BACKGROUND: Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time- and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a "rule-in" procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting. METHODS: This study aimed to distinguish 86 FND patients from 86 healthy controls acquired in four different centres using a multivariate machine learning approach based on whole-brain resting-state functional connectivity. First, previously published results were replicated in each centre individually (intra-centre cross-validation) and its robustness across inter-scanner variability was assessed by pooling all the data (pooled cross-validation). Second, we evaluated the generalizability of the method by using data from each centre once as a test set, and the data from the remaining centres as a training set (inter-centre cross-validation). RESULTS: FND patients were successfully distinguished from healthy controls in the replication step (accuracy of 74%) as well as in each individual additional centre (accuracies of 73%, 71% and 70%). The pooled cross validation confirmed that the classifier was robust with an accuracy of 72%. The results survived post-hoc adjustment for anxiety, depression, psychotropic medication intake, and symptom severity. The most discriminant features involved the angular- and supramarginal gyri, sensorimotor cortex, cingular- and insular cortex, and hippocampal regions. The inter-centre validation step did not exceed chance level (accuracy below 50%). CONCLUSIONS: The results demonstrate the applicability of RS FC to correctly distinguish FND patients from healthy controls in different centres and its robustness against inter-scanner variability. In order to generalize its use across different centres and aim for clinical application, future studies should work towards optimization of acquisition parameters and include neurological and psychiatric control groups presenting with similar symptoms.

Centre for Interventional Therapy of Movement Disorders Department of Neurology Charles University 1 Faculty of Medicine and General University Hospital Prague Czech Republic

Centre for Interventional Therapy of Movement Disorders Department of Neurology Charles University 1 Faculty of Medicine and General University Hospital Prague Czech Republic; Department of Neurology Charles University and General University Hospital Prague Prague Czech Republic

Department of Neurology University Medical Center Groningen Hanzeplein 1 9713 GZ Groningen the Netherlands; UMCG Expertise Center Movement Disorders Groningen University Medical Center Groningen Groningen the Netherlands

Department of Radiology Lausanne University Hospital and University of Lausanne Lausanne Switzerland

Institute of Bioengineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland; Department of Radiology and Medical Informatics Geneva University Hospitals Geneva Switzerland

Psychosomatic Medicine Department of Neurology Inselspital Bern University Hospital University of Bern Switzerland

Zobrazit více v PubMed

Abdulkadir A., Mortamet B., Vemuri P., Jack C.R., Krueger G., Klöppel S. Effects of hardware heterogeneity on the performance of SVM Alzheimer’s disease classifier. Neuroimage. 2011;58(3):785–792. PubMed PMC

Abraham A., Milham M.P., Di Martino A., Craddock R.C., Samaras D., Thirion B., Varoquaux G. Deriving reproducible biomarkers from multi-site resting-state data: an Autism-based example. Neuroimage. 2017;147:736–745. doi: 10.1016/j.neuroimage.2016.10.045. PubMed DOI

Aléman-Gomez Y.-M.-G., Melie-Garcia L., Valdés-Hernandez P. Annu. Meet. Organ. Hum. Brain Mapping; Florence, Italy: 2006. IBASPM: toolbox for automatic parcellation of brain structures; p. 27.

American Psychiatric Association Diagnostic and statistical manual of mental disorders. American Psychiatric Association. 2013 doi: 10.1176/appi.books.9780890425596. DOI

Aybek S., Nicholson T.R., O’Daly O., Zelaya F., Kanaan R.A., David A.S., Park S. Emotion-motion interactions in conversion disorder: an fMRI study. PLoS One. 2015;10(4):e0123273. PubMed PMC

Aybek S., Nicholson T.R., Zelaya F., O’Daly O.G., Craig T.J., David A.S., Kanaan R.A., O’Daly O.G., Craig T.J., David A.S., Kanaan R.A. Neural correlates of recall of life events in conversion disorder. JAMA Psychiatry. 2014;71:52–60. doi: 10.1001/jamapsychiatry.2013.2842. PubMed DOI

Baek K., Doñamayor N., Morris L.S., Strelchuk D., Mitchell S., Mikheenko Y., Yeoh S.Y., Phillips W., Zandi M., Jenaway A., Walsh C., Voon V. Impaired awareness of motor intention in functional neurological disorder: implications for voluntary and functional movement. Psychol. Med. 2017;47:1624–1636. doi: 10.1017/S0033291717000071. PubMed DOI PMC

