Changes in patterns of age-related network connectivity are associated with risk for schizophrenia

This is the online data repository accompanying the following manuscript:

Changes in patterns of age-related network connectivity are associated with risk for schizophrenia

Roberta Passiatore^a,b,c, Linda A. Antonucci^a, Thomas P. DeRamus^b, Leonardo Fazio^d, Giuseppe Stolfa^a, Leonardo Sportelli^a,e, Gianluca C. Kikidis^a,e, Giuseppe Blasi^a,f, Qiang Chen^e, Juergen Dukart^c,g, Aaron L. Goldman^e, Venkata S. Mattay^e,h, Teresa Popolizioⁱ, Antonio Rampino^a,f, Fabio Sambataro^j, Pierluigi Selvaggi^a,f, William Ulrich^e, Apulian Network on Risk for Psychosis^a,k,l,m,n,o, Daniel R. Weinberger^e,h,p,q,r, Alessandro Bertolino^a,f1, Vince D. Calhoun^b1, Giulio Pergola^a,e,p1

^a Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124 Bari, Italy

^b Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, 30303 Atlanta, GA

^c Institute of Neuroscience and Medicine, Brain and Behavior, Research Centre Jülich, 52428 Jülich, Germany

^d Department of Medicine and Surgery, Libera Università Mediterranea Giuseppe Degennaro, 70010 Casamassima, Italy

^e Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, MD

^f Psychiatric Unit, University Hospital, 70124 Bari, Italy

^g Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany

^h Department of Neurology and Radiology, Johns Hopkins Medical Campus, 21287 Baltimore, MD

ⁱ Neuroradiology Unit, Scientific Institute for Research, Hospitalization and Health Care, Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Foggia, Italy

^j Section of Psychiatry, Department of Neuroscience, University of Padova, 35121 Padova, Italy

^k Department of Mental Health, Azienda Sanitaria Locale Foggia, 71121 Foggia, Italy

^l Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy

^m Department of Mental Health, Azienda Sanitaria Locale Barletta-Andria-Trani, 76123 Andria, Italy

ⁿ Department of Mental Health, Azienda Sanitaria Locale Bari, 70132 Bari, Italy

^o Department of Mental Health, Azienda Sanitaria Locale Brindisi, 72100 Brindisi, Italy

^p Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 21205 Baltimore, MD

^q Department of Neuroscience, Johns Hopkins University School of Medicine, 21287 Baltimore, MD
^r Department of Genetic Medicine, Johns Hopkins University School of Medicine, 21287 Baltimore, MD

 

Members of the Apulian Network on Risk for Psychosis include Ileana Andriola, Lucia Mare, Nicolò Parente, Alessandra Raio, Veronica D. Toro, (Department of Translational Biomedicine and Neuroscience – University of Bari Aldo Moro, Bari, IT), Mario Altamura, Marimar Castrigno, Melania Difino (Department of Mental Health, ASL Foggia, Foggia, IT; Department of Clinical and Experimental Medicine, University of Foggia, Foggia, IT), Flora Brudaglio, Domenico S. Savino, Rossana Vista (Department of Mental Health, ASL Barletta-Andria-Trani, Andria, IT), Angela Carofiglio, Barbara Gelao, Marina Mancini (Department of Mental Health, ASL Bari, Bari, IT), Alessandro Saponaro, Anna Manzari, Domenico Suma (Department of Mental Health, ASL Brindisi, Brindisi, IT).

Abstract

Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single- session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age-stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlate with genetic risk for SCZ and are detectable with MRI in young participants.

DOI: 10.1073/pnas.2221533120

 

Files:

- NeuroMark_1.0_template 

1. NeuroMark_1.0.nii:  4D Nifti containing the 100 Independent Components (ICs) spatial maps that was used for individual IC extraction based on Du Y, Fu Z, Sui J, Gao S, Xing Y, Lin D, Salman M, Abrol A, Rahaman MA, Chen J, Hong LE, Kochunov P, Osuch EA, Calhoun VD; Alzheimer's Disease Neuroimaging Initiative. NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. Neuroimage Clin. 2020;28:102375. doi: 10.1016/j.nicl.2020.102375. Epub 2020 Aug 11. PMID: 32961402; PMCID: PMC7509081.
2. NeuroMark_1.0_Label.doc: document containing labels for each IC extracted including brain regions, peak coordinates of ICs, corresponding functional network in which ICs are arranged, and the corresponding image number in the NeuroMark_1.0.nii file. NOTE

