Greater Mitochondrial DNA Pathogenicity is Associated with Greater Regional Cerebral Blood Flow in Youth Bipolar Disorder

Greater Mitochondrial DNA Pathogenicity is Associated with Greater Regional Cerebral Blood Flow in Youth 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 Greater Mitochondrial DNA Pathogenicity is Associated with Greater Regional Cerebral Blood Flow in Youth Bipolar Disorder Benjamin Goldstein, Suyi Shao, Ana Mendes-Silva, Yi Zou, Kody Kennedy, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7635750/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mitochondrial dysfunction is implicated in the neuropathology of bipolar disorder (BD). Mitochondrial DNA (mtDNA) variants are associated with anomalous cerebral energy metabolism and with increased risk for BD. Little is known about the relevance of mtDNA to cerebral blood flow (CBF) in BD. Participants included 101 youth (BD, n = 56; Control group, n = 45; ages 13–20). The Miseq platform was used to sequence saliva mtDNA. The mtDNA-Server pipeline was used for variant calling and annotation. mtDNA common variants (i.e. minor allele frequency larger than 5%) were included in the analyses due to sample size. We generated an mtDNA variant functional impact (FI) score by performing functional analysis using Mutserve and summing across the MutPred, Selection Score, and MitoTool algorithms. CBF was measured using pseudo-continuous arterial spin labeling magnetic resonance imaging. Region of interest (ROI) analyses examined FI scores in relation to CBF in the anterior cingulate cortex (ACC) and global gray matter, controlling for age and sex. Voxel-based analyses were also conducted. In ROI analyses, higher mtDNA FI score was associated with higher ACC CBF in the overall sample (β = 0.20, p = 0.045). In voxel-based analyses, higher mtDNA FI score was associated with higher CBF in regions within the basal ganglia, frontal and parietal lobe, and cingulate within the overall sample and within the BD group. This study found that higher mtDNA FI score, putatively reflecting mtDNA pathogenicity, was associated with higher regional CBF among youth. Present findings add to the evidence that elevated CBF may be a compensatory mechanism in youth with BD. Health sciences/Diseases/Psychiatric disorders/Bipolar disorder Health sciences/Biomarkers/Diagnostic markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Bipolar disorder (BD) is a severe psychiatric disorder that affects approximately 1–3% of youth 1 . The disorder is characterized by recurrent episodes of mania and depression: mania is reported to increase CBF and energy, while depression has the opposite association 2 . Relatedly, mitochondrial dysfunction has been implicated in the etiology of BD 3, 4 . Mitochondria are the main source of energy for cells and supply most of the energy demands to support cerebral metabolism and energetics 3 , 5 . Magnetic resonance spectroscopy (MRS) findings in adults and youth with BD 4, 6, 7 and mitochondrial morphology abnormalities in fibroblasts from BD adults 8 suggest that abnormal mitochondrial morphology is linked to altered energy metabolism in the progression of BD 8 . Mitochondria have their own genome (mtDNA) that encodes 2 ribosomal RNA (rRNA), 22 transfer RNA (tRNA), and 13 electron transport chain (ETC) subunits, which are all related to oxidative phosphorylation 9 . There is evidence from adults with BD of mtDNA deletion 10 – 13 , depletion 14 – 16 , and mutation 17 – 23 . In a recent study, three mtDNA variants (m.10410T > A, m.13135G > A, and m.13708G > A), located near or on NADH dehydrogenase were associated with BD 24 . A controlled study in adults found that m.16519T > C, which is located in the hypervariable region, is associated with risk of BD or schizophrenia (combined sample) 18 . Further, three variants in the D-loop region (m.114 T > C, m.195 C > T, m.16300 G > A), and one variant in ND4L (m.10652 C > T) have been associated with BD in adults 17 . A study of transmitochondrial hybrid cells found that variant m.10398A > G, a risk factor for BD, was associated with differences in mitochondrial pH and intracellular calcium dynamics 22 . Additionally, mtDNA variants can lead to alteration of cerebral energy metabolism 25 , 26 , medication response, psychosis in BD 19, 23 , and mitochondrial protein function 21 , 22 . Cerebral blood flow (CBF) also plays an essential role in brain energy metabolism 27 . CBF is defined as the volume of blood delivered to a defined mass of brain tissue per unit of time and is maintained by cerebral autoregulation 28 . CBF delivers energy by carrying glucose and oxygen to active brain regions and facilitates the clearance of metabolic byproducts 27 . CBF can be measured non-invasively using arterial spin labeling (ASL), which is a functional magnetic resonance imaging (MRI) method 29 well-suited for measuring regional estimation 30 . Adults with BD have reported lower CBF in the frontal, temporal, and parietal regions across different mood states including euthymia 28 , 31 . In contrast to the numerous studies regarding CBF in adults with BD, there are few studies on this topic in youth. Prior CBF studies from our group, using ASL, have yielded a number of preliminary findings. In contrast to lower CBF in adults, there is evidence of higher global 32 , 33 and regional CBF in the medial frontal gyrus and middle cingulate cortex in youth with BD compared to a control group, differences that appear to be attenuated by a single session of acute aerobic exercise 34 . Within-BD analyses further demonstrate that anhedonia and greater severity of depressed mood are associated with lower global and regional CBF in the anterior cingulate cortex (ACC) 33 . Finally, and perhaps most relevant to mitochondrial dysfunction, we found that the cerebral metabolic rate of oxygen (CMRO 2 ), the rate of oxygen consumption by the brain, is unchanged in youth with BD despite elevated CBF 32 . This suggests an abnormal energy homeostasis, in which mitochondrial dysfunction might play a role. There is a gap in knowledge regarding mtDNA mutation and mitochondrial energy metabolism in relation to CBF in both BD and in youth. This is important given that adolescence is a key period of brain maturation, characterized by high metabolic demands 35 . The current study therefore aims to examine the association between mtDNA variants and CBF differences in youth with and without BD. We focused on whole brain gray matter CBF and specifically the ACC region of interest (ROI). The ACC regulates emotion and cognition and has been repeatedly associated with functional and structural brain abnormalities in BD 36 . In addition, higher levels of expression of genes that are related to aerobic energy metabolism and neuronal functions were found in ACC of humans compared to other primates, suggesting the greater metabolic demand and greater neuronal activity of ACC through evolution 37 . Lower pH was found in ACC of manic adolescents with BD compared to controls, suggesting mitochondrial dysfunction in the pathophysiology of BD 38 . Lower mitochondria density and higher lactate levels were found in ACC of adults with schizophrenia compared to the controls suggesting dysfunctional energy metabolism resulted from mitochondrial dysfunction 39 . Given the dearth of prior studies in youth, we also integrated a data-driven voxel-based approach. We hypothesized that the presence of mtDNA variants will be associated with higher global and regional gray matter CBF among youth with BD, but not among the controls. Methods Participants This study included 101 English-speaking youth of European ancestry based on genetic data, ages between 13–20 years. 56 participants had a diagnosis of BD (type I, II, or not otherwise specified [NOS]) and 45 were in the control group. BD participants were recruited from a tertiary subspecialty clinic at an academic health science centre in Toronto, Ontario. All participants provided informed consent and had no pre-existing cardiac, inflammatory, and/ or autoimmune illness, infectious illness in the past 14 days, substance dependence in the past 3 months, or contraindications to MRI. No participants were taking hyperglycemic, anti-hypertensive, anti-platelet, anti-lipidemic, or daily anti-inflammatory medications. Control group youth were recruited via hospital and community advertisements. Control group participants did not have any lifetime major psychiatric disorders (e.g. BD, MDD, and psychosis), alcohol/drug dependence, or first- or second-degree family history of BD or psychotic disorders. Control group participants were also excluded if they had other psychiatric disorders and/or exposure to psychiatric medications in the past 3 months. All participants and their parent/guardian(s) provided written informed consent. All procedures were approved by the research ethics board at Sunnybrook Health Sciences Centre and at Centre for Addiction and Mental Health (CAMH). Clinical Procedures and Measures The Schedule for Affective Disorders and Schizophrenia for School Age Children, Present and Life Version (K-SADS-PL) 40 was used to confirm psychiatric diagnoses, treatment, and mood symptoms for all participants. Related current and lifetime mood symptom severity scores were assessed using the KSADS Depression Rating Scale (DRS) and the Mania Rating Scale (MRS) 41 , 42 . Diagnoses were based on the Diagnosis and Statistical Manual of Mental Disorders, 4th Edition criteria (DSM-IV) since this sample was recruited from 2012 to 2019 and the DSM-5 version of the K-SADS-PL was not available until December 2016. Diagnosis of BD-NOS was based on operationalized criteria from the Course and Outcome of Bipolar Illness in Youth (COBY) study for duration of symptoms (minimum 4 hours/day) and number of hypomanic days (minimum 4 in lifetime) 43 , while retaining DSM-5 symptom count requirements (i.e. 3 symptoms when elation was the primary symptom, 4 symptoms when irritability was the primary symptom). Diagnoses were confirmed during case conferences with a licensed child-adolescent psychiatrist. Age of onset was the age at which the participant first experienced an episode of hypomania or mania that affected functioning or met diagnosis criteria for BD-NOS. The Family History Screen was used to ascertain first- and second-degree family psychiatry history 44 . Information regarding psychotropic medication and tobacco use were collected during the K-SADS-PL interview. Tanner stages (1–5 stage scale) were determined using the Pubertal Developmental Scale 45 . Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Mood symptoms were determined through items from interviews with both youth and parents using the DRS and MRS. These symptoms were assessed based on the most severe week in the preceding month. Following K-SADS-PL guidelines, summary scores were created for analysis, considering ratings from both youth and parents, along with any relevant medical records. A current DRS score ≥ 13 was classified as current depression, and a current MRS score ≥ 12 was categorized as current hypomania 41 , 42 . These definitions were derived from existing literature 46 and from our own group's previous research 28 , 33 . Saliva and DNA Extraction Participants were instructed to refrain from eating, drinking, smoking, and chewing gum 30 minutes prior to saliva collection. 2 mL of saliva samples from each participants were collected using DNA Genotek Oragene-500 (DNA Genotek Inc, Ottawa, Canada) kits. DNA extraction was performed using the CheMagic MSM I DNA extractor (Perkin-Elmer, Waltham, MA, USA) per manufacturer instructions. DNA was quantified using a Nanodrop 8000 spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA) and diluted to a concentration of 20 ng/µL. DNA extraction, quantification, and dilutions were carried out at the CAMH Biobank and Molecular Core Facility. Amplicon Generation Next-generation sequencing (NGS) amplicon libraries were prepared by the CAMH Biobank and Molecular Core Facility and sequenced at the Donnelly Sequencing Centre ( http://ccbr.utoronto.ca/donnelly-sequencing-centre ). The DNA pool of each individual contained approximatly 100 ng mtDNA, which were enriched using Takara LA Taq DNA polymerase (Illumina DNA Prep, Tagmentation kit, Cat# 20018705). The following set of primers was used: Forward mt16426: 5’-CCGCACAAGAGTGCTACTCTCCT-3’, and Reverse mt16425: 5’-GATATTGATTTCACGGAGGATGG-3’. 