Abnormal metabolic connectivity in the pilocarpine-induced epilepsy rat model: A multiscale network analysis based on persistent homology

Topological analysis of functional connectivity in Parkinson's disease

Parkinson's disease (PD) is a clinically heterogeneous disorder, which mainly affects patients' motor and non-motor function. Functional connectivity was preliminary explored and studied through resting state functional magnetic resonance imaging (rsfMRI). Through the topological analysis of 54 PD scans and 31 age-matched normal controls (NC) in the Neurocon dataset, leveraging on rsfMRI data, the brain functional connection and the Vietoris-Rips (VR) complex were constructed. The barcodes of the complex were calculated to reflect the changes of functional connectivity neural circuits (FCNC) in brain network. The 0-dimensional Betti number β0 means the number of connected branches in VR complex. The average number of connected branches in PD group was greater than that in NC group when the threshold δ ≤ 0.7. Two-sample Mann-Whitney U test and false discovery rate (FDR) correction were used for statistical analysis to investigate the FCNC changes between PD and NC groups. In PD group, under threshold of 0.7, the number of FCNC involved was significantly differences and these brain regions include the Cuneus_R, Lingual_R, Fusiform_R and Heschl_R. There are also significant differences in brain regions in the Frontal_Inf_Orb_R and Pallidum_R, when the threshold increased to 0.8 and 0.9 (p < 0.05). In addition, when the length of FCNC was medium, there was a significant statistical difference between the PD group and the NC group in the Neurocon dataset and the Parkinson's Progression Markers Initiative (PPMI) dataset. Topological analysis based on rsfMRI data may provide comprehensive information about the changes of FCNC and may provide an alternative for clinical differential diagnosis.

Development of the Functional Connectome Topology in Adolescence: Evidence from Topological Data Analysis

Adolescence is a crucial developmental period in terms of behavior and mental health. Therefore, understanding how the brain develops during this stage is a fundamental challenge for neuroscience. Recent studies have modeled the brain as a network or connectome, mainly applying measures from graph theory, showing a change in its functional organization, such as an increase in its segregation and integration. Topological Data Analysis (TDA) complements such modeling by extracting high-dimensional features across the whole range of connectivity values instead of exploring a fixed set of connections. This study inquires into the developmental trajectories of such properties using a longitudinal sample of typically developing human participants (N = 98; 53/45 female/male; 6.7-18.1 years), applying TDA to their functional connectomes. In addition, we explore the effect of puberty on individual developmental trajectories. Results showed that the adolescent brain has a more distributed topology structure compared with random networks but is more densely connected at the local level. Furthermore, developmental effects showed nonlinear trajectories for the topology of the whole brain and fronto-parietal networks, with an inflection point and increasing trajectories after puberty onset. These results add to the insights into the development of the functional organization of the adolescent brain.

Long-term ANT-DBS effects in pilocarpine-induced epileptic rats: A combined 9.4T MRI and histological study

Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) appears to be effective against seizures in animals and humans however, its therapeutic mechanisms remain elusive. This study aimed to combine 9.4T multimodal magnetic resonance imaging (MRI) with histology to investigate the longitudinal effects of long-term ANT-DBS in pilocarpine-induced epileptic rats. Status epilepsy (SE) was induced by LiCl-pilocarpine injection in 11 adult male Sprague-Dawley rats. Four weeks after SE, chronic epileptic rats underwent either ANT-DBS (n = 6) or sham-DBS (n = 5) surgery. Electroencephalography (EEG) and spontaneous recurrent seizures (SRS) were recorded for 1 week. The T2-weighted image and images from resting-state functional MRI (rs-fMRI) were acquired at three states: before SE, at 4 weeks post-SE, and at 5 weeks post-DBS. Volumes of the hippocampal subregions and hippocampal-related functional connectivity (FC) were compared longitudinally. Finally, antibodies against neuronal nuclei (NeuN) and glial fibrillary acidic proteins were used to evaluate neuronal loss and astrogliosis in the hippocampus. Long-term ANT-DBS significantly reduced seizure generalization in pilocarpine-induced epileptic rats. By analyzing the gray matter volume using T2-weighted images, long-term ANT-DBS displayed morphometric restoration of the hippocampal subregions. Neuronal protection of the hippocampal subregions and inhibition of astrogliosis in the hippocampal subregions were observed in the ANT-DBS group. ANT-DBS caused reversible regulation of FC in the insula-hippocampus and subthalamic nucleus-hippocampus. Long-term ANT-DBS provides comprehensive protection of hippocampal histology, hippocampal morphometrics, and hippocampal-related functional networks.

Topological analysis of brain dynamics in autism based on graph and persistent homology

Autism spectrum disorder (ASD) is a heterogeneous disorder with a rapidly growing prevalence. In recent years, the dynamic functional connectivity (DFC) technique has been used to reveal the transient connectivity behavior of ASDs' brains by clustering connectivity matrices in different states. However, the states of DFC have not been yet studied from a topological point of view. In this paper, this study was performed using global metrics of the graph and persistent homology (PH) and resting-state functional magnetic resonance imaging (fMRI) data. The PH has been recently developed in topological data analysis and deals with persistent structures of data. The structural connectivity (SC) and static FC (SFC) were also studied to know which one of the SC, SFC, and DFC could provide more discriminative topological features when comparing ASDs with typical controls (TCs). Significant discriminative features were only found in states of DFC. Moreover, the best classification performance was offered by persistent homology-based metrics and in two out of four states. In these two states, some networks of ASDs compared to TCs were more segregated and isolated (showing the disruption of network integration in ASDs). The results of this study demonstrated that topological analysis of DFC states could offer discriminative features which were not discriminative in SFC and SC. Also, PH metrics can provide a promising perspective for studying ASD and finding candidate biomarkers.

