Group Independent Component Analysis Research Articles

Fusion analysis of resting-state networks and its application to Alzheimer's disease

Functional networks are extracted from resting-state functional magnetic resonance imaging data to explore the biomarkers for distinguishing brain disorders in disease diagnosis. Previous works have primarily focused on using a single Resting-State Network (RSN) with various techniques. Here, we apply fusion analysis of RSNs to capturing biomarkers that can combine the complementary information among the RSNs. Experiments are carried out on three groups of subjects, i.e., Cognition Normal (CN), Early Mild Cognitive Impairment (EMCI), and Alzheimer's Disease (AD) groups, which correspond to the three progressing stages of AD; each group contains 18 subjects. First, we apply group Independent Component Analysis (ICA) to extracting the Default Mode Network (DMN) and Dorsal Attention Network (DAN) for each subject group. Then, by obtaining the common DMN and DAN as templates for each group, we employ the individual ICA to extract the DMN and DAN for each subject. Finally, we fuse the DMNs and DANs to explore the biomarkers. The results show that (1) the templates generated by group ICA can extract the RSN for each subject by individual ICA effectively; (2) the RSNs combined with the fusion analysis can obtain more informative biomarkers than without fusion analysis; (3) the most different regions of DMN and DAN are found between CN and EMCI and between EMCI and AD, which show differences. For the DMN, the difference in the medial prefrontal cortex between the EMCI and AD is smaller than that between CN and EMCI, whereas that in the posterior cingulate between EMCI and AD is larger. As for the DAN, the difference in the intraparietal sulcus is smaller than that between CN and EMCI; (4) extracting DMN and DAN for each subject via the back reconstruction of group ICA is invalid.

In search of multimodal brain alterations in Alzheimer's and Binswanger's disease.

Structural and functional brain abnormalities have been widely identified in dementia, but with variable replicability and significant overlap. Alzheimer's disease (AD) and Binswanger's disease (BD) share similar symptoms and common brain changes that can confound diagnosis. In this study, we aimed to investigate correlated structural and functional brain changes in AD and BD by combining resting-state functional magnetic resonance imaging (fMRI) and diffusion MRI. A group independent component analysis was first performed on the fMRI data to extract 49 intrinsic connectivity networks (ICNs). Then we conducted a multi-set canonical correlation analysis on three features, functional network connectivity (FNC) between ICNs, fractional anisotropy (FA) and mean diffusivity (MD). Two inter-correlated components show significant group differences. The first component demonstrates distinct brain changes between AD and BD. AD shows increased cerebellar FNC but decreased thalamic and hippocampal FNC. Such FNC alterations are linked to the decreased corpus callosum FA. AD also has increased MD in the frontal and temporal cortex, but BD shows opposite alterations. The second component demonstrates specific brain changes in BD. Increased FNC is mainly between default mode and sensory regions, while decreased FNC is mainly within the default mode domain and related to auditory regions. The FNC changes are associated with FA changes in posterior/middle cingulum cortex and visual cortex and increased MD in thalamus and hippocampus. Our findings provide evidence of linked functional and structural deficits in dementia and suggest that AD and BD have both common and distinct changes in white matter integrity and functional connectivity.

Exploring individual and group differences in latent brain networks using cross-validated simultaneous component analysis

Component models such as PCA and ICA are often used to reduce neuroimaging data into a smaller number of components, which are thought to reflect latent brain networks. When data from multiple subjects are available, the components are typically estimated simultaneously (i.e., for all subjects combined) using either tensor ICA or group ICA. As we demonstrate in this paper, neither of these approaches is ideal if one hopes to find latent brain networks that cross-validate to new samples of data. Specifically, we note that the tensor ICA model is too rigid to capture real-world heterogeneity in the component time courses, whereas the group ICA approach is too flexible to uniquely identify latent brain networks. For multi-subject component analysis, we recommend comparing a hierarchy of simultaneous component analysis (SCA) models. Our proposed model hierarchy includes a flexible variant of the SCA framework (the Parafac2 model), which is able to both (i) model heterogeneity in the component time courses, and (ii) uniquely identify latent brain networks. Furthermore, we propose cross-validation methods to tune the relevant model parameters, which reduces the potential of over-fitting the observed data. Using simulated and real data examples, we demonstrate the benefits of the proposed approach for finding credible components that reveal interpretable individual and group differences in latent brain networks.

Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition

BackgroundStability of spatial components is frequently used as a post-hoc selection criteria for choosing the dimensionality of an independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. Although the stability of the ICA temporal courses differs from that of spatial components, temporal stability has not been considered during dimensionality decisions. New methodThe current study aims to (1) develop an algorithm to incorporate temporal course stability into dimensionality selection and (2) test the impact of temporal course on the stability of the ICA decomposition of fMRI data via tensor clustering. Resting state fMRI data were analyzed with two popular ICA algorithms, InfomaxICA and FastICA, using our new method and results were compared with model order selection based on spatial or temporal criteria alone. ResultsHierarchical clustering indicated that the stability of the ICA decomposition incorporating spatiotemporal tensor information performed similarly when compared to current best practice. However, we found that component spatiotemporal stability and convergence of the model varied significantly with model order. Considering both may lead to methodological improvements for determining ICA model order. Selected components were also significantly associated with relevant behavioral variables.Comparison with Existing Method: The Kullback–Leibler information criterion algorithm suggests the optimal model order for group ICA is 40, compared to the proposed method with an optimal model order of 20. ConclusionThe current study sheds new light on the importance of temporal course variability in ICA of fMRI data.

Hybridizing EMD with cICA for fMRI Analysis of Patient Groups.

Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow.

HYPERGLYCEMIA AND NEURAL NETWORK FUNCTIONAL CONNECTIVITY: PEAKS AND THEN TROUGHS IN MID- TO LATE LIFE FOR PRECLINICAL AD AND ACROSS THE AD SPECTRUM

In normal aging, pre- and type 2 diabetes (“diabetes”) are associated with less functional connectivity within and between AD-sensitive neural networks, which may be an early AD marker. To date, however, metabolic dysfunction and intrinsic neural network functional connectivity have not been systematically examined in preclinical AD, or both MCI and AD, compared to normal aging. Data were downloaded from 13,186 UK Biobank subjects (47-81y) with no memory impairment at baseline, and 157 aged subjects from ADNI at 5 visits spanning 2 years. Mean intrinsic functional connectivity was computed and Z-scored for Group ICA derived orthogonalized networks including default mode, central executive, emotion regulation, salience, and recognition memory networks. Mixed model regression tested the main effect of diabetes vs. no diabetes and 2-way interactions with AD parental family history or Apolipoprotein E ε4 (APOE4) status. Further interactions with age in years were explored. Similar ADNI models were tested with log-transformed fasting glucose and genetic risk factors or baseline clinical diagnosis (normal, Mild Cognitive Impairment or MCI, AD). Interactions with change over Time were explored. In UK Biobank, AD Parental History or APOE4 x Diabetes x Age interactions were seen for all networks (p<.05 to p<.01, corrected). For example, in default mode network (Figure 1), among adults with no AD parental history (Figure 1, left graph), having diabetes (“red” bar) vs. no diabetes (“gray” bar) was linked to less network connectivity regardless of age. By contrast, in adults with AD parental history (Figure 1, right graph), diabetes in middle vs. old age was related to 1.5SD more vs. 2SD less network connectivity. In ADNI, using default mode as an example, higher glucose in APOE4 carriers predicted less intrinsic functional connectivity (R2=0.208, p<.001). A Diagnosis x Glucose interaction (p<.05, Figure 2) showed that higher glucose was, over 2 years, consistently related to more neural network connectivity in stable MCI and MCI progressors to AD, but less network connectivity in AD.

