O2Hb] and [HHb] at 1 Hz during each single stimulation for eight light paths (all possible combinations of right/left, caudal/cranial, shallow/deep). In the interests of decreasing carryover effects from one stimulation to the next, we included only a 10 s pre-stimulus, 15 s stimulus and 10 s post-stimulus time in our analysis. As we were interested in relative changes in [O2Hb] and [HHb], we subtracted the median of the pre-stimulus observations from all the values observed for each stimulation.
Some of the fNIRS measurements failed during single stimulations, mostly due to movement artefacts. These single stimulations that were detected visually were omitted from further analyses. In order to increase signal-to-noise ratio, we additionally used a time triggered median across all single stimulations, based on the ten stimulations per treatment (in the median, range 6-10 stimulations). We used the median to make our data more stable in respect to random variation and outliers.
2.3.2. Heart rate measurements
Continuous heart rate (RR-intervals) was measured non-invasively throughout the
experimental trials using Polar RS800CX (Polar Elektro, Oy, Finland). The heart monitor consisted of a flexible belt with two integrated electrodes, a transmitter and a separate storage device. The electrodes were placed behind the left humerus and across the sternum. In the interests of better electrical conductivity, the goats were shaved and ultrasonic gel was used (Heiland Vertriebsgesellschaft GmbH, Germany). RR-data were transmitted wirelessly to the storage device and later imported into the corresponding software (Polar Pro Trainer 5). Error correction of the RR-data was conducted using standard set-up of the software (the error rate was between 0 and 5% with the exception of one animal, where it was up to 10%). HR (heart rate in beats per minute), SDNN (standard deviation of the RR-intervals), and RMSSD (square root of the mean squared difference of successive RR-intervals) were then
continuously extracted for each 5s interval throughout the single stimulations. Additionally, the SDNN/RMSSD ratio was calculated. While the SDNN is thought to be related to
sympathetic activity and the RMSSD reflects parasympathetic (vagal) activity, the ratio gives an idea of the balance between both branches of the autonomic nervous system [69, 75].
2.3.3. Behavioural data
Behavioural data was video recorded (Panasonic HDC-SD600, Kadoma, Osaka, Japan) and then analysed using The Observer 7.0 (Noldus Information Technology, Wageningen, Netherlands). Behaviour was assigned to the same 5 s intervals according to heart rate measurements and was continuously scored. The resulting classification specified the duration of feeding (from the bowl), of being inactive (standing in place without any movement), of being active (standing in place while moving head or legs), of locomotion (walking around in the pen), rearing (raising both forelegs to a wall), of shaking the body, and of shaking the head per each 5 s interval. Simultaneously, the duration of time animals were at the bowl (contact with the bowl), near the bowl (< 0.5 m) and far from the bowl (>
0.5m) was assessed for each 5 s interval. Due to sum-to-one constraints, we restricted our behavioural evaluations to the duration of feeding, of being inactive, of locomotion, and of being at the trough. In addition, we evaluated whether the (rarely observed) behaviours of rearing, scratching, shaking body and shaking the head actually occurred at all within a specific 5 s interval.
Heart rate measurements and behaviour were evaluated in the 15 s time frame prior to onset of the stimulus (three 5 s intervals), 15 s stimulus (three 5 s intervals) and another 15 s after cessation of stimulus (three 5 s intervals), resulting in a total of nine 5 s intervals. The middle 15 s of the 45 s periods between stimuli were dropped to reduce carry-over effects.
For each stimulation, the median value for the three 5 s pre-stimulus intervals was calculated and subtracted from all nine 5 s intervals of that stimulation in all heart rate measurements and the behavioural data reflecting durations. These differences describe the absolute change from baseline in each stimulation. The ten stimulations per treatment and per animal were then averaged, again using the median, resulting in a median change from baseline for each animal and treatment throughout the nine 5 s intervals (Fig. 3).
As regards the rare behaviours (rearing, scratching, shaking body and shaking the head), we first calculated the proportion of the ten stimulations per treatment during which the
behaviour was observed, resulting in a proportion per 5 s period and per animal. We then again subtracted the median of the pre-stimulus intervals from all of the intervals,
culminating in values reflecting the absolute change from baseline (Fig. 3). Scratching, shaking body and shaking the head were omitted from further analysis because of their rare occurrence.
2.4. Statistical analysis 2.4.1. Type of model used
We used linear mixed-effects models [55, 56] in R 2.14.1 [58] to describe changes in [O2Hb]
and [HHb] as well as in heart rate and behavioural measures depending on the fixed effects time course throughout the stimulation (continuous variable as a natural spline to allow for an unrestricted yet smooth curve; package splines in the base distribution; see below),
experimental treatment (factor with two levels: covered and uncovered bowl) and location on the head ([O2Hb] and [HHb] only; laterality: indicator for left versus right hemisphere, longitudinal position: indicator for cranial location versus caudal location, measurement depth: deep versus superficial measurement). For the dependencies in our data, we used a hierarchically nested random effect which nested the data from the single paths ([O2Hb] and [HHb] only) within the experimental phase (with a given treatment) which was in turn nested in goat identity. In the 1 Hz [O2Hb] and [HHb], we additionally allowed for an auto-
regressive process of the order of 3 in the residuals to control for temporal auto-correlation.
We checked statistical assumptions, normality of errors and random effects, homoscedasticity of errors and temporal independence of errors using a graphical analysis of residuals.
