TY - GEN
T1 - Intact connectional morphometricity learning using multi-view morphological brain networks with application to autism spectrum disorder
AU - Bessadok, Alaa
AU - Rekik, Islem
PY - 2018
Y1 - 2018
N2 - The morphology of anatomical brain regions can be affected by neurological disorders, including dementia and schizophrenia, to various degrees. Hence, identifying the morphological signature of a specific brain disorder can improve diagnosis and better explain how neuroanatomical changes associate with function and cognition. To capture this signature, a landmark study introduced, brain morphometricity, a global metric defined as the proportion of phenotypic variation that can be explained by brain morphology derived from structural brain MRI scans. However, this metric is limited to investigating morphological changes using low-order measurements (e.g., regional volumes) and overlooks how these changes can be related to each other (i.e., how morphological changes in region A are influenced by changes in region B). Furthermore, it is derived from a pre-defined anatomical similarity matrix using a Gaussian function, which might not be robust to outliers and constrains the locality of data to a fixed bandwidth. To address these limitations, we propose the intact connectional brain morphometricity (ICBM), a metric that captures the variation of connectional changes in brain morphology. In particular, we use multi-view morphological brain networks estimated from multiple cortical attributes (e.g., cortical thickness) to learn an intact space that first integrates the morphological network views into a unified space. Next, we learn a multi-view morphological similarity matrix in the intact space by adaptively assigning neighbors for each data sample based on local connectivity. The learned similarity capturing the shared traits across morphological brain network views is then used to derive our ICBM via a linear mixed effect model. Our framework shows the potential of the proposed ICBM in capturing the connectional neuroanatomical signature of brain disorders such as Autism Spectrum Disorder.
AB - The morphology of anatomical brain regions can be affected by neurological disorders, including dementia and schizophrenia, to various degrees. Hence, identifying the morphological signature of a specific brain disorder can improve diagnosis and better explain how neuroanatomical changes associate with function and cognition. To capture this signature, a landmark study introduced, brain morphometricity, a global metric defined as the proportion of phenotypic variation that can be explained by brain morphology derived from structural brain MRI scans. However, this metric is limited to investigating morphological changes using low-order measurements (e.g., regional volumes) and overlooks how these changes can be related to each other (i.e., how morphological changes in region A are influenced by changes in region B). Furthermore, it is derived from a pre-defined anatomical similarity matrix using a Gaussian function, which might not be robust to outliers and constrains the locality of data to a fixed bandwidth. To address these limitations, we propose the intact connectional brain morphometricity (ICBM), a metric that captures the variation of connectional changes in brain morphology. In particular, we use multi-view morphological brain networks estimated from multiple cortical attributes (e.g., cortical thickness) to learn an intact space that first integrates the morphological network views into a unified space. Next, we learn a multi-view morphological similarity matrix in the intact space by adaptively assigning neighbors for each data sample based on local connectivity. The learned similarity capturing the shared traits across morphological brain network views is then used to derive our ICBM via a linear mixed effect model. Our framework shows the potential of the proposed ICBM in capturing the connectional neuroanatomical signature of brain disorders such as Autism Spectrum Disorder.
UR - http://www.scopus.com/inward/record.url?scp=85054358691&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00755-3_5
DO - 10.1007/978-3-030-00755-3_5
M3 - Conference contribution
AN - SCOPUS:85054358691
SN - 9783030007546
VL - 11083
T3 - Lecture Notes in Computer Science
SP - 38
EP - 46
BT - Connectomics in NeuroImaging
A2 - Wu, Guorong
A2 - Schirmer, Markus D.
A2 - Chung, Ai Wern
A2 - Rekik, Islem
A2 - Munsell, Brent
PB - Springer Verlag
CY - Switzerland
T2 - 2nd International Workshop on Connectomics in NeuroImaging, CNI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 20 September 2018 through 20 September 2018
ER -