Amaro Júnior, Edson

Biazoli Junior, Claudinei Eduardo

Bressan, Rodrigo Affonseca

Jackowski, Andrea Parolin

Miguel, Eurípedes Constantino

Pan, Pedro Mario

Rohde, Luis Augusto Paim

Salum Junior, Giovanni Abrahão

Sato, João Ricardo

Zugman, André

Frontiers in Human Neuroscience

English

Lume 5.8

GENERAL COMMENTARYpublished: 07 February 2017doi: 10.3389/fnhum.2017.00047Frontiers in Human Neuroscience | www.frontiersin.org 1 February 2017 | Volume 11 | Article 47Edited by:Lutz Jäncke,University of Zurich, SwitzerlandReviewed by:Damien Fair,Oregon Health & Science University,USA*Correspondence:Claudinei E. Biazoli Jr.cbiazoli@gmail.comReceived: 02 June 2016Accepted: 23 January 2017Published: 07 February 2017Citation:Biazoli CE Jr., Salum GA, Pan PM,Zugman A, Amaro E Jr., Rohde LA,Miguel EC, Jackowski AP, Bressan RAand Sato JR (2017) Commentary:Functional connectome fingerprint:identifying individuals using patterns ofbrain connectivity.Front. Hum. Neurosci. 11:47.doi: 10.3389/fnhum.2017.00047Commentary: Functionalconnectome fingerprint: identifyingindividuals using patterns of brainconnectivityClaudinei E. Biazoli Jr. 1, 2*, Giovanni A. Salum 3, 4, Pedro M. Pan 4, 5, André Zugman 4, 5,Edson Amaro Jr. 2, Luis A. Rohde 3, 4, Euripedes C. Miguel 4, 6, Andrea P. Jackowski 4, 5,Rodrigo A. Bressan 4, 5 and João R. Sato 1, 2, 4, 51Centre of Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo Andre, Brazil, 2Department ofRadiology, School of Medicine, University of São Paulo, São Paulo, Brazil, 3Hospital de Clinicas de Porto Alegre andDepartment of Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil, 4National Institute of DevelopmentalPsychiatry for Children and Adolescents, CNPq, São Paulo, Brazil, 5 Interdisciplinary Lab for Clinical Neurosciences,Universidade Federal de São Paulo, São Paulo, Brazil, 6Department of Psychiatry, School of Medicine, University of SãoPaulo, São Paulo, BrazilKeywords: connectome, psychopatological features, executive function, intelligence, functional networksdevelopmentA commentary onFunctional connectome fingerprint: identifying individuals using patterns of brainconnectivityby Finn, E. S., Shen, X. Scheinost, D. Rosenberg, M. D. Huang, J. Chun, M. M., et al. (2015). Nat.Neurosci. 18, 1664–1671. doi: 10.1038/nn.4135Inspired by the innovative “connectome fingerprints” analyses of resting-state functional magneticresonance imaging data conducted by Finn et al. (2015), and by the original “connectotyping”procedure proposed by Miranda-Dominguez et al. (2014), we sought to investigate whetherconnectome fingerprint constructs could convey useful information regarding not only cognitivefunction, but also the expression of emotional and behavioral problems. As Finn et al. advocate, thisapproach might offer an alternative to both the ubiquitous group-based analysis of neuroimagingdata and the current non-biologically based classification ofmental disorders (Insel, 2014;Miranda-Dominguez et al., 2014; Wang et al., 2015). This novel approach is nevertheless supported byprevious promising findings on predicting cognitive and behavioral profiles from fMRI data(Poldrack et al., 2013; Smith et al., 2015).We build upon the method of Finn et al. (2015) by including single subject connectomeinformation directly into the model. While they applied a leave-one-out cross validation procedure,we put forward a method suitable to test whether the information conveyed by a given connectomefingerprint could predict the intelligence quotient, cognitive function and also emotional andbehavioral problems at the individual level.First, we have estimated the whole-brain functional connectome of each of the 655 children andadolescents (7–15 years old; from a school-based sample, with no siblings included) participatingin the High Risk Cohort Study for Psychiatric disorders (HRC, Salum et al., 2015). This wasaccomplished using the consensual parcellation scheme proposed by Gordon et al. (2014) andthe processing detailed previously by our group (Sato et al., 2015). Briefly, 6 min of resting-statefMRI data from each participant (1.5 T GE scanner, TR = 2 s, 180 volumes) were skull striped,motion corrected, despiked, and normalized. A band-pass filter (0.01 < f < 0.1 Hz) was appliedBiazoli Jr. et al. Development of Connectome Fingerprintsand data were detrended using first and second-orderpolynomials and then spatially smoothed (8 mm FWHM).Linear registration to individual’s’ skull striped structuralscan was applied followed by non-linear registration mappingindividual’s functional native space onto the standard MNItemplate. Global, CSF, white matter signals and the six linearmotion’s parameters were regressed out. Finally, the mean BOLDsignal from each region of the parcellation was extracted andused to define the correlation matrix of the connectome.Then, the 5% of individuals with the most similar connectomefingerprints to each subject were