SCI Publications

2012

B.A. Zielinski, J.S. Anderson, A.L. Froehlich, M.B.D. Prigge, J.A. Nielsen, J.R. Cooperrider, A.N. Cariello, P.T. Fletcher, A.L. Alexander, Nicholas Lange, Erin D Bigler, Janet E Lainhart. “scMRI Reveals Large-Scale Brain Network Abnormalities in Autism,” In PloS one, Vol. 7, No. 11, pp. e49172. 2012.
DOI: 10.1371/journal.pone.0049172

Autism is a complex neurological condition characterized by childhood onset of dysfunction in multiple cognitive domains including socio-emotional function, speech and language, and processing of internally versus externally directed stimuli. Although gross brain anatomic differences in autism are well established, recent studies investigating regional differences in brain structure and function have yielded divergent and seemingly contradictory results. How regional abnormalities relate to the autistic phenotype remains unclear. We hypothesized that autism exhibits distinct perturbations in network-level brain architecture, and that cognitive dysfunction may be reflected by abnormal network structure. Network-level anatomic abnormalities in autism have not been previously described. We used structural covariance MRI to investigate network-level differences in gray matter structure within two large-scale networks strongly implicated in autism, the salience network and the default mode network, in autistic subjects and age-, gender-, and IQ-matched controls. We report specific perturbations in brain network architecture in the salience and default-mode networks consistent with clinical manifestations of autism. Extent and distribution of the salience network, involved in social-emotional regulation of environmental stimuli, is restricted in autism. In contrast, posterior elements of the default mode network have increased spatial distribution, suggesting a 'posteriorization' of this network. These findings are consistent with a network-based model of autism, and suggest a unifying interpretation of previous work. Moreover, we provide evidence of specific abnormalities in brain network architecture underlying autism that are quantifiable using standard clinical MRI.

2011

J.S. Anderson, J.A. Nielsen, A.L. Froehlich, M.B. DuBray, T.J. Druzgal, A.N. Cariello, J.R. Cooperrider, B.A. Zielinski, C. Ravichandran, P.T. Fletcher, A.L. Alexander, E.D. Bigler, N. Lange, J.E. Lainhart. “Functional connectivity magnetic resonance imaging classification of autism,” In Brain, pp. (published online). 2011.
DOI: 10.1093/brain/awr263
PubMed ID: 22006979

Group differences in resting state functional magnetic resonance imaging connectivity between individuals with autism and typically developing controls have been widely replicated for a small number of discrete brain regions, yet the whole-brain distribution of connectivity abnormalities in autism is not well characterized. It is also unclear whether functional connectivity is sufficiently robust to be used as a diagnostic or prognostic metric in individual patients with autism. We obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the entire grey matter (26.4 million connections) in a well-characterized set of 40 male adolescents and young adults with autism and 40 age-, sex- and IQ-matched typically developing subjects. A single resting state blood oxygen level-dependent scan of 8 min was used for the classification in each subject. A leave-one-out classifier successfully distinguished autism from control subjects with 83% sensitivity and 75\% specificity for a total accuracy of 79\% (P = 1.1 x 10^-7). In subjects less than 20 years of age, the classifier performed at 89\% accuracy (P = 5.4 x 10^-7). In a replication dataset consisting of 21 individuals from six families with both affected and unaffected siblings, the classifier performed at 71\% accuracy (91\% accuracy for subjects less than 20 years of age). Classification scores in subjects with autism were significantly correlated with the Social Responsiveness Scale (P = 0.05), verbal IQ (P = 0.02) and the Autism Diagnostic Observation Schedule-Generic's combined social and communication subscores (P = 0.05). An analysis of informative connections demonstrated that region of interest pairs with strongest correlation values were most abnormal in autism. Negatively correlated region of interest pairs showed higher correlation in autism (less anticorrelation), possibly representing weaker inhibitory connections, particularly for long connections (Euclidean distance greater than 10 cm). Brain regions showing greatest differences included regions of the default mode network, superior parietal lobule, fusiform gyrus and anterior insula. Overall, classification accuracy was better for younger subjects, with differences between autism and control subjects diminishing after 19 years of age. Classification scores of unaffected siblings of individuals with autism were more similar to those of the control subjects than to those of the subjects with autism. These findings indicate feasibility of a functional connectivity magnetic resonance imaging diagnostic assay for autism.

P.T. Fletcher. “Geodesic Regression on Riemannian Manifolds,” In Proceedings of the Third MICCIA International Workshop on Mathematical Foundations of Computational Anatomy (MFCA), Toronto, Canada, pp. 75--86. 2011.