Beck A.T. An inventory for measuring depression. Arch. Gen. Psychiatry. 1961;4:561. doi: 10.1001/archpsyc.1961.01710120031004. PubMed DOI

Beck A.T., Epstein N., Brown G., Steer R.A. An inventory for measuring clinical anxiety: psychometric properties. J. Consult. Clin. Psychol. 1988;56:893–897. doi: 10.1037/0022-006X.56.6.893. PubMed DOI

Blakemore R.L., Sinanaj I., Galli S., Aybek S., Vuilleumier P. Aversive stimuli exacerbate defensive motor behaviour in motor conversion disorder. Neuropsychologia. 2016;93:229–241. doi: 10.1016/j.neuropsychologia.2016.11.005. PubMed DOI

Carson A., Lehn A. Epidemiology. Handb. Clin. Neurol. 2016:47–60. doi: 10.1016/B978-0-12-801772-2.00005-9. PubMed DOI

Castrillon, J.G., Ahmadi, A., Navab, N., Richiardi, J., 2015. Learning with multi-site fMRI graph data. Conf. Rec. - Asilomar Conf. Signals, Syst. Comput. 2015-April, 608–612. https://doi.org/10.1109/ACSSC.2014.7094518.

Chang C.-C., Lin C.-J. LIBSVM: a Library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011;2(3):1–27.

Chen A.A., Srinivasan D., Pomponio R., Fan Y., Nasrallah I.M., Resnick S.M., Beason-Held L.L., Davatzikos C., Satterthwaite T.D., Bassett D.S., Shinohara R.T., Shou H. Harmonizing functional connectivity reduces scanner effects in community detection. Neuroimage. 2022;256 doi: 10.1016/j.neuroimage.2022.119198. PubMed DOI PMC

Chen H.H., Duan X., Liu F., Lu F., Ma X., Zhang Y., Uddin L.Q., Chen H.H. Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity—A multi-center study. Prog. Neuro-Psychopharmacology Biol. Psychiatry. 2016;64:1–9. doi: 10.1016/j.pnpbp.2015.06.014. PubMed DOI

Cojan Y., Waber L., Carruzzo A., Vuilleumier P. Motor inhibition in hysterical conversion paralysis. Neuroimage. 2009;47:1026–1037. doi: 10.1016/j.neuroimage.2009.05.023. PubMed DOI

Colombari M., Di Vico I.A., Turrina S., De Leo D., Tinazzi M. Medico-legal aspects of functional neurological disorders: time for an interdisciplinary dialogue. Neurol. Sci. 2021;42:3053–3055. doi: 10.1007/s10072-021-05162-w. PubMed DOI

Dansereau C., Benhajali Y., Risterucci C., Pich E.M., Orban P., Arnold D., Bellec P. Statistical power and prediction accuracy in multisite resting-state fMRI connectivity. Neuroimage. 2017;149:220–232. doi: 10.1016/j.neuroimage.2017.01.072. PubMed DOI

Daum C., Gheorghita F., Spatola M., Stojanova V., Medlin F., Vingerhoets F., Berney A., Gholam-Rezaee M., Maccaferri G.E., Hubschmid M., Aybek S. Interobserver agreement and validity of bedside ‘positive signs’ for functional weakness, sensory and gait disorders in conversion disorder: a pilot study. J. Neurol. Neurosurg. Psychiatry. 2015;86:425–430. doi: 10.1136/jnnp-2013-307381. PubMed DOI

Dewey B.E., Zhao C., Reinhold J.C., Carass A., Fitzgerald K.C., Sotirchos E.S., Saidha S., Oh J., Pham D.L., Calabresi P.A., van Zijl P.C.M., Prince J.L. DeepHarmony: a deep learning approach to contrast harmonization across scanner changes. Magn. Reson. Imaging. 2019;64:160–170. doi: 10.1016/j.mri.2019.05.041. PubMed DOI PMC

