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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

Joint fMRI/FA component that is HC–SZ discriminative, from Sui et al. (2011). Spatial maps of the identified functional blobs (A) and WM regions (B) are displayed with the correlation plot between subjects’ loadings and ages. Specifically, HC in red line, SZ in blue line, BP in green line, and trend of all subjects in black line. (C) Shows a high-level brain interaction diaphragm according to the joint component. Functional region with a red solid line frame indicates a major portion activation and the dotted line frame indicates that only small part of it is activated. Abbreviations are defined below, SLF, superior longitudinal fasciculus; CST, corticospinal tract; IFO, inferior fronto-occipital fasciculus; ILF, inferior longitudinal fasciculus; ATR, anterior thalamic radiation; CGC, cingulum; FMAJ, forceps major; FMIN, forceps minor.
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Figure 4: Joint fMRI/FA component that is HC–SZ discriminative, from Sui et al. (2011). Spatial maps of the identified functional blobs (A) and WM regions (B) are displayed with the correlation plot between subjects’ loadings and ages. Specifically, HC in red line, SZ in blue line, BP in green line, and trend of all subjects in black line. (C) Shows a high-level brain interaction diaphragm according to the joint component. Functional region with a red solid line frame indicates a major portion activation and the dotted line frame indicates that only small part of it is activated. Abbreviations are defined below, SLF, superior longitudinal fasciculus; CST, corticospinal tract; IFO, inferior fronto-occipital fasciculus; ILF, inferior longitudinal fasciculus; ATR, anterior thalamic radiation; CGC, cingulum; FMAJ, forceps major; FMIN, forceps minor.

Mentions: Sui et al. (2011) applied a blind data-driven model, mCCA + jICA, optimized for identifying correspondence across modalities, to real fMRI–DTI datasets from 164 subjects, including 62 HC, 54 SZ, and 48 bipolar disorder (BP) subjects. Only one joint group-discriminating component was detected between SZ and HC, including DLPFC and motor regions in fMRI of an auditory oddball detection task as well as parts of the ATR, SLF, and IFO WM tracts. The loading parameters of each modality also showed significant correlations with age. AOD_IC1 represents activations mainly in motor cortex, accompanied by a functional asymmetry with left dominance; see Figure 4A, consistent with the fact that the AOD task design required participants to push the button with fingers on their right hand. Controls had a very significant correlation r = 0.5, p = 4e − 5, while patient groups did not (p does not pass correction for multiple comparisons), implying that the motor regions of HC are normally more involved in the task with increasing age (Bennett et al., 2010), whereas schizophrenia patients have no such trend due to presumed motor system deficits (Rogowska et al., 2004).


A selective review of multimodal fusion methods in schizophrenia.

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

Joint fMRI/FA component that is HC–SZ discriminative, from Sui et al. (2011). Spatial maps of the identified functional blobs (A) and WM regions (B) are displayed with the correlation plot between subjects’ loadings and ages. Specifically, HC in red line, SZ in blue line, BP in green line, and trend of all subjects in black line. (C) Shows a high-level brain interaction diaphragm according to the joint component. Functional region with a red solid line frame indicates a major portion activation and the dotted line frame indicates that only small part of it is activated. Abbreviations are defined below, SLF, superior longitudinal fasciculus; CST, corticospinal tract; IFO, inferior fronto-occipital fasciculus; ILF, inferior longitudinal fasciculus; ATR, anterior thalamic radiation; CGC, cingulum; FMAJ, forceps major; FMIN, forceps minor.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Joint fMRI/FA component that is HC–SZ discriminative, from Sui et al. (2011). Spatial maps of the identified functional blobs (A) and WM regions (B) are displayed with the correlation plot between subjects’ loadings and ages. Specifically, HC in red line, SZ in blue line, BP in green line, and trend of all subjects in black line. (C) Shows a high-level brain interaction diaphragm according to the joint component. Functional region with a red solid line frame indicates a major portion activation and the dotted line frame indicates that only small part of it is activated. Abbreviations are defined below, SLF, superior longitudinal fasciculus; CST, corticospinal tract; IFO, inferior fronto-occipital fasciculus; ILF, inferior longitudinal fasciculus; ATR, anterior thalamic radiation; CGC, cingulum; FMAJ, forceps major; FMIN, forceps minor.
Mentions: Sui et al. (2011) applied a blind data-driven model, mCCA + jICA, optimized for identifying correspondence across modalities, to real fMRI–DTI datasets from 164 subjects, including 62 HC, 54 SZ, and 48 bipolar disorder (BP) subjects. Only one joint group-discriminating component was detected between SZ and HC, including DLPFC and motor regions in fMRI of an auditory oddball detection task as well as parts of the ATR, SLF, and IFO WM tracts. The loading parameters of each modality also showed significant correlations with age. AOD_IC1 represents activations mainly in motor cortex, accompanied by a functional asymmetry with left dominance; see Figure 4A, consistent with the fact that the AOD task design required participants to push the button with fingers on their right hand. Controls had a very significant correlation r = 0.5, p = 4e − 5, while patient groups did not (p does not pass correction for multiple comparisons), implying that the motor regions of HC are normally more involved in the task with increasing age (Bennett et al., 2010), whereas schizophrenia patients have no such trend due to presumed motor system deficits (Rogowska et al., 2004).

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