Resting-state fMRI studies in epilepsy

Resting-State Functional Magnetic Resonance Imaging Network Association With Mortality, Epilepsy, Cognition, and Motor Two-Year Outcomes in Suspected Severe Neonatal Acute Brain Injury

BACKGROUND AND OBJECTIVES

In acute brain injury of neonates, resting-state functional magnetic resonance imaging (MRI) (RS) showed incremental association with consciousness, mortality, cognitive and motor development, and epilepsy, with correction for multiple comparisons, at six months postgestation in neonates with suspected acute brain injury (ABI). However, there are relatively few developmental milestones at six months to benchmark against, thus, we extended this cohort study to evaluate two-year outcomes.

METHODS

In 40 consecutive neonates with ABI and RS, ordinal scores of resting-state networks; MRI, magnetic resonance spectroscopy, and electroencephalography; and up to 42-month outcomes of mortality, general and motor development, Pediatric Cerebral Performance Category Scale (PCPC), and epilepsy informed associations between tests and outcomes.

RESULTS

Mean gestational age was 37.8 weeks, 68% were male, and 60% had hypoxic-ischemic encephalopathy. Three died in-hospital, four at six to 42 months, and five were lost to follow-up. Associations included basal ganglia network with PCPC (P = 0.0003), all-mortality (P = 0.005), and motor (P = 0.0004); language/frontoparietal network with developmental delay (P = 0.009), PCPC (P = 0.006), and all-mortality (P = 0.01); default mode network with developmental delay (P = 0.003), PCPC (P = 0.004), neonatal intensive care unit mortality (P = 0.01), and motor (P = 0.009); RS seizure onset zone with epilepsy (P = 0.01); and anatomic MRI with epilepsy (P = 0.01).

CONCLUSION

For the first time, at any age, resting state functional MRI in ABI is associated with long-term epilepsy and RSNs predicted mortality in neonates. Severity of RSN abnormality was associated with incrementally worsened neurodevelopment including cognition, language, and motor function over two years.

BOLD cardiorespiratory pulsatility in the brain: from noise to signal of interest

Functional magnetic resonance imaging (fMRI) based on the Blood Oxygen Level Dependent (BOLD) contrast has been extensively used to map brain activity and connectivity in health and disease. Standard fMRI preprocessing includes different steps to remove confounds unrelated to neuronal activity. First, this narrative review explores how signal fluctuations due to cardiac and respiratory activity, usually considered as "physiological noise" and regressed out from fMRI time series. However, these signal components bear useful information about some mechanisms of brain functioning (e.g., glymphatic clearance) or cerebrovascular compliance in response to arterial pressure waves. Aging and chronic diseases can cause stiffening of the aorta and other main arteries, with a reduced dampening effect resulting in greater transmission of pressure impulses to the brain. Importantly, the continuous hammering of cardiac pulsations can produce local alterations of the mechanical properties of the small cerebral vessels, with a progressive deterioration that ultimately affects neuronal functionality. Second, the review emphasizes how fMRI can study the brain patterns most affected by cardiac pulsations in health and disease with high spatiotemporal resolution, offering the opportunity to identify much more specific risk markers than systemic factors based on measurements of the vascular compliance of large arteries or other global risk factors. In this regard, modern fast fMRI acquisition techniques allow a better characterization of these pulsatile signal components due to reduced aliasing effects, turning what has been traditionally considered as noise in a signal of interest that can be used to develop novel non-invasive biomarkers in different clinical contexts.

The altered spontaneous neural activity in patients with Parkinson's disease and its predictive value for the motor improvement of deep brain stimulation

BACKGROUND

This study aims to investigate the altered spontaneous neural activity in patients with Parkinson's disease (PD) revealed by amplitudes of low-frequency fluctuations (ALFF) of resting-state fMRI, and the feasibility of using ALFF as neuroimaging predictors for motor improvement after bilateral subthalamic nucleus (STN) deep brain stimulation (DBS).

METHODS

Fourty-four patients and 44 healthy controls were included in this study. First, the ALFF of patients with PD was compared with that of controls; then significant clusters were correlated with motor improvement after DBS (unified Parkinson's disease rating scale (UPDRS-III)) and other clinical variables. Second, regression and classification of the machine learning models were conducted to predict motor improvement after DBS. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the classification model.

RESULTS

Compared with healthy controls, patients with PD showed increased ALFF in the bilateral motor area and decreased ALFF in the bilateral temporal cortex and cerebellum. The Hoehn-Yahr stages correlated with ALFF within the bilateral cerebellum (p = 0.021), and UPDRS-III improvement correlated with ALFF in the left (p < 0.001) and right (p = 0.005) motor areas. The regression model showed a significant correlation between the predicted and observed UPDRS-III changes (R = 0.65, p < 0.001). The ROC analysis revealed an area under the curve (AUC) of 0.94 which differentiated moderate and superior DBS responders.

CONCLUSION

The results revealed altered ALFF patterns in patients with PD and their correlations with clinical variables. Both binary and continuous ALFF can potentially serve as predictive biomarkers for DBS response.

