Aberrant localization of synchronous hemodynamic activity in auditory cortex reliably characterizes schizophrenia

Calhoun, Vince D.; Kiehl, Kent A.; Liddle, Peter F.; Pearlson, Godfrey D.

doi:10.1016/j.biopsych.2004.01.011

Cited by 89 publications

(86 citation statements)

References 46 publications

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“…We expect patients with schizophrenia to share a common, widespread deficit resulting in activation differences that transcend individual cognitive domains. This viewpoint is supported by the schizophrenia literature showing differences in almost every type of cognitive or sensory task studied [Bullmore et al, 1999;Calhoun et al, 2004b;Laurens et al, 2003;Manoach et al, 2000;Schroder et al, 1999]. In this context, the use of tasks that probe different functional domains is advantageous, and joint analyses of these data may even help us to unify better the diversity of findings present in previous work.…”

Section: Introductionmentioning

confidence: 75%

“…This is a different goal than the useful process of classification using nodes that are known to be affected, such as the temporal lobe [Calhoun et al, 2004b]. In a previous work [Calhoun et al, 2004b], using solely temporal lobe function, we were able to correctly classify a new group of patients and controls with high accuracy using subject specific ICA maps of functional connectivity during the performance of a single fMRI task. The current work can be considered a precursor to developing potentially more sensitive methods for classification utilizing multiple tasks.…”

Section: Discussionmentioning

confidence: 93%

“…We take a different approach to our previous work, which focused on using temporal lobe differences in spatially independent synchronous activity to classify schizophrenia patients and controls using a single fMRI task [Calhoun et al, 2004b]. The goal of the current study is not classification of patient groups, but rather the identification of a common network, jointly revealed by the data from two tasks, which is differentially involved in patients versus controls.…”

Section: Introductionmentioning

confidence: 99%

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A method for multitask fMRI data fusion applied to schizophrenia

Calhoun

Adalı

Kiehl

et al. 2005

Human Brain Mapping

154

122

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Abstract:It is becoming common to collect data from multiple functional magnetic resonance imaging (fMRI) paradigms on a single individual. The data from these experiments are typically analyzed separately and sometimes directly subtracted from one another on a voxel-by-voxel basis. These comparative approaches, although useful, do not directly attempt to examine potential commonalities between tasks and between voxels. To remedy this we propose a method to extract maximally spatially independent maps for each task that are "coupled" together by a shared loading parameter. We first compute an activation map for each task and each individual as "features," which are then used to perform joint independent component analysis (jICA) on the group data. We demonstrate our approach on a data set derived from healthy controls and schizophrenia patients, each of which carried out an auditory oddball task and a Sternberg working memory task. Our analysis approach revealed two interesting findings in the data that were missed with traditional analyses. First, consistent with our hypotheses, schizophrenia patients demonstrate "decreased" connectivity in a joint network including portions of regions implicated in two prevalent models of schizophrenia. A second finding is that for the voxels identified by the jICA analysis, the correlation between the two tasks was significantly higher in patients than in controls. This finding suggests that schizophrenia patients activate "more similarly" for both tasks than do controls. A possible synthesis of both findings is that patients are activating less, but also activating with a less-unique set of regions for these very different tasks. Both of the findings described support the claim that examination of joint activation across multiple tasks can enable new questions to be posed about fMRI data. Our approach can also be applied to data using more than two tasks. It thus provides a way to integrate and probe brain networks using a variety of tasks and may increase our understanding of coordinated brain networks and the impact of pathology upon them. Hum Brain Mapp 27:598-610, 2006.

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Section: Introductionmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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A method for multitask fMRI data fusion applied to schizophrenia

Calhoun

Adalı

Kiehl

et al. 2005

Human Brain Mapping

154

122

View full text Add to dashboard Cite

show abstract

“…Many of the high-scoring families consisted of child ROIs with connections to bilateral frontal and temporal lobes, which have been shown using different connectivity measures to be abnormal in schizophrenia patients (Calhoun et al, 2004;Garrity et al, 2007;Winterer et al, 2003). After ICA filtering, the second highest ACL score was found between the cerebellum and dorsal lateral prefrontal cortex, which suggests that this connection might be impaired in patients with schizophrenia.…”

Section: Discussionmentioning

confidence: 95%

Hybrid ICA–Bayesian network approach reveals distinct effective connectivity differences in schizophrenia

et al. 2008

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We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge et al., 2007) 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 and Lane, 2005). 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, 1991). 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 and frontal cortices, plus cerebellum during an auditory paradigm.

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“…33 Thus, the task is now well explored in patients (and their relatives) as a putative biological marker and endophenotype. 11,12 In addition to dynamic techniques such as ERP and fMRI, MRI-derived structural endophenotypes have also been studied. While several reviews have been optimistic, e.g., Keshavan et al, 46 other recent publications have yielded disappointing results, e.g., Goldman et al 37 Thus, we focus here on functional MRI as an event-related methodology that can also provide information regarding functional connectivity among distant brain regions.…”

Section: Introductionmentioning

confidence: 99%

Physiogenomic Analysis of Localized fMRI Brain Activity in Schizophrenia

et al. 2008

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The search for genetic factors associated with disease is complicated by the complexity of the biological pathways linking genotype and phenotype. This analytical complexity is particularly concerning in diseases historically lacking reliable diagnostic biological markers, such as schizophrenia and other mental disorders. We investigate the use of functional magnetic resonance imaging (fMRI) as an intermediate phenotype (endophenotype) to identify physiogenomic associations to schizophrenia. We screened 99 subjects, 30 subjects diagnosed with schizophrenia, 13 unaffected relatives of schizophrenia patients, and 56 unrelated controls, for gene polymorphisms associated with fMRI activation patterns at two locations in temporal and frontal lobes previously implied in schizophrenia. A total of 22 single nucleotide polymorphisms (SNPs) in 15 genes from the dopamine and serotonin neurotransmission pathways were genotyped in all subjects. We identified three SNPs in genes that are significantly associated with fMRI activity. SNPs of the dopamine beta-hydroxylase (DBH) gene and of the dopamine receptor D4 (DRD4) were associated with activity in the temporal and frontal lobes, respectively. One SNP of serotonin-3A receptor (HTR3A) was associated with temporal lobe activity. The results of this study support the physiogenomic analysis of neuroimaging data to discover associations between genotype and disease-related phenotypes.

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Aberrant localization of synchronous hemodynamic activity in auditory cortex reliably characterizes schizophrenia

Cited by 89 publications

References 46 publications

A method for multitask fMRI data fusion applied to schizophrenia

A method for multitask fMRI data fusion applied to schizophrenia

Hybrid ICA–Bayesian network approach reveals distinct effective connectivity differences in schizophrenia

Physiogenomic Analysis of Localized fMRI Brain Activity in Schizophrenia

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