The brain connectome as a personalized biomarker of seizure outcomes after temporal lobectomy.
Ontology highlight
ABSTRACT: Objective
We examined whether individual neuronal architecture obtained from the brain connectome can be used to estimate the surgical success of anterior temporal lobectomy (ATL) in patients with temporal lobe epilepsy (TLE).Methods
We retrospectively studied 35 consecutive patients with TLE who underwent ATL. The structural brain connectome was reconstructed from all patients using presurgical diffusion MRI. Network links in patients were standardized as Z scores based on connectomes reconstructed from healthy controls. The topography of abnormalities in linkwise elements of the connectome was assessed on subnetworks linking ipsilateral temporal with extratemporal regions. Predictive models were constructed based on the individual prevalence of linkwise Z scores >2 and based on presurgical clinical data.Results
Patients were more likely to achieve postsurgical seizure freedom if they exhibited fewer abnormalities within a subnetwork composed of the ipsilateral hippocampus, amygdala, thalamus, superior frontal region, lateral temporal gyri, insula, orbitofrontal cortex, cingulate, and lateral occipital gyrus. Seizure-free surgical outcome was predicted by neural architecture alone with 90% specificity (83% accuracy), and by neural architecture combined with clinical data with 94% specificity (88% accuracy).Conclusions
Individual variations in connectome topography, combined with presurgical clinical data, may be used as biomarkers to better estimate surgical outcomes in patients with TLE.
SUBMITTER: Bonilha L
PROVIDER: S-EPMC4433467 | biostudies-literature | 2015 May
REPOSITORIES: biostudies-literature
Publications
The brain connectome as a personalized biomarker of seizure outcomes after temporal lobectomy.
Bonilha Leonardo L Jensen Jens H JH Baker Nathaniel N Breedlove Jesse J Nesland Travis T Lin Jack J JJ Drane Daniel L DL Saindane Amit M AM Binder Jeffrey R JR Kuzniecky Ruben I RI
Neurology 20150408 18
<h4>Objective</h4>We examined whether individual neuronal architecture obtained from the brain connectome can be used to estimate the surgical success of anterior temporal lobectomy (ATL) in patients with temporal lobe epilepsy (TLE).<h4>Methods</h4>We retrospectively studied 35 consecutive patients with TLE who underwent ATL. The structural brain connectome was reconstructed from all patients using presurgical diffusion MRI. Network links in patients were standardized as Z scores based on conne ...[more]
Similar Datasets
Project description:As mesial temporal lobe epilepsy (mTLE) has been recognized as a network disorder, a longitudinal connectome investigation may shed new light on the understanding of the underlying pathophysiology related to distinct surgical outcomes. Resting-state functional MRI data was acquired from mTLE patients before (n?=?37) and after (n?=?24) anterior temporal lobectomy. According to surgical outcome, patients were classified as seizure-free (SF, n?=?14) or non-seizure-free (NSF, n?=?10). First, we found higher network resilience to targeted attack on topologically central nodes in the SF group compared to the NSF group, preoperatively. Next, a two-way mixed analysis of variance with between-subject factor 'outcome' (SF vs. NSF) and within-subject factor 'treatment' (pre-operation vs. post-operation) revealed divergent dynamic reorganization in nodal topological characteristics between groups, in the temporoparietal junction and its connection with the ventral prefrontal cortex. We also correlated the network damage score (caused by surgical resection) with postsurgical brain function, and found that the damage score negatively correlated with postoperative global and local parallel information processing. Taken together, dynamic connectomic architecture provides vital information for selecting surgical candidates and for understanding brain recovery mechanisms following epilepsy surgery.