1% Agarose gel electrophoresis was used to check the amplified products and Nanodrop was used for the quantification of the gel-purified mtDNA. Paired-end read sequencing raw data were exported as FASTQ files for quality control and variant calling. Mitochondrial DNA Variants Calling and Filtering A custom script was used to trim the sequence reads to remove adapters and bases where the Phred quality score, which is a measure of base quality in DNA sequencing, was less than 20. Burrows-Wheeler Aligner was used to align the trimmed reads. The hg38 version of the genome is available from genomics public data ( https://console.cloud.google.com/storage/browser/genomics-public-data/references/hg38/v0;tab=objects?pageState=(%22StorageObjectListTable%22:(%22f%22:%22%255B%255D%22))&prefix=&forceOnObjectsSortingFiltering=false ) was used as the reference to perform the alignment. Variants calling in the control region (coordinates chromosome (Chr) M: 16,024–16,569 and Chr M: 1–576; observe an artificial break in the region) was done as follows: by shifting 8000 nucleotides, reads originally aligning to Chr M were realigned to a Chr M reference genome; then variants called on the shifted reference were mapped back to standard coordinates (Picard liftOver) and were combined with variants from the non-control region. The aligned reads with low-quality mapping scores, unusual insert-sizes, and cross-chromosome mapping were filtered out. Reads were re-aligned around variants and Base Quality Score Recalibration (BQSR) was done using GATK, Mutect2 caller ( https://gatk.broadinstitute.org/hc/en-us/articles/360037593851-Mutect2 ). The reference mitochondrial sequence was the revised mtDNA Cambridge Reference Sequence (rCRS) 47 of the Human Mitochondrial DNA (NC_012920.1). mtDNA variants were identified using mtDNA Serve 2 in the Mitoverse platform ( https://mitoverse.readthedocs.io/mtdna-server/mtdna-server/ ). Annotation of the variants was done using MITOMAP and Human Mitochondrial Genome Database (mtDB). Haplogroup assignment 48 was done using HaploGrep2 and estimation of mtDNA contamination 49 was done by using HaploCheck (v1.0.5). A VCF with mtDNA variants was produced for each input sample. Only biallelic SNPs and markers with less than 5% missing data were included in the analysis. Variants with the variant allele frequency (VAF) 0.95-1.00 were defined as homoplasmic variants 50 and variants with VAF 0.03–0.95 were defined as heteroplasmic variants 51 . Quality Control Quality control of genetic data was performed as follows: variants from samples identified as contaminated by HaploCheck (v1.0.5) were excluded; variants at primer regions, artifact-prone sites (positions 301, 302, 310, 316, 3107, 16182), and at the phantom mutations sites (72':['G','T'], 257':['A','C'], '414':['G','T'], 3492':['A','C'], 3511':['A','C'], 4774':['T','A'], 5290':['A','T'], '9801':['G','T'], 10306':['A','C'], '10792':['A','C'], '11090':['A','C']) were excluded; variants from samples with coverage of each strand (forward and reverse) lower than 200x and with a forward to reverse strand reading ratio of less than 0.5 or greater than 1.5 were excluded. Functional Annotation of Variants and mtDNA Functional Impact Score A combination of tools, including MutPred 52 , 53 , mtDNA Selection 54 , and Mito tool 55 , 56 , were utilized to assess the impact of amino acid changes caused by mtDNA mutations on protein function through analyses of the sequence homology, evolutionary conservation, and protein structure 57 . Functional impact (FI) scores were calculated using the same set of tools for all of the 24,206 possible amino acid variations defined by a single point mutation away from the rCRS reference sequence. The amino acid variation with a higher FI score is more likely to be pathogenic. The MutPred algorithm assigns a score between 0 and 1, and we conducted analysis for the pathogenicity levels including only variants with a MutPred score > 0.5 as those are considered as potentially "harmful". The FI score for each individual was calculated by combining the sum of predicted MutPred, mtDNA selection, and MitoTool scores for each variant across all potentially “harmful” variants (Supplementary Table 3). Two-way ANOVA was used to assess the between-group differences in individual or global mtDNA FI scores (Supplementary Table 4). Databases such as Mitomap 58 and ClinVar 59 were used to obtain information including allele frequency in several known datasets, reported variants that were associated with diseases, and whether the mutations were novel or known. The identified mtDNA variants were annotated to include the mutation type, region of the mutations, and whether they were synonymous or not. Mitochondrial DNA Global Heteroplasmy Level and Count The global heteroplasmy level, calculated by taking the sum of the heteroplasmy level for each variant per individual and dividing it by the total number of heteroplasmic sites of that individual, ranged between 0 and 1. The heteroplasmy count was reflects the number of heteroplasmic sites within an individual's mitochondrial DNA pool. Haplogroups Assignment mtDNA haplogroup for each individual was determined using Haplogrep 2 using the rCRS-orientated version of PhyloTree Build 17 48, 60 . The filtering criteria for our sample is a quality score of 0.9 or higher by HaploGrep 2. Due to the relatively small sample size, phylogenetically related haplogroups were combined into macro-haplogroups (H-HV-V (H, HV and V), J-T (J and T), U-K (U and K) and others) (Supplementary Table 5, 7). Individuals assigned as non-European haplogroups were excluded because they comprised less than 10% of the total sample. MRI Acquisition Structural and ASL-CBF images of the brain were collected with a 3 Tesla Philips Achieva MRI scanner (Philips Medical Systems, Best, Netherlands) with an 8-channel head receiver coil. High-resolution fast-field echo T1-weighted images were acquired for anatomical registration, and pseudo-continuous ASL (ASL) images were collected to derive CBF measures. Structural scans were acquired with the following parameters: repetition time (TR) of 9.5 milliseconds (ms), echo time (TE) of 2.3ms, inversion time (TI) of 1400ms, spatial resolution of 0.94 × 1.17× 1.2 mm, acquisition matrix of 256 × 164 × 140, field of view (FOV) of 240 × 191 mm, flip angle of 8°, and scan duration of 8 minutes and 56 seconds. Phase contrast angiography scout images were acquired for visualizing vascular anatomy prior to ASL imaging. ASL images were obtained with single-shot 2-dimensional echo planar imaging with the following parameters: TR of 4000 ms, TE of 9.7 ms, matrix of 64 × 64 × 18, spatial resolution of 3 × 3 × 5 mm, labeling duration of 1650-ms, post-label delay of 1600-ms for the most inferior slice, 30 control-tag pairs, and scan duration of 4 minutes and 8 seconds. ASL reference images were acquired with a TR of 10 seconds to establish initial magnetization for CBF quantification. Image Processing FMRIB Software Library (FSL) tools were used for image processing 61 . T1-weighted images were skull-stripped 62 , co-registered to ASL space and standard space, normalized, and segmented into gray matter and white matter 61 . ASL data was co-registered to a reference volume. Differences in consecutive control and tag images were calculated to obtain CBF-weighted images. Images with excess head motion were identified automatically and removed to optimize CBF signal 63 . Estimates were converted to absolute units (mL/100 g/min) using quantification values and the ASL reference image 64 . CBF maps were smoothed using a 5 mm full width at half maximum kernel. Regional CBF values were then extracted from masks of the ACC and the whole brain gray matter, which were defined using the Harvard-Oxford Cortical and subcortical Structural Atlases in FSL in 3 mm standard space. Statistical Analysis Statistical analyses were performed for clinical and demographic variables using the SPSS statistical software version 26 (IBM; NY, USA). Continuous variables were assessed for normality using the Shapiro-Wilks test. The equal variance assumptions of all continuous variables were checked using Levene’s test. Between-group differences in demographic and clinical characteristics were assessed using independent-samples t -tests, Mann-Whitney U -tests, and Kruskal-Wallis test for continuous and ordinal variables or chi-square tests for categorical variables as appropriate. Effect sizes were reported as Cramer’s V ( V ), Cohen’s d ( d ), or eta-squared (η2). An a priori approach was taken to assess the association between mtDNA FI score, mtDNA haplogroups, both mtDNA heteroplasmy level and count, and CBF. The association of mtDNA FI score, mtDNA haplogroups, mtDNA heteroplasmy level, and mtDNA heteroplasmy count with ROIs (i.e. ACC gray matter CBF and whole brain gray matter CBF) were tested using a General Linear Model (GLM) in SPSS, covarying for age and sex, within the overall sample and each diagnosis group (BD and CG). Bonferroni correction was used to correct for 6 (2 ROIs*3 groups = 6) comparisons ( p = 0.05/6 = 0.008). We also undertook a data-driven whole-brain voxel-based approach to examine regions where mtDNA FI score is associated with regional CBF, we also examined regions where haplogroup JT is associated with regional CBF. A GLM was designed in FSL using the FMRIB’s Local Analysis of Mixed Effects (FLAME1). Three group contrast CBF maps corresponding to the three analyses (i.e. within the overall sample, within BD, and within CG) were corrected using FSL cluster, a multiple comparisons correction method that controls family-wise error rate. A cluster-forming threshold of z = 1.95 (corresponding to p = 0.05) and a secondary threshold to determine cluster significance of p = 0.05 was applied. The peak voxel within each cluster was determined by finding the voxel within the cluster with the highest significance and was localized to anatomical regions using Harvard-Oxford Cortical and subcortical Structural Atlases. Lastly, we undertook sensitivity analyses by adding BMI, current depression mood symptom (presence or absence), and current manic mood symptom (presence or absence) as a covariate individually in addition to the covariates used previously. In the post-hoc analyses and sensitivity analyses for the vertex-wise findings, each significant cluster was treated as an ROI. Results Demographic and Clinical Characteristics Demographic characteristics are summarized in Supplementary Table 1. This study included 101 youth, 56 with BD (19 BD-I, 16 BD-II, 21 BD-NOS) and 45 in CG. There were no significant between-group differences in age, BMI, or race (Supplementary Table 1). BD group (70% female) had significantly more females than control group (44% female; χ2 = 6.52, V = 0.25, p = 0.01), and there were significant differences in Tanner stage between BD and CG, with BD group having greater pubertal status (H = 6.04, η2 = 0.05, p = 0.01). Clinical characteristics are summarized in Supplementary Table 2. mtDNA Analyses A comprehensive analysis of mtDNA variants was performed across the 101 participants, resulting in the identification of a total of 5833 variants. A total sequencing coverage of 1,788.2 X of the mitochondrial genome for this work was achieved. To ensure the quality and reliability of the data, a stringent quality control analysis was conducted, as outlined in the methods section. In our sample, 64 of the 2263 homoplasmic variants detected were common variants (Minor allele frequency > 0.05). Functional analysis revealed 9 non-synonymous common variants, and the variants m.4917A > G in the MT-ND2 gene, m.4216T > C in the MT-ND1 gene, and m.14798T > C in the MT-CYB were potentially harmful, indicated by MutPred scores larger than 0.5. Haplogroups Our samples consisted of European participants exclusively and were combined into four macro-haplogroups (H-HV-V, J-T, U-K, and others). The proportion of each macro-haplogroup represented in our dataset was H-HV-V (47%), J-T (20%), U-K (27%), and others (6%) (Supplementary Table 5). Moreover, haplogroup J (partially defined by the presence of the m.4216T > C and m.14798T > C variants), haplogroup K (partially defined by the presence of m.14798T > C variant only), and haplogroup T (partially defined by the presence of m.4216T > C and m.4917A > G variants). Global Heteroplasmy Level and Count The global heteroplasmy level and count, which are indicators of the mtDNA instability, did not differ significantly between BD and CG (t = 0.59, p = 0.56; and t = 0.79, p = 0.43, respectively; Supplementary Table 4). ROI CBF Analysis The association of mtDNA FI score with ACC and whole brain gray matter CBF is presented in Table 1 . Higher mtDNA FI score was nominally associated with higher ACC gray matter CBF within the overall sample (β = 0.22 p = 0.031, 95%CI [0.02, 0.41]) and within BD group (β = 0.24, p = 0.049, 95%CI [0.00, 0.47]); these associations were not significant after correcting for multiple comparisons. There were no significant associations between heteroplasmy level or heteroplasmy count and CBF in any of the ROIs (Supplementary Table 7). There were no significant associations between haplogroups