Linda Douw

Cognitive and radiological effects of radiotherapy in patients with low-grade glioma : long-term follow-up

BACKGROUND Our previous study on cognitive functioning among 195 patients with low-grade glioma (LGG) a mean of 6 years after diagnosis suggested that the tumour itself, rather than the radiotherapy used to treat it, has the most deleterious effect on cognitive functioning; only high fraction dose radiotherapy (>2 Gy) resulted in significant added cognitive deterioration. The present study assesses the radiological and cognitive abnormalities in survivors of LGG at a mean of 12 years after first diagnosis. METHODS Patients who have had stable disease since the first assessment were invited for follow-up cognitive assessment (letter-digit substitution test, concept shifting test, Stroop colour-word test, visual verbal learning test, memory comparison test, and categoric word fluency). Compound scores in six cognitive domains (attention, executive functioning, verbal memory, working memory, psychomotor functioning, and information processing speed) were calculated to detect differences between patients who had radiotherapy and patients who did not have radiotherapy. White-matter hyperintensities and global cortical atrophy were rated on MRI scans. FINDINGS 65 patients completed neuropsychological follow-up at a mean of 12 years (range 6-28 years). 32 (49%) patients had received radiotherapy (three had fraction doses >2 Gy). The patients who had radiotherapy had more deficits that affected attentional functioning at the second follow-up, regardless of fraction dose, than those who did not have radiotherapy (-1.6 [SD 2.4] vs -0.1 [1.3], p=0.003; mean difference 1.4, 95% CI 0.5-2.4). The patients who had radiotherapy also did worse in measures of executive functioning (-2.0 [3.7] vs -0.5 [1.2], p=0.03; mean difference 1.5, 0.2-2.9) and information processing speed (-2.0 [3.7] vs -0.6 [1.5], p=0.05; mean difference 0.8, 0.009-1.6]) between the two assessments. Furthermore, attentional functioning deteriorated significantly between the first and second assessments in patients who had radiotherapy (p=0.25). In total, 17 (53%) patients who had radiotherapy developed cognitive disabilities deficits in at least five of 18 neuropsychological test parameters compared with four (27%) patients who were radiotherapy naive. White-matter hyperintensities and global cortical atrophy were associated with worse cognitive functioning in several domains. INTERPRETATION Long-term survivors of LGG who did not have radiotherapy had stable radiological and cognitive status. By contrast, patients with low-grade glioma who received radiotherapy showed a progressive decline in attentional functioning, even those who received fraction doses that are regarded as safe (</=2 Gy). These cognitive deficits are associated with radiological abnormalities. Our results suggest that the risk of long-term cognitive and radiological compromise that is associated with radiotherapy should be considered when treatment is planned. FUNDING Kaptein Fonds; Schering Plough.

Long-Term Effects of Temporal Lobe Epilepsy on Local Neural Networks: A Graph Theoretical Analysis of Corticography Recordings

Purpose Pharmaco-resistant temporal lobe epilepsy (TLE) is often treated with surgical intervention at some point. As epilepsy surgery is considered a last resort by most physicians, a long history of epileptic seizures prior to surgery is not uncommon. Little is known about the effects of ongoing TLE on neural functioning. A better understanding of these effects might influence the moment of surgical intervention. Functional connectivity (interaction between spatially distributed brain areas) and network structure (integration and segregation of information processing) are thought to be essential for optimal brain functioning. We report on the impact of TLE duration on temporal lobe functional connectivity and network characteristics. Methods Functional connectivity of the temporal lobe at the time of surgery was assessed by means of interictal electrocorticography (ECoG) recordings of 27 TLE patients by using the phase lag index (PLI). Graphs (abstract network representations) were reconstructed from the PLI matrix and characterized by the clustering coefficient C (local clustering), the path length L (overall network interconnectedness), and the “small world index” S (network configuration). Results Functional connectivity (average PLI), clustering coefficients, and the small world index were negatively correlated with TLE duration in the broad frequency band (0.5–48 Hz). Discussion Temporal lobe functional connectivity is lower in patients with longer TLE history, and longer TLE duration is correlated with more random network configuration. Our findings suggest that the neural networks of TLE patients become more pathological over time, possibly due to temporal lobe changes associated with long-standing lesional epilepsy.

Indications for network regularization during absence seizures: Weighted and unweighted graph theoretical analyses

Previous studies with intracranial recordings suggested that a more random spatial structure of functional brain networks could be related to seizure generation. Here, we studied whether similar network changes in weighted and unweighted networks can be found in generalized absence seizures recorded with surface EEG. We retrospectively selected EEG recordings of eleven children with absence seizures. The functional neural networks were characterized by calculating both coherence and synchronization likelihood (SL) between 21 EEG signals that were either broad band filtered (1-48 Hz) or filtered in different frequency bands. From both weighted and unweighted networks the clustering coefficient (C) and path length (L) were computed and compared to 500 random networks. We compared the ictal with the pre-ictal network structure. During absence seizures there was an increase of synchronization in all frequency bands, seen most clearly in the SL-based networks, and the functional network topology changed towards a more ordered pattern, with an increase of C/C-s and L/L-s. This study supports the hypothesis of functional neural network changes during absence seizures. The network became more regularized in weighted and unweighted analyses, when compared to the more randomized pre-ictal network configuration.

