Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-14T19:12:46.617Z Has data issue: false hasContentIssue false

Precuneus functioning differentiates first-episode psychosis patients during the fantasy movie Alice in Wonderland

Published online by Cambridge University Press:  25 October 2016

E. Rikandi*
Affiliation:
Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland Department of Neuroscience and Biomedical Engineering, and Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland
S. Pamilo
Affiliation:
Department of Neuroscience and Biomedical Engineering, and Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland
T. Mäntylä
Affiliation:
Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland Department of Neuroscience and Biomedical Engineering, and Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland
J. Suvisaari
Affiliation:
Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland
T. Kieseppä
Affiliation:
Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland Department of Psychiatry, Helsinki University and Helsinki University Hospital, Helsinki, Finland
R. Hari
Affiliation:
Department of Neuroscience and Biomedical Engineering, and Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland Department of Art, School of Arts, Design and Architecture, Aalto University, Helsinki, Finland
M. Seppä
Affiliation:
Department of Neuroscience and Biomedical Engineering, and Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland
T. T. Raij
Affiliation:
Department of Neuroscience and Biomedical Engineering, and Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland Department of Psychiatry, Helsinki University and Helsinki University Hospital, Helsinki, Finland
*
*Address for correspondence: E. Rikandi, Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland. (Email: eva.rikandi@thl.fi)

Abstract

Background

While group-level functional alterations have been identified in many brain regions of psychotic patients, multivariate machine-learning methods provide a tool to test whether some of such alterations could be used to differentiate an individual patient. Earlier machine-learning studies have focused on data collected from chronic patients during rest or simple tasks. We set out to unravel brain activation patterns during naturalistic stimulation in first-episode psychosis (FEP).

Method

We recorded brain activity from 46 FEP patients and 32 control subjects viewing scenes from the fantasy film Alice in Wonderland. Scenes with varying degrees of fantasy were selected based on the distortion of the ‘sense of reality’ in psychosis. After cleaning the data with a novel maxCorr method, we used machine learning to classify patients and healthy control subjects on the basis of voxel- and time-point patterns.

Results

Most (136/194) of the voxels that best classified the groups were clustered in a bilateral region of the precuneus. Classification accuracies were up to 79.5% (p = 5.69 × 10−8), and correct classification was more likely the higher the patient's positive-symptom score. Precuneus functioning was related to the fantasy content of the movie, and the relationship was stronger in control subjects than patients.

