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Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression.

Frontiers in neuroscience

Kohannim O, Hibar DP, Stein JL, Jahanshad N, Hua X, Rajagopalan P, Toga AW, Jack CR, Weiner MW, de Zubicaray GI, McMahon KL, Hansell NK, Martin NG, Wright MJ, Thompson PM.
PMID: 22888310
Front Neurosci. 2012 Aug 06;6:115. doi: 10.3389/fnins.2012.00115. eCollection 2012.

We implemented least absolute shrinkage and selection operator (LASSO) regression to evaluate gene effects in genome-wide association studies (GWAS) of brain images, using an MRI-derived temporal lobe volume measure from 729 subjects scanned as part of the Alzheimer's Disease...

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Kohannim O, Hibar DP, Stein JL, et al. Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression. Front Neurosci. 2012;6:115doi: 10.3389/fnins.2012.00115.
Kohannim, O., Hibar, D. P., Stein, J. L., Jahanshad, N., Hua, X., Rajagopalan, P., Toga, A. W., Jack, C. R., Weiner, M. W., de Zubicaray, G. I., McMahon, K. L., Hansell, N. K., Martin, N. G., Wright, M. J., Thompson, P. M. (2012). Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression. Frontiers in neuroscience, 6115. https://doi.org/10.3389/fnins.2012.00115
Kohannim, Omid, et al. "Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression." Frontiers in neuroscience vol. 6 (2012): 115. doi: https://doi.org/10.3389/fnins.2012.00115
Kohannim O, Hibar DP, Stein JL, Jahanshad N, Hua X, Rajagopalan P, Toga AW, Jack CR, Weiner MW, de Zubicaray GI, McMahon KL, Hansell NK, Martin NG, Wright MJ, Thompson PM. Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression. Front Neurosci. 2012 Aug 06;6:115. doi: 10.3389/fnins.2012.00115. eCollection 2012. PMID: 22888310; PMCID: PMC3412288.
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TRACTOGRAPHY DENSITY AND NETWORK MEASURES IN ALZHEIMER'S DISEASE.

Proceedings. IEEE International Symposium on Biomedical Imaging

Prasad G, Nir TM, Toga AW, Thompson PM.
PMID: 25404994
Proc IEEE Int Symp Biomed Imaging. 2013 Apr;2013:692-695. doi: 10.1109/ISBI.2013.6556569.

Brain connectivity declines in Alzheimer's disease (AD), both functionally and structurally. Connectivity maps and networks derived from diffusion-based tractography offer new ways to track disease progression and to understand how AD affects the brain. Here we set out to...

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Prasad G, Nir TM, Toga AW, et al. TRACTOGRAPHY DENSITY AND NETWORK MEASURES IN ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging. 2013;2013:692-695doi: 10.1109/ISBI.2013.6556569.
Prasad, G., Nir, T. M., Toga, A. W., & Thompson, P. M. (2013). TRACTOGRAPHY DENSITY AND NETWORK MEASURES IN ALZHEIMER'S DISEASE. Proceedings. IEEE International Symposium on Biomedical Imaging, 2013692-695. https://doi.org/10.1109/ISBI.2013.6556569
Prasad, Gautam, et al. "TRACTOGRAPHY DENSITY AND NETWORK MEASURES IN ALZHEIMER'S DISEASE." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2013 (2013): 692-695. doi: https://doi.org/10.1109/ISBI.2013.6556569
Prasad G, Nir TM, Toga AW, Thompson PM. TRACTOGRAPHY DENSITY AND NETWORK MEASURES IN ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging. 2013 Apr;2013:692-695. doi: 10.1109/ISBI.2013.6556569. PMID: 25404994; PMCID: PMC4232938.
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Rich club network analysis shows distinct patterns of disruption in frontotemporal dementia and Alzheimer's disease.

Mathematics and visualization

Daianu M, Jahanshad N, Villalon-Reina JE, Mendez MF, Bartzokis G, Jimenez EE, Joshi A, Barsuglia J, Thompson PM.
PMID: 26161050
Math Vis. 2014;2014:13-22. doi: 10.1007/978-3-319-11182-7_2.

Diffusion imaging and brain connectivity analyses can reveal the underlying organizational patterns of the human brain, described as complex networks of densely interlinked regions. Here, we analyzed 1.5-Tesla whole-brain diffusion-weighted images from 64 participants - 15 patients with behavioral...

