Diminished Compensatory Energy Production Following Hypoxic Stress in the Prefrontal and Anterior Cingulate Cortex Among Individuals With Schizophrenia

M Burak Baytunca; Xian-Feng Shi; Nicolas A Nuñez; Danielle Boxer; Colleen Fitzgerald; Dost Ongur; Deborah Yurgelun-Todd; Perry Renshaw; Douglas Kondo

doi:10.30773/pi.2024.0150

Psychiatry Investig > Volume 22(3); 2025 > Article

Baytunca, Shi, Nuñez, Boxer, Fitzgerald, Ongur, Yurgelun-Todd, Renshaw, and Kondo: Diminished Compensatory Energy Production Following Hypoxic Stress in the Prefrontal and Anterior Cingulate Cortex Among Individuals With Schizophrenia

Original Article

Psychiatry Investigation 2025;22(3):243-251.

Published online: March 18, 2025

DOI: https://doi.org/10.30773/pi.2024.0150

Diminished Compensatory Energy Production Following Hypoxic Stress in the Prefrontal and Anterior Cingulate Cortex Among Individuals With Schizophrenia

M Burak Baytunca¹

, Xian-Feng Shi¹

, Nicolas A Nuñez^1,²

, Danielle Boxer¹

, Colleen Fitzgerald¹

, Dost Ongur³

, Deborah Yurgelun-Todd^1,⁴

, Perry Renshaw^1,⁵

, Douglas Kondo^1,⁴

¹Department of Psychiatry, University of Utah, Salt Lake City, UT, USA

²Department of Psychiatry and Psychology, Mayo Clinic Rochester, Rochester, MN, USA

³Division of Psychotic Disorder, McLean Hospital, Harvard Medical School, Belmont, MA, USA

⁴VA Salt Lake City Health Care System, George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, USA

⁵Department of Psychiatry, Utah Science Technology and Research Initiative (USTAR), Salt Lake City, UT, USA

Correspondence: M Burak Baytunca, MD Department of Psychiatry, University of Utah, 501 Chipeta Way, Salt Lake City 84108, UT, USA Tel: +1-8572008904, E-mail: baytunca@gmail.com

Received May 2, 2024 Revised October 15, 2024 Accepted December 3, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective

The purpose of this study was to explore the capacity for energy production under conditions of increased energy demand in schizophrenia (SCZ) subjects compared to healthy controls.

Methods

Twelve healthy controls (33.00±6.07 years) and 12 subjects diagnosed with SCZ or schizoaffective disorder (36.00±8.33 years) matched for age and sex, were recruited for this study. Hypoxic stress was induced during MR scans to elevate the energy demand on the subjects’ bioenergetic systems. Participants breathed air with a lower oxygen concentration (FiO₂=13%), maintaining their SpO₂ levels (86%) during the initial phase of the scan. ³¹Phosphorus MR spectroscopy was employed to examine metabolite levels, including phosphocreatine (PCr), β-adenosine triphosphate (ATP), and inorganic phosphate (Pi), as well as the ratios of PCr/Pi and PCr/β-ATP, in regions such as the prefrontal cortex (PFC), anterior cingulate cortex (ACC), and posterior cortex (POC), as well as across the entire brain, during both hypoxia and hyperoxia scans.

Results

Subjects with SCZ had significantly lower levels of Pi across the brain and particularly, in the PFC, POC, and ACC during the hypoxia scan. Moreover, levels of PCr/Pi, indicative of mitochondrial energy production, were found to be higher in the same brain regions in the SCZ group. No significant differences were found in hyperoxia scan phase.

Conclusion

These findings suggest a deficit in the bioenergetic systems of individuals with SCZ under conditions of heightened energy demand. Further studies are warranted.

Keywords: Schizophrenia, MR spectroscopy, Bioenergetics, Psychosis, Hypoxia

INTRODUCTION

Schizophrenia (SCZ) is a mental disorder which is often manifested by positive (hallucinations, delusions, paranoia), negative (anhedonia, amotivation, avolition, social withdrawal, flat affect), and cognitive symptoms [1]. Several leading hypotheses including hyperdopaminergic state in the striatum [2] as well as a disruption in the functions of glutamatergic receptors (namely, N-methyl D-aspartate) leading to aberrant dopaminergic neurotransmission have failed to fully explain the complexities in the cellular neurobiology of the disease [3,4]. Bioenergetics focuses on the metabolic pathways of the production, transformation, storage, and consumption of high energy phosphates (HEP) including phosphocreatine (PCr), adenosine triphosphate (ATP), and inorganic phosphate (Pi) in cells. There are multiple pathways involved in bioenergetics such as glycolysis, oxidative phosphorylation (OXPHOS) in the mitochondria, and the lactate shuttle [5]. A growing body of evidence supporting an alteration in the bioenergetics in those with subjects with SCZ have been reported in the literature [6]. Precisely, the mitochondria function as power houses of neurons which generate HEP (ATP, PCr, Pi) via OXPHOS, and regulate brain’s ATP metabolism in the brain. Thus, ATP is synthesized from ADP and Pi in the mitochondria and then is converted to PCr, an energy reservoir for cellular functions. Aberrant functioning of the mitochondria or impaired dynamic adaptation to meet increased energy demands can potentially result in a deterioration of neuronal homeostasis. Dysfunctions in mitochondrial respiration and the electron transport chain (ETC) as well as mitochondrial fission and fusion have been implicated in numerous genetic and postmortem studies in subjects suffering from SCZ [7-13]. Consequently, a potential shift from the major energy production pathways of the mitochondrial OXPHOS and the tricyclic carboxylic acid (TCA) cycle towards increased glycolysis leads up to an accumulation of lactate which have been reported in recent meta-analysis [14,15]. Convergently, Du et al. [16] reported a 22% reduction in creatine kinase (CK) forward rate in SCZ subjects, which would transfer phosphates from PCr to ADP to generate ATP which can be utilized for ongoing neuronal functions (PCr+ADP←CK enzyme→creatine [Cr]+ATP). The reduction in CK forward rate has also been reported in subjects with first episode psychosis [17]. Additionally, a downregulation in CK mRNA levels was also reported in SCZ subjects, which is a critical enzyme to maintain steady levels of ATP when higher energy is needed [18-20]. NAD and NADH (nicotineamide adenine dicnucleotide oxidized and reduced form) redox pair is used for the transportation of electrons to the mitochondrial ETC, which is critical to produce ATP by mitochondrial ATP synthase. A significant decrease in NAD/NADH ratio has also been shown in SCZ subjects [21,22].