and CBF in any of the ROIs (Supplementary Table 8). There were no significant findings for the whole brain gray matter CBF ROI analyses, heteroplasmy count analyses, or within-group analyses. Table 1 Association of mtDNA Functional Impact Score Main Effect with CBF in ROIs. Whole Brain Gray Matter CBF ACC Gray Matter CBF Diagnosis β 95%CI p β 95%CI p BD 0.18 (-0.07, 0.42) 0.16 0.24 (0.00, 0.47) 0.049 CG 0.10 (-0.22, 0.45) 0.49 0.17 (-0.20, 0.53) 0.36 Overall Sample 0.16 (-0.04, 0.35) 0.11 0.22 (0.02, 0.41) 0.031 ACC = Anterior cingulate cortex; BD = Bipolar disorder; Significant group effects are bolded. Voxel-based CBF Analysis Table 2 summarizes the clusters identified by the voxel-based analyses. Within the overall sample, higher mtDNA FI score was associated with higher CBF in three clusters with a peak region within the left cerebral white matter, right putamen, and posterior cingulate and precentral gyrus (Table 2 ; Fig. 1 ). The cluster with a peak region in left cerebral white matter extended into regions including basal ganglia (putamen and pallidum), left thalamus, left caudate, insular cortex, and parietal lobe (parietal operculum and supramaginal gyri). The right putamen cluster extended into regions including right caudate, right pallidum, and cingulate cortex. The precentral gyrus and posterior cingulate cortex cluster extended into ACC and parietal lobe (postcentral gyri and precuneus cortex). Table 2 Association of mtDNA Heteroplasmy Global Level and Heteroplasmy Count Main Effect with CBF in ROIs. Association of mtDNA Heteroplasmy Global Level Main Effect with CBF in ROIs Whole Brain Gray Matter CBF ACC Gray Matter CBF Diagnosis β 95%CI p β 95%CI p BD -0.08 (-0.37, 0.21) 0.59 -0.18 (-0.46, 0.10) 0.21 CG -0.15 (-0.42, 0.13) 0.27 -0.17 (-0.47, 0.14) 0.27 Overall Sample -0.15 (-0.34, 0.05) 0.14 -0.19 (-0.39, -0.00) 0.05 Association of mtDNA Heteroplasmy Count Main Effect with CBF in ROIs Whole Brain Gray Matter CBF ACC Gray Matter CBF Diagnosis β 95%CI p β 95%CI p BD 0.20 (-0.06, 0.46) 0.13 0.16 (-0.09, 0.42) 0.20 CG 0.12 (-0.18, 0.42) 0.44 0.07 (-0.26, 0.40) 0.67 Overall Sample 0.16 (-0.03, 0.36) 0.10 0.12 (-0.07, 0.32) 0.22 ACC = Anterior cingulate cortex; BD = Bipolar disorder; Significant group effects are bolded. Within the BD group, higher mtDNA FI score was associated with higher CBF in two clusters with a peak region within the right caudate and superior parietal lobule (Table 2 ; Fig. 2 ). The right caudate cluster extended into regions including left caudate, cingulate cortex, frontal lobe (middle frontal gyri, pars opercularis, pars triangularis, precentral gyri, and frontal pole), and parietal lobe (postcentral gyri). The cluster with a peak region in superior parietal lobule extended into regions including frontal lobe (middle frontal gyri and precentral gyri), occipital lobe (superior lateral occipital cortex, and parietal lobe (postcentral gyri, anterior supramaginal gyrus, and posterior supramaginal gyrus). Within the control group, higher mtDNA FI score was associated with higher CBF in one cluster in the parietal operculum cortex (Table 2 ; Fig. 3 ). Table 3 summarizes the clusters identified by the voxel-based analyses where the appearance of haplogroup JT was associated with higher CBF. Within the overall sample, haplogroup JT was associated with higher CBF in two clusters with a peak region in the left thalamus, right lateral ventricle, and bilateral white matter. The cluster with a peak region in the left thalamus extended into regions including the left putamen, left pallidum, left posterior cingulate gyrus, left posterior parahippocampal gyrus, left anterior cingulate gyrus, and left lateral ventricle. The right lateral ventricle cluster extended into regions including the right caudate, right cerebral cortex, right accumbens, right pallidum, anterior cingulate cortex, and paracingulate cortex. Table 3 Association of mtDNA Haplogroup JT with CBF from Voxel-based Analyses. Diagnosis Cluster size (voxels) cwp MAX X (vox) MAX Y (vox) MAX Z (vox) Regions Overall Sample 407 < 0.001 36 39 26 left thalamus (peak), left cerebral white matter (peak) , left putamen, left pallidum, left posterior cingulate gyrus, left posterior parahippocampal gyrus, left anterior cingulate gyrus, left lateral ventricle 115 < 0.001 23 46 31 right cerebral white matter (peak), right lateral ventricle (peak) , right anterior cingulate gyrus, right paracingulate gyrus, right caudate, right putamen BD 56 0.027 36 23 25 right lingual gyrus (peak), right posterior cingulate gyrus (peak), right precuneus cortex(peak) , right intracalcarine cortex (peak), right supracalcarine cortex, right cuneal cortex cwp = cluster wise p-value; BD = Bipolar Disorder Within the BD group, the presence of haplogroup JT was associated with higher CBF in one cluster with a peak region in the right lingual gyrus, right posterior cingulate gyrus, right precuneus cortex, and right intracalcarine cortex. This cluster extended into regions including the right supracalcarine cortex and the right cuneal cortex. Sensitivity Analysis Sensitivity analyses were undertaken for both ROI and voxel-based analyses. By adding BMI, current depression mood severity, and current manic mood severity as a covariate individually, all findings remained significant. In addition, current depression mood severity was associated with lower right caudate CBF (β=-0.247 p = 0.049, 95% CI [-0.55, 0.00]). Discussion In this study, we investigated mtDNA pathogenicity in relation to CBF in youth with and without BD. ROI analyses focused on ACC and whole brain gray matter CBF revealed that mtDNA pathogenicity, reflected in higher mtDNA FI score, was associated with higher ACC gray matter CBF in the overall sample. In voxel-based analyses, we found that higher mtDNA FI score was associated with higher regional CBF in the basal ganglia, frontal and parietal lobe, and cingulate gyrus within the overall sample and within the BD group. In addition, higher mtDNA FI score was associated with higher cerebral white matter CBF across all voxel-based findings. This study addresses a gap in the literature, integrating CBF in relation to mitochondrial dysfunction or mtDNA variants among individuals with BD. Each of the three potentially harmful variants (i.e. m.4917A > G, m.4216T > C, m.14798T > C) that were used to generate the mtDNA FI score are located on sequences in mitochondrial genes that encode functional proteins. The m.4917A > G variant is located on the MT-ND1 gene that codes for NADH dehydrogenase 1, and the m.4216T > C variant is located on the MT-ND2 gene that codes for NADH dehydrogenase 2. Both NADH dehydrogenase 1 and 2 are part of ETC complex I, which is responsible for the first step in the ETC, transferring electrons from NADH to ubiquinone 65 . The m.14798T > C is located on the MT-CYB gene that codes for cytochrome b, which is part of ETC complex III that is responsible for transferring electrons from the ubiquinol to cytochrome c 65, 66 . We therefore speculate that the identified variants lead to impaired oxidative phosphorylation and reduced energy production. In ROI analyses, higher mtDNA FI score was associated with higher CBF in the ACC, a region where both altered CBF levels and altered mitochondrial energetics have been reported in BD 31, 67–69 . In a prior study based on an overlapping sample, our group found that lower ACC CBF was associated with greater severity of symptoms including depressed mood and anhedonia in youth with BD 33 . Relatedly, neurochemical findings that were obtained using MRS suggested that altered energy metabolism in ACC was associated with altered mood states as well as anhedonia symptoms in adults with BD 67, 70 . Our recent study also found higher temperature-to-CBF ratio in the ACC and precuneus was associated with more severe depression symptoms in youth with BD 71 . Thus, the current findings add to the literature by showing anomalous ACC CBF and energy metabolism, implicating mitochondrial dysfunction. The current study provides preliminary evidence that mtDNA FI correlates with ACC CBF and energy metabolism abnormalities in youth with BD. We did not find an association of mtDNA heteroplasmic level with CBF in ROI analyses. mtDNA heteroplasmic level is indicative of mtDNA stability and is affected by numerous factors including somatic segregation of inherited heteroplasmy, acquisition of new mutations during development and aging, and selection of mtDNA through differential replication and repair 72 . A deeper comprehension of the processes behind the spread of mtDNA mutations and the rise in heteroplasmy load is necessary to understand the mechanisms underlying the current findings. Voxel-based analyses identified regions within the basal ganglia, default mode network (DMN), and cingulate, key brain regions involved in reward processing, memory, attention, and emotion regulation 73 – 75 . Findings of lower mitochondrial energy metabolism in the caudate, putamen, and pallidum, components of basal ganglia in adults with BD during depressed and manic mood states 74 , 76 – 78 have been previously reported. Lower mitochondrial energy metabolism has also been associated with lower executive functions in adults with BD 79 . However, there are also exceptions as higher thalamic mitochondrial metabolism has also been associated with lower executive functions in adults with BD and current suicide ideation 80 . We also identified a cluster extending from posterior cingulate gyrus to precuneus and ACC. As the central node in the DMN, posterior cingulate gyrus is highly connected and metabolically active, with CBF levels that tend to be higher than the whole-brain average 81 . Relatedly, increased DMN and precuneus functional connectivity, which is implicated in human attention and self-consciousness 82 , 83 , has been found in euthymic BD patients 84 , 85 , whereas decreased DMN and precuneus functional connectivity has been found in BD-II depression patients 86 . Finally, DMN and ACC hypoconnectivity has been associated with more severe anxiety and depressive symptoms in BD 87 . The association of higher mtDNA FI score with higher regional CBF may reflect compensatory processes (Fig. 4 ) to maintain metabolic demands and remove waste during mitochondrial dysfunction 32 , 88 , 89 . Mitochondrial dysfunction impairs oxidative phosphorylation, necessitating anaerobic glycolysis in BD 90, 91 . In comparison to oxidative phosphorylation, anaerobic glycolysis requires more glucose to produce similar amounts of ATP and generates more lactate as a by-product 92 . Thus, the association of higher mtDNA FI score with higher regional CBF in our results may act to compensate for increased demand for both glucose delivery and lactate removal 93 , 94 . In addition to gray matter findings, voxel-based analyses also identified five significant white matter clusters. In three of these clusters, the white matter regions are adjacent to gray matter, and therefore potentially explained by partial volume effects 95 . However, in the other two clusters, the white matter is mainly distal from the adjacent gray matter. Lower subcortical white matter CBF has been reported in BD 31 . White matter CBF is affected by the white matter integrity 96 in addition to white matter metabolism 97 . Thus, our findings suggest that mtDNA variants may affect white matter CBF by affecting white matter integrity, as white matter is composed primarily of myelinated axons and is highly vulnerable to oxidative damages that result from mitochondrial dysfunction 20 , 98 . Alternatively, the white matter CBF findings might be secondary to the gray matter CBF findings as gray matter CBF is significantly associated with white matter integrity. Since both variants mt4216 and mt4917 are from the same macrohaplogroup JT, we further took exploratory vertex-wise analyses to examine the association of haplogroup JT with CBF in the overall sample, BD group, and the CG. We observed that individuals with haplogroup JT demonstrated increased CBF in a distributed set of cortical and subcortical regions, including the left thalamus, bilateral cerebral white matter, left putamen, pallidum, anterior and posterior cingulate gyri, and medial occipital areas (e.g., lingual gyrus, precuneus, intracalcarine, and supracalcarine cortex). Notably, many of these regions overlapped with those previously identified in our analyses linking higher mtDNA FI score to increased CBF: the anterior and posterior cingulate cortex, precuneus, thalamus, and lateral ventricles are part of the DMN, a system highly active at rest and sensitive to mitochondrial efficiency 99 ; subcortical structures such as the putamen, caudate, and pallidum play roles in motor control and emotion regulation and exhibit high metabolic demands 78 . Since the FI score is