Conclusions

These findings are the first to show abnormalities in precuneus functioning during naturalistic information processing in FEP patients. Correlational findings suggest that these alterations are associated with positive psychotic symptoms and processing of fantasy. The results may provide new insights into the neuronal basis of reality distortion in psychosis.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abbott, CC, Jaramillo, A, Wilcox, CE, Hamilton, DA (2013). Antipsychotic drug effects in schizophrenia: a review of longitudinal fMRI investigations and neural interpretations. Current Medicinal Chemistry 20, 428437.Google ScholarPubMed
Andrews-Hanna, JR, Reidler, JS, Huang, C, Buckner, RL (2010). Evidence for the default network's role in spontaneous cognition. Journal of Neurophysiology 104, 322335.CrossRefGoogle ScholarPubMed
Arbabshirani, MR, Castro, E, Calhoun, VD (2014). Accurate classification of schizophrenia patients based on novel resting-state fMRI features. In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 66916694. EMBC: Chicago.Google Scholar
Bartels, A, Zeki, S (2005). Brain dynamics during natural viewing conditions – a new guide for mapping connectivity in vivo . NeuroImage 24, 339349.CrossRefGoogle ScholarPubMed
Binder, JR, Desai, RH, Graves, WW, Conant, LL (2009). Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cerebral Cortex 19, 27672796.CrossRefGoogle ScholarPubMed
Bleich-Cohen, M, Jamshy, S, Sharon, H, Weizman, R, Intrator, N, Poyurovsky, M (2014). Machine learning fMRI classifier delineates subgroups of schizophrenia patients. Schizophrenia Research 160, 196200.CrossRefGoogle ScholarPubMed
Bleuler, E (1911). Dementia praecox oder Gruppe der Schizophrenien. Franz Deuticke: Leipzig.Google Scholar
Bora, E, Fornito, A, Radua, J, Walterfang, M, Seal, M, Wood, SJ (2011). Neuroanatomical abnormalities in schizophrenia: a multimodal voxelwise meta-analysis and meta-regression analysis. Schizophrenia Research 127, 4657.CrossRefGoogle ScholarPubMed
Borgwardt, S, Koutsouleris, N, Aston, J, Studerus, E, Smieskova, R, Riecher-Rössler, A (2013). Distinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognition. Schizophrenia Bulletin 39, 11051114.CrossRefGoogle ScholarPubMed
Calhoun, VD, Maciejewski, PK, Pearlson, GD, Kiehl, KA (2008). Temporal lobe and ‘default’ hemodynamic brain modes discriminate between schizophrenia and bipolar disorder. Human Brain Mapping 29, 12651275.CrossRefGoogle ScholarPubMed
Carhart-Harris, RL, Erritzoe, D, Williams, T, Stone, JM, Reed, LJ, Colasanti, A, Tyacke, RJ, Leech, R, Malizia, AL, Murphy, K, Hobden, P, Evans, J, Feilding, A, Wise, RG, Nutt, DJ (2012). Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin. Proceedings of the National Academy of Sciences USA 109, 21382143.CrossRefGoogle ScholarPubMed
Carhart-Harris, RL, Leech, R, Erritzoe, D, Williams, TM, Stone, JM, Evans, J, Sharp, DJ, Feilding, A, Wise, RG, Nutt, DJ (2013). Functional connectivity measures after psilocybin inform a novel hypothesis of early psychosis. Schizophrenia Bulletin 39, 13431351.CrossRefGoogle ScholarPubMed
Cavanna, AE, Trimble, MR (2006). The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129, 564583.CrossRefGoogle ScholarPubMed
Collin, G, Kahn, RS, de Reus, MA, Cahn, W, van den Heuvel, MP (2014). Impaired rich club connectivity in unaffected siblings of schizophrenia patients. Schizophrenia Bulletin 40, 438439.CrossRefGoogle ScholarPubMed
Conover, WJ (1999). Practical Nonparametric Statistics. John Wiley and Sons: New York.Google Scholar
