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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

Auditory oddball/gray matter jICA analysis. Only one component demonstrated a significant difference between patients and controls. The joint source map for the auditory oddball fMRI data (A) and gray matter (B) data is presented along with the loading parameters for patients and controls (C).
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Figure 3: Auditory oddball/gray matter jICA analysis. Only one component demonstrated a significant difference between patients and controls. The joint source map for the auditory oddball fMRI data (A) and gray matter (B) data is presented along with the loading parameters for patients and controls (C).

Mentions: Figure 3 (Calhoun et al., 2006a) shows analyzed data collected from groups of schizophrenia patients and HCs using the jICA approach. The main finding was that group differences in bilateral parietal and frontal as well as posterior temporal regions in GM distinguished groups. A finding of less patient GM and less hemodynamic activity for target detection in these bilateral anterior temporal lobe regions was consistent with previous work. An unexpected corollary to this finding was that, in the regions showing the largest group differences, GM concentrations were larger in patients versus controls, suggesting that more GM may be related to less functional connectivity during performance of an auditory oddball task. Correa et al. (2008) also showed an interesting joint relationship between fMRI and GM by mCCA, with patients with schizophrenia showing more functional activity in motor areas and less activity in temporal areas associated with less GM as compared to HCs.


A selective review of multimodal fusion methods in schizophrenia.

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

Auditory oddball/gray matter jICA analysis. Only one component demonstrated a significant difference between patients and controls. The joint source map for the auditory oddball fMRI data (A) and gray matter (B) data is presented along with the loading parameters for patients and controls (C).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Auditory oddball/gray matter jICA analysis. Only one component demonstrated a significant difference between patients and controls. The joint source map for the auditory oddball fMRI data (A) and gray matter (B) data is presented along with the loading parameters for patients and controls (C).
Mentions: Figure 3 (Calhoun et al., 2006a) shows analyzed data collected from groups of schizophrenia patients and HCs using the jICA approach. The main finding was that group differences in bilateral parietal and frontal as well as posterior temporal regions in GM distinguished groups. A finding of less patient GM and less hemodynamic activity for target detection in these bilateral anterior temporal lobe regions was consistent with previous work. An unexpected corollary to this finding was that, in the regions showing the largest group differences, GM concentrations were larger in patients versus controls, suggesting that more GM may be related to less functional connectivity during performance of an auditory oddball task. Correa et al. (2008) also showed an interesting joint relationship between fMRI and GM by mCCA, with patients with schizophrenia showing more functional activity in motor areas and less activity in temporal areas associated with less GM as compared to HCs.

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