Retrosplenial Cortex Effects Contextual Fear Formation Relying on Dysgranular Constituent in Rats

Animal contextual fear conditioning (CFC) models are the most-studied forms used to explore the neural substances of posttraumatic stress disorder (PTSD). In addition to the well-recognized hippocampal–amygdalar system, the retrosplenial cortex (RSC) is getting more and more attention due to substantial involvement in CFC, but with a poor understanding of the specific roles of its two major constituents—dysgranular (RSCd) and granular (RSCg). The current study sought to identify their roles and underlying brain network mechanisms during the encoding processing of the rat CFC model. Rats with pharmacologically inactivated RSCd, RSCg, and corresponding controls underwent contextual fear conditioning. [¹⁸F]-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) scanning was performed for each animal. The 5-h and 24-h retrieval were followed to test the formation of contextual memory. Graph theoretic tools were used to identify the brain metabolic network involved in encoding phase, and changes of nodal (brain region) properties linked, respectively, to disturbed RSCd and RSCg were analyzed. Impaired retrieval occurred in disturbed RSCd animals, not in RSCg ones. The RSC, hippocampus (Hip), amygdala (Amy), piriform cortex (Pir), and visual cortex (VC) are hub nodes of the brain-wide network for contextual fear memory encoding in rats. Nodal degree and efficiency of hippocampus and its connectivity with amygdala, Pir, and VC were decreased in rats with disturbed RSCd, while not in those with suppressed RSCg. The RSC plays its role in contextual fear memory encoding mainly relying on its RSCd part, whose condition would influence the activity of the hippocampal–amygdalar system.

A Spatiotemporal Brain Network Analysis of Alzheimer's Disease Based on Persistent Homology

Current brain network studies based on persistent homology mainly focus on the spatial evolution over multiple spatial scales, and there is little research on the evolution of a spatiotemporal brain network of Alzheimer's disease (AD). This paper proposed a persistent homology-based method by combining multiple temporal windows and spatial scales to study the spatiotemporal evolution of brain functional networks. Specifically, a time-sliding window method was performed to establish a spatiotemporal network, and the persistent homology-based features of such a network were obtained. We evaluated our proposed method using the resting-state functional MRI (rs-fMRI) data set from Alzheimer's Disease Neuroimaging Initiative (ADNI) with 31 patients with AD and 37 normal controls (NCs). In the statistical analysis experiment, most network properties showed a better statistical power in spatiotemporal networks than in spatial networks. Moreover, compared to the standard graph theory properties in spatiotemporal networks, the persistent homology-based features detected more significant differences between the groups. In the clustering experiment, the brain networks on the sliding windows of all subjects were clustered into two highly structured connection states. Compared to the NC group, the AD group showed a longer residence time and a higher window ratio in a weak connection state, which may be because patients with AD have not established a firm connection. In summary, we constructed a spatiotemporal brain network containing more detailed information, and the dynamic spatiotemporal brain network analysis method based on persistent homology provides stronger adaptability and robustness in revealing the abnormalities of the functional organization of patients with AD.

Recent Developments in Diagnosis of Epilepsy: Scope of MicroRNA and Technological Advancements

Epilepsy is one of the most common neurological disorders, characterized by recurrent seizures, resulting from abnormally synchronized episodic neuronal discharges. Around 70 million people worldwide are suffering from epilepsy. The available antiepileptic medications are capable of controlling seizures in around 60-70% of patients, while the rest remain refractory. Poor seizure control is often associated with neuro-psychiatric comorbidities, mainly including memory impairment, depression, psychosis, neurodegeneration, motor impairment, neuroendocrine dysfunction, etc., resulting in poor prognosis. Effective treatment relies on early and correct detection of epileptic foci. Although there are currently a few well-established diagnostic techniques for epilepsy, they lack accuracy and cannot be applied to patients who are unsupportive or harbor metallic implants. Since a single test result from one of these techniques does not provide complete information about the epileptic foci, it is necessary to develop novel diagnostic tools. Herein, we provide a comprehensive overview of the current diagnostic tools of epilepsy, including electroencephalography (EEG) as well as structural and functional neuroimaging. We further discuss recent trends and advances in the diagnosis of epilepsy that will enable more effective diagnosis and clinical management of patients.

Promises and pitfalls of topological data analysis for brain connectivity analysis

Developing sensitive and reliable methods to distinguish normal and abnormal brain states is a key neuroscientific challenge. Topological Data Analysis, despite its relative novelty, already generated many promising applications, including in neuroscience. We conjecture its prominent tool of persistent homology may benefit from going beyond analysing structural and functional connectivity to effective connectivity graphs capturing the direct causal interactions or information flows. Therefore, we assess the potential of persistent homology to directed brain network analysis by testing its discriminatory power in two distinctive examples of disease-related brain connectivity alterations: epilepsy and schizophrenia. We estimate connectivity from functional magnetic resonance imaging and electrophysiology data, employ Persistent Homology and quantify its ability to distinguish healthy from diseased brain states by applying a support vector machine to features quantifying persistent homology structure. We show how this novel approach compares to classification using standard undirected approaches and original connectivity matrices. In the schizophrenia classification, topological data analysis generally performs close to random, while classifications from raw connectivity perform substantially better; potentially due to topographical, rather than topological, specificity of the differences. In the easier task of seizure discrimination from scalp electroencephalography data, classification based on persistent homology features generally reached comparable performance to using raw connectivity, albeit with typically smaller accuracies obtained for the directed (effective) connectivity compared to the undirected (functional) connectivity. Specific applications for topological data analysis may open when direct comparison of connectivity matrices is unsuitable - such as for intracranial electrophysiology with individual number and location of measurements. While standard homology performed overall better than directed homology, this could be due to notorious technical problems of accurate effective connectivity estimation.