Di Martino A., Yan C.-G.-G., Li Q., Denio E., Castellanos F.X., Alaerts K., Anderson J.S., Assaf M., Bookheimer S.Y., Dapretto M., Deen B., Delmonte S., Dinstein I., Ertl-Wagner B., Fair D.A., Gallagher L., Kennedy D.P., Keown C.L., Keysers C., Lainhart J.E., Lord C., Luna B., Menon V., Minshew N.J., Monk C.S., Mueller S., Müller R.-A.-A., Nebel M.B., Nigg J.T., O’Hearn K., Pelphrey K.A., Peltier S.J., Rudie J.D., Sunaert S., Thioux M., Tyszka J.M., Uddin L.Q., Verhoeven J.S., Wenderoth N., Wiggins J.L., Mostofsky S.H., Milham M.P. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry. 2014;19:659–667. doi: 10.1038/mp.2013.78. PubMed DOI PMC

Diez I., Larson A.G., Nakhate V., Dunn E.C., Fricchione G.L., Nicholson T.R., Sepulcre J., Perez D.L. Early-life trauma endophenotypes and brain circuit–gene expression relationships in functional neurological (conversion) disorder. Mol. Psychiatry. 2020;26(8):3817–3828. PubMed PMC

Diez I., Ortiz-Terán L., Williams B., Jalilianhasanpour R., Ospina J.P., Dickerson B.C., Keshavan M.S., Lafrance W.C., Sepulcre J., Perez D.L. Corticolimbic fast-tracking: enhanced multimodal integration in functional neurological disorder. J. Neurol. Neurosurg. Psychiatry. 2019;90:929–938. doi: 10.1136/jnnp-2018-319657. PubMed DOI PMC

Ding J.-R., An D., Liao W., Li J., Wu G.-R., Xu Q., Long Z., Gong Q., Zhou D., Sporns O., Chen H., Kaiser M. Altered functional and structural connectivity networks in psychogenic non-epileptic seizures. PLoS One. 2013;8(5):e63850. PubMed PMC

Donnelly-Kehoe P.A., Pascariello G.O., García A.M., Hodges J.R., Miller B., Rosen H., Manes F., Landin-Romero R., Matallana D., Serrano C., Herrera E., Reyes P., Santamaria-Garcia H., Kumfor F., Piguet O., Ibanez A., Sedeño L. Robust automated computational approach for classifying frontotemporal neurodegeneration: multimodal/multicenter neuroimaging. Alzheimer’s Dement. Diagnosis Assess. Dis. Monit. 2019;11:588–598. doi: 10.1016/j.dadm.2019.06.002. PubMed DOI PMC

Drane D.L., Fani N., Hallett M., Khalsa S.S., Perez D.L., Roberts N.A. A framework for understanding the pathophysiology of functional neurological disorder. CNS Spectr. 2020;26(6):555–561. PubMed PMC

Dyrba M., Ewers M., Wegrzyn M., Kilimann I., Plant C., Oswald A., Meindl T., Pievani M., Bokde A.L.W., Fellgiebel A., Filippi M., Hampel H., Klöppel S., Hauenstein K., Kirste T., Teipel S.J., Zhan W. Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data. PLoS One. 2013;8(5):e64925. PubMed PMC

Eickhoff S.B., Constable R.T., Yeo B.T.T. Topographic organization of the cerebral cortex and brain cartography. Neuroimage. 2018;170:332–347. doi: 10.1016/j.neuroimage.2017.02.018. PubMed DOI PMC

Erickson B.J., Korfiatis P., Akkus Z., Kline T.L. Machine learning for medical imaging. RadioGraphics. 2017;37:505–515. doi: 10.1148/rg.2017160130. PubMed DOI PMC

Espay A.J., Aybek S., Carson A., Edwards M.J., Goldstein L.H., Hallett M., LaFaver K., LaFrance W.C., Lang A.E., Nicholson T., Nielsen G., Reuber M., Voon V., Stone J., Morgante F. Current concepts in diagnosis and treatment of functional neurological disorders. JAMA Neurol. 2018;75:1132–1141. doi: 10.1001/jamaneurol.2018.1264. PubMed DOI PMC

Espay A.J., Goldenhar L.M., Voon V., Schrag A., Burton N., Lang A.E. Opinions and clinical practices related to diagnosing and managing patients with psychogenic movement disorders: an international survey of movement disorder society members. Mov. Disord. 2009;24:1366–1374. doi: 10.1002/mds.22618. PubMed DOI

Espay A.J., Maloney T., Vannest J., Norris M.M., Eliassen J.C., Neefus E., Allendorfer J.B., Chen R., Szaflarski J.P. Dysfunction in emotion processing underlies functional (psychogenic) dystonia. Mov. Disord. 2018;33:136–145. doi: 10.1002/mds.27217. PubMed DOI PMC

