McCutcheon RA, Reis Marques T, Howes OD. Schizophrenia—an overview. JAMA Psychiatry. 2020;77:201–10.
Google Scholar
Howes OD, Kambeitz J, Kim E, Stahl D, Slifstein M, Abi-Dargham A, et al. The nature of dopamine dysfunction in schizophrenia and what this means for treatment. Arch Gen Psychiatry. 2012;69:776–86.
Google Scholar
Valdés-Tovar M, Rodríguez-Ramírez AM, Rodríguez-Cárdenas L, Sotelo-Ramírez CE, Camarena B, Sanabrais-Jiménez MA, et al. Insights into myelin dysfunction in schizophrenia and bipolar disorder. World J Psychiatry. 2022;12:264–85.
Google Scholar
Zucca FA, Segura-Aguilar J, Ferrari E, Muñoz P, Paris I, Sulzer D, et al. Interactions of iron, dopamine and neuromelanin pathways in brain aging and Parkinson’s disease. Progress in Neurobiology. 2017;155:96–119.
Google Scholar
Saghazadeh A, Mahmoudi M, Shahrokhi S, Mojarrad M, Dastmardi M, Mirbeyk M, et al. Trace elements in schizophrenia: a systematic review and meta-analysis of 39 studies (N = 5151 participants). Nutrition Reviews. 2020;78:278–303.
Google Scholar
Georgieff MK. Iron deficiency in pregnancy. Am J Obstet Gynecol. 2020;223:516–24.
Google Scholar
Stevens JR. Neuropathology of schizophrenia. Arch Gen Psychiatry. 1982;39:1131–9.
Google Scholar
Casanova MF, Waldman IN, Kleinman JE. A postmortem quantitative study of iron in the globus pallidus of schizophrenic patients. Biol Psychiatry. 1990;27:143–9.
Google Scholar
Kornhuber J, Lange KW, Kruzik P, Rausch WD, Gabriel E, Jellinger K, et al. Iron, copper, zinc, magnesium, and calcium in postmortem brain tissue from schizophrenic patients. Biol Psychiatry. 1994;36:31–34.
Google Scholar
Kral VA, Lehmann HE. Further studies on the iron content of the cerebrospinal fluid in psychoses. AMA Arch Neurol Psychiatry. 1952;68:321–8.
Google Scholar
Lehmann HE, Kral VA. Studies on the iron content of cerebrospinal fluid in different psychotic conditions. AMA Arch Neurol Psychiatry. 1951;65:326–36.
Google Scholar
Lotan A, Luza S, Opazo CM, Ayton S, Lane DJR, Mancuso S, et al. Perturbed iron biology in the prefrontal cortex of people with schizophrenia. Mol Psychiatry. 2023;28:2058–70.
Google Scholar
Xu M, Guo Y, Cheng J, Xue K, Yang M, Song X, et al. Brain iron assessment in patients with First-episode schizophrenia using quantitative susceptibility mapping. Neuroimage Clin. 2021;31:102736.
Google Scholar
García Saborit M, Jara A, Muñoz N, Milovic C, Tepper A, Alliende LM, et al. Quantitative susceptibility mapping MRI in deep-brain nuclei in first-episode psychosis. Schizophr Bull. 2023;49:1355–63.
Google Scholar
Ravanfar P, Syeda WT, Jayaram M, Rushmore RJ, Moffat B, Lin AP, et al. In vivo 7-Tesla MRI investigation of brain iron and its metabolic correlates in chronic schizophrenia. Schizophr. 2022;8:1–11.
Google Scholar
Sonnenschein SF, Parr AC, Larsen B, Calabro FJ, Foran W, Eack SM, et al. Subcortical brain iron deposition in individuals with schizophrenia. J Psychiatr Res. 2022;151:272–8.
Google Scholar
Sui YV, McKenna F, Bertisch H, Storey P, Anthopolos R, Goff DC, et al. Decreased basal ganglia and thalamic iron in early psychotic spectrum disorders are associated with increased psychotic and schizotypal symptoms. Mol Psychiatry. 2022;27:5144–53.
