Open Access
2 December 2022 Psychedelics and fNIRS neuroimaging: exploring new opportunities
Felix Scholkmann, Franz X. Vollenweider
Author Affiliations +
Abstract

In this Outlook paper, we explain to the optical neuroimaging community as well as the psychedelic research community the great potential of using optical neuroimaging with functional near-infrared spectroscopy (fNIRS) to further explore the changes in brain activity induced by psychedelics. We explain why we believe now is the time to exploit the momentum of the current resurgence of research on the effects of psychedelics and the momentum of the increasing progress and popularity of the fNIRS technique to establish fNIRS in psychedelic research. With this article, we hope to contribute to this development.

1.

Introduction

Although every human experiences two main states of consciousness on a daily basis (i.e., the waking state and the state of dreaming during sleep),1,2 there are many more tangible states of consciousness that can be located in a multidimensional state space consisting of different aspects of conscious experience.3 Altered states or nonordinary states of consciousness can be induced in various ways, such as training self-awareness while dreaming (lucid dreaming),4 using meditation techniques that can lead to deep meditative absorption,5,6 during life-threatening situations triggering a near-death experience,7,8 or by the intake of psychoactive substances (such as psychedelics).9,10 These nonordinary states of consciousness are of interest not only from a phenomenological11,12 and philosophical13,14 point of view but also with regard to the specific states of brain activity associated with them.1519 Functional neuroimaging with its wide range of different techniques is an excellent way to investigate these specific states of brain activity.

The aim of this paper is to explain to the optical neuroimaging community as well as the psychedelic research community the great potential of using optical neuroimaging with functional near-infrared spectroscopy (fNIRS) to further explore the changes in brain activity induced by psychedelics.

2.

Psychology and Neurobiology of Psychedelics

Classic psychedelics or hallucinogens compromise a class of psychoactive compounds that include (i) the naturally occurring indoleamines, such as psilocybin (4-phosphoryloxy-N,N-dimethyltryptamine) contained in a variety of fungi, and dimethyltryptamine (DMT) contained in the ayahuasca brew, (ii) the phenylalkylamines, such as mescaline derived from the peyote cactus, synthetic “amphetamines,” such as 2,5-dimethoxy-4-iodoamphetamine, and (iii) ergolines such as the semisynthetic lysergic acid diethylamide (LSD).20

Classic psychedelics induce an altered state of consciousness, characterized by profound changes in perception, mood, cognitive capacities, and self-experience, including transcendence of time and space.11 Given these intense mind-altering properties, plant-derived psychedelics have been used for millennia for spiritual and medicinal purposes.21,22

During the 1950s and 1960s, classic psychedelics (mainly LSD and psilocybin) were extensively investigated in psycholytic (i.e., repeated low doses) and psychedelic (i.e., one or two high doses) substance-assisted psychotherapy.23 Although these early studies used various psychotherapeutic techniques and had serious methodological flaws by contemporary standards, systematic reviews reported impressive improvement rates in various forms of depression, anxiety disorders, and alcohol-dependence.2426 After psychedelics became schedule I substances in 1967, human research with psychedelics became severely restricted in most countries, leaving many questions unexplored.27

However, since the 1990s, several research groups have started to use modern neuroscience methods and concepts to characterize the psychological effects of psilocybin,2830 DMT,31,32 and LSD.33,34 In addition, the study of the neuronal correlates of these psychological effects were resumed in healthy volunteers.30,3538 These phase I studies provide evidence that classic psychedelics have rapid mood-enhancing properties, shift emotion processing in a positive direction, diminish self-boundaries, and reduce self-focus in combination with prosocial effects via modulation of neural circuits that are implicated in mood and affective disorders.3941 Furthermore, psychedelics have been shown to produce lasting positive changes in psychosocial behavior in healthy subjects.4244

Recent behavioral and neuroimaging studies demonstrate that psychedelics produce their psychological effects primarily via agonist action at serotonin 5-HT2A receptors in the brain,15,4547 although the 5-HT1A receptor48 and modulatory downstream effects upon the GABAergic, dopaminergic,49,50 and glutamatergic51 systems are also implicated. Moreover, psychedelics have been shown to increase glutamate-driven neuroplastic adaptations in animals,5255 which may provide a mechanism for the lasting beneficial outcomes reported in nonclinical and clinical populations.39

3.

Resurgence of Psychedelic-Assisted Psychotherapy

In parallel to the research into the neuronal correlates of the psychedelic experience, the past decade has seen a resurgence and burgeoning research interest in the clinical potential of psychedelics in the treatment of various psychiatric disorders.56 Specifically, several recent pilot and a few controlled studies have demonstrated that psilocybin reduces substance use in alcohol- and nicotine-dependent patients5759 and ameliorates both symptoms of anxiety and depression in major depression,6062 treatment-resistant depression,63,64 and in advanced cancer patients6567 for 3 to 6 months after administration of just one or two doses. Comparable results were reported for ayahuasca—a brew containing DMT—in major depression6870 and for LSD in end-of-life psychological distress related to terminal illness,71 respectively.

These modern clinical trials provide new evidence for the safety, tolerability, and efficacy of the use of classic psychedelics in a supportive psychotherapeutic framework. It has been shown that psychedelic 5-HT2A agonists are rapidly acting and produce enduring beneficial effects after only one or two administrations.56,72 However, the underlying acute and delayed neurophysiological mechanism mediating these clinical effects is yet largely unknown.

Since psychedelics can have anti-inflammatory effects by modulating inflammatory pathways via novel mechanisms,73 they are currently also being explored for the treatment of neurodegenerative diseases,7476 brain injuries,77 autoimmune diseases,78,79 as well as for chronic pain.8082

4.

Neuroimaging of Psychedelic Effects

Recent neuroimaging studies using electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic-resonance imaging (fMRI) in resting state and in combination with neuropsychological tasks in healthy subjects have advanced our understanding of the acute system-level effects and their association with behavioral changes.83 These discoveries provide a strategic scientific roadmap to further identify circumscribed neurobehavioral responses that may allow us to pinpoint the neuronal targets that may reflect specific symptom reductions in patients.

A recent review on human psychedelic research shows that during the 1950s and 1970s (i.e., the “first wave”),17 most neurophysiological studies into drug action were performed with EEG and primarily with LSD, whereas since the early 1990s and the recent renewed interest in the clinical application of psychedelics and related drugs (i.e., the “second wave”), researcher has begun to employ positron emission tomography (PET), photon emission computed tomography, and then later increasingly fMRI as well as EEG and MEG, to identify potential therapeutic targets primarily of psilocybin but also of LSD and DMT at the molecular and the neural system level (see Fig. 1). More recently, a few multimodal neuroimaging studies combining fMRI with MEG,84 magnetic resonance spectroscopy (MRS),85 and EEG86 have been also conducted. In addition, several neuroimaging studies have investigated the antidepressant of psychedelic-related drugs such as ketamine and 3,4-methylendioxymethampheamine (MDMA) in healthy subjects and clinical populations. In 2019, we explored in a single-subject pilot study the feasibility of investigating the effects of psilocybin using optical neuroimaging with fNIRS.87 The results of this pilot study showed that the application of fNIRS is safe and well tolerated during the induction of a psychedelic-induced altered state, and that this relatively new neuroimaging modality, particularly in combination with neuropsychological testing, may help unravel the therapeutic target of psychedelic drug action. This paper discusses new opportunities of fNIRS neuroimaging for psychedelic research.

Fig. 1

The development of human neuroimaging studies on psychedelics from the 1950s to 2020. Studies were identified via a search in PubMed and Google Scholar. A total of 141 studies were identified. Studies were only included when a modality of the acute effect of a psychedelic substance in a human was investigated. (a) and (c) visualize the number of studies as a function of the neuroimaging techniques or psychedelic substances used, respectively. Most of the studies employed fMRI (b) and investigated LSD (d). The “two waves” in the research development about human neuroimaging studies on psychedelics are clearly visible. In the first wave EEG was mainly used and the effect of LSD was investigated, whereas in the second wave, research opened up to other psychedelics and all available neuroimaging techniques were employed. Note: the listing contains also “salvinorin A,” which is a κ-opioid receptor agonist and considered a dissociative hallucinogen that can induce psychedelic-like effects. N,N-DMT: N,N-dimethyltryptamine, 5-MeO-DMT: 5-methoxy-dimethytryptamine, MDEA: 3,4-methylenedioxy-N-ethylamphetamine.

