Neurochemical Dynamics during Two Hypnotic States: Evidence from Magnetic Resonance Spectroscopy

doi:10.21203/rs.3.rs-4617202/v1

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Neurochemical Dynamics during Two Hypnotic States: Evidence from Magnetic Resonance Spectroscopy

https://doi.org/10.21203/rs.3.rs-4617202/v1

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This study explores neurochemical changes in the brain during hypnosis, targeting the parieto-occipital (PO) and posterior superior temporal gyrus (pSTG) regions using proton magnetic resonance spectroscopy (MRS).

We examined 52 healthy, hypnosis experienced participants to investigate how two different hypnotic states of varying depth impacted brain neurochemistry in comparison to each other and to their respective non-hypnagogic control conditions.

Alongside neurochemical assessments, we recorded respiration and heart rate variability (HRV) to further explore possible association between physiological correlates of hypnotic depth.

Significant myo-Inositol concentration changes were observed in the PO region during the deeper hypnosis state, possibly indicating reduced neuronal activity.

No significant neurochemical shifts were detected in the pSTG region.

Additionally, our findings revealed notable physiological changes during hypnosis. Respiratory rates were significantly slowed in both hypnotic states compared to the respective controls, with more pronounced slowing in the deeper hypnotic state. Furthermore, breathing rate differed significantly between both hypnosis states with a stronger decrease during the deeper state

This study contributes a first-time insight into neurochemical responses during hypnotic states. We hope offering a foundation for further research in understanding the neurobiological correlates of hypnosis in both, basic science and – down the line - clinical applications.

Biological sciences/Neuroscience

Biological sciences/Neuroscience/Cognitive neuroscience

Biological sciences/Neuroscience/Neural circuit

Biological sciences/Neuroscience/Neuronal physiology

Hypnosis has been defined by the American Psychological Association (APA) as “a state of consciousness involving focused attention and reduced peripheral awareness characterized by an enhanced capacity for response to suggestion.” (Elkins et al. 2015). To induce these altered states, a hypnotist or hypnotherapist typically begins a hypnosis session with an induction, where they provide specific verbal instructions to a subject (Terhune und Cardeña 2016). Research into the phenomenology of hypnotic states suggests a more complex scenario than that depicted by the APA definition. This includes a decrease in peripheral awareness, reduced judgment and monitoring, a loss of time and spatial orientation, and at times, experiences of automatic or involuntary motor responses (Price und Barrell 2012; Landry et al. 2017; Varga et al. 2017; Zahedi und Sommer 2021; Bicego et al. 2022). The hypnotic induction without further suggestions is also frequently referred to as “neutral hypnosis” (Kihlstrom und Edmonston 1971).

Over recent decades, extensive research has focused on the effects of neutral hypnosis on brain processes, often utilizing functional magnetic resonance imaging (fMRI). Common to the results of the studies conducted are changes in activity and connectivity in areas that are part of the default-mode (DMN), salience (SN) and central executive networks (CEN)(Deeley et al. 2012; Demertzi et al. 2011; Jiang et al. 2017; Lipari et al. 2012; McGeown et al. 2009; McGeown et al. 2015; Rainville et al. 1999b; Rainville et al. 1999a; Rainville et al. 2002). Hypnosis research has attributed particular importance to these brain networks, as they have been linked to the control and monitoring of attention, regulation of sensory and interoceptive processes, which are discussed as central elements of hypnosis (Landry et al. 2017; Landry und Raz 2015). An activation likelihood estimates (ALE) meta-analysis conducted by Landry et al. (2017) aimed at identifying consistent neural correlates of hypnosis and hypothesized the involvement of the CEN, SN and DMN as a commonality across the included neuroimaging studies. However, rather than confirming their hypotheses, ALE findings showed a link between hypnosis and medial lingual gyrus, an occipital area primarily commonly reported to be engaged in advanced visual processing. Although the lack of confirmation of the involvement of CEN, SN and DMN by the meta-analysis is partly attributable to the substantial heterogeneity of applied technical methodologies and hypnotic procedures in the included studies, the surprising finding could also be viewed as an invitation for the exploration of hypnosis correlates beyond DMN, SN, CEN and the concept of top-down model of hypnosis (Landry et al. 2017).

In a previous fMRI-study from our group, which was conducted in the context of “Project HypnoScience”, hypnosis-related brain connectivity was explored by means of a purely data driven functional connectivity multivariate pattern analysis (fc-MVPA)(Nieto-Castanon 2022; Matos et al. 2023). This method analyzes the connectivity of individual voxels with the entire brain, providing a comprehensive map of functional connectivity without the necessity of a-priori limitations on areas or networks, like for example SN, DMN and CEN. Clusters identified through this method represent areas where the pattern of connectivity with the rest of the brain significantly differs, either between different conditions or across subject groups (Nieto-Castanon 2022). In this previous work, two hypnotic states differing in hypnotic depth (Hypnotic state 1 and 2, HS1 and HS2) were compared with matched non hypnagogic control conditions (control state 1 and 2, CS1 and CS2) (Matos et al. 2023). Using the fc-MVPA approach, significant clusters were identified for the comparison for each of the hypnosis states with the corresponding control condition (HS1 vs CS1 and HS2 vs CS2). Interestingly, the most significant clusters identified in the comparisons HS1 vs CS1 and HS2 vs CS2, were highly similar in shape and location, encompassing parieto-occipital (PO) structures such as (pre-) cuneal cortex, intra- and supracalcarine cortex, lateral occipital cortex and bilateral lingual gyrus. The lingual gyrus emerged as part of these clusters, being the structure, which most strongly correlated in the meta-analysis performed by Landry et al. (2017). When comparing both hypnotic states with each other (HS1 vs HS2), a PO-cluster did not emerge, possibly pointing to its central connective role in both states. Instead, a temporal cluster (pSTG cluster) involving the posterior part of the left superior temporal gyrus, left supramarginal and middle temporal gyrus was identified as most significant (Matos et al. 2023). However, while the fMRI study's identification of the PO and pSTG indicates their significant role in differentiating network configurations between hypnosis and control states, as well as between the two hypnosis states themselves, the results do not provide information about changes in neurophysiological processes inside the clusters themselves independent of their network context.