Google Scholar
Duyn JH, Schenck J. Contributions to magnetic susceptibility of brain tissue: magnetic susceptibility of brain tissue. NMR Biomed. 2017;30:e3546.
Google Scholar
Seehaus A, Roebroeck A, Bastiani M, Fonseca L, Bratzke H, Lori N, et al. Histological validation of high-resolution DTI in human post mortem tissue. Front Neuroanat. 2015;9:98.
Google Scholar
Hallgren B, Sourander P. The effect of age on the non-haemin iron in the human brain. J Neurochem. 1958;3:41–51.
Google Scholar
Hametner S, Endmayr V, Deistung A, Palmrich P, Prihoda M, Haimburger E, et al. The influence of brain iron and myelin on magnetic susceptibility and effective transverse relaxation – a biochemical and histological validation study. NeuroImage. 2018;179:117–33.
Google Scholar
Lee J, Shmueli K, Fukunaga M, van Gelderen P, Merkle H, Silva AC, et al. Sensitivity of MRI resonance frequency to the orientation of brain tissue microstructure. Proc Natl Acad Sci USA. 2010;107:5130–5.
Google Scholar
Wharton S, Bowtell R. Effects of white matter microstructure on phase and susceptibility maps. Magn Reson Med. 2015;73:1258–69.
Google Scholar
Li X, Vikram DS, Lim IAL, Jones CK, Farrell JAD, van Zijl PCM. Mapping magnetic susceptibility anisotropies of white matter in vivo in the human brain at 7 T. Neuroimage. 2012;62:314–30.
Google Scholar
Li W, Wu B, Avram AV, Liu C. Magnetic susceptibility anisotropy of human brain in vivo and its molecular underpinnings. NeuroImage. 2012;59:2088–97.
Google Scholar
Vano LJ, McCutcheon RA, Rutigliano G, Kaar SJ, Finelli V, Nordio G, et al. Mesostriatal dopaminergic circuit dysfunction in schizophrenia: a multimodal neuromelanin-sensitive magnetic resonance imaging and [18F]-DOPA positron emission tomography study. Biol Psychiatry. 2024;96:674–83.
Google Scholar
Vano LJ, McCutcheon RA, Sedlacik J, Kaar SJ, Rutigliano G, Nordio G, et al. Reduced brain iron and striatal hyperdopaminergia in schizophrenia: a quantitative susceptibility mapping MRI and PET study. Am J Psychiatry. 2025;182:830-9.
Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489:391–9.
Google Scholar
Howes O, Marcinkowska J, Turkheimer FE, Carr R. Synaptic changes in psychiatric and neurological disorders: state-of-the art of in vivo imaging. Neuropsychopharmacol. 2024;50:164–83.
Google Scholar
First MB. Structured clinical interview for DSM-5 disorders – clinician version (SCID-5-CV). Arlington, VA: American Psychiatric Association; 2016.
Kim E, Howes OD, Veronese M, Beck K, Seo S, Park JW, et al. Presynaptic dopamine capacity in patients with treatment-resistant schizophrenia taking clozapine: an [18F]DOPA PET study. Neuropsychopharmacology. 2017;42:941–50.
Google Scholar
Smith-Kielland A, Skuterud B, Mørland J. Urinary excretion of 11-nor-9-carboxy-delta9-tetrahydrocannabinol and cannabinoids in frequent and infrequent drug users. J Anal Toxicol. 1999;23:323–32.
Google Scholar
Elbejjani M, Auer R, Jacobs DR, Haight T, Davatzikos C, Goff DC, et al. Cigarette smoking and gray matter brain volumes in middle age adults: the CARDIA brain MRI sub-study. Transl Psychiatry. 2019;9:78.
Google Scholar
Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13:261–76.
Google Scholar
Kirkpatrick B, Strauss GP, Nguyen L, Fischer BA, Daniel DG, Cienfuegos A, et al. The brief negative symptom scale: psychometric properties. Schizophr Bull. 2011;37:300–5.
Google Scholar
Busner J, Targum SD. The clinical global impressions scale: applying a research tool in clinical practice. Psychiatry (Edgmont). 2007;4:28.