2.4.2. Model selection
Model selection based on stepwise procedures has recently been criticised due to issues of bias, multiple testing and negligence of model selection uncertainty. An alternative approach based on the use of information criteria (IC) has been suggested (cp. Behavioral Ecology and Sociobiology volume 65, issue 1; [61] for a summary and an application). In short, an optimal model is sought in respect to predictive power and parsimony. Usually, the set of models to select from is an a priori set-up and each model is assigned a so-called model weight which can be interpreted as its probability among the models in the set. The details of this approach have been elaborated mainly using the Akaike information criterion (AIC) which is generally thought to yield good results where predictions are concerned because relatively large models are chosen. The Bayes information criterion (BIC), on the contrary, chooses smaller models and might be more appropriate for research into mechanisms, i.e. the causal understanding of the relation between explanatory and outcome variables [46]. In addition, some former analyses by us showed that models of [O2Hb] and [HHb] selected on the basis of the AIC included some very detailed patterns that were unsystematic and had to be considered random. This is why we used BIC values for comparison here. To compare models, we used the package AICcmodavg [41] that we adapted for a comparison of BIC values.
2.4.3. fNIRS: Outcome variables
[O2Hb] and [HHb] showed an unusual distribution in that long tails occurred in the lower as well as in the upper value range that could not be assigned to either specific animals or treatments. This is understandable in that some changes in concentration showed an increase and some animals and treatments showed an exceptionally large increase. The same was true for other animals and treatments where a decrease in concentrations was involved. Therefore, we applied a transformation that shrinks both of these tails to the centre of the distribution as
follows: [xHb]T = sign ([xHb]) * log (104 * abs ([xHb]) + 1.5), where x stands for either O2
or H and abs for taking the absolute value. Basically, we log-transformed the absolute values and reassigned the sign for the original value after transformation. The specifics of this transformation were chosen such that all values were > 1 before the logarithm transformation to ensure that there was no change in sign following that transformation. This ensured that the complete transformation was monotonous, i.e. values that were larger before the
transformation were also larger after the transformation.
The estimated signals presented in the results were based on the back-transformation of the model predictions following the formula (rounded off to four decimal places): [xHb] = sign ([xHb]T) * (exp (abs ([xHb]T)) - 1.5) * 1/104 with abs standing for taking the absolute value and exp for taking the exponential to the natural base.
2.4.4. fNIRS: model selection
We started out with a full factorial model, i.e. we included all fixed effects and all possible interactions among these fixed effects. We then performed model selection in two steps (Table 1): first we selected the necessary degrees of freedom of the splines reflecting the temporal change in [O2Hb] and [HHb] in that model. In this step, we compared the degrees of freedom of the splines that we had used before ([44], [O2Hb]: 17 dfs; [HHb]: 9 dfs) with a model with fewer ([O2Hb]: 7 dfs; [HHb]: 5 dfs) and a model with more degrees of freedom ([O2Hb]: 27 dfs; [HHb]: 19 dfs).
Secondly, we assessed whether a simplification of the fixed effects was possible. For this purpose, we compared the full model chosen in step one with (1) a model that included all fixed effects as main effects and all two-way and the one three-way interaction between treatment, time-course, right-left and the same effects of treatment, time course, caudal- cranial, (2) a model that included all fixed effects as main effects and all two way and the one
three-way interaction between treatment, time course, right-left, (3) the main effects model including all fixed effects, (4) a model including treatment and time course and their
interaction, (5) a model including the main effects of treatment and time course, (6) a model including time course only, (7) a model including treatment only, and (8) a model including the intercept only (a null model without fixed effects).
2.4.5. Heart rate and behavioural measurements: outcome variables and model selection None of the heart rate and behavioural measurements needed to be transformed to satisfy statistical assumptions. Similar to the [O2Hb] and [HHb] data, we proceeded in two steps.
These outcome variables were observed across nine points in time (5 s intervals) and thus the maximum number of degrees of freedom for a spline was eight. Therefore, we first of all compared models using splines with 8, 5, 4, 3, 2, and 1 degree of freedom to model the time course that included treatment, time course and their interaction as fixed effects. Secondly, we compared the model with the optimal number of degrees of freedom with the main effects model, with the models including only one of the two main effects and with the model
including the intercept only (Table 2).
3. Results
3.1. fNIRS measurements
The BIC values showed that the models with fewer degrees of freedom in the splines modelling the time course were adequate in both [O2Hb] and [HHb] and yielded model weights of 1 with allowance for rounding (Table 1). In respect to the structure of the explanatory variables, the most favoured model was the main effects model including treatment and time course for [O2Hb] and the same model with the additional interaction
between the two variables for [HHb] (Table 1). Therefore, the position on the head did not seem to play a major role as far as the signal was concerned.
[O2Hb] showed two peaks in the course of the presentation of the covered bowl whereas, on average, there was no change in [O2Hb] when feed was accessible (Fig. 2a). [HHb] showed negligible changes when the covered bowl was presented but showed a decrease where feed was available (Fig. 2b). Although reactions in all measured light paths were similar in the treatment with the covered feed bowl, reactions were more variable in the situation where feed was available (Fig. 2, 4).
3.2. Heart rate measurements and behaviour
High model weights enabled to choose degrees of freedom for the splines modelling the time course and these ranged between 1 and 4 (Table 2). For rearing, the models with 2 and 1 degree of freedom attained similar model weights and we chose the one with the slightly higher value in order not to miss any potential subtle effect. The null model, i.e. the
assumption that the measured values would randomly vary around a given mean over time, was strongly supported by the BIC values for SDNN, RMSSD and rearing (Table 2). The model, including interactions, was as strongly supported for SDNN/RMSSD, time spent at the trough, and locomotion. The case was less clear when it came to heart rate and periods of inactivity. For heart rate, the highest model probability was found for the null model, but the main effects model, including the time course, showed a similar probability and an evidence ratio of almost 90% in comparison to the null model. Thus it was almost as likely as the null model. As regards periods of inactivity, the largest model probability was reached by the model with the main effect of the time course, but the model with an interaction between treatment and time-course still yielded a non-negligible model probability of 0.28 (with an evidence ratio > 56 in comparison to the null model; Table 2). Since the effects of interest
may be subtle, we decided to present the more complex models for these two outcome variables.