Friedman L., Glover G.H., The FBIRN Consortium Reducing interscanner variability of activation in a multicenter fMRI study: Controlling for signal-to-fluctuation-noise-ratio (SFNR) differences. Neuroimage. 2006;33(2):471–481. PubMed

Galli S., Béreau M., Magnin E., Moulin T., Aybek S. Functional movement disorders. Rev. Neurol. (Paris) 2020;176:244–251. doi: 10.1016/j.neurol.2019.08.007. PubMed DOI

Glasser M.F., Coalson T.S., Robinson E.C., Hacker C.D., Harwell J., Yacoub E., Ugurbil K., Andersson J., Beckmann C.F., Jenkinson M., Smith S.M., Van Essen D.C. A multi-modal parcellation of human cerebral cortex. Nature. 2016;536:171–178. doi: 10.1038/nature18933. PubMed DOI PMC

Glover G.H., Mueller B.A., Turner J.A., Van Erp T.G.M., Liu T.T., Greve D.N., Voyvodic J.T., Rasmussen J., Brown G.G., Keator D.B., Calhoun V.D., Lee H.J., Ford J.M., Mathalon D.H., Diaz M., O’Leary D.S., Gadde S., Preda A., Lim K.O., Wible C.G., Stern H.S., Belger A., McCarthy G., Ozyurt B., Potkin S.G. Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies. J. Magn. Reson. Imaging. 2012;36:39–54. doi: 10.1002/jmri.23572. PubMed DOI PMC

Greicius M. Resting-state functional connectivity in neuropsychiatric disorders. Curr. Opin. Neurol. 2008;21:424–430. doi: 10.1097/WCO.0b013e328306f2c5. PubMed DOI

Gupta A., Lang A.E. Psychogenic movement disorders. Curr. Opin. Neurol. 2009;22:430–436. doi: 10.1097/WCO.0b013e32832dc169. PubMed DOI

Guyon I., Elisseeff A., Kaelbling L.P. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003;3:1157–1182.

Hallett M., Aybek S., Dworetzky B.A., McWhirter L., Staab J.P., Stone J. Functional neurological disorder: new subtypes and shared mechanisms. Lancet Neurol. 2022;21(6):537–550. PubMed PMC

Hassa T., Sebastian A., Liepert J., Weiller C., Schmidt R., Tüscher O. Symptom-specific amygdala hyperactivity modulates motor control network in conversion disorder. NeuroImage Clin. 2017;15:143–150. doi: 10.1016/j.nicl.2017.04.004. PubMed DOI PMC

Jack, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., Whitwell, J.L., Ward, C., Dale, A.M., Felmlee, J.P., Gunter, J.L., Hill, D.L.G., Killiany, R., Schuff, N., Fox-Bosetti, S., Lin, C., Studholme, C., DeCarli, C.S., Krueger, G., Ward, H.A., Metzger, G.J., Scott, K.T., Mallozzi, R., Blezek, D., Levy, J., Debbins, J.P., Fleisher, A.S., Albert, M., Green, R., Bartzokis, G., Glover, G., Mugler, J., Weiner, M.W., L. Whitwell, J., Ward, C., Dale, A.M., Felmlee, J.P., Gunter, J.L., Hill, D.L.G., Killiany, R., Schuff, N., Fox-Bosetti, S., Lin, C., Studholme, C., DeCarli, C.S., Gunnar Krueger, Ward, H.A., Metzger, G.J., Scott, K.T., Mallozzi, R., Blezek, D., Levy, J., Debbins, J.P., Fleisher, A.S., Albert, M., Green, R., Bartzokis, G., Glover, G., Mugler, J., Weiner, M.W., 2008. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27, 685–691. https://doi.org/10.1002/jmri.21049. PubMed PMC

Longarzo M., Cavaliere C., Mele G., Tozza S., Tramontano L., Alfano V., Aiello M., Salvatore M., Grossi D. Microstructural changes in motor functional conversion disorder: multimodal imaging approach on a case. Brain Sci. 2020;10:385. doi: 10.3390/brainsci10060385. PubMed DOI PMC

Ludwig J., Speier P., Seifert F., Schaeffter T., Kolbitsch C. Pilot tone–based motion correction for prospective respiratory compensated cardiac cine MRI. Magn. Reson. Med. 2021;85:2403–2416. doi: 10.1002/mrm.28580. PubMed DOI