calculated using functional weighting of variants including mt4216 and mt4917, both of which belong to haplogroup JT, it is plausible that JT is a key driver of the observed association between FI score and cerebral blood flow. These findings suggest that individuals with haplogroup JT may show a distinct neurovascular phenotype characterized by increased perfusion in regions critical for cognitive control, memory, and visual processing, potentially reflecting compensatory responses to mitochondrial inefficiency. Several limitations must be considered when interpreting present findings. First, the current sample size is relatively small, precluding several potentially informative secondary analyses such as those focused on mood states, BD subtypes, and comorbidity. In addition, larger sample size would provide greater power to detect additional variants and in turn generate a broader range of mtDNA FI scores. Second, we enrolled a naturalistic sample characterized by heterogeneity in medications, symptomatic status, BD subtypes, family psychiatric history, comorbidities, and life adversity. Third, we only included individuals of European ancestry in the current analyses, limiting the interpretability of the results in other race groups. Finally, we could not examine CMRO 2 or cerebral metabolic rate of glucose, which could provide a basis for directly testing our speculations regarding current findings. In conclusion, this study demonstrates that higher mtDNA pathogenicity is associated with higher CBF in the overall sample and within the BD group. Although FI scores are genetically based and consistent across the lifespan, regional hyperperfusion during euthymia has only been reported in youth. Future studies incorporating time-varying indices of mitochondrial pathogenicity, such as heteroplasmy, are needed to parse these developmental differences. Our results add further support to the premise that regional CBF in youth with BD is in part reflective of compensatory mechanisms that arise in response to inefficient energy metabolism. Therapeutically, this highlights the potential of interventions that optimize energy metabolism, such as ketogenic diet 100 and cardiorespiratory (i.e., aerobic) exercise 101 . Future studies using larger samples, including additional objective metrics of cerebral energy metabolism, and ideally using a lifespan approach, are needed to validate and expand upon current findings. Declarations Statement of Interest Dr. Goldstein acknowledges research funding from Canadian Institutes of Health Research, Heart and Stroke Foundation, the Department of Psychiatry at the University of Toronto, and the CAMH Foundation. Dr. Goldstein also acknowledges his position as RBC Investments Chair in Children’s Mental Health and Developmental Psychopathology at CAMH, a joint Hospital-University Chair between the University of Toronto, CAMH, and the CAMH Foundation. Dr. Mendes-Silva acknowledges support from the CIHR and CAMH Discovery Fund Fellowships. Dr. Vanessa Goncalves acknowledges the Miner's Lamp Innovation Fund (University of Toronto). All other authors report no actual or potential conflict of interests. Data Availability Statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Code Availability The codes for mtDNA variant detection and quality control analyses are publicly available at https://github.com/lmtani/wf-human-mito and mtDNA Quality Control analysis - Genetics - KCNI Knowledge Base (camh.ca). Funding Source This study was supported by the Canadian Institutes of Health Research (CIHR MOP 136947) and the Ontario Mental Health Foundation (OMHF) to Benjamin I. Goldstein. Author Contributions Suyi Shao was the writer of the manuscript and performed all relevant analyses with the support of Ana Paula Mendes-Silva, Yi Zou and Kody G. Kennedy. Vanessa F Goncalves, Bradley J. MacIntosh, Mikaela K. Dimick and all provided critical feedback and intellectual content to the paper. Additionally, Benjamin I. Goldstein is the principal investigator, participated in the conceptualization of the study, and provided critical feedback and intellectual content to the paper. References Goldstein BI, Birmaher B, Carlson GA, DelBello MP, Findling RL, Fristad M et al. The International Society for Bipolar Disorders Task Force report on pediatric bipolar disorder: Knowledge to date and directions for future research. Bipolar Disord 2017; 19 (7) : 524-543. 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Clinical and neuroimaging correlates of cardiorespiratory fitness in adolescents with bipolar disorder. Bipolar Disord 2021; 23 (3) : 274-283. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files SupplementaryTablesMolpsyc.docx supplementary tables Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7635750","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":525958486,"identity":"18d7151b-6bb7-4975-a2a7-2b1019d5cebf","order_by":0,"name":"Benjamin 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13:40:46","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":255177,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7635750/v1/6942aabd475e7cbc5f9edd1a.html"},{"id":93941662,"identity":"9a76ff9f-537e-4ce7-91c5-776c60835e03","added_by":"auto","created_at":"2025-10-20 13:40:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":530897,"visible":true,"origin":"","legend":"\u003cp\u003eCluster-corrected Z-statistic image (z=1.95, secondary threshold of \u003cem\u003ep\u003c/em\u003e=0.05) overlaid onto a standard structural image. The red to yellow color scale displays regions in which higher mtDNA pathogenicity score was associated with higher CBF within the overall sample.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7635750/v1/10fd732f8a27882e75b59dd4.png"},{"id":93941663,"identity":"4c0e847a-145a-4e4c-aa17-a5cc407fc2e2","added_by":"auto","created_at":"2025-10-20 13:40:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":518432,"visible":true,"origin":"","legend":"\u003cp\u003eCluster-corrected Z-statistic image (z=1.95, secondary threshold of \u003cem\u003ep\u003c/em\u003e=0.05) overlaid onto a standard structural image. The red to yellow color scale displays regions in which higher mtDNA pathogenicity score was associated with higher CBF within the BD group.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7635750/v1/37b3aca368d444f48fd1273f.png"},{"id":93941678,"identity":"86e1d66d-54b5-4d5e-8260-0818e65a0129","added_by":"auto","created_at":"2025-10-20 13:40:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":439437,"visible":true,"origin":"","legend":"\u003cp\u003eCluster-corrected Z-statistic image (z=1.95, secondary threshold of \u003cem\u003ep\u003c/em\u003e=0.05) overlaid onto a standard structural image. The red to yellow color scale displays regions in which higher mtDNA pathogenicity score was associated with higher CBF within the control group.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7635750/v1/c8fd35add95877578e546a95.png"},{"id":93941664,"identity":"b7e58911-fd18-4a52-9302-7618470d5cf0","added_by":"auto","created_at":"2025-10-20 13:40:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":289477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpeculated mechanism that underlie the association of mitochondrial dysfunction and CBF. \u003c/strong\u003ea) In normal conditions, CBF delivers nutrients and energy substrate to the brain tissue. Healthy mitochondria generate energy by converting pyruvate to ATP during aerobic glycolysis. b) In disease conditions, dysfunctional mitochondria have reduced glycolytic capacity necessitating that pyruvate will undergoes anaerobic glycolysis inside the cytosol. Anaerobic glycolysis requires more glucose than aerobic glycolysis to generate equivalent amounts of ATP, and results in excess lactate generation. Compensatory increases in CBF deliver more glucose and remove excess lactate.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7635750/v1/30374d8e7831e6ae5c78d09b.png"},{"id":95654878,"identity":"c437e0ca-7276-4f29-99a3-d33b3643380d","added_by":"auto","created_at":"2025-11-11 16:13:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3553221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7635750/v1/4b8aad26-c903-47d8-8bea-314f18383229.pdf"},{"id":93943648,"identity":"94dca390-1921-4722-9810-ccdc49e09e95","added_by":"auto","created_at":"2025-10-20 13:56:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":36829,"visible":true,"origin":"","legend":"supplementary tables","description":"","filename":"SupplementaryTablesMolpsyc.docx","url":"https://assets-eu.researchsquare.com/files/rs-7635750/v1/3226897b7b1c640a1b013819.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Greater Mitochondrial DNA Pathogenicity is Associated with Greater Regional Cerebral Blood Flow in Youth Bipolar Disorder","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBipolar disorder (BD) is a severe psychiatric disorder that affects approximately 1\u0026ndash;3% of youth \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The disorder is characterized by recurrent episodes of mania and depression: mania is reported to increase CBF and energy, while depression has the opposite association \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Relatedly, mitochondrial dysfunction has been implicated in the etiology of BD \u003csup\u003e3, 4\u003c/sup\u003e. Mitochondria are the main source of energy for cells and supply most of the energy demands to support cerebral metabolism and energetics \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Magnetic resonance spectroscopy (MRS) findings in adults and youth with BD \u003csup\u003e4, 6, 7\u003c/sup\u003e and mitochondrial morphology abnormalities in fibroblasts from BD adults \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e suggest that abnormal mitochondrial morphology is linked to altered energy metabolism in the progression of BD \u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMitochondria have their own genome (mtDNA) that encodes 2 ribosomal RNA (rRNA), 22 transfer RNA (tRNA), and 13 electron transport chain (ETC) subunits, which are all related to oxidative phosphorylation \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. There is evidence from adults with BD of mtDNA deletion \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, depletion \u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and mutation \u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In a recent study, three mtDNA variants (m.10410T\u0026thinsp;\u0026gt;\u0026thinsp;A, m.13135G\u0026thinsp;\u0026gt;\u0026thinsp;A, and m.13708G\u0026thinsp;\u0026gt;\u0026thinsp;A), located near or on NADH dehydrogenase were associated with BD \u003csup\u003e24\u003c/sup\u003e. A controlled study in adults found that m.16519T\u0026thinsp;\u0026gt;\u0026thinsp;C, which is located in the hypervariable region, is associated with risk of BD or schizophrenia (combined sample) \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Further, three variants in the D-loop region (m.114 T\u0026thinsp;\u0026gt;\u0026thinsp;C, m.195 C\u0026thinsp;\u0026gt;\u0026thinsp;T, m.16300 G\u0026thinsp;\u0026gt;\u0026thinsp;A), and one variant in ND4L (m.10652 C\u0026thinsp;\u0026gt;\u0026thinsp;T) have been associated with BD in adults \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. A study of transmitochondrial hybrid cells found that variant m.10398A\u0026thinsp;\u0026gt;\u0026thinsp;G, a risk factor for BD, was associated with differences in mitochondrial pH and intracellular calcium dynamics \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Additionally, mtDNA variants can lead to alteration of cerebral energy metabolism \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, medication response, psychosis in BD \u003csup\u003e19, 23\u003c/sup\u003e, and mitochondrial protein function \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCerebral blood flow (CBF) also plays an essential role in brain energy metabolism \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. CBF is defined as the volume of blood delivered to a defined mass of brain tissue per unit of time and is maintained by cerebral autoregulation \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. CBF delivers energy by carrying glucose and oxygen to active brain regions and facilitates the clearance of metabolic byproducts \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. CBF can be measured non-invasively using arterial spin labeling (ASL), which is a functional magnetic resonance imaging (MRI) method \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e well-suited for measuring regional estimation \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Adults with BD have reported lower CBF in the frontal, temporal, and parietal regions across different mood states including euthymia \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In contrast to the numerous studies regarding CBF in adults with BD, there are few studies on this topic in youth.