Subcortical atrophy and cognition Sex effects in multiple sclerosis

Objectives: Gray matter (GM) atrophy is common in multiple sclerosis (MS), as is cognitive dysfunction. Understanding the exact relationship between atrophy and cognition requires further investigation. The aim of this study was to investigate the relationship between subcortical GM atrophy and cognition in early relapsing onset MS. Methods: Structural MRI and neuropsychological evaluations were performed in 120 patients (80 women) and 50 controls (30 women), part of an early inception cohort, 6 years postdiagnosis. Deep GM volumes were segmented automatically. Cognition was assessed in 7 domains. Stepwise linear regression was used to predict average cognition in the patient group. Results: Most deep GM volumes were reduced in patients, with larger effects on average in men (−11%) than in women (−6.3%). Only the bilateral hippocampus, amygdala, and right nucleus accumbens in men, and right hippocampus and nucleus accumbens, bilateral amygdala, and putamen in women, showed no atrophy compared to controls. All cognitive domains except visuospatial memory were affected in men; none were significantly affected in women. In the MS group, average cognition was best predicted by thalamic volume, sex, and education (adjusted R2 = 0.31), while lesion volume was not a significant predictor in the model. Conclusions: Six years postdiagnosis, almost all subcortical structures were affected by MS, especially in men. Cognition was most severely affected in male patients. Thalamic volume, sex, and education best predicted average cognition. These results underline the relevance of specific subcortical structures to cognition, as well as the relevance of (sex-specific) atrophy in MS.

Epilepsy is related to theta band brain connectivity and network topology in brain tumor patients

BackgroundBoth epilepsy patients and brain tumor patients show altered functional connectivity and less optimal brain network topology when compared to healthy controls, particularly in the theta band. Furthermore, the duration and characteristics of epilepsy may also influence functional interactions in brain networks. However, the specific features of connectivity and networks in tumor-related epilepsy have not been investigated yet. We hypothesize that epilepsy characteristics are related to (theta band) connectivity and network architecture in operated glioma patients suffering from epileptic seizures. Included patients participated in a clinical study investigating the effect of levetiracetam monotherapy on seizure frequency in glioma patients, and were assessed at two time points: directly after neurosurgery (t1), and six months later (t2). At these time points, magnetoencephalography (MEG) was recorded and information regarding clinical status and epilepsy history was collected. Functional connectivity was calculated in six frequency bands, as were a number of network measures such as normalized clustering coefficient and path length.ResultsAt the two time points, MEG registrations were performed in respectively 17 and 12 patients. No changes in connectivity or network topology occurred over time. Increased theta band connectivity at t1 and t2 was related to a higher total number of seizures. Furthermore, higher number of seizures was related to a less optimal, more random brain network topology. Other factors were not significantly related to functional connectivity or network topology.ConclusionsThese results indicate that (pathologically) increased theta band connectivity is related to a higher number of epileptic seizures in brain tumor patients, suggesting that theta band connectivity changes are a hallmark of tumor-related epilepsy. Furthermore, a more random brain network topology is related to greater vulnerability to seizures. Thus, functional connectivity and brain network architecture may prove to be important parameters of tumor-related epilepsy.

‘Functional Connectivity’ Is a Sensitive Predictor of Epilepsy Diagnosis after the First Seizure

Background Although epilepsy affects almost 1% of the world population, diagnosis of this debilitating disease is still difficult. The EEG is an important tool for epilepsy diagnosis and classification, but the sensitivity of interictal epileptiform discharges (IEDs) on the first EEG is only 30–50%. Here we investigate whether using ‘functional connectivity’ can improve the diagnostic sensitivity of the first interictal EEG in the diagnosis of epilepsy. Methodology/Principal Findings Patients were selected from a database with 390 standard EEGs of patients after a first suspected seizure. Patients who were later diagnosed with epilepsy (i.e. ≥two seizures) were compared to matched non-epilepsy patients (with a minimum follow-up of one year). The synchronization likelihood (SL) was used as an index of functional connectivity of the EEG, and average SL per patient was calculated in seven frequency bands. In total, 114 patients were selected. Fifty-seven patients were diagnosed with epilepsy (20 had IEDs on their EEG) and 57 matched patients had other diagnoses. Epilepsy patients had significantly higher SL in the theta band than non-epilepsy patients. Furthermore, theta band SL proved to be a significant predictor of a diagnosis of epilepsy. When only those epilepsy patients without IEDs were considered (n = 74), theta band SL could predict diagnosis with specificity of 76% and sensitivity of 62%. Conclusion/Significance Theta band functional connectivity may be a useful diagnostic tool in diagnosing epilepsy, especially in those patients who do not show IEDs on their first EEG. Our results indicate that epilepsy diagnosis could be improved by using functional connectivity.