NPh_10_1_013506_f001.png

5.

fNIRS: Neuroimaging Technique with Much Progress and Increasing Popularity

Over the last decades, optical neuroimaging with fNIRS is rapidly gaining popularity in neuroscience, which can be seen in the exponential number of articles published88,89 and an increased number of commercially available fNIRS devices. Both fNIRS and fMRI are techniques that measure brain activity indirectly by determining the changes in vascular hemodynamics and oxygenation induced by neuronal activity (neurovascular coupling). fNIRS is based on the principle that near-infrared light (with at least two different wavelengths) is shown in the head by placing light emitters on the scalp and detecting the diffusely back-scattered light at specific distances apart [Fig. 2(a)]. This allows to perform the spectroscopic determination of changes in the concentration of oxyhemoglobin ([O2Hb]), deoxyhemoglobin ([HHb]), and total hemoglobin ([tHb]=[O2Hb]+[HHb]).90 The measurement determines the color of the blood (light red versus dark red: oxygen-rich versus oxygen-poor blood) as well as the color intensity (high color intensity: higher hemoglobin concentration). The light detectors and emitters are normally mounted on a cap [Fig. 2(b)] and measurements can be made independent of body position and even in moving subjects.91

Fig. 2

fNIRS neuroimaging: visualization of key aspects. (a) Illustration of a two-channel fNIRS measurement using a long and a short separation channel to enable a depth-resolved measurement specifically sensitive to the cerebral tissue layer. (b) A typical fNIRS headgear (covering the right and left motor cortices in this case). (c) The six main aspects that can be determined with optical neuroimaging employing fNIRS and NIRS-based oximetry. (d) The six main components of the fNIRS signal. (e) Two typical fNIRS instrumentations with regard to the spatial positioning of the light sources and detectors on the head. (f) Visualization of current trajectories of fNIRS development. (g) The fNIRS hyperscanning approach.

NPh_10_1_013506_f002.png

The fNIRS signals are rich in information and there are several ways to perform the measurements and analyze the data. In general, fNIRS can be used to measure six physiological aspects [Fig. 2(c)]:

  • (i) localized stimulus- or task-induced changes in cerebrovascular hemodynamics and oxygenation;9294

  • (ii) functional and effective connectivity of localized evoked or resting-state changes in cerebral hemodynamics and oxygenation;9597

  • (iii) oscillations and fluctuations of tissue hemodynamics and oxygenation [e.g., changes in Mayer wave power (around 0.1 Hz)98];

  • (iv) cerebral pulsatility (i.e., cardiac activity-induced changes in the fNIRS signal99102);

  • (v) cerebral tissue oxygenation [which can be measured as relative oxygenation changes with respect to a baseline (the option available in most of the commercial fNIRS devices on the market), or absolute tissue oxygenation (i.e., near-infrared spectroscopy-based oximetry based on frequency-domain, time-domain, or specific types of continuous-wave domain near-infrared spectroscopy techniques)];103,104 and

  • (vi) reactivity of extracerebral and cerebral tissue hemodynamics and oxygenation to systemic physiological changes (i.e., measuring aspects of cerebrovascular reactivity, cerebral autoregulation, and autonomic cerebrovascular control).105,106

When aiming to measure brain-activity-related changes in cerebrovascular hemodynamics and oxygenation with fNIRS, one needs to be aware that the measured fNIRS signal generally comprises six components [Fig. 2(d)]:90,107,108 the first three have their origin in the cerebral tissue compartment, and the other three in the extracerebral tissue compartment. To detect brain activity–related changes in vascular hemodynamics and oxygenation due to neurovascular coupling, only changes happening in the cerebral compartment are of interest (i.e., the first component). Systemic physiology affects both tissue compartments and can lead to changes in hemodynamics and oxygenation, for example induced by changes in the cardiorespiratory state or autonomic nervous system activity. Furthermore, spontaneous fluctuations in tone of blood vessel walls (vasomotion) cause another component also present in both tissue compartments.109 Finally, muscular evoked changes can be induced by the activity of the temporal muscle on the head.110,111 The non-neuronal driven components (i.e., components 2 to 6) are a challenge for fNIRS since they may mimic typical fNIRS signal changes normally observed due to an increase (or decrease) of brain activity (a “false positive”), or they may mask a neuronal-induced hemodynamic response so that it is not detected anymore (a “false negative”).107,112 Although the significance of non-neuronal drivers of the fNIRS signal changes is increasingly recognized, these non-neuronal drivers (e.g., systemic and vascular ones) are also increasingly in the focus in the field of fMRI due to their impact on the BOLD signal.113,114

Optical neuroimaging with fNIRS can be performed either by measuring regions of interest with a partial coverage of light emitters and detectors or using a full head coverage [Fig. 2(e)]. Measurements with different source–detector distances (short and long ones) enable depth-dependent measurements and reduction of the influence from extracerebral tissue layers,115117 and when combined with a high-density coverage of light emitters and detectors also to perform a tomographic reconstruction of cerebrovascular hemodynamics and oxygenation (also termed “diffuse optical tomography” or “near-infrared optical tomography”).118,119

As far as current trends in fNIRS neuroimaging are concerned, there is a development toward using fNIRS in combination with the measurement of systemic signals [an approach termed “systemic physiology augmented functional near-infrared spectroscopy” (SPA-fNIRS);92,120 for a review see Ref. 108], or combining fNIRS with other neuroimaging techniques, such as EEG,121123 fMRI,121,124 or PET.125128 Ideally, both approaches can then be combined [Fig. 2(f)]. In addition, future commercial fNIRS devices will probably also work with even more wavelengths (“broadband NIRS”, bNIRS), which will enable the direct measurement of metabolic parameters (e.g., the concentration of cytochrome-c-oxidase).129 Moreover, time-domain fNIRS devices are expected to play an increasingly important role,130132 the promising interferometric NIRS technology is currently being further developed and explored for fNIRS applications,133135 and the combination of fNIRS with diffuse correlation spectroscopy offers great potential for detailed measurement of hemodynamic changes.136,137 Another trend is the performance of fNIRS measurements on two or more people at the same time (the “hyperscanning” approach) [Fig. 2(g)].138140

6.

fNIRS Neuroimaging as a Promising New Technique for Psychedelic Neuroscience

Optical neuroimaging with fNIRS has specific features that make it a quite unique approach to measure neurovascular and neurometabolic changes associated with brain activity. Compared to the other neuroimaging techniques, fNIRS has its advantages but also limitations.

The main advantages are that fNIRS

  • (i) enables the measurement of a broad set of parameters related to cerebral hemodynamic, oxygenation, and metabolism [especially when specific advances technical NIRS implementation are used; see Fig. 2(c)];

  • (ii) is more cost-effective compared to the purchase and operation of an fMRI scanner;

  • (iii) does not produce disturbing noise like an fMRI (and thus avoiding stress induced by the noise in the subjects);

  • (iv) is much more robust against movement artifacts than an fMRI measurement—it can be used even when the subject is moving (an aspect that makes it ideally suited for psychedelic research since under the influence of a psychedelic substance the subject can feel and urge to move the body);

  • (v) allows measurement of the subject in different body positions (fMRI normally allows only the supine position);

  • (vi) makes it possible to perform relatively long measurements (several hours) which could cover the whole dynamics of the psychedelic experience;

  • (vii) is ideally suited for multimodal measurements combining different types of neuroimaging as well as to combine fNIRS neuroimaging with monitoring systemic physiological activity (the SPA-fNIRS approach); and

  • (viii) enables neuroimaging to be performed in many subjects in parallel (hyperscanning), ideally suited to investigate the impact of the group-setting and personal interactions during psychedelic sessions.

With regard to limitations, the main limitations of fNIRS neuroimaging are that

  • (i) the light penetration is limited so that only tissue hemodynamic, oxygenation, and metabolism originating from the cerebral cortex can be measured;

  • (ii) the measured fNIRS signals comprise different components [Fig. 2(d)] that need to be separated in order to enable a correct physiological interpretation of the signals;

  • (iii) wearing the fNIRS cap can be uncomfortable (but this can be improved considerably by optimizing the cap accordingly), which is particularly relevant for longer measurements or experiments where the subject should not be stressed by additional factors (e.g., during a psychedelic experience);

  • (iv) the fNIRS signal processing and data analysis are complicated, and the related standardization is currently still subject of discussion and development.141

Psychedelics induce changes in the activity of the autonomic nervous system, cardiorespiratory, and cardiovascular system32,142,143 (Fig. 3) in a subject- and substance-dependent manner. These systemic physiological changes will influence the fNIRS measurements and it is recommended to use a depth-resolved measurement technique, the SPA-fNIRS approach, and a careful as well as detailed analysis of the interplay between cerebral fNIRS data and systemic physiology in order to have an optimal separation between brain and systemic physiological effects. At the same time, the SPA-fNIRS approach also provides completely new insights into the interaction between brain activity and systemic physiology induced by a psychedelic. As psychedelics are affecting not only brain activity but also the physiological state of the whole body, an integrative physiological understanding of the physiological effects of psychedelics will require to investigate how the brain and the body are affected in parallel and how both interact—for example, changes in respiration will have an effect on the partial pressure of carbon dioxide in the arterial blood (PaCO2), changing cerebral hemodynamics, as well as potentially interfering/modulating neurovascular-coupling.144146 Therefore, possible PaCO2 changes must be taken into account for a correct analysis and interpretation of fNIRS data112,147 (which, incidentally, also applies to fMRI data). Furthermore, changes in cerebral and extracerebral tissue hemodynamics induced by changes in the state of the autonomic nervous system need to be considered too.148150 The SPA-fNIRS approach is an ideal method to explore these aspects.

