As stated in section 4.3, odors have a strong affective power and are ideal cues for instating contexts in which new associations can be formed or existing associations can be modified. We therefore aimed at manipulating an associative process – aversive conditioning – by exposure to an interfering olfactory context, of differential hedonic colorings: pleasant or unpleasant. We focused on odor valence given the saliency of this characteristic, and its propensity to generate positive or negative emotions. Negative emotions, by signaling threat, usually restrict attentional scope (Storbeck &
Clore, 2005) to elicit specific action tendencies. On the contrary, according to the Broaden and Build theory (Fredrickson, 2004b), non-motivational positive emotions such as happiness or contentment (Gable & Harmon-Jones, 2008, 2010), rarely cue dangerous situations, but rather widen the mindset and facilitate the recruitment of perceptual and attentional cognitive resources (Bar, 2009; Shenhav et al., 2013), as evidenced by the use of global versus local information processing in positive versus negative mood (Gasper & Clore, 2002). Additionally, positive mood has been shown to promote associative thinking, for example in word-associations tasks (Isen, Johnson, Mertz, & Robinson, 1985), as opposed to negative mood (Storbeck & Clore, 2005). For instance, a pleasant olfactory context, inducing a positive emotion, could lead to a general broadening of attention, which would in turn facilitate the perception of salient stimuli, such as an aversive US, and thus potentiate aversive conditioning. Conversely, an unpleasant context, resulting in negative emotions, would attenuate aversive conditioning by narrowing attention and external awareness.
On the other hand, an unpleasant smell could have a facilitating effect on aversive conditioning by instating an affectively congruent, aversive context. Mood-congruent cognition is “the observation that a given mood promotes the processing of information that possesses a similar affective tone or valence” (Forgas & Eich, 2006). Affective congruent effects are observed in many processes such as memory (Bower, 1981; Chepenik, Cornew, & Farah, 2007), attention (Köster, 2005) or perception (Chepenik et al., 2007). Associative processes are also affected: learning is enhanced by reading mood congruent material (Bower, 1981) and associations are eased when they are mood consistent:
associating the terms “life” and “love” occurs more often in a happy mood than associating “life” and
“death” (Bower, 1981). Affective congruence between an unpleasant olfactory context and an unpleasant task could thus facilitate the processing of the unpleasant US, and, by reinforcing its association with the CS, intensify the conditioning outcome.
The objective of the study was to investigate whether an aversive conditioning process could be influenced by the pleasantness of a task-irrelevant olfactory context in humans. Participants were
84 thus asked to rate the level of anger expressed by faces (CS), while undergoing aversive conditioning session with an unpleasant, loud white noise (US) in different olfactory contexts (pleasant, unpleasant, no odor). We used angry faces to ensure a robust association phenomenon, since
“prepared stimuli” are known to reinforce aversive conditioning (Öhman & Mineka, 2001; Soares &
Öhman, 1993). These subjective rating scores, as well as reaction times, and several physiological indicators were recorded in order to assess for the efficiency of aversive learning. The conditioned response (CR) is known to be reflected by several physiological changes - including heart rate, that is raised when presented fear-relevant compared to non-fear relevant stimuli (Cook, Hodes, & Lang, 1986); an increase of the skin electrical conductance as a reflect of emotional arousal (Lidberg &
Wallin, 1981); and a diminution of the amplitude of the pupillary light reflex (Bitsios, Szabadi, &
Bradshaw, 2004) when anticipating an aversive event. In addition, these physiology indicators are also sensitive to odor valence (Bensafi, Rouby, Farget, Bertrand, et al., 2002a; Delplanque et al., 2009; Soussignan, Ehrlé, Henry, Schaal, & Bakchine, 2005). We hypothesized that if an odor context acts via affective congruency (Aarts, Custers, & Marien, 2008), then aversive conditioning should be reinforced under a congruent, unpleasant context, and that those changes would be reflected by physiology changes and higher anger ratings. Conversely, the Broaden and Build theory states that positive emotions broaden the scope of attention and facilitate environment awareness (Fredrickson, 2004b). If a “Broaden and Build” process is at stake, conditioning should be more effective when performed under a pleasant odor context.
