Skip to main content Accessibility help
×
Hostname: page-component-848d4c4894-p2v8j Total loading time: 0.001 Render date: 2024-05-31T18:22:30.026Z Has data issue: false hasContentIssue false

Chapter 10 - Translational Neuroimaging in Psychiatry

Published online by Cambridge University Press:  01 February 2024

Andrea Fiorillo
Affiliation:
University of Campania “L. Vanvitelli”, Naples
Peter Falkai
Affiliation:
Ludwig-Maximilians-Universität München
Philip Gorwood
Affiliation:
Sainte-Anne Hospital, Paris
Get access

Summary

In recent decades, neuroimaging has been worthy of increasing attention in psychiatry research. Specifically, noninvasive imaging modalities (e.g. structural and functional magnetic resonance imaging, diffusion tensor imaging, magnetic resonance spectroscopy, and positron emission tomography) have permitted a growing understanding of brain circuit alterations in mental health disorders and a continuous development of putative biomarkers to be used for diagnostic, prognostic, and predictive purposes. Yet, the clinical utility of such biomarkers is still under investigation. This chapter describes the most common neuroimaging methods used in psychiatric research, provides an overview of specific imaging-based research findings and their contributions toward the development of neurobiological markers for psychiatric disorders (focusing on major psychoses i.e., schizophrenia and bipolar disorder), and discusses limitations and future directions in the field of translational neuroimaging in psychiatry.

Type
Chapter
Information
Mental Health Research and Practice
From Evidence to Experience
, pp. 158 - 176
Publisher: Cambridge University Press
Print publication year: 2024

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

Delvecchio, G., et al., Structural and metabolic cerebral alterations between elderly bipolar disorder and behavioural variant frontotemporal dementia: A combined MRI-PET study. Aust N Z J Psychiatry, 2019. 53(5): pp. 413423.CrossRefGoogle ScholarPubMed
Rossetti, M. G., et al., Sex and dependence related neuroanatomical differences in regular cannabis users: Findings from the ENIGMA Addiction Working Group. Transl Psychiatry, 2021. 11(1): p. 272.CrossRefGoogle ScholarPubMed
Pigoni, A., et al., Classification of first-episode psychosis using cortical thickness: A large multicenter MRI study. Eur Neuropsychopharmacol, 2021. 47: pp. 3447.CrossRefGoogle ScholarPubMed
Pigoni, A., et al., Sex differences in brain metabolites in anxiety and mood disorders. Psychiatry Res Neuroimaging, 2020. 305: p. 111196.CrossRefGoogle ScholarPubMed
Silbersweig, D. A. and Rauch, S. L., Neuroimaging in psychiatry: A quarter century of progress. Harv Rev Psychiatry, 2017. 25(5): pp. 195197.CrossRefGoogle ScholarPubMed
Insel, T. R., The NIMH research domain criteria (RDoC) project: Precision medicine for psychiatry. Am J Psychiatry, 2014. 171(4): pp. 395397.CrossRefGoogle ScholarPubMed
Kraguljac, N. V., et al., Neuroimaging biomarkers in schizophrenia. Am J Psychiatry, 2021: appiajp202020030340.Google Scholar
Uludağ, K. and Roebroeck, A., General overview on the merits of multimodal neuroimaging data fusion. Neuroimage, 2014. 102: pp. 310.CrossRefGoogle ScholarPubMed
Keller, S. S. and Roberts, N., Measurement of brain volume using MRI: Software, techniques, choices and prerequisites. J Anthropol Sci, 2009. 87: pp. 127151.Google ScholarPubMed
Martinelli, C. and Shergill, S. S., Everything you wanted to know about neuroimaging and psychiatry, but were afraid to ask. BJPsych Advances, 2015. 21(4): pp. 251260.CrossRefGoogle Scholar
