Reduced higher-dimensional resting state fMRI dynamism in clinical high-risk individuals for schizophrenia identified by meta-state analysis

Mennigen, Eva; Miller, Robyn L.; Rashid, Barnaly; Fryer, Susanna L.; Loewy, Rachel; Stuart, Barbara; Mathalon, Daniel H.; Calhoun, Vince D.

doi:10.1016/j.schres.2018.06.007

Cited by 20 publications

(19 citation statements)

References 38 publications

(51 reference statements)

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“…properties of resting-state brain activity among schizophrenia patients 29,30 . The present study also demonstrated reduced inter-individual variability in semantic representations across schizophrenia patients (Figs.…”

Section: Previous Studies Have Also Reported Reduced Intra-individualmentioning

confidence: 99%

Reduced intra- and inter-individual diversity of semantic representations in the brains of schizophrenia patients

Nishida

Matsumoto

Yoshikawa

et al. 2020

Preprint

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Schizophrenia patients often manifest semantic processing deficits. It has been proposed that these deficits stem from disorganized semantic representations in the brain. However, no study has yet examined the neural correlates of semantic disorganization by directly evaluating semantic representations in the brain. We used voxelwise modeling on functional magnetic resonance imaging signals to evaluate the semantic representations associated with several thousand words in individual patient brains. We then compared the structural properties of semantic representations to those in healthy controls. The variability of semantic representations was smaller both within individual patients and across patients compared to controls. Surrogate data analysis suggests that the observed reduction in representational variability is associated with disorganization of categorical information. To our knowledge, these findings provide the first evidence for sematic disorganization in schizophrenia at the level of brain representations.

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Section: Previous Studies Have Also Reported Reduced Intra-individualmentioning

confidence: 99%

Reduced intra- and inter-individual diversity of semantic representations in the brains of schizophrenia patients

Nishida

Matsumoto

Yoshikawa

et al. 2020

Preprint

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“…Several studies tried to characterize TVC abnormalities present in different psychiatric and neurological diseases, sometimes looking for an early diagnostic biomarker (Du et al, 2018; Mennigen et al, 2018). Modification of TVC strength, dwell time or number of transitions between states varied according to the disease status in patients affected by bipolar disorder (Rashid et al, 2014, 2016), schizophrenia (Yu et al, 2015; Cetin et al, 2016; Rashid et al, 2016; Gazula et al, 2018; Yue et al, 2018; Zhang W. et al, 2018), depression (Liao et al, 2018; Qiu et al, 2018; Zhi et al, 2018), autism (He et al, 2018; Rashid et al, 2018a), stroke (Chen et al, 2018), mild traumatic brain injury (Vergara et al, 2018), epilepsy (Ridley et al, 2017; Klugah-Brown et al, 2018), Alzheimer's disease (Quevenco et al, 2017; Jie et al, 2018), and Parkinson's disease (Engels et al, 2018).…”