Moreover, while hedonicity is among the most recognizable features of smell, trigeminality conveys information presence and directionality of a potential chemical threat. These unique and complementary features contribute to the emotional saliency of odors. It is therefore important to consider both of channels of odor perception to understand the subjective and behavioral effects of affective processes evoked by olfactory stimuli. We therefore assessed whether these two different (Zelano et al., 2007) qualities of odors might by themselves potentiate or attenuate associative aversive learning as both of them have attentional modulation properties (Rolls, Grabenhorst, Margot, Da Silva, & Velazco, 2008; Walla, Mayer, Deecke, & Lang, 2005)
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Research methodology 6
6.1 Functional MRI
The two first studies summarized in the experimental chapter used functional magnetic resonance imaging (fMRI) as a main technique of investigation. MRI enables the creation of high resolution images (1mm) of biological tissues. This is done in a non-invasive fashion, through the measurement of the nuclear magnetic resonance (NMR) signal of protons present in water. Functional MRI, based on the paramagnetic properties of hemoglobin, enables the tracing of local variations of metabolic activity in the brain with good temporal resolution (around 2 sec). Compared to invasive techniques used in animal-based experiments, fMRI does not alter the system studied, either through physical harming or chemically induced anesthesia. Compared to other imaging techniques, fMRI has a better spatial and temporal resolution than Positron Emission Tomography (PET) and does not required contrast products, plus it does not involve ionizing radiations as X-ray computed Tomography (CT).
Because of these numerous advantages, MRI is suitable for repeated use in healthy participants, and is thus employed extensively in cognitive neuroscience research to gain new insights about the brain’s anatomy and function. More particularly, a global view of neural patterns enables the study of interaction between different systems, such as olfactory perception and emotions.
6.1.1 The physical basis of MRI
MRI will create 3D images of water-containing soft tissues in the body, based on the nuclear spin of single protons constituting the nuclei of hydrogen atoms. Nuclear spin is a quantum mechanical property which can be described as giving rise to angular momentum, and can be visualized as rotation of the nucleus around its axis. Each nucleus constituted by protons in an odd number (such as hydrogen) also possesses a nuclear magnetic moment. When no external magnetic field is present, there is no net macroscopic magnetic moment, since the direction of the individual magnetic moment vectors is randomly distributed and their sum is null. However, when the protons are placed in a strong external magnetic field (B0), their spins will align in a parallel (low energy) or antiparallel (high energy) fashion with the field. A resulting longitudinal macroscopic net magnetization M0, aligned with B0 emerges, given that more protons align in a low-energy, parallel fashion, than in the opposite configuration. It is this magnetization M0 which is manipulated in order to obtain the NMR signal.
If a radio frequency electromagnetic field with a ω0 frequency (resonance frequency) is briefly turned on (RF pulse), the M0 vector tips away from its alignment with B0 and starts to precess with an
86 angular frequency known as the Larmor frequency (ω0), which is equal to the product of the gyromagnetic ratio (γ) by the external field: ω0 = γB0.
When the RF pulse is turned off, M0 returns gradually to its original alignment with B0, releasing the extra energy acquired during excitation. This emitted energy produces a NMR signal detected by a receiver coil, whose strength is proportional to B0. This relaxation process is characterized by the time constant T1 which describes longitudinal relaxation (in the direction of the B0 axis), and T2, which describes transverse relaxation (in the plane perpendicular to B0).
Both these time constants depend on certain tissue properties, and their measurement allows to distinguish between tissue types. For example, T1 weighting allows discrimination between GM and WM and can be used to produce anatomical images of the brain. Transverse relaxation is caused by local microscopic fluctuations in the magnetic field due to the movement of neighboring spins (T2), and is accelerated in the presence of macroscopic field gradients, resulting in the shorter T2*.
