Cellular Substrates of Functional Network Integration and Memory in Temporal Lobe Epilepsy.

Temporal lobe epilepsy (TLE) patients are at risk of memory deficits, which have been linked to functional network disturbances, particularly of integration of the default mode network (DMN). However, the cellular substrates of functional network integration are unknown. We leverage a unique cross-scale dataset of drug-resistant TLE patients (n = 31), who underwent pseudo resting-state functional magnetic resonance imaging (fMRI), resting-state magnetoencephalography (MEG) and/or neuropsychological testing before neurosurgery. fMRI and MEG underwent atlas-based connectivity analyses. Functional network centrality of the lateral middle temporal gyrus, part of the DMN, was used as a measure of local network integration. Subsequently, non-pathological cortical tissue from this region was used for single cell morphological and electrophysiological patch-clamp analysis, assessing integration in terms of total dendritic length and action potential rise speed. As could be hypothesized, greater network centrality related to better memory performance. Moreover, greater network centrality correlated with more integrative properties at the cellular level across patients. We conclude that individual differences in cognitively relevant functional network integration of a DMN region are mirrored by differences in cellular integrative properties of this region in TLE patients. These findings connect previously separate scales of investigation, increasing translational insight into focal pathology and large-scale network disturbances in TLE.

Human Hippocampal Neurons Track Moments in a Sequence of Events.

An indispensable feature of episodic memory is our ability to temporally piece together different elements of an experience into a coherent memory. Hippocampal time cells-neurons that represent temporal information-may play a critical role in this process. Although these cells have been repeatedly found in rodents, it is still unclear to what extent similar temporal selectivity exists in the human hippocampus. Here, we show that temporal context modulates the firing activity of human hippocampal neurons during structured temporal experiences. We recorded neuronal activity in the human brain while patients of either sex learned predictable sequences of pictures. We report that human time cells fire at successive moments in this task. Furthermore, time cells also signaled inherently changing temporal contexts during empty 10 s gap periods between trials while participants waited for the task to resume. Finally, population activity allowed for decoding temporal epoch identity, both during sequence learning and during the gap periods. These findings suggest that human hippocampal neurons could play an essential role in temporally organizing distinct moments of an experience in episodic memory.SIGNIFICANCE STATEMENT Episodic memory refers to our ability to remember the what, where, and when of a past experience. Representing time is an important component of this form of memory. Here, we show that neurons in the human hippocampus represent temporal information. This temporal signature was observed both when participants were actively engaged in a memory task, as well as during 10-s-long gaps when they were asked to wait before performing the task. Furthermore, the activity of the population of hippocampal cells allowed for decoding one temporal epoch from another. These results suggest a robust representation of time in the human hippocampus.

A European questionnaire survey on epilepsy monitoring units' current practice for postoperative psychogenic nonepileptic seizures' detection.

BACKGROUND: In cases undergoing epilepsy surgery, postoperative psychogenic nonepileptic seizures (PNES) may be underdiagnosed complicating the assessment of postsurgical seizures' outcome and the clinical management. We conducted a survey to investigate the current practices in the European epilepsy monitoring units (EMUs) and the data that EMUs could provide to retrospectively detect cases with postoperative PNES and to assess the feasibility of a subsequent postoperative PNES research project for cases with postoperative PNES. METHODS: We developed and distributed a questionnaire survey to 57 EMUs. Questions addressed the number of patients undergoing epilepsy surgery, the performance of systematic preoperative and postoperative psychiatric evaluation, the recording of sexual or other abuse, the follow-up period of patients undergoing epilepsy surgery, the performance of video-electroencephalogram (EEG) and postoperative psychiatric assessment in suspected postoperative cases with PNES, the existence of electronic databases to allow extraction of cases with postoperative PNES, the data that these bases could provide, and EMUs' interest to participate in a retrospective postoperative PNES project. RESULTS: Twenty EMUs completed the questionnaire sheet. The number of patients operated every year/per center is 26.7 ( ±â€¯19.1), and systematic preoperative and postoperative psychiatric evaluation is performed in 75% and 50% of the EMUs accordingly. Sexual or other abuse is systematically recorded in one-third of the centers, and the mean follow-up period after epilepsy surgery is 10.5 ±â€¯7.5 years. In suspected postoperative PNES, video-EEG is performed in 85% and psychiatric assessment in 95% of the centers. An electronic database to allow extraction of patients with PNES after epilepsy surgery is used in 75% of the EMUs, and all EMUs that sent the sheet completed expressed their interest to participate in a retrospective postoperative PNES project. CONCLUSION: Postoperative PNES is an underestimated and not well-studied entity. This is a European survey to assess the type of data that the EMUs surgical cohorts could provide to retrospectively detect postoperative PNES. In cases with suspected PNES, most EMUs perform video-EEG and psychiatric assessment, and most EMUs use an electronic database to allow extraction of patients developing PNES.

