Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity

Functional connectivity via the dorsolateral prefrontal cortex in the late phase of rest periods predicts offline learning

The relationship between offline learning gains and functional connectivity (FC) has been investigated in several studies. They have focused on average motor task performance and resting-state FC across subjects. Generally, individual differences are seen in both offline learning gain and neurophysiological profiles in resting-state FC. However, few studies have focused on the relationship between individual differences in offline learning gain and temporal characteristics of resting-state FC. The present study aimed to clarify this relationship between the two profiles. Thirty-four healthy right-handed participants performed a force-controlled motor task. Electroencephalography was performed during the 15-minute wakeful rest period between tasks. The results revealed a significant correlation between offline learning gain and FC between the contralateral dorsolateral prefrontal cortex (DLPFC) and contralateral primary motor cortex (M1), and ipsilateral primary somatosensory cortex (S1) during late phase of the rest interval. These results are consistent with the findings of previous studies showing the FC between M1, which is necessary for awake offline learning, and DLPFC, which is related to motor control. Additionally, sensory feedback related to force control may be caused by the interaction between contralateral DLPFC and ipsilateral S1. Our study shed light on the temporal profiles of resting-state FC associated with individual differences in offline learning.

HD-tDCS induced changes in resting-state functional connectivity: Insights from EF modeling

BACKGROUND

High-definition transcranial direct current stimulation (HD-tDCS) holds promise for therapeutic use in psychiatric disorders. One obstacle for the implementation into clinical practice is response variability. One way to tackle this obstacle is the use of Individualized head models.

OBJECTIVE

This study investigated the variability of HD-tDCS induced electric fields (EFs) and its impact on resting-state functional connectivity (rsFC) during different time windows.

METHODS

In this randomized, double-blind, and sham controlled study, seventy healthy males underwent 20 min of 1.5 mA HD-tDCS on the right inferior frontal gyrus (rIFG) while undergoing resting-state functional magnetic resonance imaging (rs-fMRI). Individual head models and EF simulations were created from anatomical images. The effects of HD-tDCS on rsFC were assessed using a seed-to-voxel analysis. A subgroup analysis explored the relationship between EF magnitude and rsFC during different stimulation time windows.

RESULTS

Results highlighted significant variability in HD-tDCS-induced EFs. Compared to the sham group, the active group showed increased rsFC between the rIFG and the left prefrontal cortex, during and after stimulation. During active stimulation, EF magnitude correlated positively with rsFC between the rIFG and the left hippocampus initially, and negatively during the subsequent period.

CONCLUSION

This study indicated an HD-tDCS induced increase of rsFC between left and right prefrontal areas. Furthermore, an interaction between the magnitude and the duration of HD-tDCS on rsFC was observed. Due to the high EF variability that was apparent, these findings highlight the need for individualized HD-tDCS protocols and the creation of head models to optimize effects and reduce response heterogeneity.

Dynamic Functional Connectivity Analysis Using Network-Based Brain State Identification, Application on Temporal Lobe Epilepsy

Epilepsy is a brain network disorder caused by discharges of interconnected groups of neurons and resulting brain dysfunction. The brain network can be characterized by intra- and inter-regional functional connectivity (FC). However, since the BOLD signal is inherently non-stationary, the FC is evidenced to be varying over time. Considering the dynamic characteristics of the functional network, we aimed to obtain dynamic brain states and their properties using network-based analyses for the comparison of healthy control and temporal lobe epilepsy (TLE) groups and also lateralization of TLE patients. We used dwelling time, transition time, and brain network connection in each state as the dynamic features for this purpose. Results showed a significant difference in dwelling time and transition time between the healthy control group and both left TLE and right TLE groups and also a significant difference in brain network connections between the left and right TLE groups.

