UNIVERSITÀ DI PISA
Facoltà di Scienze Matematiche, Fisiche e Naturali
Corso di Laurea in Biologia Applicata alla Biomedicina
Curriculum Neurobiologico
EEG correlates of visual and somesthetic imagery
as a function of hypnotizability.
Relatore:
Enrica L. Santarcangelo
Candidata:
Lisa Campioni
Anno Accademico 2016/17TABLE OF CONTENTS.
Summary 4
1. INTRODUCTION 6
1.1 The hypnotic trait and state 6
1.2 Mental imagery 12
1.3 Classical approaches to the study of imagery modalities: sensory and motor.
18
1.4 Motor imagery. 20
1.5 Hypnotizability and imagery 22
1.6 EEG-bands and cognition 24
2. AIM OF THE STUDY. 34
3. METHODS 35 3.1 Subjects. 35 3.2 Experimental Procedure 35 3.3 Variables 37 3.3.1 Subjective reports 37 3.3.2 EEG-power 38
3.4 EEG acquisition and analysis 38
3.5 Statistical analysis 40
4. RESULTS. 42
4.1 Subjective experience 42
4.2 EEG-bands power 44
Alpha band 51 Beta band 57 Gamma band 69 5. DISCUSSION. 74 5.1 General observations 74 5.2 Hypnotizability-related findings 75
5.3 Limitations of the study 79
6. CONCLUSION. 80 REFERENCES. 82 APPENDIX I 93 APPENDIX II 99 APPENDIX III 109 APPENDIX IV 125 ACKNOWLEDGEMENTS 134
Summary
The aim of study was to investigate whether subjects with high (highs) and low (lows) scores of hypnotizability differ a) in the cortical representation of visual and kinaesthetic mental images of rotated head posture and b) in the superimposition of these representations to that associated with the perception of physically rotated head posture (functional equivalence).
Subjective reports of the vividness of visual imagery and relative cognitive effort and the power of EEG spectral frequencies (alpha, theta, beta, gamma) were studied in 21 highs and 20 lows recruited among 200 participants who accepted to have their hypnotizability assessed through the Italian version of the Stanford Hypnotic Susceptibility Scale, form A.
Significant changes in EEG power bands with respect to basal conditions were considered indices of cortical representation; the absence of significant differences between the sensory and imaginative representation was considered index of functional equivalence.
The findings show that i) highs report higher vividness of mental images than lows and the same effort; ii) there is no evidence of different functional equivalence in highs and lows; iii) hypnotizability and gender interact in the task-related modulation of theta 2 (occipital), alpha1 (medioposterior, occipital), alpha2 (occipital) and beta2 (occipital) independently of the specific task.
In conclusion, findings do not provide evidence for a different task representation and functional equivalence between highs and lows. They indicate interactions of hypnotisability and gender in the task induced power changes, which
were found in posterior regions consistently across frequency bands. These task induced changes are independent of the specific task and may be related to cognitive effort.
1. INTRODUCTION.
1.1 The hypnotic trait and state.
Hypnotizability (or hypnotic susceptibility) is a cognitive trait stable throughout life and predicts the proneness to enter the hypnotic state, that is to modify the current state of consciousness experiencing an “altered” state, as indicated by self-report (Pekala and Kumar, 1984) and changes in the brain activity (McGeown et al 2015; Jamieson and Burgess 2014).
Hypnotizability is also associated with the ability to modify perception, memory and behaviour (Elkins et al., 2015) according to specific suggestions both under hypnosis and in the ordinary state of consciousness (Meyer and Lynn, 2011).
The response to suggestions has been differentially interpreted (Ruehle and Zamanski, 1997) by the neo-dissociative (Hilgard 1973; Bowers, 1992) and socio-cognitive (Lynn & Rhue, 1991) theories of hypnosis. The former assume that hypnotic responding, whose main characteristic is to be reported as involuntary is due to dissociation between behavior and conscious experience, which could be likely accounted for by recent imaging findings of modulation of the functional connection between the salience and executive circuits in highs (Hoeft et al., 2012; Huber et al., 2014). In contrast, socio-cognitive theories propose that the experience of involuntariness in action may be sustained by peculiar configurations of cognitive, emotional, relational and sociocultural traits which make the suggestion induced behavior the most adequate to a given situation so that it is triggered
automatically and experienced as effortless and involuntary (Lynn, 1997). The two views are not alternative (Lynn and Green, 2011).
Hypnotizability is measured by standard scales allowing to classify high (highs, about 15% of the population), medium (mediums, about 70%) and low hypnotizable individuals (lows, about 15%) and is very popular owing to the ability of highs to control pain through cognitive strategies (Fidanza et al., 2017). It has been classically attributed to peculiar characteristics of the supervisory attentional system (Norman and Shallice, 1986; Fan and Posner, 2004) allowing highs to maintain their attention focused on chosen objects (Tellegen and Atkinson, 1974; Zachariae et al., 2000). Higher dopaminergic tone may sustain such attentional stability, although the evidence provided by earlier studies in support of this hypothesis has been seriously challenged and the possible mechanism of the possible higher dopaminergic tone remains unknown (see Egner et al., 2005; Presciuttini et al., 2014; Bryant et al., 2013).
Recent evidence indicate, however, that hypnotizability is not only a cognitive trait but is associated with physiological correlates in the sensorimotor and autonomic domains which can be observed even in the ordinary state of consciousness and in the absence of specific suggestions (Santarcangelo and Scattina, 2016).
Brain correlates of hypnotisability. Imaging and electroencephalographic
during specific tasks. In particular, structural and functional differences between highs and lows are summarized below as reported by Landry et al 2017.
Among the functional differences summarized in the paper by Landry and coll. (2017), the most interesting observation is perhaps the higher functional connectivity between the salience and the executive circuits highlighted by Hoeft and coll. (2012) and Huber and coll. (2014), which may sustain the ability to modulate the salience of stimulation as well as the perception of involuntariness of hypnotic responding in the perspective of the neo-dissociative theories of hypnosis (Hilgard, 1973; Bowers, 1992).
Preliminary reports (Picerni et al, 2016) have shown also reduced grey matter in the left cerebellar lobule 4, 5, 6 and higher mean diffusivity in the left Crus 1. The relevance of this observation suggesting a possible variation in the highs’ cerebellar functions will become clear with the following description of the
hypnotizability-Figure 1.1 [ACC = Anterior Cingulate Cortex. DLPFC = Dorsolateral Prefrontal Cortex. FFG = Fusiform Gyrus. IFG = Inferior Frontal Gyrus. IPL = Inferior Parietal Lobule. SFG = Superior Frontal Gyrus. SPL = Superior Parietal Lobule. PCC = Posterior Cingulate Cortex.] From Landry et al., 2017.
related sensorimotor integration and pain control (Santarcangelo and Scattina, 2016).