The goal of the present study focuses on the neurochemical milieu during hypnosis. As the second study in the HypnoScience-project, neurochemical alterations within these two core areas were measured, by means of proton magnetic resonance spectroscopy (MRS).

MRS has a different strength profile, with its low spatial resolution of cubic centimeters and temporal resolution of minutes (Stagg und Rothman 2013). The strength of this method lies in the capability to directly acquire signals on the neurochemical components within regions-of-interest, such as excitatory (glutamate, Glu) and inhibitory (γ-Aminobutyric acid, GABA) neurotransmitters, neuronal viability (N-Acetylaspartate, NAA), energy (creatine) and cell membrane (choline) metabolism as well as compounds involved in cellular signaling, osmoregulation and neuronal activity (myo-Inositol, mI) (Stagg und Rothman 2014; Matos et al. 2017). Thus, this technique has the potential to provide information about the neurochemical fingerprint of the functional PO and pSTG areas during hypnosis. To the authors knowledge, this study explores for the first time by means of MRS the neurochemical milieu in two brain regions during hypnosis. The selected two brain regions possibly play fundamental roles in regulating hypnotic states as indicated by the results of the previous fMRI study. Based on the fMRI observations, we hypothesize in the present study that 1) the functional PO area shows similar neurochemical alterations in both hypnotic states compared to the control conditions, but not between the hypnotic states themselves (main effect of hypnosis). In other words, PO shows specificity for hypnosis, but not for differences in depth of the hypnotic states. Furthermore, 2) we hypothesize that pSTG shows neurochemical alterations between hypnotic states 1 and 2.

For that purpose, 52 healthy hypnosis-experienced participants were included in the study. Neurochemical assessments were complemented by respiration, heart rate (variability) measures and questionnaire data.

Subjects:

The study was approved by the ethics committee of the Canton of Zurich (BASEC No. 2018 − 00550). All participants received detailed information about the experimental procedure, the aim of the study, and gave their written informed consent before any procedures were carried out. Participants were instructed not to consume alcohol, analgesics/other medications 24 hours before the start of the experiment and to eat before arriving at the study site. The study was conducted at the MR center of the Psychiatric University Hospital Zurich, Switzerland and was performed in accordance with the Declaration of Helsinki.

For this study, a total of 52 subjects (34 females, 18 males, mean age 46.9) were recruited.

The experimental procedure was identical for the fMRI and MRS study and is described in detail in the fMRI manuscript (Matos et al. 2023). Recruitment focused on participants familiar with the hypnosis procedures used in this study. Participants underwent basic hypnosis training (Hypnose.NET GmbH/OMNI Hypnosis International). All of them practiced self-hypnosis on a weekly basis for at least two months. The rationale behind this requirement was to ensure a sufficient level of familiarity with the subjective experience in order to provide feedback on the perceived hypnotic states experienced within the scanner. As many of the participants were exposed to a MR-scanner for the first time, the question arose to what extent such an unfamiliar setting would impact the quality hypnotic states. The familiarity with the states allowed the participants to compare the perceived states inside the scanner with states typically perceived in their well-known environment. Comparability was assessed by means of the questionnaire.

Experimental Design:

The experimental design and procedure are illustrated in Fig. 1. The experiment had of a total of four conditions: Two hypnosis (Hypnotic state 1 and 2; HS1/HS2) and two corresponding control conditions (control state 1 and 2; CS1/CS2). Each condition consisted of induction and measurement phase. Participants were randomly allocated to either sequence 1 or 2 of the experimental procedure.

In the induction phase, hypnosis and control inductions were acoustically presented to the participants by experienced hypnotherapists via high-fidelity MR-headphones (MRConfon, Magdeburg, Germany) while the participants were comfortably positioned inside the scanner in a supine position. During the induction phases, no MR-measurements were done. The hypnotic inductions consisted of standardized texts based on the Dave Elman induction techniques, which were adapted to the MR scanner environment and translated to German (Hypnose.NET GmbH/OMNI Hypnosis International). The first hypnotic induction aimed at guiding the participants into a state of profound mental and physical relaxation (HS1), whereas the second hypnotic (deepening) induction aimed at further deepening the state into a deep state commonly characterized by a suspension of time and localization orientation and experience of automatic or extra-volitional own (motor) responses (Price und Barrell 2012; Landry et al. 2017; Varga et al. 2017; Zahedi und Sommer 2021; Bicego et al. 2022). Control inductions were designed based on the work of Varga et al. (2017) and consisted of a collage of Wikipedia snippets which were topic-related to the sections of the hypnotic inductions. For more details regarding hypnosis and control induction texts and hypnosis procedures, see Matos et al. (2023).

Following the induction procedures, the MRS-measurements were started and lasted approx. 10 minutes per condition. During the MRS measurements, heart rate was recorded by means of a pulse oximeter (PPU) sensor and respiration data was collected via a respiratory belt (Philips Medical Systems, Best, The Netherlands).

At the end, participants completed a questionnaire consisting of four items in which the participants provided information about the quality and stability of the experienced states, the exerted effort to remain in the states and the level of perceived sleepiness.