Pierpaoli, C., et al., Diffusion tensor MR imaging of the human brain. Radiology, 1996. 201(3): pp. 637648.CrossRefGoogle ScholarPubMed
Larvie, M. and Fischl, B., Volumetric and fiber-tracing MRI methods for gray and white matter. Handb Clinl Neurol, 2016. 135: pp. 3960.CrossRefGoogle ScholarPubMed
Giuliani, N. R., et al., Voxel-based morphometry versus region of interest: A comparison of two methods for analyzing gray matter differences in schizophrenia. Schizophr Res, 2005. 74(2-3): pp. 135147.CrossRefGoogle ScholarPubMed
Bach, M., et al., Methodological considerations on tract-based spatial statistics (TBSS). Neuroimage, 2014. 100: pp. 358369.CrossRefGoogle ScholarPubMed
Heeger, D. J. and Ress, D., What does fMRI tell us about neuronal activity? Nat Rev Neurosci, 2002. 3(2): pp. 142151.CrossRefGoogle ScholarPubMed
Kwong, K. K., et al., Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci, 1992. 89(12): pp. 56755679.CrossRefGoogle ScholarPubMed
Belliveau, J., et al., Functional mapping of the human visual cortex by magnetic resonance imaging. Science, 1991. 254(5032): pp. 716719.CrossRefGoogle ScholarPubMed
Biswal, B. B., Resting state fMRI: A personal history. Neuroimage, 2012. 62(2): pp. 938944.CrossRefGoogle ScholarPubMed
Valk, P. E., et al., Positron emission tomography: Basic science and clinical practice. Am J Neuroradiol, 2005. 26(9): p. 2429.Google Scholar
Berger, A., How does it work? Positron emission tomography. BMJ, 2003. 326(7404): p. 1449.CrossRefGoogle ScholarPubMed
Marotta, G., et al., The metabolic basis of psychosis in bipolar disorder: A positron emission tomography study. Bipolar Disord, 2019. 21(2): pp. 151158.CrossRefGoogle ScholarPubMed
Silverman, D. H., Brain 18 F-FDG PET in the diagnosis of neurodegenerative dementias: Comparison with perfusion SPECT and with clinical evaluations lacking nuclear imaging. J Nucl Med, 2004. 45(4): pp. 594607.Google ScholarPubMed
Matthews, P. M., et al., Positron emission tomography molecular imaging for drug development. Br J Clin Pharmacol, 2012. 73(2): pp. 175186.CrossRefGoogle ScholarPubMed
Squarcina, L., et al., A review of altered biochemistry in the anterior cingulate cortex of first-episode psychosis. Epidemiol Psychiatr Sci, 2017. 26(2): pp. 122128.CrossRefGoogle ScholarPubMed
Group, B. D. W., et al., Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther, 2001. 69(3): pp. 8995.Google Scholar
Abi-Dargham, A. and Horga, G., The search for imaging biomarkers in psychiatric disorders. Nat Med, 2016. 22(11): pp. 12481255.CrossRefGoogle ScholarPubMed
Kramer, U., Observer-rated coping associated with borderline personality disorder: An exploratory study. Clin Psychol Psychother, 2014. 21(3): pp. 242251.CrossRefGoogle ScholarPubMed
Bellani, M., et al., Resting state networks activity in euthymic bipolar disorder. Bipolar Disord, 2020. 22(6): pp. 593601.CrossRefGoogle ScholarPubMed
Maggioni, E., et al., Common and different neural markers in major depression and anxiety disorders: A pilot structural magnetic resonance imaging study. Psychiatry Res Neuroimaging, 2019. 290: pp. 4250.CrossRefGoogle ScholarPubMed
Fusar-Poli, P., et al., Predicting psychosis: meta-analysis of transition outcomes in individuals at high clinical risk. Arch Gen Psychiatry, 2012. 69(3): pp. 220229.CrossRefGoogle ScholarPubMed
Allen, P., et al., Transition to psychosis associated with prefrontal and subcortical dysfunction in ultra high-risk individuals. Schizophr Bull, 2012. 38(6): pp. 12681276.CrossRefGoogle ScholarPubMed