Understanding the heterogeneity of dynamic functional connectivity patterns in first-episode drug naïve depression using normative models

BACKGROUND

The heterogeneity of the clinical symptoms and presumptive neural pathologies has stunted progress toward identifying reproducible biomarkers and limited therapeutic interventions' effectiveness for the first episode drug-naïve major depressive disorders (FEDN-MDD). This study combined the dynamic features of fMRI data and normative modeling to quantitative and individualized metrics for delineating the biological heterogeneity of FEDN-MDD.

METHOD

Two hundred seventy-four adults with FEDN-MDD and 832 healthy controls from International Big-Data Center for Depression Research were included. Subject-specific dynamic brain networks and network fluctuation characteristics were computed for each subject using the group information-guided independent component analysis. Then, we mapped the heterogeneity of the dynamic features (network fluctuation characteristics and dynamic functional connectivity within brain networks) in the patients group via normative modeling.

RESULTS

The FEDN-MDD whose network fluctuation characteristics deviate from the normative model also showed significant differences within the default mode network, executive control network, and limbic network compared with healthy controls. Furthermore, the network fluctuation characteristics are significantly increased in patients with FEDN-MDD. About 4.74 % of the patients showed a deviation of dynamic functional connectivity, and only 3.35 % of the controls deviated from the normative model in above 100 connectivities. More patients than healthy controls showed extreme dynamic variabilities in above 100 connectivities.

CONCLUSIONS

This work evaluates the efficacy of an individualized approach based on normative modeling for understanding the heterogeneity of abnormal dynamic functional connectivity patterns in FEDN-MDD, and could be used as complementary to classical case-control comparisons.

Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning

The article presents research on the influence of the type of UHF antenna and the type of machine learning algorithm on the effectiveness of classification of partial discharges (PD) occurring in the insulation system of a power transformer. For this purpose, four antennas specially adapted to be installed in the transformer tank (UHF disk sensor, UHF drain valve sensor, planar inverted F-type antenna, Hilbert curve fractal antenna) and a reference log-periodic antenna were used in laboratory tests. During the research, the main types of PD, typical for oil-paper insulation, were generated, i.e., PD in oil, PD in oil wedge, PD in gas bubbles, surface discharges, and creeping sparks. For the registered UHF PD pulses, nine features in the frequency domain and four features in the wavelet domain were extracted. Then, the PD classification process was carried out with the use of selected methods of supervised machine learning. The study investigated the influence of the number and type of feature on the obtained classification results gained with the following machine-learning methods: decision tree, support vector machine, Bayes method, k-nearest neighbor, linear discriminant, and ensemble machine. As a result of the works carried out, it was found that the highest accuracies are gathered for the feature representing peak frequency using a decision tree, reaching values, depending on the type of antenna, from 89.7% to 100%, with an average of 96.8%. In addition, it was found that the MRMR method reduces the number of features from 13 to 1 while maintaining very high effectiveness. The broadband log-periodic antenna ensured the highest average efficiency (100%) in the PD classification. In the case of the tested antennas adapted to work in an energy transformer tank, the highest defect-recognition efficiency is provided by the UHF disk sensor (99.3%), and the lowest (89.7%) is by the UHF drain valve sensor.

From temporal to spatial topography: hierarchy of neural dynamics in higher- and lower-order networks shapes their complexity

The brain shows a topographical hierarchy along the lines of lower- and higher-order networks. The exact temporal dynamics characterization of this lower-higher-order topography at rest and its impact on task states remains unclear, though. Using 2 functional magnetic resonance imaging data sets, we investigate lower- and higher-order networks in terms of the signal compressibility, operationalized by Lempel-Ziv complexity (LZC). As we assume that this degree of complexity is related to the slow-fast frequency balance, we also compute the median frequency (MF), an estimation of frequency distribution. We demonstrate (i) topographical differences at rest between higher- and lower-order networks, showing lower LZC and MF in the former; (ii) task-related and task-specific changes in LZC and MF in both lower- and higher-order networks; (iii) hierarchical relationship between LZC and MF, as MF at rest correlates with LZC rest-task change along the lines of lower- and higher-order networks; and (iv) causal and nonlinear relation between LZC at rest and LZC during task, with MF at rest acting as mediator. Together, results show that the topographical hierarchy of lower- and higher-order networks converges with their temporal hierarchy, with these neural dynamics at rest shaping their range of complexity during task states in a nonlinear way.

