Transdiagnostic differences in the resting-state functional connectivity of the prefrontal cortex in depression and schizophrenia

Chen, Xi; Liu, Chang; He, Hong; Chang, Xin; Jiang, Yuchao; Li, Yingjia; Duan, Mingjun; Li, Jianfu; Chen, Luo; Yao, Dezhong

doi:10.1016/j.jad.2017.04.001

Cited by 43 publications

(40 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The brain regions with higher local FCD values mainly contained the bilateral lingual gyrus (BA17/18), precuneus (BA7) and postcentral gyrus (BA4), while the regions with greater global FCD values included the bilateral lingual and calcarine gyrus (BA17/18), middle temporal lobes (BA21), superior temporal lobes (BA48), precuneus (BA7) and supplementary motor area (BA6). The current results of the resting-state fMRI measures are consistent with the previous FOCA (Dong et al, 2015a; Chen et al, 2017a; Ma et al, 2017) and FCD (Tomasi and Volkow, 2010, 2011, 2012; Luo et al, 2014; Chen et al, 2017b) studies. Figure 9 demonstrated that changes in the BOLD signals related to GSWDs were mainly found in the right anterior cingulate (BA32), right precentral gyrus (BA44), bilateral thalamus, pallidum and putamen using traditional EEG-informed fMRI analysis.…”

Section: Resultssupporting

confidence: 91%

“…Two novel measures, called local/global functional connectivity density (lFCD/gFCD), have been proposed to characterize the distributions of hubs in the brain (Tomasi and Volkow, 2010, 2011). The FCD measures have been used in various studies such as aging (Tomasi and Volkow, 2012), functional plasticity (Luo et al, 2014) and schizophrenia (Chen et al, 2017b). The lFCD of a given voxel is defined as the total number of functional connections between the voxel and its local cluster (correlation coefficient > a threshold and spatial adjacent voxels).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Neuroscience Information Toolbox: An Open Source Toolbox for EEG–fMRI Multimodal Fusion Analysis

Dong

Chen

Liu

et al. 2018

Front. Neuroinform.

Self Cite

View full text Add to dashboard Cite

Recently, scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) multimodal fusion has been pursued in an effort to study human brain function and dysfunction to obtain more comprehensive information on brain activity in which the spatial and temporal resolutions are both satisfactory. However, a more flexible and easy-to-use toolbox for EEG–fMRI multimodal fusion is still lacking. In this study, we therefore developed a freely available and open-source MATLAB graphical user interface toolbox, known as the Neuroscience Information Toolbox (NIT), for EEG–fMRI multimodal fusion analysis. The NIT consists of three modules: (1) the fMRI module, which has batch fMRI preprocessing, nuisance signal removal, bandpass filtering, and calculation of resting-state measures; (2) the EEG module, which includes artifact removal, extracting EEG features (event onset, power, and amplitude), and marking interesting events; and (3) the fusion module, in which fMRI-informed EEG analysis and EEG-informed fMRI analysis are included. The NIT was designed to provide a convenient and easy-to-use toolbox for researchers, especially for novice users. The NIT can be downloaded for free at http://www.neuro.uestc.edu.cn/NIT.html, and detailed information, including the introduction of NIT, user’s manual and example data sets, can also be observed on this website. We hope that the NIT is a promising toolbox for exploring brain information in various EEG and fMRI studies.

show abstract

Section: Resultssupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Neuroscience Information Toolbox: An Open Source Toolbox for EEG–fMRI Multimodal Fusion Analysis

Dong

Chen

Liu

et al. 2018

Front. Neuroinform.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The largest transdiagnostic study published to date leveraged an electronic case register to include 353 mental disorders clustered across ten spectra, representing all ICD‐10 mental disorders except organic mental disorders. About one third of the studies (35%) included at least one non‐clinical sample.…”

Section: Resultsmentioning

confidence: 99%

“…Most studies (81%) were descriptive in nature. Mechanistic constructs were more infrequent (19%), and causal transdiagnostic constructs were hardly ever reported (7%) and only during the most recent years (2017‐2018).…”