\u003c/p\u003e\u003cp\u003ePrior CBF studies from our group, using ASL, have yielded a number of preliminary findings. In contrast to lower CBF in adults, there is evidence of higher global \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and regional CBF in the medial frontal gyrus and middle cingulate cortex in youth with BD compared to a control group, differences that appear to be attenuated by a single session of acute aerobic exercise \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Within-BD analyses further demonstrate that anhedonia and greater severity of depressed mood are associated with lower global and regional CBF in the anterior cingulate cortex (ACC) \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Finally, and perhaps most relevant to mitochondrial dysfunction, we found that the cerebral metabolic rate of oxygen (CMRO\u003csub\u003e2\u003c/sub\u003e), the rate of oxygen consumption by the brain, is unchanged in youth with BD despite elevated CBF \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This suggests an abnormal energy homeostasis, in which mitochondrial dysfunction might play a role.\u003c/p\u003e\u003cp\u003eThere is a gap in knowledge regarding mtDNA mutation and mitochondrial energy metabolism in relation to CBF in both BD and in youth. This is important given that adolescence is a key period of brain maturation, characterized by high metabolic demands \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The current study therefore aims to examine the association between mtDNA variants and CBF differences in youth with and without BD. We focused on whole brain gray matter CBF and specifically the ACC region of interest (ROI). The ACC regulates emotion and cognition and has been repeatedly associated with functional and structural brain abnormalities in BD \u003csup\u003e36\u003c/sup\u003e. In addition, higher levels of expression of genes that are related to aerobic energy metabolism and neuronal functions were found in ACC of humans compared to other primates, suggesting the greater metabolic demand and greater neuronal activity of ACC through evolution \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Lower pH was found in ACC of manic adolescents with BD compared to controls, suggesting mitochondrial dysfunction in the pathophysiology of BD \u003csup\u003e38\u003c/sup\u003e. Lower mitochondria density and higher lactate levels were found in ACC of adults with schizophrenia compared to the controls suggesting dysfunctional energy metabolism resulted from mitochondrial dysfunction \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Given the dearth of prior studies in youth, we also integrated a data-driven voxel-based approach. We hypothesized that the presence of mtDNA variants will be associated with higher global and regional gray matter CBF among youth with BD, but not among the controls.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThis study included 101 English-speaking youth of European ancestry based on genetic data, ages between 13\u0026ndash;20 years. 56 participants had a diagnosis of BD (type I, II, or not otherwise specified [NOS]) and 45 were in the control group. BD participants were recruited from a tertiary subspecialty clinic at an academic health science centre in Toronto, Ontario. All participants provided informed consent and had no pre-existing cardiac, inflammatory, and/ or autoimmune illness, infectious illness in the past 14 days, substance dependence in the past 3 months, or contraindications to MRI. No participants were taking hyperglycemic, anti-hypertensive, anti-platelet, anti-lipidemic, or daily anti-inflammatory medications. Control group youth were recruited via hospital and community advertisements. Control group participants did not have any lifetime major psychiatric disorders (e.g. BD, MDD, and psychosis), alcohol/drug dependence, or first- or second-degree family history of BD or psychotic disorders. Control group participants were also excluded if they had other psychiatric disorders and/or exposure to psychiatric medications in the past 3 months. All participants and their parent/guardian(s) provided written informed consent. All procedures were approved by the research ethics board at Sunnybrook Health Sciences Centre and at Centre for Addiction and Mental Health (CAMH).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical Procedures and Measures\u003c/h3\u003e\n\u003cp\u003eThe Schedule for Affective Disorders and Schizophrenia for School Age Children, Present and Life Version (K-SADS-PL) \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e was used to confirm psychiatric diagnoses, treatment, and mood symptoms for all participants. Related current and lifetime mood symptom severity scores were assessed using the KSADS Depression Rating Scale (DRS) and the Mania Rating Scale (MRS) \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Diagnoses were based on the Diagnosis and Statistical Manual of Mental Disorders, 4th Edition criteria (DSM-IV) since this sample was recruited from 2012 to 2019 and the DSM-5 version of the K-SADS-PL was not available until December 2016. Diagnosis of BD-NOS was based on operationalized criteria from the Course and Outcome of Bipolar Illness in Youth (COBY) study for duration of symptoms (minimum 4 hours/day) and number of hypomanic days (minimum 4 in lifetime) \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, while retaining DSM-5 symptom count requirements (i.e. 3 symptoms when elation was the primary symptom, 4 symptoms when irritability was the primary symptom). Diagnoses were confirmed during case conferences with a licensed child-adolescent psychiatrist. Age of onset was the age at which the participant first experienced an episode of hypomania or mania that affected functioning or met diagnosis criteria for BD-NOS. The Family History Screen was used to ascertain first- and second-degree family psychiatry history \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Information regarding psychotropic medication and tobacco use were collected during the K-SADS-PL interview. Tanner stages (1\u0026ndash;5 stage scale) were determined using the Pubertal Developmental Scale \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters.\u003c/p\u003e\u003cp\u003eMood symptoms were determined through items from interviews with both youth and parents using the DRS and MRS. These symptoms were assessed based on the most severe week in the preceding month. Following K-SADS-PL guidelines, summary scores were created for analysis, considering ratings from both youth and parents, along with any relevant medical records. A current DRS score\u0026thinsp;\u0026ge;\u0026thinsp;13 was classified as current depression, and a current MRS score\u0026thinsp;\u0026ge;\u0026thinsp;12 was categorized as current hypomania \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. These definitions were derived from existing literature \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and from our own group's previous research \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eSaliva and DNA Extraction\u003c/h3\u003e\n\u003cp\u003e Participants were instructed to refrain from eating, drinking, smoking, and chewing gum 30 minutes prior to saliva collection. 2 mL of saliva samples from each participants were collected using DNA Genotek Oragene-500 (DNA Genotek Inc, Ottawa, Canada) kits. DNA extraction was performed using the CheMagic MSM I DNA extractor (Perkin-Elmer, Waltham, MA, USA) per manufacturer instructions. DNA was quantified using a Nanodrop 8000 spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA) and diluted to a concentration of 20 ng/\u0026micro;L. DNA extraction, quantification, and dilutions were carried out at the CAMH Biobank and Molecular Core Facility.\u003c/p\u003e\n\u003ch3\u003eAmplicon Generation\u003c/h3\u003e\n\u003cp\u003eNext-generation sequencing (NGS) amplicon libraries were prepared by the CAMH Biobank and Molecular Core Facility and sequenced at the Donnelly Sequencing Centre (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ccbr.utoronto.ca/donnelly-sequencing-centre\u003c/span\u003e\u003cspan address=\"http://ccbr.utoronto.ca/donnelly-sequencing-centre\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The DNA pool of each individual contained approximatly 100 ng mtDNA, which were enriched using Takara LA Taq DNA polymerase (Illumina DNA Prep, Tagmentation kit, Cat# 20018705). The following set of primers was used: Forward mt16426: 5\u0026rsquo;-CCGCACAAGAGTGCTACTCTCCT-3\u0026rsquo;, and Reverse mt16425: 5\u0026rsquo;-GATATTGATTTCACGGAGGATGG-3\u0026rsquo;. 1% Agarose gel electrophoresis was used to check the amplified products and Nanodrop was used for the quantification of the gel-purified mtDNA. Paired-end read sequencing raw data were exported as FASTQ files for quality control and variant calling.\u003c/p\u003e\n\u003ch3\u003eMitochondrial DNA Variants Calling and Filtering\u003c/h3\u003e\n\u003cp\u003eA custom script was used to trim the sequence reads to remove adapters and bases where the Phred quality score, which is a measure of base quality in DNA sequencing, was less than 20. Burrows-Wheeler Aligner was used to align the trimmed reads. The hg38 version of the genome is available from genomics public data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://console.cloud.google.com/storage/browser/genomics-public-data/references/hg38/v0;tab=objects?pageState=(%22StorageObjectListTable%22:(%22f%22:%22%255B%255D%22))\u0026amp;prefix=\u0026amp;forceOnObjectsSortingFiltering=false\u003c/span\u003e\u003cspan address=\"https://console.cloud.google.com/storage/browser/genomics-public-data/references/hg38/v0;tab=objects?pageState=(%22StorageObjectListTable%22:(%22f%22:%22%255B%255D%22))\u0026amp;prefix=\u0026amp;forceOnObjectsSortingFiltering=false\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used as the reference to perform the alignment. Variants calling in the control region (coordinates chromosome (Chr) M: 16,024\u0026ndash;16,569 and Chr M: 1\u0026ndash;576; observe an artificial break in the region) was done as follows: by shifting 8000 nucleotides, reads originally aligning to Chr M were realigned to a Chr M reference genome; then variants called on the shifted reference were mapped back to standard coordinates (Picard liftOver) and were combined with variants from the non-control region. The aligned reads with low-quality mapping scores, unusual insert-sizes, and cross-chromosome mapping were filtered out. Reads were re-aligned around variants and Base Quality Score Recalibration (BQSR) was done using GATK, Mutect2 caller (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gatk.broadinstitute.org/hc/en-us/articles/360037593851-Mutect2\u003c/span\u003e\u003cspan address=\"https://gatk.broadinstitute.org/hc/en-us/articles/360037593851-Mutect2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe reference mitochondrial sequence was the revised mtDNA Cambridge Reference Sequence (rCRS) \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e of the Human Mitochondrial DNA (NC_012920.1). mtDNA variants were identified using mtDNA Serve 2 in the Mitoverse platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mitoverse.readthedocs.io/mtdna-server/mtdna-server/\u003c/span\u003e\u003cspan address=\"https://mitoverse.readthedocs.io/mtdna-server/mtdna-server/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Annotation of the variants was done using MITOMAP and Human Mitochondrial Genome Database (mtDB). Haplogroup assignment \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e was done using HaploGrep2 and estimation of mtDNA contamination \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e was done by using HaploCheck (v1.0.5). A VCF with mtDNA variants was produced for each input sample. Only biallelic SNPs and markers with less than 5% missing data were included in the analysis. Variants with the variant allele frequency (VAF) 0.95-1.00 were defined as homoplasmic variants \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e and variants with VAF 0.03\u0026ndash;0.95 were defined as heteroplasmic variants \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eQuality Control\u003c/h2\u003e\u003cp\u003eQuality control of genetic data was performed as follows: variants from samples identified as contaminated by HaploCheck (v1.0.5) were excluded; variants at primer regions, artifact-prone sites (positions 301, 302, 310, 316, 3107, 16182), and at the phantom mutations sites (72':['G','T'], 257':['A','C'], '414':['G','T'], 3492':['A','C'], 3511':['A','C'], 4774':['T','A'], 5290':['A','T'], '9801':['G','T'], 10306':['A','C'], '10792':['A','C'], '11090':['A','C']) were excluded; variants from samples with coverage of each strand (forward and reverse) lower than 200x and with a forward to reverse strand reading ratio of less than 0.5 or greater than 1.5 were excluded.