Anti-Seizure and Neuronal Protective Effects of Irisin in Kainic Acid-Induced Chronic Epilepsy Model with Spontaneous Seizures

An increased level of reactive oxygen species is a key factor in neuronal apoptosis and epileptic seizures. Irisin reportedly attenuates the apoptosis and injury induced by oxidative stress. Therefore, we evaluated the effects of exogenous irisin in a kainic acid (KA)-induced chronic spontaneous epilepsy rat model. The results indicated that exogenous irisin significantly attenuated the KA-induced neuronal injury, learning and memory defects, and seizures. Irisin treatment also increased the levels of brain-derived neurotrophic factor (BDNF) and uncoupling protein 2 (UCP2), which were initially reduced following KA administration. Furthermore, the specific inhibitor of UCP2 (genipin) was administered to evaluate the possible protective mechanism of irisin. The reduced apoptosis, neurodegeneration, and spontaneous seizures in rats treated with irisin were significantly reversed by genipin administration. Our findings indicated that neuronal injury in KA-induced chronic epilepsy might be related to reduced levels of BDNF and UCP2. Moreover, our results confirmed the inhibition of neuronal injury and epileptic seizures by exogenous irisin. The protective effects of irisin may be mediated through the BDNF-mediated UCP2 level. Our results thus highlight irisin as a valuable therapeutic strategy against neuronal injury and epileptic seizures.

A Study of Spontaneous Brain Activity on Resting-State Functional Magnetic Resonance Imaging in Adults with MRI-Negative Temporal Lobe Epilepsy

Purpose

Patients with magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE-N) represent an important subgroup of temporal lobe epilepsy (TLE). Here, we aimed to combine three voxel-based local brain area analysis methods of resting-state functional MRI (rs-fMRI), to examine the TLE-N patients' resting brain function based on neural synchronization and intensity of local brain areas.

Methods

The study included 47 patients with TLE, including 28 cases of drug-controlled TLE (cTLE-N) and 19 cases of drug-resistant TLE-N (rTLE-N), as well as 30 participants in the healthy control (HC) group. To comprehensively assess the altered brain function associated with TLE-N patients, we analyzed three data-driven rs-fMRI algorithms for amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF) and regional homogeneity (ReHo).

Results

Compared to the HC group, the distribution of abnormal functional brain areas in cTLE-N patients was dominated by occipital lobe activation, as measured by increased fALFF values in the superior occipital gyrus (SOG) and increased ReHo values in the lingual gyrus (Lin), fusiform gyrus, and middle occipital gyrus. Patients with rTLE-N exhibited a diffuse distribution of abnormal functional brain areas, showing increased fALFF values in the SOG, Lin, superior temporal gyrus, and postcentral gyrus, and decreased fALFF values in the inferior frontal gyrus orbital, parahippocampal gyrus, and superior frontal gyrus orbital. The ReHo values were reduced in the orbital region of the middle frontal gyrus, the precuneus, and the parietal inferior angular gyrus; while ReHo values were elevated values in several frontal, temporal, occipital, and subcortical brain areas.

Conclusion

Patients with rTLE-N have local brain activity changes in the prefrontal limbic system and default model network dysfunction, while cTLE-N patients have local brain activity changes in the visual functional areas. Different epilepsy networks exist between cTLE-N and rTLE-N.

Association of network connectivity via resting state functional MRI with consciousness, mortality, and outcomes in neonatal acute brain injury

BACKGROUND

An accurate and comprehensive test of integrated brain network function is needed for neonates during the acute brain injury period to inform on morbidity. This retrospective cohort study assessed whether integrated brain network function acquired by resting state functional MRI during the acute period in neonates with brain injury, is associated with acute exam, neonatal mortality, and 6-month outcomes.

METHODS

Study subjects included 40 consecutive neonates with resting state functional MRI acquired within 31 days after suspected brain insult from March 2018 to July 2019 at Phoenix Children's Hospital. Acute-period exam and test results were assigned ordinal scores based on severity as documented by respective treating specialists. Analyses (Fisher exact, Wilcoxon-rank sum test, ordinal/multinomial logistic regression) examined association of resting state networks with demographics, presentation, neurological exam, electroencephalogram, anatomical MRI, magnetic resonance spectroscopy, passive task functional MRI, and outcomes of discharge condition, outpatient development, motor tone, seizure, and mortality.

RESULTS

Subjects had a mean (standard deviation) gestational age of 37.8 (2.6) weeks, a majority were male (63%), with a diagnosis of hypoxic ischemic encephalopathy (68%). Findings at birth included mild distress (48%), moderately abnormal neurological exam (33%), and consciousness characterized as awake but irritable (40%). Significant associations after multiple testing corrections were detected for resting state networks: basal ganglia with outpatient developmental delay (odds ratio [OR], 14.5; 99.4% confidence interval [CI], 2.00-105; P < .001) and motor tone/weakness (OR, 9.98; 99.4% CI, 1.72-57.9; P < .001); language/frontoparietal network with discharge condition (OR, 5.13; 99.4% CI, 1.22-21.5; P = .002) and outpatient developmental delay (OR, 4.77; 99.4% CI, 1.21-18.7; P=.002); default mode network with discharge condition (OR, 3.72; 99.4% CI, 1.01-13.78; P=.006) and neurological exam (P = .002 (FE); OR, 11.8; 99.4% CI, 0.73-191; P = .01 (OLR)); and seizure onset zone with motor tone/weakness (OR, 3.31; 99.4% CI, 1.08-10.1; P=.003). Resting state networks were not detected in three neonates, who died prior to discharge.

