{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T03:06:12Z","timestamp":1790651172718,"version":"4.1.0"},"reference-count":111,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Schizophr"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Few studies using Positron Emission Tomography with\n                    <jats:sup>18<\/jats:sup>\n                    F-fluorodeoxyglucose (\n                    <jats:sup>18<\/jats:sup>\n                    F-FDG-PET) have examined the neurobiological basis of antipsychotic resistance in schizophrenia, primarily focusing on metabolic activity, with none investigating connectivity patterns. Here, we aimed to explore differential patterns of glucose metabolism between patients and controls (CTRL) through a graph theory-based approach and network comparison tests. PET scans with\n                    <jats:sup>18<\/jats:sup>\n                    F-FDG were obtained by 70 subjects, 26 with treatment-resistant schizophrenia (TRS), 28 patients responsive to antipsychotics (nTRS), and 16 CTRL. Relative brain glucose metabolism maps were processed in the automated anatomical labeling (AAL)-Merged atlas template. Inter-subject connectivity matrices were derived using Gaussian Graphical Models and group networks were compared through permutation testing. A logistic model based on machine-learning was employed to estimate the association between the metabolic signals of brain regions and treatment resistance. To account for the potential influence of antipsychotic medication, we incorporated chlorpromazine equivalents as a covariate in the network analysis during partial correlation calculations. Additionally, the machine-learning analysis employed medication dose-stratified folds. Global reduced connectivity was detected in the nTRS (\n                    <jats:italic>p<\/jats:italic>\n                    -value\u2009=\u20090.008) and TRS groups (\n                    <jats:italic>p<\/jats:italic>\n                    -value\u2009=\u20090.001) compared to CTRL, with prominent alterations localized in the frontal lobe, Default Mode Network, and dorsal dopamine pathway. Disruptions in frontotemporal and striatal-cortical connectivity were detected in TRS but not nTRS patients. After adjusting for antipsychotic doses, alterations in the anterior cingulate, frontal and temporal gyri, hippocampus, and precuneus also emerged. The machine-learning approach demonstrated an accuracy ranging from 0.72 to 0.8 in detecting the TRS condition.\n                  <\/jats:p>","DOI":"10.1038\/s41537-024-00535-4","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T05:51:55Z","timestamp":1734587515000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Addressing brain metabolic connectivity in treatment-resistant schizophrenia: a novel graph theory-driven application of 18F-FDG-PET with antipsychotic dose correction"],"prefix":"10.1038","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0422-3415","authenticated-orcid":false,"given":"Giuseppe","family":"De Simone","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7051-5013","authenticated-orcid":false,"given":"Felice","family":"Iasevoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annarita","family":"Barone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valeria","family":"Gaudieri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3431-7658","authenticated-orcid":false,"given":"Alberto","family":"Cuocolo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8944-414X","authenticated-orcid":false,"given":"Mariateresa","family":"Ciccarelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabina","family":"Pappat\u00e0","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3188-5652","authenticated-orcid":false,"given":"Andrea","family":"de Bartolomeis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"535_CR1","doi-asserted-by":"crossref","unstructured":"Correll, C.U. & Howes, O.D. Treatment-Resistant Schizophrenia: Definition, Predictors, and Therapy Options. J. Clin. Psychiatry 82, MY20096AH1C (2021).","DOI":"10.4088\/JCP.MY20096AH1C"},{"key":"535_CR2","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1176\/appi.ajp.2016.16050503","volume":"174","author":"OD Howes","year":"2017","unstructured":"Howes, O. D., McCutcheon, R., Agid, O., de Bartolomeis, A., van Beveren, N. J. & Birnbaum, M. L. et al. Treatment-Resistant Schizophrenia: Treatment Response and Resistance in Psychosis (TRRIP) Working Group Consensus Guidelines on Diagnosis and Terminology. Am. J. Psychiatry 174, 216\u2013229 (2017).","journal-title":"Am. J. Psychiatry"},{"key":"535_CR3","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.pnpbp.2015.08.010","volume":"65","author":"F Iasevoli","year":"2016","unstructured":"Iasevoli, F., Giordano, S., Balletta, R., Latte, G., Formato, M. V. & Prinzivalli, E. et al. Treatment resistant schizophrenia is associated with the worst community functioning among severely-ill highly-disabling psychiatric conditions and is the most relevant predictor of poorer achievements in functional milestones. Prog. Neuro Psychopharmacol. Biol. psychiatry 65, 34\u201348 (2016).","journal-title":"Prog. Neuro Psychopharmacol. Biol. psychiatry"},{"key":"535_CR4","doi-asserted-by":"publisher","first-page":"1843","DOI":"10.1038\/s41380-023-02043-w","volume":"28","author":"OD Howes","year":"2023","unstructured":"Howes, O. D. & Onwordi, E. C. The synaptic hypothesis of schizophrenia version III: a master mechanism. Mol. Psychiatry 28, 1843\u20131856 (2023).","journal-title":"Mol. Psychiatry"},{"key":"535_CR5","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.schres.2016.07.014","volume":"176","author":"K Friston","year":"2016","unstructured":"Friston, K., Brown, H. R., Siemerkus, J. & Stephan, K. E. The dysconnection hypothesis (2016). Schizophrenia Res. 176, 83\u201394 (2016).","journal-title":"Schizophrenia Res."