Predicting brain activity using a Bayesian spatial model

Statistical Methods in Medical Research

Volumn 22, Issue 4, 2013, Pages 382-397

  Derado, Gordana ^a   Bowman, F Dubois ^a   Zhang, Lijun ^a  

^a EMORY UNIVERSITY   (United States)

Author keywords

Alzheimer's disease; Bayesian spatial modeling; neuroimaging; prediction

Indexed keywords

ALZHEIMER DISEASE; BAYES THEOREM; CONFERENCE PAPER; CONTROLLED STUDY; DISEASE COURSE; ELECTROENCEPHALOGRAPHY; FOLLOW UP; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL NEUROIMAGING; HUMAN; POSITRON EMISSION TOMOGRAPHY;

ALZHEIMER'S DISEASE; BAYESIAN SPATIAL MODELING; NEUROIMAGING; PREDICTION;

ALGORITHMS; ALZHEIMER DISEASE; BAYES THEOREM; BIOSTATISTICS; BRAIN; CASE-CONTROL STUDIES; COMPUTER SIMULATION; DISEASE PROGRESSION; FLUORODEOXYGLUCOSE F18; FUNCTIONAL NEUROIMAGING; HUMANS; MAGNETIC RESONANCE IMAGING; MILD COGNITIVE IMPAIRMENT; MODELS, NEUROLOGICAL; NORMAL DISTRIBUTION; POSITRON-EMISSION TOMOGRAPHY; RADIOPHARMACEUTICALS;

EID: 84881422357     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280212448972     Document Type: Conference Paper

References (15)

1. Guo Y, Bowman FD, Kilts C. Predicting the brain response to treatment using a Bayesian hierarchical model with application to a study of schizophrenia. Hum Brain Mapp. 2008 ; 29: 1092-1109
2. Stonnington CM, Chu C, Klöppel S, et al. Predicting clinical scores from magnetic resonance scans in Alzheimer's disease. NeuroImage. 2010 ; 51 (4). 1405-1413
3. Marquand A, Howard M, Brammer M, et al. Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. NeuroImage. 2010 ; 49 (3). 2178-2189
4. Bowman FD, Caffo B, Bassett SS, et al. A Bayesian hierarchical framework for spatial modeling of fMRI data. NeuroImage. 2008 ; 39: 146-156 (Pubitemid 350103867)
5. Zhang L, Agravat S, Derado G, et al. BSMac: a MATLAB toolbox implementing a bayesian spatial model for brain activation and connectivity. J Neurosci Methods. 2012 ; 204: 133-143
6. Mueller SG, Weiner MW, Thal LJ, et al. The Alzheimer's disease neuroimaging initiative. Neuroimaging Clin N Am. 2005 ;: 15-15
7. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labelling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single subject brain. NeuroImage. 2002 ; 15: 273-289 (Pubitemid 34852226)
8. Gelfand AE, Vounatsou P. Proper multivariate conditional autoregressive models for spatial data analysis. Biostatistics. 2003 ; 4 (1). 11-25
9. Brodmann K. Vergleichende Lokalisationlehre der Grosshirnrinde in ihren Prinzipien Dargestellt auf Grund des Zellenbaues, translated by Garey LJ as Brodmann's Localisation in the cerebral cortex. London: Smith-Gordon, 1994 [original publication date 1909].
10. Bassett S, Yousem D, Christinzio C, et al. Familiar risk for Alzheimer's disease alters fMRI activation patterns. Brain. 2006 ; 129: 1229-1239
11. West M, Harrison J Applied Bayesian forecasting and time series analysis. Boca Raton, FL: Chapman and Hall/CRC ; 1989 :
12. Friston KJ, Holmes AP, Worsley KJ, et al. Statistical parametric maps in functional imaging: A general linear approach. Hum Brain Mapp. 1995 ; 2 (4). 189-210 (Pubitemid 124008664)
13. De Meyer G, Shapiro F, Vanderstichele H, et al. Diagnosis-independent Alzheimer disease biomarker signature in cognitively normal elderly people. Arch Neurol. 2010 ; 67 (8). 949-956
14. Anoop A, Singh PS, Jacob RS, et al. CSF biomarkers for Alzheimer's disease diagnosis. Int J Alzheimer's Dis. 2010 ; 2010: 606802-606802
15. Buerger K, Zinkowski R, Teipel SJ, et al. Differential diagnosis of Alzheimer disease with cerebrospinal fluid levels of tau protein phosphorylated at threonine 231. Arch Neurol. 2002 ; 59 (8). 1267-1272 (Pubitemid 34856379)