Fig. 3

Examples of changes in cardiovascular and autonomic nervous system activity in humans induced be the intake of psychedelics. (a), (b) Changes in heart rate and blood pressure receiving (a) doses of 0.05, 0.1, 0.2, and 0.4  mg/kg N,N-DMT32 and (b) doses of 50, 100, and 200  μg LSD.142 New visualization of the data presented in Figs. 9 and 10 of the paper of Strassman and Qualls, and Fig. 3 of the paper of Holze et al., respectively. Shown are mean values (a), (b) as well as regression functions and the confidence interval for the regression functions [(b); own calculation].

NPh_10_1_013506_f003.png

What must also be taken into account is the possibility and already existing initial indications that psychedelics can alter neurovascular coupling, as has been shown, for example in rats for psilocin (the active metabolite of psilocybin).151 The authors rightly concluded that “caution is required when making inferences about drug effects on neuronal activity from changes detected in neuroimaging signals.” This is true for fMRI as well as fNIRS. More research is urgently needed to understand how psychedelics (in a dose- and substance-dependent manner) affect neurovascular coupling and vascular reactivity (e.g., CO2 cerebrovascular reactivity, cerebral autoregulation, and autonomic cerebrovascular reactivity) in humans. SPA-fNIRS is a useful technique in this case too.

Regarding the limited depth resolution of fNIRS, the inability to measure subcortical structures is of course a disadvantage, but it is clear from previous research that the cerebral cortex (which can be measured with fNIRS) is also always involved in psychedelic effects—for example, the prefrontal cortex (PFC) is particularly enriched in 5-HTA receptors expressed in the apical dendrites of layer 5 pyramidal neurons,152154 the PFC 5-HT2A receptor occupancy correlates with the psychedelic effects of psilocybin in humans45,155 and prefrontal cortical areas activated by both psilocybin and ketamine.28,30,36,37,47 Moreover, networks of synchronized brain activity involve subcortical and cortical areas156,157 that change during the psychedelic state.35,41,158,159 Such changes in cortical network activity can, of course, also be analyzed with fNIRS neuroimaging.87,160,161 The principal disadvantage that only cortical areas can be measured with fNIRS is put into perspective by the fact that the cortex is also always influenced by psychedelics and brain activity also changes there.

7.

Conclusion and Outlook

In summary, fNIRS is a neuroimaging method that has great potential for psychedelic research. It is expected that in the near future, the number of fNIRS studies investigating psychedelic effects in humans will increase rapidly, as the technique offers certain advantages over conventional hemodynamic-based neuroimaging techniques, enables novel study designs, and also has great potential to be used for multimodal neuroimaging (e.g., fNIRS in combination with fMRI, EEG, or PET).

fNIRS will be a good method to study cognitive control (with the PFC as an important brain region associated with and the multisource interference task as a typical test), attentional capacity, and possibly emotion processing, as well as the interaction between cognition and emotion, before, during and after psychedelic administration. fNIRS will also be well suited for longitudinal studies (which are currently scarce). In addition, fNIRS has great potential to investigate social interaction in a setting with psychedelics [e.g., fNIRS neuroimaging on the subject that got the psychedelic substance and in parallel on the person that monitors the subject and provides support when necessary (the “trip sitter”)].

At the same time, certain aspects must be taken into account when using fNIRS in order to carry out the measurements correctly, to optimally analyze the data and to correctly interpret the results physiologically. It is important to avoid misinterpretation of fNIRS data [e.g., confusion between extracerebral and cerebral components in the fNIRS signal or components caused by changes in systemic physiology (e.g., respiration or blood pressure)] with those induced by neurovascular coupling. Appropriate fNIRS hardware improvements and advanced signal processing methods are necessary to be applied and/or further developed. Good progress has already been made in this respect, and it is expected to accelerate enormously in the coming years, making the measurement and interpretation of fNIRS signals more reliable and accurate.

As far as the availability of commercially available fNIRS devices is concerned, the current situation is very good: there are many different commercially available fNIRS devices and NIRS oximeters, and more and more new companies and devices are entering the market. It is undoubted that fNIRS neuroimaging will be an integral part of the repertoire of modern neuroimaging.

Now is the time to exploit the momentum of the current resurgence of research on the effects of psychedelics and the momentum of the increasing progress and popularity of the fNIRS technique to establish fNIRS in psychedelic research. With this article, we hope to contribute to this development.

Disclosures

F. S. provided scientific consulting for companies producing fNIRS devices, including NIRx GmbH (Berlin, Germany) and Kernel (Culver City, Los Angeles, USA). F. X. V. declares no conflicts of interest.

Acknowledgements

We would like to thank Martin Wolf for valuable discussion and comments on the manuscript and Rachel Scholkmann for proofreading it.

References

1. 

J. A. Hobson, E. F. Pace-Schott and R. Stickgold, “Dreaming and the brain: toward a cognitive neuroscience of conscious states,” Behav. Brain Sci., 23 (6), 793 –842 https://doi.org/10.1017/S0140525X00003976 (2000). Google Scholar

2. 

J. A. Hobson and K. J. Friston, “Waking and dreaming consciousness: neurobiological and functional considerations,” Prog. Neurobiol., 98 (1), 82 –98 https://doi.org/10.1016/j.pneurobio.2012.05.003 PGNBA5 0301-0082 (2012). Google Scholar

3. 

T. Bayne, J. Hohwy and A. M. Owen, “Are there levels of consciousness?,” Trends Cognit. Sci., 20 (6), 405 –413 https://doi.org/10.1016/j.tics.2016.03.009 TCSCFK 1364-6613 (2016). Google Scholar

4. 

S. P. La Berge et al., “Lucid dreaming verified by volitional communication during REM sleep,” Percept. Motor Skills, 52 (3), 727 –732 https://doi.org/10.2466/pms.1981.52.3.727 PMOSAZ 0031-5125 (1981). Google Scholar

5. 

L. Brasington, Right Concentration: A Practical Guide to the Jhanas, Shambhala, Boulder, Colorado (2015). Google Scholar

6. 

R. Shankman, The Experience of Samadhi: An In-depth Exploration of Buddhist Meditation, Shambhala Publications( (2008). Google Scholar

7. 

The Handbook of Near-Death Experiences: Thirty Years of Investigation, Praeger( (2009). Google Scholar

8. 

C. Martial et al., “Near-death experience as a probe to explore (disconnected) consciousness,” Trends Cognit. Sci., 24 (3), 173 –183 https://doi.org/10.1016/j.tics.2019.12.010 TCSCFK 1364-6613 (2020). Google Scholar

9. 

A. Dittrich, “The standardized psychometric assessment of altered states of consciousness (ASCs) in humans,” Pharmacopsychiatry, 31 (S2), 80 –84 https://doi.org/10.1055/s-2007-979351 PHRMEZ 0176-3679 (2007). Google Scholar

10. 

A. Dittrich, S. von Arx and S. Staub, “International study on altered states of consciousness (ISASC): summary of results,” German J. Psychol., 9 (4), 319 –339 (1985). Google Scholar

11. 

K. H. Preller and F. X. Vollenweider, “Phenomenology, structure, and dynamic of psychedelic states,” Curr. Top. Behav. Neurosci., 36 221 –256 https://doi.org/10.1007/7854_2016_459 (2018). Google Scholar

12. 

A. Houot, “Phenomenology for psychedelic researchers: a review of current methods and practices,” J. Conscious. Explor. Res., 12 (4), (2021). Google Scholar

13. 

C. Letheby, Philosophy of Psychedelics, Oxford University Press( (2021). Google Scholar

14. 

C. Timmermann et al., “Psychedelics alter metaphysical beliefs,” Sci. Rep., 11 22166 https://doi.org/10.1038/s41598-021-01209-2 (2021). Google Scholar

15. 