Heart rate showed a weak peak, reaching its maximum at around stimulus onset (Fig. 3a).
There was no consistent and statistically detectable pattern in SDNN nor in RMSSD (Fig.
3bc), whereas SDNN/RMSSD showed a dip after the covered feed bowl had been withdrawn and a strong peak and dip with onset of stimulus and cessation of stimulus respectively, in the treatment where feed was provided (Fig. 3d).
Feeding took place only when feed was available and somewhat longer. Goats fed for a median of 3.67 (range: 2.85-4.35), 5 (5-5), 5 (2.62-5) and 2.01 s (0-3.48), in the 5 s intervals starting at 0, 5, 10, and 15 s after onset of stimulus.
Time spent at the trough was high when feed was accessible and dipped when the feed bowl was covered (Fig. 3e). The goats were less inactive during the stimulus presentation and were even less so when feed was available (Fig. 3f). On the other hand, locomotion increased during stimulus presentation if the bowl was covered, but only increased after stimulus presentation if the feed was available (Fig. 3g). Finally, the proportion of single stimulations during which goats showed rearing behaviour did not change systematically throughout the stimulus presentation (Fig. 3h).
4. Discussion 4.1. Technical aspects
Some technical aspects of this experiment call for brief discussion. Until recently, the advice was against cortical fNIRS measurements if a sinus was located between skull and brain [26].
Yet recent measurements and simulations have shown that such air cavities may attenuate the signal but do not seem to interfere with the pattern of the signal [26]. In that study,
Hauessinger et al. [26] show that the depth of penetration below a sinus remains about the
same (2-3 cm at an interoptode distance of 3 cm), but the volume of grey matter that is traversed is reduced by a sinus and thus the fNIRS measurements may become less sensitive.
This is an issue mainly for studies comparing individuals who vary in the depth of their sinuses, whereas these effects are kept constant if the main comparison is between different treatment situations within subjects as in our study. An activation may therefore be more difficult to detect, but if detected it can be expected to be reliable.
We have used a non-standard transformation for the outcome variables [O2Hb] and [HHb].
This was needed because we observed, in some instances, (paths and individuals) very large absolute increases or decreases in [O2Hb] and [HHb]. It may well be the case that these changes were all similar on a relative scale, i.e. that these major changes observed started from high pre-stimulus concentration. Unfortunately, the approach to calculating [O2Hb] and [HHb] changes does not allow for an estimation of absolute [O2Hb] and [HHb] [77]. For the time being, we are thus limited to addressing the issue of statistical assumptions in our models and the presentation of absolute rather than relative changes in concentration. We thus need to postpone questions relating to exact (de-) activation mechanisms (absolute or relative compared to baseline).
The modelled change in [HHb] during accessible feed appeared to be relatively slow and delayed in comparison to the onset and cessation of the stimulus (Fig. 2b, right). This is likely to be an artefact, though, caused by the simplicity of the chosen model that averaged across paths and individuals and also used a spline with a relatively low degree of freedom. Given the raw block-averaged data visible in the same figure, many of the changes in single path [HHb] seem to be fast and consistent with at least the onset of stimulus. Given a larger sample size, a model with a higher degree of freedom and including some of the interactions between the localisations on the head might have been chosen. Estimated values of such a model would follow the raw data more closely.
Our dwarf goats were alternately subjected to the same two stimuli ten times, i.e. the significance of the stimuli might have changed in the course of their repetition in that goats became habituated, satiated or increasingly frustrated. The main purpose of the repetitions was to increase signal-to-noise ratios in our fNIRS measurements by taking a triggered median across the repetitions. For consistency and comparability reasons we dealt in an analogous way with all our measurements. Thus our focus was on whether we could at all detect a pattern between the two types of stimuli as reflected in the median of the ten replicates. Obviously, the above mentioned processes did not occur to an extent as to
completely dilute the patterns that we have found. Based on the feeding behaviour, there did not seem to be much change from one repetition to the next and goats remained motivated to feed throughout the trials. It remains currently open how much change in behaviour there was with each additional repetition and this question might be addressed in a future study
purposefully designed for that question.
4.2. Neuro-cardio behavioural reactions: emotions in action?
Emotions have been conceptualised as a "multi-component response to an emotionally potent antecedent event, causing changes in subjective feeling quality, expressive behaviour, and physiological activation" [38]. We have indeed found changes in all these aspects of
emotions in our goats if we consider changes in the activation of the cortical frontal brain as a (non-sufficient) prerequisite for subjective feelings. In humans, it has been found that
conscious reflection of feelings specifically activates the medial prefrontal cortex [49, 76]. It can thus be assumed that activation in this area is a necessary prerequisite for conscious subjective feelings. While this condition is met in our goats, we can nevertheless make no statement in as much this activation actually contributes to a conscious feeling in this species
as it is unknown to what extent other species than humans have the capability of conscious subjective experiences.
4.2.1. Behaviour
Our treatments were successful in the sense that the animals fed while confronted with the uncovered bowl and until about 5 seconds after its retrieval, thus experiencing a feed reward of the same length as when they were confronted with the wire-mesh covered bowl in the negative situation. Accordingly, time spent at the trough was high when the uncovered bowl was available, but low when the bowl was covered. The time spent in locomotion increased during the presentation of the negative stimulus, because goats moved away from the bowl when presented covered by the mesh. Also, goats showed higher locomotion in reaction to the withdrawal of the uncovered bowl which could be interpreted as a negative event too.
These reactions can be viewed as the result of an approach/withdrawal output of the emotional system (e.g. [47]). These reactions may be viewed as the result of an
approach/withdrawal output of the emotional system (e.g. [47]). Increased activity has previously been observed in response to a negative/frustrating event (e.g. [28, 74]). In contrast to another study conducted with goats [66], rearing did not reliably reflect the
valence of the stimulus and scratching, shaking the body and shaking the head were observed too rarely to serve as an indicator for valence in such experimental situations.
