Parcellation of the human brain Parcellation of the human brain by combining text mining and spatial data mining within a neuroinformatics database. Text mining: Analysis of scientific abstracts. Spatial data mining: Modeling of the distribution of Talairach coordinates. Seek communality between the the text representation and spatial representation by multivariate analysis

Finn ˚arup Nielsen

Mathematical Modelling

Nielsen, Finn Årup

Online Research Database In Technology

Text and spatial data miningFinn A˚rup NielsenLundbeck Foundation Center for Integrated Molecular Brain ImagingatNeurobiology Research UnitCopenhagen University Hospital RigshospitaletandInformatics and Mathematical ModellingTechnical University of DenmarkSeptember 29, 2006Text and spatial data miningParcellation of the human brainParcellation of the humanbrain by combining text min-ing and spatial data min-ing within a neuroinformaticsdatabase.Text mining: Analysis of sci-entific abstracts.Spatial data mining: Model-ing of the distribution of Ta-lairach coordinates.Seek communality betweenthe the text representationand spatial representation bymultivariate analysis.Finn A˚rup Nielsen, CIMBI 2-day meeting 1 September 29, 2006Text and spatial data miningBrede DatabaseFigure 1: Web-page generated for the paper (Epstein andKanwisher, 1998) with abstract and Talairach coordinatesdisplayed in a corner cube environment (Rehm et al., 1998).Brede Database (Nielsen, 2003)inspired by the BrainMapdatabase (Fox and Lancaster,1994).Presently 186 papers and theinformation used is:Abstract of the article3D coordinates representingchange in brain activation,grey matter variation or siteof lesion: so-called Talairachcoordinates (Talairach andTournoux, 1988).Finn A˚rup Nielsen, CIMBI 2-day meeting 2 September 29, 2006Text and spatial data miningNon-negative matrix factorizationMultivariate analysis: Non-negative matrix factorization (NMF) decom-poses a non-negative data matrix X(N × P ) (Lee and Seung, 1999)X =WH+U, (1)where W(N ×K) and H(K × P ) are also non-negative matrices.“Euclidean” cost function forE“eucl” = ||X−WH||2F (2)Iterative algorithm (Lee and Seung, 2001)Hkp ← Hkp(WTX)kp(WTWH)kp(3)Wnk ← Wnk(XHT)nk(WHHT)nk. (4)Finn A˚rup Nielsen, CIMBI 2-day meeting 3 September 29, 2006Text and spatial data miningPartial least squaresOne of the variations of partial least squares: Singular value decomposi-tion of a inner product matrix (McIntosh et al., 1996)ULVT = svd(XTY)(5)Probably most suitable for data that is symmetric, i.e., both positive andnegative.“Non-negative partial least squares”WH = nmf(XTY)(6)Should get two non-negative matrices (X and Y) as input.Finn A˚rup Nielsen, CIMBI 2-day meeting 4 September 29, 2006Text and spatial data miningFirst matrix: Bag-of-words matrix‘memory’ ‘visual’ ‘motor’ ‘time’ ‘retrieval’ . . .Fujii 6 0 1 0 4 . . .Maddock 5 0 0 0 0 . . .Tsukiura 0 0 4 0 0 . . .Belin 0 0 0 0 0 . . .Ellerman 0 0 0 5 0 . . .... ... ... ... ... ... . . .Representation of the abstracts of the papers in a bag-of-words matrix:(abstract × words)-matrix: Each element counts of the frequency of aword occurring in an abstract text (Salton et al., 1975).Exclusion of stop words: common words, brain anatomy, . . .Mostly wordsfor brain function left (Nielsen et al., 2005).Finn A˚rup Nielsen, CIMBI 2-day meeting 5 September 29, 2006Text and spatial data miningSecond matrix: Voxelization matrix−6 −4 −2 0 2 4 600.51Example locations−6 −4 −2 0 2 4 6012σ = 0.05 (Too small)−6 −4 −2 0 2 4 600.10.20.3σ = 3.00 (Too Large)−6 −4 −2 0 2 4 600.51σ = 0.49 (LOO CV optimal) ’Talairach coordinate’ in centimeter                                                           Probability density valueRegard the Talairach coordinates in anarticle as being generated from a distri-bution p(z), where z is in 3D Talairachspace (Fox et al., 1997).Kernel methods (L kernels centered oneach location: µl) with homogeneousGaussian kernel in 3D Talairach spacezpˆ(z) =(2piσ2)−3/2LL∑le− 12σ2(z−µl)2σ2 fixed (σ = 1cm) or optimized withleave-one-out cross-validation (Nielsenand Hansen, 2002).Finn A˚rup Nielsen, CIMBI 2-day meeting 6 September 29, 2006Text and spatial data miningDetailsCoarse sampling of the volume with 8mm voxels: pˆ(z) ≡ y. This yCorresponds to the Talairach coordinates in one scientific article and whenstacked: Y(N × P )Restriction to gray matter regions using a anatomically labeled brain“AAL” (Tzourio-Mazoyer et al., 2002).Number of components in NMF: Using a rule of thumb we select K =√N/2 (Mardia et al., 1979): K = 10 ≈√186/2.Several runs of NMF with 50’000 iterations each, and with different ini-tialization each time.Exclusive assignment: Winner-takes-all function on W and H.Finn A˚rup Nielsen, CIMBI 2-day meeting 7 September 29, 2006Text and spatial data miningResulting NMF components 1: memory, retrieval, words, neutral, episodic 2: motor, movements, somatosensory, hand, finger 3: pain, noxious, unpleasantness, perceived, hot 4: painful, perception, thermal, heat, warm 5: semantic, phonological, cognitive, verbal, decision 6: visual, eye, time, attention, mental 7: faces, face, recognition, perceptual, category 8: auditory, spatial, neglect, awareness, language 9: emotion, emotions, sadness, disgust, happiness 10: motion, word, kinetic, reading, restLoadings on words in WW(P × K) = W(470 × 10), i.e., 10components (“functions”) and 470words.Most dominant functions listed at thetop: memory, motor, pain, . . .5 most loaded words listed for eachfunction.Finn A˚rup Nielsen, CIMBI 2-day meeting 8 September 29, 2006Text and spatial data miningDorsolateral surface viewH in Talairach space: H(K ×Q) = H(10 × 2492), i.e., 10components and 2492 voxels.Occipital and parietal lobe:“visual”, “eye”.Central sulcus: “Motor”,“movements”, “somatosen-sory”.Temporal cortex: “auditory”,“spatial”, “neglect”Finn A˚rup Nielsen, CIMBI 2-day meeting 9 September 29, 2006Text and spatial data miningMedial viewFigure 2: Medial surface view of the labeled right hemisphere.Seen from the left“Memory” in posterior cingu-late area. Probably due tothe many articles about mem-ory and the posterior cingu-late in the Brede database.Episodic memory retrieval isassociated with posterior cin-gulate (Cabeza and Nyberg,2000).“Emotion” in the medialfrontal area, e.g., amygdala.“Pain” in anterior cingulate,thalamus, insula. Previouslynoted: (Ingvar, 1999).Finn A˚rup Nielsen, CIMBI 2-day meeting 10 September 29, 2006Text and spatial data miningHow stable are the results?2 4 6 8 102468102 4 6 8 10246810The parcellation varies between runs of the NMF andwhen different parts of the data set are used.The different components are not matched betweenruns, e.g., the 2nd component in the first run mightmatch the 5rd component in the second run.It is possible to match the components, e.g., withthe algorithm suggested by (Meila˘, 2002) or by the“Hungarian method” (Roth et al., 2002).Example “confusion matrices” (HHT) in the figure ap-pear with one instance of half-split resampling betweenthe all papers before and after sorting.Finn A˚rup Nielsen, CIMBI 2-day meeting 11 September 29, 2006Text and spatial data miningConsistent parcellation-Parcellation after maskingthose voxels that are con-sistently parcellated.Threshold determined bya permutation test andmatching by the Hungar-ian method.Finn A˚rup Nielsen, CIMBI 2-day meeting 12 September 29, 2006Text and spatial data mining. . . and molecular neuroimaging?WOROI: 2 Limbic lobeWOROI: 4 Cingulate gyrusWOROI: 5 Posterior cingulate gyrusWOROI: 8 Anterior cingulate gyrusWOROI: 9 Middle cingulate gyrusWOROI: 6 Left posterior cingulate gyrusWOROI: 7 Right posterior cingulate gyrus(Exp x woroi)−matrix [ X(73x114) ], row Z−score scaled, Full woroi imputation, submatrix [ X(73x10) ](38, 8) = (Epidepride binding to the D2 receptor, Pu): 1.3628411 2 3 4 5 6 7 8 9 1010203040506070−3−2−1012−4 −3 −2 −1 0 1 2Ada04For02She02Goe04Goe04Aud03Aud03vDy00vDy00vDy00vDy00She04She04Biv94Hal00Kay01Kay01vDy00vDy00vDy00vDy00Bae98Bae98Lip04Lip04Lip04Lip04Lip04Lip04Ros96Ros96Sad95Cg TLFL PL OLCb ThPuCdHiNormalized region valueExperimentCgTLFLPLOLCbThPuCdHiFew molecular neuroimag-ing studies with Talairachcoordinates — most arebased on analysis in pre-defined regions.Building a taxonomy forbrain regions.Match regions in paperswith taxonomy entriesBuild algorithms that areable to handle missingdata and different scal-ings.Finn A˚rup Nielsen, CIMBI 2-day meeting 13 September 29, 2006Text and spatial data miningSummaryIt is possible to automatically perform a high-level coarse parcellation ofthe entire human brain.The results appear in accordance with general consensus in human brainmapping.We use the Brede Database and rely only on abstract and Talairachcoordinate information.We suggest “non-negative partial least squares” as a combination of non-negative matrix factorization and partial least squares.Finn A˚rup Nielsen, CIMBI 2-day meeting 14 September 29, 2006ReferencesReferencesCabeza, R. and Nyberg, L. 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PMID: 9345485.ftp://ftp.rotman-baycrest.on.ca/pub/Randy/PLS/pls article.pdf.Finn A˚rup Nielsen, CIMBI 2-day meeting 15 September 29, 2006ReferencesMeila˘, M. (2002). Comparing clusterings. Technical Report 418, Department of Statistics, Universityof Washington, Seattle, Washington. http://www.stat.washington.edu/www/research/reports/2002-/tr418.ps.Nielsen, F. A˚. (2003). The Brede database: a small database for functional neuroimaging. NeuroImage,19(2). http://208.164.121.55/hbm2003/abstract/abstract906.htm. Presented at the 9th InternationalConference on Functional Mapping of the Human Brain, June 19–22, 2003, New York, NY. Availableon CD-Rom.Nielsen, F. A˚., Balslev, D., and Hansen, L. K. (2005). Mining the posterior cin-gulate: Segregation between memory and pain component. NeuroImage, 27(3):520–532.DOI: 10.1016/j.neuroimage.2005.04.034.Nielsen, F. A˚. and Hansen, L. K. (2002). Modeling of activation data in theBrainMapTM database: Detection of outliers. 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