Ludwig L., Pasman J.A., Nicholson T., Aybek S., David A.S., Tuck S., Kanaan R.A., Roelofs K., Carson A., Stone J. Stressful life events and maltreatment in conversion (functional neurological) disorder: systematic review and meta-analysis of case-control studies. Lancet Psychiatry. 2018;5:307–320. doi: 10.1016/S2215-0366(18)30051-8. PubMed DOI

Marapin R.S., Gelauff J.M., Marsman J.B.C., de Jong B.M., Dreissen Y.E.M., Koelman J.H.T.M., van der Horn H.J., Tijssen M.A.J. Altered posterior midline activity in patients with jerky and tremulous functional movement disorders. Brain Connect. 2021;11:584–593. doi: 10.1089/brain.2020.0779. PubMed DOI

Marapin R.S., van der Stouwe A.M.M., de Jong B.M., Gelauff J.M., Vergara V.M., Calhoun V.D., Dalenberg J.R., Dreissen Y.E.M., Koelman J.H.T.M., Tijssen M.A.J., van der Horn H.J. The chronnectome as a model for Charcot’s ‘dynamic lesion’ in functional movement disorders. NeuroImage Clin. 2020;28 doi: 10.1016/j.nicl.2020.102381. PubMed DOI PMC

Maurer C.W., LaFaver K., Ameli R., Epstein S.A., Hallett M., Horovitz S.G. Impaired self-agency in functional movement disorders: a resting-state fMRI study. Neurology. 2016;87:564–570. doi: 10.1212/WNL.0000000000002940. PubMed DOI PMC

Monsa R., Peer M., Arzy S. Self-reference, emotion inhibition and somatosensory disturbance: preliminary investigation of network perturbations in conversion disorder. Eur. J. Neurol. 2018;25:888–e62. doi: 10.1111/ene.13613. PubMed DOI

Mori Y., Miyata J., Isobe M., Son S., Yoshihara Y., Aso T., Kouchiyama T., Murai T., Takahashi H. Effect of phase-encoding direction on group analysis of resting-state functional magnetic resonance imaging. Psychiatry Clin. Neurosci. 2018;72:683–691. doi: 10.1111/pcn.12677. PubMed DOI

Morris L.S., To B., Baek K., Chang-Webb Y.-C., Mitchell S., Strelchuk D., Mikheenko Y., Phillips W., Zandi M., Jenaway A., Walsh C., Voon V. Disrupted avoidance learning in functional neurological disorder: Implications for harm avoidance theories. NeuroImage Clin. 2017;16:286–294. doi: 10.1016/j.nicl.2017.08.007. PubMed DOI PMC

Mueller K., Růžička F., Slovák M., Forejtová Z., Dušek P., Dušek P., Jech R., Serranová T. Symptom-severity-related brain connectivity alterations in functional movement disorders. NeuroImage Clin. 2022;34 doi: 10.1016/j.nicl.2022.102981. PubMed DOI PMC

Nakano T., Takamura M., Ichikawa N., Okada G., Okamoto Y., Yamada M., Suhara T., Yamawaki S., Yoshimoto J. Enhancing multi-center generalization of machine learning-based depression diagnosis from resting-state fMRI. Front. Psychiatry. 2020;11 doi: 10.3389/fpsyt.2020.00400. PubMed DOI PMC

Nielsen A.N., Barch D.M., Petersen S.E., Schlaggar B.L., Greene D.J. Machine learning with neuroimaging: evaluating its applications in psychiatry. Biol. Psychiatry Cogn. Neurosci. Neuroimaging. 2020;5:791–798. doi: 10.1016/j.bpsc.2019.11.007. PubMed DOI PMC

Nielsen G., Ricciardi L., Meppelink A.M., Holt K., Teodoro T., Edwards M. A simplified version of the psychogenic movement disorders rating scale: the simplified functional movement disorders rating scale (S-FMDRS) Mov. Disord. Clin. Pract. 2017;4:710–716. doi: 10.1002/mdc3.12475. PubMed DOI PMC