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Daianu M, Jahanshad N, Villalon-Reina JE, et al. Rich club network analysis shows distinct patterns of disruption in frontotemporal dementia and Alzheimer's disease. Math Vis. 2014;2014:13-22doi: 10.1007/978-3-319-11182-7_2.
Daianu, M., Jahanshad, N., Villalon-Reina, J. E., Mendez, M. F., Bartzokis, G., Jimenez, E. E., Joshi, A., Barsuglia, J., & Thompson, P. M. (2014). Rich club network analysis shows distinct patterns of disruption in frontotemporal dementia and Alzheimer's disease. Mathematics and visualization, 201413-22. https://doi.org/10.1007/978-3-319-11182-7_2
Daianu, Madelaine, et al. "Rich club network analysis shows distinct patterns of disruption in frontotemporal dementia and Alzheimer's disease." Mathematics and visualization vol. 2014 (2014): 13-22. doi: https://doi.org/10.1007/978-3-319-11182-7_2
Daianu M, Jahanshad N, Villalon-Reina JE, Mendez MF, Bartzokis G, Jimenez EE, Joshi A, Barsuglia J, Thompson PM. Rich club network analysis shows distinct patterns of disruption in frontotemporal dementia and Alzheimer's disease. Math Vis. 2014;2014:13-22. doi: 10.1007/978-3-319-11182-7_2. PMID: 26161050; PMCID: PMC4492471.
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The C677T variant in .

Biological psychiatry. Cognitive neuroscience and neuroimaging

Roussotte FF, Hua X, Narr KL, Small GW, Thompson PM.
PMID: 28435933
Biol Psychiatry Cogn Neurosci Neuroimaging. 2017 Apr;2(3):280-288. doi: 10.1016/j.bpsc.2016.09.005.

INTRODUCTION: The C677T functional variant in the methylene-tetrahydrofolate reductase (METHODS AND MATERIALS: Here, 3D magnetic resonance imaging data was analyzed from 738 individuals (age: 75.5 ± 6.8 years; 438 men/300 women) including 173 Alzheimer's patients, 359 subjects with mild...

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Roussotte FF, Hua X, Narr KL, et al. The C677T variant in . Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2(3):280-288doi: 10.1016/j.bpsc.2016.09.005.
Roussotte, F. F., Hua, X., Narr, K. L., Small, G. W., Thompson, P. M. (2017). The C677T variant in . Biological psychiatry. Cognitive neuroscience and neuroimaging, 2(3), 280-288. https://doi.org/10.1016/j.bpsc.2016.09.005
Roussotte, Florence F, et al. "The C677T variant in ." Biological psychiatry. Cognitive neuroscience and neuroimaging vol. 2,3 (2017): 280-288. doi: https://doi.org/10.1016/j.bpsc.2016.09.005
Roussotte FF, Hua X, Narr KL, Small GW, Thompson PM. The C677T variant in . Biol Psychiatry Cogn Neurosci Neuroimaging. 2017 Apr;2(3):280-288. doi: 10.1016/j.bpsc.2016.09.005. PMID: 28435933; PMCID: PMC5395287.
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A Novel Protocol for Directed Differentiation of C9orf72-Associated Human Induced Pluripotent Stem Cells Into Contractile Skeletal Myotubes.

Stem cells translational medicine

Swartz EW, Baek J, Pribadi M, Wojta KJ, Almeida S, Karydas A, Gao FB, Miller BL, Coppola G.
PMID: 27369896
Stem Cells Transl Med. 2016 Nov;5(11):1461-1472. doi: 10.5966/sctm.2015-0340. Epub 2016 Jul 01.

: Induced pluripotent stem cells (iPSCs) offer an unlimited resource of cells to be used for the study of underlying molecular biology of disease, therapeutic drug screening, and transplant-based regenerative medicine. However, methods for the directed differentiation of skeletal...