},{"key":"535_CR6","doi-asserted-by":"publisher","first-page":"179","DOI":"10.2183\/pjab.95.014","volume":"95","author":"K Obi-Nagata","year":"2019","unstructured":"Obi-Nagata, K., Temma, Y. & Hayashi-Takagi, A. Synaptic functions and their disruption in schizophrenia: From clinical evidence to synaptic optogenetics in an animal model. Proc. Jpn. Acad. Ser. B Phys. Biol. Sci. 95, 179\u2013197 (2019).","journal-title":"Proc. Jpn. Acad. Ser. B Phys. Biol. Sci."},{"key":"535_CR7","doi-asserted-by":"publisher","first-page":"277\u2013","DOI":"10.1176\/appi.ajp.2021.21080798","volume":"179","author":"DW Chung","year":"2022","unstructured":"Chung, D. W., Geramita, M. A. & Lewis, D. A. Synaptic Variability and Cortical Gamma Oscillation Power in Schizophrenia. Am. J. Psychiatry 179, 277\u2013\u2013287 (2022).","journal-title":"Am. J. Psychiatry"},{"key":"535_CR8","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1093\/schbul\/sbx034","volume":"44","author":"D Dong","year":"2018","unstructured":"Dong, D., Wang, Y., Chang, X., Luo, C. & Yao, D. Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity. Schizophrenia Bull. 44, 168\u2013181 (2018).","journal-title":"Schizophrenia Bull."},{"key":"535_CR9","doi-asserted-by":"publisher","first-page":"3719","DOI":"10.1038\/s41380-022-01731-3","volume":"27","author":"J Seitz-Holland","year":"2022","unstructured":"Seitz-Holland, J., Wojcik, J. D., Cetin-Karayumak, S., Lyall, A. E., Pasternak, O. & Rathi, Y. et al. Cognitive deficits, clinical variables, and white matter microstructure in schizophrenia: a multisite harmonization study. Mol. Psychiatry 27, 3719\u20133730 (2022).","journal-title":"Mol. Psychiatry"},{"key":"535_CR10","doi-asserted-by":"publisher","first-page":"1961","DOI":"10.1038\/s41386-022-01328-y","volume":"47","author":"D Lei","year":"2022","unstructured":"Lei, D., Li, W., Tallman, M. J., Strakowski, S. M., DelBello, M. P. & Rodrigo Patino, L. et al. Changes in the structural brain connectome over the course of a nonrandomized clinical trial for acute mania. Neuropsychopharmacology 47, 1961\u20131968 (2022).","journal-title":"Neuropsychopharmacology"},{"key":"535_CR11","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neubiorev.2021.06.020","volume":"128","author":"L Del Fabro","year":"2021","unstructured":"Del Fabro, L., Schmidt, A., Fortea, L., Delvecchio, G., D\u2019Agostino, A. & Radua, J. et al. Functional brain network dysfunctions in subjects at high-risk for psychosis: A meta-analysis of resting-state functional connectivity. Neurosci. Biobehav. Rev. 128, 90\u2013101 (2021).","journal-title":"Neurosci. Biobehav. Rev."},{"key":"535_CR12","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1093\/schbul\/sbad160","volume":"50","author":"S Tsugawa","year":"2023","unstructured":"Tsugawa, S., Honda, S., Noda, Y., Wannan, C., Zalesky, A. & Tarumi, R. et al. Associations Between Structural Covariance Network and Antipsychotic Treatment Response in Schizophrenia. Schizophrenia Bull. 50, 382\u2013392 (2023).","journal-title":"Schizophrenia Bull."},{"key":"535_CR13","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1093\/schbul\/sbad164","volume":"50","author":"D Sone","year":"2023","unstructured":"Sone, D., Young, A., Shinagawa, S., Tsugawa, S., Iwata, Y. & Tarumi, R. et al. Disease Progression Patterns of Brain Morphology in Schizophrenia: More Progressed Stages in Treatment Resistance. Schizophrenia Bull. 50, 393\u2013402 (2023).","journal-title":"Schizophrenia Bull."},{"key":"535_CR14","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1038\/npp.2016.258","volume":"42","author":"E Kim","year":"2017","unstructured":"Kim, E., Howes, O. D., Veronese, M., Beck, K., Seo, S. & Park, J. W. et al. Presynaptic Dopamine Capacity in Patients with Treatment-Resistant Schizophrenia Taking Clozapine: An [(18)F]DOPA PET Study. Neuropsychopharmacology 42, 941\u2013950 (2017).","journal-title":"Neuropsychopharmacology"},{"key":"535_CR15","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1176\/appi.ajp.2012.12010144","volume":"169","author":"A Demjaha","year":"2012","unstructured":"Demjaha, A., Murray, R. M., McGuire, P. K., Kapur, S. & Howes, O. D. Dopamine synthesis capacity in patients with treatment-resistant schizophrenia. Am. J. Psychiatry 169, 1203\u20131210 (2012).","journal-title":"Am. J. Psychiatry"},{"key":"535_CR16","doi-asserted-by":"publisher","first-page":"570","DOI":"10.30773\/pi.2022.0033","volume":"19","author":"S Shin","year":"2022","unstructured":"Shin, S., Jung, W. H., McCutcheon, R., Veronese, M., Beck, K. & Lee, J. S. et al. The Relationship Between Frontostriatal Connectivity and Striatal Dopamine Function in Schizophrenia: An 18F-DOPA PET and Diffusion Tensor Imaging Study in Treatment Responsive and Resistant Patients. Psychiatry Investig. 19, 570\u2013579 (2022).","journal-title":"Psychiatry Investig."},{"key":"535_CR17","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/S0893-133X(99)00096-2","volume":"22","author":"A Bertolino","year":"2000","unstructured":"Bertolino, A., Breier, A., Callicott, J. H., Adler, C., Mattay, V. S. & Shapiro, M. et al. The Relationship between Dorsolateral Prefrontal Neuronal N-Acetylaspartate and Evoked Release of Striatal Dopamine in Schizophrenia. Neuropsychopharmacology 22, 125\u2013132 (2000).","journal-title":"Neuropsychopharmacology"},{"key":"535_CR18","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1073\/pnas.94.6.2569","volume":"94","author":"A Breier","year":"1997","unstructured":"Breier, A., Su, T. P., Saunders, R., Carson, R. E., Kolachana, B. S. & de Bartolomeis, A. et al. Schizophrenia is associated with elevated amphetamine-induced synaptic dopamine concentrations: evidence from a novel positron emission tomography method. Proc. Natl Acad. Sci. USA 94, 2569\u20132574 (1997).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"535_CR19","doi-asserted-by":"crossref","unstructured":"Rubio, J.M., Lencz, T., Cao, H., Kraguljac, N., Dhamala, E., Homan, P. et al. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. Mol. Psychiatry 29, 929\u2013938 (2024).","DOI":"10.1038\/s41380-023-02381-9"},{"key":"535_CR20","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.tics.2022.11.015","volume":"27","author":"A Sala","year":"2023","unstructured":"Sala, A., Lizarraga, A., Caminiti, S. P., Calhoun, V. D., Eickhoff, S. B. & Habeck, C. et al. Brain connectomics: time for a molecular imaging perspective? Trends Cogn. Sci. 27, 353\u2013366 (2023).","journal-title":"Trends Cogn. Sci."},{"key":"535_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.116871","volume":"218","author":"ME Archila-Mel\u00e9ndez","year":"2020","unstructured":"Archila-Mel\u00e9ndez, M. E., Sorg, C. & Preibisch, C. Modeling the impact of neurovascular coupling impairments on BOLD-based functional connectivity at rest. NeuroImage 218, 116871 (2020).","journal-title":"NeuroImage"},{"key":"535_CR22","doi-asserted-by":"crossref","unstructured":"Yakushev, I., Drzezga, A., Habeck, C. Metabolic connectivity: methods and applications. Curr. Opin. Neurol. 30, 677\u2013685 (2017).","DOI":"10.1097\/WCO.0000000000000494"},{"key":"535_CR23","doi-asserted-by":"crossref","unstructured":"Caminiti, S. P., Tettamanti, M., Sala, A., Presotto, L., Iannaccone, S., Cappa, S. F. et al. Metabolic connectomics targeting brain pathology in dementia with Lewy bodies. J. Cereb. Blood Flow. Metab. 37, 1311\u20131325 (2017).","DOI":"10.1177\/0271678X16654497"},{"key":"535_CR24","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.parkreldis.2020.10.036","volume":"81","author":"G Carli","year":"2020","unstructured":"Carli, G., Caminiti, S. P., Sala, A., Galbiati, A., Pilotto, A. & Ferini-Strambi, L. et al. Impaired metabolic brain networks associated with neurotransmission systems in the \u03b1-synuclein spectrum. Parkinsonism Relat. Disord. 81, 113\u2013122 (2020).","journal-title":"Parkinsonism Relat. Disord."},{"key":"535_CR25","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1016\/j.cortex.2019.07.018","volume":"120","author":"M Malpetti","year":"2019","unstructured":"Malpetti, M., Carli, G., Sala, A., Cerami, C., Marcone, A. & Iannaccone, S. et al. Variant-specific vulnerability in metabolic connectivity and resting-state networks in behavioural variant of frontotemporal dementia. Cortex 120, 483\u2013497 (2019).","journal-title":"Cortex"},{"key":"535_CR26","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-04102-z","volume":"7","author":"A Sala","year":"2017","unstructured":"Sala, A., Caminiti, S. P., Presotto, L., Premi, E., Pilotto, A. & Turrone, R. et al. Altered brain metabolic connectivity at multiscale level in early Parkinson\u2019s disease. Sci. Rep. 7, 4256 (2017).","journal-title":"Sci. Rep."},{"key":"535_CR27","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1016\/j.neuroimage.2009.12.120","volume":"50","author":"S Huang","year":"2010","unstructured":"Huang, S., Li, J., Sun, L., Ye, J., Fleisher, A. & Wu, T. et al. Learning brain connectivity of Alzheimer\u2019s disease by sparse inverse covariance estimation. NeuroImage 50, 935\u2013949 (2010).","journal-title":"NeuroImage"},{"key":"535_CR28","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1177\/0271678X15622465","volume":"37","author":"D Titov","year":"2017","unstructured":"Titov, D., Diehl-Schmid, J., Shi, K., Perneczky, R., Zou, N. & Grimmer, T. et al. Metabolic connectivity for differential diagnosis of dementing disorders. J. Cereb. Blood Flow. Metab. 37, 252\u2013262 (2017).","journal-title":"J. Cereb. Blood Flow. Metab."},{"key":"535_CR29","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1093\/schbul\/sbac147","volume":"49","author":"F Iasevoli","year":"2022","unstructured":"Iasevoli, F., D\u2019Ambrosio, L., Ciccarelli, M., Barone, A., Gaudieri, V. & Cocozza, S. et al. Altered Patterns of Brain Glucose Metabolism Involve More Extensive and Discrete Cortical Areas in Treatment-resistant Schizophrenia Patients Compared to Responder Patients and Controls: Results From a Head-to-Head 2-[18F]-FDG-PET Study. Schizophrenia Bull. 49, 474\u2013485 (2022).","journal-title":"Schizophrenia Bull."},{"key":"535_CR30","doi-asserted-by":"publisher","first-page":"4880","DOI":"10.1017\/S003329172200174X","volume":"53","author":"L Townsend","year":"2023","unstructured":"Townsend, L., Pillinger, T., Selvaggi, P., Veronese, M., Turkheimer, F. & Howes, O. Brain glucose metabolism in schizophrenia: a systematic review and meta-analysis of (18)FDG-PET studies in schizophrenia. Psychological Med. 53, 4880\u20134897 (2023).","journal-title":"Psychological Med."},{"key":"535_CR31","doi-asserted-by":"crossref","unstructured":"de Bartolomeis, A., De Simone, G., De Prisco, M., Barone, A., Napoli, R., Beguinot, F. et al. Insulin effects on core neurotransmitter pathways involved in schizophrenia neurobiology: a meta-analysis of preclinical studies. Implications for the treatment. Mol. Psychiatry 28, 2811\u20132825 (2023).","DOI":"10.1038\/s41380-023-02065-4"},{"key":"535_CR32","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-022-02002-z","volume":"12","author":"SX Tang","year":"2022","unstructured":"Tang, S. X., Oliver, L. D., H\u00e4nsel, K., DeRosse, P., John, M. & Khairullah, A. et al. Metabolic disturbances, hemoglobin A1c, and social cognition impairment in Schizophrenia spectrum disorders. Transl. Psychiatry 12, 233 (2022).","journal-title":"Transl. Psychiatry"},{"key":"535_CR33","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/S0925-4927(02)00124-5","volume":"122","author":"M Desco","year":"2003","unstructured":"Desco, M., Gispert, J. D., Reig, S., Sanz, J., Pascau, J. & Sarramea, F. et al. Cerebral metabolic patterns in chronic and recent-onset schizophrenia. Psychiatry Res. 122, 125\u2013135 (2003).","journal-title":"Psychiatry Res."