determined using the samemetric applied by Finn et al. (2015). This procedure definedclusters of individuals with highly similar connectomes. For eachof these clusters, we assessed emotional and behavioral problemsand cognitive executive functions using well-establishedinstruments and tasks including the Child Behaviour Checklistfor psychopathology; the Go/No-Go and Conflict Control Taskfor executive function; and the block design and vocabularysub-scores of the WISC-IV for intelligence measures. In order toevaluate the potential relationship between individual functionalconnectomes and those set of measures, we performed aSpearman correlation analysis. The correlations between thescores of each measure for each individual and the mean scoresfor the 5% most similar individuals were determined. In thisapproach, the mean scores within cluster constitute the estimatedmeasures for each individual actual cognitive and behavioralmeasures. The results for these analyses, including correlationcoefficients and the respective significance levels, are presentedin Table 1.Individuals with similar connectomes were found to have (i)similar ages; (ii) similar intelligence quotients; (iii) analogousperformance on tests measuring executive function; (iv) andsimilar levels of behavioral problems. As motion artifacts arewidely recognized as a potential source of error in resting statefMRI, we repeated the analysis in a subgroup with minimallevels of head-movement to test whether such artifacts biased theresults (Power et al., 2014). In fact, recent findings demonstratedsystematic errors in correlations between behavioral measuresand functional connectivity due to head-movement (Siegel et al.,2016). Our analysis in subjects with minimal head-movementreproduced the main findings with high statistical significance,indicating that the effects are unlikely due to differences inmovement during scanning (Table 1).Some differences between the dataset accessed here andthe Human Connectome Project (HCP) data used by Finnet al. (2015) should be emphasized. The HCP functional datapresents a particularly high sampling rate and a longer thanusual resting state acquisition. They had indeed showed thatthe classification procedure to determine functional fingerprintsdegrades for smaller amounts of resting data. However, we wereable to find significant and meaningful correlations betweenconnectome profiles and non-imaging parameters even withrelatively little amount of functional data. Indeed, in the originalTABLE 1 | Spearman correlation results between closest connectomes forage and cognitive and behavioral and emotional problems measurements.5% Neighbours Only with FD < 0.1rs p-value rs p-valueAge 0.408 < 0.001 0.396 < 0.001**Total behavioral and emotionalproblems (CBCL)0.191 0.001 0.150 0.004*Estimated Intelligence Quotient 0.124 0.002 0.117 0.026*GO/NO-GO TASK% of commission errors 0.196 < 0.001 0.216 < 0.001**% of omission errors 0.108 0.007 0.052 0.334#d-prime 0.196 < 0.001 0.124 0.025*CONFLICT CONTROL TASK% of correct responses incongruent trials0.1 0.012 0.091 0.088% of correct responses inincongruent trials0.12 0.003 0.14 0.009#**p < 0.001 in the general group and in individuals with very low head movement (FD <0.1 mm); *p < 0.05 in the general group and in individuals with very low head movement;#Significant difference in the general group but not in the very low head movement group.connectoptyping report, Miranda-Dominguez et al. (2014)achieved robust predictions for individuals’ models with around2 min of resting data. Taken together, these results suggest thatthe amount of data necessary to identify individual connectomeprofiles heavily depends on the classification procedure andanalysis. We argue that, although collecting data with highertemporal and spatial resolution, as in the HCP initiative, isof surmount importance to the field, it not exclude properlyreanalysing previously collected data. Importantly, using suitableanalytical approaches, it could be possible to extend functionalfingerprinting framework to more usual datasets, particularlyallowing the use of MR data from difficult to assess populations.Our results suggested that the connectivity profile reliablychanges with neurodevelopment, and that the level of behavioralproblems of a particular individual can be predicted byconsidering only children with similar connectome architectures.We were positively surprised by the correlation found betweenthe functional connectome fingerprint and age. However,unraveling developmental effects on the connectivity profilewould only be possible with longitudinal data. Using informationcontained in the connectomes of individuals may provide acrucial tool for mental health care. One can expect remarkableadvances in the assessment of mental development based onthis biological measure—especially when large datasets withfunctional fingerprint changes over long time-scales are