In MRI (magnetic resonance imaging), the NMR signal is spatially encoded by applying magnetic field gradients that vary linearly over the x, y and z spatial directions. RF pulses are sent in the presence of a first gradient thereby limiting excitation to a given slice of the brain. The signal is then read out in the presence of 2 other within-slice gradients, and an image is formed by taking the two-dimensional inverse Fourier transform of the signal. The amount of time between successive pulses applied to the same slice is called repetition time (TR), and represents the time required to scan the whole brain. A period called the echo time (TE) is defined as the time between the time of the RF pulse and the peak of the signal induced in the coil. The combination of these two parameters has an important effect on the control of image characteristics for tissues with different T1 and T2.
6.1.2 Principles of functional MRI: BOLD and HRF
The brain consumes around 20% of the oxygen uptake by the body in order to meet its high energy demands. Oxygen is mostly provided by local blood supply, and more particularly by hemoglobin, an oxygen-carrying blood protein with an iron based-core (heme), that can exist under the form of oxyhemoglobin (oHb, Hb with oxygen) or deoxyhemoglobin (dHb, Hb without oxygen). Local changes in neural metabolism subsequent to activity induce a local increase in blood supply, which is higher than the real need for oxygen. As a result, the oHb /dHb ratio increases. Interestingly, oHb and dHb have different magnetic properties, as dHb creates an induced magnetic field in the same direction to an externally applied B0 (strongly paramagnetic), whereas the oHb has the opposite property, but much weaker (midly diamagnetic, see Pauling & Coryell, 1936). Thus, changing the amount of oxygen
87 carried by Hb modifies the degree to which the dHb introduces distortions in the local magnetic field.
A dHb decrease induces a weak rise in the T2* weighted signal. The effect, named blood-oxygenation-level dependent (BOLD, Ogawa, Lee, Kay and Tank, 1990) evolves over time following a hemodynamic response function (HRF). First, a brief, initial dip is observed, as neurons consume oxygen and the level of dHb rises. The dip is followed after 2-3 seconds by a slow increase in the BOLD signal, peaking 4-6s after the neural activity onset. Finally, the signal decreases and returns to baseline after a brief undershoot. The signal peak, generated by an overcompensation of blood flow to the active area, reflects local changes in neural activity in fMRI.
6.1.3 fMRI data statistical analysis
The analysis of the fMRI data obtained in the two studies was performed using Statistical Parametric Mapping (SPM, http://www.fil.ion.ucl.ac.uk/spm/), a matlab-based program enabling both image pre-treatment (preprocessing) and statistical analyses.
6.1.3.1 Preprocessing
Given the high spatial resolution of MRI, dealing with individual differences (e.g. brain size and shape, movement) is a central problem in imaging. Preprocessing allows to minimize these differences by implementing standardized modifications to the acquired images, before proceeding to statistical analyses. First, the images need to be corrected for head movements (which occur despite of physical restraints and instructions to participants). The images undergo realignment to a reference scan, usually the first acquired volume, by estimating the six parameters (translation and rotational movements in the x, y, and z spatial directions) of an affine “rigid-body” transformation, that minimizes the difference between each image and the reference. 6 corresponding vectors, containing one value per acquired volume, are generated, to be later used to refine subsequent analytical steps. The second step, normalization, consists in mapping each coordinate of participant’s brains (voxels) onto the corresponding coordinates of a standard brain template, in order to compensate individual anatomical differences. The Montreal Neurological Institute (MNI) template, consisting in an average of 305 human brains (Collins, Neelin, Peters and Evans, 1994), was used in both studies. Images are finally smoothed, with a Gaussian kernel of usually 8 mm full width half-maximum, which enables the gradual redistribution activity of one central voxel’s activity to its neighbors. Smoothing has several main advantages: (1) it enhances signal to noise ratio (SNR) by minimizing the impact of isolated voxels and high-spatial frequency signals (most likely reflecting noise), while enhancing those effects extending to many spatially contiguous voxels (which suggest common underlying neuronal source); (2) it improves the activation overlaps across different
88 individuals, by “blurring” over anatomical differences; (3) it increases the statistical power of subsequent analytical steps, by leading to functional data which are more normally distributed.