Default Mode Network Analysis of APOE Genotype in Cognitively Unimpaired Subjects Based on Persistent Homology

Current researches on default mode network (DMN) in normal elderly have mainly focused on finding some dysfunctional areas with decreased or increased connectivity. The global network dynamics of apolipoprotein E (APOE) e4 allele group is rarely studied. In our previous brain network study, we have demonstrated the advantage of persistent homology. It can distinguish robust and noisy topological features over multiscale nested networks, and the derived properties are more stable. In this study, for the first time we applied persistent homology to analyze APOE-related effects on whole-brain functional network. In our experiments, the risk allele group exhibited lower network radius and modularity in whole brain DMN based on graph theory, suggesting the abnormal organization structure. Moreover, two suggested measures from persistent homology detected significant differences between groups within the left hemisphere and in the whole brain in two datasets. They were more statistically sensitive to APOE genotypic differences than standard graph-based measures. In summary, we provide evidence that the e4 genotype leads to distinct DMN functional alterations in the early phases of Alzheimer's disease using persistent homology approach. Our study offers a novel insight to explore potential biomarkers in healthy elderly populations carrying APOE e4 allele.

White Matter Brain Network Research in Alzheimer's Disease Using Persistent Features

Despite the severe social burden caused by Alzheimer’s disease (AD), no drug than can change the disease progression has been identified yet. The structural brain network research provides an opportunity to understand physiological deterioration caused by AD and its precursor, mild cognitive impairment (MCI). Recently, persistent homology has been used to study brain network dynamics and characterize the global network organization. However, it is unclear how these parameters reflect changes in structural brain networks of patients with AD or MCI. In this study, our previously proposed persistent features and various traditional graph-theoretical measures are used to quantify the topological property of white matter (WM) network in 150 subjects with diffusion tensor imaging (DTI). We found significant differences in these measures among AD, MCI, and normal controls (NC) under different brain parcellation schemes. The decreased network integration and increased network segregation are presented in AD and MCI. Moreover, the persistent homology-based measures demonstrated stronger statistical capability and robustness than traditional graph-theoretic measures, suggesting that they represent a more sensitive approach to detect altered brain structures and to better understand AD symptomology at the network level. These findings contribute to an increased understanding of structural connectome in AD and provide a novel approach to potentially track the progression of AD.

Animal Models of Metabolic Epilepsy and Epilepsy Associated Metabolic Dysfunction: A Systematic Review

Epilepsy is a serious neurological disorder affecting around 70 million people globally and is characterized by spontaneous recurrent seizures. Recent evidence indicates that dysfunction in metabolic processes can lead to the alteration of neuronal and network excitability, thereby contributing to epileptogenesis. Developing a suitable animal model that can recapitulate all the clinical phenotypes of human metabolic epilepsy (ME) is crucial yet challenging. The specific environment of many symptoms as well as the primary state of the applicable neurobiology, genetics, and lack of valid biomarkers/diagnostic tests are the key factors that hinder the process of developing a suitable animal model. The present systematic review summarizes the current state of available animal models of metabolic dysfunction associated with epileptic disorders. A systematic search was performed by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model. A range of electronic databases, including google scholar, Springer, PubMed, ScienceDirect, and Scopus, were scanned between January 2000 and April 2020. Based on the selection criteria, 23 eligible articles were chosen and are discussed in the current review. Critical analysis of the selected literature delineated several available approaches that have been modeled into metabolic epilepsy and pointed out several drawbacks associated with the currently available models. The result describes available models of metabolic dysfunction associated with epileptic disorder, such as mitochondrial respiration deficits, Lafora disease (LD) model-altered glycogen metabolism, causing epilepsy, glucose transporter 1 (GLUT1) deficiency, adiponectin responsive seizures, phospholipid dysfunction, glutaric aciduria, mitochondrial disorders, pyruvate dehydrogenase (PDH) α-subunit gene (PDHA1), pyridoxine dependent epilepsy (PDE), BCL2-associated agonist of cell death (BAD), Kcna1 knock out (KO), and long noncoding RNAs (lncRNA) cancer susceptibility candidate 2 (lncRNA CASC2). Finally, the review highlights certain focus areas that may increase the possibilities of developing more suitable animal models and underscores the importance of the rationalization of animal models and evaluation methods for studying ME. The review also suggests the pressing need of developing precise robust animal models and evaluation methods for investigating ME.

Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia

Purpose

Positron emission tomography (PET) with ¹⁸F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual’s risk of conversion from present MCI to AD. We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual’s risk of conversion from MCI to AD.

Methods

FDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database. Each individual’s metabolic brain network was ascertained using the KLSE method. We compared intra- and intergroup similarity and difference between the KLSE matrix and group-level matrix, and then evaluated the network stability and inter-individual variation of KLSE. The multivariate Cox proportional hazards model and Harrell’s concordance index (C-index) were employed to assess the prediction performance of KLSE and other clinical characteristics.

Results

The KLSE method captures more pathological connectivity in the parietal and temporal lobes relative to the typical group-level method, and yields detailed individual information, while possessing greater stability of network organization (within-group similarity coefficient, 0.789 for sMCI and 0.731 for pMCI). Metabolic connectome expression was a superior predictor of conversion than were other clinical assessments (hazard ratio (HR) = 3.55; 95% CI, 2.77–4.55; P < 0.001). The predictive performance improved further upon combining clinical variables in the Cox model, i.e., C-indices 0.728 (clinical), 0.730 (group-level pattern model), 0.750 (imaging connectome), and 0.794 (the combined model).

Conclusion

The KLSE indicator identifies abnormal brain networks predicting an individual’s risk of conversion from MCI to AD, thus potentially constituting a clinically applicable imaging biomarker.

Electronic supplementary material

The online version of this article (10.1007/s00259-020-04814-x) contains supplementary material, which is available to authorized users.