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFunctional Annotation of Variants and mtDNA Functional Impact Score\u003c/h3\u003e\n\u003cp\u003eA combination of tools, including MutPred \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, mtDNA Selection \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, and Mito tool \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, were utilized to assess the impact of amino acid changes caused by mtDNA mutations on protein function through analyses of the sequence homology, evolutionary conservation, and protein structure \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Functional impact (FI) scores were calculated using the same set of tools for all of the 24,206 possible amino acid variations defined by a single point mutation away from the rCRS reference sequence. The amino acid variation with a higher FI score is more likely to be pathogenic. The MutPred algorithm assigns a score between 0 and 1, and we conducted analysis for the pathogenicity levels including only variants with a MutPred score\u0026thinsp;\u0026gt;\u0026thinsp;0.5 as those are considered as potentially \"harmful\". The FI score for each individual was calculated by combining the sum of predicted MutPred, mtDNA selection, and MitoTool scores for each variant across all potentially \u0026ldquo;harmful\u0026rdquo; variants (Supplementary Table\u0026nbsp;3). Two-way ANOVA was used to assess the between-group differences in individual or global mtDNA FI scores (Supplementary Table\u0026nbsp;4). Databases such as Mitomap \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e and ClinVar \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e were used to obtain information including allele frequency in several known datasets, reported variants that were associated with diseases, and whether the mutations were novel or known. The identified mtDNA variants were annotated to include the mutation type, region of the mutations, and whether they were synonymous or not.\u003c/p\u003e\n\u003ch3\u003eMitochondrial DNA Global Heteroplasmy Level and Count\u003c/h3\u003e\n\u003cp\u003eThe global heteroplasmy level, calculated by taking the sum of the heteroplasmy level for each variant per individual and dividing it by the total number of heteroplasmic sites of that individual, ranged between 0 and 1. The heteroplasmy count was reflects the number of heteroplasmic sites within an individual's mitochondrial DNA pool.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eHaplogroups Assignment\u003c/h2\u003e\u003cp\u003emtDNA haplogroup for each individual was determined using Haplogrep 2 using the rCRS-orientated version of PhyloTree Build 17 \u003csup\u003e48, 60\u003c/sup\u003e. The filtering criteria for our sample is a quality score of 0.9 or higher by HaploGrep 2. Due to the relatively small sample size, phylogenetically related haplogroups were combined into macro-haplogroups (H-HV-V (H, HV and V), J-T (J and T), U-K (U and K) and others) (Supplementary Table\u0026nbsp;5, 7). Individuals assigned as non-European haplogroups were excluded because they comprised less than 10% of the total sample.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMRI Acquisition\u003c/h2\u003e\u003cp\u003eStructural and ASL-CBF images of the brain were collected with a 3 Tesla Philips Achieva MRI scanner (Philips Medical Systems, Best, Netherlands) with an 8-channel head receiver coil. High-resolution fast-field echo T1-weighted images were acquired for anatomical registration, and pseudo-continuous ASL (ASL) images were collected to derive CBF measures. Structural scans were acquired with the following parameters: repetition time (TR) of 9.5 milliseconds (ms), echo time (TE) of 2.3ms, inversion time (TI) of 1400ms, spatial resolution of 0.94 \u0026times; 1.17\u0026times; 1.2 mm, acquisition matrix of 256 \u0026times; 164 \u0026times; 140, field of view (FOV) of 240 \u0026times; 191 mm, flip angle of 8\u0026deg;, and scan duration of 8 minutes and 56 seconds. Phase contrast angiography scout images were acquired for visualizing vascular anatomy prior to ASL imaging. ASL images were obtained with single-shot 2-dimensional echo planar imaging with the following parameters: TR of 4000 ms, TE of 9.7 ms, matrix of 64 \u0026times; 64 \u0026times; 18, spatial resolution of 3 \u0026times; 3 \u0026times; 5 mm, labeling duration of 1650-ms, post-label delay of 1600-ms for the most inferior slice, 30 control-tag pairs, and scan duration of 4 minutes and 8 seconds. ASL reference images were acquired with a TR of 10 seconds to establish initial magnetization for CBF quantification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eImage Processing\u003c/h2\u003e\u003cp\u003eFMRIB Software Library (FSL) tools were used for image processing \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. T1-weighted images were skull-stripped \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, co-registered to ASL space and standard space, normalized, and segmented into gray matter and white matter \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. ASL data was co-registered to a reference volume. Differences in consecutive control and tag images were calculated to obtain CBF-weighted images. Images with excess head motion were identified automatically and removed to optimize CBF signal \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Estimates were converted to absolute units (mL/100 g/min) using quantification values and the ASL reference image \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. CBF maps were smoothed using a 5 mm full width at half maximum kernel. Regional CBF values were then extracted from masks of the ACC and the whole brain gray matter, which were defined using the Harvard-Oxford Cortical and subcortical Structural Atlases in FSL in 3 mm standard space.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed for clinical and demographic variables using the SPSS statistical software version 26 (IBM; NY, USA). Continuous variables were assessed for normality using the Shapiro-Wilks test. The equal variance assumptions of all continuous variables were checked using Levene\u0026rsquo;s test. Between-group differences in demographic and clinical characteristics were assessed using independent-samples \u003cem\u003et\u003c/em\u003e-tests, Mann-Whitney \u003cem\u003eU\u003c/em\u003e-tests, and Kruskal-Wallis test for continuous and ordinal variables or chi-square tests for categorical variables as appropriate. Effect sizes were reported as Cramer\u0026rsquo;s V (\u003cem\u003eV\u003c/em\u003e), Cohen\u0026rsquo;s d (\u003cem\u003ed\u003c/em\u003e), or eta-squared (η2).\u003c/p\u003e\u003cp\u003eAn a priori approach was taken to assess the association between mtDNA FI score, mtDNA haplogroups, both mtDNA heteroplasmy level and count, and CBF. The association of mtDNA FI score, mtDNA haplogroups, mtDNA heteroplasmy level, and mtDNA heteroplasmy count with ROIs (i.e. ACC gray matter CBF and whole brain gray matter CBF) were tested using a General Linear Model (GLM) in SPSS, covarying for age and sex, within the overall sample and each diagnosis group (BD and CG). Bonferroni correction was used to correct for 6 (2 ROIs*3 groups\u0026thinsp;=\u0026thinsp;6) comparisons (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05/6\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e\u003cp\u003eWe also undertook a data-driven whole-brain voxel-based approach to examine regions where mtDNA FI score is associated with regional CBF, we also examined regions where haplogroup JT is associated with regional CBF. A GLM was designed in FSL using the FMRIB\u0026rsquo;s Local Analysis of Mixed Effects (FLAME1). Three group contrast CBF maps corresponding to the three analyses (i.e. within the overall sample, within BD, and within CG) were corrected using FSL cluster, a multiple comparisons correction method that controls family-wise error rate. A cluster-forming threshold of z\u0026thinsp;=\u0026thinsp;1.95 (corresponding to \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05) and a secondary threshold to determine cluster significance of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05 was applied. The peak voxel within each cluster was determined by finding the voxel within the cluster with the highest significance and was localized to anatomical regions using Harvard-Oxford Cortical and subcortical Structural Atlases. Lastly, we undertook sensitivity analyses by adding BMI, current depression mood symptom (presence or absence), and current manic mood symptom (presence or absence) as a covariate individually in addition to the covariates used previously. In the post-hoc analyses and sensitivity analyses for the vertex-wise findings, each significant cluster was treated as an ROI.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDemographic and Clinical Characteristics\u003c/h2\u003e\u003cp\u003eDemographic characteristics are summarized in Supplementary Table\u0026nbsp;1. This study included 101 youth, 56 with BD (19 BD-I, 16 BD-II, 21 BD-NOS) and 45 in CG. There were no significant between-group differences in age, BMI, or race (Supplementary Table\u0026nbsp;1). BD group (70% female) had significantly more females than control group (44% female; χ2\u0026thinsp;=\u0026thinsp;6.52, V\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;=\u0026thinsp;0.01), and there were significant differences in Tanner stage between BD and CG, with BD group having greater pubertal status (H\u0026thinsp;=\u0026thinsp;6.04, η2\u0026thinsp;=\u0026thinsp;0.05, p\u0026thinsp;=\u0026thinsp;0.01). Clinical characteristics are summarized in Supplementary Table\u0026nbsp;2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003emtDNA Analyses\u003c/h2\u003e\u003cp\u003eA comprehensive analysis of mtDNA variants was performed across the 101 participants, resulting in the identification of a total of 5833 variants. A total sequencing coverage of 1,788.2 X of the mitochondrial genome for this work was achieved. To ensure the quality and reliability of the data, a stringent quality control analysis was conducted, as outlined in the methods section. In our sample, 64 of the 2263 homoplasmic variants detected were common variants (Minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Functional analysis revealed 9 non-synonymous common variants, and the variants m.4917A\u0026thinsp;\u0026gt;\u0026thinsp;G in the MT-ND2 gene, m.4216T\u0026thinsp;\u0026gt;\u0026thinsp;C in the MT-ND1 gene, and m.14798T\u0026thinsp;\u0026gt;\u0026thinsp;C in the MT-CYB were potentially harmful, indicated by MutPred scores larger than 0.5.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eHaplogroups\u003c/h2\u003e\u003cp\u003e Our samples consisted of European participants exclusively and were combined into four macro-haplogroups (H-HV-V, J-T, U-K, and others). The proportion of each macro-haplogroup represented in our dataset was H-HV-V (47%), J-T (20%), U-K (27%), and others (6%) (Supplementary Table\u0026nbsp;5). Moreover, haplogroup J (partially defined by the presence of the m.4216T\u0026thinsp;\u0026gt;\u0026thinsp;C and m.14798T\u0026thinsp;\u0026gt;\u0026thinsp;C variants), haplogroup K (partially defined by the presence of m.14798T\u0026thinsp;\u0026gt;\u0026thinsp;C variant only), and haplogroup T (partially defined by the presence of m.4216T\u0026thinsp;\u0026gt;\u0026thinsp;C and m.4917A\u0026thinsp;\u0026gt;\u0026thinsp;G variants).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eGlobal Heteroplasmy Level and Count\u003c/h2\u003e\u003cp\u003eThe global heteroplasmy level and count, which are indicators of the mtDNA instability, did not differ significantly between BD and CG (t\u0026thinsp;=\u0026thinsp;0.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56; and t\u0026thinsp;=\u0026thinsp;0.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43, respectively; Supplementary Table\u0026nbsp;4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eROI CBF Analysis\u003c/h2\u003e\u003cp\u003eThe association of mtDNA FI score with ACC and whole brain gray matter CBF is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Higher mtDNA FI score was nominally associated with higher ACC gray matter CBF within the overall sample (β\u0026thinsp;=\u0026thinsp;0.22 p\u0026thinsp;=\u0026thinsp;0.031, 95%CI [0.02, 0.41]) and within BD group (β\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;=\u0026thinsp;0.049, 95%CI [0.00, 0.47]); these associations were not significant after correcting for multiple comparisons. There were no significant associations between heteroplasmy level or heteroplasmy count and CBF in any of the ROIs (Supplementary Table\u0026nbsp;7). There were no significant associations between haplogroups and CBF in any of the ROIs (Supplementary Table\u0026nbsp;8). There were no significant findings for the whole brain gray matter CBF ROI analyses, heteroplasmy count analyses, or within-group analyses.