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Reducing false positive connection in tractograms using
joint structure-function filtering
Matteo Frigo, Guillermo Gallardo, Isa Costantini, Alessandro Daducci,
Demian Wassermann, Rachid Deriche, Samuel Deslauriers-Gauthier
To cite this version:
Matteo Frigo, Guillermo Gallardo, Isa Costantini, Alessandro Daducci, Demian Wassermann, et al.. Reducing false positive connection in tractograms using joint structure-function filtering. OHBM 2018 - Organization for Human Brain Mapping, Jun 2018, Singapore, Singapore. pp.1-3. �hal-01737434�
Reducing false positive connection in tractograms using
joint structure-function filtering
Matteo Frigoo
1*, Guigllermo Gallardo
1, Isa Constantnig
1, Alessandro Daduccig
2, Demigan
Wassermann
1, Rachigd Derigche
1, Samuel Deslaurigers-Gauthiger
11Unigversigté Côte d’Azur, Inriga, France
2Department of Computer Scigence, Unigversigty of Verona, Italy *matteo.frigooiignriga.fr
INTRODUCTION
Due to igts igll-posed nature, tractooraphy oenerates a sigonigfcant number of false posigtve connectons between braign reoigons [3]. To reduce the number of false posigtves, Daduccig et al. [1] proposed the COMMIT framework, whigch has the ooal of re-establigshigno the lignk between tractooraphy and tssue migcrostructure. In thigs framework, the digfusigon MRI sigonal igs modeled as a lignear combignaton of local models associgated wigth streamlignes where the weigohts are igdentfed by solvigno a convex optmigzaton problem. Streamlignes wigth a weigoht of zero do not contrigbute to the digfusigon MRI data and are assumed to be false posigtves. Removigno these false posigtves yigelds a subset of streamlignes supportno the anatomigcal data. However, COMMIT does not make use of the lignk between structure and functon and thus weigohts all bundles equally. In thigs work, we propose a new strateoy that enhances the COMMIT framework by ignjectno the functonal ignformaton provigded by functonal MRI. The result igs an enhanced tractooram flterigno strateoy that consigders both functonal and structural data.
METHODS
We randomly selected 3 subjects from the HCP500 dataset, each processed wigth the HCP mignigmum pigpeligne [4]. For each subject, we performed probabigligstc tractooraphy usigno the vertces of the cortcal mesh as seed locatons. We then parcellated the cortex ignto 74 reoigons based on theigr extrignsigc anatomigcal connectvigty usigno the aloorigthm proposed by Gallardo et al. [2] Usigno the restno state functonal MRI of each subject, we computed the functonal connectvigty between the cortcal reoigons. To do so, we frst higoh pass fltered the tme seriges and computed the averaoe functonal MRI sigonal ignsigde of each reoigon. Fignally, we digvigded the sigonals ignto wigndows of 100 seconds and computed the maxigmum sligdigno wigndow correlaton between every paigr of reoigons. These correlaton coefcigents were used to defne a reoularigzaton coefcigent for each bundle of the tractooram. The reoularigzaton coefcigents were desigoned to favor streamlignes associgated wigth a higoh correlaton whigle penaligzigno but not excludigno those wigth a low correlaton. The ratonale igs that streamlignes connectno reoigons that are functonally correlated should be favored to explaign the digfusigon MRI data. As ign the non functonal COMMIT, we solved the optmigzaton problem and removed streamlignes wigth a weigoht of zero. For comparigson, we also fltered the tractoorams usigno equal weigohts for all bundles, ig.e. wigthout functonal prigors.
RESULTS
To measure the performance of the proposed aloorigthm we evaluated the quantty of candigdate false posigtve streamlignes that we were able to detect and the digfusigon MRI sigonal fino error. A flterigno strateoy igs consigdered superigor igf igt igs able to remove more streamlignes whigle stll explaignigno the digfusigon MRI sigonal. Figoure 1 igllustrates that igncludigno functonal ignformaton allows us to igncrease the number of candigdate false posigtves detected wigthout afectno the data fino error, therefore igndigcatno enhanced flterigno. Interestnoly, the addigton of functonal ignformaton modigfed the spatal locaton of the removed bundles ign a non-unigform manner. The streamlignes removed by both flterigno strateoiges and those removed by one or the other are igllustrated ign Figoure 2.
CONCLUSION
We proposed a new method to enhance tractooram flterigno usigno functonal ignformaton. Our preligmignary results igndigcate that ignjectno functonal data igncreases the number of streamlignes removed whigle maigntaignigno data ft and modigfed the spatal locaton of removed streamlignes across the braign.
REFERENCES
[1] Daduccig, A., et al. (2015) "COMMIT: convex optmigzaton modeligno for migcrostructure ignformed tractooraphy." IEEE
transactins in medical imaging 34.1: 246-257.
[2] Gallardo, G., et al. (2017) "Groupwigse structural parcellaton of the whole cortex: a looigstc random efects model based approach." NeuroImaoe.
[3] Maiger-Heign, K.H., et al. (2017) "The challenoe of mappigno the human connectome based on digfusigon tractooraphy."
Nature cimmunicatins 8.1: 1349.
[4] Van Essen, D.C., et al. (2012) "The Human Connectome Project: a data acquigsigton perspectve." Neuriimage 62.4: 2222-2231.
ACKNOWLEDGEMENT
Thigs work has receigved fundigno from the European Research Councigl (ERC) under the European Unigon's Horigzon 2020 research and ignnovaton prooram (ERC Advanced Grant aoreement No 694665 : CoBCoM - Computatonal Braign Connectvigty Mappigno).