MRI Data Acquisition and Analysis

MR data was acquired on a Philips Achieva 3T scanner with a dStream upgrade (Philips Medical Systems, Best, The Netherlands) using a dStream 32-Channel head coil. For information regarding the fMRI sequences and applied MVPA-based connectivity analyses, see Matos et al. (2023).

Anatomical MRI

A T1-weighted 3D TFE (Turbo-Field-Echo) sequence was done at the beginning of the measurement sessions. The MRS-voxel placements were done on these T1w images. The parameters were as follows: 160 sagittal slices, repetition time (TR) = 8.16 ms, echo time (TE) = 3.73 ms, acquisition voxel size = 1.0 × 1.0 × 1.0 mm³, flip angle = 8°, FOV = 240 × 240 × 160 mm³, acquisition matrix 240 × 240 pixels, scan duration 7 min 32 s.

MRS Protocol

A standard single-voxel PRESS (point-resolved spectroscopy) sequence (TR/TE 2500/32 ms; data points = 1024; sample frequency = 2000 Hz; readout duration = 512 ms; number of acquisitions = 128) was used to obtain the spectra. To improve spectral quality and to reduce the effects of frequency drifts, the 128 acquisitions were divided into four dynamic blocks containing 32 acquisitions each. A total of four successive PRESS sequences (4 x 32 acquisitions) per voxel were performed per block. A water-unsuppressed spectrum was acquired prior to the main spectrum for post hoc spectral corrections. Water suppression was achieved using a VAPOR (variable pulse power and optimized relaxation delays) scheme (Tkáč et al. 2021). A second-order projection-based shimming routine was used for the reduction of B₀–inhomogeneities (Gruetter 1993). Shim volume was placed manually and asymmetrically regarding the measurement voxel to avoid the inclusion of surrounding heterogeneous tissues, thus reducing B₀- inhomogeneities.

MRS Voxel Definition and Placements

As described in the introduction section, the MRS Voxels were positioned in the PO and pSTG regions, based on the fc-MVPA results revealed in Matos et al. (2023) Specifically, the PO-voxel was defined according to the observation that the most significant and largest clusters for the contrasts HS1 vs CS1 and HS2 vs CS2 strongly overlapped (For details see Clusters 1 of both, HS1 vs CS1 and HS2 vs CS2 (Fig. 3) in Matos et al., 2023). We interpreted this observation as a possible role of this region in mediating states of hypnosis independent on depth. For this reason, the commonality of these fc-MVPA clusters in the fMRI-dataset was analyzed by contrasting the hypnosis vs control conditions (main effect of condition) independent of depth (Fig. 2). The strongest cluster resulting from the main effect of hypnosis did correspond to the cluster 1 of both HS1 vs CS1 and HS2 vs CS2, as expected (Fig. 2). The peak voxel of this cluster was then estimated to identify the ideal position for the MRS measures (Fig. 2) using the SPM12-toolbox (). The identified peak voxel MNI-coordinates were: 12–80 36.

The second MRS voxel (pSTG) was defined based on the HS1 vs HS2 fc-MVPA contrast in the fMRI-study in order to focus on possible neurochemical effects linked to mediate the two depth levels of the induced hypnosis. For that purpose, the peak voxel of the fc-MVPA-cluster 1 of the HS1 vs HS2 contrast was estimated and used as reference for the placement the pSTG-MRS voxel. The resulting peak MNI-coordinates were − 58 − 42 6.

For both voxels, the same dimensions were chosen, namely: 18 mm x 17 mm x 17 mm (Fig. 2).

MRS-data preprocessing and neurochemical quantification

Before spectral quantification, following pre-processing steps were performed according to the consensus recommendations: 1) Eddy-current correction 2) combination of head coil channels, 3) frequency realignment of the 128 single acquisitions, 4) filtering of residual water signal (Near et al. 2021). LCModel was used when quantifying the preprocessed spectral data (Provencher 1993). For quantification a basis set was simulated using the GAMMA Simulation package (Smith et al. 1994) with the following model spectra: N-acetylaspartate (NAA), N-acetyl-aspartyl-glutamate (NAAG), glutamate (Glu), glutamine (Gln), gamma-aminobutyric acid (GABA), glutathione (GSH), glycerophosphocholine (GPC), phosphocholine (PCh), creatine (Cre), phosphocreatine (PCr), myo-Inositol (mI), ethanolamine and phosphorylethanolamine (PE), aspartate (Asp), glucose (Glc), lactate (Lac), scyllo-inositol (sI), and taurine (Tau). The chemical shift range for the fitting procedure was set to 4.0–0.2 ppm. Peak area concentrations were corrected for the number of contributing protons and scaled to total creatine. The advantage of the use of creatine as internal reference is that reference and metabolite peaks are both present in the same spectrum. Therefore, as the reference signal was subject to the same influences as the rest of the spectrum, residual external effects like dynamic linewidth changes due to BOLD effects are taken into account (Stagg und Rothman 2014).

Criteria for spectrum exclusion from the analysis were 1) linewidth of the NAA peak of > 10Hz and signal-to-noise ratio (SNR) of < 15 (linewidth and SNR values from the LCModel analysis were used) in one of the four conditions (HS1, HS2, CS1, CS2). This means that if at least one of the spectra met the exclusion criterion, all four spectra were excluded. In addition, 2) data sets with differences in linewidth LW and SNR between the four conditions higher than two times of the mean intrasubject standard deviation (SD) of all subjects were excluded. The rationale for this approach is based on reported systematic quantification biases of neurochemical due to differences in spectral quality measures (Bartha 2007; Matos et al. 2016).