Propofol Anesthesia Alters Spatial and Topologic Organization of Rat Brain Metabolism

BACKGROUND

Loss of consciousness during anesthesia reduces local and global rate of cerebral glucose metabolism. Despite this, the influence of gradual anesthetic-induced changes on consciousness across the entire brain metabolic network has barely been studied. The purpose of the present study was to identify specific cerebral metabolic patterns characteristic of different consciousness/anesthesia states induced by intravenous anesthetic propofol.

METHODS

At various times, 20 Sprague-Dawley adult rats were intravenously administered three different dosages of propofol to induce different anesthetic states: mild sedation (20 mg · kg · h), deep sedation (40 mg · kg · h), and deep anesthesia (80 mg · kg · h). Using [F]fluorodeoxyglucose positron emission tomography brain imaging, alterations in the spatial pattern of metabolic distribution and metabolic topography were investigated by applying voxel-based spatial covariance analysis and graph-theory analysis.

RESULTS

Evident reductions were found in baseline metabolism along with altered metabolic spatial distribution during propofol-induced anesthesia. Moreover, graph-theory analysis revealed a disruption in global and local efficiency of the metabolic brain network characterized by decreases in metabolic connectivity and energy efficiency during propofol-induced deep anesthesia (mild sedation global efficiency/local efficiency = 0.6985/0.7190, deep sedation global efficiency/local efficiency = 0.7444/0.7875, deep anesthesia global efficiency/local efficiency = 0.4498/0.6481; mild sedation vs. deep sedation, global efficiency: P = 0.356, local efficiency: P = 0.079; mild sedation vs. deep anesthesia, global efficiency: P < 0.0001, local efficiency: P < 0.0001; deep sedation vs. deep anesthesia, global efficiency: P < 0.0001, local efficiency: P < 0.0001). A strong spatial correlation was also found between cerebral metabolism and metabolic connectivity strength, which decreased significantly with deepening anesthesia level (correlation coefficients: mild sedation, r = 0.55, deep sedation, r = 0.47; deep anesthesia, r = 0.23; P < 0.0001 between the sedation and deep anesthesia groups).

CONCLUSIONS

The data revealed anesthesia-related alterations in spatial and topologic organization of metabolic brain network, as well as a close relationship between metabolic connectivity and cerebral metabolism during propofol anesthesia. These findings may provide novel insights into the metabolic mechanism of anesthetic-induced loss of consciousness.

Metabolic Brain Network Analysis of FDG-PET in Alzheimer's Disease Using Kernel-Based Persistent Features

Recent research of persistent homology in algebraic topology has shown that the altered network organization of human brain provides a promising indicator of many neuropsychiatric disorders and neurodegenerative diseases. However, the current slope-based approach may not accurately characterize changes of persistent features over graph filtration because such curves are not strictly linear. Moreover, our previous integrated persistent feature (IPF) works well on an rs-fMRI cohort while it has not yet been studied on metabolic brain networks. To address these issues, we propose a novel univariate network measurement, kernel-based IPF (KBI), based on the prior IPF, to quantify the difference between IPF curves. In our experiments, we apply the KBI index to study fluorodeoxyglucose positron emission tomography (FDG-PET) imaging data from 140 subjects with Alzheimer's disease (AD), 280 subjects with mild cognitive impairment (MCI), and 280 healthy normal controls (NC). The results show the disruption of network integration in the progress of AD. Compared to previous persistent homology-based measures, as well as other standard graph-based measures that characterize small-world organization and modular structure, our proposed network index KBI possesses more significant group difference and better classification performance, suggesting that it may be used as an effective preclinical AD imaging biomarker.

A concise and persistent feature to study brain resting-state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative

Alzheimer's disease (AD) is the most common type of dementia in the elderly with no effective treatment currently. Recent studies of noninvasive neuroimaging, resting-state functional magnetic resonance imaging (rs-fMRI) with graph theoretical analysis have shown that patients with AD and mild cognitive impairment (MCI) exhibit disrupted topological organization in large-scale brain networks. In previous work, it is a common practice to threshold such networks. However, it is not only difficult to make a principled choice of threshold values, but also worse is the discard of potential important information. To address this issue, we propose a threshold-free feature by integrating a prior persistent homology-based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure. We apply this measure to study rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative and compare our approach with five other widely used graph measures across five parcellation schemes ranging from 90 to 1,024 region-of-interests. The experimental results demonstrate that the proposed network measure shows more statistical power and stronger robustness in group difference studies in that the absolute values of the proposed measure of AD are lower than MCI and much lower than normal controls, providing empirical evidence for decreased functional integration in AD dementia and MCI.

Alteration of metabolic connectivity in a rat model of deafferentation pain: a 18F-FDG PET/CT study

OBJECTIVE

Refractory deafferentation pain has been evidenced to be related to central nervous system neuroplasticity. In this study, the authors sought to explore the underlying glucose metabolic changes in the brain after brachial plexus avulsion, particularly metabolic connectivity.

METHODS

Rats with unilateral deafferentation following brachial plexus avulsion, a pain model of deafferentation pain, were scanned by small-animal 2-deoxy-[18F]fluoro-d-glucose (18F-FDG) PET/CT to explore the changes of metabolic connectivity among different brain regions. Thermal withdrawal latency (TWL) and mechanical withdrawal threshold (MWT) of the intact forepaw were also measured for evaluating pain sensitization. Brain metabolic connectivity and TWL were compared from baseline to 1 week after brachial plexus avulsion.

RESULTS

Alterations of metabolic connectivity occurred not only within the unilateral hemisphere contralateral to the injured forelimb, but also in the other hemisphere and even in the connections between bilateral hemispheres. Metabolic connectivity significantly decreased between sensorimotor-related areas within the left hemisphere (contralateral to the injured forelimb) (p < 0.05), as well as between areas across bilateral hemispheres (p < 0.05). Connectivity between areas within the right hemisphere (ipsilateral to the injured forelimb) significantly increased (p = 0.034). TWL and MWT of the left (intact) forepaw after surgery were significantly lower than those at baseline (p < 0.001).