Smieskova, R., et al., Neuroimaging predictors of transition to psychosis: A systematic review and meta-analysis. Neurosci Biobehav Rev, 2010. 34(8): pp. 1207–22.CrossRefGoogle ScholarPubMed
Wenneberg, C., et al., Cerebral glutamate and GABA levels in high-risk of psychosis states: A focused review and meta-analysis of 1H-MRS studies. Schizophr Res, 2020. 215: pp. 3848.CrossRefGoogle Scholar
Cannon, T. D., et al., Progressive reduction in cortical thickness as psychosis develops: a multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biol Psychiatry, 2015. 77(2): pp. 147157.CrossRefGoogle ScholarPubMed
Cao, H., et al., Altered brain activation during memory retrieval precedes and predicts conversion to psychosis in individuals at clinical high risk. Schizophr Bull, 2019. 45(4): pp. 924933.CrossRefGoogle ScholarPubMed
Merritt, K., et al., Longitudinal structural MRI findings in individuals at genetic and clinical high risk for psychosis: A systematic review. Front Psychiatry, 2021. 12: p. 620401.CrossRefGoogle ScholarPubMed
Karlsgodt, K. H., et al., White matter integrity and prediction of social and role functioning in subjects at ultra-high risk for psychosis. Biol Psychiatry, 2009. 66(6): pp. 562569.CrossRefGoogle ScholarPubMed
de la Fuente-Sandoval, C., et al., Striatal glutamate and the conversion to psychosis: A prospective 1H-MRS imaging study. Int J Neuropsychopharmacol, 2013. 16(2): pp. 471475.CrossRefGoogle ScholarPubMed
Bossong, M. G., et al., Association of hippocampal glutamate levels with adverse outcomes in individuals at clinical high risk for psychosis. JAMA Psychiatry, 2019. 76(2): pp. 199207.CrossRefGoogle ScholarPubMed
Vita, A., et al., Progressive loss of cortical gray matter in schizophrenia: A meta-analysis and meta-regression of longitudinal MRI studies. Transl Psychiatry, 2012. 2(11): p. e190.CrossRefGoogle ScholarPubMed
Cropley, V. L., et al., Accelerated gray and white matter deterioration with age in schizophrenia. Am J Psychiatry, 2017. 174(3): pp. 286295.CrossRefGoogle ScholarPubMed
van Erp, T. G. M., et al., Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) consortium. Biol Psychiatry, 2018. 84(9): pp. 644654.CrossRefGoogle ScholarPubMed
van Erp, T. G., et al., Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry, 2016. 21(4): pp. 547553.CrossRefGoogle ScholarPubMed
Maggioni, E., et al., Neuroanatomical voxel-based profile of schizophrenia and bipolar disorder. Epidemiol Psychiatr Sci, 2016. 25(4): pp. 312316.CrossRefGoogle ScholarPubMed
Baglivo, V., et al., Hippocampal subfield volumes in patients with first-episode psychosis. Schizophr Bull, 2018. 44(3): pp. 552559.CrossRefGoogle ScholarPubMed
Hiser, J. and Koenigs, M., The multifaceted role of the ventromedial prefrontal cortex in emotion, decision making, social cognition, and psychopathology. Biol Psychiatry, 2018. 83(8): pp. 638647.CrossRefGoogle ScholarPubMed
Tanimizu, T., et al., Functional connectivity of multiple brain regions required for the consolidation of social recognition memory. J Neurosci, 2017. 37(15): pp. 41034116.CrossRefGoogle ScholarPubMed
Mothersill, D. and Donohoe, G., Neural effects of cognitive training in schizophrenia: A systematic review and activation likelihood estimation meta-analysis. Biol Psychiatry Cogn Neurosci Neuroimaging, 2019. 4(8): pp. 688696.Google ScholarPubMed
Lu, X., et al., Structural imaging biomarkers for bipolar disorder: Meta‐analyses of whole‐brain voxel‐based morphometry studies. Depress Anxiety, 2019. 36(4): pp. 353364.CrossRefGoogle ScholarPubMed
