Connectivity Matrices Research Articles

Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity.

Patients with schizophrenia (SCZ), as well as their unaffected siblings (SIB), show functional connectivity (FC) alterations during performance of tasks involving attention. As compared with SCZ, these alterations are present in SIB to a lesser extent and are more pronounced during high cognitive demand, thus possibly representing one of the pathways in which familial risk is translated into the SCZ phenotype. Our aim is to measure the separability of SCZ and SIB from healthy controls (HC) using attentional control-dependent FC patterns, and to test to which extent these patterns span a continuum of neurofunctional alterations between HC and SCZ. 65 SCZ with 65 age and gender-matched HC and 39 SIB with 39 matched HC underwent the Variable Attentional Control (VAC) task. Load-dependent connectivity matrices were generated according to correct responses in each VAC load. Classification performances of high, intermediate and low VAC load FC on HC-SCZ and HC-SIB cohorts were tested through machine learning techniques within a repeated nested cross-validation framework. HC-SCZ classification models were applied to the HC-SIB cohort, and vice-versa. A high load-related decreased FC pattern discriminated between HC and SCZ with 66.9% accuracy and with 57.7% accuracy between HC and SIB. A high load-related increased FC network separated SIB from HC (69.6% accuracy), but not SCZ from HC (48.5% accuracy). Our findings revealed signatures of attentional FC abnormalities shared by SCZ and SIB individuals. We also found evidence for potential, SIB-specific FC signature, which may point to compensatory neurofunctional mechanisms in persons at familial risk for schizophrenia.

Structural and functional brain connectomes in patients with systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an immune-mediated disease that may affect the nervous system. We explored the topographical organization of structural and functional brain connectivity in patients with SLE and its correlation with neuropsychiatric (NP) involvement and autoantibody profiles. Graph theoretical analysis was applied to diffusion tensor magnetic resonance imaging (MRI) and resting-state functional MRI data from 32 patients with SLE and 32 age- and sex-matched healthy controls. Structural and functional connectivity matrices between 116 cortical/subcortical brain regions were estimated using a bivariate correlation analysis, and global and nodal network metrics were calculated. Structural, but not functional, global network properties (strength, transitivity, global efficiency and path length) were abnormal in patients with SLE versus controls (P<0.0001), especially in patients with anti-double-stranded DNA (ADNA) autoantibodies (P=0.03). No difference was found according to NP involvement or anti-phospholipid autoantibody status. Patients with SLE and controls shared identical structural hubs and the majority of functional hubs. In patients with SLE, all structural hubs showed reduced strength and clustering coefficient compared with controls (P from 0.001 to <0.0001), especially in patients with ADNA autoantibodies. Only a few differences in functional hub properties were found between patients with SLE and controls. Structural and functional hub measures did not differ according to NP involvement or anti-phospholipid autoantibody status. Significant correlations were found between clinical, MRI and network measures (r from -0.56 to 0.60, P from 0.0003 to 0.05). Abnormalities of global and nodal structural connectivity occur in patients with SLE, especially with ADNA autoantibodies, with a diffuse disruption of structural integrity. Functional network integrity may contribute to preserve clinical functions.

Alterations of neural network organisation during rapid eye movement sleep and slow-wave sleep in major depression: Implications for diagnosis, classification, and treatment

The aim of this study was to empirically investigate the network organisation during rapid eye movement sleep (REMS) and slow-wave sleep (SWS) using the effective connectivity measured using the Granger causality to identify new potential biomarkers for the diagnosis, classification, and potential favourable response to treatment in major depression. Polysomnographic data were analysed from 24 healthy individuals and 16 major depressed individuals recruited prospectively. To obtain the 19×19 connectivity matrix of all possible pairwise combinations of electrodes by the Granger causality method from our electroencephalographic data, we used the Toolbox MVGC multivariate Granger causality. The computation of network measures was realised by importing these connectivity matrices into the EEGNET Toolbox. Major depressed individuals (versus healthy individuals) and those with endogenous depression (versus those with neurotic depression) present alterations of small-world network organisation during REMS, whereas major depressed individuals with potential favourable response to electroconvulsive therapy (versus those with potential unfavourable response) have a less efficient small-world network organisation during SWS. Thus, alterations in network organisation during REMS could be biomarkers for the diagnosis and classification of major depressive episodes, whereas alterations of network organisation during SWS could be a biomarker to predict potential favourable response to treatment by electroconvulsive therapy.