CONCLUSIONS

This study provides level 3 evidence (OCEBM Levels of Evidence Working Group) demonstrating that in neonatal acute brain injury, the degree of abnormality of resting state networks is associated with acute exam and outcomes. Total lack of brain network detection was only found in patients who did not survive.

[Optimized multi-scale entropy to localize epileptogenic hemisphere of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging]

Entropy model is widely used in epileptic electroencephalogram (EEG) analysis, but there are few reports on how to objectively select the parameters to compute the entropy model in the analysis of resting-state functional magnetic resonance imaging (rfMRI). Therefore, an optimization algorithm to confirm the parameters in multi-scale entropy (MSE) model was proposed, and the location of epileptogenic hemisphere was taken as an example to test the optimization effect by supervised machine learning. The rfMRI data of 20 temporal lobe epilepsy (TLE) patients with hippocampal sclerosis, positive on structural magnetic resonance imaging, were divided into left and right groups. Then, the parameters in MSE model were optimized by the receiver operating characteristic curves (ROC) and area under ROC curve (AUC) values in sensitivity analysis, and the entropy value of the brain regions with statistically significant difference between the groups were taken as sensitive features to epileptogenic hemisphere lateral. The optimized entropy values of these bio-marker brain areas were considered as feature vectors input into the support vector machine (SVM). Finally, combining optimized MSE model with SVM could accurately distinguish epileptogenic hemisphere in TLE at an average accuracy rate of 95%, which was higher than the current level. The results show that the MSE model parameter optimization algorithm can accurately extract the functional imaging markers sensitive to the epileptogenic hemisphere, and achieve the purpose of objectively selecting the parameters for MSE in rfMRI, which provides the basis for the application of entropy in advanced technology detection.

Increased resting state connectivity in the anterior default mode network of idiopathic epileptic dogs

Epilepsy is one of the most common chronic, neurological diseases in humans and dogs and considered to be a network disease. In human epilepsy altered functional connectivity in different large-scale networks have been identified with functional resting state magnetic resonance imaging. Since large-scale resting state networks have been consistently identified in anesthetised dogs’ application of this technique became promising in canine epilepsy research. The aim of the present study was to investigate differences in large-scale resting state networks in epileptic dogs compared to healthy controls. Our hypothesis was, that large-scale networks differ between epileptic dogs and healthy control dogs. A group of 17 dogs (Border Collies and Greater Swiss Mountain Dogs) with idiopathic epilepsy was compared to 20 healthy control dogs under a standardized sevoflurane anaesthesia protocol. Group level independent component analysis with dimensionality of 20 components, dual regression and two-sample t test were performed and revealed significantly increased functional connectivity in the anterior default mode network of idiopathic epileptic dogs compared to healthy control dogs (p = 0.00060). This group level differences between epileptic dogs and healthy control dogs identified using a rather simple data driven approach could serve as a starting point for more advanced resting state network analysis in epileptic dogs.

Stationary EEG pattern relates to large-scale resting state networks - An EEG-fMRI study connecting brain networks across time-scales

Relating brain dynamics acting on time scales that differ by at least an order of magnitude is a fundamental issue in brain research. The same is true for the observation of stable dynamical structures in otherwise highly non-stationary signals. The present study addresses both problems by the analysis of simultaneous resting state EEG-fMRI recordings of 53 patients with epilepsy. Confirming previous findings, we observe a generic and temporally stable average correlation pattern in EEG recordings. We design a predictor for the General Linear Model describing fluctuations around the stationary EEG correlation pattern and detect resting state networks in fMRI data. The acquired statistical maps are contrasted to several surrogate tests and compared with maps derived by spatial Independent Component Analysis of the fMRI data. By means of the proposed EEG-predictor we observe core nodes of known fMRI resting state networks with high specificity in the default mode, the executive control and the salience network. Our results suggest that both, the stationary EEG pattern as well as resting state fMRI networks are different expressions of the same brain activity. This activity is interpreted as the dynamics on (or close to) a stable attractor in phase space that is necessary to maintain the brain in an efficient operational mode. We discuss that this interpretation is congruent with the theoretical framework of complex systems as well as with the brain's energy balance.

Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere in Temporal Lobe Epilepsy Using Resting-State fMRI

The bottleneck associated with the validation of the parameters of the entropy model has limited the application of this model to modern functional imaging technologies such as the resting-state functional magnetic resonance imaging (rfMRI). In this study, an optimization algorithm that could choose the parameters of the multiscale entropy (MSE) model was developed, while the optimized effectiveness for localizing the epileptogenic hemisphere was validated through the classification rate with a supervised machine learning method. The rfMRI data of 20 mesial temporal lobe epilepsy patients with positive indicators (the indicators of epileptogenic hemisphere in clinic) in the hippocampal formation on either left or right hemisphere (equally divided into two groups) on the structural MRI were collected and preprocessed. Then, three parameters in the MSE model were statistically optimized by both receiver operating characteristic (ROC) curve and the area under the ROC curve value in the sensitivity analysis, and the intergroup significance of optimized entropy values was utilized to confirm the biomarked brain areas sensitive to the epileptogenic hemisphere. Finally, the optimized entropy values of these biomarked brain areas were regarded as the feature vectors input for a support vector machine to classify the epileptogenic hemisphere, and the classification effectiveness was cross-validated. Nine biomarked brain areas were confirmed by the optimized entropy values, including medial superior frontal gyrus and superior parietal gyrus (p < .01). The mean classification accuracy was greater than 90%. It can be concluded that combination of the optimized MSE model with the machine learning model can accurately confirm the epileptogenic hemisphere by rfMRI. With the powerful information interaction capabilities of 5G communication, the epilepsy side-fixing algorithm that requires computing power can be integrated into a cloud platform. The demand side only needs to upload patient data to the service platform to realize the preoperative assessment of epilepsy.