The most relevant observation performed through EEG and psychophysiological studies is the higher excitability of the left (anterior) hemisphere found in non-hypnotized highs (Gruzelier, 1998, 2006), which declines during hypnotic induction and shifts toward a right prevalence during hypnosis. This observation has been repeatedly confirmed (Naish, 2010; Diolaiuti et al., in press) and its robustness contrasts with the inconsistent results provided by studies based on spectral analysis which have not been able to provide EEG-based criteria able to classify highs and lows (De Pascalis et al., 1989, 1998; Graffin et al., 1995; Perlini and Spanos, 1991; Sabourin et al., 1990; Sebastiani et al., 2005; Williams and Gruzelier, 2001). However, EEG spectral frequencies have been widely
Figure 1.2 Upper panel: cerebellar grey matter volumes (left lobules 4, 5, 6); middle panel: mean diffusivity (left crus 1); lower panel: left insular grey matter volume (from Picerni et al, 2016).
investigated as a function of hypnotizability in resting and sensory/cognitive stimulation conditions (Sebastiani et al., 2003; De Pascalis, 1999).
Studies based on techniques from nonlinear dynamics views and on the combination of linear and nonlinear methods have shown significant relation between hypnotizability/hypnosis and features extracted from EEG signals (Yargholi and Nasrabadi, 2015a and 2015b, Madeo et al., 2013; Chiarucci et al, 2014). In particular, Yargoli and Nasrabadi (2015b) sorted EEG channels according to the their recurrence characteristics and could classify subjects of different hypnotizability levels as well as identify the brain regions mostly involved in different tasks (ideomotor, hallucination, challenge and memory).
In addition, Recurrence Quantification Analysis (Webber and Zbilut, 2005) has indicated a significantly higher determinism of the recurrence plot in the highs in the earliest 3 minutes of relaxation (Madeo et al., 2013; Chiarucci et al, 2014). Then, it remained stable in highs and increased progressively in lows so that it was similar in the two groups at the end of the task.
Figure 1.3 Determinism in highs (left panel) and lows (right panel as a function of time (abscissae). Ordinate: EEG channels. Red: high determinism; blue: low determinism (From Madeo et al., 2013)
Higher Determinism could parallel the trait of “automaticity” reported for highs, whose behavior seems to be pre-eminently centrally controlled (Santarcangelo and Scattina, 2016), as appears from studies of sensorimotor integration focused on posture, locomotion and visuo-motor tasks.
Sensorimotor correlates of hypnotizability. Hypnotizability-related differences
have been observed even in the absence of specific suggestions:
a) Posture and locomotion control. During visual and leg proprioceptive
alteration the highs’ body sway is larger and faster than the lows’ and it does not become slower and smaller over consecutive trials (Santarcangelo et al, 2008a). Stabilogram diffusion analysis (Collins and De Luca, 1993) shows that the highs’ postural control is less strict and mainly centrally driven. In addition, modulation of the neck proprioceptive input induced by head rotation influences the highs’ postural and locomotion control less than the lows’(Santarcangelo et al., 2008a; Menzocchi et al., 2010) indicating less ability to integrate the neck proprioceptive information. In addition, highs do not modulate their body sway (Santarcangelo et al., 2008b) and their direction of blindfolded locomotion (Menzocchi et al., 2010) as much as lows do when their head is maintained rotated toward one side.
b) Visuomotor performance. When asked to throw small wood balls toward a
target, the highs’ performance is significantly less accurate and more variable than the lows’ and does not improve over consecutive trials (Menzocchi et al., 2015).
The above observation could be supported by the above reported information of cerebellar structural variations in highs (Picerni et al, 2016). The latter may also
support the observation of paradoxical pain control after transcranial anodal stimulation of the cerebellum. In fact, the cerebellum exerts inhibitory pain control after cerebellar anodal direct transcranial stimulation (tDCS), the general population undergoing nociceptive stimulation reports reduced pain and shows smaller Laser Evoked Potentials (LEPs) amplitudes most likely due to enhanced inhibitory activity of the Purkinje cells on cerebellar nuclei (Hamada et al., 2012; Parazzini et al., 2014; Bocci et al., 2015). In contrast, highs report significantly increased pain and show significantly larger LEPs (Bocci et al., 2015). Thus, in highs cerebellar anodal tDCS seems to depolarize cerebellar nuclei rather than the cerebellar cortex, which could be accounted for by structural cerebellar peculiarities presently under investigation.
1.2 Mental imagery.
Mental imagery is described as “the ability to generate, represent and
manipulate objects and events that are not physically present” (De Borst and De
Gelder, 2016). According to this definition, mental imagery and perception (in particular the visual modality) have been proposed as two mental faculties sharing common mechanisms; this theory has been named “functional equivalence” (Bartolomeo, 1994; Kosslyn et al., 1995).
Imagery plays an important role in human cognition. This ability facilitates action planning and decision making (Ganea et al., 2017) but also memory and abstract/ spatial reasoning, skill learning and language comprehension (Kosslyn et al., 1995). As a simulation or a re-creation of perceptual experience, imagery has been demonstrated to play a key role in some psychological disorders, such as
post-traumatic stress disorder, social phobia, schizophrenia and depression (Pearson et al., 2012).
The most investigated modality of imagery is visual imagery (see Kosslyn et al., 1995, 1997; Ganis et al., 2004). However, mental imagery can be performed though all sensory modalities (De Borst and De Gelder, 2016; Prete et al., 2016; Lima et al., 2015), and can represent also actions (Jeannerod, 1994; Guillot et al., 2009; Mizuguchi et al., 2016; Ganea et al., 2017).
Recently, arguments have presented in favor of the existence of shared cognitive foundations of specific sensory information and of a supramodal functional organization of the human brain. In particular, this “supramodality” refers to the functional feature of defined brain regions to process and represent specific information content in an abstract way, independently of the sensory modality conveying such information to the brain (Papale et al., 2016).The concept of supramodality may explain the findings of similar cortical activation induced by visual and non-visual inputs in normal subjects and in congenitally blind participants (Bonino et al., 2015) and be relevant to the efficacy of mental imagery.
Since the Ancient Greece, imagery has been investigated; Plato reckoned memories were “painted” in mind such as images were painted on a wax tablet. With the development of behaviorist psychology, a problem born with the method of investigating imagery because it was not possible quantify and qualify an image with a rigorous method. Some questionnaires had been prepared but they implied the subjective judgment of the person about his own imagery, which contrasted with the rigorousness of the behaviorist method. In the late ‘70ies, however, with the
cognitive revolution imagery rose up again (Kosslyn et al., 1995) and there is consensus on the relevance of the subjective evaluation – vividness, effort- of mental images to the study of their neurophysiological basis.
In the ‘80ies, Kosslyn and colleagues proposed his model of visual imagery (then improved by Farah) in order to understand the cognitive systems and processes were necessary to generate a mental image (Pearson et al. 2012). He proposed a computational approach and he divided the entire process in subsystems, each of them with an own function. These mechanisms would be used “top-down” to recall memories and visualize generated images and “bottom-up” to display visual percepts (Bartolomeo, 2002); the subsystems include (fig. 1.4):
Visual buffer (medial occipital lobe);
Object properties encoding (middle and temporal gyri); Spatial properties encoding (inferior posterior parietal lobe); Associative memory (area 19/angular gyrus);
Information lookup (dorsolateral prefrontal cortex);
Attention shifting (frontal eye fields, superior colliculus, superior Figure 1.4 A synthetic view of Kosslyn’s model of visual imagery. (from
posterior parietal lobe).
According to the computational approach, a mental image is generated step by step and the aim of the researchers is to describe the systems and the processes beneath each stage.
