Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization.
ABSTRACT: Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis.
Project description:Time-varying connectivity analyses have indicated idiopathic generalized epilepsy (IGE) could cause significant abnormalities in dynamic connective pattern within and between resting-state sub-networks (RSNs). However, previous studies mainly focused on the IGE-induced dynamic changes of functional connectivity (FC) in specific frequency band (0.01-0.08 Hz or 0.01-0.15 Hz), ignoring the changes across different frequency bands. Here, 24 patients with IGE characterized by juvenile myoclonic epilepsy (JME) and 24 matched healthy controls were studied using a data-driven frequency decomposition approach and a sliding window approach. The RSN dynamics, including intra-RSN dynamics and inter-RSN dynamics, was further calculated to investigate dynamic FC changes within and between RSNs in JME patients in each decomposed frequency band. Compared to healthy controls, JME patients not only showed frequency-dependent decrease in intra-RSN dynamics within multiple RSNs but also exhibited fluctuant alterations in inter-RSN dynamics among several RSNs over different frequency bands especially in the ventral/dorsal attention network and the subcortical network. Additionally, the disease severity had significantly negative correlations with both intra-RSN dynamics within the subcortical network and inter-RSN dynamics between the subcortical network and the default network at the lower frequency band (0.0095-0.0195 Hz). These results suggested that abnormal dynamic FC within and between RSNs in JME occurs at multiple frequency bands and the lower frequency band (0.0095-0.0195 Hz) was probably more sensitive to JME-caused dynamic FC abnormalities. The frequency subdivision and selection are potentially helpful for detecting particular changes of dynamic FC in JME.
Project description:Functional connectivity (FC) is among the most informative features derived from EEG. However, the most straightforward sensor-space analysis of FC is unreliable owing to volume conductance effects. An alternative-source-space analysis of FC-is optimal for high- and mid-density EEG (hdEEG, mdEEG); however, it is questionable for widely used low-density EEG (ldEEG) because of inadequate surface sampling. Here, using simulations, we investigate the performance of the two source FC methods, the inverse-based source FC (ISFC) and the cortical partial coherence (CPC). To examine the effects of localization errors of the inverse method on the FC estimation, we simulated an oscillatory source with varying locations and SNRs. To compare the FC estimations by the two methods, we simulated two synchronized sources with varying between-source distance and SNR. The simulations were implemented for hdEEG, mdEEG, and ldEEG. We showed that the performance of both methods deteriorates for deep sources owing to their inaccurate localization and smoothing. The accuracy of both methods improves with the increasing between-source distance. The best ISFC performance was achieved using hd/mdEEG, while the best CPC performance was observed with ldEEG. In conclusion, with hdEEG, ISFC outperforms CPC and therefore should be the preferred method. In the studies based on ldEEG, the CPC is a method of choice.
Project description:Recent studies have highlighted the importance of an accurate individual head model for reliably using high-density electroencephalography (hdEEG) as a brain imaging technique. Correct identification of sensor positions is fundamental for accurately estimating neural activity from hdEEG recordings. We previously introduced a method of automated localization and labelling of hdEEG sensors using an infrared colour-enhanced 3D scanner. Here, we describe an extension of this method, the spatial positioning toolbox for head markers using 3D scans (SPOT3D), which integrates a graphical user interface (GUI). This enables the correction of imprecisions in EEG sensor positioning and the inclusion of additional head markers. The toolbox was validated using 3D scan data collected in four participants wearing a 256-channel hdEEG cap. We quantified the misalignment between the 3D scan and the head shape, and errors in EEG sensor locations. We assessed these parameters after using the automated approach and after manually adjusting its results by means of the GUI. The GUI overcomes the main limitations of the automated method, yielding enhanced precision and reliability of head marker positioning.