Section: Application Of Time-varying Fc Techniques To Psychiatric Andmentioning

confidence: 99%

Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis

et al. 2019

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Functional magnetic resonance imaging (fMRI) at resting state (RS) has been widely used to characterize the main brain networks. Functional connectivity (FC) has been mostly assessed assuming that FC is static across the whole fMRI examination. However, FC is highly variable at a very fast time-scale, as demonstrated by neurophysiological techniques. Time-varying functional connectivity (TVC) is a novel approach that allows capturing reoccurring patterns of interaction among functional brain networks. Aim of this review is to provide a description of the methods currently used to assess TVC on RS fMRI data, and to summarize the main results of studies applying TVC in healthy controls and patients with multiple sclerosis (MS). An overview of the main results obtained in neurodegenerative and psychiatric conditions is also provided. The most popular TVC approach is based on the so-called “sliding windows,” in which the RS fMRI acquisition is divided in small temporal segments (windows). A window of fixed length is shifted over RS fMRI time courses, and data within each window are used to calculate FC and its variability over time. Sliding windows can be combined with clustering techniques to identify recurring FC states or used to assess global TVC properties of large-scale functional networks or specific brain regions. TVC studies have used heterogeneous methodologies so far. Despite this, similar results have been obtained across investigations. In healthy subjects, the default-mode network (DMN) exhibited the highest degree of connectivity dynamism. In MS patients, abnormal global TVC properties and TVC strengths were found mainly in sensorimotor, DMN and salience networks, and were associated with more severe structural MRI damage and with more severe physical and cognitive disability. Conversely, abnormal TVC measures of the temporal network were correlated with better cognitive performances and less severe fatigue. In patients with neurodegenerative and psychiatric conditions, TVC abnormalities of the DMN, attention and executive networks were associated to more severe clinical manifestations. TVC helps to provide novel insights into fundamental properties of functional networks, and improves the understanding of brain reorganization mechanisms. Future technical advances might help to clarify TVC association with disease prognosis and response to treatment.

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“…where c(i, j) represents the Pearson correlation coefficient of features i and j, and |D| denotes the number of features in D. By (8), the average redundant degree of both the two-step feature selection method and the SVM-RFE (with best performance) are 49.8423 and 138.6289, respectively, which strongly confirms the ability of the two-step feature selection method to effectively remove redundancy. In addition to CAR, the selected differences of ICNs can be further quantified by sensitivity (SS), specificity (SC), positive predictive value (PPV) and negative predictive value (NPV), which are defined as follows SS = (9) where Corr p , InCorr p , Corr n and InCorr n denote the number of true positive, false positive, true negative and false negative samples, respectively.…”

Section: Lpmentioning

confidence: 62%

“…Therefore, resting-state fMRI (rs-fMRI) has been widely used to investigate regional associations of brain networks [4,5], which can give an insight to the brain's functional organization. In particular, it can be used to examine altered or aberrant functional networks as a result of aging or brain disorders [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Two-Step Feature Selection for Identifying Developmental Differences in Resting fMRI Intrinsic Connectivity Networks

Qiao

Gao

et al. 2019

Applied Sciences

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Functional connectivity derived from functional magnetic resonance imaging (fMRI) is used as an effective way to assess brain architecture. There has been a growing interest in its application to the study of intrinsic connectivity networks (ICNs) during different brain development stages. fMRI data are of high dimension but small sample size, and it is crucial to perform dimension reduction before pattern analysis of ICNs. Feature selection is thus used to reduce redundancy, lower the complexity of learning, and enhance the interpretability. To study the varying patterns of ICNs in different brain development stages, we propose a two-step feature selection method. First, an improved support vector machine based recursive feature elimination method is utilized to study the differences of connectivity during development. To further reduce the highly correlated features, a combination of F-score and correlation score is applied. This method was then applied to analysis of the Philadelphia Neurodevelopmental Cohort (PNC) data. The two-step feature selection was randomly performed 20 times, and those features that showed up consistently in the experiments were chosen as the essential ICN differences between different brain ages. Our results indicate that ICN differences exist in brain development, and they are related to task control, cognition, information processing, attention, and other brain functions. In particular, compared with children, young adults exhibit increasing functional connectivity in the sensory/somatomotor network, cingulo-opercular task control network, visual network, and some other subnetworks. In addition, the connectivity in young adults decreases between the default mode network and other subnetworks such as the fronto-parietal task control network. The results are coincident with the fact that the connectivity within the brain alters from segregation to integration as an individual grows.

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Reduced higher-dimensional resting state fMRI dynamism in clinical high-risk individuals for schizophrenia identified by meta-state analysis

Cited by 20 publications

References 38 publications

Reduced intra- and inter-individual diversity of semantic representations in the brains of schizophrenia patients

Reduced intra- and inter-individual diversity of semantic representations in the brains of schizophrenia patients

Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis

Two-Step Feature Selection for Identifying Developmental Differences in Resting fMRI Intrinsic Connectivity Networks

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