Google Scholar
Leucht S, Samara M, Heres S, Davis JM. Dose equivalents for antipsychotic drugs: the DDD method. Schizophr Bull. 2016;42:S90–S94.
Google Scholar
Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage. 2011;54:313–27.
Google Scholar
Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968–80.
Google Scholar
QSM Consensus Organization Committee, Bilgic B, Costagli M, Chan K-S, Duyn J, Langkammer C, et al. Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: a consensus of the ISMRM electro-magnetic tissue properties study group. Magn Reson Med. 2024;91:1834–62.
Google Scholar
Manera AL, Dadar M, Fonov V, Collins DL. CerebrA, registration and manual label correction of Mindboggle-101 atlas for MNI-ICBM152 template. Sci Data. 2020;7:237.
Google Scholar
Martinez D, Slifstein M, Broft A, Mawlawi O, Hwang D-R, Huang Y, et al. Imaging human mesolimbic dopamine transmission with positron emission tomography. Part II: amphetamine-induced dopamine release in the functional subdivisions of the striatum. J Cereb Blood Flow Metab. 2003;23:285–300.
Google Scholar
Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage. 2006;31:1487–505.
Google Scholar
Sibgatulin R, Güllmar D, Deistung A, Enzinger C, Ropele S, Reichenbach JR. Magnetic susceptibility anisotropy in normal appearing white matter in multiple sclerosis from single-orientation acquisition. NeuroImage: Clin. 2022;35:103059.
Google Scholar
Sibgatulin R, Güllmar D, Deistung A, Ropele S, Reichenbach JR. In vivo assessment of anisotropy of apparent magnetic susceptibility in white matter from a single orientation acquisition. NeuroImage. 2021;241:118442.
Google Scholar
Tournier J-D, Calamante F, Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage. 2007;35:1459–72.
Google Scholar
Wasserthal J, Neher P, Maier-Hein KH. TractSeg – fast and accurate white matter tract segmentation. NeuroImage. 2018;183:239–53.
Google Scholar
McCutcheon RA, Brown K, Nour MM, Smith SM, Veronese M, Zelaya F, et al. Dopaminergic organization of striatum is linked to cortical activity and brain expression of genes associated with psychiatric illness. Sci Adv. 2021;7:eabg1512.
Google Scholar
Morgan SE, Seidlitz J, Whitaker KJ, Romero-Garcia R, Clifton NE, Scarpazza C, et al. Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes. Proc Natl Acad Sci USA. 2019;116:9604–9.
Google Scholar
Markello RD, Arnatkeviciute A, Poline J-B, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife. 2021;10:e72129.
Google Scholar
Arnatkeviciute A, Fulcher BD, Fornito A. A practical guide to linking brain-wide gene expression and neuroimaging data. Neuroimage. 2019;189:353–67.
Google Scholar
Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2009;44:83–98.
Google Scholar
Krishnan A, Williams LJ, McIntosh AR, Abdi H. Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage. 2011;56:455–75.
Google Scholar
Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, et al. API design for machine learning software: experiences from the scikit-learn project. arXiv:1309.0238. 2013. https://arxiv.org/abs/1309.0238.
Burt JB, Helmer M, Shinn M, Anticevic A, Murray JD. Generative modeling of brain maps with spatial autocorrelation. NeuroImage. 2020;220:117038.
Google Scholar
Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics. 2009;10:48.
Google Scholar
Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLOS ONE. 2011;6:e21800.
Google Scholar
Lake BB, Chen S, Sos BC, Fan J, Kaeser GE, Yung YC, et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat Biotechnol. 2018;36:70–80.
Google Scholar
Darmanis S, Sloan SA, Zhang Y, Enge M, Caneda C, Shuer LM, et al. A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci USA. 2015;112:7285–90.
Google Scholar
Trubetskoy V, Pardiñas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604:502–8.
Google Scholar
Reinert A, Morawski M, Seeger J, Arendt T, Reinert T. Iron concentrations in neurons and glial cells with estimates on ferritin concentrations. BMC Neurosci. 2019;20:25.