Project description:INTRODUCTION: The cerebral resting state in schizophrenia is altered, as has been demonstrated separately by electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state networks (RSNs). Previous simultaneous EEG/fMRI findings in healthy controls suggest that a consistent spatiotemporal coupling between neural oscillations (EEG frequency correlates) and RSN activity is necessary to organize cognitive processes optimally. We hypothesized that this coupling is disorganized in schizophrenia and related psychotic disorders, in particular regarding higher cognitive RSNs such as the default-mode (DMN) and left-working-memory network (LWMN). METHODS: Resting state was investigated in eleven patients with a schizophrenia spectrum disorder (n?=?11) and matched healthy controls (n?=?11) using simultaneous EEG/fMRI. The temporal association of each RSN to topographic spectral changes in the EEG was assessed by creating Covariance Maps. Group differences within, and group similarities across frequencies were estimated for the Covariance Maps. RESULTS: The coupling of EEG frequency bands to the DMN and the LWMN respectively, displayed significant similarities that were shifted towards lower EEG frequencies in patients compared to healthy controls. CONCLUSIONS: By combining EEG and fMRI, each measuring different properties of the same pathophysiology, an aberrant relationship between EEG frequencies and altered RSNs was observed in patients. RSNs of patients were related to lower EEG frequencies, indicating functional alterations of the spatiotemporal coupling. SIGNIFICANCE: The finding of a deviant and shifted coupling between RSNs and related EEG frequencies in patients with a schizophrenia spectrum disorder is significant, as it might indicate how failures in the processing of internal and external stimuli, as commonly seen during this symptomatology (i.e. thought disorders, hallucinations), arise.
Project description:Altered functional characteristics have been reported in amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD); nonetheless, comprehensive analyses of the resting-state networks (RSNs) are rare. This study combined multiple imaging modalities to investigate the functional and structural changes within each RSN and between RSNs in aMCI/AD patients. Eight RSNs were identified from functional MRI data from 35 AD, 18 aMCI and 21 normal control subjects using independent component analysis. We compared functional connectivity (FC) within each RSN and found decreased FC in the several cognitive-related RSNs in AD, including the bilateral precuneus of the precuneus network, the posterior cingulate cortex and left precuneus of the posterior default mode network (DMN), and the left superior parietal lobule of the left frontoparietal network (LFP). We further compared the grey matter volumes and amplitudes of low-frequency fluctuations of these regions and found decreases in these measures in AD. Importantly, we found decreased inter-network connectivity between the visual network and the LFP and between the anterior and posterior DMNs in AD. All indices in aMCI patients were numerically between those of controls and AD patients. These results suggest that the brain networks supporting complex cognitive processes are specifically and progressively impaired over the course of AD, and the FC impairments are present not only within networks but also between networks.
Project description:In resting state fMRI (rs-fMRI), only functional connectivity (FC) reductions in the default mode network (DMN) are normally reported as a biomarker for Alzheimer's disease (AD). In this investigation we have developed a comprehensive strategy to characterize the FC changes occurring in multiple networks and applied it in a pilot study of subjects with AD and Mild Cognitive Impairment (MCI), compared to healthy controls (HC). Resting state networks (RSNs) were studied in 14 AD (70 ± 6 years), 12 MCI (74 ± 6 years), and 16 HC (69 ± 5 years). RSN alterations were present in almost all the 15 recognized RSNs; overall, 474 voxels presented a reduced FC in MCI and 1244 in AD while 1627 voxels showed an increased FC in MCI and 1711 in AD. The RSNs were then ranked according to the magnitude and extension of FC changes (gFC), putting in evidence 6 RSNs with prominent changes: DMN, frontal cortical network (FCN), lateral visual network (LVN), basal ganglia network (BGN), cerebellar network (CBLN), and the anterior insula network (AIN). Nodes, or hubs, showing alterations common to more than one RSN were mostly localized within the prefrontal cortex and the mesial-temporal cortex. The cerebellum showed a unique behavior where voxels of decreased gFC were only found in AD while a significant gFC increase was only found in MCI. The gFC alterations showed strong correlations (p < 0.001) with psychological scores, in particular Mini-Mental State Examination (MMSE) and attention/memory tasks. In conclusion, this analysis revealed that the DMN was affected by remarkable FC increases, that FC alterations extended over several RSNs, that derangement of functional relationships between multiple areas occurred already in the early stages of dementia. These results warrant future work to verify whether these represent compensatory mechanisms that exploit a pre-existing neural reserve through plasticity, which evolve in a state of lack of connectivity between different networks with the worsening of the pathology.