Direction of information flow in large-scale resting-state networks is frequency-dependent.

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease.

Neuronal hyperactivity and hyperexcitability of the cerebral cortex and hippocampal region is an increasingly observed phenomenon in preclinical Alzheimer's disease (AD). In later stages, oscillatory slowing and loss of functional connectivity are ubiquitous. Recent evidence suggests that neuronal dynamics have a prominent role in AD pathophysiology, making it a potentially interesting therapeutic target. However, although neuronal activity can be manipulated by various (non-)pharmacological means, intervening in a highly integrated system that depends on complex dynamics can produce counterintuitive and adverse effects. Computational dynamic network modeling may serve as a virtual test ground for developing effective interventions. To explore this approach, a previously introduced large-scale neural mass network with human brain topology was used to simulate the temporal evolution of AD-like, activity-dependent network degeneration. In addition, six defense strategies that either enhanced or diminished neuronal excitability were tested against the degeneration process, targeting excitatory and inhibitory neurons combined or separately. Outcome measures described oscillatory, connectivity and topological features of the damaged networks. Over time, the various interventions produced diverse large-scale network effects. Contrary to our hypothesis, the most successful strategy was a selective stimulation of all excitatory neurons in the network; it substantially prolonged the preservation of network integrity. The results of this study imply that functional network damage due to pathological neuronal activity can be opposed by targeted adjustment of neuronal excitability levels. The present approach may help to explore therapeutic effects aimed at preserving or restoring neuronal network integrity and contribute to better-informed intervention choices in future clinical trials in AD.

Selective impairment of hippocampus and posterior hub areas in Alzheimer's disease: an MEG-based multiplex network study.

Although frequency-specific network analyses have shown that functional brain networks are altered in patients with Alzheimer's disease, the relationships between these frequency-specific network alterations remain largely unknown. Multiplex network analysis is a novel network approach to study complex systems consisting of subsystems with different types of connectivity patterns. In this study, we used magnetoencephalography to integrate five frequency-band specific brain networks in a multiplex framework. Previous structural and functional brain network studies have consistently shown that hub brain areas are selectively disrupted in Alzheimer's disease. Accordingly, we hypothesized that hub regions in the multiplex brain networks are selectively targeted in patients with Alzheimer's disease in comparison to healthy control subjects. Eyes-closed resting-state magnetoencephalography recordings from 27 patients with Alzheimer's disease (60.6 ± 5.4 years, 12 females) and 26 controls (61.8 ± 5.5 years, 14 females) were projected onto atlas-based regions of interest using beamforming. Subsequently, source-space time series for both 78 cortical and 12 subcortical regions were reconstructed in five frequency bands (delta, theta, alpha 1, alpha 2 and beta band). Multiplex brain networks were constructed by integrating frequency-specific magnetoencephalography networks. Functional connections between all pairs of regions of interests were quantified using a phase-based coupling metric, the phase lag index. Several multiplex hub and heterogeneity metrics were computed to capture both overall importance of each brain area and heterogeneity of the connectivity patterns across frequency-specific layers. Different nodal centrality metrics showed consistently that several hub regions, particularly left hippocampus, posterior parts of the default mode network and occipital regions, were vulnerable in patients with Alzheimer's disease compared to control subjects. Of note, these detected vulnerable hubs in Alzheimer's disease were absent in each individual frequency-specific network, thus showing the value of integrating the networks. The connectivity patterns of these vulnerable hub regions in the patients were heterogeneously distributed across layers. Perturbed cognitive function and abnormal cerebrospinal fluid amyloid-ß42 levels correlated positively with the vulnerability of the hub regions in patients with Alzheimer's disease. Our analysis therefore demonstrates that the magnetoencephalography-based multiplex brain networks contain important information that cannot be revealed by frequency-specific brain networks. Furthermore, this indicates that functional networks obtained in different frequency bands do not act as independent entities. Overall, our multiplex network study provides an effective framework to integrate the frequency-specific networks with different frequency patterns and reveal neuropathological mechanism of hub disruption in Alzheimer's disease.