Development of Nanosensors Based Intelligent Packaging Systems: Food Quality and Medicine

The issue of medication noncompliance has resulted in major risks to public safety and financial loss. The new omnipresent medicine enabled by the Internet of things offers fascinating new possibilities. Additionally, an in-home healthcare station (IHHS), it is necessary to meet the rapidly increasing need for routine nursing and on-site diagnosis and prognosis. This article proposes a universal and preventive strategy to drug management based on intelligent and interactive packaging (I2Pack) and IMedBox. The controlled delamination material (CDM) seals and regulates wireless technologies in novel medicine packaging. As such, wearable biomedical sensors may capture a variety of crucial parameters via wireless communication. On-site treatment and prediction of these critical factors are made possible by high-performance architecture. The user interface is also highlighted to make surgery easier for the elderly, disabled, and patients. Land testing incorporates and validates an approach for prototyping I2Pack and iMedBox. Additionally, sustainability, increased product safety, and quality standards are crucial throughout the life sciences. To achieve these standards, intelligent packaging is also used in the food and pharmaceutical industries. These technologies will continuously monitor the quality of a product and communicate with the user. Data carriers, indications, and sensors are the three most important groups. They are not widely used at the moment, although their potential is well understood. Intelligent packaging should be used in these sectors and the functionality of the systems and the values presented in this analysis.

BVAR-Connect: A Variational Bayes Approach to Multi-Subject Vector Autoregressive Models for Inference on Brain Connectivity Networks

In this paper we propose BVAR-connect, a variational inference approach to a Bayesian multi-subject vector autoregressive (VAR) model for inference on effective brain connectivity based on resting-state functional MRI data. The modeling framework uses a Bayesian variable selection approach that flexibly integrates multi-modal data, in particular structural diffusion tensor imaging (DTI) data, into the prior construction. The variational inference approach we develop allows scalability of the methods and results in the ability to estimate subject- and group-level brain connectivity networks over whole-brain parcellations of the data. We provide a brief description of a user-friendly MATLAB GUI released for public use. We assess performance on simulated data, where we show that the proposed inference method can achieve comparable accuracy to the sampling-based Markov Chain Monte Carlo approach but at a much lower computational cost. We also address the case of subject groups with imbalanced sample sizes. Finally, we illustrate the methods on resting-state functional MRI and structural DTI data on children with a history of traumatic injury.

Modulation of epileptic networks by transient interictal epileptic activity: A dynamic approach to simultaneous EEG-fMRI

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EEG-fMRI has been instrumental in characterizing brain networks in epilepsy.

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Its value is documented in the pre-surgical assessment of drug-resistant epilepsy.

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The delineation of brain areas to resect is fundamental for the post-surgical outcome.

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Standard EEG-fMRI in epilepsy assesses static functional connectivity of the network.

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EEG-fMRI dynamic connectivity identifies transitory features of specific connections.

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We integrate dynamic fMRI connectivity and dynamic patterns of simultaneous scalp EEG.

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This allows to better characterize the spatiotemporal aspects of epileptic networks.

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This may help in more efficiently target the surgical intervention.

Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to define brain hemodynamic changes, measured by the Blood Oxygen Level Dependent (BOLD) signal, associated to the interictal epileptic discharges (IED), which together with ictal events constitute a signature of epileptic disease. Given the highly time-varying nature of epileptic activity, a dynamic functional connectivity (dFC) analysis of EEG-fMRI data appears particularly suitable, having the potential to identify transitory features of specific connections in epileptic networks. In the present study, we propose a novel method, defined dFC-EEG, that integrates dFC assessed by fMRI with the information recorded by simultaneous scalp EEG, in order to identify the connections characterised by a dynamic profile correlated with the occurrence of IED, forming the dynamic epileptic subnetwork. Ten patients with drug-resistant focal epilepsy were included, with different aetiology and showing a widespread (or multilobar) BOLD activation, defined as involving at least two distinct clusters, located in two different lobes and/or extended to the hemisphere contralateral to the epileptic focus. The epileptic focus was defined from the IED-related BOLD map. Regions involved in the occurrence of interictal epileptic activity; i.e., forming the epileptic network, were identified by a general linear model considering the timecourse of the fMRI-defined focus as main regressor. dFC between these regions was assessed with a sliding-window approach. dFC timecourses were then correlated with the sliding-window variance of the IED signal (VarIED), to identify connections whose dynamics related to the epileptic activity; i.e., the dynamic epileptic subnetwork. As expected, given the very different clinical picture of each individual, the extent of this subnetwork was highly variable across patients, but was but was reduced of at least 30% with respect to the initially identified epileptic network in 9/10 patients. The connections of the dynamic subnetwork were most commonly close to the epileptic focus, as reflected by the laterality index of the subnetwork connections, reported higher than the one within the original epileptic network. Moreover, the correlation between dFC timecourses and VarIED was predominantly positive, suggesting a strengthening of the dynamic subnetwork associated to the occurrence of IED. The integration of dFC and scalp IED offers a more specific description of the epileptic network, identifying connections strongly influenced by IED. These findings could be relevant in the pre-surgical evaluation for the resection or disconnection of the epileptogenic zone and help in reaching a better post-surgical outcome. This would be particularly important for patients characterised by a widespread pathological brain activity which challenges the surgical intervention.

Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach

PURPOSE

Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE).

METHODS

Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE.

RESULTS

Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%.

CONCLUSION

Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.

Characterization of sponge-associated Verrucomicrobia: microcompartment-based sugar utilization and enhanced toxin-antitoxin modules as features of host-associated Opitutales

Bacteria of the phylum Verrucomicrobia are ubiquitous in marine environments and can be found as free-living organisms or as symbionts of eukaryotic hosts. Little is known about host-associated Verrucomicrobia in the marine environment. Here we reconstructed two genomes of symbiotic Verrucomicrobia from bacterial metagenomes derived from the Atlanto-Mediterranean sponge Petrosia ficiformis and three genomes from strains that we isolated from offshore seawater of the Eastern Mediterranean Sea. Phylogenomic analysis of these five strains indicated that they are all members of Verrucomicrobia subdivision 4, order Opitutales. We compared these novel sponge-associated and seawater-isolated genomes to closely related Verrucomicrobia. Genomic analysis revealed that Planctomycetes-Verrucomicrobia microcompartment gene clusters are enriched in the genomes of symbiotic Opitutales including sponge symbionts but not in free-living ones. We hypothesize that in sponge symbionts these microcompartments are used for degradation of l-fucose and l-rhamnose, which are components of algal and bacterial cell walls and therefore may be found at high concentrations in the sponge tissue. Furthermore, we observed an enrichment of toxin-antitoxin modules in symbiotic Opitutales. We suggest that, in sponges, verrucomicrobial symbionts utilize these modules as a defence mechanism against antimicrobial activity deriving from the abundant microbial community co-inhabiting the host.

