Broadband Near-Infrared Spectroscopy in Vivo Study of Brain Mitochondrial Oxidative Metabolism and Hemodynamics in Bipolar Disorder

Broadband Near-Infrared Spectroscopy in Vivo Study of Brain Mitochondrial Oxidative Metabolism and Hemodynamics in Bipolar Disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Broadband Near-Infrared Spectroscopy in Vivo Study of Brain Mitochondrial Oxidative Metabolism and Hemodynamics in Bipolar Disorder Kutlu Kaya, Rebecca Marks, Frédéric Lange, Paola Pinti, Brian Pittman, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7915512/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 19 You are reading this latest preprint version Abstract Mitochondrial oxidative dysfunction is increasingly implicated in the neuropathophysiology of bipolar disorder (BD), yet in vivo brain assessments of cerebral oxidative metabolism and its relationship with oxygenation have been limited. Here, brain broadband near-infrared spectroscopy (bNIRS) non-invasively measured oxidation of the key enzyme of the mitochondrial electron transport chain, mitochondrial complex IV cytochrome-c-oxidase (oxCCO), alongside oxyhemoglobin (HBO) and deoxyhemoglobin (HBR) measures, in vivo in adults with BD and healthy comparison (HC) during visual stimulation. The relationship between oxCCO and the hemodynamic measures was assessed. During visual stimulation, participants with BD showed significantly higher elevations in oxCCO and significantly lower relative power (concurrence between oxCCO and oxygen use) compared to HC participants, a pattern also observed in euthymic BD participants, suggesting a trait difference in BD. The BD participants also had significantly higher levels of peripheral blood lactate, even when oxCCO levels were high, unlike the association observed for HC participants, which would be expected if oxidative phosphorylation was providing energy to meet neural demands. Together, these findings suggest that to meet neural energy demands, mitochondria in BD exhibit oxygen consumption that is not efficiently coupled to ATP production, and instead shift toward an energetically inefficient process, aerobic glycolysis. By enabling the simultaneous assessment of mitochondrial metabolism and hemodynamics, bNIRS provided a new, scalable, non-invasive tool to uncover mechanisms of neuroenergetic dysfunction in BD. This approach may facilitate the identification of novel mechanistic targets and advance biomarker development for more personalized interventions in mood disorders and potentially other neuropsychiatric disorders. Health sciences/Diseases/Psychiatric disorders/Bipolar disorder Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Bioenergetic abnormalities have been increasingly recognized as core mechanisms contributing to the pathophysiology of bipolar disorder (BD) [ 1 , 2 ]. Clinically, altered energy and activity levels are early and pervasive signs of acute BD episodes [ 3 ], suggesting that they may reflect key pathophysiological mechanisms of the disorder. Uncovering specific contributing bioenergetic mechanisms could be essential for elucidating the pathophysiology of BD, advancing early detection strategies, and developing novel, bioenergetic mechanism-based therapeutics. Converging evidence from imaging [ 4 , 5 ] and postmortem molecular [ 6 – 12 ] studies implicate brain mitochondrial metabolic abnormalities in the bioenergetic disturbances observed in BD. Mitochondrial abnormalities in the brain of persons with BD, which may also occur systemically, are further supported by findings of elevated lactate, a byproduct of anaerobic and aerobic glycolysis that reflects a reduction in the efficiency of mitochondrial energy production, in serum in adolescents and adults, and in cerebrospinal fluid in adults [ 13 – 15 ]. In vivo magnetic resonance imaging (MRS) studies in BD have shown elevated lactate levels and reduced phosphocreatine (a critical energy reservoir) in the prefrontal cortex (PFC) and occipital cortex, consistent with mitochondrial dysfunction [ 16 , 17 ]. Postmortem studies of individuals with BD further implicate mitochondrial dysfunction, revealing altered oxidative stress markers, neuroinflammatory activation, and dysregulated transcription of mitochondrial genes [ 10 , 11 , 18 , 19 ]. Mitochondrial complex IV, also known as cytochrome-c-oxidase (CCO), is the terminal enzyme of the mitochondrial electron transport chain (ETC). The oxidation of CCO (oxCCO) is essential for cellular respiration, as it drives the ETC to reduce molecular oxygen to water. This reaction helps establish the proton gradient across the inner mitochondrial membrane, which can ultimately drive ATP synthesis by ATP synthase. Transcriptional alterations have been identified in complex IV in BD [ 20 ], and its dysfunction was shown to disrupt ETC, leading to reduced ATP production and potentially cell damage [ 21 , 22 ]. Despite its importance in mitochondrial bioenergetics, oxCCO has received little study in BD, which may be at least in part attributed to the limited prior methods to examine brain oxCCO in vivo . With longstanding evidence supporting altered brain hemodynamic responses in BD [ 23 ], elucidating the relationship between mitochondrial mechanisms such as oxCCO and cerebral hemodynamics could be critical to understanding the pathophysiology underlying the brain dysfunction of BD. Advances in non-invasive optical neuroimaging methods now allow for the simultaneous assessment of mitochondrial oxidative metabolism and cerebral hemodynamics. Broadband near-infrared spectroscopy (bNIRS), an extension of the commercially available functional NIRS, enables the concurrent in vivo measurement of brain oxCCO alongside oxyhemoglobin (HBO) and deoxyhemoglobin (HBR), providing a window into real-time mitochondrial function in relation to functional brain hemodynamic responses [ 24 , 25 ]. Moreover, bNIRS allows the derivation of metrics, such as relative power (rPWR) and relative cost (rCST), to indicate distinct bioenergetic signatures by integrating mitochondrial function and cerebral hemodynamics-oxygenation [ 26 , 27 ]. In prior multimodal neuroimaging research, fluorodeoxyglucose-positron emission tomography (FDG-PET) and functional magnetic resonance imaging (fMRI) have been used to derive metabolic-hemodynamic correspondence metrics of rPWR and rCST [ 28 ]. These indices extend beyond basic measures to quantify the efficiency of mitochondrial engagement relative to hemodynamic supply, revealing potential mismatches in energy utilization and disruptions in neuroenergetic regulation. Specifically, rPWR captures concurrent intensity of oxygenation and metabolic activity, reflecting the degree to which oxygen use and energy production are coupled during neural activation; rCST captures the deviation between oxygenation and metabolism, reflecting the extent to which mitochondrial energy production exceeds or falls behind oxygen use relative to neural activity. Advantages of bNIRS over PET and MRI are portability, less sensitivity to movement, and capacity for deployment in naturalistic settings, supporting bNIRS as a robust and scalable platform for investigating mitochondrial dysfunction in BD. Prior studies using multi-wavelength NIRS or dual-channel bNIRS studies provided initial evidence supporting altered oxCCO in, and in combination with photobiomodulation as a possible therapeutic for BD [ 29 – 31 ], and therefore the potential for bNIRS to provide key information for understanding and treating BD. Here, multichannel bNIRS was employed for in vivo assessment of brain oxCCO and hemodynamics in a group of individuals with BD and a group of healthy comparison (HC) individuals while performing a visual hemifield checkerboard task, known to robustly activate the visual cortex and increase local energy demand. We hypothesized that individuals with BD would exhibit evidence of altered mitochondrial metabolic responses and impaired neuroenergetic efficiency, as reflected in altered levels of oxCCO, rPWR, and/or rCST. METHODS Participants Participants were comprised of 10 adults with BD (demographic and clinical characteristics are summarized in Table 1 ) who met criteria for BD and 10 HC adults who were without personal or first-degree relatives with a history of a major psychiatric disorder (age range 21–63 years, mean age 46.7 ± 15.1 years; 4 females; body mass index (BMI) 24.8 ± 3.7). The presence or absence of psychiatric disorders, and for BD participants, rapid cycling, history of psychosis, number of hospitalizations, and mood state, were confirmed with the Structured Clinical Interview for DSM-5 Diagnosis – Research Version [ 32 ]. Family history was obtained using the Family History Screen [ 33 ]. History of suicide attempts was determined using the Columbia Suicide History Form [ 34 ]. Mood symptoms were evaluated using the Hamilton Depression Scale 29-item version (HDRS-29) [ 35 ] and Young Mania Rating Scale (YMRS) [ 36 ]. Demographic and clinical characteristics of the BD sample are outlined in Table 1 . All participants were without major unstable medical or neurological conditions that could affect the brain. Two participants with BD had hypothyroidism treated with levothyroxine. The BD participants did not meet criteria for moderate or severe alcohol and/or substance use disorder, except for caffeine, nicotine, and cannabis, within 12 months of the study. Blood samples were collected from individuals with BD ( n = 8) and HC individuals ( n = 6), and serum lactate was measured. Participants provided written informed consent in accordance with the Yale School of Medicine Human Investigation Committee/Institutional Review Board. Table 1 Demographic and Clinical Characteristics of the Sample with Bipolar Disorder (BD). Individuals with BD ( n = 10) Mean Age in years ± standard deviation (SD), range 39.6 ± 14.1 25–62 Females, n (%) 8 (80) Mean Body Mass Index ± (SD) 26.6 ± 4.8 History of Rapid Cycling, n (%) 5 (50) History of Psychosis, n (%) 5 (50) Current Psychosis, n (%) 1 (10) History of Past Suicide Attempt, n (%) 5 (50) Mean Number of Hospitalizations ± SD 2.4 ± 1.6 Mood State at Scan Euthymic, n (%) 7 (70) Elevated, n (%) 2 (20) Depressed, n (%) 1 (10) Psychotropic Medications Anticolvulsants Antidepressants Lithium Carbonate, n (%) 6 (60) 4 (40) 3 (30) Second Generation Antipsychotics, n (%) 3 (30) Benzodiazepines, n (%) 3 (30) Stimulants, n (%) 2 (20) Medical Cannabis, n (%) 1 (10) Current Psychiatric Comorbidities Attention Deficit Hyperactivity Disorder, n (%) 3 (30) Generalized Anxiety Disorder, n (%) 3 (30) Mild Cannabis Use Disorder, n (%) 2 (20) Social Anxiety Disorder, n (%) 2 (20) Premenstrual Dysphoric Disorder, n (%) 2 (20) Post-traumatic stress disorder, n (%) 1 (10) bNIRS The bNIRS system used was developed in-house by I.T. at University College London, UK, and is described in detail here [ 27 , 37 ]. Briefly, it is a multichannel instrument, equipped with two halogen bulbs that emit light in the NIR range (504–1068 nm) and two spectrometers, which are customized lens