Two Patterns of White Matter Abnormalities in Medication-Naive Patients With First-Episode Schizophrenia Revealed by Diffusion Tensor Imaging and Cluster Analysis

Sun, Huaiqiang; Lui, Su; Yao, Li; Deng, Wei; Xiao, Yuan; Zhang, Wenjing; Huang, Xiaoqi; Hu, Junmei; Bi, Feng; Li, Tao; Sweeney, John A.; Gong, Qiyong

doi:10.1001/jamapsychiatry.2015.0505

Cited by 143 publications

(89 citation statements)

References 57 publications

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“…According to the macrocircuit theory, specific white matter tracts are disrupted either as a cause or a consequence of a disorder in the gray matter regions they connect. Consistent with previous reports, the current study also found moderate correlations between the severity of negative symptom and diffusion measurements of certain tracts, including FMAJ, FMIN, SLF, and ILF [43]. …”

Section: Results Of Human Brain Datasupporting

confidence: 92%

“…The FMIN linking DLPFC region is traversed by tracts interconnecting the frontal lobe, providing evidence for disrupted anatomical connections in the fronto-limbic circuitry [43]. FA changes in the SLF, the major white matter connection between prefrontal and parietal cortices, relate to verbal working memory performance [17].…”

Section: Results Of Human Brain Datamentioning

confidence: 99%

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Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

Calhoun

Erp

et al. 2018

IEEE Trans. Med. Imaging

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Multimodal fusion is an effective approach to take advantage of cross-information among multiple imaging data to better understand brain diseases. However, most current fusion approaches are blind, without adopting any prior information. To date, there is increasing interest to uncover the neurocognitive mapping of specific behavioral measurement on enriched brain imaging data; hence, a supervised, goal-directed model that enables a priori information as a reference to guide multimodal data fusion is in need and a natural option. Here we proposed a fusion with reference model, called “multi-site canonical correlation analysis with reference plus joint independent component analysis” (MCCAR+jICA), which can precisely identify co-varying multimodal imaging patterns closely related to reference information, such as cognitive scores. In a 3-way fusion simulation, the proposed method was compared with its alternatives on estimation accuracy of both target component decomposition and modality linkage detection. MCCAR+jICA outperforms others with higher precision. In human imaging data, working memory performance was utilized as a reference to investigate the covarying functional and structural brain patterns among 3 modalities and how they are impaired in schizophrenia. Two independent cohorts (294 and 83 subjects respectively) were used. Interestingly, similar brain maps were identified between the two cohorts, with substantial overlap in the executive control networks in fMRI, salience network in sMRI, and major white matter tracts in dMRI. These regions have been linked with working memory deficits in schizophrenia in multiple reports, while MCCAR+jICA further verified them in a repeatable, joint manner, demonstrating the potential of such results to identify potential neuromarkers for mental disorders.

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Section: Results Of Human Brain Datasupporting

confidence: 92%

Section: Results Of Human Brain Datamentioning

confidence: 99%

Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

Calhoun

Erp

et al. 2018

IEEE Trans. Med. Imaging

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“…This is notable considering that RDoC and ROAMER emphasize stratification on the basis of mappings between biological systems and cognitive domains, not just symptoms (10). To date, few studies have stratified psychiatric disorders on the basis of quantitative biological measures, and these studies have predominantly used neuroimaging-based measures (13, 16, 17, 52). This may be because of well-known problems with clustering complex, high-dimensional data (see Advantages and Disadvantages of Clustering).…”