Dynamic functional connectivity of the migraine brain: a resting-state functional magnetic resonance imaging study

Migraine headache is an episodic phenomenon, and patients with episodic migraine have ictal (headache), peri-ictal (premonitory, aura, and postdrome), and interictal (asymptomatic) phases. We aimed to find the functional characteristics of the migraine brain regardless of headache phase using dynamic functional connectivity analysis. We prospectively recruited 50 patients with migraine and 50 age- and sex-matched controls. All subjects underwent a resting-state functional magnetic resonance imaging. Significant networks were defined in a data-driven fashion from the interictal (>48 hours apart from headache phases) patients and matched controls (interictal data set) and tested to ictal or peri-ictal patients and controls (ictal/peri-ictal data set). Both static and dynamic analyses were used for the between-group comparison. A false discovery rate correction was performed. As a result, the static analysis did not reveal a network which was significant in both interictal and ictal/peri-ictal data sets. Dynamic analysis revealed significant between-group differences in 7 brain networks in the interictal data set, among which a frontoparietal network (controls > patients, P = 0.0467), 2 brainstem networks (patients > controls, P = 0.0467 and <0.001), and a cerebellar network (controls > patients, P = 0.0408 and <0.001 in 2 states) remained significant in the ictal/peri-ictal data set. Using these networks, migraine was classified with a sensitivity of 0.70 and specificity of 0.76 in the ictal/peri-ictal data set. In conclusion, the dynamic connectivity analysis revealed more functional networks related to migraine than the conventional static analysis, suggesting a substantial temporal fluctuation in functional characteristics. Our data also revealed migraine-related networks which show significant difference regardless of headache phases between patients and controls.

The Morphology of Somatosensory Evoked Potentials During Middle Cerebral Artery Aneurysm Clipping (MoSAC): A Pilot Study

Somatosensory evoked potential (SEP) monitoring is a standard tool during clipping of aneurysms of the middle cerebral artery (MCA), and the parameter used to detect a state of cortical ischemia is amplitude. We think that the sensitivity of SEP can however be improved by using other parameters. Our study moves in this direction via SEP morphology. In this pilot preliminary study, involving a small sample without postoperative neurological deficit, we aimed at investigating the value of SEP morphology (in the 15- to 35-ms time frame), in comparison with SEP amplitude (N20 peak-to-peak), as a measure of sensitivity to blood flow reduction. The changes in the SEP morphology of 16 patients undergoing clipping of an unruptured MCA aneurysm was studied. We applied the Morph-Fréchet index for each recorded SEP (at 30-second intervals), quantifying the pattern shape change with regard to the average SEP recorded after dura opening (baseline). We also compared 3 measurements of the SEP morphology, without and with GARCH-derived filter. Filtered Morph-Fréchet never exceeded the individual's "normality" range in baseline but did so in 81% of the risk phase on average across the 16 subjects, which is more than that for amplitude (36%, P = .002). This pilot study indicates that a measurement derived from the networking nature of the brain was sensitive to blood flow reduction. The SEP morphology approach promises to improve SEP monitoring sensitivity during clipping of unruptured MCA aneurysms. New and Noteworthy. The higher sensitivity to blood flow reduction of SEP morphology than amplitude promises to improve the effectiveness of intraoperative monitoring during MCA aneurysm clipping procedures.

Dynamic functional connectivity and graph theory metrics in a rat model of temporal lobe epilepsy reveal a preference for brain states with a lower functional connectivity, segregation and integration

Epilepsy is a neurological disorder characterized by recurrent epileptic seizures. The involvement of abnormal functional brain networks in the development of epilepsy and its comorbidities has been demonstrated by electrophysiological and neuroimaging studies in patients with epilepsy. This longitudinal study investigated changes in dynamic functional connectivity (dFC) and network topology during the development of epilepsy using the intraperitoneal kainic acid (IPKA) rat model of temporal lobe epilepsy (TLE). Resting state functional magnetic resonance images (rsfMRI) of 20 IPKA animals and 7 healthy control animals were acquired before and 1, 3, 6, 10 and 16 weeks after status epilepticus (SE) under medetomidine anaesthesia using a 7 T MRI system. Starting from 17 weeks post-SE, hippocampal EEG was recorded to determine the mean daily seizure frequency of each animal. Dynamic FC was assessed by calculating the correlation matrices between fMRI time series of predefined regions of interest within a sliding window of 50 s using a step length of 2 s. The matrices were classified into 6 FC states, each characterized by a correlation matrix, using k-means clustering. In addition, several time-variable graph theoretical network metrics were calculated from the time-varying correlation matrices and classified into 6 states of functional network topology, each characterized by a combination of network metrics. Our results showed that FC states with a lower mean functional connectivity, lower segregation and integration occurred more often in IPKA animals compared to control animals. Functional connectivity also became less variable during epileptogenesis. In addition, average daily seizure frequency was positively correlated with percentage dwell time (i.e. how often a state occurs) in states with high mean functional connectivity, high segregation and integration, and with the number of transitions between states, while negatively correlated with percentage dwell time in states with a low mean functional connectivity, low segregation and low integration. This indicates that animals that dwell in states of higher functional connectivity, higher segregation and higher integration, and that switch more often between states, have more seizures.