Dual-Echo Arterial Spin Labeling for Brain Perfusion Quantification and Functional Analysis

Arterial Spin Labeling (ASL) is a noninvasive MRI-based method to measure cerebral blood flow (CBF). Recently, the study of ASL as a functional tool has emerged once CBF fluctuation comes from capillaries in brain tissue, giving a more spatially specific response when compared to the standard functional MRI method, based on the blood oxygenation level-dependent (BOLD) contrast. Although the BOLD effect could be desirable to study brain function, if one aims to quantify CBF, such effect is considered contamination that can be more attenuated if short TE value is used in the image acquisition. An approach that provides both CBF and function information in a simultaneous acquisition is the use of a dual-echo ASL (DE-ASL) readout. Our purpose was to evaluate the information provided by DE-ASL regarding CBF quantification and functional connectivity with a motor task. Pseudocontinuous ASL of twenty healthy subjects (age: 32.4 ± 10.2 years, 13 male) was acquired at a 3T scanner. We analyzed the influence of TE on CBF values and brain connectivity provided by CBF and concurrent BOLD (cc-BOLD) time series. Brain networks were obtained by the general linear model and independent component analysis. Connectivity matrices were generated using a bivariate correlation (Fisher Z values). No effect of the sequence readout, but significant effect of the TE value, was observed on gray matter CBF values. Motor networks with reduced extension and more connections with important regions for brain integration were observed for CBF data acquired with short TE, proving its higher spatial specificity. Therefore, it was possible to use a dual-echo readout provided by a standard commercial ASL pulse sequence to obtain reliable quantitative CBF values and functional information simultaneously.

Alterations in White Matter Network and Microstructural Integrity Differentiate Parkinson's Disease Patients and Healthy Subjects.

Parkinson’s disease (PD) is a neurodegenerative disease, neuropathologically characterized by progressive loss of neurons in distinct brain areas. We hypothesize that quantifiable network alterations are caused by neurodegeneration. The primary motivation of this study was to assess the specific network alterations in PD patients that are distinct but appear in conjunction with physiological aging. 178 subjects (130 females) stratified into PD patients, young, middle-aged and elderly healthy controls (age- and sex-matched with PD patients), were analyzed using 3D-T1 magnetization-prepared rapid gradient-echo (MPRAGE) and diffusion weighted images acquired in 3T MRI scanner. Diffusion modeling and probabilistic tractography analysis were applied for generating voxel-based connectivity index maps from each seed voxel. The obtained connectivity matrices were analyzed using graph theoretical tools for characterization of involved network. By network-based statistic (NBS) the interregional connectivity differences between the groups were assessed. Measures evaluating local diffusion properties for anisotropy and diffusivity were computed for characterization of white matter microstructural integrity. The graph theoretical analysis showed a significant decrease in distance measures – eccentricity and characteristic path length – in PD patients in comparison to healthy subjects. Both measures as well were lower in PD patients when compared to young and middle-aged healthy controls. NBS analysis demonstrated lowered structural connectivity in PD patients in comparison to young and middle-aged healthy subject groups, mainly in frontal, cingulate, olfactory, insula, thalamus, and parietal regions. These specific network differences were distinct for PD and were not observed between the healthy subject groups. Microstructural analysis revealed diffusivity alterations within the white matter tracts in PD patients, predominantly in the body, splenium and tapetum of corpus callosum, corticospinal tract, and corona radiata, which were absent in normal aging. The identified alterations of network connectivity presumably caused by neurodegeneration indicate the disruption in global network integration in PD patients. The microstructural changes identified within the white matter could endorse network reconfiguration. This study provides a clear distinction between the network changes occurring during aging and PD. This will facilitate a better understanding of PD pathophysiology and the direct link between white matter changes and their role in the restructured network topology.

Spatiotemporal Empirical Mode Decomposition of Resting-State fMRI Signals: Application to Global Signal Regression.

Resting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze non-linear and non-stationary phenomena. For each SIMF, functional connectivity matrices were computed by means of Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high correlation and global efficiency values of the connectivity matrices related to the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, by summing up the first two SIMFs extracted from the fMRI signals, we have automatically excluded the GS which is now voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision in identifying the correct brain network connectivity, our approach demonstrated expected connectivity patterns for the default mode network and task-positive network.