CONCLUSIONS

This study revealed that unilateral brachial plexus avulsion facilitates pain sensitization in the opposite limb. A specific pattern of brain metabolic changes occurred in this procedure. Metabolic connectivity reorganized not only in the sensorimotor area corresponding to the affected forelimb, but also in extensive areas involving the bilateral hemispheres. These findings may broaden our understanding of central nervous system changes, as well as provide new information and a potential intervention target for nosogenesis of deafferentation pain.

Neuroimaging Biomarkers of Experimental Epileptogenesis and Refractory Epilepsy

This article provides an overview of neuroimaging biomarkers in experimental epileptogenesis and refractory epilepsy. Neuroimaging represents a gold standard and clinically translatable technique to identify neuropathological changes in epileptogenesis and longitudinally monitor its progression after a precipitating injury. Neuroimaging studies, along with molecular studies from animal models, have greatly improved our understanding of the neuropathology of epilepsy, such as the hallmark hippocampus sclerosis. Animal models are effective for differentiating the different stages of epileptogenesis. Neuroimaging in experimental epilepsy provides unique information about anatomic, functional, and metabolic alterations linked to epileptogenesis. Recently, several in vivo biomarkers for epileptogenesis have been investigated for characterizing neuronal loss, inflammation, blood-brain barrier alterations, changes in neurotransmitter density, neurovascular coupling, cerebral blood flow and volume, network connectivity, and metabolic activity in the brain. Magnetic resonance imaging (MRI) is a sensitive method for detecting structural and functional changes in the brain, especially to identify region-specific neuronal damage patterns in epilepsy. Positron emission tomography (PET) and single-photon emission computerized tomography are helpful to elucidate key functional alterations, especially in areas of brain metabolism and molecular patterns, and can help monitor pathology of epileptic disorders. Multimodal procedures such as PET-MRI integrated systems are desired for refractory epilepsy. Validated biomarkers are warranted for early identification of people at risk for epilepsy and monitoring of the progression of medical interventions.

Characterization of Brain Dysfunction Induced by Bacterial Lipopeptides That Alter Neuronal Activity and Network in Rodent Brains

The immunopathological states of the brain induced by bacterial lipoproteins have been well characterized by using biochemical and histological assays. However, these studies have limitations in determining functional states of damaged brains involving aberrant synaptic activity and network, which makes it difficult to diagnose brain disorders during bacterial infection. To address this, we investigated the effect of Pam₃CSK₄ (PAM), a synthetic bacterial lipopeptide, on synaptic dysfunction of female mice brains and cultured neurons in parallel. Our functional brain imaging using PET with [¹⁸F]fluorodeoxyglucose and [¹⁸F] flumazenil revealed that the brain dysfunction induced by PAM is closely aligned to disruption of neurotransmitter-related neuronal activity and functional correlation in the region of the limbic system rather than to decrease of metabolic activity of neurons in the injection area. This finding was verified by in vivo tissue experiments that analyzed synaptic and dendritic alterations in the regions where PET imaging showed abnormal neuronal activity and network. Recording of synaptic activity also revealed that PAM reorganized synaptic distribution and decreased synaptic plasticity in hippocampus. Further study using in vitro neuron cultures demonstrated that PAM decreased the number of presynapses and the frequency of miniature EPSCs, which suggests PAM disrupts neuronal function by damaging presynapses exclusively. We also showed that PAM caused aggregation of synapses around dendrites, which may have caused no significant change in expression level of synaptic proteins, whereas synaptic number and function were impaired by PAM. Our findings could provide a useful guide for diagnosis and treatment of brain disorders specific to bacterial infection.SIGNIFICANCE STATEMENT It is challenging to diagnose brain disorders caused by bacterial infection because neural damage induced by bacterial products involves nonspecific neurological symptoms, which is rarely detected by laboratory tests with low spatiotemporal resolution. To better understand brain pathology, it is essential to detect functional abnormalities of brain over time. To this end, we investigated characteristic patterns of altered neuronal integrity and functional correlation between various regions in mice brains injected with bacterial lipopeptides using PET with a goal to apply new findings to diagnosis of brain disorder specific to bacterial infection. In addition, we analyzed altered synaptic density and function using both in vivo and in vitro experimental models to understand how bacterial lipopeptides impair brain function and network.

Abnormal Metabolic Connectivity in Rats at the Acute Stage of Ischemic Stroke

Stroke at the acute stage is a major cause of disability in adults, and is associated with dysfunction of brain networks. However, the mechanisms underlying changes in brain connectivity in stroke are far from fully elucidated. In the present study, we investigated brain metabolism and metabolic connectivity in a rat ischemic stroke model of middle cerebral artery occlusion (MCAO) at the acute stage using 18F-fluorodeoxyglucose positron emission tomography. Voxel-wise analysis showed decreased metabolism mainly in the ipsilesional hemisphere, and increased metabolism mainly in the contralesional cerebellum. We used further metabolic connectivity analysis to explore the brain metabolic network in MCAO. Compared to sham controls, rats with MCAO showed most significantly reduced nodal and local efficiency in the ipsilesional striatum. In addition, the MCAO group showed decreased metabolic central connection of the ipsilesional striatum with the ipsilesional cerebellum, ipsilesional hippocampus, and bilateral hypothalamus. Taken together, the present study demonstrated abnormal metabolic connectivity in rats at the acute stage of ischemic stroke, which might provide insight into clinical research.