J. B. Burt et al., “Transcriptomics-informed large-scale cortical model captures topography of pharmacological neuroimaging effects of LSD,” eLife, 10 69320 https://doi.org/10.7554/eLife.69320 (2021). Google Scholar

16. 

J. L. Ji et al., “Mapping brain-behavior space relationships along the psychosis spectrum,” eLife, 10 e66968 https://doi.org/10.7554/eLife.66968 (2021). Google Scholar

17. 

E. Tagliazucchi, “Human neuroimaging studies of serotogenergic psychedelics,” Handbook of Medical Hallucinogens, The Guilford Press( (2021). Google Scholar

18. 

D. Vaitl et al., “Psychobiology of altered states of consciousness,” Psychol. Bull., 131 (1), 98 –127 https://doi.org/10.1037/0033-2909.131.1.98 PSBUAI 0033-2909 (2005). Google Scholar

19. 

D. Stoliker et al., “Effective connectivity of functionally anticorrelated networks under lysergic acid diethylamide,” Biol. Psychiatry, https://doi.org/10.1016/j.biopsych.2022.07.013 BIPCBF 0006-3223 (2022). Google Scholar

20. 

D. E. Nichols, “Hallucinogens,” Pharmacol. Ther., 101 (2), 131 –181 https://doi.org/10.1016/j.pharmthera.2003.11.002 (2004). Google Scholar

21. 

J. M. Rodriguez Arce and M. J. Winkelman, “Psychedelics, sociality, and human evolution,” Front. Psychol., 12 729425 https://doi.org/10.3389/fpsyg.2021.729425 1664-1078 (2021). Google Scholar

22. 

T. B. Roberts and M. J. Winkelman, Psychedelic Medicine, Praeger Publishers Inc.( (2007). Google Scholar

23. 

H. Leuner, Halluzinogene: Psychische Grenzzustände in Forschung und Psychotherapie, 1 –458 Verlag Hans Huber, Bern, Stuttgart, Wien (1981). Google Scholar

24. 

Sr. F. S. Abuzzahab and B. J. Anderson, “A review of LSD treatment in alcoholism,” Int. Pharmacopsychiatry, 6 (4), 223 –235 https://doi.org/10.1159/000468273 INPHB6 0020-8272 (1971). Google Scholar

25. 

E. Mascher, “Psycholytic therapy: statistics and indications,” (1967). Google Scholar

26. 

J. J. Rucker et al., “Psychedelics in the treatment of unipolar mood disorders: a systematic review,” J. Psychopharmacol., 30 (12), 1220 –1229 https://doi.org/10.1177/0269881116679368 JOPSEQ 0269-8811 (2016). Google Scholar

27. 

A. Pletscher and D. Ladewig, Fifty Years of LSD: Current Status and Perspectives of Hallucinogens (A Symposium of the Swiss Academy of Medical Sciences: Lugano-Agno 1993), Parthenon Publishing, New York (1994). Google Scholar

28. 

E. Gouzoulis-Mayfrank, “Neurometabolic effects of psilocybin, 3,4-methylenedioxyethylamphetamine (MDE) and d-methamphetamine in healthy volunteers a double-blind, placebo-controlled PET study with [18F]FDG,” Neuropsychopharmacology, 20 (6), 565 –581 https://doi.org/10.1016/S0893-133X(98)00089-X NEROEW 0893-133X (1999). Google Scholar

29. 

F. X. Vollenweider, “Evidence for a cortical-subcortical dysbalance of sensory information processing during altered states of consciousness using PET and FDG,” in 50 Years of LSD: Curr. Stat. Perspect. of Hallucinogens A Sympos. the Swiss Acad. Med. Sci., 67 –86 (1994). Google Scholar

30. 

F. X. Vollenweider, “Positron emission tomography and fluorodeoxyglucose studies of metabolic hyperfrontality and psychopathology in the psilocybin model of psychosis,” Neuropsychopharmacology, 16 (5), 357 –372 https://doi.org/10.1016/S0893-133X(96)00246-1 NEROEW 0893-133X (1997). Google Scholar

31. 

J. Riba et al., “Subjective effects and tolerability of the South American psychoactive beverage ayahuasca in healthy volunteers,” Psychopharmacology, 154 (1), 85 –95 https://doi.org/10.1007/s002130000606 (2001). Google Scholar

32. 

R. J. Strassman and C. R. Qualls, “Dose-response study of N,N-dimethyltryptamine in humans. I. Neuroendocrine, autonomic, and cardiovascular effects,” Arch. Gen. Psychiatry, 51 (2), 85 –97 https://doi.org/10.1001/archpsyc.1994.03950020009001 ARGPAQ 0003-990X (1994). Google Scholar

33. 

M. E. Liechti, P. C. Dolder and Y. Schmid, “Alterations of consciousness and mystical-type experiences after acute LSD in humans,” Psychopharmacology, 234 (9-10), 1499 –1510 https://doi.org/10.1007/s00213-016-4453-0 (2017). Google Scholar

34. 

Y. Schmid et al., “Acute effects of lysergic acid diethylamide in healthy subjects,” Biol. Psychiatry, 78 (8), 544 –553 https://doi.org/10.1016/j.biopsych.2014.11.015 BIPCBF 0006-3223 (2015). Google Scholar

35. 

F. X. Vollenweider and M. Kometer, “The neurobiology of psychedelic drugs: implications for the treatment of mood disorders,” Nat. Rev. Neurosci., 11 (9), 642 –651 https://doi.org/10.1038/nrn2884 NRNAAN 1471-003X (2010). Google Scholar

36. 

F. X. Vollenweider et al., “Differential psychopathology and patterns of cerebral glucose utilisation produced by (S)- and (R)-ketamine in healthy volunteers using positron emission tomography (PET),” Eur. Neuropsychopharmacol., 7 (1), 25 –38 https://doi.org/10.1016/S0924-977X(96)00042-9 EURNE8 0924-977X (1997). Google Scholar

37. 

F. X. Vollenweider et al., “Metabolic hyperfrontality and psychopathology in the ketamine model of psychosis using positron emission tomography (PET) and [18F]fluorodeoxyglucose (FDG),” Eur. Neuropsychopharmacol., 7 (1), 9 –24 https://doi.org/10.1016/S0924-977X(96)00039-9 EURNE8 0924-977X (1997). Google Scholar

38. 

J. Daumann et al., “Neuronal correlates of visual and auditory alertness in the DMT and ketamine model of psychosis,” J. Psychopharmacol., 24 (10), 1515 –1524 https://doi.org/10.1177/0269881109103227 JOPSEQ 0269-8811 (2010). Google Scholar

39. 

R. G. Dos Santos and J. E. C. Hallak, “Therapeutic use of serotoninergic hallucinogens: a review of the evidence and of the biological and psychological mechanisms,” Neurosci. Biobehav. Rev., 108 423 –434 https://doi.org/10.1016/j.neubiorev.2019.12.001 NBREDE 0149-7634 (2020). Google Scholar

40. 

K. H. Preller and F. X. Vollenweider, “Modulation of social cognition via hallucinogens and “Entactogens”,” Front Psychiatry, 10 881 https://doi.org/10.3389/fpsyt.2019.00881 (2019). Google Scholar

41. 

F. X. Vollenweider and K. H. Preller, “Psychedelic drugs: neurobiology and potential for treatment of psychiatric disorders,” Nat. Rev. Neurosci., 21 (11), 611 –624 https://doi.org/10.1038/s41583-020-0367-2 NRNAAN 1471-003X (2020). Google Scholar

42. 

A. V. Lebedev et al., “LSD-induced entropic brain activity predicts subsequent personality change,” Hum. Brain Mapp., 37 (9), 3203 –3213 https://doi.org/10.1002/hbm.23234 (2016). Google Scholar

43. 

K. A. MacLean, M. W. Johnson and R. R. Griffiths, “Mystical experiences occasioned by the hallucinogen psilocybin lead to increases in the personality domain of openness,” J. Psychopharmacol., 25 (11), 1453 –1461 https://doi.org/10.1177/0269881111420188 JOPSEQ 0269-8811 (2011). Google Scholar

44. 

L. Smigielski et al., “Characterization and prediction of acute and sustained response to psychedelic psilocybin in a mindfulness group retreat,” Sci. Rep., 9 14914 https://doi.org/10.1038/s41598-019-50612-3 (2019). Google Scholar

45. 

M. K. Madsen et al., “Psychedelic effects of psilocybin correlate with serotonin 2A receptor occupancy and plasma psilocin levels,” Neuropsychopharmacology, 44 (7), 1328 –1334 https://doi.org/10.1038/s41386-019-0324-9 NEROEW 0893-133X (2019). Google Scholar

46. 