As neurochemical-related inclusion criterion, neurochemicals with a mean Cramer-Rao-Lower-Bound (CRLB) of < 20 were included in the analysis. We opted for a separate analysis of Glu instead of Glx (sum of Glu and Gln) as the correlation between both neurochemicals were well below the recommended cutoff-criterion of r = |0.5|( PO: r = 0.079, p = 0.437; pSTG: r = 0.030, p = 0.786) (Stagg und Rothman 2014).

Additional information regarding MRS methodology – spectral acquisition/analysis – is provided as supplementary file.

Psychophysiological Data Acquisition and Analysis

As mentioned previously in the Materials & Methods section, respiration and heart rate data were recorded during the four MRS recordings to assess potential psychophysiological effects associated with the hypnosis states (Sampling rate: 496 Hz). As the spectra acquisition of each voxel was divided into four dynamic blocks, four Scanphyslog text files were created per voxel. The eight text files (four from each voxel measurement) from PO- and pSTG measurements were imported into LabChart Pro v8.1.2 (ADInstruments, Sydney, Australia) and appended into a single file before data analysis.

Heart Rate Measures

Heart rate (HR) and Heart rate variability (HRV) were calculated using the HRV module integrated in the LabChart software. This module provides automated identification of the peaks in the pulse-oximetry signal and provides HRV-estimates in the time and frequency domain. The pulse-oximetry signal was visually inspected for peak classification errors and manually corrected if necessary (Matos et al. 2020).

HR was quantified as mean beats per minute during each of the measurements. HRV was calculated as the ratio of low frequency to high-frequency (LF/HF) components in the heart rate variability signal. The LH/FH-ratio has been suggested to reflect the ratio of sympathetic (LF) to parasympathetic (HF) activity levels of the autonomous nervous system (ANS). The higher the value, the stronger the relative sympathetic activity level (Cacioppo et al. 2012).

Respiration

First, imported respiratory signal was low-pass filtered at 0.5 Hz to remove high-frequency noise. Second, the peaks of each respiratory cycle (signalling the point of maximal inhalation) were identified using the peak analysis toolbox included in the LabChart software package for estimation and extraction of respiratory cycle lengths. Data was visually checked for classification errors and erroneous periods were excluded.

Statistical Analysis

Statistical analyses were performed using SPSS 25 (IBM Corp., Armonk, NY, USA). A statistical threshold of p < 0.05 was applied as significance criterion. First, the data was checked regarding normal distribution by means of a Kolmogorov-Smirnov test of normality.

Depending on distribution characteristics, the four conditions were 1) either analysed by means of an 2x2 factor analysis of variance (ANOVA) with the factors intervention (hypnosis, control) and depth (states 1 or 2), or 2) were compared using a non-parametric Friedman-test. In case of significances, post-hoc analyses were performed by means of Wilcoxon signed-rank test.

Table 2

Descriptives from the hypnotic state quality questionnaire for both studies.
Item	Mean (SD)
Item	CS1	CS2	HS1	HS2
How comparable were the hypnotic states to those you know form OUTside the MR scanner? (1 = not at all / 10 = identical)	-	-	8.55 (1.36)	8.49 (1.52)
Did the state quality change across the measurement? (1 = not at all / 10 = changed completely)	2.40 (2.04)	2.38 (2.06)	2.49 (1.61)	2.76 (1.97)
Was it difficult to remain within the states? (1 = not at all / 10 = very difficult)	3.65 (2.74)	3.69 (2.74)	1.83 (1.27)	1.81 (1.64)
How close were you to fall asleep? (1 = not at all / fell asleep)	2.34 (2.09)	2.25 (2.05)	1.50 (1.09)	1.37 (1.06)
CS1/CS2 indicates Control States, HS1/HS2 indicates Hypnotic States, SD = standard deviation

Analyzed Datasets:

Regarding the MRS data, two datasets for the PO- (excess intra-subj. SD in LW) and 11 datasets for the pSTG-region (10 datasets had LW > 10Hz, 1 dataset had SNR < 15) had to be excluded as they did not meet spectral quality requirements as specified in Materials and Methods. Thus, a total of 50 spectra for the PO and 41 for the pSTG were analyzed.

Table 1

Mean and standard deviations (in brackets) for the physiological parameters
Heart Rate Variability (HRV) (Low Frequencies / High Frequencies)	1.10 (0.86)	1.19 (1.52)	1.48 (1.16)	1.58 (1.14)
Heart Rate (HR) (Beats per Minute)	65.64 (10.73)	65.64 (11.08)	65.75 (11.29)	65.56 (11.64)
Respiration (Amplitude peak-to-peak in seconds)	5.07 (1.62)	4.96 (1.68)	6.42 (3.23)	6.94 (3.69)
CS1/CS2 indicates Control States, HS1/HS2 indicates Hypnotic States, SD indicates standard deviation

With regard to the secondary outcomes, namely respiration, heart rate, heart rate variability and questionnaires data, the same 50 participants (evaluated in the MRS-PO analysis) were analyzed (Table 1 and 2).

Questionnaire data:

Descriptive statistics from the questionnaire provided by the participants in order to assess quality and characteristics of the different states is listed in Table 2.

Psychophysiological data:

The data from the physiological parameters including mean values and corresponding standard deviations are summarized in Table 3.

HR and HRV:

For heart rate, no differences between the conditions were found. However, the analysis of LF/HF ratios using a Friedman-test revealed significant differences. (X² = 10.85, p = 0.013), post-hoc comparisons showed higher LF/HF ratios in both hypnotic states compared to the corresponding control conditions (HS1 vs CS1 (Z = -2.457, p = 0.014); HS2 vs CS2 (Z = -3.277, p = 0.001)).