Google Scholar
Raabe FJ, Slapakova L, Rossner MJ, Cantuti-Castelvetri L, Simons M, Falkai PG, et al. Oligodendrocytes as a new therapeutic target in schizophrenia: from histopathological findings to neuron-oligodendrocyte interaction. Cells. 2019;8:1496.
Google Scholar
Wang C, Martins-Bach AB, Alfaro-Almagro F, Douaud G, Klein JC, Llera A, et al. Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging. Nat Neurosci. 2022;25:818–31.
Google Scholar
Kelly S, Jahanshad N, Zalesky A, Kochunov P, Agartz I, Alloza C, et al. Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group. Mol Psychiatry. 2018;23:1261–9.
Google Scholar
Martins-de-Souza D, Guest PC, Reis-de-Oliveira G, Schmitt A, Falkai P, Turck CW. An overview of the human brain myelin proteome and differences associated with schizophrenia. World J Biol Psychiatry. 2021;22:271–87.
Google Scholar
Reis-de-Oliveira G, Zuccoli GS, Fioramonte M, Schimitt A, Falkai P, Almeida V, et al. Digging deeper in the proteome of different regions from schizophrenia brains. J Proteomics. 2020;223:103814.
Google Scholar
Regenold WT, Phatak P, Marano CM, Gearhart L, Viens CH, Hisley KC. Myelin staining of deep white matter in the dorsolateral prefrontal cortex in schizophrenia, bipolar disorder, and unipolar major depression. Psychiatry Res. 2007;151:179–88.
Google Scholar
Lake EMR, Steffler EA, Rowley CD, Sehmbi M, Minuzzi L, Frey BN, et al. Altered intracortical myelin staining in the dorsolateral prefrontal cortex in severe mental illness. Eur Arch Psychiatry Clin Neurosci. 2017;267:369–76.
Google Scholar
Larsen B, Bourque J, Moore TM, Adebimpe A, Calkins ME, Elliott MA, et al. Longitudinal development of brain iron is linked to cognition in youth. J Neurosci. 2020;40:1810–8.
Google Scholar
Larsen B, Olafsson V, Calabro F, Laymon C, Tervo-Clemmens B, Campbell E, et al. Maturation of the human striatal dopamine system revealed by PET and quantitative MRI. Nat Commun. 2020;11:846.
Google Scholar
Kolomeets NS, Uranova NA. Numerical density of oligodendrocytes and oligodendrocyte clusters in the anterior putamen in major psychiatric disorders. Eur Arch Psychiatry Clin Neurosci. 2020;270:841–50.
Google Scholar
Vostrikov VM, Uranova NA. Reduced density of oligodendrocytes and oligodendrocyte clusters in the caudate nucleus in major psychiatric illnesses. Schizophr Res. 2020;215:211–6.
Google Scholar
Kolomeets NS, Uranova NA. Reduced oligodendrocyte density in layer 5 of the prefrontal cortex in schizophrenia. Eur Arch Psychiatry Clin Neurosci. 2019;269:379–86.
Google Scholar
Hof PR, Haroutunian V, Friedrich VL, Byne W, Buitron C, Perl DP, et al. Loss and altered spatial distribution of oligodendrocytes in the superior frontal gyrus in schizophrenia. Biol Psychiatry. 2003;53:1075–85.
Google Scholar
Vostrikov VM, Uranova NA, Orlovskaya DD. Deficit of perineuronal oligodendrocytes in the prefrontal cortex in schizophrenia and mood disorders. Schizophr Res. 2007;94:273–80.
Google Scholar
Yap CX, Vo DD, Heffel MG, Bhattacharya A, Wen C, Yang Y, et al. Brain cell-type shifts in Alzheimer’s disease, autism, and schizophrenia interrogated using methylomics and genetics. Sci Adv. 2024;10:eadn7655.
Google Scholar
Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C, et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science. 2018;359:693–7.
Google Scholar
McCutcheon RA, Krystal JH, Howes OD. Dopamine and glutamate in schizophrenia: biology, symptoms and treatment. World Psychiatry. 2020;19:15–33.
Google Scholar
Merritt K, McCutcheon RA, Aleman A, Ashley S, Beck K, Block W, et al. Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis. Mol Psychiatry. 2023;28:2039–48.