The first one is the image generation, which can be conscious or not. The image can be created from a perception and remain after the removal of the stimulus; alternatively it can be created from stored information held in long term memory (Pearson et al., 2012). In both cases, the image is generated in the visual buffer, which is a topographically organized area used in common with the perception system; this fact may lead to interferences between the visual perception and the visual image generation such as in the Perky effect (Bartolomeo et al., 2002). Lots of different categories of images can be generated, such as general images, specific images, autobiographical or episodic images and all the sensory modalities shall be involved.
Once that the image is generated, its maintenance is provided by a system that re-actives the visual memory representation in the object properties subsystem, whose function is analyzing shape, colours and so on, during either imagination or perception. By contrast, the spatial properties are registered by the spatial properties encoding subsystem, which works in a slightly different way compared to the object properties encoding one. The information from these two systems converges in an associative memory area, where amodal and multimodal representation are stored (Kosslyn et al., 1995). However others argue that image maintenance may also depend upon general attentional mechanisms. This step may be better a function of
central executive component of working memory of the attentional processes instead of Kosslyn’s separated visually-based process (Pearson et al., 2012).
Next step is the image inspection. When further cognitive processes are required, such as spatial properties or object-based characteristics, an attentional window shifts across the mental image (maintained in the visual buffer) to encode its properties. Mental scanning requires a spatial properties process; the focus of attention move along the image from point to point. The larger is the image to scan the more time is required to this step. Image inspection seems to have separate processes from those dedicated to image generation, maintenance and transformation (Pearson et al., 2012). According to Kosslyn’s studies, about two-thirds of the areas used during a visual imagery task and a visual perception task are in common; it is also suggested that the spatial-properties-encoding and the attention-shifting function operate in two separated ways (Kosslyn et al., 1997).
The last step is transformation and manipulation of mental images; these processes allow the resolution of everyday problems and the creative thinking. During mental rotation, the image is rotated and the most the subject has to rotate it the longer the process will last; according to Kosslyn’s model, this process is carried out by the object-properties encoding system and the visual buffer. In the restructuring process, the image is changed or modified in some ways, for example during component detection tasks or re-interpretation of ambiguous figures or simple changes in colour or size. The image may dissociate from the original contextual reference to be modified. Mental synthesis is a combination between the rotation and the restructuring processes and occurs when discrete parts of the image are modified to create new pattern or insights during the scientific reasoning, the
design creative thinking or general problem solving. The whole transforming process of an object can be dissociated from the original environmental context (Pearson et al., 2012).
The model proposed by Kosslyn is supported by a few studies (Marks and Isaac, 1995; Ganis et al., 2004; Guillot et al., 2009; Kilintari et al., 2016). Marks and Isaac (1995) found a decrease in alpha activity in the left posterior quadrant in vivid imagers during visual imagery and an increase during motor imagery. In an fMRI study, Ganis and coll. (2004) reported an overlap in the structures activated during visual perception and imagery (prefrontal areas, parahippocampal gyrus, calcarine cortex and a large portion of occipito-temporal cortex). These findings confirmed a previous PET study from the same research group (see Kosslyn et al., 1997). Activation of visual areas have been reported also in fMRI studies of motor imagery (during which the subjects were asked to imagine a movement or a sequence of movements) with a visual modality (Guillot et al., 2009; Kilintari et al., 2016).
Although Kosslyn’s model provides an explanation for imagery processes, the debate is still going on because visual areas seem to be not always involved in visual imagery (Mellet et al., 1996; Bartolomeo, 2002, 2008; Mechelli et al., 2004; Moro et al., 2008) and this provides an evidence against the model. Bartolomeo (2008) presented some studies in which lesions at the temporal lobes (especially the left one) caused impairments in the imagination abilities. These cases are very interesting because no occipital damage and no visual impairment were reported (Moro et al., 2008). This functional anatomic peculiarity of these patients provides a strong evidence against Kosslyn’s model, because visual imagination is impaired
without the lesion in the visual buffer. Moreover, some of these patients preserved most of the other sensory modalities imaginaries, such as the tactile one; Bartolomeo (2009) suggested that imagery abilities may be modality-specific and Moro (2008) proposed that visual properties of mental images are intrinsic to the image itself and the brain does not need to use a visuotopic pattern (such as the primary visual areas) to recall them.
The debate is still open. In order to reconcile these two views, Mellet and coll. (1998) suggested that the involvement of primary visual cortex may occur most of all in fine-grained mental representations than the spatial properties of objects and scenes which rely elsewhere (De Borst and De Gelder, 2016). The latter hypothesis is in line with the findings of Papale et al (2016) and Bonino et al (2015) suggesting a role of a “supramodality” organization for the mental representation of sensory information.
1.3 Classical approaches to the study of Imagery modalities: sensory and motor.
Everyday imagery does not imply only visual images but spreads over the sensory modalities, involving for example voices, noises, tactile perceptions and movements. According to this observation, imagery is a cross-modality cognitive process and in order to study this phenomenon it is necessary analyze also the other sensory modalities (although very few studies are present in literature, especially about auditory and tactile imagery). Auditory imagery has been shown to modulate the activity of the superior temporal gyrus (Halpern and Zatorre, 1999) and tactile imagery the somatosensory cortex (Schmidt et al., 2014). Visual imagery modality
has been widely investigated during the definition of imagery itself (see above) and will be not discussed again in this section; likewise, motor imagery will be discussed in a separate paragraph.
With an fMRI study, De Borst and De Gelder (2016) demonstrated that perception modalities discriminative patterns in primary visual cortex (V1), primary motor cortex (M1) and primary sensorimotor cortex (S1) are similar to the patterns evoked by imagery, confirming that these top-down processes rely on similar structures as bottom-up processes. In particular, during auditory imagery, they found evidence of content-specific representations of the stimuli (subjects were asked to imagine to hear voices with an emotional content, such as fear and anger) in S1 and M1 rather than in auditory cortex; a similar activation was found during auditory perception. Thus, it seems that the relationship between auditory imagery and somatosensory/motor cortices is stronger than the modulation of auditory cortex by auditory imagery.
The common role of perceptual-motor interactions for processing heard and imaged auditory information. A voxel-based morphometry study (Lima et al., 2015) showed that a greater amount of gray matter in the supplementary motor area (SMA) is the most robust predictor of vividness of auditory imagery confirming this observation. They found that vividness of auditory imagery correlates also with gray matter volume of parietal cortex, medial superior frontal gyrus and middle frontal gyrus.
These neuroanatomical findings integrate behavioral investigations about functional lateralization of the auditory cerebral structures. Prete and coll. (2016) found that the Right Ear Advantage effect (REA), which implies a left hemispheric
advantage in processing auditory information, can be extended from auditory perception to auditory imagery.
1.4 Motor imagery.
Motor imagery is defined as the mental simulation of a specific action without any corresponding motor output, hence requiring a representation of the body as generator of actions (Guillot et al., 2009). Imagery of movement differs from the imagery of postures. In fact, motor imagery consists of both sensory images (kinesthetic, visual) associated with the imagined action and of the covert effects of the motor command implicitly associated with the mental representation of actions. In contrast, proprioceptive/visual imagery is exclusively responsible for the mental representations of postures (Ganea and Longo, 2017).