spectrographs and front-illuminated CCD cameras (PIXIS512f, Princeton Instruments) [ 37 ]. Light is directed onto the scalp through four fiber-optic bundles (sources), and the back-scattered light is collected by the spectrographs through ten fiber-optic bundles (detectors), capable of 16 measurement channels with a source-detector separation of 3 cm. The bNIRS fiber bundles were secured with an optode holder cap to follow the head's curvature to maximize optical coupling. The distance from the Nasion to the Inion was measured through 10–20 anatomical landmarks along the midline to position the cap reliably across all participants. The cap was then positioned bilaterally over the visual cortex, corresponding with the Oz landmark. After cap placement and raw signal quality check, changes in brain hemodynamics and metabolism of participants were monitored at a 0.35 Hz sampling rate using 120 wavelengths from 780 to 900 nm to improve the estimation of hemodynamic and metabolic signals. The experiment was conducted in a dark room to minimize interference from ambient light. BNIRS data were acquired during visual stimulation. All participants had normal or corrected-to-normal vision and were seated comfortably. The monitor was positioned so that a central cross-hair was in the center of the participant’s view; its height was adjusted for each participant, and the distance was fixed at 60 cm. The visual stimulation paradigm was designed using Psychtoolbox (RRID: SCR_002881) in MATLAB (version R2024a, MathWorks Inc., USA; RRID: SCR_001622) and consisted of left and right hemifield stimuli composed of reversing black and white checkerboards (2 check size; reversal rate of 15 Hz) to activate visual system pathways. All participants were instructed to maintain their gaze on a white fixation cross displayed at the center of a grey screen. The visual stimulation task was structured as a block design, with 10 blocks per condition (alternating Right or Left), each lasting 20 sec, spaced out by rest periods ranging randomly from 15 to 20 sec. Left and right stimuli were alternated throughout the blocks for a total duration of 13 min. bNIRS Data Processing The bNIRS data processing and analysis pipeline, adopted from as described in methods paper in Pinti et al. [ 27 ], used an in-house developed UCLn algorithm based on the modified Beer-Lambert Law [ 38 ]. Briefly, the raw intensity data from 120 wavelengths (780–900 nm) were converted into changes in optical density and then converted using the UCLn algorithm and the specific extinction coefficients (publicly available at https://github.com/multimodalspectroscopy/UCL-NIR-Spectra ) into changes in concentrations of HBO, HBR, and oxCCO (∆HBO, ∆HBR, and ∆oxCCO, respectively). The wavelength-varying differential pathlength factor was assumed to be 6.27 at 807 nm, with the UCLn algorithm applying a wavelength dependency pathlength correction factor. Upon visual inspection of raw concentration changes, the noisy channels were excluded due to detector saturation (> 40.000 photon counts), lower signal intensity (< 800 counts), or poor optical coupling by looking at the signals both in the time domain and the frequency domain. Motion artifacts were identified and corrected using the wavelet-based method, considering an interquartile range threshold of 1.5 [ 39 – 41 ]. A band-pass filter was applied to the concentration data using a 5th -order Finite Impulse Response (FIR) band-pass filter in the range of 0.008 and 0.1 Hz to minimize very low and high frequency physiological noise (such as breathing rate). The motion-corrected and filtered data were also visually inspected. For single-subject responses at each channel, the brain hemodynamic and metabolic responses were estimated through the General Linear Model (GLM) using FIR basis functions. While the canonical GLM estimates cerebral activity by convolving the experimental design with a pre-defined hemodynamic response function (HRF), no established HRF exists for the oxCCO. Therefore, we employed a FIR-based GLM, which does not require the assumption of a pre-defined shape or timing of hemodynamic and metabolic responses. The FIR basis set was used with a bin width of 2.88 s (matching the 0.35 Hz acquisition rate) to model hemodynamic and metabolic responses for each experimental condition (Right and Left), each lasting 32 s from − 2 to 30 s around stimulus onset. This resulted in eleven 2.88 sec-long time bins per condition and formed the design matrix. Ordinary least squares were then used to estimate β-values for each bin, representing the amplitude response over time. The GLM-FIR analysis was applied separately to each channel and chromophores (i.e., ∆HBO, ∆HBR, ∆oxCCO) for each participant. Baseline correction was then applied by subtracting the median signal during a 2-second pre-task period from hemodynamic and metabolic responses to ensure consistency. For each subject, the block averages were calculated across the 20 blocks (10 Right and 10 Left hemifield stimulations) and averaged across the medial channels (i.e., channels 5–12) overlying the visual cortex. These channels were selected because they exhibited the strongest task-evoked responses and captured bilateral visual processing. To assess the relationships between the hemodynamic and metabolic responses, rPWR and rCST were computed by calculating a z-score-normalized hemodynamics-metabolism map by performing a 45° rotation of the axes adapted from Shokri-Kojori et al. [ 28 ] and previously reported in Pinti et al. using bNIRS [ 27 ]. This analysis was carried out on ∆oxCCO to assess metabolic activity and on ∆HBO to examine hemodynamic activity, given its higher signal-to-noise ratio and higher contrast signal than ∆HBR [ 42 ]. To elucidate the coupling and deviation between brain hemodynamics and metabolic activity for each channel and each participant, the peak β-values for brain oxygenation and metabolism were used to compute rPWR and rCST as: $$\:\left[\:\begin{array}{c}\text{r}\text{P}\text{W}\text{R}\\\:\text{r}\text{C}\text{S}\text{T}\end{array}\:\right]=\:\left[\:\begin{array}{cc}\text{cos}(45^\circ\:)&\:\text{sin}(45^\circ\:)\\\:-\text{sin}(45^\circ\:)&\:\text{cos}(45^\circ\:)\:\end{array}\right]\left[\begin{array}{c}\mathcal{z}\left(\text{o}\text{x}\text{y}\text{g}\text{e}\text{n}\text{a}\text{t}\text{i}\text{o}\text{n}\right)\\\:\mathcal{\:}\mathcal{z}\left(\text{m}\text{e}\text{t}\text{a}\text{b}\text{o}\text{l}\text{i}\text{s}\text{m}\right)\:\end{array}\right]$$ This procedure generates rPWR, where positive values indicate concurrent increase in oxygenation and mitochondrial metabolism, and negative values indicate reduced or opposite coupling between the two. For rCST, positive values indicate that the mitochondrial metabolic increase is greater than the oxygenation, whereas negative values indicate that the oxygenation increase exceeds the mitochondrial metabolic increase. Statistical Analyses Demographic measure analyses. Potential group differences in continuous (i.e., age, BMI, blood lactate) and categorical (i.e., sex) demographic variables were assessed using two-tailed Mann-Whitney U and Chi-square tests, respectively. bNIRS analyses Linear mixed-effect models (LMMs) were employed for each chromophore to examine group differences in peak responses, the area under the curve (AUC) in the time window of 20 sec after stimulus onset, rPWR, and rCST, with group included as a fixed effect and random intercepts modeled for subjects to account for within-subject correlation across multiple channels. Age and sex were initially included as covariates; however, it was removed from the model for parsimony due to insignificance. Exploratory Analyses Separate LMMs were fit to explore potential relationships between the bNIRS measures with lactate levels, including potential main and interactive effects with group. To explore potential effects of clinical variables, LMMs were fit to compare BD-euthymic vs. HC groups, and within BD participants, potential effects of rapid cycling (yes, no), lifetime history of psychosis (yes, no), and suicide attempters versus non-attempters, as well as associations with HDRS-29 and YMRS scores. All models were fit using the lmerTest package (version 3.1–3; RRID: SCR_015656) in R (version 4.4.2; RRID: SCR_001905). Type III analyses of variance were conducted using Kenward-Roger's approximation for degrees of freedom, and least-squares (LS) means were extracted using the emmeans package (version 1.10.7). Data are reported as LS means ± standard error of mean (SEM) unless stated otherwise, and effects were considered significant using a two-sided alpha = 0.05 threshold. RESULTS Demographic and Clinical Characteristics Groups did not differ significantly in age ( U = 38, P = 0.38), BMI ( U = 32.0, P = 0.36), or sex ( \(\:{\chi\:}_{1}^{2}\) =1.86, P = 0.17). Blood lactate levels were significantly higher among the BD group ( n = 8, 2.25 ± 0.20 mmol/L) compared to the HC group ( n = 6, 1.56 ± 0.18 mmol/L, U = 43, P = 0.02). Individuals with BD Shows Elevated Mitochondrial Oxidative Metabolism but Reduced Hemodynamic-Mitochondrial Coupling Figure 1 A and 1 B show the group-averaged estimated responses of ∆HBO, ∆HBR, and ∆oxCCO during the visual stimulation task for participants in the BD and HC groups. The groups did not significantly differ in peak values of ∆HBO (BD = 0.16 ± 0.04 µM, HC = 0.20 ± 0.05 µM, Cohen’s d = 0.29, F (1,18) = 0.51, P = 0.49; Fig. 1 C left) or ∆HBR (BD=–0.16 ± 0.02 µM, HC=–0.15 ± 0.02 µM, Cohen’s d = 0.08, F (1,18) = 0.07, P = 0.79; Fig. 1 C middle). The peak concentration of ∆oxCCO was significantly higher in the BD group (0.17 ± 0.02 µM) compared to the HC group (0.12 ± 0.02 µM, Cohen’s d = 0.63, F (1,18) = 4.81, P = 0.04; Fig. 1 C right). The groups also did not differ significantly in the AUC of ∆HBO (BD = 0.81 ± 0.24 µM×sec; HC = 0.98 ± 0.24 µM×sec; Cohen’s d = 0.21, F (1,18) = 0.25, P = 0.62; Fig. 1 D left) or of ∆HBR (BD=–0.79 ± 0.14 µM×sec, HC=–0.73 ± 0.14 µM×sec, Cohen’s d = 0.08, F (1,18) = 0.10, P = 0.76; Fig. 1 D middle). Groups differed significantly in the AUC of ∆oxCCO (Cohen’s d = 0.65, F (1,18) = 4.86, P = 0.04; Fig. 1 D right); the BD (0.87 ± 0.12 µM×sec) group had significantly higher AUC than the HC group (0.50 ± 0.12 µM×sec). The oxCCO measures were not significantly associated with age (∆oxCCO Peak: r = 0.27, F (1,18) = 0.48, P = 0.50; ∆oxCCO AUC: r = 0.27, F (1,18) = 1.12, P = 0.30) or sex (∆oxCCO Peak: r = 0.31, F (1,17) = 0.004, P = 0.95; ∆oxCCO AUC: r = 0.27, F (1,17) = 0.38, P = 0.54). Figure 2 A shows the hemodynamic vs metabolism plots for the averaged brain responses, divided into four quadrants based on the magnitude and direction of the changes in ∆HBO and ∆oxCCO. For instance, subjects in the top right quadrant exhibit a greater increase in ∆HBO and ∆oxCCO. Groups differed significantly in rPWR (Cohen’s d = 0.40, F (1,17) = 4.91, P = 0.04; Fig. 2 B left); the BD group (0.22 ± 0.10) had significantly lower values than the HC (0.55 ± 0.11). However, groups did not differ significantly