Larval connectivity patterns of the North Indo-West Pacific coral reefs

Coral reefs of the North Indo-West Pacific provide important ecosystem services to the region but are subjected to multiple local and global threats. Strengthening management measures necessitate understanding the variability of larval connectivity and bridging global connectivity models to local scales. An individual-based Lagrangian biophysical model was used to simulate connectivity between coral reefs for three organisms with different early life history characteristics: a coral (Acropora millepora), a sea urchin (Tripneustes gratilla), and a reef fish (Epinephelus sp). Connectivity metrics and reef clusters were computed from the settlement probability matrices. Fitted power law functions derived from the dispersal kernels provided relative probabilities of connection given only the distance between reefs, and demonstrated that 95% of the larvae across organisms settled within a third of their maximum settlement distances. The magnitude of the connectivity metric values of reef cells were sensitive to differences both in the type of organism and temporal variability. Seasonal variability of connections was more dominant than interannual variability. However, despite these differences, the moderate to high correlation of metrics between organisms and seasonal matrices suggest that the spatial patterns are relatively similar between reefs. A cluster analysis based on the Bray-Curtis Dissimilarity of sink and source connections synthesized the inherent variability of these multiple large connectivity matrices. Through this, similarities in regional connectivity patterns were determined at various cluster sizes depending on the scale of interest. The validity of the model is supported by 1) the simulated dispersal kernels being within the range of reported parentage analysis estimates; and, 2) the clusters that emerged reflect the dispersal barriers implied by previously published population genetics studies. The tools presented here (dispersal kernels, temporal variability maps and reef clustering) can be used to include regional patterns of connectivity into the spatial management of coral reefs.

Combining multiple connectomes improves predictive modeling of phenotypic measures

Resting-state and task-based functional connectivity matrices, or connectomes, are powerful predictors of individual differences in phenotypic measures. However, most of the current state-of-the-art algorithms only build predictive models based on a single connectome for each individual. This approach neglects the complementary information contained in connectomes from different sources and reduces prediction performance. In order to combine different task connectomes into a single predictive model in a principled way, we propose a novel prediction framework, termed multidimensional connectome-based predictive modeling. Two specific algorithms are developed and implemented under this framework. Using two large open-source datasets with multiple tasks—the Human Connectome Project and the Philadelphia Neurodevelopmental Cohort, we validate and compare our framework against performing connectome-based predictive modeling (CPM) on each task connectome independently, CPM on a general functional connectivity matrix created by averaging together all task connectomes for an individual, and CPM with a naïve extension to multiple connectomes where each edge for each task is selected independently. Our framework exhibits superior performance in prediction compared with the other competing methods. We found that different tasks contribute differentially to the final predictive model, suggesting that the battery of tasks used in prediction is an important consideration. This work makes two major contributions: First, two methods for combining multiple connectomes from different task conditions in one predictive model are demonstrated; Second, we show that these models outperform a previously validated single connectome-based predictive model approach.

Finite element model updating using Lagrange interpolation

Abstract In this paper, an iterative finite element model updating method in structural dynamics is proposed. This uses information matrices and element connectivity matrices to reconstruct the corrected model by reproducing the frequency response at measured degrees of freedom. Indicators have been proposed to quantify the mismodelling errors based on a development in Lagrange matrix interpolation. When applied on simulated truss structures, the model gives satisfactory results by detecting and quantifying the defaults of the initial model.

Detecting and testing altered brain connectivity networks with k-partite network topology

Emerging brain connectivity network studies suggest that interactions betweenvarious distributed neuronal populations may be characterized by an organized complex topological structure. Many neuropsychiatric disorders are associated with altered topological patterns of brain connectivity. Therefore, a key inquiry of connectivity analysis is to detect group-level differentially expressed connectome patterns from the massive neuroimaging data. Recently, statistical methods have been developed to detect differentially expressed connectivity features at a subnetwork level, extending more commonly applied edge level analysis. However, the graph topological structures in these methods are limited to community/cliques which may not effectively uncover the underlying complex and disease-related brain circuits/subnetworks. Building on these previous subnetwork detection methods, a new statistical approach is developed to automatically identify the latent differentially expressed brain connectivity subnetworks with k-partite graph topological structures from large brain connectivity matrices. In addition, statistical inferential techniques are provided to test the detected topological structure. The new methods are evaluated via extensive simulation studies and then applied to resting state fMRI data (24 cases and 18 controls) for Parkinson’s disease research. A differentially expressed connectivity network with the k-partite graph topological structure is detected which reveals underlying neural features distinguishing Parkinson’s disease patients from healthy control subjects.