4.2.2. Physiological activation.
The role of the autonomic system and the specificity of its reactions in respect to different emotions has been the subject of intensive discussions [21]. Though there seems to be a direct relationship between prefrontal cortex activation and HRV [70], we did not find changes in either SDNN or RMSSD corresponding to the changes in the brain that we have observed
(see below). This corresponds to some previous observations in chickens and sheep [17, 61]
with no changes in RMSSD but contrasts others [59, 60, 80] that found lower values in RMSSD in situations of negative valence in sheep and pigs. This may be explained by the fact that the situations of negative and positive valence in the current experiment were of relatively weak intensity and by the fact that reactions were highly variable between individuals.
Sympathetically mediated arousal as reflected in SDNN/RMSSD was visible in the
anticipation of the positive event, i.e. the uncovered bowl. There are many easily measurable variables for heart-rate variability and the sympatho-vagal balance in the time domain (e.g.
Table 3 in [38]), but only a few studies have been dedicated to the changes displayed in relation to emotional situations (Table 2 in [38]) and therefore, there is little evidence from human research as to what specific reactions we need to expect during frustration versus reward and vice versa [36]. However, in a study on acoustical operant conditioning in pigs, we found a comparable increase in the sympatho-vagal balance in anticipation of an
individually announced feed reward [80]. In the present study we used the same acoustical signal (counting down from 3 to 1) to announce both, the positive and the negative
stimulation and yet the goats only reacted to the positive situation with a change in autonomic balance. We assume the goats were in a state of affective arousal because of reward
anticipation before positive stimulation. In contrast, the expectation of a frustrating event (the covered bowl) did not lead to arousal as may be expected.
Some of the anticipatory reaction seen in SDNN/RMSSD was also visible in HR for both treatments and the raw data seemed to indicate that the reaction was stronger in the positive situation (Fig. 3a), though this difference was not statistically detectable. This contrasts with previous research on the situation of differing valence that found higher negative correlations between heart rate and positivity of a situation [6, 59, 60].
4.2.3. Cortical activation.
As regards the interpretation of the haemodynamic cortical changes, the location of
measurement is crucial. As shown in Fig. 1, the fNIRS sensor was positioned on top of the frontal cortex; the most frontal part (orbitofrontal cortex) was covered by the ‘cranial’ paths and a position slightly shifted to the top and back (dorsomedial/-lateral cortex) by the
‘caudal’ paths. In humans, it is known that this area plays an essential part in assessing emotions and linking these emotions with cognitive processes and behavioural actions [10, 53, 65, 79].
If neural activation takes place, O2 consumption (the cerebral metabolic rate) increases, reducing [O2Hb] and increasing [HHb]. At the same time, blood flow and blood volume are increased, carrying more [O2Hb] to the active tissue and reducing [HHb] at the same time;
this results in an increase of [O2Hb] and a decrease of [HHb]. Usually, the [O2Hb] changes are about 3-4 times stronger and the balance of the above effects are much less clear for [HHb] than for [O2Hb] [78]. This leads us to conclude that we observed a general frontal cortical activation when the goats were faced with the negative situation, i.e. the covered bowl. This activation was simultaneous with the onset of stimulus; it was also of short duration. There was a second similar activation around 10 sec after the start of the stimulus.
The most likely cause for this activation was the expectation of the bowl being withdrawn which took place at that point in time in the other (positive) situation, i.e. at the end of the presentation of the uncovered bowl. Such a reaction might not be elicited in the situation with the uncovered bowl because the animals were still eating when the bowl was withdrawn.
Thus, such a general frontal activation would seem to coincide with emotions likely to elicit frustration but also negative anticipation. This bilateral activation of the prefrontal cortex in a presumably negative situation coincides with previous observations of humans viewing
fearful compared to neutral pictures [23], pictures of what one dislikes [33] or during deception [72].
Although a general frontal cortical activation in a positive situation (grooming) modulated by mood was found in a previous study involving sheep [44], the pattern is less clear in the positive situation of the current study. The lack of a clear reaction may be related to the higher variability as seen in the insets of Fig. 2. This high variability in an emotionally positive situation may be viewed as an evolutionary effect [51] that leads to little variation between individuals as regards their reaction towards negative stimuli but allows for more variable reactions if faced with a positive stimulus [12, 20]. This discrepancy may be caused by the far-reaching and direct consequences as regards fitness of the negative compared to the positive stimuli. However, individuality in terms of reactions does not seem to be the only source of variability in our experiment. Looking at the single paths in more detail (Fig. 4), there seemed to be a differential activation in the more cranial area: a shallow (short light paths) right-sided activation was accompanied by a deep (long light paths) left-sided activation and contrasted with a deep right-sided deactivation. In the caudal part of our measurement, haemodynamic changes are less apparent. Given our sample size, this effect was not statistically detectable in interactions between the measurement locations.
Nevertheless, the pattern fits previous observations so well that we will shortly be looking at it in more detail: left-sided cortical activation has been related to positive emotional reactions and right-sided activation to negative emotional reactions in humans [10, 39, 45, 50] and animals (sheep [6], rats [68]). This asymmetry may thus be directly relevant for the
assessment of animal welfare [63]. Some recent research indicates that the asymmetric frontal cortical activation may not, however, correspond to valence (negativity – positivity) but that left cortical activation may coincide with approach motivation [11, 22, 27] or that the left- right gradient may just reflect relative differences in magnitude of e.g. numerals, valence of
emotions [31]. All in all, the activation pattern reflected by the long paths in our current study fits the expectation of asymmetric activation in respect to positivity of a situation or tendency to approach, thus, indicating that our brain structures coinciding with the measurements in the long paths (deeper in the tissue) were responsible for the emotional processing of the stimuli.