in rCST (BD = 0.01 ± 0.06, HC=–0.08 ± 0.06, Cohen’s d = 0.18, F (1,17) = 0.94, P = 0.35; Fig. 2 B right). These metabolic-hemodynamic coupling measures were also not significantly associated with age or sex. Individuals with BD Shows Elevated Lactate Despite High Mitochondrial Oxidative Metabolism, unlike HC Individuals There was a significant interaction between group and blood lactate levels when predicting ∆oxCCO AUC ( F (1,9) = 8.85, P = 0.01; Fig. 3 ). Specifically, the relationship between blood lactate and ∆oxCCO AUC was flat among BD (slope = 0.03 ± 0.18, r = 0.02, F (1,8) = 0.02, P = 0.90), however negative for HC (slope=–1.03 ± 0.30, r =–0.63, F (10) = 11.42, P = 0.01). Explatory Analyses of Clinical Factors Compared to HC individuals, euthymic BD individuals exhibited significantly lower rPWR (BD-euthymic: 0.08 ± 012, HC: 0.55 ± 0.11, Cohen’s d = 0.56,w F (1,13) = 8.01, P = 0.01). The other clinical factors assessed did not show significance. Suicide attempters with BD showed a higher oxCCO peak (0.23 ± 0.03 µM) than non-attempters (0.14 ± 0.02 µM), with a large effect size (Cohen’s d = 0.90; F (1,8) = 4.86; P = 0.06), indicating a robust difference, although the comparison did not reach statistical significance. DISCUSSION This is the first multichannel bNIRS study aimed to investigate brain mitochondrial function and mitochondrial-hemodynamic association in vivo in individuals with BD. The data provide evidence that individuals with BD exhibit higher peak and AUC levels of oxCCO changes, indicative of elevated electron transport. Additionally, the reduced rPWR indicates a weaker coupling between oxygen delivery and mitochondrial oxidation in individuals with BD. Alongside these findings, individuals with BD showed elevated serum lactate levels that were not negatively correlated with high oxCCO levels, as they were for HC participants, which would be expected if oxidative phosphorylation was providing energy to meet neural demands. Taken together, these findings suggest that in individuals with BD, mitochondria engage in elevated oxygen consumption that is uncoupled from ATP synthesis. Instead, energy demands appear to be met through increased reliance on aerobic glycolysis, a less efficient pathway for meeting neural energy demands (Fig. 4). The higher oxCCO peak and AUC values in individuals with BD suggest an amplified and prolonged mitochondrial oxidative response during neural stimulation. This pattern is consistent with increased electron transport in an attempt to meet the neural demands. Importantly, however, greater oxCCO does not necessarily imply more efficient metabolism; rather, it may reflect increased mitochondrial oxygen consumption that is uncoupled from ATP production, indicating an inefficient mode of energy generation. While oxCCO serves as a valuable standalone marker of mitochondrial oxidation, its interpretation gains important nuance when considered in relation to oxygenation. This study showed reduced rPWR in individuals with BD, indicating weaker coupling between HBO and oxCCO and supporting mitochondrial inefficiency resulting from a decoupling between oxygen consumption and energy generation. Such a deficit would limit the capacity to rapidly replenish ATP, reducing the efficiency of translating increased energy supply into usable high-energy phosphate. In HCs, negative rCST values reflected greater oxygen delivery relative to mitochondrial oxidation, consistent with lower metabolic cost and efficient coupling (i.e., higher rPWR). In contrast, BD participants showed rCST values closer to zero or slightly positive, suggesting proportionally greater mitochondrial oxidation relative to oxygen delivery. This pattern may reflect increased oxygen consumption in BD that is not fully coupled to ATP synthesis, consistent with mitochondrial inefficiency and potential uncoupling of oxCCO activity. Mitochondrial mechanisms such as leak pathways could represent an adaptive mechanism, helping to maintain mitochondrial membrane gradients and reduce oxidative stress in the context of greater reliance on aerobic glycolysis. Notably, lower rPWR was also evident in euthymic individuals with BD, compared to the HCs, indicating that such mitochondrial impairments may be a trait feature of BD. The findings are consistent with prior findings from 31 P-MRS studies during visual stimulation that support inability to replenish ATP in individuals with BD to meet neural demands [16, 17]. The MRS studies suggest impaired phosphocreatine-mediated buffering, while the current findings support alternate contributing mechanisms. Thus, there may be multiple converging pathways involved. Consistent with prior studies, individuals with BD exhibited elevated peripheral blood lactate levels alongside altered brain mitochondrial responses measured with bNIRS [43, 44]. Under normal conditions, elevated mitochondrial oxygen consumption is typically associated with reduced lactate levels, reflecting use of oxidative phosphorylation for efficient energy production to meet neural demands. Indeed, in HCs, this expected inverse relationship was observed between oxCCO responses and peripheral lactate. However, this relationship was absent in BD. Together, these findings suggest that, rather than efficiently coupling oxygen consumption to ATP synthesis through oxidative phosphorylation, mitochondria in BD rely on aerobic glycolysis to meet energy demands, a process that is inherently less efficient in generating energy [44]. The results also support the view that lactate may serve as a peripheral marker of mitochondrial dysfunction in BD, while bNIRS provides a window into the mechanisms involved. This study builds upon previous optical imaging studies in BD in which oxCCO was measured. Those studies were limited to single- or dual-channel recordings with bNIRS [30, 31] or to precursor methods using multi-wavelength NIRS [29]. These pioneering efforts included initial evidence that photobiomodulation improves altered oxCCO and oxygenation responses in older adults with BD, suggesting a potential therapeutic approach for targeting mitochondrial oxidative abnormalities in BD. The present study employed an advanced multi-channel bNIRS system for robust quantification of oxCCO alongside oxygenation and for assessment of regional heterogeneity, enabling simultaneous assessment of occipital cortical dynamics to demonstrate elevated and sustained oxCCO responses and their relationship to hemodynamics in individuals with BD. For this study, measurements were made only in the occipital cortex. While this provided valuable information that supports BD trait abnormalities that are present in occipital regions, future multichannel study extending coverage to other brain regions is warranted. This includes frontal regions that show prominent mood-state-related differences in BD [45]. We speculate that frontal differences may be more pronounced, especially during acute BD episodes. Several limitations of the present study should be acknowledged. First, although bNIRS offers a non-invasive measure of cerebral mitochondrial function, it is intrinsically limited by assumptions of consistent head anatomy and uniform extracerebral interference across individuals. Our models also assumed identical optical properties for all participants, which may introduce variability in signal interpretation. Second, while the visual checkerboard task reliably induces cortical activation and metabolic demand, it does not engage the cognitive or affective systems most relevant to BD. Notably, findings were detected during the visual task, which is potentially consistent with broad systemic abnormalities. Future work with tasks that probe other brain functions could potentially reveal greater mitochondrial metabolic abnormalities in brain areas most implicated in the symptoms of the disorder. Third, although we observed an altered association between serum blood lactate and cerebral oxCCO responses, lactate was measured at rest and peripherally, limiting inference about its dynamic relationship to brain metabolism during task engagement. Fourth, most individuals with BD were receiving psychotropic medications, including antidepressants, mood stabilizers, and antipsychotics, all of which have been reported to influence, and in some cases inhibit, mitochondrial ETC activity [46]. Finally, the sample size was small and did not provide sufficient power to detect significant effects of clinical variations. Given the importance of reducing the high risk of suicide in BD, it is of interest that suicide attempters showed higher oxCCO than non-attempters with a high effect size; however, the result did not reach significance. The findings do provide support that larger studies are warranted. Overall, these findings have important mechanistic and potential clinical implications. The findings suggest that mitochondrial dysfunction in BD, increasingly recognized as having a central role in BD pathophysiology, is contributed to by bioenergetic abnormalities that result in inefficient aerobic glycolysis to meet elevations in neural demands. Notably, these physiologic alterations may be modifiable. The findings align with mechanisms that might be affected by lithium [47], which remains a mainstay of treatment for BD, and suggest novel future targets for the treatment. The ability of bNIRS to non-invasively capture real-time fluctuations in oxidative metabolism offers a promising avenue for identifying metabolically vulnerable subgroups and monitoring treatment effects. However, further research is needed to validate and extend these findings, particularly through longitudinal designs, larger samples, and integration with complementary imaging modalities. Declarations AKCNOWLEDGEMENTS We thank our participants for their time and interest in the study. We also thank Erin Carubba and Bernadette Lecza for their help in recruiting and assessing participants. AUTHOR CONTRIBUTIONS KK and HPB contributed to all aspects of the study, including funding, design of the study, data acquisition and analysis, statistical analysis, interpretation of findings, drafting the paper, and revising it critically for intellectual content. RM made substantial contributions to the design of the study, data acquisition, interpretation of findings, and revising the paper. FL and PP made substantial contributions to establishing the optical system, helping with the analysis methodology, and critical revisions of the paper. BP provided statistical expertise on the analyses performed and substantially revised the paper. MH made substantial contributions to the data acquisition and to revising the paper. SQ substantially contributed to recruiting participants, data acquisition, and revising the paper. EJ and JH made substantial contributions to the design of the study, interpretation of findings, and revising the paper. IT substantially contributed to the provision of the optical system, conception, supervision, and design of the study, data analysis, interpretation of findings, and critical revisions of the paper. All authors have read and agreed to the published version of the manuscript. COMPETING INTERESTS HPB has consulted to Boehringer Ingelheim, Lilly and Biohaven. IT is the founder and CEO of Metabolight Ltd., which operates in a field unrelated to this work. Other authors do not have any potential competing interests to disclose. FUNDING This research was funded in part by BD 2 : Breakthrough Discoveries for thriving with Bipolar Disorders Discovery Research Grant (#DG230102; KK, SQ, EJ, JH, IT, HPB), an internal pilot project funding through the Yale School of Medicine Promotion of Interdisciplinary Team Science award program (KK, SQ, EJ, HPB), and the John and Hope Furth Endowment (HPB). PP was supported by the Wellcome Trust (#212979/Z/18/Z). FL was supported by the Wellcome Trust (#219610/Z/19/Z). References Andreazza AC, Duong A, Young LT. Bipolar Disorder as a Mitochondrial Disease. Biol Psychiatry 2018; 83 (9) : 720-721. Andreazza AC, Nierenberg AA. Mitochondrial Dysfunction: At the Core of Psychiatric Disorders? Biol Psychiatry 2018; 83 (9) : 718-719. Merikangas KR, Swendsen J, Hickie IB, Cui L, Shou H, Merikangas AK et al. 