Studies using 31Phosphorus Magnetic Resonance Spectroscopy (31P-MRS), a neuroimaging tool to quantify the concentrations of HEP and membrane phospholipids as well as CK forward rate in living brain in vivo [16], have also revealed some inconclusive data regarding brain bioenergetics in SCZ subjects. Although, several researchers have reported significantly lower levels of ATP, PCr, and Pi in the prefrontal cortices of SCZ subjects [23-27] and PCr/ATP ratio in unaffected siblings of SCZ subjects [22]; others have reported no change [28-31] or an increased PCr levels [32]. Noteworthy, significantly lower levels of PCr in the basal ganglia [33-35], the temporal cortex [24] but increased HEP concentrations (PCr, Pi, and ATP) in the anterior cingulate cortex [30] have also been reported. Overall, 31P-MRS findings regarding HEP in SCZ subjects seem inconclusive and have shown regional differences.

To the best our knowledge, the dynamic change in cerebral bioenergetics during 31P-MRS scan has not been assessed before. In this current study, we chose to induce hypoxia as a challenge, necessitating increased energy demand compared to the baseline. A 10% reduction in oxygen saturation proved sufficient to induce alterations in cerebral bioenergetics among individuals with chronic obstructive sleep apnea [36]. It is a well-replicated finding that PCr reduces, and Pi increases in skeletal muscle during physical exercise in order to maintain steady levels of ATP, which shows the natural process during a state requiring higher energy demand than baseline [37].

Considering the potential disruption in mitochondrial ETC, a shift toward anerobic glycolysis, a decrease in CK forward rate in SCZ subjects; we hypothesized: 1) SCZ subjects would fail to maintain a steady level of ATP during hypoxia challenge; 2) Pi would increase in healthy controls (HCs); 3) PCr might not decrease during hypoxic stress due to reduction in CK forward rate; 4) lower brain ph levels in SCZ subjects.

METHODS

Subjects

Adult subjects (18 to 65 years of age) with a diagnosis of SCZ or schizoaffective disorder (SZA) were recruited from the outpatient and inpatient clinics of Hunstman Mental Health Institute at University of Utah. Subjects diagnosis was made base on clinical evaluation based on the structured clinical interview for DSM-5 [38]. Severity of psychotic symptoms was measured with the Positive and Negative Syndrome Scale (PANNS) [39], depressive symptoms with the Montgomery-Asberg Depression Rating Scale (MADRS) [40], and manic/hypomanic symptoms with the Young Mania Rating Scale (YMRS) [41]. Subjects with active substance use disorder, neurological and pulmonary diseases, diabetes mellitus, heart failure, mental retardation, or aged less than 18 and more than 65 were excluded. Subjects with SCZ or SZA were allowed to continue their psychiatric treatments during the study participation. Their antipsychotic doses were converted to chlorpromazine equivalent doses [42]. The clinical trial was approved by the IRB of the University of Utah #00138477 on 9/22/2021 and informed consent was obtained from each patient. This study was conducted according to United States government regulations and Institutional research policies and procedures as well as comply with the Helsinki Declaration of 1975, as revised in 2008.

Statistical analyses

Independent samples t-tests were employed to compare the neuro-metabolite ratios (PCr/total phosphate [Tp], β-ATP/Tp, Pi/Tp, PCr/β-ATP, and PCr/Pi) in the study groups after verifying that the data displayed normal distribution. Categorical variables were compared by χ². Linear regression analysis was used to rule out the carryover effect of hypoxia on the metabolites of PCr, β-ATP, and Pi levels of the participants during hyperoxia: Y=β₀+Xβ+ε. Y denotes for phosphorus metabolite ratio (PCr, β-ATP, and Pi) during hyperoxia; X for corresponding metabolite during the hypoxia challenge; and ε standard error. This model did not quantify any statistically significant association between the metabolite ratios during hypoxia on the corresponding ones during hyperoxia scan. This lack of significant effect of hypoxia on metabolites during hyperoxia was also verified in the scatter diagrams. Pearson’s correlation analysis was utilized to investigate the association between the clinical variables (disease severity, duration of illness, medication dose) and the statistically significant neuro-metabolite ratios.