Altered spontaneous brain activity patterns in patients with neovascular glaucoma using amplitude of low-frequency fluctuations: A functional magnetic resonance imaging study

BACKGROUND

Neovascular glaucoma (NVG) can cause irreversible visual impairment and abnormal spontaneous changes in brain's visual system and other systems. There is little research on this aspect at present. However, amplitude of low-frequency fluctuations (ALFFs) can be used as an rs-fMRI analysis technique for testing changes in spontaneous brain activity patterns.

PURPOSE

The aim of this study was to probe the local characteristics of spontaneous brain activity in NVG patients and analyze their correlation with clinical behaviors.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) scans were obtained from eighteen patients with NVG (8 males, 10 females) and eighteen healthy controls (HCs; 8 males and 10 females) who were matched in age, gender, and education level. We evaluated spontaneous brain activity with the ALFF method. A receiver operating characteristic (ROC) curve was used to compare the average ALFF values for altered brain regions of NVG patients with those of HCs.

RESULTS

Compared with HCs, NVG patients had lower ALFF values in the right cuneus, right middle occipital gyrus, left cingulate gyrus, right precuneus, and left medial frontal gyrus (p < 0.001). Higher ALFF values were observed in the right superior frontal gyrus and left middle frontal gyrus (p < 0.001). Analysis of the ROC curves of the brain regions showed that the specificity and accuracy of ALFF values between NVG and HCs in the area under the curve were acceptable (p < 0.001).

CONCLUSION

The patients with NVG exhibited anomalous spontaneous activity in different brain regions; these finding should establish the foundation for a more comprehensive understanding of the pathological mechanisms of NVG. Furthermore, these abnormal variations in specific brain regions can be considered possible clinical indices of NVG.

Intrinsic brain activity changes in temporal lobe epilepsy patients revealed by regional homogeneity analysis

PURPOSE

Temporal lobe epilepsy is increasingly being recognized as a disorder associated with brain networks extending outside the seizure onset zone. In the current study, we aim to clarify regional functional changes using a regional homogeneity method.

METHODS

We retrospectively included resting-state fMRI data from 14 left and 18 right temporal lobe epilepsy patients. Data from the control group were acquired from an open dataset. Regional homogeneity was calculated, and a two-sample t-test was performed to compare the left and right temporal lobe epilepsy groups with the control group.

RESULTS

Compared with the healthy control group, the left temporal lobe epilepsy group showed increased regional homogeneity in the left anterior and middle cingulate cortex, and putamen; right inferior frontal gyrus; bilateral temporal lobe and precentral gyrus and decreased regional homogeneity in the left superior parietal gyrus, cuneus and inferior occipital gyrus; right inferior parietal lobule and bilateral rectus. The right temporal lobe epilepsy group showed increased regional homogeneity in the left middle cingulate cortex, precuneus, precentral and postcentral gyrus; right insula and bilateral temporal lobe and decreased regional homogeneity in the left cuneus and superior occipital gyrus; right supramarginal gyrus, fusiform gyrus, lingual gyrus, inferior occipital gyrus and putamen; and the bilateral rectus.

CONCLUSION

Regional homogeneity measurements provide evidence supporting that temporal lobe epilepsy is a complex network disease. Functional disruption of temporal lobe epilepsy at the brain region level was revealed, which may provide novel insights for any potential diagnostic and therapeutic approaches.

Respiratory-related brain pulsations are increased in epilepsy-a two-centre functional MRI study

Resting-state functional MRI has shown potential for detecting changes in cerebral blood oxygen level-dependent signal in patients with epilepsy, even in the absence of epileptiform activity. Furthermore, it has been suggested that coefficient of variation mapping of fast functional MRI signal may provide a powerful tool for the identification of intrinsic brain pulsations in neurological diseases such as dementia, stroke and epilepsy. In this study, we used fast functional MRI sequence (magnetic resonance encephalography) to acquire ten whole-brain images per second. We used the functional MRI data to compare physiological brain pulsations between healthy controls (n = 102) and patients with epilepsy (n = 33) and furthermore to drug-naive seizure patients (n = 9). Analyses were performed by calculating coefficient of variation and spectral power in full band and filtered sub-bands. Brain pulsations in the respiratory-related frequency sub-band (0.11-0.51 Hz) were significantly (P < 0.05) increased in patients with epilepsy, with an increase in both signal variance and power. At the individual level, over 80% of medicated and drug-naive seizure patients exhibited areas of abnormal brain signal power that correlated well with the known clinical diagnosis, while none of the controls showed signs of abnormality with the same threshold. The differences were most apparent in the basal brain structures, respiratory centres of brain stem, midbrain and temporal lobes. Notably, full-band, very low frequency (0.01-0.1 Hz) and cardiovascular (0.8-1.76 Hz) brain pulses showed no differences between groups. This study extends and confirms our previous results of abnormal fast functional MRI signal variance in epilepsy patients. Only respiratory-related brain pulsations were clearly increased with no changes in either physiological cardiorespiratory rates or head motion between the subjects. The regional alterations in brain pulsations suggest that mechanisms driving the cerebrospinal fluid homeostasis may be altered in epilepsy. Magnetic resonance encephalography has both increased sensitivity and high specificity for detecting the increased brain pulsations, particularly in times when other tools for locating epileptogenic areas remain inconclusive.

