The relevance of transdiagnostic shared networks to the severity of symptoms and cognitive deficits in schizophrenia: a multimodal brain imaging fusion study.
Journal
Translational psychiatry
ISSN: 2158-3188
Titre abrégé: Transl Psychiatry
Pays: United States
ID NLM: 101562664
Informations de publication
Date de publication:
18 05 2020
18 05 2020
Historique:
received:
09
12
2019
accepted:
28
04
2020
revised:
06
04
2020
entrez:
20
5
2020
pubmed:
20
5
2020
medline:
22
6
2021
Statut:
epublish
Résumé
Schizophrenia (SZ) is frequently concurrent with substance use, depressive symptoms, social communication and attention deficits. However, the relationship between common brain networks (e.g., SZ vs. substance use, SZ vs. depression, SZ vs. developmental disorders) with SZ on specific symptoms and cognition is unclear. Symptom scores were used as a reference to guide fMRI-sMRI fusion for SZ (n = 94), substance use with drinking (n = 313), smoking (n = 104), major depressive disorder (MDD, n = 260), developmental disorders with autism spectrum disorder (ASD, n = 421) and attention-deficit/hyperactivity disorder (ADHD, n = 244) respectively. Common brain regions were determined by overlapping the symptom-related components between SZ and these other groups. Correlation between the identified common brain regions and cognition/symptoms in an independent SZ dataset (n = 144) was also performed. Results show that (1): substance use was related with cognitive deficits in schizophrenia through gray matter volume (GMV) in anterior cingulate cortex and thalamus; (2) depression was linked to PANSS negative dimensions and reasoning in SZ through a network involving caudate-thalamus-middle/inferior temporal gyrus in GMV; (3) developmental disorders pattern was correlated with poor attention, speed of processing and reasoning in SZ through inferior temporal gyrus in GMV. This study reveals symptom driven transdiagnostic shared networks between SZ and other mental disorders via multi-group data mining, indicating that some potential common underlying brain networks associated with schizophrenia differently with respect to symptoms and cognition. These results have heuristic value and advocate specific approaches to refine available treatment strategies for comorbid conditions in schizophrenia.
Identifiants
pubmed: 32424299
doi: 10.1038/s41398-020-0834-6
pii: 10.1038/s41398-020-0834-6
pmc: PMC7235018
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
149Subventions
Organisme : NIMH NIH HHS
ID : R01 MH117107
Pays : United States
Organisme : NIGMS NIH HHS
ID : P30 GM122734
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB020407
Pays : United States
Organisme : NIGMS NIH HHS
ID : P20 GM103472
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH118695
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB005846
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH094524
Pays : United States
Références
Insel, T. R. Rethinking schizophrenia. Nature 468, 187–193 (2010).
pubmed: 21068826
pmcid: 21068826
Clark, L. A., Cuthbert, B., Lewis-Fernandez, R., Narrow, W. E. & Reed, G. M. Three Approaches to Understanding and Classifying Mental Disorder: ICD-11, DSM-5, and the National Institute of Mental Health’s Research Domain Criteria (RDoC). Psychol. Sci. Publ. Int. 18, 72–145 (2017).
doi: 10.1177/1529100617727266
Ruderfer, D. M. et al. Genomic dissection of bipolar disorder and Schizophrenia, including 28 subphenotypes. Cell 173, 1705–170, https://doi.org/10.1016/j.cell.2018.05.046 (2018). ARTN 1715.e16.
doi: 10.1016/j.cell.2018.05.046
pmcid: 6432650
Reininghaus, U. et al. Transdiagnostic dimensions of psychosis in the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP). World Psychiatry 18, 67–76 (2019).
doi: 10.1002/wps.20607
Goodkind, M. et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72, 305–315 (2015).
doi: 10.1001/jamapsychiatry.2014.2206
Kaufmann, T. et al. Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nat. Neurosci. 22, 1617–1623 (2019).
doi: 10.1038/s41593-019-0471-7
Cross-Disorder Group of the Psychiatric Genomics, C. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).
doi: 10.1016/S0140-6736(12)62129-1
Network & Pathway Analysis Subgroup of Psychiatric Genomics, C Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci. 18, 199–209 (2015).
Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).
doi: 10.1126/science.aad6469
Buckley, P. F., Miller, B. J., Lehrer, D. S. & Castle, D. J. Psychiatric comorbidities and schizophrenia. Schizophrenia Bull. 35, 383–402 (2009).
doi: 10.1093/schbul/sbn135
Siu, M. W., Chong, C. S. & Lo, W. T. Prevalence and clinicians’ awareness of psychiatric comorbidities among first-episode schizophrenia. Early Intervention Psychiatry 12, 1128–1136 (2018).
doi: 10.1111/eip.12426
Upthegrove, R., Marwaha, S. & Birchwood, M. Depression and schizophrenia: cause, consequence, or trans-diagnostic issue? Schizophrenia Bull. 43, 240–244 (2017).
Ford, T. C., Apputhurai, P., Meyer, D. & Crewther, D. P. Confirmatory factor analysis of autism and schizophrenia spectrum traits. Pers Indiv Differ 110, 80–84. https://doi.org/10.1016/j.paid.2017.01.033 (2017).
doi: 10.1016/j.paid.2017.01.033
Groom, M. J. et al. Cognitive deficits in early-onset schizophrenia spectrum patients and their non-psychotic siblings: a comparison with ADHD. Schizophrenia Res. 99, 85–95 (2008).
doi: 10.1016/j.schres.2007.11.008
Koenders, L. et al. Brain volume in male patients with recent onset schizophrenia with and without cannabis use disorders. J. Psychiatry Neurosci. 40, 197–206 (2015).
doi: 10.1503/jpn.140081
Onwuameze, O. E. et al. MAPK14 and CNR1 gene variant interactions: effects on brain volume deficits in schizophrenia patients with marijuana misuse. Psychological Med. 43, 619–631 (2013).
doi: 10.1017/S0033291712001559
Chen, H. et al. Shared atypical default mode and salience network functional connectivity between autism and schizophrenia. Autism Res. 10, 1776–1786 (2017).
doi: 10.1002/aur.1834
Crump, C., Winkleby, M. A., Sundquist, K. & Sundquist, J. Comorbidities and mortality in persons with schizophrenia: a Swedish national cohort study. Am. J. Psychiatry 170, 324–333 (2013).
doi: 10.1176/appi.ajp.2012.12050599
Sha, Z. Q., Wager, T. D., Mechelli, A. & He, Y. Common dysfunction of large-scale neurocognitive networks across psychiatric disorders. Biol. Psychiatry 85, 379–388 (2019).
doi: 10.1016/j.biopsych.2018.11.011
Walters, R. K. et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat. Neurosci. 21, 1656–1669 (2018).
doi: 10.1038/s41593-018-0275-1
Jiang, R. et al. Multimodal data revealed different neurobiological correlates of intelligence between males and females. Brain Imag. Behavior. https://doi.org/10.1007/s11682-019-00146-z (2019).
Qi, S. et al. Electroconvulsive therapy treatment responsive multimodal brain networks. Human Brain Mapping https://doi.org/10.1002/hbm.24910 (2020).
Sui, J. et al. Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion. Nat. Commun. 9, 3028, https://doi.org/10.1038/s41467-018-05432-w (2018).
doi: 10.1038/s41467-018-05432-w
pubmed: 30072715
pmcid: 6072778
Vergara, V. M., Weiland, B. J., Hutchison, K. E. & Calhoun, V. D. The impact of combinations of alcohol, nicotine, and cannabis on dynamic brain connectivity. Neuropsychopharmacology 43, 877–890 (2018).
doi: 10.1038/npp.2017.280
Qi, S. et al. MicroRNA132 associated multimodal neuroimaging patterns in unmedicated major depressive disorder. Brain 141, 916–926 (2018).
doi: 10.1093/brain/awx366
Fu, Z. N. et al. Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism. Neuroimage 190, 191–204 (2019).
doi: 10.1016/j.neuroimage.2018.06.003
Turner, J. A. et al. A multi-site resting state fMRI study on the amplitude of low frequency fluctuations in schizophrenia. Front. Neurosci. 7, 137, https://doi.org/10.3389/fnins.2013.00137 (2013).