Hypoxia experiment during the MR scan

Two experimental oxygenation conditions were applied to each participant during the scan: hypoxia and hyperoxia. A hypoxia chamber enveloping the upper part of the participants as well as a head coil inside the MR scan was installed. A generator, which was connected to the chamber, was used to regulate the oxygen concentration (FiO₂) in this chamber to induce hypoxia challenge and hyperoxia state. All participants were inspiring air with lower O₂ concentration during the first part of the scan. Study co-ordinators continuously monitored the SpO₂ levels of subjects during the scan, and manually adjusted the FiO₂ level in the chamber to maintain SpO₂ level around 86%. The first part of the scan (hypoxia scan), which was completed in 30 minutes, was initiated when the SpO₂ levels of the participants was at 86%. Although, FiO₂ levels to reach a steady level of 86% of SpO₂ differed for each subject; a FiO₂ level of 13% was sufficient to maintain this SpO₂ level. Of note, FiO₂ level was manually increased if SpO₂ level of any participant dropped lower than 86% for safety reasons. Upon the completion of hypoxia scan; the participants were given 25% of FiO₂. The second part of the scan started when SpO₂ levels of the subjects reached 100%. In the present study, hyperoxia instead of normoxia was used to expedite the energy restoration in a relatively shorter scan time (approximately 30 minutes). After installing hypoxia chamber in place, we tested whether the hypoxia chamber was sealed off properly and SpO₂ levels of subjects were trending towards to 86% before the initiation of the scan. Hence, a comprehensive quality analysis of the system was conducted before initiating the MRI protocol. Nevertheless, during the scan, an interruption was necessitated as we had to enter the scan room to verify system integrity, including checking for air leakage and ensuring proper installation of the chamber. Such interventions, albeit essential, risked compromising the stability of the magnetic field.

Conversely, our preliminary endeavors uncovered a notable challenge in attaining the desired SpO₂ levels of 86% when commencing the scan under hyperoxic conditions. This observation underscores the practical complexities encountered in achieving and maintaining physiological parameters within specified ranges during experimental protocols.

³¹P-MRS scan and data processing

All spectra analysis was pre-processed locally written Matlab (The Mathworks, Inc.) programs. Each spectrum is apodized with 5 Hz of exponential decay line broadening before zero-filling and fast Fourier transform. Zero- and first-order phase corrections are performed on all spectra. The signal intensity of each metabolite is obtained using the Advanced Magnetic Resonance (AMARES) fitting algorithm within jMRUI [43]. The AMARES routine incorporates the use of prior spectral knowledge such as J-coupling constants, chemical shifts and linewidth. In our spectral fitting template, we model PCr, Pi, dinucleotide, and the α-, β-, and γ-NTP as Lorenztian lineshapes. During data preprocessing, zero- and first-order phase correction was applied manually. Metabolite signals were calculated as a percentage of the total phosphorus (TP) signal acquired from the region of interest (ROI): prefrontal, anterior cingulate and posterior cortices in addition to whole brain analysis for the present study (Figure 1). Our hypothesis was tested using Student’s t-test. All metabolite ratios were also compared between subjects using Mathlab statistics toolbox [44].

RESULTS

Demographics and study sample

We enrolled 12 HCs (mean age, 33.00±6.07 years; 33.3% of females), and 12 subjects with SCZ/SZA (mean age, 36.00±8.33 years; 25% of females) with no statistical difference in age and sex (p=0.32, p>0.999, respectively). There was no statistical difference with regards to age, sex, SpO₂ levels, and respiration rates during hypoxia and hyperoxia between the study groups (Table 1). One SCZ patient was lost to follow up during the study, another one wanted to quit the study before the MR scan. Two subjects experienced anxiety, and could not complete the scan. Another patient terminated the scan prematurely due to numbness in his fingertips. One HC prematurely terminated the scan due to feeling anxious.

Hypoxia condition-HCs vs. SCZ/SZA

We detected statistically significant lower Pi levels (0.05 vs. 0.06, p=0.004) and higher PCr/Pi ratio (3.28 vs. 2.97, p=0.02) in the prefrontal cortex (PFC) of SCZ subjects compared to HCs. Our primary analyses also revealed significantly lower Pi levels (0.059 vs. 0.051, p=0.04) as well as higher PCr/Pi (3.46 vs. 2.9, p=0.04) in the anterior cingulate cortex (ACC) regions of subjects. Similarly, Pi levels were significantly lower (0.058 vs. 0.062, p=0.01) in the POC, and whole brain analysis (3.19 vs. 3.03, p=0.02).

In Figures 2 and 3, we show the comparison of neuro-metabolites and Pi/TP levels in the brain regions between study groups during hypoxia, respectively.

Hyperoxic condition-HCs vs. SCZ/SZA

No statistically significant differences were found while comparing (PCr/Tp, β-ATP/Tp, Pi/Tp, and Pcr/Pi) between the study groups during hyperoxia scan. In Table 2, we provide the neuro-metabolite ratios for the study groups.

Intragroup analyses

We also contrasted the neuro-metabolite concentrations in each brain region (ACC, posterior cortex [POC], PFC) as well as across the brain during hypoxia versus hyperoxia within group analyses. Our primary goal was to assess the effect of hypoxic stress on neuro-metabolites in study subjects.

SCZ group: intragroup analyses

There was no statistical difference in any parameters in the sch group between hypoxia and hyperoxia scan.

HC group: intragroup analyses

We detected significantly higher Pi levels in POC during hypoxia scan in the healthy subjects (p=0.036).