Altered Brain Regional Homogeneity Following Contralateral Acupuncture at Quchi (LI 11) and Zusanli (ST 36) in Ischemic Stroke Patients with Left Hemiplegia: An fMRI Study

OBJECTIVE

To study the effect of contralateral acupuncture (CAT) at acupoints of Quchi (LI 11) and Zusanli (ST 36) on the unaffected limbs of ischemic stroke patients with left hemiplegia based on regional homogeneity (ReHo) indices.

METHODS

Ten ischemic stroke patients with left hemiplegia received CAT on right side at LI 11 and ST 36. Functional magnetic resonance imaging (fMRI) was performed before and after acupuncture. A ReHo analytical method was used to compare brain responses of patients before and after CAT operated by REST software.

RESULTS

The stimulation at both LI 11 and ST 36 on the unaffected limbs produced significantly different neural activities. CAT elicited increased ReHo values at the right precentral gyrus and superior frontal gyrus, decreased ReHo value at right superior parietal lobule, left fusiform gyrus and left supplementary motor area.

CONCLUSIONS

Acupuncture at one side could stimulate bilateral regions. CAT could evoke the gyrus which was possibly related to motor recovery from stroke. A promising indicator of neurobiological deficiencies could be represented by ReHo values in post-stroke patients.

Quantifying the performance of MEG source reconstruction using resting state data

In magnetoencephalography (MEG) research there are a variety of inversion methods to transform sensor data into estimates of brain activity. Each new inversion scheme is generally justified against a specific simulated or task scenario. The choice of this scenario will however have a large impact on how well the scheme performs. We describe a method with minimal selection bias to quantify algorithm performance using human resting state data. These recordings provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. We used a Hidden Markov model to spatio-temporally partition data into self-similar dynamic states. To test the anatomical precision that could be achieved, we then inverted these data onto libraries of systematically distorted subject-specific cortical meshes and compared the quality of the fit using cross validation and a Free energy metric. This revealed which inversion scheme was able to identify the least distorted (most accurate) anatomical models, and allowed us to quantify an upper bound on the mean anatomical distortion accordingly. We used two resting state datasets, one recorded with head-casts and one without. In the head-cast data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm/LORETA (6.0 mm) and Multiple Sparse Priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer (non-significant) prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods and/or recording paradigms.

Altered physiological brain variation in drug-resistant epilepsy

INTRODUCTION

Functional magnetic resonance imaging (fMRI) combined with simultaneous electroencephalography (EEG-fMRI) has become a major tool in mapping epilepsy sources. In the absence of detectable epileptiform activity, the resting state fMRI may still detect changes in the blood oxygen level-dependent signal, suggesting intrinsic alterations in the underlying brain physiology.

METHODS

In this study, we used coefficient of variation (CV) of critically sampled 10 Hz ultra-fast fMRI (magnetoencephalography, MREG) signal to compare physiological variance between healthy controls (n = 10) and patients (n = 10) with drug-resistant epilepsy (DRE).

RESULTS

We showed highly significant voxel-level (p < 0.01, TFCE-corrected) increase in the physiological variance in DRE patients. At individual level, the elevations range over three standard deviations (σ) above the control mean (μ) CV_MREG values solely in DRE patients, enabling patient-specific mapping of elevated physiological variance. The most apparent differences in group-level analysis are found on white matter, brainstem, and cerebellum. Respiratory (0.12-0.4 Hz) and very-low-frequency (VLF = 0.009-0.1 Hz) signal variances were most affected.

CONCLUSIONS

The CV_MREG increase was not explained by head motion or physiological cardiorespiratory activity, that is, it seems to be linked to intrinsic physiological pulsations. We suggest that intrinsic brain pulsations play a role in DRE and that critically sampled fMRI may provide a powerful tool for their identification.

Cerebral activation effects of acupuncture at Yanglinquan(GB34) point acquired using resting-state fMRI

OBJECTIVE

To explore the central mechanism of acupuncture points for regional homogeneity(ReHo) of resting state in brain function after acupuncture at GB34.

METHODS

Ten healthy volunteers were enrolled, which included 4 males and 6 females, aged 20-34 years old with median age of 23. The GE Signa HDxt 3.0 T magnetic resonance imaging were performed before (control group) and after acupuncture at GB34, and differences of different brain ReHo of 2 groups by statistical parametric mapping (SPM8) software and ReHo data processing methods were analyzed. The statistically different brain regions were obtained by false discovery rate corrected (FDR-Corrected).

RESULTS

Compared with control group, the anterior cingulated gyrus, left temporal gyrus, right inferior parietal lobule, right frontal gyrus were enhanced ReHo after acupuncture at GB34. The left thalamus, right insular cortex, left inferior frontal gyrus, right anterior cingulate were decreased ReHo after acupuncture at GB34.