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Swartz EW, Baek J, Pribadi M, et al. A Novel Protocol for Directed Differentiation of C9orf72-Associated Human Induced Pluripotent Stem Cells Into Contractile Skeletal Myotubes. Stem Cells Transl Med. 2016;5(11):1461-1472doi: 10.5966/sctm.2015-0340.
Swartz, E. W., Baek, J., Pribadi, M., Wojta, K. J., Almeida, S., Karydas, A., Gao, F. B., Miller, B. L., & Coppola, G. (2016). A Novel Protocol for Directed Differentiation of C9orf72-Associated Human Induced Pluripotent Stem Cells Into Contractile Skeletal Myotubes. Stem cells translational medicine, 5(11), 1461-1472. https://doi.org/10.5966/sctm.2015-0340
Swartz, Elliot W, et al. "A Novel Protocol for Directed Differentiation of C9orf72-Associated Human Induced Pluripotent Stem Cells Into Contractile Skeletal Myotubes." Stem cells translational medicine vol. 5,11 (2016): 1461-1472. doi: https://doi.org/10.5966/sctm.2015-0340
Swartz EW, Baek J, Pribadi M, Wojta KJ, Almeida S, Karydas A, Gao FB, Miller BL, Coppola G. A Novel Protocol for Directed Differentiation of C9orf72-Associated Human Induced Pluripotent Stem Cells Into Contractile Skeletal Myotubes. Stem Cells Transl Med. 2016 Nov;5(11):1461-1472. doi: 10.5966/sctm.2015-0340. Epub 2016 Jul 01. PMID: 27369896; PMCID: PMC5070503.
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FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE.

Proceedings. IEEE International Symposium on Biomedical Imaging

Prasad G, Joshi SH, Nir TM, Toga AW, Thompson PM.
PMID: 25067993
Proc IEEE Int Symp Biomed Imaging. 2013;2013:258-261. doi: 10.1109/ISBI.2013.6556461.

We present a new flow-based method for modeling brain structural connectivity. The method uses a modified maximum-flow algorithm that is robust to noise in the diffusion data and guided by biologically viable pathways and structure of the brain. A...

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Prasad G, Joshi SH, Nir TM, et al. FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging. 2013;2013:258-261doi: 10.1109/ISBI.2013.6556461.
Prasad, G., Joshi, S. H., Nir, T. M., Toga, A. W., & Thompson, P. M. (2013). FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE. Proceedings. IEEE International Symposium on Biomedical Imaging, 2013258-261. https://doi.org/10.1109/ISBI.2013.6556461
Prasad, Gautam, et al. "FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2013 (2013): 258-261. doi: https://doi.org/10.1109/ISBI.2013.6556461
Prasad G, Joshi SH, Nir TM, Toga AW, Thompson PM. FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging. 2013;2013:258-261. doi: 10.1109/ISBI.2013.6556461. PMID: 25067993; PMCID: PMC4109645.
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Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease.

Frontiers in aging neuroscience

Zhan L, Zhou J, Wang Y, Jin Y, Jahanshad N, Prasad G, Nir TM, Leonardo CD, Ye J, Thompson PM, For The Alzheimer's Disease Neuroimaging Initiative.
PMID: 25926791
Front Aging Neurosci. 2015 Apr 14;7:48. doi: 10.3389/fnagi.2015.00048. eCollection 2015.

Alzheimer's disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods...

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Zhan L, Zhou J, Wang Y, et al. Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease. Front Aging Neurosci. 2015;7:48doi: 10.3389/fnagi.2015.00048.
Zhan, L., Zhou, J., Wang, Y., Jin, Y., Jahanshad, N., Prasad, G., Nir, T. M., Leonardo, C. D., Ye, J., Thompson, P. M., & For The Alzheimer's Disease Neuroimaging Initiative. (2015). Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease. Frontiers in aging neuroscience, 748. https://doi.org/10.3389/fnagi.2015.00048
Zhan, Liang, et al. "Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease." Frontiers in aging neuroscience vol. 7 (2015): 48. doi: https://doi.org/10.3389/fnagi.2015.00048
Zhan L, Zhou J, Wang Y, Jin Y, Jahanshad N, Prasad G, Nir TM, Leonardo CD, Ye J, Thompson PM, For The Alzheimer's Disease Neuroimaging Initiative. Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease. Front Aging Neurosci. 2015 Apr 14;7:48. doi: 10.3389/fnagi.2015.00048. eCollection 2015. PMID: 25926791; PMCID: PMC4396191.
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OPTIMIZING BRAIN CONNECTIVITY NETWORKS FOR DISEASE CLASSIFICATION USING EPIC.

Proceedings. IEEE International Symposium on Biomedical Imaging

Prasad G, Joshi SH, Thompson PM.
PMID: 25405000
Proc IEEE Int Symp Biomed Imaging. 2014 May;2014:834-837. doi: 10.1109/ISBI.2014.6868000.