Association between brain tissue composition on Pi levels in the PFC, ACC, POC, and whole brain analyses

Our analyses have revealed larger whole brain cerebrospinal fluid (CSF) volume in the patient group (p=0.05). Gray and white matter percentages in the PFC, ACC, POC, and whole brain did not differ between the study groups. A tendency towards increased CSF volume in the PFC of subjects with SCZ was noted, although it did not attain statistical significance (p=0.055) (Supplementary Material). Furthermore, no statistically significant correlation was observed between CSF levels in the whole brain and Pi levels across various brain regions in the SCZ group (p>0.05 for each analysis).

pH levels

The pH levels exhibited a statistically significant reduction in the PFC of individuals with SCZ during hypoxic conditions compared to HCs (7.053±0.013 vs. 7.060±0.014, p=0.0474). Conversely, no statistically significant distinctions were observed in pH levels across other brain regions during either hypoxia or hyperoxia.

DISCUSSION

To our knowledge, this is the first study which has underscored disruption in the compensatory dynamic response in the brain energy metabolism during hypoxic stress in the SCZ subjects. This deficit seems to exist across the brain as evidenced by significantly lower levels of Pi in the PFC, ACC, and POC of SCZ subjects relative to HCs during the hypoxia challenge (higher metabolic demand). However, it appears to be more prominent in the PFC and ACC, which are critical brain regions for the top-down control (brain’s ability to use higher-level cognitive functions to guide and modulate lower-level sensory and motor processes), and have been reported to be dysfunctional in subjects with SCZ [45]. PCr/Pi ratio, an indicator of mitochondrial function [46-48], was also quantified significantly higher in the PFC and ACC regions in the SCZ group during hypoxia. It should be noted that a reduction in PCr, an increase in Pi, and consequently, a decrease in the PCr/Pi ratio during a state of heightened energy demand compared to baseline levels represents the anticipated physiological response. Therefore, a higher PCr/Pi ratio in SCZ subjects underscores the disruption in the compensatory response of energy production pathways.

The dynamic chemical interplay between HEP molecules during the high energy demanding states is catalyzed by the CK enzyme (PCr+ADP←CK enzyme→Cr+ATP). The CK enzyme was reported to be coupled to brain activity in the human brain [48,49]. A significant increase in the CK forward rate without any depletion in PCr and β-ATP or elevation in the Pi levels during visual stimulation paradigms and mild hypoxia challenge were reported in HCs [50-53]. However, the recent 31P-MRS studies have revealed a substantial reduction in the CK forward rate (22%) in vivo in subjects with SCZ and first-episode psychosis [16,17,54]. A decrease in the brain isoform of CK enzyme in SCZ has also been reported in postmortem studies [19,20]. Therefore, our findings might be related to the reduced CK forward rate in our study. Notably, Pi levels at baseline were not found statistically different in the several previous 31P-MRS studies [16,17,55]. We also did not identify a difference in the Pi levels between the groups during hyperoxia condition [23,28,56-60].

Our findings showed no significant differences observed in the baseline levels of PCr and ATP between the study groups which aligns with previous literature [16,25,28,29,59,61]. However, other authors such as Jensen et al. [62] found elevated levels of PCr, ATP, and Pi levels in the ACCs but not in the PFCs of subjects with first episode psychosis. Plausible explanations for these differences may relate to disease state (most of the subjects were in their first psychotic episode), and approximately 83% were antipsychotic-naïve. Additionally, hypoactive ACC in SCZ subjects have been reported, which might explain elevated baseline HEPs in the ACCs due to decreased utilization of HEPs for neural activity in this region [63-66].

We should acknowledge several limitations while interpreting these findings. Firstly, the impact of antipsychotics and mood stabilizers on mitochondrial function and energy metabolism remains unclear. For example, studies investigating HCs undergoing a 15-day olanzapine treatment reported no significant changes in HEPs [67]. Similarly, Nenadic et al. [68] observed no alterations in HEPs in the prefrontal cortices (PFCs) of SCZ subjects—comprising both drug-naïve individuals and those off antipsychotics—following a 6-week olanzapine regimen. Conversely, a six-week risperidone treatment was shown to elevate levels of PCr and ATP in the left PFCs of SCZ subjects, interpreted as a restoration of metabolic activity [69]. In our study, all participants diagnosed with SCZ or SZA continued their antipsychotic regimens, with three schizoaffective subjects additionally taking mood stabilizers (one on valproic acid and two on gabapentin). Future investigations involving antipsychotic and mood stabilizer-naïve subjects are warranted to elucidate the potential effects of psychotropic medications on dynamic changes in bioenergetics. Second, tissue segmentation was not conducted in our study, reflecting a gap in the available literature. Past proton magnetic resonance spectroscopy investigations have indicated increased ATP levels in white matter and decreased ATP levels in the fronto-temporal-striatal region among individuals experiencing first-episode psychosis, with no observed alterations in HEPs in the frontal region [70]. Third, absolute concentrations of phosphate metabolites were not quantified in our study, with relative percentages of these metabolites to TP levels being analyzed instead. It is worth noting that MRI scanners with higher magnetic fields offer enhanced accuracy in results. However, our study is the first one that assessed the dynamic change in cerebral bioenergetics.

These findings reflect a deficit in bioenergetics systems in SCZ subjects which can become quantifiable at higher energy demanding states. Although this perturbation seems to exist across the brain, it is markedly noted in the PFC and ACC regions. Studies including drug naive first-episode psychosis and chronic SCZ subjects might provide some data as to investigate if medication status and duration of illness have significant effects on bioenergetics.

Further studies assessing the dynamic changes in CK forward rate during hypoxia scan are warranted.

Supplementary Materials

The Supplement is available with this article at https://doi.org/10.30773/pi.2024.0150.

