Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states.Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure–function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings.Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10−13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10−3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10−12). Structure–function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10−21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones.These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.

Brain functional connectivity (FC) networks inferred from functional magnetic resonance imaging (fMRI) have shown altered or aberrant brain functional connectome in various neuropsychiatric disorders. Recent application of deep neural networks to connectome-based classification mostly relies on traditional convolutional neural networks (CNNs) using input FCs on a regular Euclidean grid to learn spatial maps of brain networks neglecting the topological information of the brain networks, leading to potentially sub-optimal performance in brain disorder identification. We propose a novel graph deep learning framework that leverages non-Euclidean information inherent in the graph structure for classifying brain networks in major depressive disorder (MDD). We introduce a novel graph autoencoder (GAE) architecture, built upon graph convolutional networks (GCNs), to embed the topological structure and node content of large fMRI networks into low-dimensional representations. For constructing the brain networks, we employ the Ledoit-Wolf (LDW) shrinkage method to efficiently estimate high-dimensional FC metrics from fMRI data. We explore both supervised and unsupervised techniques for graph embedding learning. The resulting embeddings serve as feature inputs for a deep fully-connected neural network (FCNN) to distinguish MDD from healthy controls (HCs). Evaluating our model on resting-state fMRI MDD dataset, we observe that the GAE-FCNN outperforms several state-of-the-art methods for brain connectome classification, achieving the highest accuracy when using LDW-FC edges as node features. The graph embeddings of fMRI FC networks also reveal significant group differences between MDD and HCs. Our framework demonstrates the feasibility of learning graph embeddings from brain networks, providing valuable discriminative information for diagnosing brain disorders.

The nigrostriatal and mesocorticolimbic dopamine networks regulate reward-driven behavior. Regional alterations to mesolimbic dopamine D_2/3receptor expression are described in drug-seeking and addiction disorders. Parkinson's disease (PD) patients are frequently prescribed D₂-like dopamine agonist (DAgonist) therapy for motor symptoms, yet a proportion develop clinically significant behavioral addictions characterized by impulsive and compulsive behaviors (ICBs). Until now, changes in D_2/3receptor binding in both striatal and extrastriatal regions have not been concurrently quantified in this population. We identified 35 human PD patients (both male and female) receiving DAgonist therapy, with (n= 17) and without (n= 18) ICBs, matched for age, disease duration, disease severity, and dose of dopamine therapy. In the off-dopamine state, all completed PET imaging with [¹⁸F]fallypride, a high affinity D₂-like receptor ligand that can measure striatal and extrastriatal D_2/3nondisplaceable binding potential (BP_ND). Striatal differences between ICB+/ICB− patients localized to the ventral striatum and putamen, where ICB+ subjects had reduced BP_ND. In this group, self-reported severity of ICB symptoms positively correlated with midbrain D_2/3receptor BP_ND. Group differences in regional D_2/3BP_NDrelationships were also notable: ICB+ (but not ICB−) patients expressed positive correlations between midbrain and caudate, putamen, globus pallidus, and amygdala BP_NDs. These findings support the hypothesis that compulsive behaviors in PD are associated with reduced ventral and dorsal striatal D_2/3expression, similar to changes in comparable behavioral disorders. The data also suggest that relatively preserved ventral midbrain dopaminergic projections throughout nigrostriatal and mesolimbic networks are characteristic of ICB+ patients, and may account for differential DAgonist therapeutic response.SIGNIFICANCE STATEMENTThe biologic determinants of compulsive reward-based behaviors have broad clinical relevance, from addiction to neurodegenerative disorders. Here, we address biomolecular distinctions in Parkinson's disease patients with impulsive compulsive behaviors (ICBs). This is the first study to image a large cohort of ICB+ patients using positron emission tomography with [18F]fallypride, allowing quantification of D_2/3receptors throughout the mesocorticolimbic network. We demonstrate widespread differences in dopaminergic networks, including (1) D2-like receptor distinctions in the ventral striatum and putamen, and (2) a preservation of widespread dopaminergic projections emerging from the midbrain, which is associated with the severity of compulsive behaviors. This clearly illustrates the roles of D_2/3receptors and medication effects in maladaptive behaviors, and localizes them specifically to nigrostriatal and extrastriatal regions.