Maggioni, E., et al., Common and distinct structural features of schizophrenia and bipolar disorder: The European Network on Psychosis, Affective disorders and Cognitive Trajectory (ENPACT) study. PLOS ONE, 2017. 12: p. e0188000.CrossRefGoogle ScholarPubMed
Hibar, D. P., et al., Cortical abnormalities in bipolar disorder: An MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol Psychiatry, 2018. 23(4): p. 932942.CrossRefGoogle ScholarPubMed
Arnone, D., et al., Magnetic resonance imaging studies in bipolar disorder and schizophrenia: Meta-analysis. Br J Psychiatry, 2009. 195(3): pp. 194201.CrossRefGoogle ScholarPubMed
Dempster, K., et al., Cognitive performance is associated with gray matter decline in first-episode psychosis. Psychiatry Res Neuroimaging, 2017. 264: pp. 4651.CrossRefGoogle ScholarPubMed
Jung, S., et al., Gray matter abnormalities in language processing areas and their associations with verbal ability and positive symptoms in first-episode patients with schizophrenia spectrum psychosis. Neuroimage Clin, 2019. 24: p. 102022.CrossRefGoogle ScholarPubMed
Padmanabhan, J. L., et al., Correlations between brain structure and symptom dimensions of psychosis in schizophrenia, schizoaffective, and psychotic bipolar I disorders. Schizophr Bull, 2015. 41(1): pp. 154162.CrossRefGoogle ScholarPubMed
Crespo-Facorro, B., et al., Reduced thalamic volume in first-episode non-affective psychosis: Correlations with clinical variables, symptomatology and cognitive functioning. NeuroImage, 2007. 35(4): pp. 16131623.CrossRefGoogle ScholarPubMed
Ferro, A., et al., Longitudinal investigation of the parietal lobe anatomy in bipolar disorder and its association with general functioning. Psychiatry Res Neuroimaging, 2017. 267: pp. 2231.CrossRefGoogle ScholarPubMed
Delvecchio, G., et al., Cingulate abnormalities in bipolar disorder relate to gender and outcome: a region-based morphometry study [corrected]. Eur Arch Psychiatry Clin Neurosci, 2019. 269(7): pp. 777784.CrossRefGoogle ScholarPubMed
Dietsche, B., Kircher, T., and Falkenberg, I., Structural brain changes in schizophrenia at different stages of the illness: A selective review of longitudinal magnetic resonance imaging studies. Aust N Z J Psychiatry, 2017. 51(5): pp. 500508.CrossRefGoogle ScholarPubMed
Brambilla, P., et al., Schizophrenia severity, social functioning and hippocampal neuroanatomy: Three-dimensional mapping study. Br J Psychiatry, 2013. 202(1): pp. 5055.CrossRefGoogle ScholarPubMed
Friston, K., et al., The dysconnection hypothesis (2016). Schizophr Res, 2016. 176(2–3): pp. 8394.CrossRefGoogle ScholarPubMed
van Dellen, E., et al., Structural brain network disturbances in the psychosis spectrum. Schizophr Bull, 2015. 42(3): pp. 782789.CrossRefGoogle ScholarPubMed
Zovetti, N., et al., Default mode network activity in bipolar disorder. Epidemiol Psychiatr Sci, 2020. 29: p. e166.CrossRefGoogle ScholarPubMed
van den Heuvel, M. P. and Fornito, A., Brain networks in schizophrenia. Neuropsychol Rev, 2014. 24(1): pp. 3248.CrossRefGoogle ScholarPubMed
O’Donoghue, S., et al., Applying neuroimaging to detect neuroanatomical dysconnectivity in psychosis. Epidemiol Psychiatr Sci, 2015. 24(4): pp. 298302.CrossRefGoogle ScholarPubMed
Sheffield, J. M. and Barch, D. M., Cognition and resting-state functional connectivity in schizophrenia. Neurosci Biobehav Rev, 2016. 61: pp. 108120.CrossRefGoogle ScholarPubMed
Squarcina, L., et al., Similar white matter changes in schizophrenia and bipolar disorder: A tract-based spatial statistics study. PLOS ONE, 2017. 12(6): p. e0178089.CrossRefGoogle ScholarPubMed