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Structured Interview Guide for the Hamilton Depression Rating Scale, Seasonal Affective Disorder Version (SIGH-SAD) , 1988. Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry 1978; 133: 429-435. Phan P, Highton D, Lai J, Smith M, Elwell C, Tachtsidis I. Multi-channel multi-distance broadband near-infrared spectroscopy system to measure the spatial response of cellular oxygen metabolism and tissue oxygenation. Biomed Opt Express 2016; 7 (11) : 4424-4440. Matcher SJ, Elwell CE, Cooper CE, Cope M, Delpy DT. Performance comparison of several published tissue near-infrared spectroscopy algorithms. Anal Biochem 1995; 227 (1) : 54-68. Molavi B, Dumont GA. Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiol Meas 2012; 33 (2) : 259-270. Brigadoi S, Phan P, Highton D, Powell S, Cooper RJ, Hebden J et al. Image reconstruction of oxidized cerebral cytochrome C oxidase changes from broadband near-infrared spectroscopy data. Neurophotonics 2017; 4 (2) : 021105. Huppert TJ, Diamond SG, Franceschini MA, Boas DA. HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. Appl Opt 2009; 48 (10) : D280-298. Pinti P, Tachtsidis I, Hamilton A, Hirsch J, Aichelburg C, Gilbert S et al. The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Ann N Y Acad Sci 2020; 1464 (1) : 5-29. Andreazza AC, Barros LF, Behnke A, Ben-Shachar D, Berretta S, Chouinard V-A et al. Brain and body energy metabolism and potential for treatment of psychiatric disorders. Nat Ment Heal 2025; 3 (7) : 763-771. Kuang H, Duong A, Jeong H, Zachos K, Andreazza AC. Lactate in bipolar disorder: A systematic review and meta-analysis. Psychiatry Clin Neurosci 2018; 72 (8) : 546-555. Colic L, Sankar A, Goldman DA, Kim JA, Blumberg HP. Towards a neurodevelopmental model of bipolar disorder: a critical review of trait- and state-related functional neuroimaging in adolescents and young adults. Mol Psychiatry 2025; 30 (3) : 1089-1101. Manji H, Kato T, Di Prospero NA, Ness S, Beal MF, Krams M et al. Impaired mitochondrial function in psychiatric disorders. Nat Rev Neurosci 2012; 13 (5) : 293-307. Stern S, Sarkar A, Stern T, Mei A, Mendes APD, Stern Y et al. Mechanisms Underlying the Hyperexcitability of CA3 and Dentate Gyrus Hippocampal Neurons Derived From Patients With Bipolar Disorder. Biol Psychiatry 2020; 88 (2) : 139-149. Additional Declarations Yes HPB has consulted to Boehringer Ingelheim, Lilly and Biohaven. IT is the founder and CEO of Metabolight Ltd., which operates in a field unrelated to this work. Other authors do not have any potential competing interests to disclose. 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1","display":"","copyAsset":false,"role":"figure","size":67262,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBroadband Near-Infrared Spectroscopy (bNIRS) Brain Measures in Individuals with Bipolar Disorder (BD) and Healthy Comparison (HC) Individuals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe graphs\u003cstrong\u003e \u003c/strong\u003eshow the\u003cstrong\u003e \u003c/strong\u003egrand-averaged responses across bNIRS channels in scanning the BD group \u003cstrong\u003e(A)\u003c/strong\u003e and HC group \u003cstrong\u003e(B) \u003c/strong\u003eduring the visual hemifield checkerboard task, indicated by the gray area. Changes in oxyhemoglobin (∆HBO) are represented in red, in deoxyhemoglobin (∆HBR) in blue, and oxidation states of cytochrome-c-oxidase (∆oxCCO) in green. Concentration values are micromolar (μM), and ∆oxCCO values are multiplied by 3 for better visualization. After performing linear mixed-effects analysis, group differences are shown in peak \u003cstrong\u003e(C)\u003c/strong\u003e and area under the curve (AUC) \u003cstrong\u003e(D)\u003c/strong\u003e of ∆HBO, ∆HBR, and ∆oxCCO. The peak \u003cstrong\u003e(C right)\u003c/strong\u003e and AUC \u003cstrong\u003e(D right) \u003c/strong\u003eresponses of ∆oxCCO were significantly higher in the BD group than in the HC group. Data are shown as mean ± standard error of the mean (SEM) for \u003cstrong\u003eA \u003c/strong\u003eand\u003cstrong\u003e B\u003c/strong\u003e, and least-squares (LS) means ± SEM for \u003cstrong\u003eC\u003c/strong\u003eand \u003cstrong\u003eD\u003c/strong\u003e. \u003cstrong\u003e*\u003c/strong\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-7915512/v1/04f33de6158c3b82aa8851e8.png"},{"id":95918853,"identity":"ab59118d-75d2-4a71-815b-d0b35bf5a5db","added_by":"auto","created_at":"2025-11-14 12:26:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBroadband Near-Infrared Spectroscopy (bNIRS) Brain Measures of Coupling Between Metabolic and Hemodynamic Changes in Individuals with Bipolar Disorder (BD) and Healthy Comparison (HC) Individuals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisualization of relative power (rPWR) and relative cost (rCST) metrics during the visual hemifield checkerboard task. These metrics were computed using peak β-values and z-scores represent the relationship between changes in oxyhemoglobin (∆HBO) and oxidized cytochrome-c-oxidase (∆oxCCO). The rPWR quantifies the degree of concurrent change between ∆oxCCO and ∆HBO \u003cstrong\u003e(A)\u003c/strong\u003e; arrows from the lower left to the upper right indicate increasing rPWR and reflecting neuroenergetic coupling. The rCST captures the relative deviation between ∆oxCCO and ∆HBO \u003cstrong\u003e(A)\u003c/strong\u003e; arrows from the lower right to the upper left indicate increasing rCST with higher values indicating greater metabolic cost relative to utilization. After performing linear mixed-effects analysis, group differences in rPWR and rCST are shown in \u003cstrong\u003eB\u003c/strong\u003e. The rPWR was significantly lower in the BD group than in the HC group \u003cstrong\u003e(B left)\u003c/strong\u003e. The BD group is represented in maroon, the HC group in teal. In each quadrant, the direction of ∆HBO is indicated by the red arrows and ∆oxCCO by the green arrow. Data are shown as least-squares (LS) means ± standard error of the mean. \u003cstrong\u003e*\u003c/strong\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-7915512/v1/71755fb6077fb138d67cf464.png"},{"id":96243718,"identity":"3d11c056-efcb-4e37-98ee-aad482dbe082","added_by":"auto","created_at":"2025-11-19 07:16:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":15192,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation Between Serum Lactate Levels and Broadband Near-Infrared Spectroscopy (bNIRS) Measures of Brain Oxidized Cytochrome-c-oxidase (oxCCO) in Individuals with Bipolar Disorder (BD) and Healthy Comparison (HC) Individuals.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear mixed-effects interaction analysis examining the relationship between blood lactate (mmol/L) and the area under the curve (AUC) of ∆oxCCO showed a significant group × lactate interaction with a negative association between blood lactate and ∆oxCCO AUC in HCs, which was not present in individuals with BD. Regression lines depict least-squares means with a 95% confidence interval. The BD group is shown in maroon and the HC group in teal. *\u003cstrong\u003e*\u003c/strong\u003e\u003cem\u003eP\u003c/em\u003e=0.01.\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-7915512/v1/4b6868c5c6c811baf7dcf585.png"},{"id":95918859,"identity":"b8899556-6b49-4dcb-8ce1-4c55756b2fbb","added_by":"auto","created_at":"2025-11-14 12:26:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":226210,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBroadband near-infrared spectroscopy (bNIRS) framework for oxygenation and metabolism in healthy controls (HC) and bipolar disorder (BD).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe schematic illustrates how bNIRS captures vascular oxygenation (oxyhemoglobin, HBO; deoxyhemoglobin, HBR) and mitochondrial metabolism (oxidized cytochrome-c-oxidase, oxCCO), highlighted in orange. In healthy individuals, glucose metabolism proceeds through oxidative phosphorylation (OXPHOS) when oxygen is sufficient, producing ATP efficiently within mitochondria. In OXPHOS, only a small fraction of pyruvate is converted to lactate, whereas in aerobic glycolysis, a large portion of pyruvate becomes lactate, and in anaerobic glycolysis, nearly all pyruvate is converted to lactate. Under conditions of reduced oxygen or altered regulation, pyruvate is increasingly shunted toward lactate production. In healthy individuals, oxCCO signals reflect efficient coupling of oxygen use with energy production, with OXPHOS meeting neuronal demands and serum lactate levels remaining low. In contrast, in individuals with BD, elevated and sustained oxCCO indicates amplified yet inefficient oxidative responses, characterized by increased oxygen consumption uncoupled from ATP synthesis and greater dependence on aerobic glycolysis, resulting in higher serum lactate levels. This framework highlights mitochondrial dysfunction and disrupted neuroenergetic coupling in BD pathophysiology.\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-7915512/v1/19e5e1b8cc61b002c26d2acc.png"},{"id":96255369,"identity":"db715acf-59de-4b41-a54e-671dc4fbc933","added_by":"auto","created_at":"2025-11-19 07:48:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1524986,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7915512/v1/2f758523-c4e6-4b68-87e1-265815098ca0.