Respiration:

The Friedman-test revealed significant differences in respiration cycle duration between the four conditions (X² = 45.745, p = < 0.001). Post-hoc analyses by means of Wilcoxon signed-rank tests found that respiratory cycles were significantly longer during HS1 compared to CS1 (Z = -3.364, p < 0.001). Between HS2 and CS2, the time distance between respiratory cycles was even more pronounced (Z = -4.088, p < 0.001). Regarding the comparison of both hypnosis states, mean respiration cycles were significantly longer in HS2 compared to HS1 (Z = -2.800, p = 0.005). No differences in respiration were observed when comparing CS1 and CS2.

Table 3

Estimates of spectral quality and fitting errors (CRLB) for each condition and voxel
Region	Parieto-occipital (PO) Mean (SD)
Condition	CS1	CS2	HS1	HS2	CS1	CS2	HS1	HS2
Spectral Quality
FWHM_H20 [Hz]	7.45 (0.66)	7.40 (0.61)	7.36 (0.63)	7.34 (0.64)	8.26 (0.67)	8.33 (0.68)	8.2 (0.60)	8.29 (0.60)
FWHM_NAA [Hz]	5.23 (0.83)	5.15 (0.74)	4.98 (0.74)	5.11 (0.65)	5.78 (0.82)	5.77 (0.94)	5.67 (0.76)	5.87 (0.78)
SNR_NAA	21.56 (2.63)	21.68 (2.67)	22.00 (3.05)	21.7 (3.12)	20.83 (3.33)	20.49 (3.07)	20.98 (3.17)	20.41 (3.18)
CRLB [%]
tCre	2.98 (0.32)	2.94 (0.31)	2.96 (0.35)	2.86 (0.40)	2.95 (0.44)	2.98 (0.42)	2.93 (0.35)	2.98 (0.42)
Glu	6.86 (0.73)	6.74 (0.75)	6.70 (0.81)	6.86 (1.09)	6.68 (1.08)	6.90 (1.18)	6.73 (1.05)	6.78 (1.17)
tCho	5.54 (0.61)	5.5 (0.68)	5.48 (0.86)	5.38 (0.81)	4.83 (0.74)	4.98 (0.76)	4.83 (0.70)	4.90 (0.70)
tmI	5.02 (0.74)	4.94 (0.68)	4.98 (0.71)	4.88 (0.69)	4.83 (0.83)	4.95 (0.92)	4.85 (0.69)	4.85 (0.85)
tNAA	2.70 (0.46)	2.66 (0.48)	2.58 (0.50)	2.64 (0.48)	2.68 (0.47)	2.80 (0.40)	2.76 (0.43)	2.71 (0.46)
CS1/CS2 = control states 1 and 2; HS1/HS2 = hypnotic States 1 and 2, SD = standard deviation, FWHM_H20 = full width at half maximum of the water peak; FWHM_NAA = full width at half maximum of the NAA-peak; SNR_NAA = Signal-to-noise ratio for the NAA-peak; CRLB = Cramer-Rao lower bounds; tCre = total creatine; Glu = glutamate; tCho = total choline; tmI = totam myo-inositol; tNAA = total NAA

MRS-data

Spectral quality:

Mean spectral quality and fitting error estimates of all four states in both measured brain regions are shown in Table 3. Exemplary spectra with the spectral fits of the analyzed neurochemicals are shown in Fig. 3. Figure 4 shows an overlay of all analyzed spectra for each condition and region in order to provide an overview of the overall spectral quality. Spectral quality measures revealed good overall data quality with very low levels of intrasubject coefficient of variation (CV) between conditions. For the PO region, mean linewidth of unsuppressed water signal is 7.39 (SD = 0.62; CV = 2.4%), for pSTG 8.27 (SD = 0.63; CV = 2.2%). SNR estimates also reflect good spectra quality with mean SNR for PO of 21.92 (SD = 2.89; CV = 4.2%) and 20.68 (SD = 3.17; CV = 5.7%). This is also reflected in small fitting errors indicated by mean CRLB values of 4.56 (SD = 1.30) for PO and 4.45 (SD = 1.66) for pSTG.

Neurochemical concentrations

Table 4 presents the concentration ratios of the analysed neurochemicals using creatine as reference. For PO, significant changes in tmI-concentrations were found between the four conditions as only neurochemical. The ANOVA analysis revealed a main effect of depth (F = 5.736(1), p = 0.02) and an interaction effect of Condition (Hypnosis /Control condition) x depth (F = 5.668(1), p = 0.02). Post-hoc analyses using paired t-tests found significantly higher tmI-concentrations for HS2 vs CS2 (t = -2.073, p = 0.043) and HS2 vs HS1 (t = -3.685, p = 0.001) (Fig. 5).

Table 4

Neurochemical concentrations (relative to creatine) for both voxels.
Region	Parieto occipital (PO) Mean (SD)				Posterior superior temporal gyrus (pSTG) Mean (SD)
Condition	CS1	CS2	HS1	HS2	CS1	CS2	HS1	HS2
Neurochemicals
Glu	1.361 (0.104)	1.366 (0.117)	1.362 (0.104)	1.347 (0.114)	1.425 (0.135)	1.403 (0.133)	1.409 (0.140)	1.410 (0.146)
tCho	0.174 (0.016)	0.176 (0.018)	0.174 (0.019)	0.175 (0.017)	0.213 (0.022)	0.212 (0.023)	0.213 (0.022)	0.212 (0.021)
tmI	0.825 (0.071)	0.825 (0.067)	0.818 (0.062)	0.837 (0.062)	0.839 (0.76)	0.833 (0.088)	0.845 (0.078)	0.840 (0.076)
tNAA	1.583 (0.114)	1.588 (0.123)	1.584 (0.111)	1.575 (0.112)	1.512 (0.119)	1.491 (0.109)	1.503 (0.101)	1.509 (0.128)
CS1/CS2 = control states 1 and 2; HS1/HS2 = hypnotic States 1 and 2; SD = standard deviation; tCre = total creatine; Glu = glutamate; tCho = total choline; tmI = totam myo-inositol; tNAA = total NAA

Regarding pSTG, no effects were found for the reported neurochemicals.

