Construction and Evaluation of Hierarchical Parcellation of the Brain using fMRI with Prewhitening

Brain​ ​atlases​ ​are​ ​a​ ​ubiquitous​ ​tool​ ​used​ ​for​ ​analyzing​ ​and​ ​interpreting​ ​brain​ ​imaging​ ​datasets. Traditionally,​ ​brain​ ​atlases​ ​divided​ ​the​ ​brain​ ​into​ ​regions​ ​separated​ ​by​ ​anatomical​ ​landmarks.​ ​In​ ​the​ ​last decade,​ ​several​ ​attempts​ ​have​ ​been​ ​made​ ​to​ ​parcellate​ ​the​ ​brain​ ​into​ ​regions​ ​with​ ​distinct​ ​functional activity​ ​using​ ​fMRI.​ ​To​ ​construct​ ​a​ ​brain​ ​atlas​ ​using​ ​fMRI,​ ​data​ ​driven​ ​algorithms​ ​are​ ​used​ ​to​ ​group​ ​voxels with​ ​similar​ ​functional​ ​activity​ ​together​ ​to​ ​form​ ​regions.​ ​Hierarchical​ ​clustering​ ​is​ ​one​ ​parcellation​ ​method that​ ​has​ ​been​ ​used​ ​for​ ​functional​ ​parcellation​ ​of​ ​the​ ​brain,​ ​resulting​ ​in​ ​parcellations​ ​that​ ​align​ ​well​ ​with cytoarchitectonic​ ​divisions​ ​of​ ​the​ ​brain.​ ​However,​ ​few​ ​rigorous​ ​data​ ​driven​ ​evaluations​ ​of​ ​the​ ​method​ ​have been​ ​performed.​ ​Moreover,​ ​the​ ​effect​ ​of​ ​removing​ ​autocorrelation​ ​trends​ ​from​ ​fMRI​ ​time​ ​series (prewhitening)​ ​on​ ​the​ ​structure​ ​of​ ​the​ ​resultant​ ​atlas​ ​has​ ​not​ ​been​ ​previously​ ​explored.​ ​In​ ​this​ ​paper,​ ​we use​ ​hierarchical​ ​clustering​ ​to​ ​produce​ ​functional​ ​parcellations​ ​of​ ​the​ ​brain​ ​using​ ​hierarchical​ ​clustering. We​ ​use​ ​both​ ​prewhitened​ ​and​ ​raw​ ​fMRI​ ​time​ ​series​ ​to​ ​construct​ ​the​ ​atlas.​ ​The​ ​resultant​ ​atlases​ ​were​ ​then evaluated​ ​for​ ​their​ ​homogeneity,​ ​separation​ ​between​ ​regions,​ ​reproducibility​ ​across​ ​subjects,​ ​and reproducibility​ ​across​ ​scans.