utilized.AUTHOR CONTRIBUTIONSAll authors listed, have made substantial, direct and intellectualcontribution to the work, and approved it for publication.Frontiers in Human Neuroscience | www.frontiersin.org 2 February 2017 | Volume 11 | Article 47Biazoli Jr. et al. Development of Connectome FingerprintsREFERENCESFinn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun,M. M., et al. (2015). Functional connectome fingerprinting: identifyingindividuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671.doi: 10.1038/nn.4135Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W.M., and Petersen, S. E. (2014). Generation and evaluation of a corticalarea parcellation from resting-state correlations. Cereb. Cortex 26, 288–303.doi: 10.1093/cercor/bhu239Insel, T. R. (2014). The NIMH research domain criteria (RDoC) project:precision medicine for psychiatry. Am. J. Psychiatry 171, 395–397.doi: 10.1176/appi.ajp.2014.14020138Miranda-Dominguez, O., Mills, B. D., Carpenter, S. D., Grant, K. A.,Kroenke, C. D., Nigg, J. T., et al. (2014). Connectotyping: modelbased fingerprinting of the functional connectome. PLoS ONE 9:e111048.doi: 10.1371/journal.pone.0111048Poldrack, R. A., Barch, D. M., Mitchell, J. P., Wager, T. D., Wagner, A. D.,Devlin, J. T., et al. (2013). Toward open sharing of task-based fMRI data:the OpenfMRI project. Front. Neuroinformatics 7:12. doi: 10.3389/fninf.2013.00012Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B.L., Petersen, S. E., et al. (2014). Methods to detect, characterize, andremove motion artifact in resting state fMRI. Neuroimage 84, 320–341.doi: 10.1016/j.neuroimage.2013.08.048Salum, G. A., Gadelha, A., Pan, P. M., Moriyama, T. S., Graeff-Martins, A. S.,Tamanaha, A. C., et al. (2015). High risk cohort study for psychiatric disordersin childhood: rationale, design, methods and preliminary results. Int. J. MethodsPsychiatr. Res. 24, 58–73. doi: 10.1002/mpr.1459Sato, J. R., Salum, G. A., Gadelha, A., Vieira, G., Zugman, A., Picon, F. A.,et al. (2015). Decreased centrality of subcortical regions during the transitionto adolescence: a functional connectivity study. Neuroimage 104, 44–51.doi: 10.1016/j.neuroimage.2014.09.063Siegel, J. S., Mitra, A., Laumann, T. O., Seitzman, B. A., Raichle, M., Corbetta,M., et al. (2016). Data quality influences observed links between functionalconnectivity and behavior. Cereb. Cortex. doi: 10.1093/cercor/bhw253. [Epubahead of print].Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E., Glasser,M. F., et al. (2015). A positive-negative mode of population covariation linksbrain connectivity, demographics and behavior. Nat. Neurosci. 18, 1565–1567.doi: 10.1038/nn.4125Wang, D., Buckner, R. L., Fox, M. D., Holt, D. J., Holmes, A. J., Stoecklein, S., et al.(2015). Parcellating cortical functional networks in individuals. Nat. Neurosci.18, 1853–1860. doi: 10.1038/nn.4164Conflict of Interest Statement: LR has been on the speakers’ bureau/advisoryboard and/or acted as a consultant for Eli-Lilly, Janssen-Cilag, Novartis, and Shirein the last three years. The ADHD and Juvenile Bipolar Disorder OutpatientPrograms that he chaired received unrestricted educational and research supportfrom the following pharmaceutical companies in the last three years: Eli-Lilly,Janssen-Cilag, Novartis, and Shire. LR has received travel grants from Shireto take part of the 2014 APA and 2015 WFADHD congresses. He receivesauthorship royalties from Oxford Press and ArtMed. RB has been on the speakers’bureau/advisory board of AstraZeneca, Bristol, Janssen, and Lundbeck. He hasreceived research grants from Janssen, Eli Lilly, Lundbeck, Novartis, Roche,Fundação E.J. Safra, and Fundação ABAHDS and is a shareholder in BiomolecularTechnology Ltda. EA has received research grants from Fundação E.J. Safra andFundação ABAHDS.The other authors declares that the research was conducted in the absenceof any commercial or financial relationships that could be construed as a potentialconflict of interest.Copyright © 2017 Biazoli, Salum, Pan, Zugman, Amaro, Rohde, Miguel, Jackowski,Bressan and Sato. This is an open-access article distributed under the terms ofthe Creative Commons Attribution License (CC BY). The use, distribution orreproduction in other forums is permitted, provided the original author(s) or licensorare credited and that the original publication in this journal is cited, in accordancewith accepted academic practice. No use, distribution or reproduction is permittedwhich does not comply with these terms.Frontiers in Human Neuroscience | www.frontiersin.org 3 February 2017 | Volume 11 | Article 47

Commentary : functional connectome fingerprint : identifying individuals using patterns of brain connectivity

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Commentary : functional connectome fingerprint : identifying individuals using patterns of brain connectivity

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