FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1998937103> ?p ?o ?g. }

• W1998937103 endingPage "247" @default.
• W1998937103 startingPage "227" @default.
• W1998937103 abstract "Neuroimage phenotyping for psychiatric and neurological disorders is performed using voxelwise analyses also known as voxel based analyses or morphometry (VBM). A typical voxelwise analysis treats measurements at each voxel (e.g., fractional anisotropy, gray matter probability) as outcome measures to study the effects of possible explanatory variables (e.g., age, group) in a linear regression setting. Furthermore, each voxel is treated independently until the stage of correction for multiple comparisons. Recently, multi-voxel pattern analyses (MVPA), such as classification, have arisen as an alternative to VBM. The main advantage of MVPA over VBM is that the former employ multivariate methods which can account for interactions among voxels in identifying significant patterns. They also provide ways for computer-aided diagnosis and prognosis at individual subject level. However, compared to VBM, the results of MVPA are often more difficult to interpret and prone to arbitrary conclusions. In this paper, first we use penalized likelihood modeling to provide a unified framework for understanding both VBM and MVPA. We then utilize statistical learning theory to provide practical methods for interpreting the results of MVPA beyond commonly used performance metrics, such as leave-one-out-cross validation accuracy and area under the receiver operating characteristic (ROC) curve. Additionally, we demonstrate that there are challenges in MVPA when trying to obtain image phenotyping information in the form of statistical parametric maps (SPMs), which are commonly obtained from VBM, and provide a bootstrap strategy as a potential solution for generating SPMs using MVPA. This technique also allows us to maximize the use of available training data. We illustrate the empirical performance of the proposed framework using two different neuroimaging studies that pose different levels of challenge for classification using MVPA." @default.
• W1998937103 created "2016-06-24" @default.
• W1998937103 creator A5002695463 @default.
• W1998937103 creator A5003019326 @default.
• W1998937103 creator A5011266024 @default.
• W1998937103 creator A5011613330 @default.
• W1998937103 creator A5044580005 @default.
• W1998937103 creator A5052801688 @default.
• W1998937103 date "2013-02-10" @default.
• W1998937103 modified "2023-10-16" @default.
• W1998937103 title "Penalized Likelihood Phenotyping: Unifying Voxelwise Analyses and Multi-Voxel Pattern Analyses in Neuroimaging" @default.
• W1998937103 cites W1963521532 @default.
• W1998937103 cites W1967252112 @default.
• W1998937103 cites W1981640845 @default.
• W1998937103 cites W1987599891 @default.
• W1998937103 cites W1992395739 @default.
• W1998937103 cites W2000292092 @default.
• W1998937103 cites W2002619293 @default.
• W1998937103 cites W2003905570 @default.
• W1998937103 cites W2004006028 @default.
• W1998937103 cites W2006096283 @default.
• W1998937103 cites W2013115634 @default.
• W1998937103 cites W2013270816 @default.
• W1998937103 cites W2017806092 @default.
• W1998937103 cites W2019583087 @default.
• W1998937103 cites W2022530159 @default.
• W1998937103 cites W2024165284 @default.
• W1998937103 cites W2030360178 @default.
• W1998937103 cites W2041050058 @default.
• W1998937103 cites W2042587503 @default.
• W1998937103 cites W2045185094 @default.
• W1998937103 cites W2046557060 @default.
• W1998937103 cites W2052570168 @default.
• W1998937103 cites W2054540100 @default.
• W1998937103 cites W2069552222 @default.
• W1998937103 cites W2087941344 @default.
• W1998937103 cites W2088796077 @default.
• W1998937103 cites W2097850441 @default.
• W1998937103 cites W2101095383 @default.
• W1998937103 cites W2101282194 @default.
• W1998937103 cites W2106364249 @default.
• W1998937103 cites W2113792310 @default.
• W1998937103 cites W2119390010 @default.
• W1998937103 cites W2120259577 @default.
• W1998937103 cites W2121747917 @default.
• W1998937103 cites W2123923307 @default.
• W1998937103 cites W2124262127 @default.
• W1998937103 cites W2124757386 @default.
• W1998937103 cites W2126728600 @default.
• W1998937103 cites W2128251808 @default.
• W1998937103 cites W2128431628 @default.
• W1998937103 cites W2132055400 @default.
• W1998937103 cites W2135046866 @default.
• W1998937103 cites W2138263042 @default.
• W1998937103 cites W2138790588 @default.
• W1998937103 cites W2139158372 @default.
• W1998937103 cites W2145714441 @default.
• W1998937103 cites W2146888763 @default.
• W1998937103 cites W2148412534 @default.
• W1998937103 cites W2158485497 @default.
• W1998937103 cites W2163198669 @default.
• W1998937103 cites W2169272937 @default.
• W1998937103 cites W2489822048 @default.
• W1998937103 cites W2514569044 @default.
• W1998937103 cites W2787894218 @default.
• W1998937103 cites W3124546987 @default.
• W1998937103 cites W4235037173 @default.
• W1998937103 cites W4238284510 @default.
• W1998937103 cites W4238893454 @default.
• W1998937103 cites W4294541781 @default.
• W1998937103 cites W4301861531 @default.
• W1998937103 cites W1968663451 @default.
• W1998937103 doi "https://doi.org/10.1007/s12021-012-9175-9" @default.
• W1998937103 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3624987" @default.
• W1998937103 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23397550" @default.
• W1998937103 hasPublicationYear "2013" @default.
• W1998937103 type Work @default.
• W1998937103 sameAs 1998937103 @default.
• W1998937103 citedByCount "3" @default.
• W1998937103 countsByYear W19989371032017 @default.
• W1998937103 countsByYear W19989371032021 @default.
• W1998937103 countsByYear W19989371032023 @default.
• W1998937103 crossrefType "journal-article" @default.
• W1998937103 hasAuthorship W1998937103A5002695463 @default.
• W1998937103 hasAuthorship W1998937103A5003019326 @default.
• W1998937103 hasAuthorship W1998937103A5011266024 @default.
• W1998937103 hasAuthorship W1998937103A5011613330 @default.
• W1998937103 hasAuthorship W1998937103A5044580005 @default.
• W1998937103 hasAuthorship W1998937103A5052801688 @default.
• W1998937103 hasBestOaLocation W19989371032 @default.
• W1998937103 hasConcept C105795698 @default.
• W1998937103 hasConcept C117251300 @default.
• W1998937103 hasConcept C119857082 @default.
• W1998937103 hasConcept C126838900 @default.
• W1998937103 hasConcept C143409427 @default.
• W1998937103 hasConcept C149550507 @default.
• W1998937103 hasConcept C153180895 @default.
• W1998937103 hasConcept C154945302 @default.

• next