CONCLUSION

It is demonstrated that the signal synchronization change ReHo in different brain regions including cognitive, motor, default network, limbic system and other parts of encephalic region after acupuncture at GB34, suggesting that the central mechanism of acupuncture at GB34 is the result of all levels of the combined effects of brain networks.

The value of resting-state functional magnetic resonance imaging for detecting epileptogenic zones in patients with focal epilepsy

Objective

To determine the value of resting-state functional magnetic resonance imaging (RS-fMRI) based on the local analysis methods regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF), for detecting epileptogenic zones (EZs).

Methods

A total of 42 consecutive patients with focal epilepsy were enrolled. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of visually assessed RS-fMRI, MRI, magnetic resonance spectroscopy (MRS), video electroencephalography (VEEG), and positron-emission tomography computed tomography (PET-CT) in EZ localization were evaluated to assess their diagnostic abilities. ReHo, ALFF, and fALFF were also compared for their diagnostic values.

Results

RS-fMRI showed comparable sensitivity to PET (83.3%) and specificity to VEEG (66.7%), respectively, for EZ localization in patients with focal epilepsy. There were no significant differences between RS-fMRI and the other localization techniques in terms of sensitivity, specificity, PPV, and NPV. The sensitivities of ReHo, ALFF, and fALFF were 69.4%, 52.8%, and 38.9%, respectively, and for specificities of 66.7%, 83.3%, and 66.7%, respectively. There were no significant differences among ReHo, ALFF, and fALFF, except that ReHo was more sensitive than fALFF.

Conclusions

RS-fMRI may be an efficient tool for detecting EZs in focal epilepsy patients.

Sex and Age Effects of Functional Connectivity in Early Adulthood

Functional connectivity (FC) in resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to find coactivating regions in the human brain. Despite its widespread use, the effects of sex and age on resting FC are not well characterized, especially during early adulthood. Here we apply regression and graph theoretical analyses to explore the effects of sex and age on FC between the 116 AAL atlas parcellations (a total of 6670 FC measures). rs-fMRI data of 494 healthy subjects (203 males and 291 females; age range: 22–36 years) from the Human Connectome Project were analyzed. We report the following findings. (1) Males exhibited greater FC than females in 1352 FC measures (1025 survived Bonferroni correction; \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$p < 7.49{ \rm{E}} - 6$$ \end{document}). In 641 FC measures, females exhibited greater FC than males but none survived Bonferroni correction. Significant FC differences were mainly present in frontal, parietal, and temporal lobes. Although the average FC values for males and females were significantly different, FC values of males and females exhibited large overlap. (2) Age effects were present only in 29 FC measures and all significant age effects showed higher FC in younger subjects. Age and sex differences of FC remained significant after controlling for cognitive measures. (3) Although sex \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$\times$$ \end{document} age interaction did not survive multiple comparison correction, FC in females exhibited a faster cross-sectional decline with age. (4) Male brains were more locally clustered in all lobes but the cerebellum; female brains had a higher clustering coefficient at the whole-brain level. Our results indicate that although both male and female brains show small-world network characteristics, male brains were more segregated and female brains were more integrated. Findings of this study further our understanding of FC in early adulthood and provide evidence to support that age and sex should be controlled for in FC studies of young adults.

Frequency Specificity of fMRI in Mesial Temporal Lobe Epilepsy

Medial temporal lobe epilepsy (mTLE) is a system-level disease characterized by aberrant neuronal synchronization and widespread alterations in function. Previous studies have focused on the amplitude analysis of the blood oxygenation level-dependent (BOLD) signals to reveal the aberrant alterations in mTLE. However, these methods did not work well in the cases where the amplitudes of two oscillations are correlated but the underlying oscillations are neither phase coherent nor frequency consistent. To address this problem, we investigated the differences of frequency specificity between patients with mTLE and healthy controls using the extreme-point symmetric mode decomposition (ESMD) method. In this method, the BOLD signals were decomposed into a set of intrinsic mode functions (IMFs) and the instantaneous frequency of each IMF was calculated using the direct interpolation strategy. The intrinsic frequency (denoted as Freq) for every voxel was obtained by the weighted sum of the instantaneous frequencies of all the IMFs. The Freq was used as an index to evaluate the altered frequency specificity of 41 patients with mTLE (17 right-side, 24 left-side) and 24 healthy control subjects. The results show that the peak of frequency distribution curve for the patients moves towards the higher frequency than that for the healthy controls. Compared with the healthy control group, the patients with left mTLE demonstrate higher Freq in the default mode network, middle frontal gyrus, insula, middle temporal gyrus and calcarine gyrus; the patients with right mTLE demonstrate higher Freq in the precuneus and occipital lobe. For the three groups, the distinct frequency distribution appears in the left and right hippocampus due to the hippocampal structural and functional asymmetries. The preliminary results imply that the frequency-specific correlated oscillations in the distributed brain regions can provide information about the nature of diseases affecting the brain and the alterations of frequency specificity are associated with the pathological characteristics of mTLE.