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e\nHPB has consulted to Boehringer Ingelheim, Lilly and Biohaven. IT is the founder and CEO of Metabolight Ltd., which operates in a field unrelated to this work. Other authors do not have any potential competing interests to disclose.","formattedTitle":"Broadband Near-Infrared Spectroscopy in Vivo Study of Brain Mitochondrial Oxidative Metabolism and Hemodynamics in Bipolar Disorder","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBioenergetic abnormalities have been increasingly recognized as core mechanisms contributing to the pathophysiology of bipolar disorder (BD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Clinically, altered energy and activity levels are early and pervasive signs of acute BD episodes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], suggesting that they may reflect key pathophysiological mechanisms of the disorder. Uncovering specific contributing bioenergetic mechanisms could be essential for elucidating the pathophysiology of BD, advancing early detection strategies, and developing novel, bioenergetic mechanism-based therapeutics.\u003c/p\u003e\u003cp\u003eConverging evidence from imaging [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and postmortem molecular [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] studies implicate brain mitochondrial metabolic abnormalities in the bioenergetic disturbances observed in BD. Mitochondrial abnormalities in the brain of persons with BD, which may also occur systemically, are further supported by findings of elevated lactate, a byproduct of anaerobic and aerobic glycolysis that reflects a reduction in the efficiency of mitochondrial energy production, in serum in adolescents and adults, and in cerebrospinal fluid in adults [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. \u003cem\u003eIn vivo\u003c/em\u003e magnetic resonance imaging (MRS) studies in BD have shown elevated lactate levels and reduced phosphocreatine (a critical energy reservoir) in the prefrontal cortex (PFC) and occipital cortex, consistent with mitochondrial dysfunction [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Postmortem studies of individuals with BD further implicate mitochondrial dysfunction, revealing altered oxidative stress markers, neuroinflammatory activation, and dysregulated transcription of mitochondrial genes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Mitochondrial complex IV, also known as cytochrome-c-oxidase (CCO), is the terminal enzyme of the mitochondrial electron transport chain (ETC). The oxidation of CCO (oxCCO) is essential for cellular respiration, as it drives the ETC to reduce molecular oxygen to water. This reaction helps establish the proton gradient across the inner mitochondrial membrane, which can ultimately drive ATP synthesis by ATP synthase. Transcriptional alterations have been identified in complex IV in BD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and its dysfunction was shown to disrupt ETC, leading to reduced ATP production and potentially cell damage [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Despite its importance in mitochondrial bioenergetics, oxCCO has received little study in BD, which may be at least in part attributed to the limited prior methods to examine brain oxCCO \u003cem\u003ein vivo\u003c/em\u003e. With longstanding evidence supporting altered brain hemodynamic responses in BD [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], elucidating the relationship between mitochondrial mechanisms such as oxCCO and cerebral hemodynamics could be critical to understanding the pathophysiology underlying the brain dysfunction of BD.\u003c/p\u003e\u003cp\u003eAdvances in non-invasive optical neuroimaging methods now allow for the simultaneous assessment of mitochondrial oxidative metabolism and cerebral hemodynamics. Broadband near-infrared spectroscopy (bNIRS), an extension of the commercially available functional NIRS, enables the concurrent \u003cem\u003ein vivo\u003c/em\u003e measurement of brain oxCCO alongside oxyhemoglobin (HBO) and deoxyhemoglobin (HBR), providing a window into real-time mitochondrial function in relation to functional brain hemodynamic responses [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, bNIRS allows the derivation of metrics, such as relative power (rPWR) and relative cost (rCST), to indicate distinct bioenergetic signatures by integrating mitochondrial function and cerebral hemodynamics-oxygenation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In prior multimodal neuroimaging research, fluorodeoxyglucose-positron emission tomography (FDG-PET) and functional magnetic resonance imaging (fMRI) have been used to derive metabolic-hemodynamic correspondence metrics of rPWR and rCST [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These indices extend beyond basic measures to quantify the efficiency of mitochondrial engagement relative to hemodynamic supply, revealing potential mismatches in energy utilization and disruptions in neuroenergetic regulation. Specifically, rPWR captures concurrent intensity of oxygenation and metabolic activity, reflecting the degree to which oxygen use and energy production are coupled during neural activation; rCST captures the deviation between oxygenation and metabolism, reflecting the extent to which mitochondrial energy production exceeds or falls behind oxygen use relative to neural activity. Advantages of bNIRS over PET and MRI are portability, less sensitivity to movement, and capacity for deployment in naturalistic settings, supporting bNIRS as a robust and scalable platform for investigating mitochondrial dysfunction in BD. Prior studies using multi-wavelength NIRS or dual-channel bNIRS studies provided initial evidence supporting altered oxCCO in, and in combination with photobiomodulation as a possible therapeutic for BD [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and therefore the potential for bNIRS to provide key information for understanding and treating BD.\u003c/p\u003e\u003cp\u003eHere, multichannel bNIRS was employed for \u003cem\u003ein vivo\u003c/em\u003e assessment of brain oxCCO and hemodynamics in a group of individuals with BD and a group of healthy comparison (HC) individuals while performing a visual hemifield checkerboard task, known to robustly activate the visual cortex and increase local energy demand. We hypothesized that individuals with BD would exhibit evidence of altered mitochondrial metabolic responses and impaired neuroenergetic efficiency, as reflected in altered levels of oxCCO, rPWR, and/or rCST.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eParticipants were comprised of 10 adults with BD (demographic and clinical characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) who met criteria for BD and 10 HC adults who were without personal or first-degree relatives with a history of a major psychiatric disorder (age range 21\u0026ndash;63 years, mean age 46.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1 years; 4 females; body mass index (BMI) 24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7). The presence or absence of psychiatric disorders, and for BD participants, rapid cycling, history of psychosis, number of hospitalizations, and mood state, were confirmed with the Structured Clinical Interview for DSM-5 Diagnosis \u0026ndash; Research Version [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Family history was obtained using the Family History Screen [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. History of suicide attempts was determined using the Columbia Suicide History Form [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Mood symptoms were evaluated using the Hamilton Depression Scale 29-item version (HDRS-29) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and Young Mania Rating Scale (YMRS) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Demographic and clinical characteristics of the BD sample are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All participants were without major unstable medical or neurological conditions that could affect the brain. Two participants with BD had hypothyroidism treated with levothyroxine. The BD participants did not meet criteria for moderate or severe alcohol and/or substance use disorder, except for caffeine, nicotine, and cannabis, within 12 months of the study. Blood samples were collected from individuals with BD (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8) and HC individuals (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6), and serum lactate was measured. Participants provided written informed consent in accordance with the Yale School of Medicine Human Investigation Committee/Institutional Review Board.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and Clinical Characteristics of the Sample with Bipolar Disorder (BD).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndividuals with BD\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Age in years\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD),\u003c/p\u003e\u003cp\u003erange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e\u003cp\u003e25\u0026ndash;62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (80)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Body Mass Index \u0026plusmn; (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of Rapid Cycling, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of Psychosis, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent Psychosis, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of Past Suicide Attempt, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Number of Hospitalizations\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMood State at Scan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEuthymic, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevated, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepressed, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychotropic Medications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnticolvulsants\u003c/p\u003e\u003cp\u003eAntidepressants\u003c/p\u003e\u003cp\u003eLithium Carbonate, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (60)\u003c/p\u003e\u003cp\u003e4 (40)\u003c/p\u003e\u003cp\u003e3 (30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecond Generation Antipsychotics, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenzodiazepines, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStimulants, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Cannabis, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCurrent Psychiatric Comorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttention Deficit Hyperactivity Disorder, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneralized Anxiety Disorder, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild Cannabis Use Disorder, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Anxiety Disorder, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremenstrual Dysphoric Disorder, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-traumatic stress disorder, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ebNIRS\u003c/h3\u003e\n\u003cp\u003eThe bNIRS system used was developed in-house by I.T. at University College London, UK, and is described in detail here [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Briefly, it is a multichannel instrument, equipped with two halogen bulbs that emit light in the NIR range (504\u0026ndash;1068 nm) and two spectrometers, which are customized lens spectrographs and front-illuminated CCD cameras (PIXIS512f, Princeton Instruments) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Light is directed onto the scalp through four fiber-optic bundles (sources), and the back-scattered light is collected by the spectrographs through ten fiber-optic bundles (detectors), capable of 16 measurement channels with a source-detector separation of 3 cm. The bNIRS fiber bundles were secured with an optode holder cap to follow the head's curvature to maximize optical coupling. The distance from the Nasion to the Inion was measured through 10\u0026ndash;20 anatomical landmarks along the midline to position the cap reliably across all participants. The cap was then positioned bilaterally over the visual cortex, corresponding with the Oz landmark. After cap placement and raw signal quality check, changes in brain hemodynamics and metabolism of participants were monitored at a 0.35 Hz sampling rate using 120 wavelengths from 780 to 900 nm to improve the estimation of hemodynamic and metabolic signals. The experiment was conducted in a dark room to minimize interference from ambient light.