Motor imagery and real execution of the same movement share two characteristics. The time taken to perform the action and the time requested for its image are often highly correlated. In addition, peripheral activity of the autonomic nervous system (for example heart rate and pulmonary frequencies) shows a similar pattern between image and execution (Guillot et al., 2009).
Motor images can be experienced in three different ways: as a first person visual
image in which the subject sees him/herself performing the movement; as a third person visual image in which the subject sees himself as a third person performing
the movement; as a kinesthetic image in which the subject feels the muscles, the joints and the tendons performing the movement (Jeannerod, 1995; Guillot et a., 2009). This distinction is important because a general motor imagery task can be performed with visual or kinesthetic modality; the uncertainty about the employed
strategy may lead to ambiguous interpretation of results (Stinear et al., 2006; Kilintari et al., 2016).
In general, motor imagery activates motor-related structures such as the premotor cortex, the SMA and also basal ganglia and cerebellum, but also the superior parietal lobe (Guillot et al., 2009). The activation of the primary motor area (M1) is still debated (see below).
Various techniques have been used to investigate the contribution of the visual and kinesthetic imagery to motor representations. In an fMRI study, Guillot and coll. (2009) reported greater recruitment of motor structures such as SMA and an exclusive activation of bilateral basal ganglia and cerebellum in kinesthetic imagery. In contrast, visual imagery was associated with exclusive activation in occipital areas. Interestingly, both the types of imagery activated the lateral premotor cortex but visual imagery recruited a postero-superior area whereas kinesthetic imagery activated more strongly the anterior and posterior parts of SMA.
As shown by Kilintari and coll. (2016), the substantial network of the two imagery processes almost overlaps between each other, except for the activation of visual areas; in addition, execution differed from imagery for the activation of M1. This may be due to the inhibitory effect of the SMA on M1 during imagery in order to suppress involuntary movements (Kilintari et al., 2016). However, the involvement of M1 in kinesthetic imagery is still debated because Guillot (2009) and Stinear (2005) reported its activation not only during movement but also in kinesthetic imagery.
Visual motor imagery activates the occipital areas. FMRI results from Guillot and coll. (2009) and Kilintari and coll. (2016) showed an activation of primary visual areas (V1) and Mizuguchi and coll. (2017) reported an increase of the cortical excitability in V1 during visual imagery but not during kinesthetic one.
Inconsistent results of different studies could be accounted for by individual variability in mental strategies and, in particular, by a different contribution of supramodal processing of images (Papale et al., 2016; Bonino et al., 2015)
1.5 Hypnotizability and imagery.
The imagery ability is a relevant part of hypnotic responding. The interactions of hypnotic performance, hypnotizability and imagery are complex (Kogon et al., 1998). Questionnaires evaluating the vividness of mental imagery have seldom shown significant differences between highs and mediums/lows (Szrich et al 2016), whereas hypnotizability-related differences have been found in the preferred modality of imagery (Carli et al., 2007a), in the EEG spectral correlates of the mental images of visual and somesthetic experiences (Cavallaro et al., 2010) and in the strength of the imagery-perception functional equivalence. The latter has been repeatedly hypothesized on the basis of findings of eye movements during imagery of head rotation (Rodionov et al., 2004), arm lowering during suggestions of arm heaviness (Santarcangelo et al., 2005) during explicit as well as explicit suggestions of backward falling (Carli et al., 2007b) and during suggestions of pain in one leg (Scattina et al., 2012). In addition, highs show greater ability to visually identify haptically explored non meaningful objects (Castellani et al., 2011) and to reproduce haptically explored angles (Menzocchi et al., 2012), which may be a
consequence of a possible greater ability to form visual mental images of somesthetic information.
The topic of hypnotizability related functional equivalence between imagery and perception is particularly intriguing because, theoretically, stronger functional equivalence between imagery and perception could account for the involuntariness in action reported by highs receiving sensory suggestions and behaving accordingly (Santarcangelo, 2014).
In this respect, behavioural studies have shown that obstructive suggestions aimed at reducing perception (like the suggestions for anaesthesia) can be effective also in lows. In contrast, only highs display the ability to accept “constructive” suggestions, which is experiencing sensory contexts entirely different from the current one, and that seems to occur through deeper embodiment of the imagined perception (Santarcangelo et al., 2010).
In particular, stronger functional equivalence in highs was demonstrated in standing participants by using the vestibulo-spinal reflex (VSR) as a probe to assess the excitability of the neural circuits which sustain the VSR whose earlier component cannot be voluntarily modulated (Guerraz and Day 2005; Reynolds, 2010). The vestibulo-spinal reflex can be elicited by galvanic stimulation of the labyrinth and induces body sway mainly in the frontal plane when the head is directed forward and mainly in the sagittal plane when the head is kept rotated toward one side (Lund and Broberg 1983). Such change in the preferred direction of body sway occurs under cerebellar control (Manzoni, 2005; Shaikh et al., 2005) and is impaired in subjects with cerebellar lesions (Kammermeier et al. 2009) or functional inactivation (Lam et al., 2016).
Interestingly, in the presence of similar high vividness of imagery, both highs and lows exhibited a reduced amplitude of the VSR earlier component in the frontal plane while keeping their head directed forward and receiving suggestions of anaesthesia. In contrast, even in the presence of similar vividness of the mental image of “having the head rotated toward the right side”, only highs exhibited a similar body sway in the sagittal plane during the real and the imagined head posture (Santarcangelo et al., 2010). In this study, participants were invited to imagine the head posture by “seeing their chin in axis with their right shoulder” (visual imagery) and by “feeling tension in their neck muscles” (kinaesthetic imagery) and, in line with earlier findings (Carli et al., 2007a), a larger number of highs reported to have imagined through the kinaesthetic modality or also through it.
Studies of mental imagery of scenes described to the participants through the somesthetic or the visual sensory modality have shown hypnotizability-related differences in that highs exhibited a widespread alpha modulation whereas lows showed a more segregated modulation of both alpha and theta EEG bands. In both groups the observed modulation was independent of the specific task suggesting different cognitive highly distributed information processing in highs.
1.6 EEG bands and cognition.
Electroencephalogram is often used to investigate brain activity during cognitive tasks. The most important findings about theta, alpha, beta and gamma bands are reported, also regarding to hypnotizability and hypnosis.
Theta rhythm (4-8 Hz). It is known that theta rhythm arises from hippocampus
during the wake predominantly in the midtemporal regions in young adults within a small temporal window; by contrast, when this activity is extended, it is typical of the first stage of sleep (Aminoff, 2012; D’Angelo and Peres, 2007; Mitchell et al., 2008). On this regard, an important distinction can be made between phasic and tonic activity: phasic theta occurs in response to an event or activity, it is located in the frontal regions and its time window is discrete; tonic theta has a more diffused activity and coincides with more stable and global characteristics or phenomena (Mitchell et al., 2008).
“Hippocampal” theta was so called because a rhythmical activity was discovered in animals and its range was the same as human theta. Nowadays, above all the species, “hippocampal” theta is no more defined by its frequency but by the fact that it arises from a pacemaker in the medial septum (which sends inhibitory inputs) and induces the rhythmicity in the hippocampal neurons (which fire when the inhibitory input is released). The information may be stored not only in the areas that show theta activity but also in the phase of the rhythm (Mitchell et al., 2008).