Topographical Distribution of Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex

Tuberous sclerosis complex is a multisystem genetic syndrome often affecting the central nervous system. The purpose of the current study was to identify topographical patterns in the distribution specific to epileptogenic (n = 37) and nonepileptogenic (n = 544) tubers throughout the brain for a cohort of 23 tuberous sclerosis complex patients with a history of seizures. Tubers localized to the inferior parietal lobes, middle frontal lobes, middle temporal lobes, or central sulcus regions were associated with a high frequency of epileptogenic tubers. Epileptogenic tubers occurred statistically more frequently within the inferior parietal lobe and within the central sulcus region in children younger than 1 or between 1 and 3 years old, respectively. Results imply seizure activity in tuberous sclerosis complex patients can be associated with the location of cortical tubers.

Head Motion and Correction Methods in Resting-state Functional MRI

Resting-state functional magnetic resonance imaging (RS-fMRI) is used to investigate brain functional connectivity at rest. However, noise from human physiological motion is an unresolved problem associated with this technique. Following the unexpected previous result that group differences in head motion between control and patient groups caused group differences in the resting-state network with RS-fMRI, we reviewed the effects of human physiological noise caused by subject motion, especially motion of the head, on functional connectivity at rest detected with RS-fMRI. The aim of the present study was to review head motion artifact with RS-fMRI, individual and patient population differences in head motion, and correction methods for head motion artifact with RS-fMRI. Numerous reports have described new methods [e.g., scrubbing, regional displacement interaction (RDI)] for motion correction on RS-fMRI, many of which have been successful in reducing this negative influence. However, the influence of head motion could not be entirely excluded by any of these published techniques. Therefore, in performing RS-fMRI studies, head motion of the participants should be quantified with measurement technique (e.g., framewise displacement). Development of a more effective correction method would improve the accuracy of RS-fMRI analysis.

Aberrant Cross-Brain Network Interaction in Children With Attention-Deficit/Hyperactivity Disorder and Its Relation to Attention Deficits: A Multisite and Cross-Site Replication Study

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is increasingly viewed as a disorder stemming from disturbances in large-scale brain networks, yet the exact nature of these impairments in affected children is poorly understood. We investigated a saliency-based triple-network model and tested the hypothesis that cross-network interactions between the salience network (SN), central executive network, and default mode network are dysregulated in children with ADHD. We also determined whether network dysregulation measures can differentiate children with ADHD from control subjects across multisite datasets and predict clinical symptoms.

METHODS

Functional magnetic resonance imaging data from 180 children with ADHD and control subjects from three sites in the ADHD-200 database were selected using case-control design. We investigated between-group differences in resource allocation index (RAI) (a measure of SN-centered triple network interactions), relation between RAI and ADHD symptoms, and performance of multivariate classifiers built to differentiate children with ADHD from control subjects.

RESULTS

RAI was significantly lower in children with ADHD than in control subjects. Severity of inattention symptoms was correlated with RAI. Remarkably, these findings were replicated in three independent datasets. Multivariate classifiers based on cross-network coupling measures differentiated children with ADHD from control subjects with high classification rates (72% to 83%) for each dataset. A novel cross-site classifier based on training data from one site accurately (62% to 82%) differentiated children with ADHD on test data from the two other sites.

CONCLUSIONS

Aberrant cross-network interactions between SN, central executive network, and default mode network are a reproducible feature of childhood ADHD. The triple-network model provides a novel, replicable, and parsimonious systems neuroscience framework for characterizing childhood ADHD and predicting clinical symptoms in affected children.

Lateralization of Temporal Lobe Epilepsy Based on Resting-State Functional Magnetic Resonance Imaging and Machine Learning

Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

Frequency-Specific Alterations of Local Synchronization in Idiopathic Generalized Epilepsy

Recurrently and abnormally hypersynchronous discharge is a striking feature of idiopathic generalized epilepsy (IGE). Resting-state functional magnetic resonance imaging has revealed aberrant spontaneous brain synchronization, predominately in low-frequency range (<0.1 Hz), in individuals with IGE. Little is known, however, about these changes in local synchronization across different frequency bands. We examined alterations to frequency-specific local synchronization in terms of spontaneous blood oxygen level-dependent (BOLD) fluctuations across 5 bands, spanning 0 to 0.25 Hz. Specifically, we compared brain activity in a large cohort of IGE patients (n = 86) to age- and sex-matched normal controls (n = 86). IGE patients showed decreased local synchronization in low frequency (<0.073 Hz), primarily in the default mode network (DMN). IGE patients also exhibited increased local synchronization in high-frequency (>0.073 Hz) in a "conscious perception network," which is anchored by the pregenual and dorsal anterior cingulate cortex, as well as the bilateral insular cortices, possibly contributing to impaired consciousness. Furthermore, we found frequency-specific alternating local synchronization in the posterior portion of the DMN relative to the anterior part, suggesting an interaction between the disease and frequency bands. Importantly, the aberrant high-frequency local synchronization in the middle cingulate cortex was associated with disease duration, thus linking BOLD frequency changes to disease severity. These findings provide an overview of frequency-specific local synchronization of BOLD fluctuations, and may be helpful in uncovering abnormal synchronous neuronal activity in patients with IGE at specific frequency bands.