},{"key":"535_CR34","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1055\/s-2007-984399","volume":"40","author":"V Molina","year":"2007","unstructured":"Molina, V., Sanz, J., Sarramea, F. & Palomo, T. Marked hypofrontality in clozapine-responsive patients. Pharmacopsychiatry 40, 157\u2013162 (2007).","journal-title":"Pharmacopsychiatry"},{"key":"535_CR35","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-023-02716-8","volume":"14","author":"J Lee","year":"2024","unstructured":"Lee, J., Xue, X., Au, E., McIntyre, W. B., Asgariroozbehani, R. & Panganiban, K. et al. Glucose dysregulation in antipsychotic-naive first-episode psychosis: in silico exploration of gene expression signatures. Transl. Psychiatry 14, 19 (2024).","journal-title":"Transl. Psychiatry"},{"key":"535_CR36","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1111\/bdi.13270","volume":"25","author":"E Mazza","year":"2023","unstructured":"Mazza, E., Calesella, F., Paolini, M., di Pasquasio, C., Poletti, S. & Lorenzi, C. et al. Insulin resistance disrupts white matter microstructure and amplitude of functional spontaneous activity in bipolar disorder. Bipolar Disord. 25, 32\u201342 (2023).","journal-title":"Bipolar Disord."},{"key":"535_CR37","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1007\/s00259-021-05603-w","volume":"49","author":"E Guedj","year":"2022","unstructured":"Guedj, E., Varrone, A., Boellaard, R., Albert, N. L., Barthel, H. & van Berckel, B. et al. EANM procedure guidelines for brain PET imaging using [(18)F]FDG, version 3. Eur. J. Nucl. Med. Mol. imaging 49, 632\u2013651 (2022).","journal-title":"Eur. J. Nucl. Med. Mol. imaging"},{"key":"535_CR38","doi-asserted-by":"crossref","unstructured":"Kuang, L., Jia, J., Zhao, D., Xiong, F., Han, X., Wang, Y. et al. Default Mode Network Analysis of APOE Genotype in Cognitively Unimpaired Subjects Based on Persistent Homology. Front. Aging Neurosci. 12, 188 (2020).","DOI":"10.3389\/fnagi.2020.00188"},{"key":"535_CR39","doi-asserted-by":"publisher","first-page":"5049","DOI":"10.1093\/bioinformatics\/btac657","volume":"38","author":"V Bernal","year":"2022","unstructured":"Bernal, V., Soancatl-Aguilar, V., Bulthuis, J., Guryev, V., Horvatovich, P. & Grzegorczyk, M. GeneNetTools: tests for Gaussian graphical models with shrinkage. Bioinformatics 38, 5049\u20135054 (2022).","journal-title":"Bioinformatics"},{"key":"535_CR40","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/6495417","volume":"2016","author":"Y Wang","year":"2016","unstructured":"Wang, Y. & Zhu, L. Variable Selection and Parameter Estimation with the Atan Regularization Method. J. Probab. Stat. 2016, 6495417 (2016).","journal-title":"J. Probab. Stat."},{"key":"535_CR41","doi-asserted-by":"publisher","unstructured":"Williams, D. R. GGMncv: Nonconvex Penalized Gaussian Graphical Models in R (2020). PsyArXiv. https:\/\/doi.org\/10.31234\/osf.io\/6jz5m.","DOI":"10.31234\/osf.io\/6jz5m"},{"key":"535_CR42","doi-asserted-by":"publisher","unstructured":"Williams, D. R. Beyond Lasso: A Survey of Nonconvex Regularization in Gaussian Graphical Models. PsyArXiv (2020). https:\/\/doi.org\/10.31234\/osf.io\/ad57p.","DOI":"10.31234\/osf.io\/ad57p"},{"key":"535_CR43","doi-asserted-by":"publisher","unstructured":"Watson. brainGraph: Graph Theory Analysis of Brain MRI Data (2024). Retrieved from CRAN. https:\/\/doi.org\/10.32614\/CRAN.package.brainGraph.","DOI":"10.32614\/CRAN.package.brainGraph"},{"key":"535_CR44","doi-asserted-by":"publisher","first-page":"247","DOI":"10.31887\/DCNS.2013.15.3\/osporns","volume":"15","author":"O Sporns","year":"2013","unstructured":"Sporns, O. Structure and function of complex brain networks. Dialogues Clin. Neurosci. 15, 247\u2013262 (2013).","journal-title":"Dialogues Clin. Neurosci."},{"key":"535_CR45","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1385\/NI:2:2:145","volume":"2","author":"O Sporns","year":"2004","unstructured":"Sporns, O. & Zwi, J. D. The small world of the cerebral cortex. Neuroinformatics 2, 145\u2013162 (2004).","journal-title":"Neuroinformatics"},{"key":"535_CR46","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1016\/j.neuron.2013.07.035","volume":"79","author":"JD Power","year":"2013","unstructured":"Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N. & Petersen, S. E. Evidence for hubs in human functional brain networks. Neuron 79, 798\u2013813 (2013).","journal-title":"Neuron"},{"key":"535_CR47","unstructured":"Cs\u00e1rdi, G., Nepusz, T., M\u00fcller, K., Horv\u00e1t, S., Traag, V., Zanini, F., & Noom, D. igraph for R: R interface of the igraph library for graph theory and network analysis (v1.5.0.1) (Zenodo, 2023)."},{"key":"535_CR48","unstructured":"Csardi, G. & Nepusz, T. The Igraph Software Package for Complex Network Research (InterJournal, 2005)."},{"key":"535_CR49","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.600","volume":"7","author":"A Arratia","year":"2021","unstructured":"Arratia, A. & Renedo Mirambell, M. Clustering assessment in weighted networks. PeerJ Computer Sci. 7, e600 (2021).","journal-title":"PeerJ Computer Sci."},{"key":"535_CR50","unstructured":"RStudio: Integrated Development for R. RStudio P, Boston, MA, http:\/\/www.rstudio.com\/ (2020)."},{"key":"535_CR51","doi-asserted-by":"publisher","first-page":"e68910","DOI":"10.1371\/journal.pone.0068910","volume":"8","author":"M Xia","year":"2013","unstructured":"Xia, M., Wang, J. & He, Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PloS one 8, e68910 (2013).","journal-title":"PloS one"},{"key":"535_CR52","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1080\/00031305.2021.1902856","volume":"76","author":"WF Christensen","year":"2022","unstructured":"Christensen, W. F. & Zabriskie, B. N. When Your Permutation Test is Doomed to Fail. Am. Statistician 76, 53\u201363 (2022).","journal-title":"Am. Statistician"},{"key":"535_CR53","doi-asserted-by":"crossref","unstructured":"Leem, S., Huh, I. & Park, T. Enhanced Permutation Tests via Multiple Pruning. Front. Genet. 11, 509 (2020).","DOI":"10.3389\/fgene.2020.00509"},{"key":"535_CR54","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-39005-8","volume":"9","author":"M Veronese","year":"2019","unstructured":"Veronese, M., Moro, L., Arcolin, M., Dipasquale, O., Rizzo, G. & Expert, P. et al. Covariance statistics and network analysis of brain PET imaging studies. Sci. Rep. 9, 2496 (2019).","journal-title":"Sci. Rep."