Supplementary Matarial

The individual levels of ATP and PCr levels in study groups during hypoxia

pi-2024-0150-Supplementary-Matarial-1.pdf

Notes

Availability of Data and Material

The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: M Burak Baytunca, Deborah Yurgelun-Todd, Perry Renshaw, Douglas Kondo. Data curation: Danielle Boxer, Douglas Kondo. Formal analysis: M Burak Baytunca, Xian-Feng Shi. Funding acquisition: M Burak Baytunca, Perry Renshaw, Douglas Kondo, Dost Ongur. Investigation: M Burak Baytunca, Xian-Feng Shi, Danielle Boxer, Colleen Fitzgerald, Deborah Yurgelun-Todd, Perry Renshaw, Douglas Kondo. Supervision: Deborah Yurgelun-Todd, Perry Renshaw, Douglas Kondo, Dost Ongur. Writing—original draft: M Burak Baytunca, Nicolas A Nuñez. Writing—review & editing: all authors.

Funding Statement

Dr Baytunca received a pilot research fund from the Department of Psychiatry of University of Utah for this research project. This work was also partially supported by VA grant 2-I01-CX000812 to Dr. Renshaw, and VA grant 1-I01-CX001611 to Dr. Kondo, and by the U.S. Department of Veterans Affairs VISN 19 Rocky Mountain MIRECC for Veteran Suicide Prevention. Dr. Ongur received support from P50 MH115846.

ACKNOWLEDGEMENTS

None

Figure 1.

Illustrations of the ROI in the present study (prefrontal cortex, anterior cingulate cortex, posterior cortex, and whole brain voxels, respectively).

Figure 2.

Comparison of neurometabolites between the study groups during hypoxia scan. PFC, prefrontal cortex; ACC, anterior cingulate cortex; POC, posterior cortex; WB, whole brain; HC, healthy control; PCr, phosphocreatine; Tp, total phosphate; ATP, adenosine triphosphate; Pi, inorganic phosphate.

Figure 3.

Pi/Tp levels in the PFC, ACC, and POC between the groups during hypoxia. PFC, prefrontal cortex; ACC, anterior cingulate cortex; POC, posterior cortex; HC, healthy control; Tp, total phosphate; Pi, inorganic phosphate.

Table 1.

Sociodemographic information and clinical variables of the study groups during hypoxia and hyperoxia

	Schizophrenia group (N=12)	Healthy controls (N=12)	p
Age (yr)	36.00±8.33	33.00±6.07	0.32
Male/female ratio	9/3	8/4	>0.999
Duration of illness (yr)	11.95±10.52	NA	NA
CPZ equivalent doses (mg)	472.22±156.34	NA	NA
SAPS	21.00±2.79	NA	NA
SANS	21.80±6.36	NA	NA
YMRS	1.66±1.30	NA	NA
MADRS	12.30±6.80	NA	NA
CGI-S	0.75±0.96	NA	NA
SpO₂ levels (hypoxia scan)	86.52	87.24	0.13
Respiration rates (hypoxia scan)	24.88	25.17	0.92
SpO₂ levels (hyperoxia scan)	98.70	98.07	0.26
Respiration rates (hyperoxia scan)	25.09	23.92	0.66

Values are presented as mean±standard deviation unless otherwise indicated. CPZ, chlorpromazine; SAPS, Scale for the Assessment of Positive Symptoms; SANS, Scale for the Assessment of Negative Symptoms; YMRS, Young Mania Rating Scale; MADRS, Montgomery-Asberg Depression Rating Scale; CGI-S, Clinical Global Impressions-Severity scale; NA, not available

Table 2.

Comparison of neuro-metabolite ratios in study groups in hyperoxia condition

	PCr/Tp	β-ATP/Tp	Pi/Tp	PCr/β-ATP	PCr/Pi
Schizophrenia (PFC)	0.17±0.005	0.10±0.012	0.053±0.08	1.68±0.23	3.30±0.51
HC (PFC)	0.17±0.006	0.10±0.101	0.057±0.09	1.62±0.19	3.08±0.51
Schizophrenia (ACC)	0.17±0.01	0.10±0.013	0.056±0.01	1.69±0.28	3.49±1.31
HC (ACC)	0.17±0.01	0.10±0.017	0.060±0.01	1.70±0.31	3.04±0.66
Schizophrenia (POC)	0.17±0.007	0.11±0.010	0.058±0.005	1.57±0.18	3.11±0.34
HC (POC)	0.17±0.006	0.11±0.006	0.058±0.004	1.55±0.12	3.07±0.31
Schizophrenia (WB)	0.18±0.007	0.11±0.006	0.055±0.003	1.67±0.12	3.32±0.23
HC (WB)	0.18±0.006	0.11±0.003	0.057±0.003	1.60±0.08	3.15±0.17

Values are presented as mean±standard deviation. PFC, prefrontal cortex; ACC, anterior cingulate cortex; POC, posterior cortex; WB, whole brain; HC, healthy control; PCr, phosphocreatine; Tp, total phosphate; ATP, adenosine triphosphate; Pi, inorganic phosphate

REFERENCES

1. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (5th ed). Arlington: American Psychiatric Association; 2013.

2. Sekiguchi H, Pavey G, Dean B. Altered levels of dopamine transporter in the frontal pole and dorsal striatum in schizophrenia. NPJ Schizophr 2019;5:20

3. Howes O, McCutcheon R, Stone J. Glutamate and dopamine in schizophrenia: an update for the 21st century. J Psychopharmacol 2015;29:97-115.