Whole brain functional connectivity using phase locking measures of resting state magnetoencephalography

The analysis of spontaneous functional connectivity (sFC) reveals the statistical connections between regions of the brain consistent with underlying functional communication networks within the brain. In this work, we describe the implementation of a complete all-to-all network analysis of resting state neuronal activity from magnetoencephalography (MEG). Using graph theory to define networks at the dipole level, we established functionally defined regions by k-means clustering cortical surface locations using Eigenvector centrality (EVC) scores from the all-to-all adjacency model. Permutation testing was used to estimate regions with statistically significant connections compared to empty room data, which adjusts for spatial dependencies introduced by the MEG inverse problem. In order to test this model, we performed a series of numerical simulations investigating the effects of the MEG reconstruction on connectivity estimates. We subsequently applied the approach to subject data to investigate the effectiveness of our method in obtaining whole brain networks. Our findings indicated that our model provides statistically robust estimates of functional region networks. Application of our phase locking network methodology to real data produced networks with similar connectivity to previously published findings, specifically, we found connections between contralateral areas of the arcuate fasciculus that have been previously investigated. The use of data-driven methods for neuroscientific investigations provides a new tool for researchers in identifying and characterizing whole brain functional connectivity networks.

Resting state FMRI reveals diminished functional connectivity in a mouse model of amyloidosis

Introduction

Functional connectivity (FC) studies have gained immense popularity in the evaluation of several neurological disorders, such as Alzheimer’s disease (AD). AD is a complex disorder, characterised by several pathological features. The problem with FC studies in patients is that it is not straightforward to focus on a specific aspect of pathology. In the current study, resting state functional magnetic resonance imaging (rsfMRI) is applied in a mouse model of amyloidosis to assess the effects of amyloid pathology on FC in the mouse brain.

Methods

Nine APP/PS1 transgenic and nine wild-type mice (average age 18.9 months) were imaged on a 7T MRI system. The mice were anesthetized with medetomidine and rsfMRI data were acquired using a gradient echo EPI sequence. The data were analysed using a whole brain seed correlation analysis and interhemispheric FC was evaluated using a pairwise seed analysis. Qualitative histological analyses were performed to assess amyloid pathology, inflammation and synaptic deficits.

Results

The whole brain seed analysis revealed an overall decrease in FC in the brains of transgenic mice compared to wild-type mice. The results showed that interhemispheric FC was relatively preserved in the motor cortex of the transgenic mice, but decreased in the somatosensory cortex and the hippocampus when compared to the wild-type mice. The pairwise seed analysis confirmed these results. Histological analyses confirmed the presence of amyloid pathology, inflammation and synaptic deficits in the transgenic mice.

Conclusions

In the current study, rsfMRI demonstrated decreased FC in APP/PS1 transgenic mice compared to wild-type mice in several brain regions. The APP/PS1 transgenic mice had advanced amyloid pathology across the brain, as well as inflammation and synaptic deficits surrounding the amyloid plaques. Future studies should longitudinally evaluate APP/PS1 transgenic mice and correlate the rsfMRI findings to specific stages of amyloid pathology.

Disrupted causal connectivity in mesial temporal lobe epilepsy

Although mesial temporal lobe epilepsy (mTLE) is characterized by the pathological changes in mesial temporal lobe, function alteration was also found in extratemporal regions. Our aim is to investigate the information flow between the epileptogenic zone (EZ) and other brain regions. Resting-state functional magnetic resonance imaging (RS-fMRI) data were recorded from 23 patients with left mTLE and matched controls. We first identified the potential EZ using the amplitude of low-frequency fluctuation (ALFF) of RS-fMRI signal, then performed voxel-wise Granger causality analysis between EZ and the whole brain. Relative to controls, patients demonstrated decreased driving effect from EZ to thalamus and basal ganglia, and increased feedback. Additionally, we found an altered causal relation between EZ and cortical networks (default mode network, limbic system, visual network and executive control network). The influence from EZ to right precuneus and brainstem negatively correlated with disease duration, whereas that from the right hippocampus, fusiform cortex, and lentiform nucleus to EZ showed positive correlation. These findings demonstrate widespread brain regions showing abnormal functional interaction with EZ. In addition, increased ALFF in EZ was positively correlated with the increased driving effect on EZ in patients, but not in controls. This finding suggests that the initiation of epileptic activity depends not only on EZ itself, but also on the activity emerging in large-scale macroscopic brain networks. Overall, this study suggests that the causal topological organization is disrupted in mTLE, providing valuable information to understand the pathophysiology of this disorder.

Neuroimaging biomarkers for epilepsy: advances and relevance to glial cells

Glial cells play an important role in normal brain function and emerging evidence would suggest that their dysfunction may be responsible for some epileptic disease states. Neuroimaging of glial cells is desirable, but there are no clear methods to assess neither their function nor localization. Magnetic resonance imaging (MRI) is now part of a standardized epilepsy imaging protocol to assess patients. Structural volumetric and T2-weighted imaging changes can assist in making a positive diagnosis in a majority of patients. The alterations reported in structural and T2 imaging is predominantly thought to reflect early neuronal loss followed by glial hypertrophy. MR spectroscopy for myo-inositol is a being pursued to identify glial alterations along with neuronal markers. Diffusion weighted imaging (DWI) is ideal for acute epileptiform events, but is not sensitive to either glial cells or neuronal long-term changes found in epilepsy. However, DWI variants such as diffusion tensor imaging or q-space imaging may shed additional light on aberrant glial function in the future. The sensitivity and specificity of PET radioligands, including those targeting glial cells (translocator protein) hold promise in being able to image glial cells. As the role of glial function/dysfunction in epilepsy becomes more apparent neuroimaging methods will evolve to assist the clinician and researcher in visualizing their location and function.