Sarrazin, S., et al., A multicenter tractography study of deep white matter tracts in bipolar I disorder: Psychotic features and interhemispheric disconnectivity. JAMA Psychiatry, 2014. 71(4): pp. 388396.CrossRefGoogle ScholarPubMed
Fatemi, S. H. and Folsom, T. D., The neurodevelopmental hypothesis of schizophrenia, revisited. Schizophr Bull, 2009. 35(3): pp. 528548.CrossRefGoogle ScholarPubMed
Kloiber, S., et al., Neurodevelopmental pathways in bipolar disorder. Neurosci Biobehav Rev, 2020. 112: pp. 213226.CrossRefGoogle ScholarPubMed
Crossley, N. A., et al., Superior temporal lobe dysfunction and frontotemporal dysconnectivity in subjects at risk of psychosis and in first-episode psychosis. Hum Brain Mapp, 2009. 30(12): pp. 4129–37.CrossRefGoogle ScholarPubMed
Hare, S. M., et al., Salience-default mode functional network connectivity linked to positive and negative symptoms of schizophrenia. Schizophr Bull, 2019. 45(4): pp. 892901.CrossRefGoogle ScholarPubMed
O’Neill, A., Mechelli, A., and Bhattacharyya, S., Dysconnectivity of large-scale functional networks in early psychosis: A meta-analysis. Schizophr Bull, 2019. 45(3): pp. 579590.CrossRefGoogle ScholarPubMed
Bopp, M. H. A., et al., White matter integrity and symptom dimensions of schizophrenia: A diffusion tensor imaging study. Schizophr Res, 2017. 184: pp. 5968.CrossRefGoogle ScholarPubMed
Lan, M. J., et al., White matter tract integrity is associated with antidepressant response to lurasidone in bipolar depression. Bipolar Disord, 2017. 19(6): pp. 444449.CrossRefGoogle ScholarPubMed
Reis Marques, T., et al., White matter integrity as a predictor of response to treatment in first episode psychosis. Brain, 2014. 137(Pt 1): pp. 172182.CrossRefGoogle ScholarPubMed
Müller, N., Inflammation in schizophrenia: Pathogenetic aspects and therapeutic considerations. Schizophr Bull, 2018. 44(5): pp. 973982.CrossRefGoogle ScholarPubMed
Stertz, L., Magalhães, P. V., and Kapczinski, F., Is bipolar disorder an inflammatory condition? The relevance of microglial activation. Curr Opin Psychiatry, 2013. 26(1): pp. 1926.CrossRefGoogle ScholarPubMed
Marques, T. R., et al., Neuroinflammation in schizophrenia: meta-analysis of in vivo microglial imaging studies. Psychol Med, 2019. 49(13): pp. 21862196.CrossRefGoogle ScholarPubMed
Giridharan, V. V., et al., Postmortem evidence of brain inflammatory markers in bipolar disorder: a systematic review. Mol Psychiatry, 2020. 25(1): pp. 94113.CrossRefGoogle ScholarPubMed
Bloomfield, P. S., et al., Microglial activity in people at ultra high risk of psychosis and in schizophrenia: An [(11)C]PBR28 PET brain imaging study. Am J Psychiatry, 2016. 173(1): pp. 4452.CrossRefGoogle Scholar
Van Berckel, B. N., et al., Microglia activation in recent-onset schizophrenia: A quantitative (R)-[11 C] PK11195 positron emission tomography study. Biol Psychiatry, 2008. 64(9): pp. 820822.CrossRefGoogle ScholarPubMed
Doorduin, J., et al., Neuroinflammation in schizophrenia-related psychosis: A PET study. J Nucl Med, 2009. 50(11): pp. 18011807.CrossRefGoogle ScholarPubMed
Takano, A., et al., Peripheral benzodiazepine receptors in patients with chronic schizophrenia: A PET study with [11 C] DAA1106. Int J Neuropsychopharmacol, 2010. 13(7): pp. 943950.CrossRefGoogle ScholarPubMed
Kenk, M., et al., Imaging neuroinflammation in gray and white matter in schizophrenia: An in-vivo PET study with [18 F]-FEPPA. Schizophr Bull, 2015. 41(1): pp. 8593.CrossRefGoogle ScholarPubMed