Another important feature of theta activity is the rhythmic trains in the midline (FM theta), especially in the frontal electrodes (F3, Fz and F4) (Mitchell et al., 2008). This activity is closely associated with top down attentional processes of cognitive control; this fact has been interpreted as an evidence that the hypnotic phenomena engages the executive attentional control because theta activity increases during hypnosis (Jamieson and Burgess, 2014) and during concentrated performances of mental tasks or meditative concentration in normal subjects (Aftanas and Golochikine, 2001). However, the debate is still going on, thus the
contemporary dissociation theorists reckon that during hypnosis there is a breakdown in the frontal executive system as indicating that a fundamental reorganization of higher level control processes is being implemented (Jamieson and Burgess, 2014).
Theta power increases during tasks of episodic and working memory, error detection, semantic processes, orienting and affective processing mechanisms (Jamieson and Burgess, 2014; Mitchell et al., 2008; Aftanas and Golochikine, 2001). It has been closely linked to the coordination of transient functional coupling between distant cortical regions (exchange of information). This synchronization is essential to coordinate bottom-up processing activity in widely separated areas at the specific times as required by controlling cognitive processing (Jamieson and Burgess, 2014; Mitchell et al., 2008). Theta synchronization correlates with working memory or episodic memory performance in particular, by contrast upper alpha desynchronization correlates with semantic memory performance (Klimesch, 1999). In addition, an increase of theta power has been shown in mental states of internalized attention such as meditation (Aftanas and Golochikine, 2001) and hypnosis (Jamieson and Burgess, 2014; Vanhaudenhuyse et al., 2014).
In fact, theta rhythm seems to play a key role also in both hypnosis and hypnotizability. In highs, there is greater hypnotic-related increase of theta activity during resting conditions then in lows, with a larger difference in the frontal regions (Jensen et al., 2015); for this reason, it has been suggested that level of theta rhythm may be a function of hypnotizability but other studies failed in replicate this difference. Theta power also increases during hypnosis in both highs and lows (but greater enhancement has been reported in highs than in lows), suggesting this
activity as an index of relaxation rather than hypnotizability (Vanhaudenhuyse et al., 2014) and hypnosis (Williams and Gruzelier, 2001) and does not predict hypnotizability (De Pascalis 1993). In fact, theta power has been associated with inhibitory processes that can lead to relaxation (Vogel et al., 1968) and also with sustained focused attention (Mitchell et al., 2008).
Theta power may be related with hypnotic susceptibility and reflect the highs’ ability to focus their attention and ignore competing stimuli (Ray, 1997), which could be sustained by the significant increase in the corrected theta coherence (that is after controlling for volume effects) observed after hypnotic induction between frontal midline and left lateral scalp sites (Jamieson and Burgess, 2014). In brief, theta rhythm may represent the tool through which the central nervous system regulated the balance between the salience and the executive circuits.
Theta 1 and theta 2 power have been found higher in highs and have shown different changes in highs and lows during emotional imagery (Sebastiani et al., 2003).
Alpha rhythm (8-13 Hz). Alpha activity is usually recorded in the posterior
areas (but it can be present also in parietal and/or temporal regions) while the subject is with closed eyes; usually it is faster at posterior than at anterior sites (Klimesch et al., 1998). Alpha frequencies include 8-13 Hz (Aminoff, 2012; Sebastiani et al., 2003) but other researchers suggest that it must include frequencies from 6.5 Hz (Klimesch et al., 1998), depending to the criteria used to define the band range (Klimesch, 1999).
The alpha rhythm has a peculiar shape; it is present as a beating spindle that disappears with the eyes opening and its amplitude is attenuated by attentional processes (Klimesch et al., 1998) visual imagery, mental alerting activities or anxiety (Aminoff, 2012). Cortical alpha is considered to be occasionally generated by input from extra-thalamic networks (Mitchell et al., 2008). The alpha rhythm can normally be up to 50% greater in amplitude over the right hemisphere, maybe because usually it is not the dominant hemisphere or maybe this phenomenon is due to a thicker portion of the skull. However, a difference of 1-2 Hz or more between the two hemispheres is considered abnormal (Aminoff, 2012).
Alpha band power desynchronization reflects attentional processes. The whole band can be divided in lower and upper alpha. Lower alpha is included between transitional frequency (from theta to alpha) and individual alpha frequency (i.e. alpha peak of each subject); higher alpha is defined as the 2 Hz band after the individual alpha frequency (Klimesch, 1999). Lower alpha is located all over the scalp and desynchronizes during general attentional processes, such as alertness (the very first Hz) and expectancy (the latest portion of the band range). Higher alpha indeed seems to respond to semantic processing and is more topographically restricted (Klimesch et al., 1998).
Alpha activity modulation has been often associated with mental imagery tasks. According to Kosslyn model (Pearson et al. 2012), visual buffer should be located in occipital cortex and its alpha activity should desynchronize during imagery. Marks and Isaac (1995) found evidence for decrease in alpha power during visual imagery in vivid imagers and alpha enhancement during motor imagery in non-vivid imagers, both in the left posterior region. Although many studies of functional
imaging confirm these hypothesis (Kosslynn et al., 1997), the observation of alpha changes during various tasks are not consistent (Mellet et al., 1998). Mental imagery is known to be associated with widespread modulation of alpha activity in highs and with segregated modulation of both alpha and theta power in lows (Cavallaro et al., 2010). Alpha percentage has been found different in highs and lows during emotional imagery (Sebastiani et al., 2003).
In line with earlier studies (Sabourin et al., 1990) higher alpha percentage has been reported in not hypnotized highs with respect to lows independently of tasks (Sebastiani et al., 2003); it exhibited similar decreases in highs and lows during unpleasant imagery and increases during pleasant imagery (Sebastiani et al., 2003). As for the role of the alpha band as a marker of hypnosis, findings indicate a scarce specificity alpha changes which are associated also with meditation (Kihlstrom, 2013) and may simply indicate relaxation (Williams and Gruzelier, 2005). It has also been suggested, but not consistently replicated (Ray, 1997; Kihlstrom, 2013), that hypnotic responding may be sustained by different alpha activation of the left and right hemispheres in that the highs’ lower fronto-parietal alpha synchronization may be involved in the highs’ proneness to modulate their state of consciousness and accept suggestions (Terhune et al 2011).
Beta activity (13-36 Hz). Its amplitude is usually minor of 30 µV and is very
variable, both in space and frequency (Pfurtscheller and Lopez da Silva, 1999). It is usually classified as responsive to eyes-opening and non-responsive, which has a more generalized distribution but in some instances it is more localized centrally
and it is attenuated by contralateral movements or tactile stimulations. (Aminoff, 2012).
Beta activity can be found during some sleep stages (drowsiness, light sleep, REM phase), cognitive tasks (Aminoff, 2012) such as top-down attentional processing (Engel and Fries, 2010). Turella et al. (2016) found that abstract action-related information may be coded in lower beta frequency band (< 20 Hz). In fact, a sub-classification of beta frequencies identifies two or more sub bands: beta 1 (13-16 Hz), beta 2 ((13-16-20 Hz) and beta 3 (20-36 Hz) (De Pascalis, 1999; Sebastiani et al, 2003).