Loss of EEG Network Efficiency Is Related to Cognitive Impairment in Dementia With Lewy Bodies.

OBJECTIVE: The aim of this study was to test whether disturbed EEG resting-state functional connectivity and network organization are a potential neurophysiological substrate of cognitive impairment in dementia with Lewy bodies. METHODS: EEG recordings were obtained in dementia with Lewy bodies patients, Alzheimer's disease patients and controls, matched for age and sex (N = 66 for each group; 14 [21%] female; mean age: 70 years). We analyzed functional connectivity of band-filtered EEG time series using the phase lag index. Functional brain network topology was analyzed with the minimum spanning tree. Mini-Mental State Examination, Trail Making Test A, and Visual Association Test were used as cognitive measures. RESULTS: Dementia with Lewy bodies patients showed lower connectivity strength in the alpha frequency band, compared to both controls and Alzheimer's disease patients (P < 0.001). Functional network topology in dementia with Lewy bodies patients was less efficient and contained less hubs (P < 0.01). Network characteristics in Alzheimer's disease patients were in between (but did not differ from) those of the other two groups. In dementia with Lewy bodies patients, lower alpha band phase lag index correlated with Visual Association Test scores and Trail Making Test scores (ρ = 0.33 and ρ = 0.31, respectively), whereas leaf fraction (a measure of 'network efficiency') correlated with Visual Association Test scores (ρ = 0.29) and Mini-Mental State Examination scores (ρ = 0.27). CONCLUSION: Functional networks of dementia with Lewy bodies patients are characterized by decreased connectivity strength and a loss of network efficiency and hubs. Severity of these disturbances is related to cognitive impairment, suggesting that network disturbances mediate between neuropathology and the clinical syndrome in dementia with Lewy bodies.

Declining functional connectivity and changing hub locations in Alzheimer's disease: an EEG study.

BACKGROUND: EEG studies have shown that patients with Alzheimer's disease (AD) have weaker functional connectivity than controls, especially in higher frequency bands. Furthermore, active regions seem more prone to AD pathology. How functional connectivity is affected in AD subgroups of disease severity and how network hubs (highly connected brain areas) change is not known. We compared AD patients with different disease severity and controls in terms of functional connections, hub strength and hub location. METHODS: We studied routine 21-channel resting-state electroencephalography (EEG) of 318 AD patients (divided into tertiles based on disease severity: mild, moderate and severe AD) and 133 age-matched controls. Functional connectivity between EEG channels was estimated with the Phase Lag Index (PLI). From the PLI-based connectivity matrix, the minimum spanning tree (MST) was derived. For each node (EEG channel) in the MST, the betweenness centrality (BC) was computed, a measure to quantify the relative importance of a node within the network. Then we derived color-coded head plots based on BC values and calculated the center of mass (the exact middle had x and y values of 0). A shifting of the hub locations was defined as a shift of the center of mass on the y-axis across groups. Multivariate general linear models with PLI or BC values as dependent variables and the groups as continuous variables were used in the five conventional frequency bands. RESULTS: We found that functional connectivity decreases with increasing disease severity in the alpha band. All, except for posterior, regions showed increasing BC values with increasing disease severity. The center of mass shifted from posterior to more anterior regions with increasing disease severity in the higher frequency bands, indicating a loss of relative functional importance of the posterior brain regions. CONCLUSIONS: In conclusion, we observed decreasing functional connectivity in the posterior regions, together with a shifted hub location from posterior to central regions with increasing AD severity. Relative hub strength decreases in posterior regions while other regions show a relative rise with increasing AD severity, which is in accordance with the activity-dependent degeneration theory. Our results indicate that hubs are disproportionally affected in AD.