\u003c/p\u003e\u003cp\u003eBNIRS data were acquired during visual stimulation. All participants had normal or corrected-to-normal vision and were seated comfortably. The monitor was positioned so that a central cross-hair was in the center of the participant\u0026rsquo;s view; its height was adjusted for each participant, and the distance was fixed at 60 cm. The visual stimulation paradigm was designed using Psychtoolbox (RRID: SCR_002881) in MATLAB (version R2024a, MathWorks Inc., USA; RRID: SCR_001622) and consisted of left and right hemifield stimuli composed of reversing black and white checkerboards (2 check size; reversal rate of 15 Hz) to activate visual system pathways. All participants were instructed to maintain their gaze on a white fixation cross displayed at the center of a grey screen. The visual stimulation task was structured as a block design, with 10 blocks per condition (alternating Right or Left), each lasting 20 sec, spaced out by rest periods ranging randomly from 15 to 20 sec. Left and right stimuli were alternated throughout the blocks for a total duration of 13 min.\u003c/p\u003e\n\u003ch3\u003ebNIRS Data Processing\u003c/h3\u003e\n\u003cp\u003eThe bNIRS data processing and analysis pipeline, adopted from as described in methods paper in Pinti et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], used an in-house developed UCLn algorithm based on the modified Beer-Lambert Law [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Briefly, the raw intensity data from 120 wavelengths (780\u0026ndash;900 nm) were converted into changes in optical density and then converted using the UCLn algorithm and the specific extinction coefficients (publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/multimodalspectroscopy/UCL-NIR-Spectra\u003c/span\u003e\u003cspan address=\"https://github.com/multimodalspectroscopy/UCL-NIR-Spectra\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) into changes in concentrations of HBO, HBR, and oxCCO (∆HBO, ∆HBR, and ∆oxCCO, respectively). The wavelength-varying differential pathlength factor was assumed to be 6.27 at 807 nm, with the UCLn algorithm applying a wavelength dependency pathlength correction factor. Upon visual inspection of raw concentration changes, the noisy channels were excluded due to detector saturation (\u0026gt;\u0026thinsp;40.000 photon counts), lower signal intensity (\u0026lt;\u0026thinsp;800 counts), or poor optical coupling by looking at the signals both in the time domain and the frequency domain.\u003c/p\u003e\u003cp\u003eMotion artifacts were identified and corrected using the wavelet-based method, considering an interquartile range threshold of 1.5 [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A band-pass filter was applied to the concentration data using a 5th -order Finite Impulse Response (FIR) band-pass filter in the range of 0.008 and 0.1 Hz to minimize very low and high frequency physiological noise (such as breathing rate). The motion-corrected and filtered data were also visually inspected.\u003c/p\u003e\u003cp\u003eFor single-subject responses at each channel, the brain hemodynamic and metabolic responses were estimated through the General Linear Model (GLM) using FIR basis functions. While the canonical GLM estimates cerebral activity by convolving the experimental design with a pre-defined hemodynamic response function (HRF), no established HRF exists for the oxCCO. Therefore, we employed a FIR-based GLM, which does not require the assumption of a pre-defined shape or timing of hemodynamic and metabolic responses. The FIR basis set was used with a bin width of 2.88 s (matching the 0.35 Hz acquisition rate) to model hemodynamic and metabolic responses for each experimental condition (Right and Left), each lasting 32 s from \u0026minus;\u0026thinsp;2 to 30 s around stimulus onset. This resulted in eleven 2.88 sec-long time bins per condition and formed the design matrix. Ordinary least squares were then used to estimate β-values for each bin, representing the amplitude response over time. The GLM-FIR analysis was applied separately to each channel and chromophores (i.e., ∆HBO, ∆HBR, ∆oxCCO) for each participant. Baseline correction was then applied by subtracting the median signal during a 2-second pre-task period from hemodynamic and metabolic responses to ensure consistency. For each subject, the block averages were calculated across the 20 blocks (10 Right and 10 Left hemifield stimulations) and averaged across the medial channels (i.e., channels 5\u0026ndash;12) overlying the visual cortex. These channels were selected because they exhibited the strongest task-evoked responses and captured bilateral visual processing.\u003c/p\u003e\u003cp\u003eTo assess the relationships between the hemodynamic and metabolic responses, rPWR and rCST were computed by calculating a z-score-normalized hemodynamics-metabolism map by performing a 45\u0026deg; rotation of the axes adapted from Shokri-Kojori et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and previously reported in Pinti et al. using bNIRS [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This analysis was carried out on ∆oxCCO to assess metabolic activity and on ∆HBO to examine hemodynamic activity, given its higher signal-to-noise ratio and higher contrast signal than ∆HBR [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. To elucidate the coupling and deviation between brain hemodynamics and metabolic activity for each channel and each participant, the peak β-values for brain oxygenation and metabolism were used to compute rPWR and rCST as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\left[\\:\\begin{array}{c}\\text{r}\\text{P}\\text{W}\\text{R}\\\\\\:\\text{r}\\text{C}\\text{S}\\text{T}\\end{array}\\:\\right]=\\:\\left[\\:\\begin{array}{cc}\\text{cos}(45^\\circ\\:)\u0026amp;\\:\\text{sin}(45^\\circ\\:)\\\\\\:-\\text{sin}(45^\\circ\\:)\u0026amp;\\:\\text{cos}(45^\\circ\\:)\\:\\end{array}\\right]\\left[\\begin{array}{c}\\mathcal{z}\\left(\\text{o}\\text{x}\\text{y}\\text{g}\\text{e}\\text{n}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\right)\\\\\\:\\mathcal{\\:}\\mathcal{z}\\left(\\text{m}\\text{e}\\text{t}\\text{a}\\text{b}\\text{o}\\text{l}\\text{i}\\text{s}\\text{m}\\right)\\:\\end{array}\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis procedure generates rPWR, where positive values indicate concurrent increase in oxygenation and mitochondrial metabolism, and negative values indicate reduced or opposite coupling between the two. For rCST, positive values indicate that the mitochondrial metabolic increase is greater than the oxygenation, whereas negative values indicate that the oxygenation increase exceeds the mitochondrial metabolic increase.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eDemographic measure analyses.\u003c/em\u003e Potential group differences in continuous (i.e., age, BMI, blood lactate) and categorical (i.e., sex) demographic variables were assessed using two-tailed Mann-Whitney U and Chi-square tests, respectively.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ebNIRS analyses\u003c/strong\u003e\u003cp\u003eLinear mixed-effect models (LMMs) were employed for each chromophore to examine group differences in peak responses, the area under the curve (AUC) in the time window of 20 sec after stimulus onset, rPWR, and rCST, with group included as a fixed effect and random intercepts modeled for subjects to account for within-subject correlation across multiple channels. Age and sex were initially included as covariates; however, it was removed from the model for parsimony due to insignificance.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExploratory Analyses\u003c/strong\u003e\u003cp\u003eSeparate LMMs were fit to explore potential relationships between the bNIRS measures with lactate levels, including potential main and interactive effects with group. To explore potential effects of clinical variables, LMMs were fit to compare BD-euthymic vs. HC groups, and within BD participants, potential effects of rapid cycling (yes, no), lifetime history of psychosis (yes, no), and suicide attempters versus non-attempters, as well as associations with HDRS-29 and YMRS scores.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eAll models were fit using the \u003cem\u003elmerTest\u003c/em\u003e package (version 3.1\u0026ndash;3; RRID: SCR_015656) in \u003cem\u003eR\u003c/em\u003e (version 4.4.2; RRID: SCR_001905). Type III analyses of variance were conducted using Kenward-Roger's approximation for degrees of freedom, and least-squares (LS) means were extracted using the \u003cem\u003eemmeans\u003c/em\u003e package (version 1.10.7). Data are reported as LS means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of mean (SEM) unless stated otherwise, and effects were considered significant using a two-sided alpha\u0026thinsp;=\u0026thinsp;0.05 threshold.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDemographic and Clinical Characteristics\u003c/h2\u003e\u003cp\u003eGroups did not differ significantly in age (\u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.38), BMI (\u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.36), or sex (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{1}^{2}\\)\u003c/span\u003e\u003c/span\u003e=1.