Functional fractionation of default mode network in first episode schizophrenia

A disruption in the connectivity between brain regions may underlie the core pathology in schizophrenia. One of the most consistent observations in human functional imaging is a network of brain regions referred to as the default network (DMN) that contains core subsystem, the dorsomedial prefrontal cortex (dMPFC) subsystem and the medial temporal lobe (MTL) subsystem, with differential contributions. The goal of this study was to examine abnormalities of different DMN subsystems in first episode schizophrenia and associations between these abnormalities and individual psychopathology. We recruited 203 patients and 131 healthy controls. A seed-based resting-state functional connectivity (RSFC) analysis on the 2D surface was conducted. Individual DMN functional connectivity matrices were then obtained by calculating spatial correlations between pairs of RSFC maps, characterizing the functional fractionation of the DMN. Patients showed patterns similar to controls but markedly reduced strength of DMN fractionation, with the degree centrality of the MTL subsystem significantly reduced, including the posterior inferior parietal lobule (pIPL), parahippocampal cortex (PHC) and lateral temporal cortex (LTC). Patients also exhibited hypo-connectivity within the MTL subsystem and between the MTL and dMPFC subsystems. Clinical symptoms were negatively correlated with degree centrality of LTC, pIPL and PHC in patients. Hyper-fractionation of different DMN components implied that communication and coordination throughout the dissociated components of the DMN are functionally over-segregated in schizophrenia. The associations between the hyper-fractionation with clinical symptoms suggest a role of the high fractionation in the DMN in the abnormal neuropathology observed in schizophrenia.

Variability and Reproducibility of Directed and Undirected Functional MRI Connectomes in the Human Brain.

A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing the inter- and intra-subject variability of connectivity matrices, as well as graph-theoretical measures, in a large (n = 1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected, as opposed to directed, methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra-subject variabilities in both directed and undirected connectomic measures.

Abnormalities in hubs location and nodes centrality predict cognitive slowing and increased performance variability in first-episode schizophrenia patients

Introducing the Minimum Spanning Tree (MST) algorithms to neural networks science eliminated the problem of arbitrary setting of the threshold for connectivity strength. Despite these advantages, MST has been rarely used to study network abnormalities in schizophrenia. An MST graph mapping a network structure is its simplification, therefore, it is important to verify whether the reconfigured network is significantly related to the behavioural dimensions of the clinical picture of schizophrenia. 35 first-episode schizophrenia patients and 35 matched healthy controls underwent an assessment of information processing speed, cognitive inter-trial variability modelled with ex-Gaussian distributional analysis of reaction times and resting-state EEG recordings to obtain frequency-specific functional connectivity matrices from which MST graphs were computed. The patients’ network had a more random structure and star-like arrangement with overloaded hubs positioned more posteriorly than it was in the case of the control group. Deficient processing speed in the group of patients was predicted by increased maximal betweenness centrality in beta and gamma bands, while decreased consistency in cognitive processing was predicted by the betweenness centrality of posterior nodes in the gamma band, together with duration of illness. The betweenness centrality of posterior nodes in the gamma band was also significantly correlated with positive psychotic symptoms in the clinical group.

Characteristic changes in the EEG signals between microsleeps and preceding responsive states.

This work aims at identifying characteristic features of EEG to demarcate a microsleep from preceding responsive states. The EEG signals, after reference electrode standardization technique (REST) re-referencing, were processed through a time-varying general linear Kalman filter (TVGLKF) to derive time-varying auto-regressive (TVAR) parameters. The time-varying effective connectivity measure of orthogonal partial directed coherence (OPDC) was obtained for every instant at 256 Hz. Effective connectivity matrices formed using these OPDC measures, with the scalp electrodes as nodes were processed further using graph theory. Community-based measures were investigated and statistical significances compared. Non-parametric Wilcoxon signed rank test was used for significance analysis, with Cohen-type and Common Language effect size (CLES) as measures of effect sizes. The results showed a decrease in directional modularity from anterior to posterior, in theta, alpha, and beta bands in microsleeps. The alpha band showed the highest significance with a Cohen-type effect size of 1.25 and a median percentage difference of 23% across subjects, with a range of 13-28%. Flexibility and integration also decreased with average percentage of 25% (17-35%) and 20% (16-32%), respectively, while recruitment increased on an average of 11% (3-16%), wherever significant across all bands. These community-based measures can help characterize and explain changes in brain mechanisms, and can also serve as potential biomarkers for microsleep detection.