Individualized epidemic spreading models predict epilepsy surgery outcomes: A pseudo-prospective study.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome on a patient-by-patient basis, we developed a framework of individualized computational models that combines epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). ESSES parameters were fitted in a retrospective study (N = 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES reproduced the iEEG-recorded seizures, and significantly better so for patients with good (seizure-free, SF) than bad (nonseizure-free, NSF) outcome. We illustrate here the clinical applicability of ESSES with a pseudo-prospective study (N = 34) with a blind setting (to the resection strategy and surgical outcome) that emulated presurgical conditions. By setting the model parameters in the retrospective study, ESSES could be applied also to patients without iEEG data. ESSES could predict the chances of good outcome after any resection by finding patient-specific model-based optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan overlapped more with the model-based optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated with a fully independent cohort and without the need for iEEG recordings.

Individualized computational models of epilepsy surgery capture some of the key aspects of seizure propagation and the resective surgery. It is to be established whether this information can be integrated during the presurgical evaluation of the patient to improve surgical planning and the chances of a good surgical outcome. Here we address this question with a pseudo-prospective study that applies a computational framework of seizure propagation and epilepsy surgery­the ESSES framework­in a pseudo-prospective study mimicking the presurgical conditions. We found that within this pseudo-prospective setting, ESSES could correctly predict 75% of NSF and 80.8% of SF cases. This finding suggests the potential of individualised computational models to inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection.

Clinical observation: Rhythmic and periodic EEG patterns in postanoxic coma can possibly be related to propofol discontinuation.

OBJECTIVES: To investigate whether rhythmic/periodic EEG patterns (RPP) appearing after propofol discontinuation are more likely to be related to the elimination phase of propofol, or are an expression of severe brain damage. METHODS: In a retrospective cohort of comatose postanoxic patients, EEG was assessed one hour before (baseline) and on hour after discontinuation of propofol. Presence and duration of RPP were related to (changes in) EEG background pattern and duration of sedation. RESULTS: In eleven (of 36 eligible) patients RPP appeared after propofol discontinuation and disappeared in seven of these patients within one hour. A continuous background pattern at baseline and shorter duration of propofol infusion seemed associated with (earlier) spontaneous disappearance of RPP. In ten patients with RPP at baseline, the EEG did not change, and in one patient it changed into burst-suppression. CONCLUSION: Our findings suggest that RPP after propofol discontinuation could be propofol-related. DISCUSSION: RPP might be related to propofol discontinuation rather than an expression of severe brain damage, especially in case of, and congruent with, a continuous pattern at the time of propofol discontinuation. This opens a new insight in this phenomenon and its transient nature. In clinical practice, we suggest to consider the timing of propofol discontinuation when assessing the EEG signal in postanoxic patients.

Activity dependent degeneration explains hub vulnerability in Alzheimer's disease.