K. H. Preller et al., “The Fabric of meaning and subjective effects in LSD-induced states depend on serotonin 2A receptor activation,” Curr. Biol., 27 (3), 451 –457 https://doi.org/10.1016/j.cub.2016.12.030 CUBLE2 0960-9822 (2017). Google Scholar

47. 

F. X. Vollenweider et al., “Psilocybin induces schizophrenia-like psychosis in humans via a serotonin-2 agonist action,” Neuroreport, 9 (17), 3897 –3902 https://doi.org/10.1097/00001756-199812010-00024 NERPEZ 0959-4965 (1998). Google Scholar

48. 

T. Pokorny et al., “5-HT1A receptor agonist buspirone reduces visual hallucinations induced by the mixed 5-HT2A/1A agonist psilocybin in healthy humans,” in ZNZ Symp. 1994, (2014). Google Scholar

49. 

K. S. Murnane, “The renaissance in psychedelic research: what do preclinical models have to offer,” Prog. Brain Res., 242 25 –67 https://doi.org/10.1016/bs.pbr.2018.08.003 PBRRA4 0079-6123 (2018). Google Scholar

50. 

F. X. Vollenweider et al., “5-HT modulation of dopamine release in basal ganglia in psilocybin-induced psychosis in man—a PET study with [11C]raclopride,” Neuropsychopharmacology, 20 (5), 424 –433 https://doi.org/10.1016/S0893-133X(98)00108-0 NEROEW 0893-133X (1999). Google Scholar

51. 

N. L. Mason et al., “Me, myself, bye: regional alterations in glutamate and the experience of ego dissolution with psilocybin,” Neuropsychopharmacology, 45 (12), 2003 –2011 https://doi.org/10.1038/s41386-020-0718-8 NEROEW 0893-133X (2020). Google Scholar

52. 

M. de la Fuente Revenga et al., “Prolonged epigenomic and synaptic plasticity alterations following single exposure to a psychedelic in mice,” Cell Rep., 37 (3), 109836 https://doi.org/10.1016/j.celrep.2021.109836 (2021). Google Scholar

53. 

C. Ly et al., “Psychedelics promote structural and functional neural plasticity,” Cell Rep., 23 (11), 3170 –3182 https://doi.org/10.1016/j.celrep.2018.05.022 (2018). Google Scholar

54. 

N. R. Raval et al., “A single dose of psilocybin increases synaptic density and decreases 5-HT2A receptor density in the pig brain,” Int. J. Mol. Sci., 22 (2), 835 https://doi.org/10.3390/ijms22020835 1422-0067 (2021). Google Scholar

55. 

L. X. Shao et al., “Psilocybin induces rapid and persistent growth of dendritic spines in frontal cortex in vivo,” Neuron, 109 (16), 2535 –2544.e4 https://doi.org/10.1016/j.neuron.2021.06.008 NERNET 0896-6273 (2021). Google Scholar

56. 

L. J. Mertens and K. H. Preller, “Classical psychedelics as therapeutics in psychiatry—current clinical evidence and potential therapeutic mechanisms in substance use and mood disorders,” Pharmacopsychiatry, 54 (4), 176 –190 https://doi.org/10.1055/a-1341-1907 PHRMEZ 0176-3679 (2021). Google Scholar

57. 

M. W. Johnson et al., “Pilot study of the 5-HT2AR agonist psilocybin in the treatment of tobacco addiction,” J. Psychopharmacol., 28 (11), 983 –992 https://doi.org/10.1177/0269881114548296 JOPSEQ 0269-8811 (2014). Google Scholar

58. 

M. W. Johnson, A. Garcia-Romeu and R. R. Griffiths, “Long-term follow-up of psilocybin-facilitated smoking cessation,” Am. J. Drug Alcohol Abuse, 43 (1), 55 –60 https://doi.org/10.3109/00952990.2016.1170135 AJDABD 0095-2990 (2017). Google Scholar

59. 

M. P. Bogenschutz et al., “Psilocybin-assisted treatment for alcohol dependence: a proof-of-concept study,” J. Psychopharmacol., 29 (3), 289 –299 https://doi.org/10.1177/0269881114565144 JOPSEQ 0269-8811 (2015). Google Scholar

60. 

A. K. Davis et al., “Effects of psilocybin-assisted therapy on major depressive disorder: a randomized clinical trial,” JAMA Psychiatry, 78 (5), 481 –489 https://doi.org/10.1001/jamapsychiatry.2020.3285 (2021). Google Scholar

61. 

R. L. Carhart-Harris et al., “Trial of psilocybin versus escitalopram for depression,” N. Engl. J. Med., 384 (15), 1402 –1411 https://doi.org/10.1056/NEJMoa2032994 NEJMAG 0028-4793 (2021). Google Scholar

62. 

N. Gukasyan et al., “Efficacy and safety of psilocybin-assisted treatment for major depressive disorder: prospective 12-month follow-up,” J. Psychopharmacol., 36 (2), 151 –158 https://doi.org/10.1177/02698811211073759 JOPSEQ 0269-8811 (2022). Google Scholar

63. 

R. L. Carhart-Harris et al., “Psilocybin with psychological support for treatment-resistant depression: six-month follow-up,” Psychopharmacology, 235 (2), 399 –408 https://doi.org/10.1007/s00213-017-4771-x (2018). Google Scholar

64. 

R. L. Carhart-Harris et al., “Psilocybin with psychological support for treatment-resistant depression: an open-label feasibility study,” Lancet Psychiatry, 3 (7), 619 –627 https://doi.org/10.1016/S2215-0366(16)30065-7 (2016). Google Scholar

65. 

R. R. Griffiths et al., “Psilocybin produces substantial and sustained decreases in depression and anxiety in patients with life-threatening cancer: a randomized double-blind trial,” J. Psychopharmacol., 30 (12), 1181 –1197 https://doi.org/10.1177/0269881116675513 JOPSEQ 0269-8811 (2016). Google Scholar

66. 

C. S. Grob et al., “Pilot study of psilocybin treatment for anxiety in patients with advanced-stage cancer,” Arch. Gen. Psychiatry, 68 (1), 71 –78 https://doi.org/10.1001/archgenpsychiatry.2010.116 ARGPAQ 0003-990X (2011). Google Scholar

67. 

S. Ross et al., “Rapid and sustained symptom reduction following psilocybin treatment for anxiety and depression in patients with life-threatening cancer: a randomized controlled trial,” J. Psychopharmacol., 30 (12), 1165 –1180 https://doi.org/10.1177/0269881116675512 JOPSEQ 0269-8811 (2016). Google Scholar

68. 

L. Osorio Fde et al., “Antidepressant effects of a single dose of ayahuasca in patients with recurrent depression: a preliminary report,” Rev. Brasil. Psiquiatria, 37 (1), 13 –20 (2015). Google Scholar

69. 

F. Palhano-Fontes et al., “Rapid antidepressant effects of the psychedelic ayahuasca in treatment-resistant depression: a randomized placebo-controlled trial,” Psychol. Med., 49 (4), 655 –663 https://doi.org/10.1017/S0033291718001356 PSMDCO 0033-2917 (2019). Google Scholar

70. 

R. F. Sanches et al., “Antidepressant effects of a single dose of ayahuasca in patients with recurrent depression: a SPECT study,” J. Clin. Psychopharmacol., 36 (1), 77 –81 https://doi.org/10.1097/JCP.0000000000000436 JCPYDR 0271-0749 (2016). Google Scholar

71. 

P. Gasser et al., “Safety and efficacy of lysergic acid diethylamide-assisted psychotherapy for anxiety associated with life-threatening diseases,” J. Nervous Mental Disease, 202 (7), 513 –520 https://doi.org/10.1097/NMD.0000000000000113 (2014). Google Scholar

72. 

S. B. Goldberg et al., “The experimental effects of psilocybin on symptoms of anxiety and depression: a meta-analysis,” Psychiatry Res., 284 112749 https://doi.org/10.1016/j.psychres.2020.112749 PSRSDR (2020). Google Scholar

73. 

T. W. Flanagan and C. D. Nichols, “Psychedelics as anti-inflammatory agents,” Int. Rev. Psychiatry, 30 (4), 363 –375 https://doi.org/10.1080/09540261.2018.1481827 IRPSE2 (2018). Google Scholar

74. 

S. A. Vann Jones and A. O’Kelly, “Psychedelics as a treatment for Alzheimer’s disease dementia,” Front. Synaptic Neurosci., 12 34 https://doi.org/10.3389/fnsyn.2020.00034 (2020). Google Scholar

75. 