4.2.4. Anticipation.
We observed several reactions in our goats that showed that they might have anticipated events: the second peak in the cortical activation when confronted with the covered bowl, a peak in heart rate and SDNN/RMSSD reached slightly before the start of stimulus
presentation. We think that all other reactions that coincided with stimulus onset or end were caused directly by the stimuli. Nevertheless, in order to completely disentangle the reactions in respect to the stimuli themselves and anticipatory effects, the pause between stimuli
presentations should be randomly varied in future experiments [73] as well as the sequence of stimuli of different types. Also, stimulus presentation should be automated so that the
animals’ attention is not drawn to the impending next stimulus by audible counting down for coordinating stimulus presentation and fNIRS recording.
5. Conclusions
In this study, goats confronted with a covered feed bowl (and thus presumably in a state of frustration) directed their behaviour away from the feed bowl and increased activity with no autonomic arousal, but accompanied by a clear activation of the prefrontal cortex.
Contrastingly, behaviour was directed at the trough and there was a decrease in activity accompanied by sympathetically mediated autonomic arousal and specific left hemispheric prefrontal activation when allowed to eat and thus being faced with a rewarding situation.
Therefore, we observed patterns in behaviour, sympathetic reaction and brain activity that
identified a frustrating and rewarding situation in goats and consisted of a well-coordinated set of reactions appropriate as regards the emotional content of the stimuli used.
6. Acknowledgements
We would like to thank T. Mühlemann for his on-going real-time advice in conjunction with the fNIRS measurements. Special thanks go to D. Sehland and H. Deike for excellent
technical assistance and to K. Siebert for behavioural scoring and extracting heart rate
measurements. Further thanks go to E. Hillmann, B. Puppe and two anonymous reviewers for commenting on earlier versions of the manuscript.
7. References
[1] Banich MT, Mackiewicz KL , Depue BE, Whitmer AJ, Miller GA, Heller W. Cognitive control mechanisms, emotion and memory: A neural perspective with implications for psychopathology. Neurosci Biobehav Rev 2009; 33: 613–30.
[2] Beauchaine T. Vagal tone, development, and Gray’s motivational theory: toward an integrated model of autonomic nervous system functioning in psychopathology. Dev Psychopath 2001; 13: 183-214.
[3] Boissy A, Manteuffel G, Jensen MB, Moe RO, Spruijt B, Keeling LJ, et al. Assessment of positive emotions in animals to improve their welfare. Physiol Behav 2007; 92: 375- 97.
[4] Braesicke K, Parkinson JA, Reekie Y, Man S, Hopewell L, Pears A, et al. Autonomic arousal in an appetitive context in primates: a behavioural and neural analysis. Eur J Neurosci 2005; 21: 1733-40.
[5] Critchley HD, Corfield DR, Chandler MP, Mathias CJ, Dolan RJ. Cerebral correlates of autonomic cardiovascular arousal: a functional neuroimaging investigation in humans. J Physiol 2000; 523: 259–70.
[6] da Costa AP, Leigh AE, Man M-S, Kendrick KM. Face pictures reduce behavioural, autonomic, endocrine and neural indices of stress an fear in sheep. Proc R Soc B 2004;
271: 2077-84.
[7] Dantzer R. Can farm animal welfare be understood without taking into account the issues of emotion and cognition? J Anim Sci 2002; 80: E1-E9.
[8] Davidson RJ. Anterior cerebral asymmetry and the nature of emotion. Brain Cognit 1992; 20: 125–51.
[9] Davidson RJ. Cerebral asymmetry, emotion, and affective style. In: Davidson, R.J.
editor. Brain Asymmetry, MIT Press, Cambridge, MA; 1995, pp. xiv, 735.
[10] Davidson RJ, Irwin W. The functional neuroanatomy of emotion and affective style.
Trends Cogn Sci 1999; 3: 11-21.
[11] Davidson RJ, Shackman AJ, Maxwell JS. Asymmetries in face and brain related emotion. Trends Cogn Sci 2004; 8: 389-91.
[12] Dawkins MS. Evolution and animal welfare. Q Rev Biol 1998; 73: 305–28.
[13] Désiré L, Veissier I, Després G, Delval E, Toporenko G, Boissy A. Appraisal process in sheep (Ovis aries): interactive effect of suddenness and unfamiliarity on cardiac and behavioral responses. J Comp Psych 2006; 120: 280–87.
[14] de Waal FBM. What is an animal emotion? Ann N Y Acad Sci 2011; 1224: 191-206.
[15] de Boyer des Roches A, Richard-Yris MA, Henry S, Ezzaouia M, Hausberger M.
Laterality and emotions: Visual laterality in the domestic horse (Equus caballus) differs with objects' emotional value. Physiol Behav 2008; 94: 487-90
[16] Duepjan S, Tuchscherer A, Langbein J, Schoen P-C, Manteuffel G, Puppe B.
Behavioural and cardiac responses towards conspecific distress calls in domestic pigs (Sus scrofa). Physiol Behav 2011; 103: 445–52.
[17] Edgar JL, Lowe JC, Paul ES, Nicol CJ. Avian maternal response to chick distress. Proc R Soc B 2011; 278: 3129-34.
[18] European Parliament. Report on a Community Action Plan on the Protection and Welfare of Animals 2006-2010 (2006/2046(INI)), 2006. Committee on Agriculture and Rural Development. Rapporteur: Elisabeth Jeggle.
[19] Feldmann-Barrett BL, Wager TD. The structure of emotion - evidence from neuroimaging studies. Curr Dir Psychol Sci 2006; 15: 79-83.
[20] Fraser D, Duncan IJH. ‘Pleasures’, ‘pains’ and animal welfare: toward a natural history of affect. Anim Welf 1998; 7: 383–96.
[21] Friedman BH. Feelings and the body: the Jamesian perspective on autonomic specificity of emotion. Biol Psychol 2010; 84: 383-93.