Depression comorbidity in epileptic rats is related to brain glucose hypometabolism and hypersynchronicity in the metabolic network architecture

OBJECTIVE

Temporal lobe epilepsy (TLE) is one of the most common types of epilepsy syndromes in the world. Depression is an important comorbidity of epilepsy, which has been reported in patients with TLE and in different experimental models of epilepsy. However, there is no established consensus on which brain regions are associated with the manifestation of depression in epilepsy. Here, we investigated the alterations in cerebral glucose metabolism and the metabolic network in the pilocarpine-induced rat model of epilepsy and correlated it with depressive behavior during the chronic phase of epilepsy.

METHODS

Fluorodeoxyglucose (¹⁸ F-FDG) was used to investigate the cerebral metabolism, and a cross-correlation matrix was used to examine the metabolic network in chronically epileptic rats using micro-positron emission tomography (microPET) imaging. An experimental model of epilepsy was induced by pilocarpine injection (320 mg/kg, ip). Forced swim test (FST), sucrose preference test (SPT), and eating-related depression test (ERDT) were used to evaluate depression-like behavior.

RESULTS

Our results show an association between epilepsy and depression comorbidity based on changes in both cerebral glucose metabolism and the functional metabolic network. In addition, we have identified a significant correlation between brain glucose hypometabolism and depressive-like behavior in chronically epileptic rats. Furthermore, we found that the epileptic depressed group presents a hypersynchronous brain metabolic network in relation to the epileptic nondepressed group.

SIGNIFICANCE

This study revealed relevant alterations in glucose metabolism and the metabolic network among the brain regions of interest for both epilepsy and depression pathologies. Thus it seems that depression in epileptic animals is associated with a more diffuse hypometabolism and altered metabolic network architecture and plays an important role in chronic epilepsy.

Disrupted Resting State Network of Fibromyalgia in Theta frequency

Fibromyalgia (FM), chronic widespread pain, exhibits spontaneous pain without external stimuli and is associated with altered brain activities during resting state. To understand the topological features of brain network in FM, we employed persistent homology which is a multiple scale network modeling framework not requiring thresholding. Spontaneous magnetoencephalography (MEG) activity was recorded in 19 healthy controls (HCs) and 18 FM patients. Barcode, single linkage dendrogram and single linkage matrix were generated based on the proposed modeling framework. In theta band, the slope of decrease in the number of connected components in barcodes showed steeper in HC, suggesting FM patients had decreased global connectivity. FM patients had reduced connectivity within default mode network, between middle/inferior temporal gyrus and visual cortex. The longer pain duration was correlated with reduced connectivity between inferior temporal gyrus and visual cortex. Our findings demonstrated that the aberrant resting state network could be associated with dysfunction of sensory processing in chronic pain. The spontaneous nature of FM pain may accrue to disruption of resting state network.

Dynamic brain glucose metabolism identifies anti-correlated cortical-cerebellar networks at rest

It remains unclear whether resting state functional magnetic resonance imaging (rfMRI) networks are associated with underlying synchrony in energy demand, as measured by dynamic 2-deoxy-2-[¹⁸F]fluoroglucose (FDG) positron emission tomography (PET). We measured absolute glucose metabolism, temporal metabolic connectivity (t-MC) and rfMRI patterns in 53 healthy participants at rest. Twenty-two rfMRI networks emerged from group independent component analysis (gICA). In contrast, only two anti-correlated t-MC emerged from FDG-PET time series using gICA or seed-voxel correlations; one included frontal, parietal and temporal cortices, the other included the cerebellum and medial temporal regions. Whereas cerebellum, thalamus, globus pallidus and calcarine cortex arose as the strongest t-MC hubs, the precuneus and visual cortex arose as the strongest rfMRI hubs. The strength of the t-MC linearly increased with the metabolic rate of glucose suggesting that t-MC measures are strongly associated with the energy demand of the brain tissue, and could reflect regional differences in glucose metabolism, counterbalanced metabolic network demand, and/or differential time-varying delivery of FDG. The mismatch between metabolic and functional connectivity patterns computed as a function of time could reflect differences in the temporal characteristics of glucose metabolism as measured with PET-FDG and brain activation as measured with rfMRI.

Gating of memory encoding of time-delayed cross-frequency MEG networks revealed by graph filtration based on persistent homology

To explain gating of memory encoding, magnetoencephalography (MEG) was analyzed over multi-regional network of negative correlations between alpha band power during cue (cue-alpha) and gamma band power during item presentation (item-gamma) in Remember (R) and No-remember (NR) condition. Persistent homology with graph filtration on alpha-gamma correlation disclosed topological invariants to explain memory gating. Instruction compliance (R-hits minus NR-hits) was significantly related to negative coupling between the left superior occipital (cue-alpha) and the left dorsolateral superior frontal gyri (item-gamma) on permutation test, where the coupling was stronger in R than NR. In good memory performers (R-hits minus false alarm), the coupling was stronger in R than NR between the right posterior cingulate (cue-alpha) and the left fusiform gyri (item-gamma). Gating of memory encoding was dictated by inter-regional negative alpha-gamma coupling. Our graph filtration over MEG network revealed these inter-regional time-delayed cross-frequency connectivity serve gating of memory encoding.

[18F]FDG PET signal is driven by astroglial glutamate transport

Contributions of glial cells to neuroenergetics have been the focus of extensive debate. Here we provide the first positron emission tomography (PET) evidence that activation of the astrocytic glutamate transport via GLT-1 triggers widespread but graded glucose uptake in the rodent brain. Our results highlight the need for a reevaluation of the interpretation of [¹⁸F]FDG PET data, whereby astrocytes would be recognized to contribute significantly to the [¹⁸F]FDG signal.