Non-Hermitian quasilocalization and ring attractor neural networks.

Eigenmodes of a broad class of "sparse" random matrices, with interactions concentrated near the diagonal, exponentially localize in space, as initially discovered in 1957 by Anderson for quantum systems. Anderson localization plays ubiquitous roles in varieties of problems from electrons in solids to mechanical and optical systems. However, its implications in neuroscience (where the connections can be strongly asymmetric) have been largely unexplored, mainly because synaptic connectivity matrices of neural systems are often "dense," which makes the eigenmodes spatially extended. Here we explore roles that Anderson localization could be playing in neural networks by focusing on "spatially structured" disorder in synaptic connectivity matrices. Recently neuroscientists have experimentally confirmed that the local excitation and global inhibition (LEGI) ring attractor model can functionally represent head direction cells in Drosophila melanogaster central brain. We first study a non-Hermitian (i.e., asymmetric) tight-binding model with disorder and then establish a connection to the LEGI ring attractor model. We discover that (1) principal eigenvectors of the LEGI ring attractor networks with structured nearest-neighbor disorder are "quasilocalized," even with fully dense inhibitory connections; and (2) the quasilocalized eigenvectors play dominant roles in the early time neural dynamics, and the location of the principal quasilocalized eigenvectors predicts an initial location of the "bump of activity" representing, for example, a head direction of an insect. Our investigations open up venues for explorations at the intersection between the theory of Anderson localization and neural networks with spatially structured disorder.

Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks.

Recent advances in image acquisition and processing techniques, along with the success of novel deep learning architectures, have given the opportunity to develop innovative algorithms capable to provide a better characterization of neurological related diseases. In this work, we introduce a neural network based approach to classify Multiple Sclerosis (MS) patients into four clinical profiles. Starting from their structural connectivity information, obtained by diffusion tensor imaging and represented as a graph, we evaluate the classification performances using unweighted and weighted connectivity matrices. Furthermore, we investigate the role of graph-based features for a better characterization and classification of the pathology. Ninety MS patients (12 clinically isolated syndrome, 30 relapsing-remitting, 28 secondary-progressive, and 20 primary-progressive) along with 24 healthy controls, were considered in this study. This work shows the great performances achieved by neural networks methods in the classification of the clinical profiles. Furthermore, it shows local graph metrics do not improve the classification results suggesting that the latent features created by the neural network in its layers have a much important informative content. Finally, we observe that graph weights representation of brain connections preserve important information to discriminate between clinical forms.

Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study.

Spatial pattern of the brain network changes dynamically. This change is closely linked to the brain-state transition, which vary depending on a dynamic stream of thoughts. To date, many dynamic methods have been developed for decoding brain-states. However, most of them only consider changes over time, not the brain-state transition itself. Here, we propose a novel dynamic functional connectivity analysis method, brain-state extraction algorithm based on state transition (BEST), which constructs connectivity matrices from the duration of brain-states and decodes the proper number of brain-states in a data-driven way. To set the duration of each brain-state, we detected brain-state transition time-points using spatial standard deviation of the brain activity pattern that changes over time. Furthermore, we also used Bayesian information criterion to the clustering method to estimate and extract the number of brain-states. Through validations, it was proved that BEST could find brain-state transition time-points and could estimate the proper number of brain-states without any a priori knowledge. It has also shown that BEST can be applied to resting state fMRI data and provide stable and consistent results.

A Novel Method for Extracting Hierarchical Functional Subnetworks Based on a Multisubject Spectral Clustering Approach.

