<i>DynamicBC</i>: A MATLAB Toolbox for Dynamic Brain Connectome Analysis

Liao, Wei; Wu, Guo‐Rong; Xu, Qiang; Ji, Gong‐Jun; Zhang, Zhiqiang; Zang, Yu‐Feng; Lu, Guangming

doi:10.1089/brain.2014.0253

Cited by 231 publications

(174 citation statements)

References 57 publications

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“…The matrix shows edgewise contribution for each classification, while the circular diagram indicates the top contributors within (colored lines) and between (gray lines) networks. The circular diagram was designed using DynamicBC software, http://restfmri.net/forum/DynamicBC (Liao et al, ) [Color figure can be viewed at wileyonlinelibrary.com]…”

Section: Resultsmentioning

confidence: 99%

Classification of schizophrenia by intersubject correlation in functional connectome

Chen

Bai

et al. 2019

Human Brain Mapping

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Functional connectomes have been suggested as fingerprinting for individual identification. Accordingly, we hypothesized that subjects in the same phenotypic group have similar functional connectome features, which could help to discriminate schizophrenia (SCH) patients from healthy controls (HCs) and from depression patients. To this end, we included resting‐state functional magnetic resonance imaging data of SCH, depression patients, and HCs from three centers. We first investigated the characteristics of connectome similarity between individuals, and found higher similarity between subjects belonging to the same group (i.e., SCH–SCH) than different groups (i.e., HC–SCH). These findings suggest that the average connectome within group (termed as group‐specific functional connectome [GFC]) may help in individual classification. Consistently, significant accuracy (75–77%) and area under curve (81–86%) were found in discriminating SCH from HC or depression patients by GFC‐based leave‐one‐out cross‐validation. Cross‐center classification further suggests a good generalizability of the GFC classification. We additionally included normal aging data (255 young and 242 old subjects with different scanning sequences) to show factors could be improved for better classification performance, and the findings emphasized the importance of increasing sample size but not temporal resolution during scanning. In conclusion, our findings suggest that the average functional connectome across subjects contained group‐specific biological features and may be helpful in clinical diagnosis for schizophrenia.

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Section: Resultsmentioning

confidence: 99%

Classification of schizophrenia by intersubject correlation in functional connectome

Chen

Bai

et al. 2019

Human Brain Mapping

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“…The sliding window method was applied to evaluate the dALFF for each participant using the DynamicBC toolbox (Liao et al, ). Previous studies proposed that the window length is an open but essential parameter in sliding‐window‐based resting‐state dynamics computation (Li et al, ; Li, Duan, et al, ; Liao et al, ) To avoid the introduction of spurious fluctuations, the minimum window length should be larger than 1/ f min , where f min is the minimum frequency of time series (Leonardi & Van De Ville, ; Li, Wang, et al, ).…”

Section: Methodsmentioning

confidence: 99%

Dynamic changes of amplitude of low‐frequency fluctuations in patients with generalized anxiety disorder

Cui

Sheng

Chen

et al. 2019

Human Brain Mapping

106

101

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Previous neuroimaging studies have mainly focused on alterations of static and dynamic functional connectivity in patients with generalized anxiety disorder (GAD). However, the characteristics of local brain activity over time in GAD are poorly understood. This study aimed to investigate the abnormal time‐varying local brain activity of GAD by using the amplitude of low‐frequency fluctuation (ALFF) method combined with sliding‐window approach. Group comparison results showed that compared with healthy controls (HCs), patients with GAD exhibited increased dynamic ALFF (dALFF) variability in widespread regions, including the bilateral dorsomedial prefrontal cortex, hippocampus, thalamus, striatum; and left orbital frontal gyrus, inferior parietal lobule, temporal pole, inferior temporal gyrus, and fusiform gyrus. The abnormal dALFF could be used to distinguish between patients with GAD and HCs. Increased dALFF variability values in the striatum were positively correlated with GAD symptom severity. These findings suggest that GAD patients are associated with abnormal temporal variability of local brain activity in regions implicated in executive, emotional, and social function. This study provides insight into the brain dysfunction of GAD from the perspective of dynamic local brain activity, highlighting the important role of dALFF variability in understanding neurophysiological mechanisms and potentially informing the diagnosis of GAD.

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“…Dynamic FC changes before and after IEDs were estimated using the DynamicBC toolbox . The flexible least squares (FLS) method and seed‐to‐voxel–based connectivity measure was applied.…”

Section: Methodsmentioning

confidence: 99%

Real‐time effects of interictal spikes on hippocampus and amygdala functional connectivity in unilateral temporal lobe epilepsy: An EEG‐fMRI study

Tong

Xiao

et al. 2019

Epilepsia

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Summary Objective To explore the real‐time effects of interictal epileptiform discharges (IEDs) on hippocampus and amygdala functional connectivity (FC) in unilateral temporal lobe epilepsy (TLE). Methods Patients with unilateral TLE were recruited and underwent simultaneous electroencephalography–functional magnetic resonance imaging (EEG‐fMRI) scanning. Simultaneous EEG was used to define the prespike (10 s before spike) and postspike (10 s after spike) periods. Dynamic FC analysis was applied using the left/right hippocampus and amygdala separately as a seed region to map the network alterations after IED occurrence. Results A total of 261 IED events (133 left, 128 right) from 21 patients with unilateral TLE (10 left, 11 right) were analyzed. Left IEDs had a greater influence on the hippocampus‐seeded networks, whereas right IEDs affected the amygdala‐seeded networks more. Left IEDs disconnected the ipsilateral hippocampus and the default mode network, which might be related to cognitive impairment in TLE. The reward‐emotion network (more of the prefrontal‐limbic system) and visual network also showed FC changes after left IEDs. The reward‐emotion network (more of the reward system) was coactivated after right IEDs, indicating a possible mechanism of forced normalization. Significance This study directly linked the disrupted functional networks in TLE to epileptic activities and offered a unique tool for future research to investigate mechanisms of comorbidities in TLE.

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DynamicBC: A MATLAB Toolbox for Dynamic Brain Connectome Analysis

Cited by 231 publications

References 57 publications

Classification of schizophrenia by intersubject correlation in functional connectome

Classification of schizophrenia by intersubject correlation in functional connectome

Dynamic changes of amplitude of low‐frequency fluctuations in patients with generalized anxiety disorder

Real‐time effects of interictal spikes on hippocampus and amygdala functional connectivity in unilateral temporal lobe epilepsy: An EEG‐fMRI study

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