4. Carlsson A, Lindqvist M, Magnusson T. 3,4-dihydroxyphenylalanine and 5-hydroxytryptophan as reserpine antagonists. Nature 1957;180:1200

5. Sullivan CR, O’Donovan SM, McCullumsmith RE, Ramsey A. Defects in bioenergetic coupling in schizophrenia. Biol Psychiatry 2018;83:739-750.

6. Henkel ND, Wu X, O’Donovan SM, Devine EA, Jiron JM, Rowland LM, et al. Schizophrenia: a disorder of broken brain bioenergetics. Mol Psychiatry 2022;27:2393-2404.

7. Iwamoto K, Bundo M, Kato T. Altered expression of mitochondria-related genes in postmortem brains of patients with bipolar disorder or schizophrenia, as revealed by large-scale DNA microarray analysis. Hum Mol Genet 2005;14:241-253.

8. Ben-Shachar D, Karry R. Neuroanatomical pattern of mitochondrial complex I pathology varies between schizophrenia, bipolar disorder and major depression. PLoS One 2008;3:e3676.

9. Gonçalves VF, Cappi C, Hagen CM, Sequeira A, Vawter MP, Derkach A, et al. A comprehensive analysis of nuclear-encoded mitochondrial genes in schizophrenia. Biol Psychiatry 2018;83:780-789.

10. Cavelier L, Jazin EE, Eriksson I, Prince J, Båve U, Oreland L, et al. Decreased cytochrome-c oxidase activity and lack of age-related accumulation of mitochondrial DNA deletions in the brains of schizophrenics. Genomics 1995;29:217-224.

11. Roberts RC. Postmortem studies on mitochondria in schizophrenia. Schizophr Res 2017;187:17-25.

12. Maurer I, Zierz S, Möller H. Evidence for a mitochondrial oxidative phosphorylation defect in brains from patients with schizophrenia. Schizophr Res 2001;48:125-136.

13. Flippo KH, Strack S. An emerging role for mitochondrial dynamics in schizophrenia. Schizophr Res 2017;187:26-32.

14. Pruett BS, Meador-Woodruff JH. Evidence for altered energy metabolism, increased lactate, and decreased pH in schizophrenia brain: a focused review and meta-analysis of human postmortem and magnetic resonance spectroscopy studies. Schizophr Res 2020;223:29-42.

15. Rowland LM, Pradhan S, Korenic S, Wijtenburg SA, Hong LE, Edden RA, et al. Elevated brain lactate in schizophrenia: a 7 T magnetic resonance spectroscopy study. Transl Psychiatry 2016;6:e967.

16. Du F, Cooper AJ, Thida T, Sehovic S, Lukas SE, Cohen BM, et al. In vivo evidence for cerebral bioenergetic abnormalities in schizophrenia measured using 31P magnetization transfer spectroscopy. JAMA Psychiatry 2014;71:19-27.

17. Yuksel C, Chen X, Chouinard VA, Nickerson LD, Gardner M, Cohen T, et al. Abnormal brain bioenergetics in first-episode psychosis. Schizophr Bull Open 2021;2:sgaa073

18. MacDonald ML, Naydenov A, Chu M, Matzilevich D, Konradi C. Decrease in creatine kinase messenger RNA expression in the hippocampus and dorsolateral prefrontal cortex in bipolar disorder. Bipolar Disord 2006;8:255-264.

19. Burbaeva GSh, Savushkina OK, Boksha IS. Creatine kinase BB in brain in schizophrenia. World J Biol Psychiatry 2003;4:177-183.

20. Klushnik TP, Spunde AYa, Yakovlev AG, Khuchua ZA, Saks VA, Vartanyan ME. Intracellular alterations of the creatine kinase isoforms in brains of schizophrenic patients. Mol Chem Neuropathol 1991;15:271-280.

21. Kim SY, Cohen BM, Chen X, Lukas SE, Shinn AK, Yuksel AC, et al. Redox dysregulation in schizophrenia revealed by in vivo NAD+/NADH measurement. Schizophr Bull 2017;43:197-204.

22. Chouinard VA, Kim SY, Valeri L, Yuksel C, Ryan KP, Chouinard G, et al. Brain bioenergetics and redox state measured by 31P magnetic resonance spectroscopy in unaffected siblings of patients with psychotic disorders. Schizophr Res 2017;187:11-16.

23. Volz HR, Riehemann S, Maurer I, Smesny S, Sommer M, Rzanny R, et al. Reduced phosphodiesters and high-energy phosphates in the frontal lobe of schizophrenic patients: a 31P chemical shift spectroscopic-imaging study. Biol Psychiatry 2000;47:954-961.

24. Smesny S, Rosburg T, Nenadic I, Fenk KP, Kunstmann S, Rzanny R, et al. Metabolic mapping using 2D 31P-MR spectroscopy reveals frontal and thalamic metabolic abnormalities in schizophrenia. Neuroimage 2007;35:729-737.

25. Volz HP, Rzanny R, Rössger G, Hübner G, Kreitschmann-Andermahr I, Kaiser WA, et al. 31Phosphorus magnetic resonance spectroscopy of the dorsolateral prefrontal region in schizophrenics--a study including 50 patients and 36 controls. Biol Psychiatry 1998;44:399-404.

26. Deicken RF, Calabrese G, Merrin EL, Meyerhoff DJ, Dillon WP, Weiner MW, et al. 31phosphorus magnetic resonance spectroscopy of the frontal and parietal lobes in chronic schizophrenia. Biol Psychiatry 1994;36:503-510.

27. Potwarka JJ, Drost DJ, Williamson PC, Carr T, Canaran G, Rylett WJ, et al. A 1H-decoupled 31P chemical shift imaging study of medicated schizophrenic patients and healthy controls. Biol Psychiatry 1999;45:687-693.