},{"key":"535_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/hbm.1058","volume":"15","author":"TE Nichols","year":"2002","unstructured":"Nichols, T. E. & Holmes, A. P. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15, 1\u201325 (2002).","journal-title":"Hum. Brain Mapp."},{"key":"535_CR56","doi-asserted-by":"publisher","first-page":"4372","DOI":"10.3390\/ijms24054372","volume":"24","author":"A Barone","year":"2023","unstructured":"Barone, A., De Simone, G., Ciccarelli, M., Buonaguro, E. F., Tomasetti, C. & Eramo, A. et al. A Postsynaptic Density Immediate Early Gene-Based Connectome Analysis of Acute NMDAR Blockade and Reversal Effect of Antipsychotic Administration. Int J. Mol. Sci. 24, 4372 (2023).","journal-title":"Int J. Mol. Sci."},{"key":"535_CR57","doi-asserted-by":"crossref","unstructured":"van Borkulo, C.D., van Bork, R., Boschloo, L., Kossakowski, J.J., Tio, P., Schoevers, R.A. et al. Comparing network structures on three aspects: A permutation test. Psychological Methods 28, 1273\u20131285 (2023).","DOI":"10.1037\/met0000476"},{"key":"535_CR58","doi-asserted-by":"publisher","unstructured":"Wei, T., Simko, V., Levy, M., Xie, Y., Jin, Y., Zemla, J.J. R. pv. corrplot: Visualization of a correlation matrix. 230 (2013). Retrieved from CRAN. https:\/\/doi.org\/10.32614\/CRAN.package.corrplot.","DOI":"10.32614\/CRAN.package.corrplot"},{"key":"535_CR59","doi-asserted-by":"publisher","DOI":"10.1093\/bioadv\/vbad048","volume":"3","author":"MJ Lewis","year":"2023","unstructured":"Lewis, M. J., Spiliopoulou, A., Goldmann, K., Pitzalis, C., McKeigue, P. & Barnes, M. R. nestedcv: an R package for fast implementation of nested cross-validation with embedded feature selection designed for transcriptomics and high-dimensional data. Bioinforma. Adv. 3, vbad048 (2023).","journal-title":"Bioinforma. Adv."},{"key":"535_CR60","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.biopsych.2023.11.012","volume":"96","author":"J Radua","year":"2024","unstructured":"Radua, J. & Koutsouleris, N. Ten Simple Rules for Using Machine Learning in Mental Health Research. Biol. Psychiatry 96, 511\u2013513 (2024).","journal-title":"Biol. Psychiatry"},{"key":"535_CR61","doi-asserted-by":"publisher","first-page":"e0224365","DOI":"10.1371\/journal.pone.0224365","volume":"14","author":"A Vabalas","year":"2019","unstructured":"Vabalas, A., Gowen, E., Poliakoff, E. & Casson, A. J. Machine learning algorithm validation with a limited sample size. PloS One 14, e0224365 (2019).","journal-title":"PloS One"},{"key":"535_CR62","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1016\/j.jad.2024.06.061","volume":"361","author":"M Amanollahi","year":"2024","unstructured":"Amanollahi, M. et al. Machine learning applied to the prediction of relapse, hospitalization, and suicide in bipolar disorder using neuroimaging and clinical data: A systematic review. J. Affect. Disord. 361, 778\u2013797 (2024).","journal-title":"J. Affect. Disord."},{"key":"535_CR63","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-12-77","volume":"12","author":"X Robin","year":"2011","unstructured":"Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F. & Sanchez, J.-C. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinforma. 12, 77 (2011).","journal-title":"BMC Bioinforma."},{"key":"535_CR64","doi-asserted-by":"crossref","unstructured":"Yu, Q., Plis, S., Erhardt, E., Allen, E., Sui, J., Kiehl, K. et al. Modular Organization of Functional Network Connectivity in Healthy Controls and Patients with Schizophrenia during the Resting State. Front. Syst. Neurosci. 5, 103 (2012).","DOI":"10.3389\/fnsys.2011.00103"},{"key":"535_CR65","doi-asserted-by":"crossref","unstructured":"Odkhuu, S., Kim, W.S., Tsogt, U., Shen, J., Cheraghi, S., Li, L. et al. Network biomarkers in recovered psychosis patients who discontinued antipsychotics. Mol. Psychiatry 28, 3717\u20133726 (2023).","DOI":"10.1038\/s41380-023-02279-6"},{"key":"535_CR66","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.schres.2015.01.028","volume":"162","author":"M Choudhary","year":"2015","unstructured":"Choudhary, M., Kumar, A., Tripathi, M., Bhatia, T., Shivakumar, V. & Beniwal, R. P. et al. F-18 fluorodeoxyglucose positron emission tomography study of impaired emotion processing in first episode schizophrenia. Schizophrenia Res. 162, 103\u2013107 (2015).","journal-title":"Schizophrenia Res."},{"key":"535_CR67","doi-asserted-by":"crossref","unstructured":"Ding, Y., Ou, Y., Su, Q., Pan, P., Shan, X., Chen, J. et al. Enhanced Global-Brain Functional Connectivity in the Left Superior Frontal Gyrus as a Possible Endophenotype for Schizophrenia. Front. Neurosci. 13, 145 (2019).","DOI":"10.3389\/fnins.2019.00145"},{"key":"535_CR68","doi-asserted-by":"publisher","first-page":"4138","DOI":"10.1002\/hbm.20835","volume":"30","author":"B Jeong","year":"2009","unstructured":"Jeong, B., Wible, C. G., Hashimoto, R. & Kubicki, M. Functional and anatomical connectivity abnormalities in left inferior frontal gyrus in schizophrenia. Hum. Brain Mapp. 30, 4138\u20134151 (2009).","journal-title":"Hum. Brain Mapp."},{"key":"535_CR69","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2221533120","volume":"120","author":"R Passiatore","year":"2023","unstructured":"Passiatore, R., Antonucci, L. A., DeRamus, T. P., Fazio, L., Stolfa, G. & Sportelli, L. et al. Changes in patterns of age-related network connectivity are associated with risk for schizophrenia. Proc. Natl Acad. Sci. USA 120, e2221533120 (2023).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"535_CR70","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-018-0464-x","volume":"17","author":"Q Zhu","year":"2018","unstructured":"Zhu, Q., Huang, J. & Xu, X. Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI. Biomed. Eng. online 17, 32 (2018).","journal-title":"Biomed. Eng. online"},{"key":"535_CR71","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1093\/schbul\/sbq074","volume":"38","author":"H Liu","year":"2010","unstructured":"Liu, H., Kaneko, Y., Ouyang, X., Li, L., Hao, Y. & Chen, E. Y. H. et al. Schizophrenic Patients and Their Unaffected Siblings Share Increased Resting-State Connectivity in the Task-Negative Network but Not Its Anticorrelated Task-Positive Network. Schizophrenia Bull. 38, 285\u2013294 (2010).","journal-title":"Schizophrenia Bull."