Section: Applications To Stratify Psychiatric Disordersmentioning

confidence: 99%

Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders

Marquand

Wolfers

Mennes

et al. 2016

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

171

187

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Heterogeneity is a key feature of all psychiatric disorders that manifests on many levels, including symptoms, disease course, and biological underpinnings. These form a substantial barrier to understanding disease mechanisms and developing effective, personalized treatments. In response, many studies have aimed to stratify psychiatric disorders, aiming to find more consistent subgroups on the basis of many types of data. Such approaches have received renewed interest after recent research initiatives, such as the National Institute of Mental Health Research Domain Criteria and the European Roadmap for Mental Health Research, both of which emphasize finding stratifications that are based on biological systems and that cut across current classifications. We first introduce the basic concepts for stratifying psychiatric disorders and then provide a methodologically oriented and critical review of the existing literature. This shows that the predominant clustering approach that aims to subdivide clinical populations into more coherent subgroups has made a useful contribution but is heavily dependent on the type of data used; it has produced many different ways to subgroup the disorders we review, but for most disorders it has not converged on a consistent set of subgroups. We highlight problems with current approaches that are not widely recognized and discuss the importance of validation to ensure that the derived subgroups index clinically relevant variation. Finally, we review emerging techniques—such as those that estimate normative models for mappings between biology and behavior—that provide new ways to parse the heterogeneity underlying psychiatric disorders and evaluate all methods to meeting the objectives of such as the National Institute of Mental Health Research Domain Criteria and Roadmap for Mental Health Research.

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“…Thus, an alternative approach resolving the heterogeneity with neurobiologic parameters, such as anatomic and functional imaging features, may represent an advantageous strategy to identify subgroups of patients, after which the clinical dimensions of these subgroups in terms of symptoms and treatment response to different therapies can be examined. Recently, Sun et al (108) have used a data-driven patient-clustering method to identify two distinct patterns of white matter abnormalities in the early phase of schizophrenia, suggesting qualitatively distinct genetic influences or neurodevelopmental alterations. This…”

Section: State Of the Art: Psychoradiologymentioning

confidence: 99%

Psychoradiology: The Frontier of Neuroimaging in Psychiatry

Lui¹,

et al. 2016

Self Cite

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Unlike neurologic conditions, such as brain tumors, dementia, and stroke, the neural mechanisms for all psychiatric disorders remain unclear. A large body of research obtained with structural and functional magnetic resonance imaging, positron emission tomography/single photon emission computed tomography, and optical imaging has demonstrated regional and illness-specific brain changes at the onset of psychiatric disorders and in individuals at risk for such disorders. Many studies have shown that psychiatric medications induce specific measurable changes in brain anatomy and function that are related to clinical outcomes. As a result, a new field of radiology, termed psychoradiology, seems primed to play a major clinical role in guiding diagnostic and treatment planning decisions in patients with psychiatric disorders. This article will present the state of the art in this area, as well as perspectives regarding preparations in the field of radiology for its evolution. Furthermore, this article will (a) give an overview of the imaging and analysis methods for psychoradiology; (b) review the most robust and important radiologic findings and their potential clinical value from studies of major psychiatric disorders, such as depression and schizophrenia; and (c) describe the main challenges and future directions in this field. An ongoing and iterative process of developing biologically based nomenclatures with which to delineate psychiatric disorders and translational research to predict and track response to different therapeutic drugs is laying the foundation for a shift in diagnostic practice in psychiatry from a psychologic symptom-based approach to an imaging-based approach over the next generation. This shift will require considerable innovations for the acquisition, analysis, and interpretation of brain images, all of which will undoubtedly require the active involvement of radiologists.q RSNA, 2016 Online supplemental material is available for this article.This copy is for personal use only. To order printed copies, contact reprints@rsna.org STATE OF THE ART: PsychoradiologyLui et al imaging study of schizophrenia with CT revealed bilaterally enlarged ventricles, which was an important con firmation of the neuropathology of the disorder (1). Since then, many psychiatry researchers have used brain imaging to elucidate the profile of brain abnormalities associated with different psychiatric disorders. This effort accelerated considerably in recent years because of the rapid and extensive growth and development in MR imaging, molecular imaging, and other diagnostic imaging techniques.Increased understanding of neurobiologic mechanisms of psychiatric disorders, increased technical capacities of human brain imaging, and many compelling demonstrations of differences between patients and control subjects in terms of brain anatomy and function provide the basis for the emergence of the clinical subspecialty of psychoradiology. Although clinical application of psychoradiology is not on the immediate horizon, th...

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Two Patterns of White Matter Abnormalities in Medication-Naive Patients With First-Episode Schizophrenia Revealed by Diffusion Tensor Imaging and Cluster Analysis

Cited by 143 publications

References 57 publications

Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders

Psychoradiology: The Frontier of Neuroimaging in Psychiatry

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