28. Yacubian J, de Castro CC, Ometto M, Barbosa E, de Camargo CP, Tavares H, et al. 31P-spectroscopy of frontal lobe in schizophrenia: alterations in phospholipid and high-energy phosphate metabolism. Schizophr Res 2002;58:117-122.

29. Pettegrew JW, Keshavan MS, Panchalingam K, Strychor S, Kaplan DB, Tretta MG, et al. Alterations in brain high-energy phosphate and membrane phospholipid metabolism in first-episode, drug-naive schizophrenics: a pilot study of the dorsal prefrontal cortex by in vivo phosphorus 31 nuclear magnetic resonance spectroscopy. Arch Gen Psychiatry 1991;48:563-568.

30. Jensen JE, Miller J, Williamson PC, Neufeld RW, Menon RS, Malla A, et al. Focal changes in brain energy and phospholipid metabolism in first-episode schizophrenia: 31P-MRS chemical shift imaging study at 4 tesla. Br J Psychiatry 2004;184:409-415.

31. Shirayama Y, Yano T, Takahashi K, Takahashi S, Ogino T. In vivo 31P NMR spectroscopy shows an increase in glycerophosphorylcholine concentration without alterations in mitochondrial function in the prefrontal cortex of medicated schizophrenic patients at rest. Eur J Neurosci 2004;20:749-756.

32. Volz HP, Rzanny R, Rössger G, Hübner G, Kreitschmann-Andermahr I, Kaiser WA, et al. Decreased energy demanding processes in the frontal lobes of schizophrenics due to neuroleptics? A 31P-magneto-resonance spectroscopic study. Psychiatry Res 1997;76:123-129.

33. Gangadhar BN, Jayakumar PN, Subbakrishna DK, Janakiramaiah N, Keshavan MS. Basal ganglia high-energy phosphate metabolism in neuroleptic-naive patients with schizophrenia: a 31-phosphorus magnetic resonance spectroscopic study. Am J Psychiatry 2004;161:1304-1306.

34. Jayakumar PN, Venkatasubramanian G, Keshavan MS, Srinivas JS, Gangadhar BN. MRI volumetric and 31P MRS metabolic correlates of caudate nucleus in antipsychotic-naïve schizophrenia. Acta Psychiatr Scand 2006;114:346-351.

35. Jayakumar PN, Gangadhar BN, Venkatasubramanian G, Desai S, Velayudhan L, Subbakrishna D, et al. High energy phosphate abnormalities normalize after antipsychotic treatment in schizophrenia: a longitudinal 31P MRS study of basal ganglia. Psychiatry Res 2010;181:237-240.

36. Rae C, Bartlett DJ, Yang Q, Walton D, Denotti A, Sachinwalla T, et al. Dynamic changes in brain bioenergetics during obstructive sleep apnea. J Cereb Blood Flow Metab 2009;29:1421-1428.

37. Meyerspeer M, Boesch C, Cameron D, Dezortová M, Forbes SC, Heerschap A, et al. 31P magnetic resonance spectroscopy in skeletal muscle: experts’ consensus recommendations. NMR Biomed 2020;34:e4246

38. Structured Clinical Interview for the DSM (SCID) [Internet]. Available at: https://doi.org/10.1002/9781118625392.wbecp351. Accessed March 7, 2023.

39. Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull 1987;13:261-276.

40. Davidson J, Turnbull CD, Strickland R, Miller R, Graves K. The Montgomery-Åsberg depression scale: reliability and validity. Acta Psychiatr Scand 1986;73:544-548.

41. Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry 1978;133:429-435.

42. Leucht S, Samara M, Heres S, Patel MX, Woods SW, Davis JM. Dose equivalents for second-generation antipsychotics: the minimum effective dose method. Schizophr Bull 2014;40:314-326.

43. Naressi A, Couturier C, Devos JM, Janssen M, Mangeat C, de Beer R, et al. Java-based graphical user interface for the MRUI quantitation package. MAGMA 2001;12:141-152.

44. MathWorks. MATLAB version: 9.13.0 (R2022b) [Internet]. Available at: https://kr.mathworks.com/support/requirements/previous-releases.html. Accessed March 7, 2023.

45. Sakurai T, Gamo NJ, Hikida T, Kim SH, Murai T, Tomoda T, et al. Converging models of schizophrenia--network alterations of prefrontal cortex underlying cognitive impairments. Prog Neurobiol 2015;134:178-201.

46. Rietzler A, Steiger R, Mangesius S, Walchhofer LM, Gothe RM, Schocke M, et al. Energy metabolism measured by 31P magnetic resonance spectroscopy in the healthy human brain. J Neuroradiol 2022;49:370-379.

47. Chance B, Eleff S, Leigh JS Jr, Sokolow D, Sapega A. Mitochondrial regulation of phosphocreatine/inorganic phosphate ratios in exercising human muscle: a gated 31P NMR study. Proc Natl Acad Sci U S A 1981;78:6714-6718.

48. Chen C, Stephenson MC, Peters A, Morris PG, Francis ST, Gowland PA. 31P magnetization transfer magnetic resonance spectroscopy: assessing the activation induced change in cerebral ATP metabolic rates at 3 T. Magn Reson Med 2018;79:22-30.

49. Du F, Zhu XH, Zhang Y, Friedman M, Zhang N, Ugurbil K, et al. Tightly coupled brain activity and cerebral ATP metabolic rate. Proc Natl Acad Sci U S A 2008;105:6409-6414.