Brain connectivity studies have revealed that highly connected 'hub' regions are particularly vulnerable to Alzheimer pathology: they show marked amyloid-ß deposition at an early stage. Recently, excessive local neuronal activity has been shown to increase amyloid deposition. In this study we use a computational model to test the hypothesis that hub regions possess the highest level of activity and that hub vulnerability in Alzheimer's disease is due to this feature. Cortical brain regions were modeled as neural masses, each describing the average activity (spike density and spectral power) of a large number of interconnected excitatory and inhibitory neurons. The large-scale network consisted of 78 neural masses, connected according to a human DTI-based cortical topology. Spike density and spectral power were positively correlated with structural and functional node degrees, confirming the high activity of hub regions, also offering a possible explanation for high resting state Default Mode Network activity. 'Activity dependent degeneration' (ADD) was simulated by lowering synaptic strength as a function of the spike density of the main excitatory neurons, and compared to random degeneration. Resulting structural and functional network changes were assessed with graph theoretical analysis. Effects of ADD included oscillatory slowing, loss of spectral power and long-range synchronization, hub vulnerability, and disrupted functional network topology. Observed transient increases in spike density and functional connectivity match reports in Mild Cognitive Impairment (MCI) patients, and may not be compensatory but pathological. In conclusion, the assumption of excessive neuronal activity leading to degeneration provides a possible explanation for hub vulnerability in Alzheimer's disease, supported by the observed relation between connectivity and activity and the reproduction of several neurophysiologic hallmarks. The insight that neuronal activity might play a causal role in Alzheimer's disease can have implications for early detection and interventional strategies.

The role of epidemic spreading in seizure dynamics and epilepsy surgery.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but only leads to seizure freedom for roughly two in three patients. To address this problem, we designed a patient-specific epilepsy surgery model combining large-scale magnetoencephalography (MEG) brain networks with an epidemic spreading model. This simple model was enough to reproduce the stereo-tactical electroencephalography (SEEG) seizure propagation patterns of all patients (N = 15), when considering the resection areas (RA) as the epidemic seed. Moreover, the goodness of fit of the model predicted surgical outcome. Once adapted for each patient, the model can generate alternative hypothesis of the seizure onset zone and test different resection strategies in silico. Overall, our findings indicate that spreading models based on patient-specific MEG connectivity can be used to predict surgical outcomes, with better fit results and greater reduction on seizure propagation linked to higher likelihood of seizure freedom after surgery. Finally, we introduced a population model that can be individualized by considering only the patient-specific MEG network, and showed that it not only conserves but improves the group classification. Thus, it may pave the way to generalize this framework to patients without SEEG recordings, reduce the risk of overfitting and improve the stability of the analyses.

Disturbed oscillatory brain dynamics in subcortical ischemic vascular dementia.

BACKGROUND: White matter hyperintensities (WMH) can lead to dementia but the underlying physiological mechanisms are unclear. We compared relative oscillatory power from electroencephalographic studies (EEGs) of 17 patients with subcortical ischemic vascular dementia, based on extensive white matter hyperintensities (SIVD-WMH) with 17 controls to investigate physiological changes underlying this diagnosis. RESULTS: Differences between the groups were large, with a decrease of relative power of fast activity in patients (alpha power 0.25 ± 0.12 versus 0.38 ± 0.13, p = 0.01; beta power 0.08 ± 0.04 versus 0.19 ± 0.07; p<0.001) and an increase in relative powers of slow activity in patients (theta power 0.32 ± 0.11 versus 0.14 ± 0.09; p<0.001 and delta power 0.31 ± 0.14 versus 0.23 ± 0.09; p<0.05). Lower relative beta power was related to worse cognitive performance in a linear regression analysis (standardized beta = 0.67, p<0.01). CONCLUSIONS: This pattern of disturbance in oscillatory brain activity indicate loss of connections between neurons, providing a first step in the understanding of cognitive dysfunction in SIVD-WMH.

Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients. However, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. In this study we have developed an individualized computational model based on MEG brain networks to explore seizure propagation and the efficacy of different virtual resections. Eventually, the goal is to obtain individualized models to optimize resection strategy and outcome. We have modelled seizure propagation as an epidemic process using the susceptible-infected (SI) model on individual brain networks derived from presurgical MEG. We included 10 patients who had received epilepsy surgery and for whom the surgery outcome at least one year after surgery was known. The model parameters were tuned in in order to reproduce the patient-specific seizure propagation patterns as recorded with invasive EEG. We defined a personalized search algorithm that combined structural and dynamical information to find resections that maximally decreased seizure propagation for a given resection size. The optimal resection for each patient was defined as the smallest resection leading to at least a 90% reduction in seizure propagation. The individualized model reproduced the basic aspects of seizure propagation for 9 out of 10 patients when using the resection area as the origin of epidemic spreading, and for 10 out of 10 patients with an alternative definition of the seed region. We found that, for 7 patients, the optimal resection was smaller than the resection area, and for 4 patients we also found that a resection smaller than the resection area could lead to a 100% decrease in propagation. Moreover, for two cases these alternative resections included nodes outside the resection area. Epidemic spreading models fitted with patient specific data can capture the fundamental aspects of clinically observed seizure propagation, and can be used to test virtual resections in silico. Combined with optimization algorithms, smaller or alternative resection strategies, that are individually targeted for each patient, can be determined with the ultimate goal to improve surgery outcome. MEG-based networks can provide a good approximation of structural connectivity for computational models of seizure propagation, and facilitate their clinical use.

Intracranial human recordings reveal association between neural activity and perceived intensity for the pain of others in the insula.

Based on neuroimaging data, the insula is considered important for people to empathize with the pain of others. Here, we present intracranial electroencephalographic (iEEG) recordings and single-cell recordings from the human insula while seven epilepsy patients rated the intensity of a woman's painful experiences seen in short movie clips. Pain had to be deduced from seeing facial expressions or a hand being slapped by a belt. We found activity in the broadband 20-190 Hz range correlated with the trial-by-trial perceived intensity in the insula for both types of stimuli. Within the insula, some locations had activity correlating with perceived intensity for our facial expressions but not for our hand stimuli, others only for our hand but not our face stimuli, and others for both. The timing of responses to the sight of the hand being hit is best explained by kinematic information; that for our facial expressions, by shape information. Comparing the broadband activity in the iEEG signal with spiking activity from a small number of neurons and an fMRI experiment with similar stimuli revealed a consistent spatial organization, with stronger associations with intensity more anteriorly, while viewing the hand being slapped.

Corpus callosum tissue loss and development of motor and global cognitive impairment: the LADIS study.

OBJECTIVE: To examine the impact of corpus callosum (CC) tissue loss on the development of global cognitive and motor impairment in the elderly. METHODS: This study was based on the Leukoaraiosis and Disability (LADIS) study. Assessment of cognitive and motor functions and magnetic resonance imaging (MRI) were done at baseline and at a 3-year follow-up in nondemented elderly subjects. RESULTS: 328 of 639 LADIS subjects had MRIs at baseline and at the 3-year follow-up, which allowed for assessment of CC. Logistic regression revealed differential tissue loss rates in posterior CC in subjects converting to dementia, compared to nonconverters (p < 0.05). Anterior and posterior CC tissue loss was significantly correlated with self-perceived memory impairment in nonconverters (p < 0.05). CC tissue loss was also significantly associated with impaired single leg stance time (p < 0.01). CONCLUSION: The present longitudinal study on CC supports the role of callosal tissue loss in the development of global cognitive as well as motor impairment.

Optimization of epilepsy surgery through virtual resections on individual structural brain networks.

The success of epilepsy surgery in patients with refractory epilepsy depends upon correct identification of the epileptogenic zone (EZ) and an optimal choice of the resection area. In this study we developed individualized computational models based upon structural brain networks to explore the impact of different virtual resections on the propagation of seizures. The propagation of seizures was modelled as an epidemic process [susceptible-infected-recovered (SIR) model] on individual structural networks derived from presurgical diffusion tensor imaging in 19 patients. The candidate connections for the virtual resection were all connections from the clinically hypothesized EZ, from which the seizures were modelled to start, to other brain areas. As a computationally feasible surrogate for the SIR model, we also removed the connections that maximally reduced the eigenvector centrality (EC) (large values indicate network hubs) of the hypothesized EZ, with a large reduction meaning a large effect. The optimal combination of connections to be removed for a maximal effect were found using simulated annealing. For comparison, the same number of connections were removed randomly, or based on measures that quantify the importance of a node or connection within the network. We found that 90% of the effect (defined as reduction of EC of the hypothesized EZ) could already be obtained by removing substantially less than 90% of the connections. Thus, a smaller, optimized, virtual resection achieved almost the same effect as the actual surgery yet at a considerably smaller cost, sparing on average 27.49% (standard deviation: 4.65%) of the connections. Furthermore, the maximally effective connections linked the hypothesized EZ to hubs. Finally, the optimized resection was equally or more effective than removal based on structural network characteristics both regarding reducing the EC of the hypothesized EZ and seizure spreading. The approach of using reduced EC as a surrogate for simulating seizure propagation can suggest more restrictive resection strategies, whilst obtaining an almost optimal effect on reducing seizure propagation, by taking into account the unique topology of individual structural brain networks of patients.