[22] Gadea M, Expert R, Salvador A, Marti-Bonmati L. The sad, the angry, and the
asymmetrical brain: dichotic listening studies of negative affect and depression. Brain Cogn 2011; 76: 294-99.
[23] Glotzbach E, Muhlberger A, Geschwendtner K, Fallgatter A, Pauli P, Herrmann MJ.
Prefrontal brain activation during emotional processing: a functional near infrared spectroscopy study (fNIRS). Open Neuroim J 2011; 5: 33-9.
[24] Greiveldinger L, Veissier I, Boissy A. Emotional experience in sheep: predictability of a sudden event lowers subsequent emotional responses. Physiol Behav 2007; 92: 675- 83.
[25] Haensse D, Szabo P, Brown D, Fauchère J-C, Niederer P, Bucher H-U, et al. A new multichannel near infrared spectrophotometry system for functional studies of the brain in adults and neonates. Opt Expr 2005; 13: 4525–38.
[26] Haeussinger FB, Heinzel S, Hahn T, Schecklmann M, Ehlis A-C, Fallgatter AJ.
Simulation of near-infrared light absorption considering individual head and prefrontal cortex anatomy: implications for optical neuroimaging. PLoS ONE 2011; 6: e26377 (1- 12).
[27] Harmon-Jones E, Gable PA, Peterson CK. The role of asymmetric frontal activity in emotion-related phenomena: a review and update. Biol Psychol 2010; 84: 451-62.
[28] Haskell MJ, Coerse NCA, Taylor PAE, McCorquodale C. The effect of previous experience over control of access to food and light on the level of frustration-induced aggression in the domestic hen. Ethol 2004; 110: 501-13.
[29] Haynes RP. Do regulations of animal welfare need to develop a theory of psychological well-being? J Agric Environ Ethic 2001; 14: 231–40.
[30] Hespos SJ. What is optical imaging? J Cogn Dev 2010; 11: 1–13.
[31] Holmes KJ, Lourenco SF. Common spatial organization of number and emotional expression: a mental magnitude line. Brain Cogn 2011; 77: 315-23.
[32] Hoshi Y. Functional near-infrared spectroscopy: current status and future prospects. J Biomed Opt 2007; 12: 062106.
[33] Hosseini SMH, Mano Y, Rostami M, Takahashi M, Sugiura M, Kawashima R.
Decoding what one likes or dislikes from single-trial fNIRS measurements.
Neuroreport 2011; 22: 269–73.
[34] Joanette Y, Ansaldo AI, de Mattos Pimenta Parente MA, Fonseca R P, Kristensen CH, Scherer LC. Neuroimaging investigation of executive functions: evidence from fNIRS.
PSICO 2008; 39: 267-74.
[35] Koboroff A, Kaplan G, Rogers LJ. Lateralized anti-predator behaviour in Australian magpies (Gymnorhina tibicen). Brain Research Bulletin 2008; 76: 304–6.
[36] Kop WJ, Synowski SJ, Newell ME, Schmidt LA, Waldstein SR, Fox NA. Autonomic nervous system reactivity to positive and negative mood induction: the role of acute psychological responses and frontal electrocortical activity. Biol Psychol 2011; 86:
230-8.
[37] Korte SM. Corticosteroids in relation to fear, anxiety and psychopathology. Neurosci Biobehav Rev 2001; 25: 117-42.
[38] Kreibig SD. Autonomic nervous system activity in emotion: a review. Biol Psychol 2010; 84: 394-421.
[39] Lewis PA, Critchley HD, Rotshtein P, Dolan RJ. Neural correlates of processing valence and arousal in affective words. Cereb Cort 2007; 17: 742-48.
[40] Matsunaga M, Isowa T, Kimura K, Miyakoshi M, Kanayama N, Murakami H, et al.
Associations among positive mood, brain, and cardiovascular activities in an affectively positive situation. Brain Res 2009; 1263: 93-103.
[41] Mazerolle MJ. AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c). 2011; R package version 1.21. http://CRAN.R-
project.org/package=AICcmodavg (last accessed 26.06.2012).
[42] Mendl M, Burman OHP, Paul ES. An integrative and functional framework for the study of animal emotion and mood. Proc R Soc B 2010; 277: 2895-2904.
[43] Muehlemann T, Haensse D, Wolf M. Wireless miniaturized in-vivo near infrared imaging. Opt Expr 2008; 16: 10323–30.
[44] Muehlemann T, Reefmann N, Wechsler B, Wolf M, Gygax L. In vivo functional near- infrared spectroscopy measures mood-modulated cerebral responses to a positive emotional stimulus in sheep. NeuroImage 2011; 54: 1625-33.
[45] Murphy FC, Nimmo-Smith I, Lawrence AD. Functional neuroanatomy of emotions: a meta-analysis. Cogn Affec Behav Neurosci 2003; 3: 207-33.
[46] Myung IJ. The importance of complexity in model selection. J Math Psychol 2000; 44:
190-204.
[47] Norris CJ, Gollan J, Berntson GG, Cacioppo JT. The current status of research on the structure of evaluative space. Biol Psych 2010; 84: 394-421.
[48] Ochsner KN, Gross JJ. Cognitive emotion regulation - insights from social cognitive and affective neuroscience. Curr Dir Psych Sci 2008; 17: 153-58.
[49] Ochsener KN, Knierim K, Ludlow DH, Hanelin J, Ramachandran T, Glover G, Mackey SC. Reflecting upon feelings: an fMRI study of neural systems supporting the
attribution of emotion to self and other. J Cog Neurosci 2004; 16: 1746-72.
[50] O’Doherty J, Kringlebach ML, Rolls ET, Hornak J, Andrews C. Abstract reward and punishment representation in the human orbitofrontal cortex. Nature Neurosci 2001; 4:
95-102.
[51] Panksepp J. The basic emotional circuits of mammalian brains: do animals have affective lives? Neurosci Biobehav Rev 2011; 35: 1791–804.