\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\u003eAssociation of mtDNA Functional Impact Score Main Effect with CBF in ROIs.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eWhole Brain Gray Matter CBF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eACC Gray Matter CBF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.07, 0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.00, 0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.22, 0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-0.20, 0.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverall Sample\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.04, 0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.02, 0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eACC\u0026thinsp;=\u0026thinsp;Anterior cingulate cortex; BD\u0026thinsp;=\u0026thinsp;Bipolar disorder; Significant group effects are bolded.\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\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eVoxel-based CBF Analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the clusters identified by the voxel-based analyses. Within the overall sample, higher mtDNA FI score was associated with higher CBF in three clusters with a peak region within the left cerebral white matter, right putamen, and posterior cingulate and precentral gyrus (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The cluster with a peak region in left cerebral white matter extended into regions including basal ganglia (putamen and pallidum), left thalamus, left caudate, insular cortex, and parietal lobe (parietal operculum and supramaginal gyri). The right putamen cluster extended into regions including right caudate, right pallidum, and cingulate cortex. The precentral gyrus and posterior cingulate cortex cluster extended into ACC and parietal lobe (postcentral gyri and precuneus cortex).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of mtDNA Heteroplasmy Global Level and Heteroplasmy Count Main Effect with CBF in ROIs.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eAssociation of mtDNA Heteroplasmy Global Level Main Effect with CBF in ROIs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eWhole Brain Gray Matter CBF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eACC Gray Matter CBF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.37, 0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-0.46, 0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.42, 0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-0.47, 0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverall Sample\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.34, 0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-0.39, -0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAssociation of mtDNA Heteroplasmy Count Main Effect with CBF in ROIs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eWhole Brain Gray Matter CBF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e\u003cb\u003eACC Gray Matter CBF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiagnosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e95%CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e95%CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.06, 0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-0.09, 0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.18, 0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-0.26, 0.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverall Sample\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.03, 0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(-0.07, 0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eACC\u0026thinsp;=\u0026thinsp;Anterior cingulate cortex; BD\u0026thinsp;=\u0026thinsp;Bipolar disorder; Significant group effects are bolded.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWithin the BD group, higher mtDNA FI score was associated with higher CBF in two clusters with a peak region within the right caudate and superior parietal lobule (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The right caudate cluster extended into regions including left caudate, cingulate cortex, frontal lobe (middle frontal gyri, pars opercularis, pars triangularis, precentral gyri, and frontal pole), and parietal lobe (postcentral gyri). The cluster with a peak region in superior parietal lobule extended into regions including frontal lobe (middle frontal gyri and precentral gyri), occipital lobe (superior lateral occipital cortex, and parietal lobe (postcentral gyri, anterior supramaginal gyrus, and posterior supramaginal gyrus). Within the control group, higher mtDNA FI score was associated with higher CBF in one cluster in the parietal operculum cortex (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the clusters identified by the voxel-based analyses where the appearance of haplogroup JT was associated with higher CBF. Within the overall sample, haplogroup JT was associated with higher CBF in two clusters with a peak region in the left thalamus, right lateral ventricle, and bilateral white matter. The cluster with a peak region in the left thalamus extended into regions including the left putamen, left pallidum, left posterior cingulate gyrus, left posterior parahippocampal gyrus, left anterior cingulate gyrus, and left lateral ventricle. The right lateral ventricle cluster extended into regions including the right caudate, right cerebral cortex, right accumbens, right pallidum, anterior cingulate cortex, and paracingulate cortex.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of mtDNA Haplogroup JT with CBF from Voxel-based Analyses.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCluster size (voxels)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecwp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMAX X (vox)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMAX Y (vox)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMAX Z (vox)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRegions\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOverall Sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eleft thalamus (peak), left cerebral white matter (peak)\u003c/b\u003e, left putamen, left pallidum, left posterior cingulate gyrus, left posterior parahippocampal gyrus, left anterior cingulate gyrus, left lateral ventricle\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eright cerebral white matter (peak), right lateral ventricle (peak)\u003c/b\u003e, right anterior cingulate gyrus, right paracingulate gyrus, right caudate, right putamen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eright lingual gyrus (peak), right posterior cingulate gyrus (peak), right precuneus cortex(peak)\u003c/b\u003e, right intracalcarine cortex (peak), right supracalcarine cortex, right cuneal cortex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003ecwp\u0026thinsp;=\u0026thinsp;cluster wise p-value; BD\u0026thinsp;=\u0026thinsp;Bipolar Disorder\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWithin the BD group, the presence of haplogroup JT was associated with higher CBF in one cluster with a peak region in the right lingual gyrus, right posterior cingulate gyrus, right precuneus cortex, and right intracalcarine cortex. This cluster extended into regions including the right supracalcarine cortex and the right cuneal cortex.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity Analysis\u003c/h2\u003e\u003cp\u003eSensitivity analyses were undertaken for both ROI and voxel-based analyses. By adding BMI, current depression mood severity, and current manic mood severity as a covariate individually, all findings remained significant. In addition, current depression mood severity was associated with lower right caudate CBF (β=-0.247 p\u0026thinsp;=\u0026thinsp;0.049, 95% CI [-0.55, 0.00]).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated mtDNA pathogenicity in relation to CBF in youth with and without BD. ROI analyses focused on ACC and whole brain gray matter CBF revealed that mtDNA pathogenicity, reflected in higher mtDNA FI score, was associated with higher ACC gray matter CBF in the overall sample. In voxel-based analyses, we found that higher mtDNA FI score was associated with higher regional CBF in the basal ganglia, frontal and parietal lobe, and cingulate gyrus within the overall sample and within the BD group. In addition, higher mtDNA FI score was associated with higher cerebral white matter CBF across all voxel-based findings. This study addresses a gap in the literature, integrating CBF in relation to mitochondrial dysfunction or mtDNA variants among individuals with BD.\u003c/p\u003e\n\u003cp\u003eEach of the three potentially harmful variants (i.e. m.4917A\u0026thinsp;\u0026gt;\u0026thinsp;G, m.4216T\u0026thinsp;\u0026gt;\u0026thinsp;C, m.14798T\u0026thinsp;\u0026gt;\u0026thinsp;C) that were used to generate the mtDNA FI score are located on sequences in mitochondrial genes that encode functional proteins. The m.4917A\u0026thinsp;\u0026gt;\u0026thinsp;G variant is located on the \u003cem\u003eMT-ND1\u003c/em\u003e gene that codes for NADH dehydrogenase 1, and the m.4216T\u0026thinsp;\u0026gt;\u0026thinsp;C variant is located on the \u003cem\u003eMT-ND2\u003c/em\u003e gene that codes for NADH dehydrogenase 2. Both NADH dehydrogenase 1 and 2 are part of ETC complex I, which is responsible for the first step in the ETC, transferring electrons from NADH to ubiquinone \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. The m.14798T\u0026thinsp;\u0026gt;\u0026thinsp;C is located on the \u003cem\u003eMT-CYB\u003c/em\u003e gene that codes for cytochrome b, which is part of ETC complex III that is responsible for transferring electrons from the ubiquinol to cytochrome c \u003csup\u003e65, 66\u003c/sup\u003e. We therefore speculate that the identified variants lead to impaired oxidative phosphorylation and reduced energy production.\u003c/p\u003e\n\u003cp\u003eIn ROI analyses, higher mtDNA FI score was associated with higher CBF in the ACC, a region where both altered CBF levels and altered mitochondrial energetics have been reported in BD \u003csup\u003e31, 67\u0026ndash;69\u003c/sup\u003e. In a prior study based on an overlapping sample, our group found that lower ACC CBF was associated with greater severity of symptoms including depressed mood and anhedonia in youth with BD \u003csup\u003e33\u003c/sup\u003e. Relatedly, neurochemical findings that were obtained using MRS suggested that altered energy metabolism in ACC was associated with altered mood states as well as anhedonia symptoms in adults with BD \u003csup\u003e67, 70\u003c/sup\u003e. Our recent study also found higher temperature-to-CBF ratio in the ACC and precuneus was associated with more severe depression symptoms in youth with BD \u003csup\u003e71\u003c/sup\u003e. Thus, the current findings add to the literature by showing anomalous ACC CBF and energy metabolism, implicating mitochondrial dysfunction. The current study provides preliminary evidence that mtDNA FI correlates with ACC CBF and energy metabolism abnormalities in youth with BD.\u003c/p\u003e\n\u003cp\u003eWe did not find an association of mtDNA heteroplasmic level with CBF in ROI analyses. mtDNA heteroplasmic level is indicative of mtDNA stability and is affected by numerous factors including somatic segregation of inherited heteroplasmy, acquisition of new mutations during development and aging, and selection of mtDNA through differential replication and repair \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. A deeper comprehension of the processes behind the spread of mtDNA mutations and the rise in heteroplasmy load is necessary to understand the mechanisms underlying the current findings.