A. Garcia-Romeu et al., “Psychedelics as novel therapeutics in Alzheimer’s disease: rationale and potential mechanisms,” Curr. Top. Behav. Neurosci., 56 287 –317 https://doi.org/10.1007/7854_2021_267 (2021). Google Scholar

76. 

U. Kozlowska et al., “From psychiatry to neurology: psychedelics as prospective therapeutics for neurodegenerative disorders,” J. Neurochem., 162 89 –108 https://doi.org/10.1111/jnc.15509 JONRA9 0022-3042 (2021). Google Scholar

77. 

S. M. Khan et al., “Psychedelics for brain injury: a mini-review,” Front. Neurol., 12 685085 https://doi.org/10.3389/fneur.2021.685085 (2021). Google Scholar

78. 

C. Thompson and A. Szabo, “Psychedelics as a novel approach to treating autoimmune conditions,” Immunol. Lett., 228 45 –54 https://doi.org/10.1016/j.imlet.2020.10.001 IMLED6 0165-2478 (2020). Google Scholar

79. 

A. Szabo, “Psychedelics and immunomodulation: novel approaches and therapeutic opportunities,” Front. Immunol., 6 358 https://doi.org/10.3389/fimmu.2015.00358 (2015). Google Scholar

80. 

J. Bornemann et al., “Self-medication for chronic pain using classic psychedelics: a qualitative investigation to inform future research,” Front. Psychiatry, 12 735427 https://doi.org/10.3389/fpsyt.2021.735427 (2021). Google Scholar

81. 

J. P. Castellanos et al., “Chronic pain and psychedelics: a review and proposed mechanism of action,” Region. Anesth. Pain Med., 45 (7), 486 –494 https://doi.org/10.1136/rapm-2020-101273 (2020). Google Scholar

82. 

V. N. Hedau and A. P. Anjankar, “Psychedelics: their limited understanding and future in the treatment of chronic pain,” Cureus, 14 (8), e28413 https://doi.org/10.7759/cureus.28413 (2022). Google Scholar

83. 

F. X. Vollenweider and J. W. Smallridge, “Classic psychedelic drugs: update on biological mechanisms,” Pharmacopsychiatry, 55 (3), 121 –138 https://doi.org/10.1055/a-1721-2914 PHRMEZ 0176-3679 (2022). Google Scholar

84. 

R. L. Carhart-Harris et al., “Neural correlates of the LSD experience revealed by multimodal neuroimaging,” Proc. Natl. Acad. Sci. U. S. A., 113 (17), 4853 –4858 https://doi.org/10.1073/pnas.1518377113 (2016). Google Scholar

85. 

K. H. Preller et al., “Effects of serotonin 2A/1A receptor stimulation on social exclusion processing,” Proc. Natl. Acad. Sci. U. S. A., 113 (18), 5119 –5124 https://doi.org/10.1073/pnas.1524187113 (2016). Google Scholar

86. 

P. Duerler et al., “Psilocybin induces aberrant prediction error processing of tactile mismatch responses—a simultaneous EEG-FMRI study,” Cereb. Cortex, 32 (1), 186 –196 https://doi.org/10.1093/cercor/bhab202 (2021). Google Scholar

87. 

F. Scholkman et al., “Effects of psilocybin on functional connectivity measured with fNIRS: insights from a single-subject pilot study,” Matters, 1 –12 https://doi.org/10.5167/uzh-181782 (2019). Google Scholar

88. 

W. Yan et al., “Bibliometric evaluation of 2000–2019 publications on functional near-infrared spectroscopy,” NeuroImage, 220 117121 https://doi.org/10.1016/j.neuroimage.2020.117121 NEIMEF 1053-8119 (2020). Google Scholar

89. 

M. A. M. Devezas, “Shedding light on neuroscience: two decades of functional near-infrared spectroscopy applications and advances from a bibliometric perspective,” J. Neuroimaging, 31 (4), 641 –655 https://doi.org/10.1111/jon.12877 JNERET 1051-2284 (2021). Google Scholar

90. 

F. Scholkmann et al., “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology,” NeuroImage, 85 (Pt 1), 6 –27 https://doi.org/10.1016/j.neuroimage.2013.05.004 NEIMEF 1053-8119 (2014). Google Scholar

91. 

P. Pinti et al., “A review on the use of wearable functional near-infrared spectroscopy in naturalistic environments,” Jpn. Psychol. Res., 60 (4), 347 –373 https://doi.org/10.1111/jpr.12206 (2018). Google Scholar

92. 

H. Zohdi et al., “Color-dependent changes in humans during a verbal fluency task under colored light exposure assessed by SPA-fNIRS,” Sci. Rep., 11 9654 https://doi.org/10.1038/s41598-021-88059-0 (2021). Google Scholar

93. 

T. Karen et al., “Cerebral hemodynamic responses in preterm-born neonates to visual stimulation: classification according to subgroups and analysis of frontotemporal–occipital functional connectivity,” Neurophotonics, 6 (4), 045005 https://doi.org/10.1117/1.NPh.6.4.045005 (2019). Google Scholar

94. 

A. T. Eggebrecht et al., “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” NeuroImage, 61 (4), 1120 –1128 https://doi.org/10.1016/j.neuroimage.2012.01.124 NEIMEF 1053-8119 (2012). Google Scholar

95. 

B. Blanco et al., “Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants,” Neurophotonics, 8 (2), 025011 https://doi.org/10.1117/1.NPh.8.2.025011 (2021). Google Scholar

96. 

C. Bulgarelli et al., “Dynamic causal modelling on infant fNIRS data: a validation study on a simultaneously recorded fNIRS-fMRI dataset,” NeuroImage, 175 413 –424 https://doi.org/10.1016/j.neuroimage.2018.04.022 NEIMEF 1053-8119 (2018). Google Scholar

97. 

J. Uchitel et al., “Reliability and similarity of resting state functional connectivity networks imaged using wearable, high-density diffuse optical tomography in the home setting,” NeuroImage, 263 119663 https://doi.org/10.1016/j.neuroimage.2022.119663 NEIMEF 1053-8119 (2022). Google Scholar

98. 

A. J. Metz et al., “Physiological effects of continuous colored light exposure on Mayer wave activity in cerebral hemodynamics: a functional near-infrared spectroscopy (fNIRS) study,” Adv. Exp. Med. Biol., 977 277 –283 https://doi.org/10.1007/978-3-319-55231-6_38 AEMBAP 0065-2598 (2017). Google Scholar

99. 

H. Mohammadi et al., “Coronary artery disease and its impact on the pulsatile brain: a functional NIRS study,” Hum. Brain Mapp., 42 (12), 3760 –3776 https://doi.org/10.1002/hbm.25463 (2021). Google Scholar

100. 

M. Fabiani et al., “Taking the pulse of aging: mapping pulse pressure and elasticity in cerebral arteries with optical methods,” Psychophysiology, 51 (11), 1072 –1088 https://doi.org/10.1111/psyp.12288 PSPHAF 0048-5772 (2014). Google Scholar

101. 

G. Themelis et al., “Near-infrared spectroscopy measurement of the pulsatile component of cerebral blood flow and volume from arterial oscillations,” J. Biomed. Opt., 12 (1), 014033 https://doi.org/10.1117/1.2710250 JBOPFO 1083-3668 (2007). Google Scholar

102. 

C. H. Tan et al., “Mapping cerebral pulse pressure and arterial compliance over the adult lifespan with optical imaging,” PLoS One, 12 (2), e0171305 https://doi.org/10.1371/journal.pone.0171305 POLNCL 1932-6203 (2017). Google Scholar

103. 

M. Wolf, M. Ferrari and V. Quaresima, “Progress of near-infrared spectroscopy and topography for brain and muscle clinical applications,” J. Biomed. Opt., 12 (6), 062104 https://doi.org/10.1117/1.2804899 JBOPFO 1083-3668 (2007). Google Scholar

104. 

V. Quaresima and M. Ferrari, “Medical near-infrared spectroscopy: a prestigious history and a bright future,” NIR News, 27 (1), 10 –13 https://doi.org/10.1255/nirn.1575 (2016). Google Scholar

105. 

J. K. Lee et al., “Cerebrovascular reactivity measured by near-infrared spectroscopy,” Stroke, 40 (5), 1820 –1826 https://doi.org/10.1161/STROKEAHA.108.536094 SJCCA7 0039-2499 (2009). Google Scholar

106. 

L. Thewissen et al., “Measuring near-infrared spectroscopy derived cerebral autoregulation in neonates: from research tool toward bedside multimodal monitoring,” Front. Pediatr., 6 117 https://doi.org/10.3389/fped.2018.00117 (2018). Google Scholar

107. 