[52] Paul ES, Harding EJ, Mendl M. Measuring emotional processes in animals: the utility of a cognitive approach. Neurosci Biobehav Rev 2005; 29: 469-91.
[53] Phan KL, Wager T, Taylor SF, Liberzon I. Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI. NeuroImage 2002; 16:
331-48.
[54] Phan L, Wager Tor D, Taylor SF, Liberzon I. Functional neuroimaging studies of human emotions. CNS Spectr 2004; 9: 258-66.
[55] Pinheiro JC, Bates DM. Mixed-Effects Models in S and S-PLUS. New York: Springer;
2000.
[56] Pinheiro J, Bates D, DebRoy S, Sarkar D. R Development Core Team. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-102; 2011.
[57] Porges SW. The polyvagal theory: phylogenetic contributions to social behavior.
Physiol Behav 2003; 79: 503-13.
[58] R Development Core Team, 2011. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2012;
http://www.R-project.org/ (last accessed 26.06.2012).
[59] Reefmann R, Wechsler B, Gygax L. Behavioural and physiological assessment of positive and negative emotion in sheep. Anim Behav 2009; 78: 651–59.
[60] Reefmann R, Bütikofer Kaszàs F, Wechsler B, Gygax L. Physiological expression of emotional reactions in sheep. Physiol Behav 2009; 98: 235–41.
[61] Reefmann N, Muehlemann T, Wechsler B, Gygax L. Housing induced mood modulates reactions to emotional stimuli in sheep. Appl Anim Behav Sci 2012; 136: 146-55.
[62] Rempel-Clower N. Role of orbitofrontal cortex connections in emotion. Ann NY Acad Sci 2007; 1121: 72–86.
[63] Rogers LJ. Relevance of brain and behavioural lateralization to animal welfare. Appl Anim Behav Sci 2010; 127: 1–11.
[64] Rolls ET, Grabenhorst F. The orbitofrontal cortex and beyond: from affect to decision- making. Progr Neurobiol 2008; 86: 216–24.
[65] Roy M, Shohamy D, Wager TD. Ventromedial prefrontal-subcortical systems and the generation of affective meaning. Trends Cogn Sci 2012; 16: 147-56.
[66] Siebert K, Langbein J, Schön P-C, Tuchscherer A, Puppe B. Degree of social isolation affects behavioural and vocal response patterns in dwarf goats (Capra hircus). Appl Anim Behav Sci 2011; 131: 53-62.
[67] Siniscalchi M, Sasso R, Pepe AM, Vallortigara G, Quaranta A. Dogs turn left to emotional stimuli. Behav Brain Res 2010; 208: 516-21.
[68] Sullivan RM. Hemispheric asymmetry in stress processing in rat prefrontal cortex and the role of mesocortical dopamine. Stress 2004; 7: 131-43.
[69] Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 1996; 93:1043– 65.
[70] Thayer JF, Fredrikson FAM, Sollers JJ, Wagere TD. A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci Biobehav Rev 2012; 36: 747-56.
[71] Thayer JF, Lane RD. Claude Bernard and the heart–brain connection: further
elaboration of a model of neurovisceral integration. Neurosci Biobehav Rev 2009; 33:
81–8.
[72] Tian F, Sharma V, Kozel FA, Liu H. Functional near-infrared spectroscopy to investigate hemodynamic responses to deception in the prefrontal cortex. Brain Res 2009; 1303: 120-30.
[73] Toronov V, Webb A, Choi JH, Wolf M, Michalos A, Gratton E, et al. Investigation of human brain hemodynamics by simultaneous near-infrared spectroscopy and functional magnetic resonance imaging. Med Phys 2001; 28: 521-27.
[74] Veissier I, Andanson S, Dubroeucq H, Pomies D. The motivation of cows to walk as thwarted by tethering. J Anim Sci 2008; 86: 2723-29.
[75] von Borell E, Langbein J, Després G, Hansen S, Leterrier C, Marchant-Forde J, et al.
Heart rate variability as a measure of autonomic regulation of cardiac activity for assessing stress and welfare in farm animals – a review. Physiol Behav 2007; 92: 293 – 316.
[76] Winkielman P, Berridge KC, Wilbarger JL. Emotion, Behavior and Conscious Experience. In: Barrett LF , Niedenthal PM, Winkielman P. editors. Emotion and Consciousness. The Guilford Press, NY; 2005, Chapter 14, 335-362.
[77] Wolf M, Ferrari M, Quaresima V. Progress of near-infrared spectroscopy and
topography for brain and muscle clinical applications. J Biomed Opt 2007; 12: 062104.
[78] Wolf M, Wolf U, Toronov V, Michalos A, Paunescu LA, Choi JH, et al. Different time evolution of oxyhemoglobin and deoxyhemoglobin concentration changes in the visual and motor cortices during functional stimulation: a near-infrared spectroscopy study.
NeuroImage 2002; 16: 704-12.
[79] Wood J, Grafman J. Human prefrontal cortex: processing and representational perspectives. Nature Rev Neurosci 2003; 4: 139-47.
[80] Zebunke M, Langbein J, Manteuffel G, Puppe B. Autonomic reactions indicating positive affect during acoustic reward learning in domestic pigs. Anim Behav 2011; 81:
481-89.
Table 1 Model selection fNIRS Measures used in BIC based model selection of the haemodynamic changes relating to degrees of freedom of the splines and the explanatory variables (see text for definitions of the models). aInt: all interactions, int2: several expected interactions, itn1: some expected interactions, aM: all main effects, V*T: valence, time course and interaction, V+T: main effects valence and time course, V: valence as a main effect only, T: time course as a main effect only.