Detecting resting-state brain activity using OEF-weighted imaging

Traditional resting-state functional magnetic resonance imaging (fMRI) is mainly based on the blood oxygenation level-dependent (BOLD) contrast. The oxygen extraction fraction (OEF) represents an important parameter of brain metabolism and is a key biomarker of tissue viability, detecting the ratio of oxygen utilization to oxygen delivery. Investigating spontaneous fluctuations in the OEF-weighted signal is crucial for understanding the underlying mechanism of brain activity because of the immense energy budget during the resting state. However, due to the poor temporal resolution of OEF mapping, no studies have reported using OEF contrast to assess resting-state brain activity. In this fMRI study, we recorded brain OEF-weighted fluctuations for 10 min in healthy volunteers across two scanning visits, using our recently developed pulse sequence that can acquire whole-brain voxel-wise OEF-weighted signals with a temporal resolution of 3 s. Using both group-independent component analysis and seed-based functional connectivity analysis, we robustly identified intrinsic brain networks, including the medial visual, lateral visual, auditory, default mode and bilateral executive control networks, using OEF contrast. Furthermore, we investigated the resting-state local characteristics of brain activity based on OEF-weighted signals using regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF). We demonstrated that the gray matter regions of the brain, especially those in the default mode network, showed higher ReHo and fALFF values with the OEF contrast. Moreover, voxel-wise test-retest reliability comparisons across the whole brain demonstrated that the reliability of resting-state brain activity based on the OEF contrast was moderate for the network indices and high for the local activity indices, especially for ReHo. Although the reliabilities of the OEF-based indices were generally lower than those based on BOLD, the reliability of OEF-ReHo was slightly higher than that of BOLD-ReHo, with a small effect size, which indicated that OEF-ReHo could be used as a reliable index for characterizing resting-state local brain activity as a complement to BOLD. In conclusion, OEF can be used as an effective contrast to study resting-state brain activity with a medium to high test-retest reliability.

Neural evidence for long-term marriage shaping the functional brain network organization between couples

Long-term married couples have been reported to share personality and behavioural similarities, but whether long-term marriage would shape the brain is hitherto unknown. In this study, 35 pairs of long-term married couples, who have married and living together at least 30 years, were recruited, and resting state functional magnetic resonance imaging was used to examine the neural correlates of long-term marriage between couples. Seven intrinsic connectivity networks were extracted using spatially constrained group independent component analysis, and the spatial similarity of each network as well as functional connectome similarity between couples were investigated respectively. The significant spatial similarities in the salience and frontoparietal networks as well as marginally significant connectome similarity were observed in long-term married couples. In addition, the marital duration showed a significantly positive correlation with the spatial similarity in the frontoparietal network and connectome similarity. The results provide objective evidence that long-term marriage would shape brain network organization, and the combination of initial personality traits and long-term common experience of the couples may be potential factors that account for similar brain network organizations between couples.

Adolescent sex differences in cortico-subcortical functional connectivity during response inhibition.

Numerous lines of evidence have shown that cognitive processes engaged during response inhibition tasks are associated with structure and functional integration of regions within fronto-parietal networks. However, while prior studies have started to characterize how intrinsic connectivity during resting state differs between boys and girls, comparatively less is known about how functional connectivity differs between males and females when brain function is exogenously driven by the processing demands of typical Go/No-Go tasks that assess both response inhibition and error processing. The purpose of this study was to characterize adolescent sex differences and possible changes in sexually dimorphic regional functional connectivity across adolescent development in both cortical and subcortical brain connectivity elicited during a visual Go/No-Go task. A total of 130 healthy adolescents (ages 12-25 years) performed a Go/No-Go task during functional magnetic resonance imaging. High model-order group independent component analysis was used to characterize whole-brain network functional connectivity during response inhibition and then a univariate technique used to evaluate differences related to sex and age. As predicted and similar to previously described findings from non-task-driven resting state connectivity studies, functional connectivity sex differences were observed in several subcortical regions, including the amygdala, caudate, thalamus, and cortical regions, including inferior frontal gyrus engaged most strongly during successful response inhibition and/or error processing. Importantly, adolescent boys and girls exhibited different normative profiles of age-related changes in several default mode networks of regions and anterior cingulate cortex. These results suggest that cortical-subcortical functional networks supporting response inhibition operate differently between sexes during adolescence.

Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia.

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called "parallel group ICA+ICA" that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.

3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI.