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17). Blood lactate levels were significantly higher among the BD group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, 2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20 mmol/L) compared to the HC group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, 1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 mmol/L, \u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIndividuals with BD Shows Elevated Mitochondrial Oxidative Metabolism but Reduced Hemodynamic-Mitochondrial Coupling\u003c/h3\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB show the group-averaged estimated responses of ∆HBO, ∆HBR, and ∆oxCCO during the visual stimulation task for participants in the BD and HC groups. The groups did not significantly differ in peak values of ∆HBO (BD\u0026thinsp;=\u0026thinsp;0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 \u0026micro;M, HC\u0026thinsp;=\u0026thinsp;0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 \u0026micro;M, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,18)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC left) or ∆HBR (BD=\u0026ndash;0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 \u0026micro;M, HC=\u0026ndash;0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 \u0026micro;M, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,18)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.07, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.79; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC middle). The peak concentration of ∆oxCCO was significantly higher in the BD group (0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 \u0026micro;M) compared to the HC group (0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 \u0026micro;M, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.63, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,18)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.81, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC right).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe groups also did not differ significantly in the AUC of ∆HBO (BD\u0026thinsp;=\u0026thinsp;0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 \u0026micro;M\u0026times;sec; HC\u0026thinsp;=\u0026thinsp;0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 \u0026micro;M\u0026times;sec; Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.21, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,18)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.25, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.62; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD left) or of ∆HBR (BD=\u0026ndash;0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 \u0026micro;M\u0026times;sec, HC=\u0026ndash;0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 \u0026micro;M\u0026times;sec, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,18)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD middle). Groups differed significantly in the AUC of ∆oxCCO (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,18)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD right); the BD (0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 \u0026micro;M\u0026times;sec) group had significantly higher AUC than the HC group (0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 \u0026micro;M\u0026times;sec). The oxCCO measures were not significantly associated with age (∆oxCCO Peak: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,18)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.48, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.50; ∆oxCCO AUC: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,18)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.12, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30) or sex (∆oxCCO Peak: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,17)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.004, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95; ∆oxCCO AUC: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,17)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.54).\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows the hemodynamic vs metabolism plots for the averaged brain responses, divided into four quadrants based on the magnitude and direction of the changes in ∆HBO and ∆oxCCO. For instance, subjects in the top right quadrant exhibit a greater increase in ∆HBO and ∆oxCCO. Groups differed significantly in rPWR (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,17)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB left); the BD group (0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10) had significantly lower values than the HC (0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11). However, groups did not differ significantly in rCST (BD\u0026thinsp;=\u0026thinsp;0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06, HC=\u0026ndash;0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.18, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,17)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.94, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.35; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB right). These metabolic-hemodynamic coupling measures were also not significantly associated with age or sex.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eIndividuals with BD Shows Elevated Lactate Despite High Mitochondrial Oxidative Metabolism, unlike HC Individuals\u003c/h3\u003e\n\u003cp\u003eThere was a significant interaction between group and blood lactate levels when predicting ∆oxCCO AUC (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,9)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8.85, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, the relationship between blood lactate and ∆oxCCO AUC was flat among BD (slope\u0026thinsp;=\u0026thinsp;0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,8)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90), however negative for HC (slope=\u0026ndash;1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30, \u003cem\u003er\u003c/em\u003e=\u0026ndash;0.63, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(10)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;11.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003eExplatory Analyses of Clinical Factors\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eCompared to HC individuals, euthymic BD individuals exhibited significantly lower rPWR (BD-euthymic: 0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;012, HC: 0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56,w \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,13)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). The other clinical factors assessed did not show significance. Suicide attempters with BD showed a higher oxCCO peak (0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 \u0026micro;M) than non-attempters (0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 \u0026micro;M), with a large effect size (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90; \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,8)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.86; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06), indicating a robust difference, although the comparison did not reach statistical significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis is the first multichannel bNIRS study aimed to investigate brain mitochondrial function and mitochondrial-hemodynamic association \u003cem\u003ein vivo\u003c/em\u003e in individuals with BD. The data provide evidence that individuals with BD exhibit higher peak and AUC levels of oxCCO changes, indicative of elevated electron transport. Additionally, the reduced rPWR indicates a weaker coupling between oxygen delivery and mitochondrial oxidation in individuals with BD. Alongside these findings, individuals with BD showed elevated serum lactate levels that were not negatively correlated with high oxCCO levels, as they were for HC participants, which would be expected if oxidative phosphorylation was providing energy to meet neural demands. Taken together, these findings suggest that in individuals with BD, mitochondria engage in elevated oxygen consumption that is uncoupled from ATP synthesis. Instead, energy demands appear to be met through increased reliance on aerobic glycolysis, a less efficient pathway for meeting neural energy demands (Fig. 4).\u003c/p\u003e\n\u003cp\u003eThe higher oxCCO peak and AUC values in individuals with BD suggest an amplified and prolonged mitochondrial oxidative response during neural stimulation. This pattern is consistent with increased electron transport in an attempt to meet the neural demands. Importantly, however, greater oxCCO does not necessarily imply more efficient metabolism; rather, it may reflect increased mitochondrial oxygen consumption that is uncoupled from ATP production, indicating an inefficient mode of energy generation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile oxCCO serves as a valuable standalone marker of mitochondrial oxidation, its interpretation gains important nuance when considered in relation to oxygenation. This study showed reduced rPWR in individuals with BD, indicating weaker coupling between HBO and oxCCO and supporting mitochondrial inefficiency resulting from a decoupling between oxygen consumption and energy generation. Such a deficit would limit the capacity to rapidly replenish ATP, reducing the efficiency of translating increased energy supply into usable high-energy phosphate. In HCs, negative rCST values reflected greater oxygen delivery relative to mitochondrial oxidation, consistent with lower metabolic cost and efficient coupling (i.e., higher rPWR). In contrast, BD participants showed rCST values closer to zero or slightly positive, suggesting proportionally greater mitochondrial oxidation relative to oxygen delivery. This pattern may reflect increased oxygen consumption in BD that is not fully coupled to ATP synthesis, consistent with mitochondrial inefficiency and potential uncoupling of oxCCO activity. Mitochondrial mechanisms such as leak pathways could represent an adaptive mechanism, helping to maintain mitochondrial membrane gradients and reduce oxidative stress in the context of greater reliance on aerobic glycolysis. Notably, lower rPWR was also evident in euthymic individuals with BD, compared to the HCs, indicating that such mitochondrial impairments may be a trait feature of BD. The findings are consistent with prior findings from \u003csup\u003e31\u003c/sup\u003eP-MRS studies during visual stimulation that support inability to replenish ATP in individuals with BD to meet neural demands\u0026nbsp;[16, 17]. The MRS studies suggest impaired phosphocreatine-mediated buffering, while the current findings support alternate contributing mechanisms. Thus, there may be multiple converging pathways involved.\u003c/p\u003e\n\u003cp\u003eConsistent with prior studies, individuals with BD exhibited elevated peripheral blood lactate levels alongside altered brain mitochondrial responses measured with bNIRS [43, 44]. Under normal conditions, elevated mitochondrial oxygen consumption is typically associated with reduced lactate levels, reflecting use of oxidative phosphorylation for efficient energy production to meet neural demands. Indeed, in HCs, this expected inverse relationship was observed between oxCCO responses and peripheral lactate. However, this relationship was absent in BD. Together, these findings suggest that, rather than efficiently coupling oxygen consumption to ATP synthesis through oxidative phosphorylation, mitochondria in BD rely on aerobic glycolysis to meet energy demands, a process that is inherently less efficient in generating energy [44]. The results also support the view that lactate may serve as a peripheral marker of mitochondrial dysfunction in BD, while bNIRS provides a window into the mechanisms involved.