Beta activity has been found modulated as a function of action. Every aspect of motor control has been associated with changes in beta frequency bands, from movement planning and motor imagery to action execution (Turella et al., 2016). About 2 seconds prior the movement, the beta activity begins to desynchronize in the contralateral Rolandic region; this change occurs also in the ipsilateral cortex immediately before the movement execution (Pfurtscheller and Lopes da Silva, 1999).
Sometimes, short-lasting bursts of beta oscillations (beta rebounds) are recorded after a movement or a somatosensory stimulation in the hand or foot representation areas; interestingly, they can be presented also after motor imagery (Pfurtscheller et al., 2005; Keinrath et al., 2006). Beta rebounds have been associated with a state of cortical deactivation of local neural networks involved in the movement (Pfuscheller et al., 2005).
A stronger decrease in alpha and beta bands is correlated with stronger recruitment of the motor system; furthermore higher increase in beta power may be
indicative of a higher sensorimotor demand during motor planning processes. The higher increase in beta power observed when action planning was followed by execution with respect to planning without execution has been interpreted as the effect of the integration of bottom-up sensorimotor information due to movement (Turella et al., 2016). All these findings about the involvement of beta band in motor control seem to support the physiological findings obtained in monkeys and showing that cortical beta activity primarily originates from the same deep cortical layers from where feedback projections arise; in contrast, gamma activity sources may lie in superficial layers from which feed-forward projections arise (Bosman et al., 2012; Jamieson and Burgess, 2014).
Beta activity has been investigated also in emotional responses and also as a function of hypnotizability and of the hypnotic state. Changes in beta (until 24 Hz) activity has been reported during positive emotional tasks in the general population (De Pascalis et al., 1998). In an experiment in which unpleasant and neutral mental images were presented to highs and lows during hypnosis, Sebastiani et al. (2003) reported higher beta 1 (13-16 Hz), beta 2 (16-20 Hz) and beta 3 (20-36 Hz) relative power in lows than in highs; however, beta 1 and beta 2 bands were modulated by unpleasant imagination only in highs and beta 3 was modulated by the same stimulus independently of hypnotizability. Pleasant and unpleasant imagery generally increase and decrease beta power, respectively. After hypnotic induction, beta 1 (13-19.9 Hz) has been reported to decrease only in highs in a fronto-central hub and corrected (that is free form volume effects) coherence between salience and executive circuits is reduced (Jamieson and Burgess 2014).
Gamma (> 36 Hz). The role of the gamma band is much more debated than the
role of all other EEG frequency bands. Neither in its frequency range nor in its functions are the findings coherent (Jensen et al., 2015). Like beta oscillations, gamma activity is generally spatially localized and covers a wide range of frequencies (Pfurtscheller and Lopes da Silva, 1999); some researchers divide the whole gamma range into a narrow-band (until 80 Hz) and a broad-band gamma (above 80 Hz) (Jensen et al., 2015). In humans very often only frequency around 40 Hz are analysed (Sebastiani et al, 2003; De Pascalis, 1999).
This frequency band seems to be involved in auditory and visual perception, attentional responses (Başar at al., 2001), learning and memory (D’Angelo and Peres, 2007) and also in motor behaviour (Pfurtscheller and Lopes da Silva, 1999). Because of the gamma response to different stimuli and to motor tasks, it has been proposed as an operator that integrates sensorimotor information (De Pascalis, 1999).
Sometimes it can happen that gamma oscillations are in phase with theta ones (the so called “theta-gamma coupling”) in different regions of the neocortex. It has been proposed that, when theta activity is present, it can control the downstream high-frequency signals from the cortex letting only the gamma phase-locked frequencies arise. Jensen (2015) proposed a model of hypnosis based on this coupling: slow waves (theta) would increase in response to hypnotic suggestion in highs and faster frequencies (such as gamma) would increase or decrease based on the suggestions and the tasks.
The most interesting sub band of gamma is the range between 36 and 44 Hz because it seems related to the attentional arousal and it is investigated in hypnosis
and hypnotizability, although its role and changes are still debated (De Pascalis, 1999; Vanhaudenhuyese et al., 2013; Jensen et al., 2015). Gamma amplitude has been found higher in highs than in lows (De Pascalis 1993; 1998). Sebastiani et al. (2003, 2005) reported higher relative spectral power in highs than in lows, both during relaxation and imagery tasks, and in anterior sites than in central or posterior; in addition, during the emotional imagination task (independently of the specific content) gamma activity decreased with respect to relaxation only in lows. However, other studies reported a lower gamma activity in highs than in lows (Jensen et al., 2015; De Pascalis, 1999) and the debate is still open.
2. AIM OF THE STUDY.
Given
the observed hypnotisability related differences in the reported vividness of the visual and kinaesthetic mental images of the rotated head posture; the apparent stronger functional equivalence between imagery and
perception of that posture in highs;
the aim of the study was to characterize the real and imagined head posture through EEG spectral frequency bands.
3. METHODS
3.1. Subjects
Hypnotizability was measured in a sample of 200 healthy students of the University of Pisa through the Italian version (Organizzazioni Speciali, Firenze) of the Stanford Scale of Hypnotic Susceptibility (SSHS), form A (Weitzenhoffer and Hilgard, 1959).
Among them, fifty subjects with negative anamnesis of neurological and psychiatric disease and drug free from at least 2 weeks were invited to complete questionnaires aimed at characterizing their handedness (Edinburgh Handedness Inventory) (Oldfield, 1971) and their imagery ability through visual and somesthetic modalities (Betts’ questionnaire upon mental imagery) (Richardson, 1999) and to participate in the second part of the study consisting of EEG recording during various experimental conditions.
Nine subjects were excluded from the analysis because they did not result right-handed or due to technical problems. At the end, a group of 41 subjects (age: 19-25 yrs) was studied. It was composed of 21 Highs (SHSS score > 8 out of 12, 11 females) and 20 Lows (SHSS score < 4 out of 12, 10 females).
3.2 Experimental procedure
During the experimental sessions, which were conducted between 11 a.m and 2 p.m., at least 2 hours after the last meal and caffeine containing beverage, participants were comfortably seated in a semi-reclined arm-chair in a sound- and light-attenuated, temperature controlled (21°) room.
The experimental procedure consisted of 5 trials. Each trial included a basal and a task condition lasting 1 minute each. In basal conditions participants were invited to relax avoiding to focus their attention on specific images/thoughts. The tasks consisted of the visual or kinesthetic imagery of a rotated head posture performed before (v1, k1) or after (v2, k2) keeping a really rotated head posture (rt).
The order of the visual and kinesthetic imagery (which was the same for the trials performed before and after the real head rotation) was randomized among subjects.
The scripts for the visual and kinesthetic imagery were prepared ad hoc and aimed at presenting the same mental images studied in earlier behavioral experiments (Santarcangelo et al., 2010). They were read to each participant immediately before the condition of visual (v1, v2) and kinesthetic imagery (k1, k2):
Visual imagery: “Please imagine that your head is rotated towards the right side;
try to see your chin aligned with your shoulder and maintain this mental imagine until I will tell you to stop”;
Kinesthetic imagery: “Please imagine that you head is rotated towards the right
side; try to feel the tension of your neck muscles and maintain this mental imagine until I will tell you to stop”.