},{"key":"535_CR72","first-page":"436","volume":"43","author":"T Li","year":"2016","unstructured":"Li, T., Wang, Q., Zhang, J., Rolls, E. T., Yang, W. & Palaniyappan, L. et al. Brain-Wide Analysis of Functional Connectivity in First-Episode and Chronic Stages of Schizophrenia. Schizophrenia Bull. 43, 436\u2013448 (2016).","journal-title":"Schizophrenia Bull."},{"key":"535_CR73","doi-asserted-by":"crossref","unstructured":"Zarei, M, J. Precentral gyrus abnormal connectivity in male and female patients with schizophrenia. Neuroimmunol. Neuroinflamm. 5, 13 (2018).","DOI":"10.20517\/2347-8659.2018.02"},{"key":"535_CR74","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1017\/S003329171400110X","volume":"45","author":"S Lui","year":"2015","unstructured":"Lui, S., Yao, L., Xiao, Y., Keedy, S. K., Reilly, J. L. & Keefe, R. S. et al. Resting-state brain function in schizophrenia and psychotic bipolar probands and their first-degree relatives. Psychological Med. 45, 97\u2013108 (2015).","journal-title":"Psychological Med."},{"key":"535_CR75","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1038\/s41537-022-00311-2","volume":"8","author":"M Cai","year":"2022","unstructured":"Cai, M., Wang, R., Liu, M., Du, X., Xue, K. & Ji, Y. et al. Disrupted local functional connectivity in schizophrenia: An updated and extended meta-analysis. Schizophrenia 8, 93 (2022).","journal-title":"Schizophrenia"},{"key":"535_CR76","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.schres.2011.10.019","volume":"134","author":"HJ Li","year":"2012","unstructured":"Li, H. J., Chan, R. C., Gong, Q. Y., Liu, Y., Liu, S. M. & Shum, D. et al. Facial emotion processing in patients with schizophrenia and their non-psychotic siblings: a functional magnetic resonance imaging study. Schizophrenia Res. 134, 143\u2013150 (2012).","journal-title":"Schizophrenia Res."},{"key":"535_CR77","doi-asserted-by":"crossref","unstructured":"Li, R.- R., Lyu, H.- L., Liu, F., Lian, N., Wu, R.- R., Zhao, J.- P. et al. Altered functional connectivity strength and its correlations with cognitive function in subjects with ultra-high risk for psychosis at rest. CNS Neurosci. Ther. 24, 1140\u20131148 (2018).","DOI":"10.1111\/cns.12865"},{"key":"535_CR78","doi-asserted-by":"crossref","unstructured":"Koshiyama, D., Miyakoshi, M., Tanaka-Koshiyama, K., Joshi, Y. B., Molina, J. L., Sprock, J. et al. Neurophysiologic Characterization of Resting State Connectivity Abnormalities in Schizophrenia Patients. Front Psychiatry. 11, 608154 (2020).","DOI":"10.3389\/fpsyt.2020.608154"},{"key":"535_CR79","doi-asserted-by":"crossref","unstructured":"Nyatega, C. O., Qiang, L., Adamu, M. J., Younis, A. & Kawuwa, H. B. Altered Dynamic Functional Connectivity of Cuneus in Schizophrenia Patients: A Resting-State fMRI Study. Appl Sci. 11, 11392 (2021).","DOI":"10.3390\/app112311392"},{"key":"535_CR80","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s00406-013-0465-5","volume":"264","author":"D Berg\u00e9","year":"2014","unstructured":"Berg\u00e9, D., Carmona, S., Salgado, P., Rovira, M., Bulbena, A. & Vilarroya, O. Limbic activity in antipsychotic na\u00efve first-episode psychotic subjects during facial emotion discrimination. Eur. Arch. Psychiatry Clin. Neurosci. 264, 271\u2013283 (2014).","journal-title":"Eur. Arch. Psychiatry Clin. Neurosci."},{"key":"535_CR81","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.schres.2018.03.018","volume":"199","author":"E Rikandi","year":"2018","unstructured":"Rikandi, E., M\u00e4ntyl\u00e4, T., Lindgren, M., Kiesepp\u00e4, T., Suvisaari, J. & Raij, T. T. Connectivity of the precuneus-posterior cingulate cortex with the anterior cingulate cortex-medial prefrontal cortex differs consistently between control subjects and first-episode psychosis patients during a movie stimulus. Schizophrenia Res. 199, 235\u2013242 (2018).","journal-title":"Schizophrenia Res."},{"key":"535_CR82","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.schres.2020.03.022","volume":"220","author":"S Liang","year":"2020","unstructured":"Liang, S., Deng, W., Li, X., Wang, Q., Greenshaw, A. J. & Guo, W. et al. Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: A machine learning study. Schizophrenia Res. 220, 187\u2013193 (2020).","journal-title":"Schizophrenia Res."},{"key":"535_CR83","doi-asserted-by":"publisher","DOI":"10.1038\/srep14655","volume":"5","author":"H Wang","year":"2015","unstructured":"Wang, H., Zeng, L.-L., Chen, Y., Yin, H., Tan, Q. & Hu, D. Evidence of a dissociation pattern in default mode subnetwork functional connectivity in schizophrenia. Sci. Rep. 5, 14655 (2015).","journal-title":"Sci. Rep."},{"key":"535_CR84","doi-asserted-by":"publisher","first-page":"998","DOI":"10.3758\/s13415-019-00698-z","volume":"19","author":"JPY Hua","year":"2019","unstructured":"Hua, J. P. Y., Karcher, N. R., Merrill, A. M., O\u2019Brien, K. J., Straub, K. T. & Trull, T. J. et al. Psychosis risk is associated with decreased resting-state functional connectivity between the striatum and the default mode network. Cogn. Affect. Behav. Neurosci. 19, 998\u20131011 (2019).","journal-title":"Cogn. Affect. Behav. Neurosci."},{"key":"535_CR85","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.neubiorev.2019.07.001","volume":"104","author":"C Molent","year":"2019","unstructured":"Molent, C., Olivo, D., Wolf, R. C., Balestrieri, M. & Sambataro, F. Functional neuroimaging in treatment resistant schizophrenia: A systematic review. Neurosci. Biobehav. Rev. 104, 178\u2013190 (2019).","journal-title":"Neurosci. Biobehav. Rev."