Costafreda, SG, Fu, CH, Picchioni, M, Toulopoulou, T, McDonald, C, Kravariti, E, Walshe, M, Prata, D, Murray, RM, McGuire, PK (2011). Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder. BMC Psychiatry 11, 18.CrossRefGoogle ScholarPubMed
Crossley, NA, Mechelli, A, Scott, J, Carletti, F, Fox, PT, McGuire, P, Bullmore, T (2014). The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137, 23822395.CrossRefGoogle ScholarPubMed
Davatzikos, C, Ruparel, K, Fan, Y, Shen, DG, Acharyya, M, Loughead, JW, Gur, RC, Langleben, DD (2005). Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. NeuroImage 28, 663668.CrossRefGoogle ScholarPubMed
Fatouros-Bergman, H, Cervenka, S, Flyckt, L, Edman, G, Farde, L (2014). Meta-analysis of cognitive performance in drug-naïve patients with schizophrenia. Schizophrenia Research 158, 156162.CrossRefGoogle ScholarPubMed
First, MB, Spitzer, RL, Gibbon, M, Williams, JBW (2007). Structured Clinical Interview for DSM-IV-TR Axis I Disorders. Research Version, Patient edn. (SCID-I/P): New York, Biometrics Research, New York: State Psychiatric Institute.Google Scholar
Friston, KJ (1998). The disconnection hypothesis. Schizophrenia Research 30, 115125.CrossRefGoogle ScholarPubMed
Friston, KJ, Frith, CD (1995). Schizophrenia: a disconnection syndrome? Journal of Clinical Neuroscience 3, 8997.Google ScholarPubMed
Goghari, VM, Sponheim, SR, MacDonald, AW (2010). The functional neuroanatomy of symptom dimensions in schizophrenia: a qualitative and quantitative review of a persistent question. Neuroscience & Biobehavioral Reviews 34, 468486.CrossRefGoogle ScholarPubMed
González-Hernández, JA, Pita-Alcorta, C, Padrón, A, Finalé, A, Galán, L, Martínez, E, Diáz-Comas, L, Samper-Gonzáles, JA, LEncer, R, Marot, M (2014). Basic visual dysfunction allows classification of patients with schizophrenia with exceptional accuracy. Schizophrenia Research 159, 226233.CrossRefGoogle ScholarPubMed
Hasson, U, Honey, CJ (2012). Future trends in Neuroimaging: neural processes as expressed within real-life contexts. NeuroImage 62, 12721278.CrossRefGoogle ScholarPubMed
Hasson, U, Nir, Y, Levy, I, Fuhrmann, G, Malach, R (2004). Intersubject synchronization of cortical activity during natural vision. Science 303, 16341640.CrossRefGoogle ScholarPubMed
Howes, OD, Kapur, S (2014). A neurobiological hypothesis for the classification of schizophrenia: type A (hyperdopaminergic) and type B (normodopaminergic). British Journal of Psychiatry 205, 13.CrossRefGoogle ScholarPubMed
Jenkinson, M, Bannister, P, Brady, M, Smith, S (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825841.CrossRefGoogle ScholarPubMed
Karageorgiou, E, Schulz, SC, Gollub, RL, Andreasen, NC, Ho, BC, Lauriello, J, Georgopoulos, AP (2011). Neuropsychological testing and structural magnetic resonance imaging as diagnostic biomarkers early in the course of schizophrenia and related psychoses. Neuroinformatics 9, 321333.CrossRefGoogle ScholarPubMed
Kircher, TT, Senior, C, Phillips, ML, Benson, PJ, Bullmore, ET, Brammer, M, Simmons, A, Williams, SC, Bartels, M, David, AS (2000). Towards a functional neuroanatomy of self-processing: effects of faces and words. Cognitive Brain Research 10, 133144.CrossRefGoogle ScholarPubMed
Kotsiantis, SB (2007). Supervised machine learning: a review of classification techniques. In Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies (ed. Maglogiannis, I., Karpouzis, K., Wallace, M. and Soldatos, J.), pp. 3224. IOS Press: Amsterdam.Google Scholar
Koutsouleris, N, Borgwardt, S, Meisenzahl, EM, Bottlender, R, Möller, H-J, Riecher-Rössler, A (2012). Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study. Schizophrenia Bulletin 38, 12341246.CrossRefGoogle ScholarPubMed