Noble S., Scheinost D., Finn E.S., Shen X., Papademetris X., McEwen S.C., Bearden C.E., Addington J., Goodyear B., Cadenhead K.S., Mirzakhanian H., Cornblatt B.A., Olvet D.M., Mathalon D.H., McGlashan T.H., Perkins D.O., Belger A., Seidman L.J., Thermenos H., Tsuang M.T., van Erp T.G.M., Walker E.F., Hamann S., Woods S.W., Cannon T.D., Constable R.T. Multisite reliability of MR-based functional connectivity. Neuroimage. 2017;146:959–970. doi: 10.1016/j.neuroimage.2016.10.020. PubMed DOI PMC

Nunes A., Schnack H.G., Ching C.R.K., Agartz I., Akudjedu T.N., Alda M., Alnæs D., Alonso-Lana S., Bauer J., Baune B.T., Bøen E., Bonnin C.D.M., Busatto G.F., Canales-Rodríguez E.J., Cannon D.M., Caseras X., Chaim-Avancini T.M., Dannlowski U., Díaz-Zuluaga A.M., Dietsche B., Doan N.T., Duchesnay E., Elvsåshagen T., Emden D., Eyler L.T., Fatjó-Vilas M., Favre P., Foley S.F., Fullerton J.M., Glahn D.C., Goikolea J.M., Grotegerd D., Hahn T., Henry C., Hibar D.P., Houenou J., Howells F.M., Jahanshad N., Kaufmann T., Kenney J., Kircher T.T.J., Krug A., Lagerberg T.V., Lenroot R.K., López-Jaramillo C., Machado-Vieira R., Malt U.F., McDonald C., Mitchell P.B., Mwangi B., Nabulsi L., Opel N., Overs B.J., Pineda-Zapata J.A., Pomarol-Clotet E., Redlich R., Roberts G., Rosa P.G., Salvador R., Satterthwaite T.D., Soares J.C., Stein D.J., Temmingh H.S., Trappenberg T., Uhlmann A., van Haren N.E.M., Vieta E., Westlye L.T., Wolf D.H., Yüksel D., Zanetti M.V., Andreassen O.A., Thompson P.M., Hajek T. Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group. Mol. Psychiatry. 2020;25(9):2130–2143. PubMed PMC

Orrù G., Pettersson-Yeo W., Marquand A.F., Sartori G., Mechelli A. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. 2012;36:1140–1152. doi: 10.1016/j.neubiorev.2012.01.004. PubMed DOI

Patel M.J., Andreescu C., Price J.C., Edelman K.L., Reynolds C.F., Aizenstein H.J. Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int. J. Geriatr. Psychiatry. 2015;30(10):1056–1067. PubMed PMC

Perez D.L., Barsky A.J., Daffner K., Silbersweig D.A. Motor and somatosensory conversion disorder: a functional unawareness Syndrome? J. Neuropsychiatry Clin. Neurosci. 2012;24:141–151. doi: 10.1176/appi.neuropsych.11050110. PubMed DOI

Perez, D.L., Matin, N., Barsky, A., Costumero-ramos, V., Makaretz, S.J., Young, S.S., Sepulcre, J., LaFrance, W.C., Keshavan, M.S., Dickerson, B.C., LaFranceJr, W.C., Keshavan, M.S., Dickerson, B.C., Sara, J., Young, S.S., Sepulcre, J., Jr, W.C.L., Matcheri, S., Dickerson, B.C., 2017. Cingulo-insular structural alterations associated with psychogenic symptoms, childhood abuse and PTSD in functional neurological disorders. J. Neurol. Neurosurg. Psychiatry 88, 491–497. https://doi.org/10.1136/jnnp-2016-314998. PubMed PMC

Pessoa L. On the relationship between emotion and cognition. Nat. Rev. Neurosci. 2008;9:148–158. doi: 10.1038/nrn2317. PubMed DOI

Power J.D., Mitra A., Laumann T.O., Snyder A.Z., Schlaggar B.L., Petersen S.E. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage. 2014;84:320–341. doi: 10.1016/j.neuroimage.2013.08.048. PubMed DOI PMC

Richiardi J., Eryilmaz H., Schwartz S., Vuilleumier P., Van De Ville D. Decoding brain states from fMRI connectivity graphs. Neuroimage. 2011;56:616–626. doi: 10.1016/j.neuroimage.2010.05.081. PubMed DOI

Richiardi J., Van De Ville D., Riesen K., Bunke H. Vector space embedding of undirected graphs with fixed-cardinality vertex sequences for classification. Proc. - Int. Conf. Pattern Recognit. 2010;902–905 doi: 10.1109/ICPR.2010.227. DOI

