PMID- 18602482
OWN - NLM
STAT- MEDLINE
DCOM- 20081229
LR  - 20211216
IS  - 1095-9572 (Electronic)
IS  - 1053-8119 (Print)
IS  - 1053-8119 (Linking)
VI  - 42
IP  - 4
DP  - 2008 Oct 1
TI  - Hybrid ICA-Bayesian network approach reveals distinct effective connectivity 
      differences in schizophrenia.
PG  - 1560-8
LID - 10.1016/j.neuroimage.2008.05.065 [doi]
AB  - We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge, J., Lane, 
      T., Link, H., Qiu, S., Clark, V.P., 2007. Discrete dynamic Bayesian network 
      analysis of fMRI data. Hum Brain Mapp.) to determine differences in brain regions 
      between patients with schizophrenia and healthy controls on a measure of 
      effective connectivity, termed the approximate conditional likelihood score (ACL) 
      (Burge, J., Lane, T., 2005. Learning Class-Discriminative Dynamic Bayesian 
      Networks. Proceedings of the International Conference on Machine Learning, Bonn, 
      Germany, pp. 97-104.). The ACL score represents a class-discriminative measure of 
      effective connectivity by measuring the relative likelihood of the correlation 
      between brain regions in one group versus another. The algorithm is capable of 
      finding non-linear relationships between brain regions because it uses discrete 
      rather than continuous values and attempts to model temporal relationships with a 
      first-order Markov and stationary assumption constraint (Papoulis, A., 1991. 
      Probability, random variables, and stochastic processes. McGraw-Hill, New York.). 
      Since Bayesian networks are overly sensitive to noisy data, we introduced an 
      independent component analysis (ICA) filtering approach that attempted to reduce 
      the noise found in fMRI data by unmixing the raw datasets into a set of 
      independent spatial component maps. Components that represented noise were 
      removed and the remaining components reconstructed into the dimensions of the 
      original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients 
      with schizophrenia and 35 matched healthy controls using an ICA filtered and 
      unfiltered approach. We determined that filtering the data significantly improved 
      the magnitude of the ACL score. Patients showed the greatest ACL scores in 
      several regions, most markedly the cerebellar vermis and hemispheres. Our 
      findings suggest that schizophrenia patients exhibit weaker connectivity than 
      healthy controls in multiple regions, including bilateral temporal, frontal, and 
      cerebellar regions during an auditory paradigm.
FAU - Kim, D
AU  - Kim D
AD  - The Mind Research Network, Albuquerque, NM 87131, USA. dkim@mrn.org
FAU - Burge, J
AU  - Burge J
FAU - Lane, T
AU  - Lane T
FAU - Pearlson, G D
AU  - Pearlson GD
FAU - Kiehl, K A
AU  - Kiehl KA
FAU - Calhoun, V D
AU  - Calhoun VD
LA  - eng
GR  - 1R01MH076282-01/MH/NIMH NIH HHS/United States
GR  - R01 EB000840-03/EB/NIBIB NIH HHS/United States
GR  - R01 MH085010/MH/NIMH NIH HHS/United States
GR  - R01 EB000840-06/EB/NIBIB NIH HHS/United States
GR  - R01 MH076282/MH/NIMH NIH HHS/United States
GR  - R01 EB000840/EB/NIBIB NIH HHS/United States
GR  - 1R01EB 000840/EB/NIBIB NIH HHS/United States
GR  - R01 EB000840-05/EB/NIBIB NIH HHS/United States
GR  - R01 EB000840-07/EB/NIBIB NIH HHS/United States
GR  - R01 EB000840-04/EB/NIBIB NIH HHS/United States
GR  - R01 EB020407/EB/NIBIB NIH HHS/United States
GR  - R01 EB000840-02/EB/NIBIB NIH HHS/United States
GR  - R01 MH076282-01/MH/NIMH NIH HHS/United States
GR  - R01 EB000840-01/EB/NIBIB NIH HHS/United States
GR  - R01 EB006841/EB/NIBIB NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20080617
PL  - United States
TA  - Neuroimage
JT  - NeuroImage
JID - 9215515
SB  - IM
MH  - Adolescent
MH  - Adult
MH  - Artificial Intelligence
MH  - Bayes Theorem
MH  - Brain/*physiopathology
MH  - Brain Mapping/*methods
MH  - Female
MH  - Humans
MH  - Male
MH  - Middle Aged
MH  - Neural Pathways/*physiopathology
MH  - Pattern Recognition, Automated/*methods
MH  - Principal Component Analysis
MH  - Schizophrenia/*physiopathology
MH  - Young Adult
PMC - PMC2566775
MID - NIHMS70469
EDAT- 2008/07/08 09:00
MHDA- 2008/12/30 09:00
PMCR- 2009/10/01
CRDT- 2008/07/08 09:00
PHST- 2008/03/26 00:00 [received]
PHST- 2008/05/13 00:00 [revised]
PHST- 2008/05/31 00:00 [accepted]
PHST- 2008/07/08 09:00 [pubmed]
PHST- 2008/12/30 09:00 [medline]
PHST- 2008/07/08 09:00 [entrez]
PHST- 2009/10/01 00:00 [pmc-release]
AID - S1053-8119(08)00718-0 [pii]
AID - 10.1016/j.neuroimage.2008.05.065 [doi]
PST - ppublish
SO  - Neuroimage. 2008 Oct 1;42(4):1560-8. doi: 10.1016/j.neuroimage.2008.05.065. Epub 
      2008 Jun 17.