6.1.3.2 Statistical analysis: 1st and 2nd levels.
Statistical analysis of fMRI data usually occurs in two steps, a 1st level analysis, in which effects are estimated for each individual subject, and a 2nd level analysis, in which the effects estimated in the 1st level analysis are compared across different subjects. The general linear model (GLM) is the most commonly employed to fit the functional signal from each individual voxel according to the following equation: Y = X ∙ β + ε, where Y is the BOLD signal, and X represents the design matrix, which comprehends all regresors or variables that might explain changes in the BOLD signal. These regressors can be of interest, variables/conditions that were intentionally manipulated, or of no interest, nuisance variables that are not the main objective of the experimental manipulation but might nevertheless influence the signal (e.g., the movement parameters defined during the realignment). Physiological variation related to cardiac and respiratory activity has a direct effect in the BOLD signal, and is usually filtered by a standard high pass filter of 1/128 Hz. Nevertheless, experimental constraints particular to olfactory experiments (e.g. specific sniffing pattern) sometimes necessitate the separate modelling of cardiac and respiratory noise as nuisance regressors, as this was the case in experiment no 2. In the GLM, each regressor X represents a of event onsets modelled through a delta function and convolved with a canonical hemodynamic response function to mimic the induced BOLD response. The GLM estimates the parameters β, representing the degree to which Y is explained by each regressor X, together with the error term ε, which is the difference between the observed data and the model’s prediction. More specifically β estimates reflect the increase of neural signal during each condition of interest relative to the implicit baseline, which is in turn defined as the activity when all regressors X are set to 0 (e.g., the pauses between one experimental trial and the subsequent).
The second level assesses the common patterns of activation across subjects. The individual β parameters resulting from the first level analysis are entered into a second level (random-effects) analysis, with different individual subjects modeled as random factors, to account for functional interindividual variability. As for the case of the first level analysis, also the second level analyses lead to parameter estimates β, which represent the estimated activity of each condition across the overall population, and an error term ε, which represents the unexplained variance. The statistical significance of one parameter β, or of the combination of many βs, is tested through contrast matrixes, which lead to t-tests or F-tests. T-tests are used to assess the significance of individual contrasts through the formula
=
𝑆𝑆𝑆(𝑐 β)𝑐β , which represents the ratio between the magnitude of a89 contrast cβ and its estimated standard error. F-tests instead compare the goodness of fit of the model (i.e., the residual sum of squares, ∑ ε2) with that of a reduced model in which multiple conditions (described by corresponding contrast matrixes) are missing: the resulting F value represents the ratio between the variability explained by the contrasts of interest and the unexplained variability
𝐹 =
∑ ε2𝑟𝑟𝑆𝑟𝑐𝑟𝑆 𝑚𝑚𝑆𝑟𝑚− ∑ ε2𝑓𝑟𝑚𝑚 𝑚𝑚𝑆𝑟𝑚∑ ε2 𝑓𝑟𝑚𝑚 𝑚𝑚𝑆𝑟𝑚 . Each test employed will lead
to a statistical parametric map, describing the magnitude of ts-and Fs (plus the corresponding significance p values) in each brain coordinate.
Given that each brain slice contains more than 10’000 coordinates, statistical analysis of the whole brain volume might be subjected to inflation of the Type I error, with high likelihood of many voxels associated with significant effects being false positives. Bonferroni correction for multiple comparisons might be a too conservative method to prevent inflation of Type I error, as it would increase the chance of missing out important effects in specific brain regions (type II error, false negatives). Moreover, a Bonferroni correction works under the assumption that the multiple tests are independent from one another, and this is not the case of fMRI data in which the activity of each voxel is highly correlated to that of its neighbors (especially if the data has been smoothed). Less conservative approaches seek to identify a family-wise corrected Type I error (FWE) from random fields theory (Worsley et al. 1996). Alternatively, the false discovery rate (FDR) correction (Benjamini
& Hochberg, 1995) can be used to insure that only 1% of positive effects might be false positives.
However, the most popular approach is to inspect data at an arbitrary uncorrected Type I error (e.g., p ≤ 0.001). Although such threshold does not prevent the occurrence of false positives, true effects are expected to occur as large clusters of consecutively active voxels, which share a common underlying neuronal source. On the other hand, false positive might manifest itself as isolated voxels throughout the brain volume, and are therefore ignored.