Functional connectivity changes in multiple sclerosis patients: A graph analytical study of MEG resting state data

Multiple sclerosis (MS) is characterized by extensive damage in the central nervous system. Within this field, there is a strong need for more advanced, functional imaging measures, as abnormalities measured with structural imaging insufficiently explain clinicocognitive decline in MS. In this study we investigated functional connectivity changes in MS using resting‐state magnetoencephalography (MEG). Data from 34 MS patients and 28 age and gender‐matched controls was assessed using synchronization likelihood (SL) as a measure of functional interaction strength between brain regions, and graph analysis to characterize topological patterns of connectivity changes. Cognition was assessed using extensive neuropsychological evaluation. Structural measures included brain and lesion volumes, using MRI. Results show SL increases in MS patients in theta, lower alpha and beta bands, with decreases in the upper alpha band. Graph analysis revealed a more regular topology in the lower alpha band in patients, indicated by an increased path length (λ) and clustering coefficient (γ). Attention and working memory domains were impaired, with decreased brain volumes. A stepwise linear regression model using clinical, MRI and MEG parameters as predictors revealed that only increases in lower alpha band γ predicted impaired cognition. Cognitive impairments and related altered connectivity patterns were found to be especially predominant in male patients. These results show specific functional changes in MS as measured with MEG. Only changes in network topology were related to poorer cognitive outcome. This indicates the value of graph analysis beyond traditional structural and functional measures, with possible implications for diagnostic and/or prognostic purposes in MS. Hum Brain Mapp, 2013.

Epilepsy surgery outcome and functional network alterations in longitudinal MEG: A minimum spanning tree analysis

Seizure freedom after resective epilepsy surgery is not obtained in a substantial number of patients with medically intractable epilepsy. Functional neural network analysis is a promising technique for more accurate identification of the target areas for epilepsy surgery, but a better understanding of the correlations between changes in functional network organization due to surgery and postoperative seizure status is required. We explored these correlations in longitudinal magnetoencephalography (MEG) recordings of 20 lesional epilepsy patients. Resting-state MEG recordings were obtained at baseline (preoperatively; T0) and at 3-7 (T1) and 9-15months after resection (T2). We assessed frequency-specific functional connectivity and performed a minimum spanning tree (MST) network analysis. The MST captures the most important connections in the network. We found a significant positive correlation between functional connectivity in the lower alpha band and seizure frequency at T0, especially in regions where lesions were located. MST leaf fraction, a measure of integration of information in the network, was significantly increased between T0 and T2, only for the seizure-free patients. This is in line with previous work, which showed that lower functional network integration in lesional epilepsy patients is related to higher epilepsy burden. Finally, eccentricity and betweenness centrality, which are measures of hub-status, decreased between T0 and T2 in seizure free patients, also in regions that were anatomically close to resection cavities. Our results increase insight into functional network changes in successful epilepsy surgery and might eventually be utilized for optimization of neurosurgical approaches.

Altered Structural Connectome in Temporal Lobe Epilepsy

PURPOSE To study differences in the whole-brain structural connectomes of patients with left temporal lobe epilepsy (TLE) and healthy control subjects. MATERIALS AND METHODS This study was approved by the institutional review board, and all individuals gave signed informed consent. Sixty-direction diffusion-tensor imaging and magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) magnetic resonance imaging volumes were analyzed in 24 patients with left TLE and in 24 healthy control subjects. MP-RAGE volumes were segmented into 1015 regions of interest (ROIs) spanning the entire brain. Deterministic white matter tractography was performed after voxelwise tensor calculation. Weighted structural connectivity matrices were generated by using the pairwise density of connecting fibers between ROIs. Graph theoretical measures of connectivity networks were compared between groups by using linear models with permutation testing. RESULTS Patients with TLE had 22%-45% reduced (P < .01) distant connectivity in the medial orbitofrontal cortex, temporal cortex, posterior cingulate cortex, and precuneus, compared with that in healthy subjects. However, local connectivity, as measured by means of network efficiency, was increased by 85%-270% (P < .01) in the medial and lateral frontal cortices, insular cortex, posterior cingulate cortex, precuneus, and occipital cortex in patients with TLE as compared with healthy subjects. CONCLUSION This study suggests that TLE involves altered structural connectivity in a network that reaches beyond the temporal lobe, especially in the default mode network.

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

Significance A description of the structural and functional connections in the human brain is necessary for the understanding of both normal and abnormal brain functioning. Although it has become clear in recent years that stable patterns of functional connectivity can be observed during the resting state, to date, it remains unclear what the dominant patterns of information flow are in this functional connectome and how these relate to the integration of brain function. Our results are the first to describe the large-scale frequency-specific patterns of information flow in the human brain, showing that different subsystems form a loop through which information “reverberates” or “circulates.” These results could be extended to give insights into how such flow optimizes integrative cognitive processing. 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.