Houenou, J., et al., Neuroimaging biomarkers in bipolar disorder. Front Biosci (Elite Ed), 2012. 4: pp. 593606.CrossRefGoogle ScholarPubMed
Weickert, C. S., et al., Biomarkers in schizophrenia: A brief conceptual consideration. Dis Markers, 2013. 35(1): pp. 3.CrossRefGoogle ScholarPubMed
Tarcijonas, G. and Sarpal, D. K., Neuroimaging markers of antipsychotic treatment response in schizophrenia: An overview of magnetic resonance imaging studies. Neurobiol Dis, 2019. 131: pp. 104209.CrossRefGoogle ScholarPubMed
Egerton, A., et al., Effects of antipsychotic administration on brain glutamate in schizophrenia: A systematic review of longitudinal 1H-MRS studies. Frontiers Psych, 2017. 8(66).Google Scholar
Egerton, A., et al., Response to initial antipsychotic treatment in first episode psychosis is related to anterior cingulate glutamate levels: A multicentre 1 H-MRS study (OPTiMiSE). Mol Psychiatry, 2018. 23(11): pp. 21452155.CrossRefGoogle Scholar
Sarpal, D. K., et al., Baseline striatal functional connectivity as a predictor of response to antipsychotic drug treatment. Am J Psychiatry, 2016. 173(1): pp. 6977.CrossRefGoogle ScholarPubMed
Kraguljac, N. V., et al., Aberrant hippocampal connectivity in unmedicated patients with schizophrenia and effects of antipsychotic medication: A longitudinal resting state functional MRI study. Schizophr Bull, 2016. 42(4): pp. 10461055.CrossRefGoogle ScholarPubMed
Kraguljac, N. V., et al., Abnormalities in large scale functional networks in unmedicated patients with schizophrenia and effects of risperidone. NeuroImage Clin, 2016. 10: pp. 146158.CrossRefGoogle ScholarPubMed
Huhtaniska, S., et al., Long-term antipsychotic use and brain changes in schizophrenia – a systematic review and meta-analysis. Hum Psychopharmacol, 2017. 32(2).CrossRefGoogle ScholarPubMed
Palaniyappan, L., et al., Cortical folding defects as markers of poor treatment response in first-episode psychosis. JAMA Psychiatry, 2013. 70(10): pp. 10311040.CrossRefGoogle ScholarPubMed
Hajek, T., et al., Neuroprotective effect of lithium on hippocampal volumes in bipolar disorder independent of long-term treatment response. Psychol Med, 2014. 44(3): pp. 507517.CrossRefGoogle ScholarPubMed
Bearden, C. E., et al., Three-dimensional mapping of hippocampal anatomy in unmedicated and lithium-treated patients with bipolar disorder. Neuropsychopharmacology, 2008. 33(6): pp. 12291238.CrossRefGoogle ScholarPubMed
Penadés, R., et al., Neuroimaging studies of cognitive remediation in schizophrenia: A systematic and critical review. World J Psychiatry, 2017. 7(1): pp. 3443.CrossRefGoogle ScholarPubMed
Eack, S. M., et al., Neuroprotective effects of cognitive enhancement therapy against gray matter loss in early schizophrenia: Results from a 2-year randomized controlled trial. Arch Gen Psychiatry, 2010. 67(7): pp. 674682.CrossRefGoogle ScholarPubMed
Isaac, C. and Januel, D., Neural correlates of cognitive improvements following cognitive remediation in schizophrenia: A systematic review of randomized trials. Socioaffect Neurosci Psychol, 2016. 6: p. 30054.CrossRefGoogle ScholarPubMed
Bellani, M., et al., Cognitive remediation in schizophrenia: The earlier the better? Epidemiol Psychiatr Sci, 2019. 29: p. e57.CrossRefGoogle ScholarPubMed
Walter, M., et al., Translational machine learning for psychiatric neuroimaging. Prog Neuropsychopharmacol Biol Psychiatry, 2019. 91: pp. 113121.CrossRefGoogle ScholarPubMed
Linden, D. E., The challenges and promise of neuroimaging in psychiatry. Neuron, 2012. 73(1): pp. 822.CrossRefGoogle ScholarPubMed