P.T. Fletcher, J. Moeller, J. Phillips, S. Venkatasubramanian. “Horoball hulls and extents in positive definite space,” In Algorithms and Data Structures, Lecture Notes in Computer Science (LNCS), Vol. 6844/2011, pp. 386--398. 2011.
DOI: 10.1007/978-3-642-22300-6_33

X. Hao, R.T. Whitaker, P.T. Fletcher. “Adaptive Riemannian Metrics for Improved Geodesic Tracking of White Matter,” In Information Processing in Medical Imaging (IPMI), Lecture Notes in Computer Science (LNCS), Vol. 6801/2011, pp. 13--24. 2011.
DOI: 10.1007/978-3-642-22092-0_2

W. Liu, S. Awate, J. Anderson, D. Yurgelun-Todd, P.T. Fletcher. “Monte Carlo expectation maximization with hidden Markov models to detect functional networks in resting-state fMRI,” In Machine Learning in Medical Imaging, Lecture Notes in Computer Science (LNCS), Vol. 7009/2011, pp. 59--66. 2011.
DOI: 10.1007/978-3-642-24319-6_8

N. Sadeghi, M.W. Prastawa, P.T. Fletcher, J.H. Gilmore, W. Lin, G. Gerig. “Statistical Growth Modeling of Longitudinal DT-MRI for Regional Characterization of Early Brain Development,” In Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2011 Workshop on Image Analysis of Human Brain Development, pp. 1507--1510. 2011.
DOI: 10.1109/ISBI.2012.6235858

2010

E.B. Dam, P.T. Fletcher, S.M. Pizer. “Automatic shape model building based on principal geodesic analysis bootstrapping,” In Medical Image Analysis, Vol. 12, No. 2, Note: Epub Feb 2 2010, pp. 136--151. 2010.
PubMed ID: 18178124

B.C. Davis, P.T. Fletcher, E. Bullitt, S. Joshi. “Population Shape Regression from Random Design Data,” In International Journal of Computer Vision, Vol. 90, No. 1, Note: Marr Prize Special Issue, pp. 255--266. October, 2010.
DOI: 10.1109/ICCV.2007.4408977

P.T. Fletcher, R.T. Whitaker, R. Tao, M.B. DuBray, A. Froehlich, C. Ravichandran, A.L. Alexander, E.D. Bigler, N. Lange, J.E. Lainhart. “Microstructural connectivity of the arcuate fasciculus in adolescents with high-functioning autism,” In NeuroImage, Vol. 51, No. 3, Note: Epub Feb 2 2010, pp. 1117--1125. July, 2010.
PubMed ID: 20132894

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R.T. Whitaker, the Alzheimers Disease Neuroimaging Initiative (ADNI). “Manifold modeling for brain population analysis,” In Medical Image Analysis, Special Issue on the 12th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009, Vol. 14, No. 5, Note: Awarded MICCAI 2010, Best of the Journal Issue Award, pp. 643--653. 2010.
ISSN: 1361-8415
DOI: 10.1016/j.media.2010.05.008
PubMed ID: 20579930

N. Lange, M.B. Dubray, J.E. Lee, M.P. Froimowitz, A. Froehlich, N. Adluru, B. Wright, C. Ravichandran, P.T. Fletcher, E.D. Bigler, A.L. Alexander, J.E. Lainhart. “Atypical diffusion tensor hemispheric asymmetry in autism,” In Autism Research, Vol. 3, No. 6, pp. 350--358. 2010.
DOI: 10.1002/aur.162
PubMed ID: 21182212

W. Liu, P. Zhu, J.S. Anderson, D. Yurgelun-Todd, P.T. Fletcher. “Spatial Regularization of Functional Connectivity Using High-Dimensional Markov Random Fields,” In Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010), Vol. 14, pp. 363--370. 2010.
PubMed ID: 20879336

N.P. Singh, P.T. Fletcher, J.S. Preston, L. Ha, R. King, J.S. Marron, M. Wiener, S. Joshi. “Multivariate Statistical Analysis of Deformation Momenta Relating Anatomical Shape to Neuropsychological Measures,” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Lecture Notes in Computer Science (LCNS), Vol. 6363/2010, pp. 529-537. 2010.
DOI: 10.1007/978-3-642-15711-0_66
PubMed ID: 20879441

2009

M. Datar, J. Cates, P.T. Fletcher, S. Gouttard, G. Gerig, R.T. Whitaker. “Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging,” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science LNCS, Vol. 5762, pp. 167--174. 2009.
DOI: 10.1007/978-3-642-04271-3_21
PubMed ID: 20426109

P.T. Fletcher PT, S. Venkatasubramanian, S. Joshi. “The geometric median on Riemannian manifolds with application to robust atlas estimation,” In Neuroimage, Vol. 45, No. 1, pp. S143--S152. March, 2009.
PubMed ID: 19056498

P.T. Fletcher, J. Moeller, J.M. Phillips, S. Venkatasubramanian. “Computing Hulls In Positive Definite Space,” In In Proceedings of the 19th Fall Workshop on Computational Geometry, November, 2009.

C. Goodlett, P.T. Fletcher, J.H. Gilmore, G. Gerig. “Group analysis of DTI fiber tract statistics with application to neurodevelopment,” In Neuroimage, Vol. 45, No. 1 (suppl 1), pp. S133--S142. 2009.
PubMed ID: 19059345

C. Goodlett, P.T. Fletcher, J.H. Gilmore, G. Gerig. “Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment,” In NeuroImage, Vol. 45, pp. S133--S142. December, 2009.
DOI: 10.1016/j.neuroimage.2008.10.060
PubMed ID: 19059345
PubMed Central ID: PMC2727755

J. Hinkle, P.T. Fletcher, Brian Wang, B. Salter, S. Joshi. “4D MAP image reconstruction incorporating organ motion,” In Information Processing in Medical Imaging, Lecture Notes in Computer Science LNCS, Vol. 5636, pp. 676--687. 2009.
PubMed ID: 19694303