\u003c/p\u003e\n\u003cp\u003eVoxel-based analyses identified regions within the basal ganglia, default mode network (DMN), and cingulate, key brain regions involved in reward processing, memory, attention, and emotion regulation \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Findings of lower mitochondrial energy metabolism in the caudate, putamen, and pallidum, components of basal ganglia in adults with BD during depressed and manic mood states \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e have been previously reported. Lower mitochondrial energy metabolism has also been associated with lower executive functions in adults with BD \u003csup\u003e79\u003c/sup\u003e. However, there are also exceptions as higher thalamic mitochondrial metabolism has also been associated with lower executive functions in adults with BD and current suicide ideation \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe also identified a cluster extending from posterior cingulate gyrus to precuneus and ACC. As the central node in the DMN, posterior cingulate gyrus is highly connected and metabolically active, with CBF levels that tend to be higher than the whole-brain average \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Relatedly, increased DMN and precuneus functional connectivity, which is implicated in human attention and self-consciousness \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e, has been found in euthymic BD patients \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, whereas decreased DMN and precuneus functional connectivity has been found in BD-II depression patients \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Finally, DMN and ACC hypoconnectivity has been associated with more severe anxiety and depressive symptoms in BD \u003csup\u003e87\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe association of higher mtDNA FI score with higher regional CBF may reflect compensatory processes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) to maintain metabolic demands and remove waste during mitochondrial dysfunction \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. Mitochondrial dysfunction impairs oxidative phosphorylation, necessitating anaerobic glycolysis in BD \u003csup\u003e90, 91\u003c/sup\u003e. In comparison to oxidative phosphorylation, anaerobic glycolysis requires more glucose to produce similar amounts of ATP and generates more lactate as a by-product \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. Thus, the association of higher mtDNA FI score with higher regional CBF in our results may act to compensate for increased demand for both glucose delivery and lactate removal \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn addition to gray matter findings, voxel-based analyses also identified five significant white matter clusters. In three of these clusters, the white matter regions are adjacent to gray matter, and therefore potentially explained by partial volume effects \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. However, in the other two clusters, the white matter is mainly distal from the adjacent gray matter. Lower subcortical white matter CBF has been reported in BD \u003csup\u003e31\u003c/sup\u003e. White matter CBF is affected by the white matter integrity \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e in addition to white matter metabolism \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e. Thus, our findings suggest that mtDNA variants may affect white matter CBF by affecting white matter integrity, as white matter is composed primarily of myelinated axons and is highly vulnerable to oxidative damages that result from mitochondrial dysfunction \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. Alternatively, the white matter CBF findings might be secondary to the gray matter CBF findings as gray matter CBF is significantly associated with white matter integrity.\u003c/p\u003e\n\u003cp\u003eSince both variants mt4216 and mt4917 are from the same macrohaplogroup JT, we further took exploratory vertex-wise analyses to examine the association of haplogroup JT with CBF in the overall sample, BD group, and the CG. We observed that individuals with haplogroup JT demonstrated increased CBF in a distributed set of cortical and subcortical regions, including the left thalamus, bilateral cerebral white matter, left putamen, pallidum, anterior and posterior cingulate gyri, and medial occipital areas (e.g., lingual gyrus, precuneus, intracalcarine, and supracalcarine cortex). Notably, many of these regions overlapped with those previously identified in our analyses linking higher mtDNA FI score to increased CBF: the anterior and posterior cingulate cortex, precuneus, thalamus, and lateral ventricles are part of the DMN, a system highly active at rest and sensitive to mitochondrial efficiency \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e; subcortical structures such as the putamen, caudate, and pallidum play roles in motor control and emotion regulation and exhibit high metabolic demands \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Since the FI score is calculated using functional weighting of variants including mt4216 and mt4917, both of which belong to haplogroup JT, it is plausible that JT is a key driver of the observed association between FI score and cerebral blood flow. These findings suggest that individuals with haplogroup JT may show a distinct neurovascular phenotype characterized by increased perfusion in regions critical for cognitive control, memory, and visual processing, potentially reflecting compensatory responses to mitochondrial inefficiency.\u003c/p\u003e\n\u003cp\u003eSeveral limitations must be considered when interpreting present findings. First, the current sample size is relatively small, precluding several potentially informative secondary analyses such as those focused on mood states, BD subtypes, and comorbidity. In addition, larger sample size would provide greater power to detect additional variants and in turn generate a broader range of mtDNA FI scores. Second, we enrolled a naturalistic sample characterized by heterogeneity in medications, symptomatic status, BD subtypes, family psychiatric history, comorbidities, and life adversity. Third, we only included individuals of European ancestry in the current analyses, limiting the interpretability of the results in other race groups. Finally, we could not examine CMRO\u003csub\u003e2\u003c/sub\u003e or cerebral metabolic rate of glucose, which could provide a basis for directly testing our speculations regarding current findings.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study demonstrates that higher mtDNA pathogenicity is associated with higher CBF in the overall sample and within the BD group. Although FI scores are genetically based and consistent across the lifespan, regional hyperperfusion during euthymia has only been reported in youth. Future studies incorporating time-varying indices of mitochondrial pathogenicity, such as heteroplasmy, are needed to parse these developmental differences. Our results add further support to the premise that regional CBF in youth with BD is in part reflective of compensatory mechanisms that arise in response to inefficient energy metabolism. Therapeutically, this highlights the potential of interventions that optimize energy metabolism, such as ketogenic diet \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e and cardiorespiratory (i.e., aerobic) exercise \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. Future studies using larger samples, including additional objective metrics of cerebral energy metabolism, and ideally using a lifespan approach, are needed to validate and expand upon current findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatement of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Goldstein acknowledges research funding from Canadian Institutes of Health Research, Heart and Stroke Foundation, the Department of Psychiatry at the University of Toronto, and the CAMH Foundation. Dr. Goldstein also acknowledges his position as RBC Investments Chair in Children\u0026rsquo;s Mental Health and Developmental Psychopathology at CAMH, a joint Hospital-University Chair between the University of Toronto, CAMH, and the CAMH Foundation. Dr. Mendes-Silva acknowledges support from the CIHR and CAMH Discovery Fund Fellowships. Dr. Vanessa Goncalves acknowledges the Miner\u0026apos;s Lamp Innovation Fund (University of Toronto). All other authors report no actual or potential conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe codes for mtDNA variant detection and quality control analyses are publicly available at https://github.com/lmtani/wf-human-mito and mtDNA Quality Control analysis - Genetics - KCNI Knowledge Base (camh.ca).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Canadian Institutes of Health Research (CIHR MOP 136947) and the Ontario Mental Health Foundation (OMHF) to Benjamin I. Goldstein.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSuyi Shao was the writer of the manuscript and performed all relevant analyses with the support of Ana Paula Mendes-Silva, Yi Zou and Kody G. Kennedy. Vanessa F Goncalves, Bradley J. MacIntosh, Mikaela K. Dimick and all provided critical feedback and intellectual content to the paper. Additionally, Benjamin I. Goldstein is the principal investigator, participated in the conceptualization of the study, and provided critical feedback and intellectual content to the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGoldstein BI, Birmaher B, Carlson GA, DelBello MP, Findling RL, Fristad M\u003cem\u003e et al.\u003c/em\u003e The International Society for Bipolar Disorders Task Force report on pediatric bipolar disorder: Knowledge to date and directions for future research. \u003cem\u003eBipolar Disord\u003c/em\u003e 2017; \u003cstrong\u003e19\u003c/strong\u003e(7)\u003cstrong\u003e: \u003c/strong\u003e524-543.\u003c/li\u003e\n\u003cli\u003eJeong H, Dimick MK, Sultan A, Duong A, Park SS, El Soufi El Sabbagh D\u003cem\u003e et al.\u003c/em\u003e Peripheral biomarkers of mitochondrial dysfunction in adolescents with bipolar disorder. \u003cem\u003eJ Psychiatr Res\u003c/em\u003e 2020; \u003cstrong\u003e123: \u003c/strong\u003e187-193.\u003c/li\u003e\n\u003cli\u003eCuperfain AB, Zhang ZL, Kennedy JL, Goncalves VF. 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Clinical and neuroimaging correlates of cardiorespiratory fitness in adolescents with bipolar disorder. \u003cem\u003eBipolar Disord\u003c/em\u003e 2021; \u003cstrong\u003e23\u003c/strong\u003e(3)\u003cstrong\u003e: \u003c/strong\u003e274-283.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7635750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7635750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMitochondrial dysfunction is implicated in the neuropathology of bipolar disorder (BD). Mitochondrial DNA (mtDNA) variants are associated with anomalous cerebral energy metabolism and with increased risk for BD. Little is known about the relevance of mtDNA to cerebral blood flow (CBF) in BD. Participants included 101 youth (BD, n\u0026thinsp;=\u0026thinsp;56; Control group, n\u0026thinsp;=\u0026thinsp;45; ages 13\u0026ndash;20). The Miseq platform was used to sequence saliva mtDNA. The mtDNA-Server pipeline was used for variant calling and annotation. mtDNA common variants (i.e. minor allele frequency larger than 5%) were included in the analyses due to sample size. We generated an mtDNA variant functional impact (FI) score by performing functional analysis using Mutserve and summing across the MutPred, Selection Score, and MitoTool algorithms. CBF was measured using pseudo-continuous arterial spin labeling magnetic resonance imaging. Region of interest (ROI) analyses examined FI scores in relation to CBF in the anterior cingulate cortex (ACC) and global gray matter, controlling for age and sex. Voxel-based analyses were also conducted. In ROI analyses, higher mtDNA FI score was associated with higher ACC CBF in the overall sample (β\u0026thinsp;=\u0026thinsp;0.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045). In voxel-based analyses, higher mtDNA FI score was associated with higher CBF in regions within the basal ganglia, frontal and parietal lobe, and cingulate within the overall sample and within the BD group. This study found that higher mtDNA FI score, putatively reflecting mtDNA pathogenicity, was associated with higher regional CBF among youth. Present findings add to the evidence that elevated CBF may be a compensatory mechanism in youth with BD.\u003c/p\u003e","manuscriptTitle":"Greater Mitochondrial DNA Pathogenicity is Associated with Greater Regional Cerebral Blood Flow in Youth Bipolar Disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 13:40:38","doi":"10.21203/rs.3.rs-7635750/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f57813f-3da0-4c70-8ecb-8967230a1981","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55899783,"name":"Health sciences/Diseases/Psychiatric disorders/Bipolar disorder"},{"id":55899784,"name":"Health sciences/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2025-11-10T11:06:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 13:40:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7635750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7635750","identity":"rs-7635750","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}