Brain network modularity analysis has attracted increasing interest due to its capability in measuring the level of integration and segregation across subnetworks. Most studies have focused on extracting modules at a single level, although brain network modules are known to be organized in a hierarchical manner. A few techniques have been developed to extract hierarchical modularity in human functional brain networks using resting-state functional magnetic resonance imaging (fMRI) data; however, the focus of those methods is binary networks produced by applying arbitrary thresholds of correlation coefficients to the connectivity matrices. In this study, we propose a new multisubject spectral clustering technique, called group-level network hierarchical clustering (GNetHiClus), to extract the hierarchical structure of the functional network based on full weighted connectivity information. The most reliable results of hierarchical clustering are then estimated using a bootstrap aggregation algorithm. Specifically, we employ a voting-based ensemble method, that is, majority voting; random subsamples with replacement are created for clustering brain regions, which are further aggregated to select the most reliable clustering results. The proposed method is evaluated over a range of group sample sizes, based on resting-state fMRI data from the Human Connectome Project. Our results show that GNetHiClus can extract relatively consistent hierarchical network structures across a range of sample sizes investigated. In addition, the results demonstrate that GNetHiClus can hierarchically cluster brain functional networks into specialized subnetworks from upper-to-lower level, including the high-level cognitive and the low-level perceptual networks. Conversely, from lower-to-upper level, information processed by specialized lower level subnetworks is integrated into upper level for achieving optimal efficiency for brain functional communications. Importantly, these findings are consistent with the concept of network segregation and integration, suggesting that the proposed technique can be helpful to promote the understanding of brain network from a hierarchical point of view.

275-LB: Connectome Predictive Modeling May Identify Brain Connectivity Signatures to Help Predict Who Will Benefit from Low-Calorie Diet

While studies have investigated changes in brain connectivity following diet and weight loss, it has not yet been explored whether baseline brain connectivity can be used to predict weight loss during diet or to identify individual subjects for which a particular diet may be effective. The following study uses Connectome-based predictive modeling (CPM), a data-driven analysis approach for producing predictive models of individual brain-behavior relationships from brain connectivity data using cross-validation, to help identify individual participants who will lose weight after an 8-week low calorie diet. 16 Healthy OB subjects (10F/6M, age 44.4±8 years, BMI 32.7±2) and 9 T2DM subjects (5F/4M, age 48±9, BMI 33.9±2) underwent functional MRI (fMRI) during a hyperglycemic-euglycemic clamp. Blood-oxygen-level-dependent (BOLD) brain activity was assessed while subjects viewed food (high-calorie, low-calorie) pictures and non-food images. The study was repeated after 8 weeks of a reduced calorie diet. CPM was applied to BOLD activity during hyperglycemia and euglycemia. Analyses show that models built on individual subject brain connectivity matrices averaged between hyperglycemia and euglycemia before an 8-week low calorie diet are able to predict 50.2% and 53.4% of the variance in BMI and weight loss (kg), respectively (p&amp;lt;0.05). While models built on brain connectivity matrices during euglycemia (similar to the post-prandial state), but not hyperglycemia predicted 42.2% of the variance in BMI change (p=0.017). These results suggest the possibility that individual brain connectivity signatures may be used to identify subjects for whom a low calorie diet might be an effective weight loss strategy, while also revealing the relevant functional connections associated with weight loss success. Disclosure D. Groskreutz: None. W. Lam: None. C. Lacadie: None. A. Elshafie: None. J.J. Hwang: None. D. Seo: None. M. Savoye: None. R. Sinha: None. T. Constable: None. R. Sherwin: Other Relationship; Self; ICON plc., IQVIA, MannKind Corporation. R. Belfort-DeAguiar: Research Support; Self; Silver Palate Kitchens, Inc. Funding National Institutes of Health

Dynamics and Distant Effects of Frontal/Temporal Epileptogenic Focus Using Functional Connectivity Maps.

Connectivity patterns of interictal epileptiform discharges are all subtle indicators of where the three-dimensional (3D) source of a seizure could be located. These specific patterns are explored in the recorded electroencephalogram (EEG) signals of 20 individuals diagnosed with focal epilepsy to assess how their functional brain maps could be affected by the 3D onset of a seizure. Functional connectivity maps, estimated by phase synchrony among EEG electrodes, were obtained by applying a data-driven recurrence-based method. This is augmented through a novel approach for selecting optimal parameters that produce connectivity matrices that are deemed significant for assessing epileptiform activity in context to the 3D source localization of seizure onset. These functional connectivity matrices were evaluated in different brain areas to gauge the regional effects of the 3D epileptic source. Empirical evaluations indicate high synchronization in the temporal and frontal areas of the effected epileptic hemisphere, whereas strong links connect the irritated area to frontal and temporal lobes of the opposite hemisphere. Epileptic activity originating in the temporal or frontal areas is seen to affect these areas in both hemispheres. The results obtained express the dynamics of focal epilepsy in context to both the epileptogenic zone and the affected distant areas of the brain.