Google Scholar
Suárez-Pozos E, Thomason EJ, Fuss B. Glutamate transporters: expression and function in oligodendrocytes. Neurochem Res. 2019. https://doi.org/10.1007/s11064-018-02708-x.
Google Scholar
Haber SN, Calzavara R. The cortico-basal ganglia integrative network: the role of the thalamus. Brain Research Bulletin. 2009;78:69–74.
Google Scholar
Nelson AJD. The anterior thalamic nuclei and cognition: a role beyond space? Neurosci Biobehav Rev. 2021;126:1–11.
Google Scholar
Mair RG, Francoeur MJ, Krell EM, Gibson BM. Where actions meet outcomes: medial prefrontal cortex, Central Thalamus, and the Basal Ganglia. Front Behav Neurosci. 2022;16:928610.
Google Scholar
Fan L, Li H, Zhuo J, Zhang Y, Wang J, Chen L, et al. The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb Cortex. 2016;26:3508–26.
Google Scholar
Howes OD, Shatalina E. Integrating the neurodevelopmental and dopamine hypotheses of schizophrenia and the role of cortical excitation-inhibition balance. Biol Psychiatry. 2022;92:501–13.
Google Scholar
Cocchi L, Harding IH, Lord A, Pantelis C, Yucel M, Zalesky A. Disruption of structure–function coupling in the schizophrenia connectome. NeuroImage: Clin. 2014;4:779–87.
Google Scholar
Lorio S, Sedlacik J, So P-W, Parkes HG, Gunny R, Löbel U, et al. Quantitative MRI susceptibility mapping reveals cortical signatures of changes in iron, calcium and zinc in malformations of cortical development in children with drug-resistant epilepsy. Neuroimage. 2021;238:118102.
Google Scholar
Tortora D, Severino M, Sedlacik J, Toselli B, Malova M, Parodi A, et al. Quantitative susceptibility map analysis in preterm neonates with germinal matrix-intraventricular hemorrhage. J Magn Reson Imaging. 2018;48:1199–207.
Google Scholar
Lancione M, Tosetti M, Donatelli G, Cosottini M, Costagli M. The impact of white matter fiber orientation in single-acquisition quantitative susceptibility mapping. NMR in Biomedicine. 2017;30:e3798.
Google Scholar
Pfefferbaum A, Adalsteinsson E, Rohlfing T, Sullivan EV. Diffusion tensor imaging of deep gray matter brain structures: effects of age and iron concentration. Neurobiol Aging. 2010;31:482.
Google Scholar
Karsa A, Punwani S, Shmueli K. An optimized and highly repeatable MRI acquisition and processing pipeline for quantitative susceptibility mapping in the head-and-neck region. Magn Reson Med. 2020;84:3206–22.
Google Scholar
Li W, Wang N, Yu F, Han H, Cao W, Romero R, et al. A method for estimating and removing streaking artifacts in quantitative susceptibility mapping. Neuroimage. 2015;108:111–22.
Google Scholar
Haacke EM, Cheng NYC, House MJ, Liu Q, Neelavalli J, Ogg RJ, et al. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging. 2005;23:1–25.
Google Scholar
Colgan TJ, Knobloch G, Reeder SB, Hernando D. Sensitivity of quantitative relaxometry and susceptibility mapping to microscopic iron distribution. Magn Reson Med. 2020;83:673–80.
Google Scholar
Dietrich O, Levin J, Ahmadi S-A, Plate A, Reiser MF, Bötzel K, et al. MR imaging differentiation of Fe2+ and Fe3+ based on relaxation and magnetic susceptibility properties. Neuroradiology. 2017;59:403–9.
Google Scholar
Birkl C, Birkl-Toeglhofer AM, Kames C, Goessler W, Haybaeck J, Fazekas F, et al. The influence of iron oxidation state on quantitative MRI parameters in post mortem human brain. NeuroImage. 2020;220:117080.
Google Scholar
Ghassaban K, Liu S, Jiang C, Haacke EM. Quantifying iron content in magnetic resonance imaging. NeuroImage. 2019;187:77–92.
Google Scholar