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

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.

Fatigue Detection during Sit-To-Stand Test Based on Surface Electromyography and Acceleration: A Case Study

The latest studies of the 30-second sit-to-stand (30-STS) test aim to describe it by employing kinematic variables, muscular activity, or fatigue through electromyography (EMG) instead of a number of repetitions. The aim of the present study was to develop a detection system based on acceleration measured using a smartphone to analyze fatigue during the 30-STS test with surface electromyography as the criterion. This case study was carried out on one woman, who performed eight trials. EMG data from the lower limbs and trunk muscles, as well as trunk acceleration were recorded. Both signals from eight trials were preprocessed, being averaged and temporarily aligned. The EMG signal was processed, calculating the spectral centroid (SC) by Discrete Fourier Transform, while the acceleration signal was processed by Discrete Wavelet Transform to calculate its energy percentage. Regarding EMG, fatigue in the vastus medialis of the quadriceps appeared as a decrease in SC, with a descending slope of 12% at second 12, indicating fatigue. However, acceleration analysis showed an increase in the percentage of relative energy, acting like fatigue firing at second 19. This assessed fatigue according to two variables of a different nature. The results will help clinicians to obtain information about fatigue using an accessible and inexpensive device, i.e., as a smartphone.

Resting brain dynamics at different timescales capture distinct aspects of human behavior

Linking human behavior to resting-state brain function is a central question in systems neuroscience. In particular, the functional timescales at which different types of behavioral factors are encoded remain largely unexplored. The behavioral counterparts of static functional connectivity (FC), at the resolution of several minutes, have been studied but behavioral correlates of dynamic measures of FC at the resolution of a few seconds remain unclear. Here, using resting-state fMRI and 58 phenotypic measures from the Human Connectome Project, we find that dynamic FC captures task-based phenotypes (e.g., processing speed or fluid intelligence scores), whereas self-reported measures (e.g., loneliness or life satisfaction) are equally well explained by static and dynamic FC. Furthermore, behaviorally relevant dynamic FC emerges from the interconnections across all resting-state networks, rather than within or between pairs of networks. Our findings shed new light on the timescales of cognitive processes involved in distinct facets of behavior.

Identification of epileptic brain states by dynamic functional connectivity analysis of simultaneous EEG-fMRI: a dictionary learning approach

Most fMRI studies of the brain's intrinsic functional connectivity (FC) have assumed that this is static; however, it is now clear that it changes over time. This is particularly relevant in epilepsy, which is characterized by a continuous interchange between epileptic and normal brain states associated with the occurrence of epileptic activity. Interestingly, recurrent states of dynamic FC (dFC) have been found in fMRI data using unsupervised learning techniques, assuming either their sparse or non-sparse combination. Here, we propose an l₁-norm regularized dictionary learning (l₁-DL) approach for dFC state estimation, which allows an intermediate and flexible degree of sparsity in time, and demonstrate its application in the identification of epilepsy-related dFC states using simultaneous EEG-fMRI data. With this l₁-DL approach, we aim to accommodate a potentially varying degree of sparsity upon the interchange between epileptic and non-epileptic dFC states. The simultaneous recording of the EEG is used to extract time courses representative of epileptic activity, which are incorporated into the fMRI dFC state analysis to inform the selection of epilepsy-related dFC states. We found that the proposed l₁-DL method performed best at identifying epilepsy-related dFC states, when compared with two alternative methods of extreme sparsity (k-means clustering, maximum; and principal component analysis, minimum), as well as an l₀-norm regularization framework (l₀-DL), with a fixed amount of temporal sparsity. We further showed that epilepsy-related dFC states provide novel insights into the dynamics of epileptic networks, which go beyond the information provided by more conventional EEG-correlated fMRI analysis, and which were concordant with the clinical profile of each patient. In addition to its application in epilepsy, our study provides a new dFC state identification method of potential relevance for studying brain functional connectivity dynamics in general.