Noninvasive Imaging of Myocardial Inflammation in Myocarditis using 68Ga-tagged Mannosylated Human Serum Albumin Positron Emission Tomography

The diagnosis of myocarditis traditionally relies on invasive endomyocardial biopsy but none of the imaging studies so far are specific for infiltration of the inflammatory cells itself. We synthesized ⁶⁸Ga-2-(p-isothiocyanatobenzyl)-1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA) mannosylated human serum albumin (MSA) by conjugating human serum albumin with mannose, followed by conjugation with NOTA and labeling it with ⁶⁸Ga. The efficacy of ⁶⁸Ga-NOTA-MSA positron emission tomography (PET) for imaging myocardial inflammation was tested in a rat myocarditis model. A significant number of mannose receptor-positive inflammatory cells infiltrated the myocardium in both human and rat myocarditis tissue. ⁶⁸Ga-NOTA-MSA uptake was upregulated in organs of macrophage accumulation, such as liver, spleen, bone marrow and myocardium (0.32 (0.31~0.33) for normal versus 1.02 (0.86~1.06) for myocarditis (median (range), SUV); n=4~6 per group, p-value=0.01). ⁶⁸Ga-NOTA-MSA uptake in the left ventricle was upregulated in myocarditis compared with normal rats (2.29 (1.42~3.40) for normal versus 4.18 (3.43~6.15) for myocarditis (median (range), average standard uptake value ratio against paraspinal muscle); n=6 per group, p-value<0.01), which was downregulated in rats with cyclosporine-A treated myocarditis (3.69 (2.59~3.86) for myocarditis versus 2.28 (1.76~2.60) for cyclosporine-A treated myocarditis; n=6 per group, p-value<0.01). The specificity of the tracer was verified by administration of excess non-labeled MSA. ⁶⁸Ga-NOTA-MSA uptake was significantly enhanced earlier in the evolution of myocarditis before any signs of inflammation could be seen on echocardiography. These results demonstrate the potential utility of visualizing infiltration of mannose receptor-positive macrophages with ⁶⁸Ga-NOTA-MSA PET in the early diagnosis of as well as in the monitoring of treatment response of myocarditis.

Disrupted brain metabolic connectivity in a 6-OHDA-induced mouse model of Parkinson's disease examined using persistent homology-based analysis

Movement impairments in Parkinson’s disease (PD) are caused by the degeneration of dopaminergic neurons and the consequent disruption of connectivity in the cortico-striatal-thalamic loop. This study evaluated brain metabolic connectivity in a 6-Hydroxydopamine (6-OHDA)-induced mouse model of PD using ¹⁸F-fluorodeoxy glucose positron emission tomography (FDG PET). Fourteen PD-model mice and ten control mice were used for the analysis. Voxel-wise t-tests on FDG PET results yielded no significant regional metabolic differences between the PD and control groups. However, the PD group showed lower correlations between the right caudoputamen and the left caudoputamen and right visual cortex. Further network analyses based on the threshold-free persistent homology framework revealed that brain networks were globally disrupted in the PD group, especially between the right auditory cortex and bilateral cortical structures and the left caudoputamen. In conclusion, regional glucose metabolism of PD was preserved, but the metabolic connectivity of the cortico-striatal-thalamic loop was globally impaired in PD.

Insights into Intrinsic Brain Networks based on Graph Theory and PET in right- compared to left-sided Temporal Lobe Epilepsy

The human brain exhibits marked hemispheric differences, though it is not fully understood to what extent lateralization of the epileptic focus is relevant. Preoperative [(18)F]FDG-PET depicts lateralization of seizure focus in patients with temporal lobe epilepsy and reveals dysfunctional metabolic brain connectivity. The aim of the present study was to compare metabolic connectivity, inferred from inter-regional [(18)F]FDG PET uptake correlations, in right-sided (RTLE; n = 30) and left-sided TLE (LTLE; n = 32) with healthy controls (HC; n = 31) using graph theory based network analysis. Comparing LTLE and RTLE and patient groups separately to HC, we observed higher lobar connectivity weights in RTLE compared to LTLE for connections of the temporal and the parietal lobe of the contralateral hemisphere (CH). Moreover, especially in RTLE compared to LTLE higher local efficiency were found in the temporal cortices and other brain regions of the CH. The results of this investigation implicate altered metabolic networks in patients with TLE specific to the lateralization of seizure focus, and describe compensatory mechanisms especially in the CH of patients with RTLE. We propose that graph theoretical analysis of metabolic connectivity using [(18)F]FDG-PET offers an important additional modality to explore brain networks.

Two's company, three (or more) is a simplex : Algebraic-topological tools for understanding higher-order structure in neural data

The language of graph theory, or network science, has proven to be an exceptional tool for addressing myriad problems in neuroscience. Yet, the use of networks is predicated on a critical simplifying assumption: that the quintessential unit of interest in a brain is a dyad – two nodes (neurons or brain regions) connected by an edge. While rarely mentioned, this fundamental assumption inherently limits the types of neural structure and function that graphs can be used to model. Here, we describe a generalization of graphs that overcomes these limitations, thereby offering a broad range of new possibilities in terms of modeling and measuring neural phenomena. Specifically, we explore the use of simplicial complexes: a structure developed in the field of mathematics known as algebraic topology, of increasing applicability to real data due to a rapidly growing computational toolset. We review the underlying mathematical formalism as well as the budding literature applying simplicial complexes to neural data, from electrophysiological recordings in animal models to hemodynamic fluctuations in humans. Based on the exceptional flexibility of the tools and recent ground-breaking insights into neural function, we posit that this framework has the potential to eclipse graph theory in unraveling the fundamental mysteries of cognition.