We propose a method to adaptively select an optimal cortical segmentation for brain connectivity analysis that maximizes feature-based disease classification performance. In standard structural connectivity analysis, the cortex is typically subdivided (parcellated) into

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Prasad G, Joshi SH, Thompson PM. OPTIMIZING BRAIN CONNECTIVITY NETWORKS FOR DISEASE CLASSIFICATION USING EPIC. Proc IEEE Int Symp Biomed Imaging. 2014;2014:834-837doi: 10.1109/ISBI.2014.6868000.
Prasad, G., Joshi, S. H., & Thompson, P. M. (2014). OPTIMIZING BRAIN CONNECTIVITY NETWORKS FOR DISEASE CLASSIFICATION USING EPIC. Proceedings. IEEE International Symposium on Biomedical Imaging, 2014834-837. https://doi.org/10.1109/ISBI.2014.6868000
Prasad, Gautam, et al. "OPTIMIZING BRAIN CONNECTIVITY NETWORKS FOR DISEASE CLASSIFICATION USING EPIC." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2014 (2014): 834-837. doi: https://doi.org/10.1109/ISBI.2014.6868000
Prasad G, Joshi SH, Thompson PM. OPTIMIZING BRAIN CONNECTIVITY NETWORKS FOR DISEASE CLASSIFICATION USING EPIC. Proc IEEE Int Symp Biomed Imaging. 2014 May;2014:834-837. doi: 10.1109/ISBI.2014.6868000. PMID: 25405000; PMCID: PMC4232940.
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A single nucleotide polymorphism associated with reduced alcohol intake in the RASGRF2 gene predicts larger cortical volumes but faster longitudinal ventricular expansion in the elderly.

Frontiers in aging neuroscience

Roussotte FF, Gutman BA, Hibar DP, Jahanshad N, Madsen SK, Jack CR, Weiner MW, Thompson PM.
PMID: 24409144
Front Aging Neurosci. 2013 Dec 19;5:93. doi: 10.3389/fnagi.2013.00093. eCollection 2013.

A recent genome-wide association meta-analysis showed a suggestive association between alcohol intake in humans and a common single nucleotide polymorphism in the ras-specific guanine nucleotide releasing factor 2 gene. Here, we tested whether this variant - associated with lower...

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Roussotte FF, Gutman BA, Hibar DP, et al. A single nucleotide polymorphism associated with reduced alcohol intake in the RASGRF2 gene predicts larger cortical volumes but faster longitudinal ventricular expansion in the elderly. Front Aging Neurosci. 2013;5:93doi: 10.3389/fnagi.2013.00093.
Roussotte, F. F., Gutman, B. A., Hibar, D. P., Jahanshad, N., Madsen, S. K., Jack, C. R., Weiner, M. W., Thompson, P. M. (2013). A single nucleotide polymorphism associated with reduced alcohol intake in the RASGRF2 gene predicts larger cortical volumes but faster longitudinal ventricular expansion in the elderly. Frontiers in aging neuroscience, 593. https://doi.org/10.3389/fnagi.2013.00093
Roussotte, Florence F, et al. "A single nucleotide polymorphism associated with reduced alcohol intake in the RASGRF2 gene predicts larger cortical volumes but faster longitudinal ventricular expansion in the elderly." Frontiers in aging neuroscience vol. 5 (2013): 93. doi: https://doi.org/10.3389/fnagi.2013.00093
Roussotte FF, Gutman BA, Hibar DP, Jahanshad N, Madsen SK, Jack CR, Weiner MW, Thompson PM. A single nucleotide polymorphism associated with reduced alcohol intake in the RASGRF2 gene predicts larger cortical volumes but faster longitudinal ventricular expansion in the elderly. Front Aging Neurosci. 2013 Dec 19;5:93. doi: 10.3389/fnagi.2013.00093. eCollection 2013. PMID: 24409144; PMCID: PMC3867747.
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Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition.

Frontiers in neuroscience

Zhan L, Liu Y, Wang Y, Zhou J, Jahanshad N, Ye J, Thompson PM.
PMID: 26257601
Front Neurosci. 2015 Jul 24;9:257. doi: 10.3389/fnins.2015.00257. eCollection 2015.

Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages...

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Zhan L, Liu Y, Wang Y, et al. Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition. Front Neurosci. 2015;9:257doi: 10.3389/fnins.2015.00257.
Zhan, L., Liu, Y., Wang, Y., Zhou, J., Jahanshad, N., Ye, J., Thompson, P. M. (2015). Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition. Frontiers in neuroscience, 9257. https://doi.org/10.3389/fnins.2015.00257
Zhan, Liang, et al. "Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition." Frontiers in neuroscience vol. 9 (2015): 257. doi: https://doi.org/10.3389/fnins.2015.00257
Zhan L, Liu Y, Wang Y, Zhou J, Jahanshad N, Ye J, Thompson PM. Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition. Front Neurosci. 2015 Jul 24;9:257. doi: 10.3389/fnins.2015.00257. eCollection 2015. PMID: 26257601; PMCID: PMC4513242.
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Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease.

