Source‐based morphometry: The use of independent component analysis to identify gray matter differences with application to schizophrenia

Xu, Lai; Groth, Karyn M.; Pearlson, Godfrey D.; Schretlen, David J.; Calhoun, Vince D.

doi:10.1002/hbm.20540

Cited by 318 publications

(483 citation statements)

References 37 publications

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“…T1 MRI images of 89 male subjects (43 diseased and 46 control subjects) from ABIDE online database were considered [18]. SBM, a multivariate approach was used to study grey matter differences between samples [14]. This method is based on the detection and decomposition of mixed signal coming from structural images by the Independent Components Analysis (ICA).…”

Section: Discussionmentioning

confidence: 99%

“…This is a great advantage as compared to standard analyses (such as Voxel-based Morphometry) used in previous studies. In this way, a data-driven method is able to provide a way to gather signal information from different voxels so as to identify unpredictable patterns [14]. Results showed a significantly different independent component (IC2) between groups.…”

Section: Discussionmentioning

confidence: 99%

“…Although often used with functional data, an independent component analysis approach applied to structural data can be very useful in identifying grey matter patterns in a whole brain network perspective [14]. SPM12 software (http:// www.fil.ion.ucl.ac.uk/spm/software) and dedicated toolboxes were used.…”

Section: Source Based Morphometry Analysismentioning

confidence: 99%

“…Only recently, Grecucci and colleagues (in prep) used a novel approach to structural MRI data analysis called Source-Based Morphometry (SBM), that is a multivariate method to detect activation patterns of gray matter distributed over several large and independent cortical components [13,14]. With this methodology, Grecucci found abnormalities in large temporal, frontal and cerebellar networks in adult autistics (age 18-39) as compared with controls [13].…”

Section: Introductionmentioning

confidence: 99%

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An Abnormal Cerebellar Network in Children with Autistic Spectrum Disorder: A Morphometric Study

Pappaianni

Grecucci²,

Siugzdaite³

2016

Autism Open Access

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Autism is a neurodevelopmental disorder characterized by poor social abilities, communication deficiency and restricted behavioural patterns. Recently, scholars started to consider the possibility of detecting biological markers for better and faster diagnosing autism. This problem has been approached from different perspectives considering biochemical, neurophysiological, and neuroanatomical markers. Following this perspective, our intent was to investigate whether a structural brain signature of autism can be detected in children by using a whole brain morphological analysis. To this aim, we selected 43 male children with autistic spectrum disorder and 46 male controls, matched for age. Structural brain images (T1 image), intelligence scores (Full IQ, Verbal IQ, Performance IQ), and Autism Diagnostic Observation Schedule (ADOS) scores were considered for analyses. Source-Based Morphometry, a multivariate method based on Independent Component Analysis to detect maximally independent cortical networks of gray matter differences was applied to autistic and control brains. Results showed a statistically different network between ASD children and controls, including several cerebellar regions (Inferior Semi-lunar lobule, Tuble, Uvula, Pyramis, Declive, Cerebellar Tonsil) and the Fusiform Gyrus, confirming, but, also expanding previous results. In addition, separate temporal, frontal, and parietal networks were found to be significantly correlated with the Stereotyped Behaviour ADOS scores. These morphologic differences may be particularly useful in paving the way for future objective methods to diagnose autism.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Source Based Morphometry Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Abnormal Cerebellar Network in Children with Autistic Spectrum Disorder: A Morphometric Study

Pappaianni

Grecucci²,

Siugzdaite³

2016

Autism Open Access

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“…Our approach incorporates an ROI mask as the prior constraint and evaluates the possibility of a structural network in both gray matter (GM) and white matter (WM) associated with ROIs throughout the whole brain. It possesses the inherent advantages of the purely data-driven method SBM (Caprihan et al, 2011;Xu et al, 2009). The cross-voxel information within the MRI data is utilized, and the structural networks showing similar intersubject co-variance can be detected.…”

mentioning

confidence: 99%

Constrained Source-Based Morphometry Identifies Structural Networks Associated with Default Mode Network

Luo

Jung

et al. 2012

Brain Connectivity

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We present constrained source-based morphometry (SBM), a multivariate semiblind data-driven approach, to explore a possible brain-wide structural network in both gray matter (GM) and white matter (WM) associated with the functional default mode network (DMN). With this approach, we utilize seed regions associated with the DMN as constraints on GM maps and derive a joint GM and WM structural network automatically through a multivariate data-driven approach. In this article, we first provide a simulation to validate the constrained SBM approach. The approach was then applied to structural magnetic resonance imaging and diffusion tensor imaging data obtained from 102 healthy controls. Regions that have consistently reported to be associated with the DMN were used to create an a priori mask that was integrated within an independent component analysis framework to derive the structural network associated with the DMN. We identified a set of GM and corresponding WM regions contributing to a structural network underlying the functional DMN. The GM regions consisted mainly of the precuneus, superior and medial frontal gyri, middle temporal gyrus, hippocampus, cuneus, and cerebellum. The WM regions included the cingulum, corpus callosum, corona radiata, association fibers, and middle cerebellar peduncle. Significant gender differences in the relationship between intelligence quotient (IQ) and the identified structural network were observed. Our findings suggest that the functional DMN is underpinned by a corresponding brain-wide structural network. The constrained SBM approach is additionally applicable to a wide variety of problems identifying structural networks from seed regions.

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The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease

Yan,

Tang,

Chen

et al. 2024

Ann Clin Transl Neurol

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ObjectiveAbnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico‐cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI).MethodsWe performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD‐CI), 85 patients without CI (CSVD‐NC), and 108 healthy controls using source‐based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population.ResultsThe results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD‐CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD‐NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier.InterpretationOur findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.

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Source‐based morphometry: The use of independent component analysis to identify gray matter differences with application to schizophrenia

Cited by 318 publications

References 37 publications

An Abnormal Cerebellar Network in Children with Autistic Spectrum Disorder: A Morphometric Study

An Abnormal Cerebellar Network in Children with Autistic Spectrum Disorder: A Morphometric Study

Constrained Source-Based Morphometry Identifies Structural Networks Associated with Default Mode Network

The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease

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