We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task. MMSE scores were predicted using: linear least square regression (LLSR), support vector regression, bagging-based ensemble regression, and tree regression with group independent component analysis (gICA) features. To improve MMSE regression performance, we applied feature optimization methods including least absolute shrinkage and selection operator and support vector machine-based recursive feature elimination (SVM-RFE). The mean balanced test accuracy was 85.27% for the classification of AD versus healthy controls. The medial visual, default mode, dorsal attention, executive, and auditory related networks were mainly associated with AD. The maximum clinical MMSE score prediction accuracy with the LLSR method applied on gICA combined with SVM-RFE features had the lowest root mean square error (3.27 ± 0.58) and the highest R2 value (0.63 ± 0.02). Classification of AD and healthy controls can be successfully achieved using only rs-fMRI and MMSE scores can be accurately predicted using functional independent component features. In the absence of trained clinicians, AD disease status and clinical MMSE scores can be jointly predicted using 3-D deep learning and regression learning approaches with rs-fMRI data.

The Dynamics of Speech Motor Control Revealed with Time-Resolved fMRI.

Holding a conversation means that speech must be started, maintained, and stopped continuously. The brain networks that underlie these aspects of speech motor control remain poorly understood. Here we collected functional magnetic resonance imaging (fMRI) data while participants produced normal and fast rate speech in response to sequences of visually presented objects. We took a non-conventional approach to fMRI data analysis that allowed us to study speech motor behavior as it unfolded over time. To this end, whole-brain fMRI signals were extracted in stimulus-locked epochs using slice-based fMRI. These data were then subjected to group independent component analysis to discover spatially independent networks that were associated with different temporal activation profiles. The results revealed two basic brain networks with different temporal dynamics: a cortical network that was activated continuously during speech production, and a second cortico-subcortical network that increased in activity during the initiation and suppression of speech production. Additional analyses explored whether key areas involved in motor suppression such as the right inferior frontal gyrus, sub-thalamic nucleus and pre-supplementary motor area provide first-order signals to stop speech. The results reveal for the first time the brain networks associated with the initiation, maintenance, and suppression of speech motor behavior.

State-Dependent Effects of Ventromedial Prefrontal Cortex Continuous Thetaburst Stimulation on Cocaine Cue Reactivity in Chronic Cocaine Users.

Cue-induced craving is a significant barrier to obtaining abstinence from cocaine. Neuroimaging research has shown that cocaine cue exposure evokes elevated activity in a network of frontal-striatal brain regions involved in drug craving and drug seeking. Prior research from our laboratory has demonstrated that when targeted at the medial prefrontal cortex (mPFC), continuous theta burst stimulation (cTBS), an inhibitory form of non-invasive brain stimulation, can decrease drug cue-related activity in the striatum in cocaine users and alcohol users. However, it is known that there are individual differences in response to repetitive transcranial magnetic stimulation (rTMS), with some individuals being responders and others non-responders. There is some evidence that state-dependent effects influence response to rTMS, with baseline neural state predicting rTMS treatment outcomes. In this single-blind, active sham-controlled crossover study, we assess the striatum as a biomarker of treatment response by determining if baseline drug cue reactivity in the striatum influences striatal response to mPFC cTBS. The brain response to cocaine cues was measured in 19 cocaine-dependent individuals immediately before and after real and sham cTBS (110% resting motor threshold, 3600 total pulses). Group independent component analysis (ICA) revealed a prominent striatum network comprised of bilateral caudate, putamen, and nucleus accumbens, which was modulated by the cocaine cue reactivity task. Baseline drug cue reactivity in this striatal network was inversely related to change in striatum reactivity after real (vs. sham) cTBS treatment (ρ = -.79; p < .001; R 2 Adj = .58). Specifically, individuals with a high striatal response to cocaine cues at baseline had significantly attenuated striatal activity after real but not sham cTBS (t 9 = -3.76; p ≤ .005). These data demonstrate that the effects of mPFC cTBS on the neural circuitry of craving are not uniform and may depend on an individual’s baseline frontal-striatal reactivity to cues. This underscores the importance of assessing individual variability as we develop brain stimulation treatments for addiction.

Aberrant interactions of cortical networks in chronic migraine: A resting-state fMRI study.