Rozycki M., Satterthwaite T.D., Koutsouleris N., Erus G., Doshi J., Wolf D.H., Fan Y., Gur R.E., Gur R.C., Meisenzahl E.M., Zhuo C., Yin H., Yan H., Yue W., Zhang D., Davatzikos C. Multisite machine learning analysis provides a robust structural imaging signature of schizophrenia detectable across diverse patient populations and within individuals. Schizophr. Bull. 2018;44:1035–1044. doi: 10.1093/schbul/sbx137. PubMed DOI PMC

Smith S.M., Miller K.L., Salimi-Khorshidi G., Webster M., Beckmann C.F., Nichols T.E., Ramsey J.D., Woolrich M.W. Network modelling methods for FMRI. Neuroimage. 2011;54:875–891. doi: 10.1016/j.neuroimage.2010.08.063. PubMed DOI

Sokolov, A.A., Granziera, C., Fischi-Gomez, E., Preti, M.G., Ryvlin, P., Van De Ville, D., Friston, K.J., 2019. Brain network analyses in clinical neuroscience. Swiss Arch. Neurol. Psychiatry Psychother. https://doi.org/10.4414/sanp.2019.03074.

Spielberger, C., Gorsuch, R., Lushene, R., Vagg, P., Jacobs, G., 1983. Manual for the State-Trait Anxiety Inventory (Form Y1 - Y2).

Stone J., Carson A. Functional neurologic disorders. Contin. Lifelong Learn. Neurol. 2015;21:818–837. doi: 10.1212/01.CON.0000466669.02477.45. PubMed DOI

Stone J., LaFrance W.C., Brown R., Spiegel D., Levenson J.L., Sharpe M. Conversion disorder: current problems and potential solutions for DSM-5. J. Psychosom. Res. 2011;71(6):369–376. PubMed

Stone J., Zeman A., Simonotto E., Meyer M., Azuma R., Flett S., Sharpe M. fMRI in patients with motor conversion symptoms and controls with simulated weakness. Psychosom. Med. 2007;69:961–969. doi: 10.1097/PSY.0b013e31815b6c14. PubMed DOI

Syed T.U., LaFrance W.C., Kahriman E.S., Hasan S.N., Rajasekaran V., Gulati D., Borad S., Shahid A., Fernandez-Baca G., Garcia N., Pawlowski M., Loddenkemper T., Amina S., Koubeissi M.Z. Can semiology predict psychogenic nonepileptic seizures? A prospective study. Ann. Neurol. 2011;69(6):997–1004. PubMed

Szaflarski J.P., Allendorfer J.B., Nenert R., LaFrance W.C., Barkan H.I., DeWolfe J., Pati S., Thomas A.E., Ver Hoef L. Facial emotion processing in patients with seizure disorders. Epilepsy Behav. 2018;79:193–204. doi: 10.1016/j.yebeh.2017.12.004. PubMed DOI

Takamura T., Hanakawa T. Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders. J. Neural Transm. 2017;124:821–839. doi: 10.1007/s00702-017-1710-2. PubMed DOI

Teipel S.J., Wohlert A., Metzger C., Grimmer T., Sorg C., Ewers M., Meisenzahl E., Klöppel S., Borchardt V., Grothe M.J., Walter M., Dyrba M. Multicenter stability of resting state fMRI in the detection of Alzheimer’s disease and amnestic MCI. NeuroImage Clin. 2017;14:183–194. doi: 10.1016/j.nicl.2017.01.018. PubMed DOI PMC

Tzourio-Mazoyer N., Landeau B., Papathanassiou D., Crivello F., Etard O., Delcroix N., Mazoyer B., Joliot M. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273–289. doi: 10.1006/nimg.2001.0978. PubMed DOI

Vabalas A., Gowen E., Poliakoff E., Casson A.J., Hernandez-Lemus E. Machine learning algorithm validation with a limited sample size. PLoS One. 2019;14(11):e0224365. doi: 10.1371/journal.pone.0224365. PubMed DOI PMC

van den Heuvel M.P., Hulshoff Pol H.E. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 2010;20:519–534. doi: 10.1016/j.euroneuro.2010.03.008. PubMed DOI

van der Kruijs S.J.M., Bodde N.M.G., Vaessen M.J., Lazeron R.H.C., Vonck K., Boon P., Hofman P.A.M., Backes W.H., Aldenkamp A.P., Jansen J.F.A. Functional connectivity of dissociation in patients with psychogenic non-epileptic seizures. J. Neurol. Neurosurg. Psychiatry. 2012;83:239–247. doi: 10.1136/jnnp-2011-300776. PubMed DOI