Section: Resultsmentioning

confidence: 99%

Transdiagnostic psychiatry: a systematic review

et al. 2019

View full text Add to dashboard Cite

The usefulness of current psychiatric classification, which is based on ICD/DSM categorical diagnoses, remains questionable. A promising alternative has been put forward as the "transdiagnostic" approach. This is expected to cut across existing categorical diagnoses and go beyond them, to improve the way we classify and treat mental disorders. This systematic review explores whether self-defining transdiagnostic research meets such high expectations. A multi-step Web of Science literature search was performed according to an a priori protocol, to identify all studies that used the word "transdiagnostic" in their title, up to May 5, 2018. Empirical variables which indexed core characteristics were extracted, complemented by a bibliometric and conceptual analysis. A total of 111 studies were included. Most studies were investigating interventions, followed by cognition and psychological processes, and neuroscientific topics. Their samples ranged from 15 to 91,199 (median 148) participants, with a mean age from 10 to more than 60 (median 33) years. There were several methodological inconsistencies relating to the definition of the gold standard (DSM/ICD diagnoses), of the outcome measures and of the transdiagnostic approach. The quality of the studies was generally low and only a few findings were externally replicated. The majority of studies tested transdiagnostic features cutting across different diagnoses, and only a few tested new classification systems beyond the existing diagnoses. About one fifth of the studies were not transdiagnostic at all, because they investigated symptoms and not disorders, a single disorder, or because there was no diagnostic information. The bibliometric analysis revealed that transdiagnostic research largely restricted its focus to anxiety and depressive disorders. The conceptual analysis showed that transdiagnostic research is grounded more on rediscoveries than on true innovations, and that it is affected by some conceptual biases. To date, transdiagnostic approaches have not delivered a credible paradigm shift that can impact classification and clinical care. Practical "TRANSD"iagnostic recommendations are proposed here to guide future research in this field.

show abstract

“…To date, DTI has been widely used to characterize the structural architecture of the white matter; however, it is unsuitable for the detection of the neural activity and brain functions inside the white matter. Functional magnetic resonance imaging (fMRI) based on blood oxygen level‐dependent (BOLD) signals is an effective technique to uncover the neural activity and relevant functions of the gray matter in neuropsychology and clinical neurological diseases (Chen, Liu, et al, ; Dong et al, ; Duan et al, ; Jia et al, ; Zhong et al, ). Recently, greater attention focused on the detection of neural activation and functional organization in the white matter by using the fMRI (Ding et al, ; Fabri & Polonara, ; Fabri, Polonara, Mascioli, Salvolini, & Manzoni, ; Gawryluk, Mazerolle, Brewer, Beyea, & D'Arcy, ; Gawryluk, Mazerolle, & D'Arcy, ; Jiang, Luo, Li, Li, et al, ; Marussich, Lu, Wen, & Liu, ; Peer, Nitzan, Bick, Levin, & Arzyt, ).…”

Section: Introductionmentioning

confidence: 99%

Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Jiang

et al. 2019

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

Benign epilepsy with centrotemporal spikes (BECT) is the most common childhood idiopathic focal epilepsy syndrome, which characterized with white‐matter abnormalities in the rolandic cortex. Although diffusion tensor imaging research could characterize white‐matter structural architecture, it cannot detect neural activity or white‐matter functions. Recent studies demonstrated the functional organization of white‐matter by using functional magnetic resonance imaging (fMRI), suggesting that it is feasible to investigate white‐matter dysfunctions in BECT. Resting‐state fMRI data were collected from 24 new‐onset drug‐naive (unmedicated [NMED]), 21 medicated (MED) BECT patients, and 27 healthy controls (HC). Several white‐matter functional networks were obtained using a clustering analysis on voxel‐by‐voxel correlation profiles. Subsequently, conventional functional connectivity (FC) was calculated in four frequency sub‐bands (Slow‐5:0.01–0.027, Slow‐4:0.027–0.073, Slow‐3:0.073–0.198, and Slow‐2:0.198–0.25 Hz). We also employed a functional covariance connectivity (FCC) to estimate the covariant relationship between two white‐matter networks based on their correlations with multiple gray‐matter regions. Compared with HC, the NMED showed increased FC and/or FCC in rolandic network (RN) and precentral/postcentral network, and decreased FC and/or FCC in dorsal frontal network, while these alterations were not observed in the MED group. Moreover, the changes exhibited frequency‐specific properties. Specifically, only two alterations were shared in at least two frequency bands. Most of these alterations were observed in the frequency bands of Slow‐3 and Slow‐4. This study provided further support on the existence of white‐matter functional networks which exhibited frequency‐specific properties, and extended abnormalities of rolandic area from the perspective of white‐matter dysfunction in BECT.

show abstract

Transdiagnostic differences in the resting-state functional connectivity of the prefrontal cortex in depression and schizophrenia

Cited by 43 publications

References 53 publications

Neuroscience Information Toolbox: An Open Source Toolbox for EEG–fMRI Multimodal Fusion Analysis

Neuroscience Information Toolbox: An Open Source Toolbox for EEG–fMRI Multimodal Fusion Analysis

Transdiagnostic psychiatry: a systematic review

Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Contact Info

Product

Resources

About