Intraoperative optical mapping of epileptogenic cortices during non-ictal periods in pediatric patients

Complete removal of epileptogenic cortex while preserving eloquent areas is crucial in patients undergoing epilepsy surgery. In this manuscript, the feasibility was explored of developing a new methodology based on dynamic intrinsic optical signal imaging (DIOSI) to intraoperatively detect and differentiate epileptogenic from eloquent cortices in pediatric patients with focal epilepsy. From 11 pediatric patients undergoing epilepsy surgery, negatively-correlated hemodynamic low-frequency oscillations (LFOs, ~ 0.02-0.1 Hz) were observed from the exposed epileptogenic and eloquent cortical areas, as defined by electrocorticography (ECoG), using a DIOSI system. These LFOs were classified into multiple groups in accordance with their unique temporal profiles. Causal relationships within these groups were investigated using the Granger causality method, and 83% of the ECoG-defined epileptogenic cortical areas were found to have a directed influence on one or more cortical areas showing LFOs within the field of view of the imaging system. To understand the physiological origins of LFOs, blood vessel density was compared between epileptogenic and normal cortical areas and a statistically-significant difference (p < 0.05) was detected. The differences in blood-volume and blood-oxygenation dynamics between eloquent and epileptogenic cortices were also uncovered using a stochastic modeling approach. This, in turn, yielded a means by which to separate epileptogenic from eloquent cortex using hemodynamic LFOs. The proposed methodology detects epileptogenic cortices by exploiting the effective connectivity that exists within cortical regions displaying LFOs and the biophysical features contributed by the altered vessel networks within the epileptogenic cortex. It could be used in conjunction with existing technologies for epileptogenic/eloquent cortex localization and thereby facilitate clinical decision-making.

Maturation of metabolic connectivity of the adolescent rat brain

Neuroimaging has been used to examine developmental changes of the brain. While PET studies revealed maturation-related changes, maturation of metabolic connectivity of the brain is not yet understood. Here, we show that rat brain metabolism is reconfigured to achieve long-distance connections with higher energy efficiency during maturation. Metabolism increased in anterior cerebrum and decreased in thalamus and cerebellum during maturation. When functional covariance patterns of PET images were examined, metabolic networks including default mode network (DMN) were extracted. Connectivity increased between the anterior and posterior parts of DMN and sensory-motor cortices during maturation. Energy efficiency, a ratio of connectivity strength to metabolism of a region, increased in medial prefrontal and retrosplenial cortices. Our data revealed that metabolic networks mature to increase metabolic connections and establish its efficiency between large-scale spatial components from childhood to early adulthood. Neurodevelopmental diseases might be understood by abnormal reconfiguration of metabolic connectivity and efficiency.

DOI:http://dx.doi.org/10.7554/eLife.11571.001

The brain consumes a great deal of a sugar called glucose, which is delivered to the brain through blood vessels. Active regions of the brain need more glucose, and so the brain has a metabolic network that controls when and where glucose is metabolized. Yet precisely how this metabolic network changes during brain development is not yet understood.

Choi et al. have now monitored the patterns of glucose metabolism in the brains of awake rats as they matured from 'childhood' to early adulthood. The experiments involved injecting the rats with radioactive glucose, and then using a technique called positron emission tomography (commonly known as 'PET scan') to monitor the metabolism of these radioactive sugar molecules in the animals’ brains.

Choi et al. showed that the patterns of glucose consumption in the brain shift drastically as the rats mature. Importantly, the findings showed that these shifts in glucose metabolism seem to support the activity of long distance connections that develop as the brain matures. The findings also showed that the increased long distance connections were energy efficient. The results suggest that these metabolic changes are likely a way of maintaining high-energy efficiency that is crucial for the brain to perform normally.

Finally, in addition to revealing the changes involved in normal brain development, these findings may have implications in neurological and psychiatric disorders in which the brain fails to achieve efficient metabolic networks as it matures.

DOI:http://dx.doi.org/10.7554/eLife.11571.002

Resting-state functional MRI and [18F]-FDG PET demonstrate differences in neuronal activity between commonly used mouse strains

The existence of numerous interesting mouse models of neurological disorders enables the investigation of causal relations between pathological events and the effect of treatment regimes. However, mouse models of a specific neurological disease are often generated using different background strains, which raises the question whether the observed effects are specific to pathology or depend on the used strain. This study used two independent in vivo functional imaging techniques to evaluate whether mouse strain differences exist in functional connectivity (FC) and brain glucose metabolism i.e. indirect measures of neuronal activity. For this purpose, C57BL/6, BALB/C and SJL mice (N=15/group, male) were evaluated using resting-state functional MRI (rsfMRI) and static [18F]-fluorodeoxyglucose Positron Emission Tomography ([18F]-FDG PET). RsfMRI and [18F]-FDG PET data were analyzed with independent component analysis (ICA). FC was quantified by calculating the mean network-specific FC strength and [18F]-FDG uptake was quantified by calculating the mean network-specific standard uptake value corrected for plasma glucose levels and body weight (SUVglu). The ICA results showed spatially similar neurological components in the rsfMRI and [18F]-FDG PET data, suggesting that patterns of metabolic covariance in the mouse brain reflect FC networks. Comparing FC and [18F]-FDG data showed that strain-dependent differences in brain activity exist for several brain networks i.e. the frontal, cingulate, (hypo)thalamus, striatum, and sensorimotor networks. The results of this study have implications for the interpretation of in vivo functional imaging data in mouse models of neurological disorders generated on different background strains.

Mapping epileptic activity: sources or networks for the clinicians?

Epileptic seizures of focal origin are classically considered to arise from a focal epileptogenic zone and then spread to other brain regions. This is a key concept for semiological electro-clinical correlations, localization of relevant structural lesions, and selection of patients for epilepsy surgery. Recent development in neuro-imaging and electro-physiology and combinations, thereof, have been validated as contributory tools for focus localization. In parallel, these techniques have revealed that widespread networks of brain regions, rather than a single epileptogenic region, are implicated in focal epileptic activity. Sophisticated multimodal imaging and analysis strategies of brain connectivity patterns have been developed to characterize the spatio-temporal relationships within these networks by combining the strength of both techniques to optimize spatial and temporal resolution with whole-brain coverage and directional connectivity. In this paper, we review the potential clinical contribution of these functional mapping techniques as well as invasive electrophysiology in human beings and animal models for characterizing network connectivity.