We investigated resting-state (RS)-fMRI using independent component analysis (ICA) to determine the functional connectivity (FC) between networks in chronic migraine (CM) patients and their correlation with clinical features. Twenty CM patients without preventive therapy or acute medication overuse underwent 3T MRI scans and were compared to a group of 20 healthy controls (HC). We used MRI to collect RS data in 3 selected networks, identified using group ICA: the default mode network (DMN), the executive control network (ECN), and the dorsal attention system (DAS). Compared to HC, CM patients had significantly reduced functional connectivity between the DMN and the ECN. Moreover, in patients, the DAS showed significantly stronger FC with the DMN and weaker FC with the ECN. The higher the severity of headache, the increased the strength of DAS connectivity, and the lower the strength of ECN connectivity. These results provide evidence for large-scale reorganization of functional cortical networks in chronic migraine. They suggest that the severity of headache is associated with opposite connectivity patterns in frontal executive and dorsal attentional networks.

Structural and functional connectivity correlates with motor impairment in chronic supratentorial stroke: a multimodal magnetic resonance imaging study.

The aim of this study was to identify differences in structural and functional brain connectivity between poststroke mild and severe motor impairment. Twenty-four chronic stroke patients who underwent resting-state functional MRI and diffusion tensor image were retrospectively included. All patients were classified into two groups (mild motor impairment: 11 patients and severe motor impairment: 13 patients) according to their Fugl-Meyer motor assessment score. Tract-based spatial statistics and group independent component analyses were applied to investigate between-group differences in structural and functional connectivity, respectively. The fractional anisotropy values of motor-related brain regions in the affected hemisphere were significantly higher in mild motor impairment than in severe motor impairment (corrected P<0.05). The internetwork functional connectivity between (i) the supplementary motor area and primary motor cortex in the affected hemisphere, (ii) the supplementary motor area and dorsolateral prefrontal cortex in the unaffected hemisphere, and (iii) the ischemic lesion and primary motor cortex in the unaffected hemisphere was significantly higher in mild motor impairment than in severe motor impairment (PFWE<0.05). Better motor recovery after stroke could be facilitated by means of treatments aimed at enhancing structural and functional connectivity among motor-related brain regions such as noninvasive brain stimulation and neurodevelopmental therapy.

The negative correlation between energy consumption and communication efficiency in motor network.

Motor network plays an important role in people's daily lives. However, until now, the energy consumption mechanism of motor network remains unclear. In this study, we aimed to investigate the energy consumption of motor network. Fluorine-18-fluorodeoxyglucose PET ([F]FDG PET) data of 81 healthy male Sprague-Dawley rats were included in this study. Metabolic motor network was constructed on the basis of group independent component analysis. Properties of motor network such as degree and nodal efficiency were investigated using graph theory-based analysis. Furthermore, the relationships between [F]FDG standardized uptake value ratio and these properties of each node were investigated. A motor network comprising of the following 11 regions were found: left primary motor cortex, right primary motor cortex, left secondary motor cortex, right secondary motor cortex, left primary somatosensory cortex, right primary somatosensory cortex, left secondary somatosensory cortex, right secondary somatosensory cortex, left insular cortex, right insular cortex, and left orbital cortex. Graph theory-based analysis indicated that right primary somatosensory cortex and left secondary somatosensory cortex were the hubs of motor network, and the nodal efficiency and nodal degree share the same order. Further investigation found a significantly negative correlation between nodal efficiency and [F]FDG standardized uptake value ratios. This study investigated the energy consumption of motor network and found a relationship between energy consumption and communication efficiency. These results may provide insights into the understanding of energy consumption mechanism underlying motor network.Video abstract: http://links.lww.com/NMC/A142.

Classification of cocaine-dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data.

Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine-dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine-dependent participants while performing a motor response inhibition task, stop signal task. Group independent component analysis was carried out on all participant data to compute spatially independent components (ICs). Eight ICs were selected manually as relevant brain networks, which were used to classify healthy versus cocaine-dependent participants. FC and DFC measures of the chosen IC pairs were used as features for the classification algorithm. Support Vector Machines were used for both feature selection/reduction and participant classification. Based on DFC with only seven IC pairs, participants were successfully classified with 95% accuracy (and with 90% accuracy with three IC pairs), whereas static FC yielded only 81% accuracy. Visual, sensorimotor, default mode, and executive control networks, amygdala, and insula played the most significant role in the DFC-based classification. These findings support the use of DFC-based classification of fMRI data as a potential biomarker for the identification of cocaine dependence.

Cerebral cortex functional networks of magnetic stimulation at acupoints along the pericardium meridian.