Lahnakoski, JM, Glerean, E, Jääskeläinen, IP, Hyönä, J, Hari, R, Sams, M, Nummenmaa, L (2014). Synchronous brain activity across individuals underlies shared psychological perspectives. NeuroImage 100, 316324.CrossRefGoogle ScholarPubMed
Leech, R, Braga, R, Sharp, DJ (2012). Echoes of the brain within the posterior cingulate cortex. Journal of Neuroscience 32, 215222.CrossRefGoogle ScholarPubMed
Leech, R, Sharp, DJ (2014). The role of the posterior cingulate cortex in cognition and disease. Brain 137, 1232.CrossRefGoogle ScholarPubMed
Lynall, M-E, Bassett, DS, Kerwin, R, McKenna, PJ, Kitzbichler, M, Muller, U, Bullmore, E (2010). Functional connectivity and brain networks in schizophrenia. Journal of Neuroscience 30, 94779487.CrossRefGoogle ScholarPubMed
Malinen, S, Hlushchuk, Y, Hari, R (2007). Towards natural stimulation in fMRI – issues of data analysis. NeuroImage 35, 131139.CrossRefGoogle ScholarPubMed
Margulies, DS, Vincent, JL, Kelly, C, Lohmann, G, Uddin, LQ, Biswal, BB, Villringer, A, Castellanos, XF, Milham, MP, Petrides, M (2009). Precuneus shares intrinsic functional architecture in humans and monkeys. Proceedings of the National Academy of Sciences USA 106, 2006920074.CrossRefGoogle ScholarPubMed
Mashal, N, Vishne, T, Laor, N (2014). The role of the precuneus in metaphor comprehension: evidence from an fMRI study in people with schizophrenia and healthy participants. Frontiers in Human Neuroscience 8, 818.CrossRefGoogle ScholarPubMed
Mestres-Missé, A, Càmara, E, Rodriguez-Fornells, A, Rotte, M, Münte, TF (2008). Functional neuroanatomy of meaning acquisition from context. Journal of Cognitive Neuroscience 20, 21532166.CrossRefGoogle ScholarPubMed
Nummenmaa, L, Glerean, E, Viinikainen, M, Jääskeläinen, IP, Hari, R, Sams, M (2012). Emotions promote social interaction by synchronizing brain activity across individuals. Proceedings of the National Academy of Sciences USA 109, 95999604.CrossRefGoogle ScholarPubMed
Orrù, G, Pettersson-Yeo, W, Marquand, AF, Sartori, G, Mechelli, A (2012). Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neuroscience & Biobehavioral Reviews 36, 11401152.CrossRefGoogle ScholarPubMed
Palhano-Fontes, F, Andrade, KC, Tofoli, LF, Santos, AC, Crippa, JAS, Hallak, JEC, Ribeiro, S, Araujo, DB (2015). The psychedelic state induced by ayahuasca modulates the activity and connectivity of the default mode network. PLoS ONE 10, e0118143.CrossRefGoogle ScholarPubMed
Pamilo, S, Malinen, S, Hotta, J, Seppä, M (2015). A correlation-based method for extracting subject-specific components and artifacts from group-fMRI data. European Journal of Neuroscience 42, 27262741.CrossRefGoogle ScholarPubMed
Pedregosa, F, Varoquaux, G, Gramfort, A, Michel, V, Thirion, B, Grisel, O, Blondel, M, Prettenhoffer, P, Weiss, R, Dubourg, V, Vanderplas, J, Passos, A, Cournapeau, D, Brucher, M, Perrot, M, Duchesnay, E (2011). Scikit-learn: machine learning in Python. Journal of Machine Learning Research 12, 28252830.Google Scholar
Peruzzo, D, Castellani, U, Perlini, C, Bellani, M, Marinelli, V, Rambaldelli, G, Lasalvia, A, Tosato, S, De Santi, K, Murino, V, Ruggeri, M, Brambilla, P (2014). Classification of first-episode psychosis: a multi-modal multi-feature approach integrating structural and diffusion imaging. Journal of Neural Transmission 122, 897905.CrossRefGoogle ScholarPubMed
Pettersson-Yeo, W, Benetti, S, Marquand, AF, Dell'Acqua, F, Williams, SCR, Allen, P, Prata, D, McGuire, P, Mechelli, A (2013). Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level. Psychological Medicine 43, 25472562.CrossRefGoogle ScholarPubMed