},{"key":"535_CR86","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-023-02714-w","volume":"14","author":"Y Chen","year":"2024","unstructured":"Chen, Y., Liu, S., Zhang, B., Zhao, G., Zhang, Z. & Li, S. et al. Baseline symptom-related white matter tracts predict individualized treatment response to 12-week antipsychotic monotherapies in first-episode schizophrenia. Transl. Psychiatry 14, 23 (2024).","journal-title":"Transl. Psychiatry"},{"key":"535_CR87","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.schres.2016.12.008","volume":"184","author":"EP Ganella","year":"2017","unstructured":"Ganella, E. P., Bartholomeusz, C. F., Seguin, C., Whittle, S., Bousman, C. & Phassouliotis, C. et al. Functional brain networks in treatment-resistant schizophrenia. Schizophrenia Res. 184, 73\u201381 (2017).","journal-title":"Schizophrenia Res."},{"key":"535_CR88","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1016\/j.biopsych.2009.11.017","volume":"67","author":"A Vercammen","year":"2010","unstructured":"Vercammen, A., Knegtering, H., den Boer, J. A., Liemburg, E. J. & Aleman, A. Auditory Hallucinations in Schizophrenia Are Associated with Reduced Functional Connectivity of the Temporo-Parietal Area. Biol. Psychiatry 67, 912\u2013918 (2010).","journal-title":"Biol. Psychiatry"},{"key":"535_CR89","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1503\/jpn.110008","volume":"36","author":"ND Wolf","year":"2011","unstructured":"Wolf, N. D., Sambataro, F., Vasic, N., Frasch, K., Schmid, M. & Sch\u00f6nfeldt-Lecuona, C. et al. Dysconnectivity of multiple resting-state networks in patients with schizophrenia who have persistent auditory verbal hallucinations. J. Psychiatry Neurosci. JPN 36, 366\u2013374 (2011).","journal-title":"J. Psychiatry Neurosci. JPN"},{"key":"535_CR90","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.schres.2014.10.047","volume":"161","author":"A Alonso-Sol\u00eds","year":"2015","unstructured":"Alonso-Sol\u00eds, A., Vives-Gilabert, Y., Grasa, E., Portella, M. J., Rabella, M. & Sauras, R. B. et al. Resting-state functional connectivity alterations in the default network of schizophrenia patients with persistent auditory verbal hallucinations. Schizophrenia Res. 161, 261\u2013268 (2015).","journal-title":"Schizophrenia Res."},{"key":"535_CR91","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/j.brs.2017.01.578","volume":"10","author":"PA Thomann","year":"2017","unstructured":"Thomann, P. A., Wolf, R. C., Nolte, H. M., Hirjak, D., Hofer, S. & Seidl, U. et al. Neuromodulation in response to electroconvulsive therapy in schizophrenia and major depression. Brain Stimulation 10, 637\u2013644 (2017).","journal-title":"Brain Stimulation"},{"key":"535_CR92","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1093\/schbul\/sbv114","volume":"42","author":"M Mondino","year":"2015","unstructured":"Mondino, M., Jardri, R., Suaud-Chagny, M.-F., Saoud, M., Poulet, E. & Brunelin, J. Effects of Fronto-Temporal Transcranial Direct Current Stimulation on Auditory Verbal Hallucinations and Resting-State Functional Connectivity of the Left Temporo-Parietal Junction in Patients With Schizophrenia. Schizophrenia Bull. 42, 318\u2013326 (2015).","journal-title":"Schizophrenia Bull."},{"key":"535_CR93","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.jpsychires.2020.05.016","volume":"127","author":"A Rold\u00e1n","year":"2020","unstructured":"Rold\u00e1n, A., Portella, M. J., Sampedro, F., Alonso-Sol\u00eds, A., Sarr\u00f3, S. & Rabella, M. et al. Brain metabolic changes in patients with treatment resistant schizophrenia treated with deep brain stimulation: A series of cases. J. Psychiatr. Res. 127, 57\u201361 (2020).","journal-title":"J. Psychiatr. Res."},{"key":"535_CR94","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1093\/schbul\/sbaa128","volume":"47","author":"A Egerton","year":"2020","unstructured":"Egerton, A., Murphy, A., Donocik, J., Anton, A., Barker, G. J. & Collier, T. et al. Dopamine and Glutamate in Antipsychotic-Responsive Compared With Antipsychotic-Nonresponsive Psychosis: A Multicenter Positron Emission Tomography and Magnetic Resonance Spectroscopy Study (STRATA). Schizophrenia Bull. 47, 505\u2013516 (2020).","journal-title":"Schizophrenia Bull."},{"key":"535_CR95","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1093\/schbul\/sbv151","volume":"42","author":"E Mouchlianitis","year":"2015","unstructured":"Mouchlianitis, E., Bloomfield, M. A. P., Law, V., Beck, K., Selvaraj, S. & Rasquinha, N. et al. Treatment-Resistant Schizophrenia Patients Show Elevated Anterior Cingulate Cortex Glutamate Compared to Treatment-Responsive. Schizophrenia Bull. 42, 744\u2013752 (2015).","journal-title":"Schizophrenia Bull."},{"key":"535_CR96","doi-asserted-by":"publisher","first-page":"110493","DOI":"10.1016\/j.pnpbp.2021.110493","volume":"114","author":"M Matrone","year":"2022","unstructured":"Matrone, M., Kotzalidis, G. D., Romano, A., Bozzao, A., Cuomo, I. & Valente, F. et al. Treatment-resistant schizophrenia: Addressing white matter integrity, intracortical glutamate levels, clinical and cognitive profiles between early- and adult-onset patients. Prog. Neuro Psychopharmacol. Biol. psychiatry 114, 110493 (2022).","journal-title":"Prog. Neuro Psychopharmacol. Biol. psychiatry"},{"key":"535_CR97","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.schres.2023.08.020","volume":"260","author":"K Griffiths","year":"2023","unstructured":"Griffiths, K., Smart, S. E., Barker, G. J., Deakin, B., Lawrie, S. M. & Lewis, S. et al. Treatment resistance NMDA receptor pathway polygenic score is associated with brain glutamate in schizophrenia. Schizophrenia Res. 260, 152\u2013159 (2023).","journal-title":"Schizophrenia Res."