doi: 10.3389/fnins.2013.00137
pubmed: 23964193
pmcid: 3737471
Zhi, D. et al. Aberrant dynamic functional network connectivity and graph properties in major depressive disorder. Front. Psychiatry 9, 339, https://doi.org/10.3389/fpsyt.2018.00339 (2018).
doi: 10.3389/fpsyt.2018.00339
pubmed: 30108526
pmcid: 6080590
Di Martino, A. et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19, 659–667 (2014).
doi: 10.1038/mp.2013.78
Di Martino, A. et al. Data Descriptor: Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci. Data 4, ARTN 170010, https://doi.org/10.1038/sdata.2017.10 (2017).
van Erp, T. G. et al. Neuropsychological profile in adult schizophrenia measured with the CMINDS. Psychiatry Res. 230, 826–834 (2015).
doi: 10.1016/j.psychres.2015.10.028
Zou, Q. H. et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J. Neurosci. Methods 172, 137–141 (2008).
doi: 10.1016/j.jneumeth.2008.04.012
Ashburner, J. & Friston, K. J. Unified segmentation. Neuroimage 26, 839–851 (2005).
doi: 10.1016/j.neuroimage.2005.02.018
Qi, S. et al. Multimodal fusion with reference: searching for joint neuromarkers of working memory deficits in Schizophrenia. IEEE Trans. Med. Imaging 37, 93–105 (2018).
doi: 10.1109/TMI.2017.2725306
Pergola, G., Selvaggi, P., Trizio, S., Bertolino, A. & Blasi, G. The role of the thalamus in schizophrenia from a neuroimaging perspective. Neurosci. Biobehav. Rev. 54, 57–75 (2015).
doi: 10.1016/j.neubiorev.2015.01.013
Samsom, J. N. & Wong, A. H. C. Schizophrenia an depression co-morbidity: what we have learned from animal models. Front. Psychiatry 6, ARTN 13, https://doi.org/10.3389/fpsyt.2015.00013 (2015).
Allen, P. et al. Emerging temporal lobe dysfunction in people at clinical high risk for psychosis. Front. Psychiatry 10, ARTN 298, https://doi.org/10.3389/fpsyt.2019.00298 (2019).
Hugdahl, K., Loberg, E. M. & Nygard, M. Left temporal lobe structural and functional abnormality underlying auditory hallucinations in schizophrenia. Front. Neurosci. 3, 34–45 (2009).
pubmed: 19753095
pmcid: 2695389
Amaral, D. G., Schumann, C. M. & Nordahl, C. W. Neuroanatomy of autism. Trends Neurosci. 31, 137–145 (2008).
doi: 10.1016/j.tins.2007.12.005
Bedford, S. A. et al. Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder. Mol. Psychiatry https://doi.org/10.1038/s41380-019-0420-6 (2019).
Cai, J. et al. Increased left inferior temporal gyrus was found in both low function autism and high function autism. Front. Psychiatry 9, ARTN 542, https://doi.org/10.3389/fpsyt.2018.00542 (2018).
Vaidya, C. J. Neurodevelopmental abnormalities in ADHD. Curr. Top. Behav. Neurosci. 9, 49–66 (2012).
doi: 10.1007/7854_2011_138
Zhang, L. et al. Decreased middle temporal gyrus connectivity in the language network in schizophrenia patients with auditory verbal hallucinations. Neurosci. Lett. 653, 177–182 (2017).
doi: 10.1016/j.neulet.2017.05.042
Liu, S. et al. Linked 4-way multimodal brain differences in Schizophrenia in a large Chinese Han population. Schizophrenia Bull. https://doi.org/10.1093/schbul/sby045 (2018).
Schumann, G. et al. Stratified medicine for mental disorders. Eur. Neuropsychopharm. 24, 5–50 (2014).
doi: 10.1016/j.euroneuro.2013.09.010
Groenman, A. P., Janssen, T. W. P. & Oosterlaan, J. Childhood psychiatric disorders as risk factor for subsequent substance abuse: a meta-analysis. J. Am. Acad. Child Adolesc. Psychiatry 56, 556–569 (2017).
doi: 10.1016/j.jaac.2017.05.004
Qi, S. et al. Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia. Human Brain Mapping https://doi.org/10.1002/hbm.24632 (2019).