Van Dijk K.R.A., Sabuncu M.R., Buckner R.L. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage. 2012;59:431–438. doi: 10.1016/j.neuroimage.2011.07.044. PubMed DOI PMC

Vasta R., Cerasa A., Sarica A., Bartolini E., Martino I., Mari F., Metitieri T., Quattrone A., Gambardella A., Guerrini R., Labate A. The application of artificial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures. Epilepsy Behav. 2018;87:167–172. doi: 10.1016/j.yebeh.2018.09.008. PubMed DOI

Voon V., Brezing C., Gallea C., Hallett M. Aberrant supplementary motor complex and limbic activity during motor preparation in motor conversion disorder. Mov. Disord. 2011;26:2396–2403. doi: 10.1002/mds.23890. PubMed DOI PMC

Voon V., Cavanna A.E., Coburn K., Sampson S., Reeve A., LaFrance W.C. Functional neuroanatomy and neurophysiology of functional neurological disorders (Conversion disorder) J. Neuropsychiatry Clin. Neurosci. 2016;28(3):168–190. PubMed

Voon V., Gallea C., Hattori N., Bruno M., Ekanayake V., Hallett M. The involuntary nature of conversion disorder. Neurology. 2010;74:223–228. doi: 10.1212/WNL.0b013e3181ca00e9. PubMed DOI PMC

Wegrzyk J., Kebets V., Richiardi J., Galli S., de Ville D., Van A., S., Identifying motor functional neurological disorder using resting-state functional connectivity. NeuroImage Clin. 2018;17:163–168. doi: 10.1016/j.nicl.2017.10.012. PubMed DOI PMC

World Health Organization . World Health Organization; Genève, Switzerland: 1993. The ICD-10 Classification of Mental and Behavioural Disorders.

Xia M., Wang J., He Y., Csermely P. BrainNet viewer: a network visualization tool for human brain connectomics. PLoS One. 2013;8(7):e68910. doi: 10.1371/journal.pone.0068910. PubMed DOI PMC

Yamashita A., Yahata N., Itahashi T., Lisi G., Yamada T., Ichikawa N., Takamura M., Yoshihara Y., Kunimatsu A., Okada N., Yamagata H., Matsuo K., Hashimoto R., Okada G.o., Sakai Y., Morimoto J., Narumoto J., Shimada Y., Kasai K., Kato N., Takahashi H., Okamoto Y., Tanaka S.C., Kawato M., Yamashita O., Imamizu H., Macleod M.R. Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias. PLoS Biol. 2019;17(4):e3000042. doi: 10.1371/journal.pbio.3000042. PubMed DOI PMC

Yu M., Linn K.A., Cook P.A., Phillips M.L., McInnis M., Fava M., Trivedi M.H., Weissman M.M., Shinohara R.T., Sheline Y.I. Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Hum. Brain Mapp. 2018;39:4213–4227. doi: 10.1002/hbm.24241. PubMed DOI PMC

Zeng L.-L.-L., Wang H., Hu P., Yang B., Pu W., Shen H., Chen X., Liu Z., Yin H., Tan Q., Wang K., Hu D. Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI. EBioMedicine. 2018;30:74–85. doi: 10.1016/j.ebiom.2018.03.017. PubMed DOI PMC

Zhao N., Yuan L.-X.-X., Jia X.-Z.-Z., Zhou X.-F.-F., Deng X.-P.-P., He H.-J.-J., Zhong J., Wang J., Zang Y.-F.-F. Intra- and inter-scanner reliability of voxel-wise whole-brain analytic metrics for resting state fMRI. Front. Neuroinform. 2018;12:1–9. doi: 10.3389/fninf.2018.00054. PubMed DOI PMC

Zhuang H., Liu R., Wu C., Meng Z., Wang D., Liu D., Liu M., Li Y. Multimodal classification of drug-naïve first-episode schizophrenia combining anatomical, diffusion and resting state functional resonance imaging. Neurosci. Lett. 2019;705:87–93. doi: 10.1016/j.neulet.2019.04.039. PubMed DOI