After each imagery task, subjects were invited to score the vividness of their mental images (range: min=0, maximum=10) and the effort (range: min=0, maximum=10) they experienced. The questions were: “How vivid was your image
of rotated head on a scale from 0 to 10?” and “How effortful was the task on a scale from 0 to 10?” In addition they were asked to indicate whether they had a better
mental image at the beginning or at the end of the condition, in order to be able to select a posteriori for EEG analysis the 20 seconds in which they had performed the requested task at their best.
During the task of real head rotation, the subject were instructed to maintain their head rotated toward the right side until the experimenter will allow them to change their head posture.
3.3 Variables.
3.3.1 Subjective reports.
Edinburgh Handedness Inventory test (EHI). The questionnaire consists of
18 daily actions and the subject has to put a cross in the column of right and/or left according to the hand used to perform the action. Each cross value is 1 point. Only subjects who reported scores equal or superior to 16 in the right column have been selected for the experiment (see Appendix I).
Betts’ Questionnaire upon Mental Imagery. The questionnaire consists of 150
items covering 7 sensory modalities (40 items on visual imagery, 20 on auditory imagery, 20 on cutaneous imagery, 20 on kinesthetic imagery, 20 on gustatory imagery, 20 on olfactory imagery and 10 on organic imagery) (Richardson, 1999). During our experiments, only 20 items of the visual imagery section and the items
on kinesthetic and cutaneous imagery have been administered to the subjects (60 items in total) (see Appendix I).
Interview. Vividness of the mental images (range 0-10); effort associated with
the imagery tasks (range: 0-10).
3.3.2 EEG power.
EEG power has been calculated in the theta 1 (4-6 Hz), theta 2 (6-8 Hz), alpha 1 (8-10 Hz), alpha 2 (10-13 Hz) beta 1 (13-16 Hz), beta 2 (16-20 Hz), beta 3 (20-36 Hz), gamma (36-45 Hz) spectral bands.
3.4. EEG acquisition and analysis
EEG was acquired (sample rate: 1000 Hz) through a Quick-CapEEG and QuickCell® (Compumedics NeuroMedical Supplies) standard system. Technical details are reported in Appendix II. Filters were applied a posteriori (notch: 50 Hz, bandpass: 0,5-50 Hz) as signal preprocessing.
Thirty-two EEG electrodes (plus the ground electrode, GND) were placed on the scalp according to the 10-20 International system: FP1, FP2, F7, F8, F3, F4, Fz, FT7, FT8, FC3, FC4, FCz, T3, T4, C3, C4, CZ, TP7, TP8, CP3, CP4, CPz, T5, T6, P3, P4, Pz, PO1, PO2, O1, O2, Oz. In addition, 2 auricular electrodes (A1 and A2) and 4 eye electrodes have been attached directly to the cap, instead 2 EKG electrodes have been connected to amplifier channels. The electrode used as reference during acquisition was FCz; off-line the signal was referred to A1/A2. Electrodes impedance was kept under 10 kΩ.
for Matlab (R2016a – The Mathworks, Inc.) by the Swartz Center for Computational Neuroscience (La Jolla, California) to processing collections of EEG data (Delorme and Makeig, 2004). Scripts used in our work are available in Appendix IV.
The first steps for the preprocessing are the load of the EEG data and its channel location. After those, filtering, rereferencing and ICA decomposition are performed. Two filters have been applied to raw data: a notch (50 Hz) and a bandpass (0.5-45 Hz) and then the whole signal has been referenced to an artificial electrode, obtained from the average of A1 and A2 (earlobe electrodes). No down sampling and resampling have been performed over data.
EEG pre-treatment has been performed through Independent Component Analysis (ICA) using Matlab tools. ICA (Delorme and Makeig, 2004).identifies signal components that are mutually independent between each other and allows to detect and artifacts. On EEGlab many algorithms are available including runica, which is the easier one to customize, and it is based on infomax algorithm developed for neural networks.
After pre-treatment, the 1-minute signal has been cut into 20 seconds (20.000 samples). For basal conditions and the condition of physically (real) rotated head, the epochs which had required less artifacts removal (indicating the best signal quality) have been chosen. Twenty seconds of each imaginative task have been chosen according to the subjective report of vividness.
The goal of spectral estimation is to describe the distribution (over frequency) of the power contained in a signal, based on a finite set of data. The power spectral density has been estimated using pwelch, a Matlab function. Welch’s method is
based on the Fast Fourier Transformation and it averages periodograms of overlapped, windowed signal epochs.
In our experiment, the 20 seconds epoch has been divided in two overlapped epochs of 10 seconds (10.000 samples per each) and then pwelch has been performed over them to obtain a three dimensional matrix (bins of frequency, epochs and channels as dimensions). All of this data has been collected into a Matlab structure and then gathered into an Excel (Microsoft Office) file. The absolute power of alpha 1, alpha 2, theta 1, theta 2, beta 1, beta 2, beta 3 and gamma frequency bands have been studied.
3.5. Statistical analysis
Subjective reports. The vividness and effort of kinesthetic and visual imagery
were analysed through repeated measures ANOVA with Hypnotizability and Gender as between subjects’ factors. Imagery modality (visual, kinesthetic) and Time (before RT: v1, k1; after RT; k2, v2) were within subjects factors.
EEG power. For each EEG band, power values of the 2 epochs were averaged,
log transformed and then analyzed for each electrode according to a 2 Hypnotizability x 2 Gender x 2 Sides (right, left) x 5 Trial x 2 Conditions (basal, task) design in order to assess whether the data from a few electrodes could be averaged. In fact, they were pooled in 5 regions: frontal (FP1, FP2, F3, F4, F7, F8), medioanterior (FC3, FC4, FT7, FT8, T3 and T4), medioposterior (C3, C4, TP7, TP8, CP3, CP4), occipital (T5, T6, P3, P4, PO1, PO2, O1, O2) and midline (Fz, FCz, Cz, CPz, Pz, Oz).
Hypnotizability x 2 Gender x 2 Sides x 5 Trial x 2 Conditions design. Greenhouse-Geisser ε correction for non sphericity was applied when necessary. In addition, repeated measures ANOVA was performed on task induced changes [(task – basal)/basal] according to the following design: 2 Hypnotizability x 2 Gender x 2 Side x 5 Trial. Post-hoc comparisons were performed through contrast analysis/paired t test for trials/conditions or unpaired t test between hypnotizability/gender groups. The level of significance was set at p=.05.
4. RESULTS.
4.1Subjective experience
Betts’ questionnaire. A significantly higher vividness of visual imagery was
found in highs (F(1,40)=4.859, p=.034, η2=.116). In fact, higher scores indicate lower ability of imagery (highs (mean ± SEM): visual; 41.91± 2.77; kinaesthetic, 45.76± 4.28; lows (mean ± SEM): visual 50.75± 2.84; kinaesthetic, 56.85±4.38). A similar trend, although not significant, was observed for the kinaesthetic imagery (F= 3.148, p= .084, η2=.078). No Gender effect and interactions were observed
Interview: vividness. Vividness (Tab 4.1; Fig. 4.1, panel A). Highs reported
significantly greater vividness than lows in all conditions, independently of the imagery modality and time (Hypnotizability effect).