},{"key":"535_CR98","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1038\/s41380-021-01297-6","volume":"27","author":"T Nakahara","year":"2022","unstructured":"Nakahara, T., Tsugawa, S., Noda, Y., Ueno, F., Honda, S. & Kinjo, M. et al. Glutamatergic and GABAergic metabolite levels in schizophrenia-spectrum disorders: a meta-analysis of 1H-magnetic resonance spectroscopy studies. Mol. Psychiatry 27, 744\u2013757 (2022).","journal-title":"Mol. Psychiatry"},{"key":"535_CR99","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/j.biopsych.2018.09.009","volume":"85","author":"Y Iwata","year":"2019","unstructured":"Iwata, Y., Nakajima, S., Plitman, E., Caravaggio, F., Kim, J. & Shah, P. et al. Glutamatergic Neurometabolite Levels in Patients With Ultra-Treatment-Resistant Schizophrenia: A Cross-Sectional 3T Proton Magnetic Resonance Spectroscopy Study. Biol. Psychiatry 85, 596\u2013605 (2019).","journal-title":"Biol. Psychiatry"},{"key":"535_CR100","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1038\/s41386-019-0589-z","volume":"45","author":"R Tarumi","year":"2020","unstructured":"Tarumi, R., Tsugawa, S., Noda, Y., Plitman, E., Honda, S. & Matsushita, K. et al. Levels of glutamatergic neurometabolites in patients with severe treatment-resistant schizophrenia: a proton magnetic resonance spectroscopy study. Neuropsychopharmacology 45, 632\u2013640 (2020).","journal-title":"Neuropsychopharmacology"},{"key":"535_CR101","doi-asserted-by":"publisher","first-page":"3220","DOI":"10.1017\/S003329172100533X","volume":"53","author":"M Abdullah","year":"2023","unstructured":"Abdullah, M., Chen, K. C., Huang, L.-C., Lin, S.-H., Tseng, H.-H. & Yang, Y. K. Altered glutamate level and its association with working memory among patients with treatment-resistant schizophrenia (TRS): a proton magnetic resonance spectroscopy study. Psychological Med. 53, 3220\u20133227 (2023).","journal-title":"Psychological Med."},{"key":"535_CR102","doi-asserted-by":"publisher","first-page":"E1","DOI":"10.1503\/jpn.210113","volume":"47","author":"R Ochi","year":"2022","unstructured":"Ochi, R., Plitman, E., Patel, R., Tarumi, R., Iwata, Y. & Tsugawa, S. et al. Investigating structural subdivisions of the anterior cingulate cortex in schizophrenia, with implications for treatment resistance and glutamatergic levels. J. Psychiatry Neurosci. JPN 47, E1\u2013e10 (2022).","journal-title":"J. Psychiatry Neurosci. JPN"},{"key":"535_CR103","doi-asserted-by":"crossref","unstructured":"Li, C. -T., Chen, M. -H., Lin, W. -C., Hong, C. -J., Yang, B. -H., Liu, R. -S. et al. The effects of low-dose ketamine on the prefrontal cortex and amygdala in treatment-resistant depression: A randomized controlled study. Hum. Brain Mapp. 37, 1080\u20131090 (2016).","DOI":"10.1002\/hbm.23085"},{"key":"535_CR104","doi-asserted-by":"crossref","unstructured":"Li, C. -T., Yang, K. -C. & Lin, W. -C. Glutamatergic Dysfunction and Glutamatergic Compounds for Major Psychiatric Disorders: Evidence From Clinical Neuroimaging Studies. Front. Psychiatry. 9, 767 (2019).","DOI":"10.3389\/fpsyt.2018.00767"},{"key":"535_CR105","doi-asserted-by":"crossref","unstructured":"Nelson, E. A., Kraguljac, N. V., Maximo, J. O., Armstrong, W. & Lahti, A. C. Dorsal striatal hypoconnectivity predicts antipsychotic medication treatment response in first-episode psychosis and unmedicated patients with schizophrenia. Brain Behav. 12, e2625 (2022).","DOI":"10.1002\/brb3.2625"},{"key":"535_CR106","doi-asserted-by":"publisher","first-page":"2503","DOI":"10.1007\/s00213-022-06138-0","volume":"239","author":"A Blazer","year":"2022","unstructured":"Blazer, A., Chengappa, K. N. R., Foran, W., Parr, A. C., Kahn, C. E. & Luna, B. et al. Changes in corticostriatal connectivity and striatal tissue iron associated with efficacy of clozapine for treatment\u2011resistant schizophrenia. Psychopharmacology 239, 2503\u20132514 (2022).","journal-title":"Psychopharmacology"},{"key":"535_CR107","doi-asserted-by":"publisher","first-page":"282","DOI":"10.3389\/fpsyt.2018.00282","volume":"9","author":"S Gao","year":"2018","unstructured":"Gao, S., Lu, S., Shi, X., Ming, Y., Xiao, C. & Sun, J. et al. Distinguishing Between Treatment-Resistant and Non-Treatment-Resistant Schizophrenia Using Regional Homogeneity. Front. Psychiatry 9, 282 (2018).","journal-title":"Front. Psychiatry"},{"key":"535_CR108","unstructured":"Jose, M. R., Todd, L., Anita, B., Franchesica, B., Gabriela, V., Nicole, G. et al. Striatal functional connectivity in psychosis relapse: A comparison between antipsychotic adherent and non-adherent patients at the time of relapse. medRxiv, https:\/\/www.medrxiv.org\/content\/10.1101\/2020.07.07.20148452v1.full (2020)."},{"key":"535_CR109","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-024-03067-8","volume":"14","author":"L Liu","year":"2024","unstructured":"Liu, L., Li, Z., Kong, D., Huang, Y., Wu, D. & Zhao, H. et al. Neuroimaging markers of aberrant brain activity and treatment response in schizophrenia patients based on brain complexity. Transl. Psychiatry 14, 365 (2024).","journal-title":"Transl. Psychiatry"},{"key":"535_CR110","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-15263-2","volume":"7","author":"T Iizuka","year":"2017","unstructured":"Iizuka, T., Iizuka, R. & Kameyama, M. Cingulate island sign temporally changes in dementia with Lewy bodies. Sci. Rep. 7, 14745 (2017).","journal-title":"Sci. Rep."},{"key":"535_CR111","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1038\/s41537-022-00231-1","volume":"8","author":"A De Rosa","year":"2022","unstructured":"De Rosa, A., Fontana, A., Nuzzo, T., Garofalo, M., Di Maio, A. & Punzo, D. et al. Machine Learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain. Schizophrenia 8, 8 (2022).","journal-title":"Schizophrenia"}],"container-title":["Schizophrenia"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41537-024-00535-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41537-024-00535-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41537-024-00535-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T06:09:56Z","timestamp":1734588596000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41537-024-00535-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,19]]},"references-count":111,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["535"],"URL":"https:\/\/doi.org\/10.1038\/s41537-024-00535-4","relation":{},"ISSN":["2754-6993"],"issn-type":[{"value":"2754-6993","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,19]]},"assertion":[{"value":"28 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"116"}}