The coordinated regulating mechanism of magnetic stimulation at acupoints along the pericardium meridian was studied by cerebral cortical functional networks. Electroencephalogram signals of 14 subjects (eight males and six females) were recorded in resting state and following magnetic stimulation at Neiguan and Daling acupoints along the pericardium meridian. The corresponding cortical functional networks were constructed and analyzed by group independent component analysis, standard low-resolution brain electromagnetic tomography, short-time Fourier transform, and complex network theory. Results showed that during magnetic stimulation at Daling and Neiguan acupoints, the functional connections of the nodes in the brain areas associated with movement respectively decreased by 7.3% and 19.9%, and the functional connections of the nodes in brain areas associated with advanced cognitive functions such as emotion, memory and language respectively increased by 24.9% and 18.8%. Changes of topological structure were similarly related to the efficacy of acupoints along the pericardium meridian. Magnetic stimulation also caused different topological changes consistent with the therapeutic function of specific acupoint. This study provided new evidence revealing mechanisms of brain functional integration and network synergy in the acupoint stimulation pathway.

Increased connectivity of pain matrix in chronic migraine: a resting-state functional MRI study

ObjectiveTo investigate the whole-brain resting-state functional connectivity in patients with chronic migraine (CM) using a data-driven method.MethodsWe prospectively recruited patients with either episodic migraine (EM) or CM aged 18–60 years who visited the headache clinic of the Samsung Medical Center from July 2016 to December 2017. All patients underwent 3 T MRI using an identical scanner. Patients were considered interictal if they did not have a migraine headache at the day and ± 1 days of functional MRI acquisition. Using the group-independent component analysis (ICA), connectivity analysis with a weighted and undirected network model was performed. The between-group differences in degree centrality (DC) values were assessed using 5000 permutation tests corrected with false discovery rate (FDR).ResultsA total of 62 patients (44 EM and 18 CM) were enrolled in this study. Among the seven functionally interpretable spatially independent components (ICs) identified, only one IC, interpreted as the pain matrix, showed a significant between-group difference in DC (CM > EM, p = 0.046). This association remained significant after adjustment for age, sex, migraine with aura (MWA), allodynia, depression, and anxiety (p = 0.038). The pain matrix was functionally correlated with the hypothalamus (p = 0.040, EM > CM) and dorsal raphe nucleus (p = 0.039, CM > EM) with different levels of strength in EM and CM.ConclusionCM patients have a stronger connectivity in the pain matrix than do EM patients. Functional alteration of the pain network might play a role in migraine chronification.

(266) Impact of Opioid Analgesics on the Dynamic Functional Connectivity of Resting-State Brain Networks in Patients with Chronic Low Back Pain

Growing body of research has described iatrogenic consequences of prescription opioid use in chronic pain including hyperalgesia and impaired executive function. While changes in pain and behavior have been noted, little is known about the brain mechanisms that change with prescription opioid use in chronic pain. Here, we examined whether long-term opioid therapy altered whole-brain functional connectivity in patients with chronic low back pain (CLBP). We focused on dynamic functional connectivity (DFC) because connectivity patterns can be time-varying, and as such it can better capture brain function. Furthermore, DFC approach may be well suited for chronic pain research because chronic pain can fluctuate over time, and the static connectivity measures may not capture the overall intensity or chronicity of the pain (i.e., state vs trait pain). Using resting-state fMRI data of 10 CLBP patients on opioid (CLBP+; 5 males, mean age ± SD = 48.5 ± 14.8 years) and 10 matched opioid-naive CLBP patients (CLBP-, 5 males, mean age ± SD = 43.6 ± 12.6 years), we performed high-dimensional group independent component analysis (GICA; N = 75) and extracted thirty-six components as intrinsic connectivity networks (ICNs). DFC analysis of ICNs was carried out in GIFT software (Medical Image Analysis Lab, USA) with sliding time-window method and k-means clustering algorithm to categorize DFC by matching the time-windowed connectivity patterns (cluster centroids) to features in a finite set of connectivity patterns (states). Different dwell times for cluster states were observed in CLBP- vs CLBP+. Two-sample t-test further revealed different non-stationary, time-varying connectivity patterns particularly involving the default-mode, salience (insula and anterior cingulate cortex), subcortical (amygdala and hippocampus), and sensorimotor networks (p