- AveragedMatrix

1. vectorized_FNC_LIBD.RData, vectorized_FNC_UNIBA1.RData, vectorized_FNC_UNIBA1.RData:
2. FNC_all_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects averaged across groups, sessions and cohorts
3. FNC_all_rest (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during resting state fMRI averaged across groups and cohorts
4. FNC_all_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during encoding (RISE/PEAR) fMRI averaged across groups and cohorts
5. FNC_all_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during retrieval (RISE/PEAR) fMRI averaged across groups and cohorts
6. FNC_all_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during working memory (2-Back) fMRI averaged across groups and cohorts
7. FNC_all_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix across subjects during emotion recognition (Faces/FMT) fMRI averaged across groups and cohorts
8. FNC_yNC_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC averaged across sessions
9. FNC_yNC_rest (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during resting state fMRI
10. FNC_yNC_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during encoding (RISE/PEAR) fMRI
11. FNC_yNC_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during retrieval (RISE/PEAR) fMRI
12. FNC_yNC_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during working memory (2-Back) fMRI
13. FNC_yNC_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during emotion recognition (Faces/FMT) fMRI
14. FN_oNC_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC averaged across sessions
15. FNC_oNC_rest (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during resting state fMRI
16. FNC_oNC_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during encoding (RISE/PEAR) fMRI
17. FNC_oNC_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during retrieval (RISE/PEAR) fMRI
18. FNC_oNC_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during working memory (2-Back) fMRI
19. FNC_oNC_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during emotion recognition (Faces/FMT) fMRI
20. FNC_ySIB_averaged (UNIBA1, LIBD): mean FNC matrix on ySIB averaged across sessions
21. FNC_ySIB_rest (LIBD): mean FNC matrix on ySIB during resting state fMRI
22. FNC_ySIB_enc (UNIBA1, LIBD): mean FNC matrix on ySIB during encoding (PEAR) fMRI
23. FNC_ySIB_ret (LIBD): mean FNC matrix on ySIB during retrieval (PEAR) fMRI
24. FNC_ySIB_nback (UNIBA1, LIBD): mean FNC matrix on ySIB during working memory (2-Back) fMRI
25. FNC_ySIB_emo (UNIBA1, LIBD): mean FNC matrix on ySIB during emotion recognition (Faces/FMT) fMRI
26. FNC_oSIB_averaged (UNIBA1, LIBD): mean FNC matrix on oSIB averaged across sessions
27. FNC_oSIB_rest (LIBD): mean FNC matrix on oSIB during resting state fMRI
28. FNC_oSIB_enc (UNIBA1, LIBD): mean FNC matrix on oSIB during encoding (PEAR) fMRI
29. FNC_oSIB_ret (LIBD): mean FNC matrix on oSIB during retrieval (PEAR) fMRI
30. FNC_oSIB_nback (UNIBA1, LIBD): mean FNC matrix on oSIB during working memory (2-Back) fMRI
31. FNC_oSIB_emo (LIBD): mean FNC matrix on oSIB during emotion recognition (FMT) fMRI
32. FNC_yPSY_averaged (UNIBA2): mean FNC matrix on yPSY averaged across sessions
33. FNC_yPSY_rest (UNIBA2): mean FNC matrix on yPSY during resting state fMRI
34. FNC_yPSY_enc (UNIBA2): mean FNC matrix on yPSY during encoding (RISE) fMRI
35. FNC_yPSY_ret (UNIBA2): mean FNC matrix on yPSY during retrieval (RISE) fMRI
36. FNC_yPSY_nback (UNIBA2): mean FNC matrix on yPSY during working memory (2-Back) fMRI
37. FNC_yPSY_emo (UNIBA2): mean FNC matrix on yPSY during emotion recognition (Faces) fMRI
38. FNC_oSCZ_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ averaged across sessions
39. FNC_oSCZ_rest (UNIBA2, LIBD): mean FNC matrix on oSCZ during resting state fMRI
40. FNC_oSCZ_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during encoding (PEAR/RISE) fMRI
41. FNC_oSCZ_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during retrieval (PEAR/RISE) fMRI
42. FNC_oSCZ_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during working memory (2-Back) fMRI
43. FNC_oSCZ_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during emotion recognition (Faces/FMT) fMRI
44. PlotMatrixFNC.m (Script): code used to generate FNC plots. Example of FNC plots are included in Figure 1.

- Connectogram

1. MeanMatrix.mat: matrix representing the difference (delta) in functional network connectivity (FNC) along the 54 ICs between oNC and yNC. This matrix was used to generate Figure 2. Matlab object.
2. MeanMatrix.txt: matrix representing the difference (delta) in functional network connectivity (FNC) along the 54 ICs between oNC and yNC. This matrix was used to generate Figure 2. Text file.
3. ConnectogramNeuroMark_1.0.m (Script): code used to generate Figure 2. Graphical and statistical options are provided. Please refer to https://trendscenter.wpengine.com/trends/software/gift/docs/v4.0b_gica_manual.pdf for further details.