Decreased subregional specificity of the putamen in Parkinson's Disease revealed by dynamic connectivity-derived parcellation

Parkinson's Disease (PD) is associated with decreased ability to perform habitual tasks, relying instead on goal-directed behaviour subserved by different cortical/subcortical circuits, including parts of the putamen. We explored the functional subunits in the putamen in PD using novel dynamic connectivity features derived from resting state fMRI recorded from thirty PD subjects and twenty-eight age-matched healthy controls (HC). Dynamic functional segmentation of the putamina was obtained by determining the correlation between each voxel in each putamen along a moving window and applying a joint temporal clustering algorithm to establish cluster membership of each voxel at each window. Contiguous voxels that had consistent cluster membership across all windows were then considered to be part of a homogeneous functional subunit. As PD subjects robustly had two homogenous clusters in the putamina, we also segmented the putamina in HC into two dynamic clusters for a fair comparison. We then estimated the dynamic connectivity using sliding windowed correlation between the mean signal from the identified homogenous subunits and 56 other predefined cortical and subcortical ROIs. Specifically, the mean dynamic connectivity strength and connectivity deviation were then compared to evaluate subregional differences. HC subjects had significant differences in mean dynamic connectivity and connectivity deviation between the two putaminal subunits. The posterior subunit connected strongly to sensorimotor areas, the cerebellum, as well as the middle frontal gyrus. The anterior subunit had strong mean dynamic connectivity to the nucleus accumbens, hippocampus, amygdala, caudate and cingulate. In contrast, PD subjects had fewer differences in mean dynamic connectivity between subunits, indicating a degradation of subregional specificity. Overall UPDRS III and MoCA scores could be predicted using mean dynamic connectivity strength and connectivity deviation. Side of onset of the disease was also jointly related with functional connectivity features. Our results suggest a robust loss of specificity of mean dynamic connectivity and connectivity deviation in putaminal subunits in PD that is sensitive to disease severity. In addition, altered mean dynamic connectivity and connectivity deviation features in PD suggest that looking at connectivity dynamics offers an additional dimension for assessment of neurodegenerative disorders.

A Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome after Anterior Temporal Lobe Resection

We develop an integrative Bayesian predictive modeling framework that identifies individual pathological brain states based on the selection of fluoro-deoxyglucose positron emission tomography (PET) imaging biomarkers and evaluates the association of those states with a clinical outcome. We consider data from a study on temporal lobe epilepsy (TLE) patients who subsequently underwent anterior temporal lobe resection. Our modeling framework looks at the observed profiles of regional glucose metabolism in PET as the phenotypic manifestation of a latent individual pathologic state, which is assumed to vary across the population. The modeling strategy we adopt allows the identification of patient subgroups characterized by latent pathologies differentially associated to the clinical outcome of interest. It also identifies imaging biomarkers characterizing the pathological states of the subjects. In the data application, we identify a subgroup of TLE patients at high risk for post-surgical seizure recurrence after anterior temporal lobe resection, together with a set of discriminatory brain regions that can be used to distinguish the latent subgroups. We show that the proposed method achieves high cross-validated accuracy in predicting post-surgical seizure recurrence.