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A selective review of multimodal fusion methods in schizophrenia.

Sui J, Yu Q, He H, Pearlson GD, Calhoun VD - Front Hum Neurosci (2012)

Bottom Line: Though strong evidence for functional, structural, and genetic abnormalities associated with this disease exists, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009), and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a).It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a).We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research question.

View Article: PubMed Central - PubMed

Affiliation: The Mind Research Network Albuquerque, NM, USA.

ABSTRACT
Schizophrenia (SZ) is one of the most cryptic and costly mental disorders in terms of human suffering and societal expenditure (van Os and Kapur, 2009). Though strong evidence for functional, structural, and genetic abnormalities associated with this disease exists, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009), and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a). It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a). It is becoming increasingly clear that multimodal fusion, a technique which takes advantage of the fact that each modality provides a limited view of the brain/gene and may uncover hidden relationships, is an important tool to help unravel the black box of schizophrenia. In this review paper, we survey a number of multimodal fusion applications which enable us to study the schizophrenia macro-connectome, including brain functional, structural, and genetic aspects and may help us understand the disorder in a more comprehensive and integrated manner. We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research question.

No MeSH data available.


Related in: MedlinePlus

Functional magnetic resonance imaging/EEG/sMRI fusion by mCCA from Correa et al. (2010a): a set of associated components estimated by mCCA that showed significantly different loading for schizophrenia patients versus controls, as well as the t-test comparison between three-way and two-way fusion.
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Figure 2: Functional magnetic resonance imaging/EEG/sMRI fusion by mCCA from Correa et al. (2010a): a set of associated components estimated by mCCA that showed significantly different loading for schizophrenia patients versus controls, as well as the t-test comparison between three-way and two-way fusion.

Mentions: Correa et al. (2010b) also tried to differentiate schizophrenia from controls by applying multimodal canonical correlation analysis (mCCA) to fMRI–EEG data during performance of an auditory oddball (AOD) task (Kiehl and Liddle, 2001). Significant group differences were found in the bilateral temporal lobe/middle anterior cingulate region in fMRI, associated with the N2 and P3 peak in EEG. Multimodal CCA allows a different mixing matrix for each modality and is able to find a transformed coordinate system that maximizes inter-subject covariation across two or more data sets (Li et al., 2009). As shown in Figure 2, a set of linked components derived from a three-way fusion of sMRI, fMRI, and EEG were detected (Correa et al., 2010a), which significantly discriminate schizophrenia patients from controls. On examining the inter-subject modulation in conjunction with the spatial and temporal components, the results imply that patients with schizophrenia have less functional activity and less GM in the motor and temporal areas and also in part of the ERP N2/P3 complex. The comparison of t-tests for three modality (fMRI, sMRI, and EEG) versus two modality (fMRI and sMRI) analyses for this set of components is also listed, showing that the three-way analysis was more significant than a two- or one-way analysis, which further validates our motivation for multimodal fusion.


A selective review of multimodal fusion methods in schizophrenia.

Sui J, Yu Q, He H, Pearlson GD, Calhoun VD - Front Hum Neurosci (2012)

Functional magnetic resonance imaging/EEG/sMRI fusion by mCCA from Correa et al. (2010a): a set of associated components estimated by mCCA that showed significantly different loading for schizophrenia patients versus controls, as well as the t-test comparison between three-way and two-way fusion.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3285795&req=5

Figure 2: Functional magnetic resonance imaging/EEG/sMRI fusion by mCCA from Correa et al. (2010a): a set of associated components estimated by mCCA that showed significantly different loading for schizophrenia patients versus controls, as well as the t-test comparison between three-way and two-way fusion.
Mentions: Correa et al. (2010b) also tried to differentiate schizophrenia from controls by applying multimodal canonical correlation analysis (mCCA) to fMRI–EEG data during performance of an auditory oddball (AOD) task (Kiehl and Liddle, 2001). Significant group differences were found in the bilateral temporal lobe/middle anterior cingulate region in fMRI, associated with the N2 and P3 peak in EEG. Multimodal CCA allows a different mixing matrix for each modality and is able to find a transformed coordinate system that maximizes inter-subject covariation across two or more data sets (Li et al., 2009). As shown in Figure 2, a set of linked components derived from a three-way fusion of sMRI, fMRI, and EEG were detected (Correa et al., 2010a), which significantly discriminate schizophrenia patients from controls. On examining the inter-subject modulation in conjunction with the spatial and temporal components, the results imply that patients with schizophrenia have less functional activity and less GM in the motor and temporal areas and also in part of the ERP N2/P3 complex. The comparison of t-tests for three modality (fMRI, sMRI, and EEG) versus two modality (fMRI and sMRI) analyses for this set of components is also listed, showing that the three-way analysis was more significant than a two- or one-way analysis, which further validates our motivation for multimodal fusion.

Bottom Line: Though strong evidence for functional, structural, and genetic abnormalities associated with this disease exists, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009), and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a).It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a).We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research question.

View Article: PubMed Central - PubMed

Affiliation: The Mind Research Network Albuquerque, NM, USA.

ABSTRACT
Schizophrenia (SZ) is one of the most cryptic and costly mental disorders in terms of human suffering and societal expenditure (van Os and Kapur, 2009). Though strong evidence for functional, structural, and genetic abnormalities associated with this disease exists, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009), and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a). It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a). It is becoming increasingly clear that multimodal fusion, a technique which takes advantage of the fact that each modality provides a limited view of the brain/gene and may uncover hidden relationships, is an important tool to help unravel the black box of schizophrenia. In this review paper, we survey a number of multimodal fusion applications which enable us to study the schizophrenia macro-connectome, including brain functional, structural, and genetic aspects and may help us understand the disorder in a more comprehensive and integrated manner. We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research question.

No MeSH data available.


Related in: MedlinePlus