- ResFNCAgeDifferences

1. data_example.RData (Data): example data frame for AgeDiff code.
2. AgeDiff_real.R (Script): code to test age differences through Linear mixed effect model on multiple sessions across age-groups.
3. AgeDiff_permutation.R (Script): code to permute age differences through Linear mixed effect model on multiple sessions across age-groups.
4. ResFNCAgeDifference.RData: summary statistics obtained on UNIBA1, UNIBA2, LIBD cohorts included in the manuscript.
5. boxplot_permutation_p_LIBD.pdf, boxplot_permutation_p_UNIBA1.pdf, boxplot_permutation_p_UNIBA2.pdf: error bars showing results obtained after permutation (10,000 iteration) across IC pairs.

- dataFNC

1. data_final.RData: de-identified individuals' aggregate FNC data for each cohort (UNIBA1, UNIBA2, LIBD, PNC, ABCD, UKB)
2. plotData.R (Script): code used to plot FNC differences across groups (Figure 3). Code used to assess group differences - Wilcoxon Rank sum test - is included.

- ResFNCRiskDifferences

1. res_cerebellar_occipitoparietal_ICs.csv: summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjusted_FDR<0.05) on the cerebellar-occipitoparietal FNC
2. res_dorsolateralPFC_sensorimotor_ICs.csv: summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjusted_FDR<0.05) on medial PFC-sensorimotor FNC
3. res_medialPFC_sensorimotor_ICs.csv: summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjusted_FDR<0.05) on dorsolateral PFC-sensorimotor FNC

- ResFNCxPRS

1. ResMetanalysisPRS_FNC_R.csv: summary statistics of metanalysis on the averaged FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups
2. ResMetanalysisPRS_FNC_children_R.csv: summary statistics of  metanalysis on the averaged FNC x PRS association across PNC and ABCD cohorts on children.
3. t2r.R (Script): code for t to R² transform
4. MetanalysisAveragedFNC_PRS.R (Script): code used perform metanalysis on the averaged FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups.
5. MetanalysisAveragedFNC_PRS_children.R (Script): code used perform metanalysis on the averaged FNC x PRS association across PNC and ABCD cohorts on children.
6. MetanalysisPRS_FNC_sessions_R.R (Script): code used perform metanalysis on the session-specific FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups.

 

The present work includes data from UK Biobank (ID #41655), Adolescence Brain Cognitive Development (ABCD; ID #12036) Study, and Philadelphia Neurodevelopmental Cohort (PNC; ID #20998). Data are publicly accessible under approved request at the following links:

- UK Biobank: https://www.ukbiobank.ac.uk/

- ABCD: https://abcdstudy.org/

- PNC: https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html

The ABCD data used in this report came from NIMH Data Archive DOI: https://doi.org/10.15154/1528793.

Additional code for MRI data processing and Independent Component Analysis are publicly available at the following link: https://trendscenter.org/software/

 

Please, check https://doi.org/10.5281/zenodo.7853030 for future updates.

For any data inquiries please contact:

• Roberta Passiatore: Roberta.Passiatore@uniba.it
• Giulio Pergola: Giulio.Pergola@uniba.it - Giulio.Pergola@libd.org
• Vince D Calhoun: VCalhoun@gsu.edu
• Alessandro Bertolino: Alessandro.Bertolino@uniba.it

 

Funding

This work received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 798181 awarded to G.P. (PI) and A.B., D.R.W.; from the European Union funding within the MUR PNRR Extended Partnership initiative on Neuroscience and Neuropharmacology (Project no. PE00000006 CUP H93C22000660006 “MNESYS, A multiscale integrated approach to the study of the nervous system in health and disease”) to G.B., A.R., A.B., and G.P.; from the funding initiative Horizon Europe Seeds 2021 (Next Generation 8 EU–MUR D.M. 737/2021) for the project S68 CUP: H99J21017550006 to G.P. (PI) and L.A.A., G.B., A.R., A.B.; from the Apulian regional government for the project: “Early Identification of Psychosis Risk” to A.B.; from the Research Projects of National Relevance (PRIN) 2017 Prot. 2017K2NEF4 awarded to G.P.; from a Collaboration Grant from Exprivia Spa to G.P. under the ministerial decree D.M. n. 352/22 and A.B. under a collaboration agreement; from a Collaboration Grant from ITEL Telecomunicazioni Srl awarded to A.B.; from the 2021 Helmholtz Information and Data Science Academy Grant No. 12429 awarded to R.P. and J.D.; and from the NIH grant #R01MH118695 awarded to V.D.C. The collection of the MRI and genetic data for the LIBD cohort was supported by direct funding from the Intramural Research Program of the NIMH to the Clinical Brain Disorders Branch (PI: D.R.W., protocol 95-M-0150).