Project description:This study aimed to investigate the changes in functional connectivity (FC) within each resting-state network (RSN) and between RSNs in subcortical stroke patients who were well recovered in global motor function. Eleven meaningful RSNs were identified via functional magnetic resonance imaging data from 25 subcortical stroke patients and 22 normal controls using independent component analysis. Compared with normal controls, stroke patients exhibited increased intranetwork FC in the sensorimotor (SMN), visual (VN), auditory (AN), dorsal attention (DAN), and default mode (DMN) networks; they also exhibited decreased intranetwork FC in the frontoparietal network (FPN) and anterior DMN. Stroke patients displayed a shift from no FC in controls to negative internetwork FC between the VN and AN as well as between the VN and SMN. Stroke patients also exhibited weakened positive (anterior and posterior DMN; posterior DMN and right FPN) or negative (AN and right FPN; posterior DMN and dorsal SMN) internetwork FC when compared with normal controls. We suggest that subcortical stroke may induce connectivity changes in multiple functional networks, affecting not only the intranetwork FC within RSNs but also the internetwork FC between these RSNs.
Project description:Compared to linear independent component analysis (ICA), non-linear ICA is more suitable for the decomposition of mixed components. Existing studies of functional magnetic resonance imaging (fMRI) data by using linear ICA assume that the brain's mixed signals, which are caused by the activity of brain, are formed through the linear combination of source signals. But the application of the non-linear combination of source signals is more suitable for the mixed signals of brain. For this reason, we investigated statistical differences in resting state networks (RSNs) on 32 healthy controls (HC) and 38 mild cognitive impairment (MCI) patients using post-nonlinear ICA. Post-nonlinear ICA is one of the non-linear ICA methods. Firstly, the fMRI data of all subjects was preprocessed. The second step was to extract independent components (ICs) of fMRI data of all subjects. In the third step, we calculated the correlation coefficient between ICs and RSN templates, and selected ICs of the largest spatial correlation coefficient. The ICs represent the corresponding RSNs. After finding out the eight RSNs of MCI group and HC group, one sample t-tests were performed. Finally, in order to compare the differences of RSNs between MCI and HC groups, the two-sample t-tests were carried out. We found that the functional connectivity (FC) of RSNs in MCI patients was abnormal. Compared with HC, MCI patients showed the increased and decreased FC in default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), somato-motor network (SMN), visual network(VN), MCI patients displayed the specifically decreased FC in auditory network (AN), self-referential network (SRN). The FC of core network (CN) did not reveal significant group difference. The results indicate that the abnormal FC in RSNs is selective in MCI patients.
Project description:Drug-resistant focal epilepsy is a major clinical problem and surgery is under-used. Better non-invasive techniques for epileptogenic zone localization are needed when MRI shows no lesion or an extensive lesion. The problem is interictal and ictal localization before propagation from the epileptogenic zone. High-density EEG (HDEEG) and magnetoencephalography (MEG) offer millisecond-order temporal resolution to address this but co-acquisition is challenging, ictal MEG studies are rare, long-term prospective studies are lacking, and fundamental questions remain. Should HDEEG-MEG discharges be assessed independently [electroencephalographic source localization (ESL), magnetoencephalographic source localization (MSL)] or combined (EMSL) for source localization? Which phase of the discharge best characterizes the epileptogenic zone (defined by intracranial EEG and surgical resection relative to outcome)? Does this differ for interictal and ictal discharges? Does MEG detect mesial temporal lobe discharges? Thirteen patients (10 non-lesional, three extensive-lesional) underwent synchronized HDEEG-MEG (72-94 channel EEG, 306-sensor MEG). Source localization (standardized low-resolution tomographic analysis with MRI patient-individualized boundary-element method) was applied to averaged interictal epileptiform discharges (IED) and ictal discharges at three phases: 'early-phase' (first latency 90% explained variance), 'mid-phase' (first of 50% rising-phase, 50% mean global field power), 'late-phase' (negative peak). 