Raichle, ME, Snyder, AZ (2007). A default mode of brain function: a brief history of an evolving idea. NeuroImage 37, 10831090; discussion 1097–1099.CrossRefGoogle ScholarPubMed
Röder, CH, Dieleman, S, van der Veen, FM, Linden, D (2013). Systematic review of the influence of antipsychotics on the blood oxygenation level-dependent signal of functional magnetic resonance imaging. Current Medicinal Chemistry 20, 448461.Google Scholar
Savla, GN, Vella, L, Armstrong, CC, Penn, DL, Twamley, EW (2013). Deficits in domains of social cognition in schizophrenia: a meta-analysis of the empirical evidence. Schizophrenia Bulletin 39, 979992.CrossRefGoogle ScholarPubMed
Schurz, M, Aichhorn, M, Martin, A, Perner, J (2013). Common brain areas engaged in false belief reasoning and visual perspective taking: a meta-analysis of functional brain imaging studies. Frontiers in Human Neuroscience 7, 712.CrossRefGoogle ScholarPubMed
Shallice, T, Fletcher, P, Frith, CD, Grasby, P, Frackowiak, RSJ, Dolan, RJ (1994). Brain regions associated with acquisition and retrieval of verbal episodic memory. Nature 368, 633635.CrossRefGoogle ScholarPubMed
Shen, H, Wang, L, Liu, Y, Hu, D (2010). Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. NeuroImage 49, 31103121.CrossRefGoogle ScholarPubMed
Sun, D, van Erp, TGM, Thompson, PM, Bearden, CE, Daley, M, Kushan, L, Hardt, ME, Nuechterlein, KH, Toga, AW, Cannon, TD (2009). Elucidating a magnetic resonance imaging-based neuroanatomic biomarker for psychosis: classification analysis using probabilistic brain atlas and machine learning algorithms. Biological Psychiatry 66, 10551060.CrossRefGoogle ScholarPubMed
Tulving, E, Kapur, S, Markowitsch, HJ, Craik, FI, Habib, R, Houle, S (1994). Neuroanatomical correlates of retrieval in episodic memory: auditory sentence recognition. Proceedings of the National Academy of Sciences USA 91, 20122015.CrossRefGoogle ScholarPubMed
Utevsky, AV, Smith, DV, Huettel, SA (2014). Precuneus is a functional core of the default-mode network. Journal of Neuroscience 34, 932940.CrossRefGoogle ScholarPubMed
Van den Heuvel, MP, Fornito, A (2014). Brain networks in schizophrenia. Neuropsychology Review 24, 3248.CrossRefGoogle ScholarPubMed
van den Heuvel, MP, Sporns, O (2011). Rich-club organization of the human connectome. Journal of Neuroscience 31, 1577515786.CrossRefGoogle ScholarPubMed
Ventura, J, Green, MF, Shaner, A, Liberman, RP (1993). Training and quality assurance with the brief psychiatric rating scale: “The drift busters”. International Journal of Methods in Psychiatry Research 3, 221244.Google Scholar
Viinikainen, M, Glerean, E, Jääskeläinen, IP, Kettunen, J, Sams, M, Nummenmaa, L (2012). Nonlinear neural representation of emotional feelings elicited by dynamic naturalistic stimulation. Open Journal of Neuroscience 4, 24.Google Scholar
Vossel, S, Geng, JJ, Fink, GR (2014). Dorsal and ventral attention systems: distinct neural circuits but collaborative roles. Neuroscientist 20, 150159.CrossRefGoogle ScholarPubMed
Wojciulik, E, Kanwisher, N (2000). Visual attention: insights from brain imaging. Nature Reviews Neuroscience 1, 91100.Google Scholar
Zhang, F, Qiu, L, Yuan, L, Ma, H, Ye, R, Yu, F, Hu, P, Dong, Y, Wang, K (2014). Evidence for progressive brain abnormalities in early schizophrenia: a cross-sectional structural and functional connectivity study. Schizophrenia Research 159, 3135.CrossRefGoogle ScholarPubMed
Zhang, S, Li, CR (2012). Functional connectivity mapping of the human precuneus by resting state fMRI. NeuroImage 59, 35483562.CrossRefGoogle ScholarPubMed
Supplementary material: File

Rikandi supplementary material

Rikandi supplementary material

Download Rikandi supplementary material(File)
File 39.4 KB