We explored neurochemical changes associated with two hypnotic states of varying depth as second investigation of a larger multimodal hypnosis project (Project HypnoScience). The present work builds on the initial fMRI related connectivity findings, applying an identical study setting with 52 hypnosis-experienced healthy participants and the same standardized hypnosis method (Matos et al. 2023). Complementary to fMRI, we focused on alterations in the neurochemical milieu – measured via Magnetic Resonance Spectroscopy (MRS) - in two key areas identified in Matos et al. (2023). The first region (PO) - localized in the parieto-occipital junction - was defined as most strongly differentiating the hypnotic states from the control conditions, whereas the second voxel placement was defined based on most strongly differentiating both hypnotic states. This area is located within the posterior division of the left superior temporal gyrus (pSTG, see Fig. 2). Thus, we addressed the question as to whether the two hypnosis states - compared to respective control states/conditions as well as to each other - are associated with altered neurochemical profiles in the PO and pSTG voxels.

All participants were familiar with the hypnosis states, thus assessments regarding the validity of the states perceived inside the scanner was possible. Respective questionnaire data unveiled high levels of similarity inside vs outside the MR scanner with mean values of 8.55 for HS1 and and 8.49 for HS2 out of a maximum of 10 (10 corresponding to “identical”). Furthermore, questionnaire data confirmed high levels of stability and low levels of effort exerted to maintain the hypnotic and control states. Also, levels of sleepiness over the time course of the MRS measurements were low.

Note that a comprehensive neurophenomenological assessment was acquired in the EEG study of Project HypnoScience with the administration of the Phenomenology of Consciousness Questionnaire (PCI) and Altered States of Consciousness Questionnaire (ASC-11D). Due to the identical study design, study requirements and a strong overlap of participants, those results are embedded (cautiously) in the context of this study to better characterize neurochemical-behavioral associations. Questionnaire data not only uncovered evidence for HS1 and HS2 significantly differing from CS1 and CS2 in their neurophenomenological attributes, but also showed significant hypnosis depth-dependent alterations in subjective experience such as changes in emotion, attentional absorption and body perception (Niedernhuber et al. 2024).

In addition to the neurochemical and questionnaire data, respiration and heart rate data were recorded during the measurements. We found significantly slowed breathing rates in hypnosis compared to the control conditions, which is on par with the observations from the fMRI-study and with a recently published review (Fernandez et al. 2022). Furthermore, we also found differences in breathing rate, with prolonged breathing cycle durations during HS2 compared to HS1. Note that slowing in respiration in HS2 compared to HS1 was also observed in Matos et al (2023), although the effect missed significancy levels (Mean respiratory cycle duration for the fMRI-study: HS1 = 6.26, HS2 = 7.17). The difference between both studies regarding this comparison is most likely attributable to the larger number of respiration datasets analyzed in the MRS compared to the fMRI study, thus exhibiting higher levels of statistical power.

We also observed significant alterations in heart rate variability (HRV) in both hypnotic states compared to their control conditions, thus HS1 vs CS1 and HS2 vs CS2. Specifically, higher LF/HF ratios were observed in hypnosis. However, we would like to weight these results with particular caution. The HF component of the HRV signal corresponds to the spectral power of the 0.15–0.4 Hz frequency range from the inter-beat-interval (IBI) time course (Shaffer und Ginsberg 2017). The HF frequency range also called the respiratory band as it reflects HR variations coupled with the respiratory cycle under the assumption that the respiratory frequency corresponds to the 0.15–0.4 Hz range (Shaffer und Ginsberg 2017). However, this is not the case in the present study, as we observed mean respiration cycle durations of 6.94 for the HS2 conditions, which corresponds do frequency values of 0.14 Hz (1/6.94). Thus, the increases in the LF/HF ratios during hypnosis are most likely due to wrong attribution of the respiratory signal to the LF component (0.04–0.15 Hz), thus impede a valid interpretation of the results. For that reason, we will not discuss the HRV results further.

Neurochemical changes induced by hypnosis

The topic on neurochemical aspects of hypnosis is a novel field, with an almost inexistent body of literature. We are only aware of a conceptual work on the possible involvement on neurochemicals in hypnotic suggestions (Acunzo et al. 2021) and an experimental study which explored the link between neurochemical concentrations and hypnotic suggestibility in the anterior cingulate cortex (DeSouza et al. 2020).

With regard to hypnotic states, the present work is - to the best of our knowledge - the first study exploring the link between hypnotic states and neurochemical alterations. Thus, the presented results must interpreted with adequate caution and need to be regarded as preliminary until further studies have been conducted.

Due to the lack of existing work on neurochemical changes induced by hypnosis, the discussion will focus on the role of myo-inositol in neurophysiology and its role in neuroimaging work investing other modalities in order to create a framework for the imbedding of our finding.

Hypnosis induced changes within the Parieto-Occipital Area - PO:

In regard to the PO voxel, we identified changes in the total myo-Inositol (tmI) concentrations. The ANOVA revealed a main effect of depth and interaction effect of condition (Hypnosis /Control condition) x depth. The post-hoc t-tests identified significant differences for the comparisons HS2 vs CS2 and HS2 vs HS1 as drivers of the observed ANOVA results.