Behavioural and psychological symptoms in vascular dementia; differences between small- and large-vessel disease.

AIM: The authors investigated the prevalence of behavioural and psychological symptoms in vascular dementia (VaD) from baseline data of the VantagE study and compared the severity and relative frequency of symptoms between small-vessel VaD and large-vessel VaD. METHODS: Behavioural and psychological symptoms of 484 VaD patients included in a large multicentre clinical trial (registration number NCT00099216) were determined using the 12-item Neuropsychiatric Inventory (NPI). Symptoms were considered present when the score was > or =1. Based on MRI, patients were classified as having small-vessel VaD (83%) or large-vessel VaD (17%). RESULTS: Behavioural and psychological symptoms were reported in 92% of the VaD patients. The median NPI score of the total study population was 9 (0-76), with a median number of three symptoms per patient. Apathy (65%) was most prevalent, followed by depressive symptoms (45%), irritability (42%) and agitation/aggression (40%). Patients with small-vessel VaD reported more apathy, aberrant motor behaviour and hallucinations than patients with large-vessel VaD (p<0.05). In contrast, patients with large-vessel VaD reported a higher severity of agitation/aggression and euphoria (p<0.05). CONCLUSION: Behavioural and psychological symptoms are common in VaD. Patients with small-vessel and large-vessel VaD demonstrate different profiles of symptoms, with especially more apathy in small-vessel VaD and more agitation/agression in large-vessel VaD.

The road ahead in clinical network neuroscience.

Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like "How do dynamic processes alter the underlying structural network?" and "Can we use network neuroscience for disease classification?" This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.

Neurological signs in relation to type of cerebrovascular disease in vascular dementia.

BACKGROUND AND PURPOSE: The aim of this study was to describe the prevalence of a number of neurological signs in a large population of patients with vascular dementia (VaD) and to compare the relative frequency of specific neurological signs dependent on type of cerebrovascular disease. METHODS: Seven hundred six patients with VaD (NINDS-AIREN) were included from a large multicenter clinical trial (registration number NCT00099216). At baseline neurological examination, the presence of 16 neurological signs was assessed. Based on MRI, patients were classified as having large vessel VaD (18%; large territorial or strategical infarcts on MRI), small vessel VaD (74%; white matter hyperintensities [WMH], multiple lacunes, bilateral thalamic lesions on MRI), or a combination of both (8%). RESULTS: A median number of 4.5 signs per patient was presented (maximum 16). Reflex asymmetry was the most prevalent symptom (49%), hemianopia was most seldom presented (10%). Measures of small vessel disease were associated with an increased prevalence of dysarthria, dysphagia, parkinsonian gait disorder, rigidity, and hypokinesia and as well to hemimotor dysfunction. By contrast, in the presence of a cerebral infarct, aphasia, hemianopia, hemimotor dysfunction, hemisensory dysfunction, reflex asymmetry, and hemiplegic gait disorder were more often observed. CONCLUSIONS: The specific neurological signs demonstrated by patients with VaD differ according to type of imaged cerebrovascular disease. Even in people who meet restrictive VaD criteria, small vessel disease is often seen with more subtle signs, including extrapyramidal signs, whereas large vessel disease is more often related to lateralized sensorimotor changes and aphasia.