Veronese, E., et al., Machine learning approaches: From theory to application in schizophrenia. Comput Math Methods Med, 2013. 2013: p. 867924.CrossRefGoogle ScholarPubMed
Woo, C.-W., et al., Building better biomarkers: Brain models in translational neuroimaging. Nat Neurosci, 2017. 20(3): p. 365.CrossRefGoogle ScholarPubMed
Squarcina, L., et al., Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques. Neuroimage, 2017. 145(Pt B): p. 238245.CrossRefGoogle Scholar
Squarcina, L., et al., Automated cortical thickness and skewness feature selection in bipolar disorder using a semi-supervised learning method. J Affect Disord, 2019. 256: pp. 416423.CrossRefGoogle ScholarPubMed
Schwarz, E., et al., Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder. Transl Psychiatry, 2019. 9(1): p. 12.CrossRefGoogle ScholarPubMed
Peruzzo, D., et al., Classification of first-episode psychosis: A multi-modal multi-feature approach integrating structural and diffusion imaging. J Neural Transm, 2015. 122(6): p. 897905.CrossRefGoogle ScholarPubMed
Koutsouleris, N., et al., Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study. Schizophr Bull, 2012. 38(6): pp. 12341246.CrossRefGoogle ScholarPubMed
Koutsouleris, N., et al., Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers. Schizophr Bull, 2015. 41(2): pp. 471482.CrossRefGoogle ScholarPubMed
Koutsouleris, N., et al., Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry, 2009. 66(7): pp. 700712.CrossRefGoogle ScholarPubMed
Koutsouleris, N., et al., Predicting response to repetitive transcranial magnetic stimulation in patients with schizophrenia using structural magnetic resonance imaging: A multisite machine learning analysis. Schizophr Bull, 2018. 44(5): pp. 10211034.CrossRefGoogle ScholarPubMed
Chand, G. B., et al., Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain, 2020. 143(3): pp. 10271038.CrossRefGoogle ScholarPubMed
Cao, B., et al., Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity. Mol Psychiatry, 2020. 25(4): pp. 906913.CrossRefGoogle ScholarPubMed
Adams, H. H., et al., Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nat Neurosci, 2016. 19(12): pp. 15691582.CrossRefGoogle ScholarPubMed
Hibar, D. P., et al., Common genetic variants influence human subcortical brain structures. Nature, 2015. 520(7546): pp. 224229.CrossRefGoogle ScholarPubMed
Gurung, R. and Prata, D. P., What is the impact of genome-wide supported risk variants for schizophrenia and bipolar disorder on brain structure and function? A systematic review. Psychol Med, 2015. 45(12): pp. 2461–80.CrossRefGoogle ScholarPubMed
van Haren, N. E., Bakker, S. C., and Kahn, R. S., Genes and structural brain imaging in schizophrenia. Curr Opin Psychiatry, 2008. 21(2): pp. 161167.CrossRefGoogle ScholarPubMed
Harari, J. H., et al., The association between gene variants and longitudinal structural brain changes in psychosis: A systematic review of longitudinal neuroimaging genetics studies. NPJ Schizophr, 2017. 3(1): p. 40.CrossRefGoogle ScholarPubMed
Kapur, S., Phillips, A. G., and Insel, T. R., Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Mol Psychiatry, 2012. 17(12): p. 1174–9.CrossRefGoogle Scholar
Cuthbert, B. N., Research domain criteria: Toward future psychiatric nosologies. Dialogues Clin Neurosci, 2015. 17(1): p. 89.CrossRefGoogle ScholarPubMed
Rossetti, M. G., et al., Gender-related neuroanatomical differences in alcohol dependence: Findings from the ENIGMA Addiction Working Group. Neuroimage Clin, 2021. 30: p. 102636.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×