The tmI-signal reflects the sum consisting of the spectra of myo-inositol (mI) and Glycine (Gly). At field strengths of 3T, mI and Gly resonances are usually fitted as combined spectrum due to the strongly overlapping patterns (illustrated in Fig. 3). That said, in this study it is not possible to disentangle the distinct contributions from mI and Gly to the observed signal drop in tmI.

With regard to Gly, its most prominent role of Gly is its function as an inhibitory neurotransmitter and co-agonist of glutamatergic N-methyl-D-aspartate (NMDA) receptors (Legendre 2001). Gly is synthetized in higher concentrations in the spinal cord and brainstem and to a lesser extent in neocortex regions (Choi et al. 2011; Probst et al. 1986).

Investigations on visual processing point towards an involvement of Gly in the visual cortex during visual stimulation in areas comparable to our PO-voxel (Lin et al. 2012; Schaller et al. 2013). Lin et al. (2012) examined neurochemical dynamics in the parieto-occipital compartment of the brain using MRS at 7T. Neurochemical changes were induced using visual stimuli. They observed no effect in mI, but significant concentration reduction in Gly as response to visual stimuli. They interpreted the findings as evidence for strong neuronal activity, as Gly is a precursor for glutathione, a prominent antioxidant in the brain. Hence, the increase in glutathione synthesis during neuronal activity may result in a reduction in Gly (Lin et al. 2012). Altered processes in areas linked to visual processing have been suggested to play a role in the HS2-state and altered states induced by psychoactive substances such as psilocybin and Lysergic acid diethylamide (LSD) (Moujaes et al. 2023). Based on these observations, it is possible that the observed alterations in tmI in the HS2 state reflect increases in Gly, possibly due to hypnosis-induced changed in such parieto-occipital areas. However, the confirmation of this hypothesis necessitates a series of future studies with corresponding specific settings.

With regard to Myo-inositol, is one of the nine stereoisomer forms of the simple C₆ sugar alcohol, which together make up the inositol group. It is mainly an intracellular molecule with an overall bias towards higher concentrations in glia cells relative to neurons, substantiating its common application as a marker for glial proliferation in clinical settings (Stagg und Rothman 2014). This neurochemical compound usually receives less attention in functional studies, probably due to its lesser understood role in the context of neuronal activity.

There is some research showing mI alterations in processing other modalities in both, animals and humans (but no hypnosis intervention). For example, an animal study observed an acute reduction in mI in the contralateral somatosensory cortex during electrical forepaw stimulation in anaesthetized rats (Xu et al. 2005). In the same vein, Gutzeit et al. performed a series of experiments which measured neurochemical reactions to an experimental dental pain stimulus in the human insular cortex. In their first study, a reduction of 9.7% were observed in the left insula during pain stimulation (Gutzeit et al. 2011). In a follow-up study, Gutzeit et al. observed again a reduction in mI during pain stimulation in sub-areas of the insular cortex (Gutzeit et al. 2013). Previous work from the same group investigating fMRI-correlates of acute dental pain using the same experimental dental pain model found an increase in the BOLD-contrast during pain in the same areas of the insular cortex (Brügger et al. 2011; Brügger et al. 2012). Therefore, it could be assumed that the drop in mI goes along with an increase in excitatory neuronal activity, which also was proposed by Xu et al. (2005).

As possible driver behind alterations in mI in such functional MRS studies, the role of mI as a precursor in intracellular second messenger cycle has been discussed (Rango et al. 2008; Stagg und Rothman 2014). Myo-Inositol plays a central role in the phosphoinositol (PI) cycle, in which the inositol-based signaling molecule IP₃ is synthetized. IP₃ is part of the intracellular signaling cascades which evokes the release of Ca²⁺ from the endoplasmic reticulum inter alia enabling long-lasting synaptic modulations such as glutamate-dependent long-term potentiation (LTP) (Anwyl 2009). However, the percentage of intracellular mI involved in the PI cycle is relatively low (Stagg und Rothman 2014), thus limiting the probability of this hypothesis as possible origin of the observed effects. An alternative explanation may be the influence of neuronal activity related metabolic effects on mI uptake by glia and neurons, such as pH-levels, lactate concentration and extracellular acidification (Stagg und Rothman 2014). As mentioned at the beginning, the neurochemical effects induced by hypnosis are, of course, quite different from pain. We are also aware that neurochemical response cascades are much more complex than those we observed here by means of the change in mI.

As a first conclusion, studies investigating Gly and mI point towards a negative correlation of neuronal activity and concentrations of both substances. Thus, we put forward the cautious hypothesis that the observed significant increase in tmI may reflect a tonic reduction in neuronal activity in the PO region while subjects are in the deeper hypnotic state (HS2). This hypothesis needs to be validated in the future, ideally by combining MRS measurements in the PO magnetic field strengths of at least 7 Tesla with resting-state fMRI measurements in the same setting.

Posterior superior temporal gyrus - pSTG:

In contrast to the PO-region, no statistically significant changes in neurochemical concentrations were identified.

A possible reason could be rooted in the spectral quality acquired from the pSTG-region. Although the overall spectral quality of the included pSTG datasets were of good quality, their SNR and LW estimates were not as good as for the PO-region. The slightly broader linewidth and lower SNR levels could have been enough to induce sufficient levels of variance to over-mask hypnosis evoked neurochemical effects. Further on, the proximity of the voxel to the skull could have negatively impacted shimming performance, thus resulting in reduced spectral quality (Kreis 2004; Stagg und Rothman 2014). Furthermore, a total of 41 pSTG datasets were analyzed in comparison to the 50 included datasets in the PO analysis. The reduced number of included datasets could thus have impacted statistical sensitivity.