Decomposition of the significant Hypnotizability x Time x Modality interaction revealed no significant difference between modalities and between times in highs (v1=k1=v1=k2) and a significant Time x Modality interaction in lows (mean values and SEM are reported in fig. 4.1). Decomposition of the latter showed a
TAB. 4.1 vividness
effect F(1,38) p η2 contrasts
hypn 6.004 .019 .136 highs>lows
timex hypn 5.377 .026 .124
time x mod 11.188 .002 .227
time x mod xhypn 4.679 .037 .110 highs ns v1=v2=k1=k2
lows time x mod
F(1,20)=9.803, p<.005 v1>v2 t(1,20)=3.237, p<.004 k1=k2 v1>k1 t(1,20)=2.211, p<.039
significantly lower vividness in v2 with respect to v1, no difference between k1 and k2, v1 and k1, k1 and k2.
Interview: effort (Tab. 4.2; Fig. 4.1, panel B). No Hypnotizability effect was
found. Decomposition of the significant Time x Hypnotizability x Gender interaction revealed no significant difference between times in the females of both hypnotizability groups; in contrast, among males, highs reported a significantly greater effort in T1 than in T2 and lows reported greater effort in T2 than in T1.
In addition, significant differences (df=1,19) were found within highs between females and males for v1 (t=3.219, p=.005; females (mean ± SEM): 5.91±.53; males: 3.1±,71), k1 (t=2.254, p=.037; females: 5.82±.78, males: 3.5±.65), v2 (t=2.343, p=.03; females: 5.32±.85; males: 2.7±.70), k2 (t=3.221, p=.005; females: 5.55±.68; males: 2.8±.49). In contrast, no significant gender difference was found
within lows (v1 (females (mean ± SEM): 4.8±.86; males: 4.4± .52), k1 (females: 5.7±.91; males: 5.9±.62), v2 (females: 4.95±.81; males: 6.2±.68), k2 (females: 4.75±.61; males: 5.8±.65).
Briefly, higher vividness was always experienced by highs, visual imagery vividness decreased in lows after the real head rotated posture. Gender differences were observed only among highs. Among males only, imagery effort increased after rotation in lows and decreased in highs.
4.2 EEG bands power.
Mean values and SEM are reported in Appendix III.
A table reporting significant main effects and interactions is presented before the description of the results obtained for each frequency band.
Results are described in view of:
a) Representation of the visual and kinesthetic images and of the physically rotated head (significant changes with respect to basal conditions);
TAB 4.2 effort
effect F(1,38) p η2 contrasts
time x hypn x gender 7.104 0,011 .158
hypn x gender 6.775 .054 .151
time x hypn x gender 3.943 .013 .094
highs F ns highs M time T1>T2 F(1,9)=5.762, p<.040 lows F ns lows M time T1<T2 F(1,19)=5.898, p<.038 Note. t1: v1, k1 t2:v2,k2
b) Functional equivalence (no significant difference between imaginatively and physically task-induced normalized changes).
THETA BAND (Tab. 4.3)
THETA 1 MEDIOANTERIOR OCCIPITAL MIDLINE
effect F p η2 F p η2 F p η2
Cond (df: 1.37) 6.574 .015 .151 4.173 0.048 0.101
B>A B>A
Side (df: 1.37) 56.812 0.0001 0.606
ant >post
Side X Trial X Cond (df:
4.148) 2.798 .028 .070
Gender(df: 1.39) 4.949 0.032 0.018
Side*Gender (df: 1.37) 5.568 0.024 0.131
THETA 2 FRONTAL MEDIOANTERIOR MEDIOPOSTERIOR OCCIPITAL MIDLINE
effect F p η2 F p η2 F p η2 F p η2 F p η2 Cond (df: 1.37) 4.352 0.044 0.105 5.374 0.026 0.127 10.478 0.003 0.221 15.649 0.0001 0.297 8.741 0.005 0.191 Trial*Cond (df: 4.148) 2.778 0.029 0.07 Gender (df: ) 3.888 0.056 0.055 2.422 .051 .061 females > males Side*Gender (df: 1.37) 4.737 0.036 0.113 left: females=males
right: females < males
t(1,39)= 2.142, p<.039
Cond*Hypn*Gender (df:
Theta 1 (Tab. 4.3)
Original power values (mean, SEM) are shown in Appendix III.
As shown in Table 4.3, a significant Condition effect was found at occipital (basal > task ) and midline sites, with no significant differences between normalized task- induced changes.
A significant Side x Trial x Condition interaction was found at occipital sites. Its decomposition showed, with respect to basal conditions, significant decrease (df=40) only on the right side in
k1 (t=2.691, p= .010) v1 (t=2.147, p= .038) v2 (t=1.989, p= .054).
The comparison between normalized changes [(task-basal)/basal] did not reveal significant difference between k1, v1, v2.
Thus, theta1 a) does not show functional equivalence between imagery and perception, b) shows similar visual and kinaesthetic representation before real rotation of the head; c) exhibits differences between the earlier and the later kinematic mental image, as the latter does not induce power changes.
On the midline, anterior power was significantly larger than posterior power . At medioanterior sites decomposition of a significant Side x Gender interaction revealed that there was no side differences in males but larger power on the left side in females.
Theta 2 (Tab. 4.3)
Original power values (mean, SEM) are shown in Appendix III.
As shown in Tab 4.3, theta2 power decreased significantly during tasks with respect to basal conditions all over the brain (Condition effect). Also Trial x Condition interactions were observed.
At medioposterior level, the significant Trial x Condition interaction revealed a significant decrease (df=40) on the left side in
k1 (t=2.42, p=.020) v1 (t=3.028, p=.004) v2 (t=2.786, p=.008)
and a significant decrease on the right side in
v1 (t=3.775, p=.001).
No significant difference between normalized task induced changes were observed.
Since theta 2 power did not respond to rt, no functional equivalence can be proposed. No significant difference between the earlier and later visual imagery was found.
At occipital level, a significant Side x Trial x Condition interaction was found. Decomposition revealed, with respect to basal conditions, significant increases (df=40) on the left side in
k1 (t=2.060, p=.046) v1 (t=3.753, p=.001) v2 (t=2.83, p=.044)
k1 (t=2.319, p=.026) v1 (t=5.373, p=.0001).
Comparisons between normalized changes [(task-basal conditions)/basal] did not reveal significant differences between the changes occurred in the above conditions.
Thus, at occipital sites, theta 2 a) shows functional equivalence between imagery and perception (on left side in k1, k2, v2 and on right side in k2) and b) shows similar visual and kinaesthetic representation both before and after real rotation of the head.
In addition, a significant Side x Gender interaction was observed at
medioanterior level. Its decomposition indicated lower theta 2 power in females
than in males on the right side (t(1,39)=2.142, p=.039) and no gender difference on the left side.
Figure 4.2. Normalized task induced changes in theta 2 power at occipital level.
The absence of significant differences (lines, p<.05) indicates functional equivalence between rt and imagery tasks. Original values (mean, SEM) are reported. Significant differences refer to analysis of logtransformed.