I. Tachtsidis and F. Scholkmann, “False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward,” Neurophotonics, 3 (3), 031405 https://doi.org/10.1117/1.NPh.3.3.031405 (2016). Google Scholar

108. 

F. Scholkmann et al., “Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain,” Neurophotonics, 9 (3), 030801 https://doi.org/10.1117/1.NPh.9.3.030801 (2022). Google Scholar

109. 

C. Aalkjaer, D. Boedtkjer and V. Matchkov, “Vasomotion—what is currently thought?,” Acta Physiol., 202 (3), 253 –269 https://doi.org/10.1111/j.1748-1716.2011.02320.x (2011). Google Scholar

110. 

M. Schecklmann et al., “The temporal muscle of the head can cause artifacts in optical imaging studies with functional near-infrared spectroscopy,” Front. Hum. Neurosci., 11 456 https://doi.org/10.3389/fnhum.2017.00456 (2017). Google Scholar

111. 

G. A. Zimeo Morais et al., “Non-neuronal evoked and spontaneous hemodynamic changes in the anterior temporal region of the human head may lead to misinterpretations of functional near-infrared spectroscopy signals,” Neurophotonics, 5 (1), 011002 https://doi.org/10.1117/1.NPh.5.1.011002 (2018). Google Scholar

112. 

M. Caldwell et al., “Modelling confounding effects from extracerebral contamination and systemic factors on functional near-infrared spectroscopy,” NeuroImage, 143 91 –105 https://doi.org/10.1016/j.neuroimage.2016.08.058 NEIMEF 1053-8119 (2016). Google Scholar

113. 

A. Das, K. Murphy and P. J. Drew, “Rude mechanicals in brain haemodynamics: non-neural factors that influence blood flow,” Philos. Trans. R. Soc. Lond. Ser. B, Biol. Sci., 376 (1815), 20190635 https://doi.org/10.1098/rstb.2019.0635 (2021). Google Scholar

114. 

K. A. Tsvetanov, R. N. A. Henson and J. B. Rowe, “Separating vascular and neuronal effects of age on fMRI BOLD signals,” Philos. Trans. R. Soc. Lond. Ser. B, Biol. Sci., 376 (1815), 20190631 https://doi.org/10.1098/rstb.2019.0631 (2021). Google Scholar

115. 

L. Gagnon et al., “Short separation channel location impacts the performance of short channel regression in NIRS,” NeuroImage, 59 (3), 2518 –2528 https://doi.org/10.1016/j.neuroimage.2011.08.095 NEIMEF 1053-8119 (2012). Google Scholar

116. 

D. Wyser et al., “Short-channel regression in functional near-infrared spectroscopy is more effective when considering heterogeneous scalp hemodynamics,” Neurophotonics, 7 (3), 035011 https://doi.org/10.1117/1.NPh.7.3.035011 (2020). Google Scholar

117. 

S. Suzuki et al., “Tissue oxygenation monitor using NIR spatially resolved spectroscopy,” Proc. SPIE, 3597 582 –592 https://doi.org/10.1117/12.356862 PSISDG 0277-786X (1999). Google Scholar

118. 

A. T. Eggebrecht et al., “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics, 8 (6), 448 –454 https://doi.org/10.1038/nphoton.2014.107 NPAHBY 1749-4885 (2014). Google Scholar

119. 

M. D. Wheelock, J. P. Culver and A. T. Eggebrecht, “High-density diffuse optical tomography for imaging human brain function,” Rev. Sci. Instrum., 90 (5), 051101 https://doi.org/10.1063/1.5086809 RSINAK 0034-6748 (2019). Google Scholar

120. 

A. J. Metz et al., “Continuous coloured light altered human brain haemodynamics and oxygenation assessed by systemic physiology augmented functional near-infrared spectroscopy,” Sci. Rep., 7 10027 https://doi.org/10.1038/s41598-017-09970-z (2017). Google Scholar

121. 

J. Uchitel et al., “Wearable, integrated EEG-fNIRS technologies: a review,” Sensors, 21 (18), https://doi.org/10.3390/s21186106 SNSRES 0746-9462 (2021). Google Scholar

122. 

Z. Liu et al., “A systematic review on hybrid EEG/fNIRS in brain-computer interface,” Biomed. Signal Process. Control, 68 102595 https://doi.org/10.1016/j.bspc.2021.102595 (2021). Google Scholar

123. 

P. Pinti et al., “An analysis framework for the integration of broadband NIRS and EEG to assess neurovascular and neurometabolic coupling,” Sci. Rep., 11 3977 https://doi.org/10.1038/s41598-021-83420-9 (2021). Google Scholar

124. 

R. Li et al., “Concurrent fNIRS and EEG for brain function investigation: a systematic, methodology-focused review,” Sensors, 22 (15), 5865 https://doi.org/10.3390/s22155865 SNSRES 0746-9462 (2022). Google Scholar

125. 

Y. Hoshi et al., “Non-synchronous behavior of neuronal activity, oxidative metabolism and blood supply during mental tasks in man,” Neurosci. Lett., 172 (1–2), 129 –133 https://doi.org/10.1016/0304-3940(94)90679-3 NELED5 0304-3940 (1994). Google Scholar

126. 

C. Hock et al., “Decrease in parietal cerebral hemoglobin oxygenation during performance of a verbal fluency task in patients with Alzheimer’s disease monitored by means of near-infrared spectroscopy (NIRS)—correlation with simultaneous rCBF-PET measurements,” Brain Res., 755 (2), 293 –303 https://doi.org/10.1016/S0006-8993(97)00122-4 BRREAP 0006-8993 (1997). Google Scholar

127. 

E. Rostrup et al., “Cerebral hemodynamics measured with simultaneous PET and near-infrared spectroscopy in humans,” Brain Res., 954 (2), 183 –193 https://doi.org/10.1016/S0006-8993(02)03246-8 BRREAP 0006-8993 (2002). Google Scholar

128. 

H. A. Polinder-Bos et al., “Changes in cerebral oxygenation and cerebral blood flow during hemodialysis—a simultaneous near-infrared spectroscopy and positron emission tomography study,” J. Cereb. Blood Flow Metab., 40 (2), 328 –340 https://doi.org/10.1177/0271678X18818652 (2018). Google Scholar

129. 

G. Bale, C. E. Elwell and I. Tachtsidis, “From Jobsis to the present day: a review of clinical near-infrared spectroscopy measurements of cerebral cytochrome-c-oxidase,” J. Biomed. Opt., 21 (9), 091307 https://doi.org/10.1117/1.JBO.21.9.091307 JBOPFO 1083-3668 (2016). Google Scholar

130. 

A. Torricelli et al., “Time domain functional NIRS imaging for human brain mapping,” NeuroImage, 85 (Pt 1), 28 –50 https://doi.org/10.1016/j.neuroimage.2013.05.106 NEIMEF 1053-8119 (2014). Google Scholar

131. 

Y. Yamada, H. Suzuki and Y. Yamashita, “Time-domain near-infrared spectroscopy and imaging: a review,” Appl. Sci., 9 (6), 1127 https://doi.org/10.3390/app9061127 (2019). Google Scholar

132. 

F. Lange and I. Tachtsidis, “Clinical brain monitoring with time domain NIRS: a review and future perspectives,” Appl. Sci., 9 (8), 1612 https://doi.org/10.3390/app9081612 (2019). Google Scholar

133. 

O. Kholiqov et al., “Scanning interferometric near-infrared spectroscopy,” Opt. Lett., 47 (1), 110 –113 https://doi.org/10.1364/OL.443533 OPLEDP 0146-9592 (2022). Google Scholar

134. 

O. Kholiqov et al., “Time-of-flight resolved light field fluctuations reveal deep human tissue physiology,” Nat. Commun., 11 (1), 391 https://doi.org/10.1038/s41467-019-14228-5 NCAOBW 2041-1723 (2020). Google Scholar

135. 

W. Zhou et al., “Functional interferometric diffusing wave spectroscopy of the human brain,” Sci. Adv., 7 (20), eabe0150 https://doi.org/10.1126/sciadv.abe0150 (2021). Google Scholar

136. 

M. Ferrari and V. Quaresima, “The future of noninvasive neonatal brain assessment: the measure of cerebral blood flow by diffuse correlation spectroscopy in combination with near-infrared spectroscopy oximetry,” J. Perinatol., 41 (11), 2690 –2691 https://doi.org/10.1038/s41372-021-00996-w JOPEEI 0743-8346 (2021). Google Scholar

137. 