'Earliest-solution' was the first of the three early-phase solutions (ESL, MSL, EMSL). Prospective follow-up was 3-21 (median 12) months before surgery, 14-39 (median 21) months after surgery. IEDs (n = 1474) were recorded, seen in: HDEEG only, 626 (42%); MEG only, 232 (16%); and both 616 (42%). Thirty-three seizures were captured, seen in: HDEEG only, seven (21%); MEG only, one (3%); and both 25 (76%). Intracranial EEG was done in nine patients. Engel scores were I (9/13, 69%), II (2/13,15%), and III (2/13). MEG detected baso-mesial temporal lobe epileptogenic zone sources. Epileptogenic zone OR [odds ratio(s)] were significantly higher for earliest-solution versus early-phase IED-surgical resection and earliest-solution versus all mid-phase and late-phase solutions. ESL outperformed EMSL for ictal-surgical resection [OR 3.54, 95% confidence interval (CI) 1.09-11.55, P = 0.036]. MSL outperformed EMSL for IED-intracranial EEG (OR 4.67, 95% CI 1.19-18.34, P = 0.027). ESL outperformed MSL for ictal-surgical resection (OR 3.73, 95% CI 1.16-12.03, P = 0.028) but was outperformed by MSL for IED-intracranial EEG (OR 0.18, 95% CI 0.05-0.73, P = 0.017). Thus, (i) HDEEG and MEG source solutions more accurately localize the epileptogenic zone at the earliest resolvable phase of interictal and ictal discharges, not mid-phase (as is common practice) or late peak-phase (when signal-to-noise ratios are maximal); (ii) from empirical observation of the differential timing of HDEEG and MEG discharges and based on the superiority of ESL plus MSL over either modality alone and over EMSL, concurrent HDEEG-MEG signals should be assessed independently, not combined; (iii) baso-mesial temporal lobe sources are detectable by MEG; and (iv) MEG is not 'more accurate' than HDEEG-emphasis is best placed on the earliest signal (whether HDEEG or MEG) amenable to source localization. Our findings challenge current practice and our reliance on invasive monitoring in these patients. 10.1093/brain/awz015_video1 awz015media1 6018582479001.
Project description:Executive control function (ECF) deficit is a common complication of temporal lobe epilepsy (TLE). Characteristics of brain network connectivity in TLE with ECF dysfunction are still unknown. The aim of this study was to investigate resting-state functional connectivity (FC) changes in patients with unilateral intractable TLE with impaired ECF. Forty right-handed patients with left TLE confirmed by comprehensive preoperative evaluation and postoperative pathological findings were enrolled. The patients were divided into normal ECF (G1) and decreased ECF (G2) groups according to whether they showed ECF impairment on the Wisconsin Card Sorting Test (WCST). Twenty-three healthy volunteers were recruited as the healthy control (HC) group. All subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI). Group-information-guided independent component analysis (GIG-ICA) was performed to estimate resting-state networks (RSNs) for all subjects. General linear model (GLM) was employed to analyze intra-network FC (<i>p</i> < 0.05, false discovery rate, FDR correction) and inter-network FC (<i>p</i> < 0.05, Bonferroni correction) of RSN among three groups. Pearson correlations between FC and neuropsychological tests were also determined through partial correlation analysis (<i>p</i> < 0.05). Eleven meaningful RSNs were identified from 40 left TLE and 23 HC subjects. Comparison of intra-network FC of all 11 meaningful RSNs did not reveal significant difference among the three groups (<i>p</i> > 0.05, FDR correction). For inter-network analysis, G2 exhibited decreased FC between the executive control network (ECN) and default-mode network (DMN) when compared with G1 (<i>p</i> = 0.000, Bonferroni correction) and HC (<i>p</i> = 0.000, Bonferroni correction). G1 showed no significant difference of FC between ECN and DMN when compared with HC. Furthermore, FC between ECN and DMN had significant negative correlation with perseverative responses (RP), response errors (RE) and perseverative errors (RPE) and had significant positive correlation categories completed (CC) in both G1 and G2 (<i>p</i> < 0.05). No significant difference of Montreal Cognitive Assessment (MoCA) was found between G1 and G2, while intelligence quotient (IQ) testing showed significant difference between G1and G2.There was no correlation between FC and either MoCA or IQ performance. Our findings suggest that ECF impairment in unilateral TLE is not confined to the diseased temporal lobe. Decreased FC between DMN and ECN may be an important characteristic of RSN in intractable unilateral TLE.