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study builds upon previous optical imaging studies in BD in which oxCCO was measured. Those studies were limited to single- or dual-channel recordings with bNIRS [30, 31] or to precursor methods using multi-wavelength NIRS [29]. These pioneering efforts included initial evidence that photobiomodulation improves altered oxCCO and oxygenation responses in older adults with BD, suggesting a potential therapeutic approach for targeting mitochondrial oxidative abnormalities in BD. The present study employed an advanced multi-channel bNIRS system for robust quantification of oxCCO alongside oxygenation and for assessment of regional heterogeneity, enabling simultaneous assessment of occipital cortical dynamics to demonstrate elevated and sustained oxCCO responses and their relationship to hemodynamics in individuals with BD. For this study, measurements were made only in the occipital cortex. While this provided valuable information that supports BD trait abnormalities that are present in occipital regions, future multichannel study extending coverage to other brain regions is warranted. This includes frontal regions that show prominent mood-state-related differences in BD [45]. We speculate that frontal differences may be more pronounced, especially during acute BD episodes.\u003c/p\u003e\n\u003cp\u003eSeveral limitations of the present study should be acknowledged. First, although bNIRS offers a non-invasive measure of cerebral mitochondrial function, it is intrinsically limited by assumptions of consistent head anatomy and uniform extracerebral interference across individuals. Our models also assumed identical optical properties for all participants, which may introduce variability in signal interpretation. Second, while the visual checkerboard task reliably induces cortical activation and metabolic demand, it does not engage the cognitive or affective systems most relevant to BD. Notably, findings were detected during the visual task, which is potentially consistent with broad systemic abnormalities. Future work with tasks that probe other brain functions could potentially reveal greater mitochondrial metabolic abnormalities in brain areas most implicated in the symptoms of the disorder. Third, although we observed an altered association between serum blood lactate and cerebral oxCCO responses, lactate was measured at rest and peripherally, limiting inference about its dynamic relationship to brain metabolism during task engagement. Fourth, most individuals with BD were receiving psychotropic medications, including antidepressants, mood stabilizers, and antipsychotics, all of which have been reported to influence, and in some cases inhibit, mitochondrial ETC activity [46]. Finally, the sample size was small and did not provide sufficient power to detect significant effects of clinical variations. Given the importance of reducing the high risk of suicide in BD, it is of interest that suicide attempters showed higher oxCCO than non-attempters with a high effect size; however, the result did not reach significance. The findings do provide support that larger studies are warranted.\u003c/p\u003e\n\u003cp\u003eOverall, these findings have important mechanistic and potential clinical implications. The findings suggest that mitochondrial dysfunction in BD, increasingly recognized as having a central role in BD pathophysiology, is contributed to by bioenergetic abnormalities that result in inefficient aerobic glycolysis to meet elevations in neural demands. Notably, these physiologic alterations may be modifiable. The findings align with mechanisms that might be affected by lithium [47], which remains a mainstay of treatment for BD, and suggest novel future targets for the treatment. The ability of bNIRS to non-invasively capture real-time fluctuations in oxidative metabolism offers a promising avenue for identifying metabolically vulnerable subgroups and monitoring treatment effects. However, further research is needed to validate and extend these findings, particularly through longitudinal designs, larger samples, and integration with complementary imaging modalities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAKCNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank our participants for their time and interest in the study. We also thank Erin Carubba and Bernadette Lecza for their help in recruiting and assessing participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;KK and HPB contributed to all aspects of the study, including funding, design of the study, data acquisition and analysis, statistical analysis, interpretation of findings, drafting the paper, and revising it critically for intellectual content. RM made substantial contributions to the design of the study, data acquisition, interpretation of findings, and revising the paper. FL and PP made substantial contributions to establishing the optical system, helping with the analysis methodology, and critical revisions of the paper. BP provided statistical expertise on the analyses performed and substantially revised the paper. MH made substantial contributions to the data acquisition and to revising the paper. SQ substantially contributed to recruiting participants, data acquisition, and revising the paper. EJ and JH made substantial contributions to the design of the study, interpretation of findings, and revising the paper. IT substantially contributed to the provision of the optical system, conception, supervision, and design of the study, data analysis, interpretation of findings, and critical revisions of the paper. All authors have read and agreed to the published version of the manuscript.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHPB has consulted to Boehringer Ingelheim, Lilly and Biohaven. IT is the founder and CEO of Metabolight Ltd., which operates in a field unrelated to this work. Other authors do not have any potential competing interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded in part by BD\u003csup\u003e2\u003c/sup\u003e: Breakthrough Discoveries for thriving with Bipolar Disorders Discovery Research Grant (#DG230102; KK, SQ, EJ, JH, IT, HPB), an internal pilot project funding through the Yale School of Medicine Promotion of Interdisciplinary Team Science award program (KK, SQ, EJ, HPB), and the John and Hope Furth Endowment (HPB). PP was supported by the Wellcome Trust (#212979/Z/18/Z). FL was supported by the Wellcome Trust (#219610/Z/19/Z).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndreazza AC, Duong A, Young LT. Bipolar Disorder as a Mitochondrial Disease. \u003cem\u003eBiol Psychiatry\u003c/em\u003e 2018; \u003cstrong\u003e83\u003c/strong\u003e(9)\u003cstrong\u003e: \u003c/strong\u003e720-721.\u003c/li\u003e\n\u003cli\u003eAndreazza AC, Nierenberg AA. 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Here, brain broadband near-infrared spectroscopy (bNIRS) non-invasively measured oxidation of the key enzyme of the mitochondrial electron transport chain, mitochondrial complex IV cytochrome-c-oxidase (oxCCO), alongside oxyhemoglobin (HBO) and deoxyhemoglobin (HBR) measures, \u003cem\u003ein vivo\u003c/em\u003e in adults with BD and healthy comparison (HC) during visual stimulation. The relationship between oxCCO and the hemodynamic measures was assessed. During visual stimulation, participants with BD showed significantly higher elevations in oxCCO and significantly lower relative power (concurrence between oxCCO and oxygen use) compared to HC participants, a pattern also observed in euthymic BD participants, suggesting a trait difference in BD. The BD participants also had significantly higher levels of peripheral blood lactate, even when oxCCO levels were high, unlike the association observed for HC participants, which would be expected if oxidative phosphorylation was providing energy to meet neural demands. Together, these findings suggest that to meet neural energy demands, mitochondria in BD exhibit oxygen consumption that is not efficiently coupled to ATP production, and instead shift toward an energetically inefficient process, aerobic glycolysis. By enabling the simultaneous assessment of mitochondrial metabolism and hemodynamics, bNIRS provided a new, scalable, non-invasive tool to uncover mechanisms of neuroenergetic dysfunction in BD. This approach may facilitate the identification of novel mechanistic targets and advance biomarker development for more personalized interventions in mood disorders and potentially other neuropsychiatric disorders.\u003c/p\u003e","manuscriptTitle":"Broadband Near-Infrared Spectroscopy in Vivo Study of Brain Mitochondrial Oxidative Metabolism and Hemodynamics in Bipolar Disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 12:25:55","doi":"10.21203/rs.3.rs-7915512/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-01-16T16:32:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-01-06T12:54:51+00:00","index":6,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-12-08T00:19:03+00:00","index":7,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-11-26T19:20:40+00:00","index":1,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-11-26T17:34:14+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-11-21T12:02:40+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-11-19T14:29:06+00:00","index":7,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-11-19T12:10:21+00:00","index":6,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-11-19T11:54:49+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-11-15T14:21:51+00:00","index":5,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-11-13T09:12:23+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-11-10T15:55:25+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-11-04T18:34:40+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-11-04T17:17:58+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-11-04T17:05:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-23T13:51:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-23T13:42:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Psychiatry","date":"2025-10-22T12:28:39+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-10-22T10:32:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"525981db-f954-4db0-b311-db8057f4f7d0","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":57437929,"name":"Health sciences/Diseases/Psychiatric disorders/Bipolar disorder"},{"id":57437930,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-01-16T16:36:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 12:25:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7915512","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7915512","identity":"rs-7915512","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}