Spline1 Choice of model complexity
df wi Measures2 aInt int2 int1 aM V*T V+T V T Intercept
[O2Hb] 7 1.00 Total dfs3 135 56 41 19 23 16 9 15 8
BIC 24880.14 24284.13 24178.41 24033.88 24036.47 24010.91 24021.39 24011.62 24022.11
∆i 869.23 273.22 167.5 22.97 25.56 0 10.47 0.70 11.20
ER0 > 116 wi 0 0 0 0 0 0.58! 0 0.41 0
[HHb] 5 1.00 Total dfs3 103 44 33 17 19 14 9 13 8
BIC 23288.77 22881.67 22798.41 22722.83 22693.64 22698.39 22705.1 22696.45 22703.04
∆i 595.13 188.03 104.77 29.19 0 4.75 11.45 2.81 9.4
ER0 =74 wi 0 0 0 0 0.74! 0.07 0 0.18 0.01
1degrees of freedom chosen among the values 7, 17, 27 [O2Hb] and 5, 9, 19 [HHb] for the spline used for modelling the time course by the minimal BIC value and model weight of that model. 2Measures used in BIC-based model selection. BIC: BIC-value; ∆i: Differences in BIC in comparison to the optimal model (having the lowest BIC value) within the set of models; wi: Bayes weight which may be interpreted as the probability of the given model within the set presented. 3df: degrees of freedom used by respective model. !: Final model reported. ER0: Evidence ratio of the chosen model in relation to the null model (including the intercept only), i.e. the chosen model is ER0 times more likely than the null model.
Table 2: Measurements used in BIC based model selection of heart rate and behavioural outcome variables in relation to degrees of freedom of the splines and the explanatory variables valence (V) and time course (T). See text for further explanations.
Spline df1 Choice of model complexity
df wi Measures2 V * T V + T V T Intercept
Total dfs fixed effects3 1+2*df 1+df 1 0+df 0
Heart rate 3 0.79 BIC 908.28 904.91 904.68 900.48 900.25
∆i 8.03 4.67 4.43 0.24 0
ER0 = 0.89 wi 0.01 0.05 0.05 0.42! 0.47
SDNN 1 0.88 BIC 757.51 753.64 749.99 750.46 746.82
∆i 10.69 6.82 3.18 3.64 0
wi 0 0.02 0.15 0.12 0.71!
RMSSD 1 0.99 BIC 728.86 724.08 719.32 719.7 714.94
∆i 13.92 9.14 4.38 4.76 0
wi 0 0.01 0.09 0.08 0.82!
SDNN/RMSSD 3 0.99 BIC 64.96 72.83 71.12 69.69 67.98
∆i 0 7.88 6.16 4.73 3.02
ER0 = 4.5 wi 0.72! 0.01 0.03 0.07 0.16
Being at trough 4 1.00 BIC 397.99 466.08 478.9 469.96 482.35
∆i 0 68.09 80.91 71.97 84.37
ER0 > 200 wi 1.00! 0 0 0 0
Being inactive 2 0.80 BIC 399.1 396.8 412.98 395.18 411.36
∆i 3.93 1.62 17.8 0 16.18
ER0 > 56 wi 0.09 0.28! 0 0.63 0
Locomotion 2 0.95 BIC 259.72 276.35 270.28 272.45 266.37
∆i 0 16.63 10.55 12.72 6.64
ER0 = 32 wi 0.96! 0 0 0 0.03
Rearing 2 0.50 BIC -147.19 -149.08 -156.27 -153.91 -161.1
(1 0.44) ∆i 13.91 12.02 4.83 7.19 0
wi 0 0 0.08 0.02 0.89!
1degrees of freedom chosen among the values 1, 2, 3, 4, 5, 8 for the spline used for modelling the time course by the minimal BIC value and model weight of that model. 2Measurements used in BIC-based model selection. BIC: BIC-value; ∆i: Differences in BIC in comparison to the optimal model (having the lowest BIC value) within the set of models; wi: Bayes weight which may be interpreted as the probability of the given model within the set presented. 3df: degrees of freedom used by the fixed effects in the model, depend largely on the degrees of freedom of the spline (all other estimated parameters, i.e. the intercept, the random effect of sheep, the variability of the error are included in all these models). !: Final model reported. ER0: Evidence ratio of the chosen model in relation to the null model (including the intercept only), i.e. the chosen model is ER0 times more likely than the null model.
Figure Captions Figure 1
Position of the fNIRS sensor on the head of a goat: a) shaved area on the forehead, b) position of the sensor, c) attachment of the sensor, and d) saggital cross section with the approximate position of the fNIRS device (see arrows).
Figure 2
Changes in (a) oxy- [O2Hb] and (b) deoxy- [HHb] haemoglobin concentrations over the time course of the presentation of the covered feed bowl (negative; feed frustration) and accessible feed (positive; feed reward) treatment. A two-peak activation is visible in [O2Hb] for the negative situation, whereas a strong average decrease in [HHb] was found in the positive situation (but see also Fig. 4). Stimulation periods are indicated by the grey shading. Wide lines indicate model estimates with thin lines reflecting 95% confidence intervals. Insets:
signals from the single paths (eight paths per animal and experimental treatment).
Figure 3
Changes in heart rate measurements (a: heart rate; b: SDNN; c: RMSSD; d: SDNN/RMSSD) and behavioural measurements (e: being close to trough; f: being inactive; g: locomotion; h:
rearing) over the time course (indicated in s) of the presentation of the covered feed bowl (negative; feed frustration) and accessible feed (positive; feed reward) treatment. Grey area:
stimulus, thin grey lines: time course for the single individuals, thick black line: model estimate: thin black lines 95% confidence intervals.
Figure 4
Summed signal across the stimulus phase (area under the curve, µmol/l) during accessible feed (seconds 0-15) in relation to the spatial location of the light paths. A differential left deep activation is visible in the cranial location specifically for [O2Hb]. Shading indicates the proportion of animals for which the summed signal was positive (white = 0, black= 1).
(a) (b) (d)
(c)