Computational diffusion MRI : MICCAI Workshop, Boston, MA, USA, September 2014. CDMRI (Workshop) (6th : 2014 : Boston, Mass.)

Daianu M, Jahanshad N, Nir TM, Leonardo CD, Jack CR, Weiner MW, Bernstein MA, Thompson PM.
PMID: 26640830
Comput Diffus MRI (2014). 2014;2014:55-64. doi: 10.1007/978-3-319-11182-7_6. Epub 2014 Dec 12.

Measures of network topology and connectivity aid the understanding of network breakdown as the brain degenerates in Alzheimer's disease (AD). We analyzed 3-Tesla diffusion-weighted images from 202 patients scanned by the Alzheimer's Disease Neuroimaging Initiative - 50 healthy controls,...

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Daianu M, Jahanshad N, Nir TM, et al. Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease. Comput Diffus MRI (2014). 2014;2014:55-64doi: 10.1007/978-3-319-11182-7_6.
Daianu, M., Jahanshad, N., Nir, T. M., Leonardo, C. D., Jack, C. R., Weiner, M. W., Bernstein, M. A., & Thompson, P. M. (2014). Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease. Computational diffusion MRI : MICCAI Workshop, Boston, MA, USA, September 2014. CDMRI (Workshop) (6th : 2014 : Boston, Mass.), 201455-64. https://doi.org/10.1007/978-3-319-11182-7_6
Daianu, Madelaine, et al. "Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease." Computational diffusion MRI : MICCAI Workshop, Boston, MA, USA, September 2014. CDMRI (Workshop) (6th : 2014 : Boston, Mass.) vol. 2014 (2014): 55-64. doi: https://doi.org/10.1007/978-3-319-11182-7_6
Daianu M, Jahanshad N, Nir TM, Leonardo CD, Jack CR, Weiner MW, Bernstein MA, Thompson PM. Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease. Comput Diffus MRI (2014). 2014;2014:55-64. doi: 10.1007/978-3-319-11182-7_6. Epub 2014 Dec 12. PMID: 26640830; PMCID: PMC4669194.
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Cortical thickness and semantic fluency in Alzheimer's disease and mild cognitive impairment.

American journal of Alzheimer's disease (Columbia, Mo.)

Eastman JA, Hwang KS, Lazaris A, Chow N, Ramirez L, Babakchanian S, Woo E, Thompson PM, Apostolova LG.
PMID: 25346870
Am J Alzheimers Dis (Columbia). 2013;1(2):81-92. doi: 10.7726/ajad.2013.1006.

The hallmark of Alzheimer's disease (AD) is declarative memory loss, but deficits in semantic fluency are also observed. We assessed how semantic fluency relates to cortical atrophy to identify specific regions that play a role in the loss of...

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Eastman JA, Hwang KS, Lazaris A, et al. Cortical thickness and semantic fluency in Alzheimer's disease and mild cognitive impairment. Am J Alzheimers Dis (Columbia). 2013;1(2):81-92doi: 10.7726/ajad.2013.1006.
Eastman, J. A., Hwang, K. S., Lazaris, A., Chow, N., Ramirez, L., Babakchanian, S., Woo, E., Thompson, P. M., & Apostolova, L. G. (2013). Cortical thickness and semantic fluency in Alzheimer's disease and mild cognitive impairment. American journal of Alzheimer's disease (Columbia, Mo.), 1(2), 81-92. https://doi.org/10.7726/ajad.2013.1006
Eastman, Jennifer A, et al. "Cortical thickness and semantic fluency in Alzheimer's disease and mild cognitive impairment." American journal of Alzheimer's disease (Columbia, Mo.) vol. 1,2 (2013): 81-92. doi: https://doi.org/10.7726/ajad.2013.1006
Eastman JA, Hwang KS, Lazaris A, Chow N, Ramirez L, Babakchanian S, Woo E, Thompson PM, Apostolova LG. Cortical thickness and semantic fluency in Alzheimer's disease and mild cognitive impairment. Am J Alzheimers Dis (Columbia). 2013;1(2):81-92. doi: 10.7726/ajad.2013.1006. PMID: 25346870; PMCID: PMC4207059.
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