50. Vidyasagar R, Kauppinen RA. 31P magnetic resonance spectroscopy study of the human visual cortex during stimulation in mild hypoxic hypoxia. Exp Brain Res 2008;187:229-235.

51. Barreto FR, Costa TB, Landim RC, Castellano G, Salmon CE. 31P-MRS using visual stimulation protocols with different durations in healthy young adult subjects. Neurochem Res 2014;39:2343-2350.

52. van de Bank BL, Maas MC, Bains LJ, Heerschap A, Scheenen TWJ. Is visual activation associated with changes in cerebral high-energy phosphate levels? Brain Struct Funct 2018;223:2721-2731.

53. Chen W, Zhu XH, Adriany G, Ugurbil K. Increase of creatine kinase activity in the visual cortex of human brain during visual stimulation: a 31P magnetization transfer study. Magn Reson Med 1997;38:551-557.

54. Song X, Chen X, Yuksel C, Yuan J, Pizzagalli DA, Forester B, et al. Bioenergetics and abnormal functional connectivity in psychotic disorders. Mol Psychiatry 2021;26:2483-2492.

55. Du F, Cooper AJ, Thida T, Sehovic S, Lukas SE, Cohen BM, et al. In vivo evidence for cerebral bioenergetic abnormalities in schizophrenia measured using 31P magnetization transfer spectroscopy. JAMA Psychiatry 2014;71:19-27.

56. Deicken RF, Calabrese G, Merrin EL, Fein G, Weiner MW. Basal ganglia phosphorous metabolism in chronic schizophrenia. Am J Psychiatry 1995;152:126-129.

57. Shioiri T, Kato T, Inubushi T, Murashita J, Takahashi S. Correlations of phosphomonoesters measured by phosphorus-31 magnetic resonance spectroscopy in the frontal lobes and negative symptoms in schizophrenia. Psychiatry Res 1994;55:223-235.

58. Kato T, Shioiri T, Murashita J, Hamakawa H, Inubushi T, Takahashi S. Lateralized abnormality of high-energy phosphate and bilateral reduction of phosphomonoester measured by phosphorus-31 magnetic resonance spectroscopy of the frontal lobes in schizophrenia. Psychiatry Res 1995;61:151-160.

59. Nenadic I, Langbein K, Weisbrod M, Maitra R, Rzanny R, Gussew A, et al. 31P-MR spectroscopy in monozygotic twins discordant for schizophrenia or schizoaffective disorder. Schizophr Res 2012;134:296-297.

60. Puri BK, Counsell SJ, Hamilton G, Bustos MG, Horrobin DF, Richardson AJ, et al. Cerebral metabolism in male patients with schizophrenia who have seriously and dangerously violently offended: a 31P magnetic resonance spectroscopy study. Prostaglandins Leukot Essent Fatty Acids 2004;70:409-411.

61. Stanley JA, Williamson PC, Drost DJ, Carr TJ, Rylett RJ, Malla A, et al. An in vivo study of the prefrontal cortex of schizophrenic patients at different stages of illness via phosphorus magnetic resonance spectroscopy. Arch Gen Psychiatry 1995;52:399-406.

62. Jensen JE, Miller J, Williamson PC, Neufeld RW, Menon RS, Malla A, et al. Focal changes in brain energy and phospholipid metabolism in first-episode schizophrenia: 31P-MRS chemical shift imaging study at 4 tesla. Br J Psychiatry 2004;184:409-415.

63. Gradin VB, Waiter G, O’Connor A, Romaniuk L, Stickle C, Matthews K, et al. Salience network-midbrain dysconnectivity and blunted reward signals in schizophrenia. Psychiatry Res 2013;211:104-111.

64. Nelson BD, Bjorkquist OA, Olsen EK, Herbener ES. Schizophrenia symptom and functional correlates of anterior cingulate cortex activation to emotion stimuli: an fMRI investigation. Psychiatry Res 2015;234:285-291.

65. Ioakeimidis V, Haenschel C, Yarrow K, Kyriakopoulos M, Dima D. A meta-analysis of structural and functional brain abnormalities in early-onset schizophrenia. Schizophr Bull Open 2020;1:sgaa016

66. Fusar-Poli P. Voxel-wise meta-analysis of fMRI studies in patients at clinical high risk for psychosis. J Psychiatry Neurosci 2012;37:106-112.

67. Chouinard VA, Chen X, Gardner ME, Valeri L, Kuan E, Cohen BM, et al. Effects of olanzapine on brain energy metabolism measured by 31P-magnetic resonance spectroscopy in healthy individuals without history of psychiatric disorders. Biol Psychiatry 2020;87:S167

68. Nenadic I, Dietzek M, Langbein K, Rzanny R, Gussew A, Reichenbach JR, et al. Effects of olanzapine on 31P MRS metabolic markers in schizophrenia. Hum Psychopharmacol 2013;28:91-93.

69. Smesny S, Langbein K, Rzanny R, Gussew A, Burmeister HP, Reichenbach JR, et al. Antipsychotic drug effects on left prefrontal phospholipid metabolism: a follow-up 31P-2D-CSI study of haloperidol and risperidone in acutely ill chronic schizophrenia patients. Schizophr Res 2012;138:164-170.

70. Jensen JE, Miller J, Williamson PC, Neufeld RW, Menon RS, Malla A, et al. Grey and white matter differences in brain energy metabolism in first episode schizophrenia: 31P-MRS chemical shift imaging at 4 Tesla. Psychiatry Res 2006;146:127-135.