However, also reasons non-related to the spectral quality and statistical power could be taken into account. As discussed in the PO-section above, the lack of significant neurochemical alterations between the measurements might be attributable to the temporal characteristics of MRS-measurements in general. It is possible that although the pSTG region is strongly involved from a functional connectivity perspective - shown by the fMRI findings in (Matos et al. 2023), the “snapshot” feature of the MRS method could have averaged out neurochemical dynamics in the 0.01–0.1 Hz range responsible for the observed fMRI results. In that sense, possibly in contrast to the PO-region, the pSTG does not show tonic changes in the analyzed neurochemicals between the HS1, HS2, CS1 and CS2 conditions. Please note, that this interpretation should be currently treated with caution as this study reflects the first approach measuring directly the neurochemical milieu while subjects are in hypnosis. Future research with dedicated and improved MRS-methodology as well as optimally suited MRS compatible hypnosis paradigms are needed to fully resolve this question.

Furthermore, several other regions need to be neurochemically investigated, because - as clearly shown in (Matos et al. 2023) - it is a far-reaching network of different brain areas involved in mediating hypnosis.

Limitations

First, familiarity of the study population with the hypnotic states was a prerequisite for enrollment in the study. Although this requirement allowed the comparison of the perceived states inside to outside the MR scanner, a certain confirmation bias cannot be excluded. Thus, it would be of significant importance to to investigate a hypnosis-naïve population.

Second, the study was conducted at a field strength of 3 Tesla which is insufficient to independently resolve mI and Gly spectra. Thus, an option would be to replicate this approach at field strengths equal or higher 7 Tesla to enable an improved individual concentration estimates of mI and Gly.

In addition, we investigated two regions using MRS based on the results from our previous fMRI study (Matos et al. 2023). In our opinion it is entirely possible that neurochemical shifts linked to hypnotic states are more pronounced reflected in other brain regions. Future studies are required to delve deeper into this topic.

Last, although neurophenomenological assessments during the EEG-study (Niedernhuber et al. 2024) provide insights about the phenomenological landscape of the hypnotic states, a direct reference to this study is not ideal. Future studies need synchronous neurophenomenological assessments in the same setting for optimal interpretability.

To our knowledge, this is the first study investigating the association between neurochemical concentrations and experimentally induced hypnosis states. Thus, it is crucial to emphasize that the results must be interpreted with the scientifically correct caution. We – reluctantly - interpret the outcomes as an encouraging first contribution in order to understand the neural effects of hypnosis induced changes within the neurochemical milieu.

The novelty of the presented approach limits the comparability to other studies. However, an overview of non-hypnosis based MRS studies suggest a link of both - mI and Gly – to levels of neuronal activity. Relating these findings to our work suggests a potential tonic reduction in parieto-occipital neuronal activity in the HS2 state. It is also important to mention, that the results do not suggest that altered tmI levels is a hypnosis specific neurochemical reaction. Brain function in general involves a myriad of altered neurochemical compounds still to uncover in future (hypnosis related) examinations.

The lack of significant findings on neurochemical effects in the pSTG region does not invalidate the findings in (Matos et al. 2023), as fMRI and MRS represent two methods measuring different aspects of brain function on a disparate timeline. As mentioned, MRS represents very slow and tonic effects as concentration estimates represent an average over the whole measurement (approx. 5 min per area). The applied functional connectivity measurements using fMRI are different with signal frequencies ranging from 0.01–0.1 Hz and thus reflect fundamentally different aspects of neurophysiology.

Future research on hypnosis evoked effects in the human neurochemical milieu need to include a larger number of regions-of-interest to better explore the underlying (neurochemical) patterns linked to these altered states of consciousness. In this vein, modern magnetic resonance spectroscopy imaging techniques (MRSI) could be applied in order to simultaneously investigate multiple brain sections (Nassirpour et al. 2018; Henning 2018). Other non-invasive techniques for whole-head examination of single neurochemicals with the spatial resolution of fMRI are being developed and applied. This methods are based on the chemical exchange saturation transfer (CEST) effect and are particularly used to measure glutamate (Jia et al. 2020), but adaptations for imaging mI are under developement (Haris et al. 2011).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the University of Zurich and Hypnose.NET GmbH/OMNI Hypnosis International. This study received funding from Hypnose.NET GmbH/OMNI Hypnosis International. The funder was not involved in the study design, collection, analysis and interpretation of data, the writing of this article or the decision to submit it for publication. All authors declare no other competing interests.

Author Contribution

NM: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing, Data curation, Funding acquisition, Resources, Software, Validation. PS: Investigation, Supervision, Writing – review & editing, Resources. NZ: Formal analysis, Software, Validation, review & editing. ES: Review & editing. MB: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

Acknowledgments

This study would not have been possible without the help of hypnosis experts Sandra Blabl, Chris Burch, Mike Schwarz, Claude Ribaux, and Hansruedi Wipf. We would also like to thank the volunteers who made themselves available free of charge for the experiments.

Data Availability

Data availability statementThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Neurochemical Dynamics during Two Hypnotic States: Evidence from Magnetic Resonance Spectroscopy

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Abstract

Figures

Introduction

Materials & Methods

Subjects:

Experimental Design:

MRI Data Acquisition and Analysis

Psychophysiological Data Acquisition and Analysis

Statistical Analysis

Results

Analyzed Datasets:

Questionnaire data:

Psychophysiological data:

MRS-data

Neurochemical concentrations

Discussion

Neurochemical changes induced by hypnosis

Limitations

Conclusions and Outlook

Declarations

Conflict of interest

Funding

Author Contribution

Acknowledgments

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1