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Items: 1 to 20 of 56

1.

Different partial volume correction methods lead to different conclusions: An (18)F-FDG-PET study of aging.

Greve DN, Salat DH, Bowen SL, Izquierdo-Garcia D, Schultz AP, Catana C, Becker JA, Svarer C, Knudsen GM, Sperling RA, Johnson KA.

Neuroimage. 2016 May 15;132:334-343. doi: 10.1016/j.neuroimage.2016.02.042. Epub 2016 Feb 23.

2.

A novel relational regularization feature selection method for joint regression and classification in AD diagnosis.

Zhu X, Suk HI, Wang L, Lee SW, Shen D; Alzheimer’s Disease Neuroimaging Initiative.

Med Image Anal. 2017 May;38:205-214. doi: 10.1016/j.media.2015.10.008. Epub 2015 Nov 10.

3.

Development and aging of cortical thickness correspond to genetic organization patterns.

Fjell AM, Grydeland H, Krogsrud SK, Amlien I, Rohani DA, Ferschmann L, Storsve AB, Tamnes CK, Sala-Llonch R, Due-Tønnessen P, Bjørnerud A, Sølsnes AE, Håberg AK, Skranes J, Bartsch H, Chen CH, Thompson WK, Panizzon MS, Kremen WS, Dale AM, Walhovd KB.

Proc Natl Acad Sci U S A. 2015 Dec 15;112(50):15462-7. doi: 10.1073/pnas.1508831112. Epub 2015 Nov 2.

4.

Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment.

Zu C, Jie B, Liu M, Chen S, Shen D, Zhang D; Alzheimer’s Disease Neuroimaging Initiative.

Brain Imaging Behav. 2016 Dec;10(4):1148-1159.

PMID:
26572145
5.

Patterns of effective connectivity during memory encoding and retrieval differ between patients with mild cognitive impairment and healthy older adults.

Hampstead BM, Khoshnoodi M, Yan W, Deshpande G, Sathian K.

Neuroimage. 2016 Jan 1;124(Pt A):997-1008. doi: 10.1016/j.neuroimage.2015.10.002. Epub 2015 Oct 13.

6.

Discriminative multi-task feature selection for multi-modality classification of Alzheimer's disease.

Ye T, Zu C, Jie B, Shen D, Zhang D; Alzheimer’s Disease Neuroimaging Initiative.

Brain Imaging Behav. 2016 Sep;10(3):739-49. doi: 10.1007/s11682-015-9437-x.

7.

Construction and comparative evaluation of different activity detection methods in brain FDG-PET.

Buchholz HG, Wenzel F, Gartenschläger M, Thiele F, Young S, Reuss S, Schreckenberger M.

Biomed Eng Online. 2015 Aug 18;14:79. doi: 10.1186/s12938-015-0073-x.

8.

Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2.

Jack CR Jr, Barnes J, Bernstein MA, Borowski BJ, Brewer J, Clegg S, Dale AM, Carmichael O, Ching C, DeCarli C, Desikan RS, Fennema-Notestine C, Fjell AM, Fletcher E, Fox NC, Gunter J, Gutman BA, Holland D, Hua X, Insel P, Kantarci K, Killiany RJ, Krueger G, Leung KK, Mackin S, Maillard P, Malone IB, Mattsson N, McEvoy L, Modat M, Mueller S, Nosheny R, Ourselin S, Schuff N, Senjem ML, Simonson A, Thompson PM, Rettmann D, Vemuri P, Walhovd K, Zhao Y, Zuk S, Weiner M.

Alzheimers Dement. 2015 Jul;11(7):740-56. doi: 10.1016/j.jalz.2015.05.002.

9.

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception.

Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Green RC, Harvey D, Jack CR, Jagust W, Luthman J, Morris JC, Petersen RC, Saykin AJ, Shaw L, Shen L, Schwarz A, Toga AW, Trojanowski JQ; Alzheimer's Disease Neuroimaging Initiative.

Alzheimers Dement. 2015 Jun;11(6):e1-120. doi: 10.1016/j.jalz.2014.11.001. Review.

10.

Spatial pattern separation differences in older adult carriers and non-carriers for the apolipoprotein E epsilon 4 allele.

Sheppard DP, Graves LV, Holden HM, Delano-Wood L, Bondi MW, Gilbert PE.

Neurobiol Learn Mem. 2016 Mar;129:113-9. doi: 10.1016/j.nlm.2015.04.011. Epub 2015 May 5.

11.

Network-Guided Sparse Learning for Predicting Cognitive Outcomes from MRI Measures.

Yan J, Huang H, Risacher SL, Kim S, Inlow M, Moore JH, Saykin AJ, Shen L.

Multimodal Brain Image Anal (2013). 2013;8159:202-210.

12.

JOINT IDENTIFICATION OF IMAGING AND PROTEOMICS BIOMARKERS OF ALZHEIMER'S DISEASE USING NETWORK-GUIDED SPARSE LEARNING.

Yan J, Huang H, Kim S, Moore J, Saykin A, Shen L.

Proc IEEE Int Symp Biomed Imaging. 2014 May;2014:665-668.

13.

Sparse Multi-Task Regression and Feature Selection to Identify Brain Imaging Predictors for Memory Performance.

Wang H, Nie F, Huang H, Risacher S, Ding C, Saykin AJ, Shen L; ADNI.

Proc IEEE Int Conf Comput Vis. 2011:557-562.

14.

Manifold regularized multitask feature learning for multimodality disease classification.

Jie B, Zhang D, Cheng B, Shen D; Alzheimer's Disease Neuroimaging Initiative.

Hum Brain Mapp. 2015 Feb;36(2):489-507. doi: 10.1002/hbm.22642. Epub 2014 Oct 3.

15.

Effects of alcohol consumption on cognition and regional brain volumes among older adults.

Downer B, Jiang Y, Zanjani F, Fardo D.

Am J Alzheimers Dis Other Demen. 2015 Jun;30(4):364-74. doi: 10.1177/1533317514549411. Epub 2014 Sep 7.

16.

How does it STAC up? Revisiting the scaffolding theory of aging and cognition.

Reuter-Lorenz PA, Park DC.

Neuropsychol Rev. 2014 Sep;24(3):355-70. doi: 10.1007/s11065-014-9270-9. Epub 2014 Aug 21. Review.

17.

Retrograde memory for public events in mild cognitive impairment and its relationship to anterograde memory and neuroanatomy.

Smith CN.

Neuropsychology. 2014 Nov;28(6):959-72. doi: 10.1037/neu0000117. Epub 2014 Jul 28.

18.

A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis.

Zhu X, Suk HI, Shen D.

Neuroimage. 2014 Oct 15;100:91-105. doi: 10.1016/j.neuroimage.2014.05.078. Epub 2014 Jun 7.

19.

Structural brain network constrained neuroimaging marker identification for predicting cognitive functions.

De W, Nie F, Huang H, Yan J, Risacher SL, Saykin AJ, Shen L.

Inf Process Med Imaging. 2013;23:536-47.

20.

What is normal in normal aging? Effects of aging, amyloid and Alzheimer's disease on the cerebral cortex and the hippocampus.

Fjell AM, McEvoy L, Holland D, Dale AM, Walhovd KB; Alzheimer's Disease Neuroimaging Initiative.

Prog Neurobiol. 2014 Jun;117:20-40. doi: 10.1016/j.pneurobio.2014.02.004. Epub 2014 Feb 16. Review.

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