M. Giovannella et al., “BabyLux device: a diffuse optical system integrating diffuse correlation spectroscopy and time-resolved near-infrared spectroscopy for the neuromonitoring of the premature newborn brain,” Neurophotonics, 6 (2), 025007 https://doi.org/10.1117/1.NPh.6.2.025007 (2019). Google Scholar

138. 

F. Scholkmann et al., “A new methodical approach in neuroscience: assessing inter-personal brain coupling using functional near-infrared imaging (fNIRI) hyperscanning,” Front. Hum. Neurosci., 7 813 https://doi.org/10.3389/fnhum.2013.00813 (2013). Google Scholar

139. 

A. Czeszumski et al., “Hyperscanning: a valid method to study neural inter-brain underpinnings of social interaction,” Front. Hum. Neurosci., 14 39 https://doi.org/10.3389/fnhum.2020.00039 (2020). Google Scholar

140. 

S. Guglielmini et al., “Systemic physiology augmented functional near-infrared spectroscopy hyperscanning: a first evaluation investigating entrainment of spontaneous activity of brain and body physiology between subjects,” Neurophotonics, 9 (2), 026601 https://doi.org/10.1117/1.NPh.9.2.026601 (2022). Google Scholar

141. 

M. A. Yücel et al., “Best practices for fNIRS publications,” Neurophotonics, 8 (1), 012101 https://doi.org/10.1117/1.NPh.8.1.012101 (2021). Google Scholar

142. 

F. Holze et al., “Acute dose-dependent effects of lysergic acid diethylamide in a double-blind placebo-controlled study in healthy subjects,” Neuropsychopharmacology, 46 (3), 537 –544 https://doi.org/10.1038/s41386-020-00883-6 NEROEW 0893-133X (2021). Google Scholar

143. 

S. Olbrich, K. H. Preller and F. X. Vollenweider, “LSD and ketanserin and their impact on the human autonomic nervous system,” Psychophysiology, 58 (6), e13822 https://doi.org/10.1111/psyp.13822 PSPHAF 0048-5772 (2021). Google Scholar

144. 

C. H. B. van Niftrik et al., “Impact of baseline CO2 on blood-oxygenation-level-dependent MRI measurements of cerebrovascular reactivity and task-evoked signal activation,” Magn. Reson. Imaging, 49 123 –130 https://doi.org/10.1016/j.mri.2018.02.002 MRIMDQ 0730-725X (2018). Google Scholar

145. 

P. Maggio et al., “Influence of CO2 on neurovascular coupling: interaction with dynamic cerebral autoregulation and cerebrovascular reactivity,” Physiol. Rep., 2 (3), e00280 https://doi.org/10.1002/phy2.280 (2014). Google Scholar

146. 

K. Szabo et al., “Hypocapnia induced vasoconstriction significantly inhibits the neurovascular coupling in humans,” J. Neurol. Sci., 309 (1-2), 58 –62 https://doi.org/10.1016/j.jns.2011.07.026 JNSCAG 0022-510X (2011). Google Scholar

147. 

F. Scholkmann et al., “End-tidal CO2: an important parameter for a correct interpretation in functional brain studies using speech tasks,” NeuroImage, 66 71 –79 https://doi.org/10.1016/j.neuroimage.2012.10.025 NEIMEF 1053-8119 (2013). Google Scholar

148. 

P. S. Ozbay et al., “Sympathetic activity contributes to the fMRI signal,” Commun. Biol., 2 421 https://doi.org/10.1038/s42003-019-0659-0 (2019). Google Scholar

149. 

P. D. Drummond and S. H. Quah, “The effect of expressing anger on cardiovascular reactivity and facial blood flow in Chinese and Caucasians,” Psychophysiology, 38 (2), 190 –196 https://doi.org/10.1111/1469-8986.3820190 PSPHAF 0048-5772 (2001). Google Scholar

150. 

P. D. Drummond and J. W. Lance, “Facial flushing and sweating mediated by the sympathetic nervous system,” Brain: J. Neurol., 110 (3), 793 –803 https://doi.org/10.1093/brain/110.3.793 (1987). Google Scholar

151. 

A. Spain et al., “Neurovascular and neuroimaging effects of the hallucinogenic serotonin receptor agonist psilocin in the rat brain,” Neuropharmacology, 99 210 –220 https://doi.org/10.1016/j.neuropharm.2015.07.018 NEPHBW 0028-3908 (2015). Google Scholar

152. 

H. Hall et al., “Autoradiographic localization of 5-HT2A receptors in the human brain using [3H]M100907 and [11C]M100907,” Synapse, 38 (4), 421 –431 https://doi.org/10.1002/1098-2396(20001215)38:4<421::AID-SYN7>3.0.CO;2-X SYNAET 1098-2396 (2000). Google Scholar

153. 

A. Saulin, M. Savli and R. Lanzenberger, “Serotonin and molecular neuroimaging in humans using PET,” Amino Acids, 42 (6), 2039 –2057 https://doi.org/10.1007/s00726-011-1078-9 AACIE6 0939-4451 (2011). Google Scholar

154. 

P. Celada, M. V. Puig and F. Artigas, “Serotonin modulation of cortical neurons and networks,” Front. Integr. Neurosci., 7 https://doi.org/10.3389/fnint.2013.00025 (2013). Google Scholar

155. 

B. Quednow, M. Geyer and A. Halberstadt, “Serotonin and schizophrenia,” Handb. Behav. Neurosci., 31 711 –743 https://doi.org/10.1016/B978-0-444-64125-0.00039-6 (2020). Google Scholar

156. 

B. A. Seitzman et al., “The state of resting state networks,” Top. Magn. Reson. Imaging, 28 (4), 189 –196 https://doi.org/10.1097/RMR.0000000000000214 TMRIEY 0899-3459 (2019). Google Scholar

157. 

B. Biswal et al., “Functional connectivity in the motor cortex of resting human brain using echo-planar MRI,” Magn. Reson. Med., 34 (4), 537 –541 https://doi.org/10.1002/mrm.1910340409 MRMEEN 0740-3194 (1995). Google Scholar

158. 

D. E. W. McCulloch et al., “Psychedelic resting-state neuroimaging: a review and perspective on balancing replication and novel analyses,” Neurosci. Biobehavioral Rev., 138 104689 https://doi.org/10.1016/j.neubiorev.2022.104689 (2022). Google Scholar

159. 

L. Smigielski et al., “Psilocybin-assisted mindfulness training modulates self-consciousness and brain default mode network connectivity with lasting effects,” NeuroImage, 196 207 –215 https://doi.org/10.1016/j.neuroimage.2019.04.009 NEIMEF 1053-8119 (2019). Google Scholar

160. 

L. Duan, Y. J. Zhang and C. Z. Zhu, “Quantitative comparison of resting-state functional connectivity derived from fNIRS and fMRI: a simultaneous recording study,” NeuroImage, 60 (4), 2008 –2018 https://doi.org/10.1016/j.neuroimage.2012.02.014 NEIMEF 1053-8119 (2012). Google Scholar

161. 

T. Nguyen et al., “Exploring brain functional connectivity in rest and sleep states: a fNIRS study,” Sci. Rep., 8 16144 https://doi.org/10.1038/s41598-018-33439-2 (2018). Google Scholar

Biography

Felix Scholkmann received his PhD from the University of Zurich, Switzerland, in 2014. He is a lecturer at the University of Zurich and a research associate at the University Hospital Zurich and University of Bern. His research mainly concerns the fields of neurophotonics and biomedical signal processing as well as integrative neuroscience and physiology. He has published more than 140 peer-reviewed papers and book chapters, and his work was awarded by several national and international awards.

Franz X. Vollenweider is a director of the Consciousness Research Unit at the Psychiatric University Hospital and a professor of psychiatry at the University of Zürich. He is internationally known for his pioneering work into the mechanism of action of classic psychedelics and related drugs in humans. His current work focuses on the neuronal basis of the effect of psychedelics on the sense of self, emotion regulation, and social interaction, all relevant for the development of innovative treatments for psychiatric disorders. He has published more than 180 peer-reviewed papers and book chapters, and his work was awarded by several national and international awards.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Felix Scholkmann and Franz X. Vollenweider "Psychedelics and fNIRS neuroimaging: exploring new opportunities," Neurophotonics 10(1), 013506 (2 December 2022). https://doi.org/10.1117/1.NPh.10.1.013506
Received: 28 July 2022; Accepted: 14 November 2022; Published: 2 December 2022
Lens.org Logo
CITATIONS
Cited by 5 scholarly publications.
Advertisement
Advertisement
KEYWORDS
Neuroimaging

Brain

Functional magnetic resonance imaging

Hemodynamics

Oxygenation

Tissue optics

Electroencephalography

Back to Top