| S-EPMC4802388 | biostudies-literature
Project description:ObjectivesThe objective of this study was to evaluate whether patients with surgically refractory medial temporal lobe epilepsy (MTLE) exhibit a distinct pattern of structural network organization involving the temporal lobes and extratemporal regions.MethodsWe retrospectively studied 18 healthy controls and 20 patients with medication refractory unilateral MTLE who underwent anterior temporal lobectomy for treatment of seizures. Patients were classified as seizure-free or not seizure-free at least 1 year after surgery. The presurgical brain connectome was calculated through probabilistic connectivity from MRI-diffusion tensor imaging from 83 anatomically defined regions of interest encompassing the whole brain. The connectivity patterns were analyzed regarding group differences in regional connectivity and network graph properties.ResultsCompared with controls, patients exhibited a decrease in connectivity involving ipsilateral thalamocortical regions, with a pathologic increase in ipsilateral medial temporal lobe, insular, and frontal connectivity. Among patients, those not seizure-free exhibited a higher connectivity between structures in 1) the ipsilateral medial and lateral temporal lobe, 2) the ipsilateral medial temporal and parietal lobe, and 3) the contralateral temporal pole and parietal lobe. Patients not seizure-free also exhibited lower small-worldness in the subnetwork within the ipsilateral temporal lobe, with higher subnetwork integration at the expense of segregation.ConclusionsMTLE is associated with network rearrangement within, but not restricted to, the temporal lobe ipsilateral to the onset of seizures. Networks involving key components of the medial temporal lobe and structures traditionally not removed during surgery may be associated with seizure control after surgical treatment of MTLE.
| S-EPMC3812102 | biostudies-literature
Project description:ObjectiveThe question of whether a patient with presumed temporal lobe seizures should proceed directly to temporal lobectomy surgery versus undergo intracranial monitoring arises commonly. We evaluate the effect of intracranial monitoring on seizure outcome in a retrospective cohort of consecutive subjects who specifically underwent an anterior temporal lobectomy (ATL) for refractory temporal lobe epilepsy (TLE).MethodsWe performed a retrospective analysis of 85 patients with focal refractory TLE who underwent ATL following: (a) intracranial monitoring via craniotomy and subdural/depth electrodes (SDE/DE), (b) intracranial monitoring via stereotactic electroencephalography (sEEG), or (c) no intracranial monitoring (direct ATL-dATL). For each subject, the presurgical primary hypothesis for epileptogenic zone localization was characterized as unilateral TLE, unilateral TLE plus (TLE+), or TLE with bilateral/poor lateralization.ResultsAt one-year and most recent follow-up, Engel Class I and combined I/II outcomes did not differ significantly between the groups. Outcomes were better in the dATL group compared to the intracranial monitoring groups for lesional cases but were similar in nonlesional cases. Those requiring intracranial monitoring for a hypothesis of TLE+had similar outcomes with either intracranial monitoring approach. sEEG was the only approach used in patients with bilateral or poorly lateralized TLE, resulting in 77.8% of patients seizure-free at last follow-up. Importantly, for 85% of patients undergoing SEEG, recommendation for ATL resulted from modifying the primary hypothesis based on iEEG data.SignificanceOur study highlights the value of intracranial monitoring in equalizing seizure outcomes in difficult-to-treat TLE patients undergoing ATL.
| S-EPMC8886064 | biostudies-literature
Project description:ObjectivePatients undergoing frontal lobectomy demonstrate lower seizure-freedom rates than patients undergoing temporal lobectomy and several other resective interventions. We attempted to utilize automated preoperative quantitative analysis of focal and global cortical volume loss to develop predictive volumetric indicators of seizure outcome after frontal lobectomy.MethodsNinety patients who underwent frontal lobectomy were stratified based on seizure freedom at a mean follow-up time of 3.5 (standard deviation [SD] 2.5) years. Automated quantitative analysis of cortical volume loss organized by distinct brain region and laterality was performed on preoperative T1-weighted magnetic resonance imaging (MRI) studies. Univariate statistical analysis was used to select potential predictors of seizure freedom. Backward variable selection and multivariate logistical regression were used to develop models to predict seizure freedom.ResultsForty-eight of 90 (53.3%) patients were seizure-free at the last follow-up. Several frontal and extrafrontal brain regions demonstrated statistically significant differences in both volumetric cortical volume loss and volumetric asymmetry between the left and right sides in the seizure-free and non-seizure-free cohorts. A final multivariate logistic model utilizing only preoperative quantitative MRI data to predict seizure outcome was developed with a c-statistic of 0.846. Using both preoperative quantitative MRI data and previously validated clinical predictors of seizure outcomes, we developed a model with a c-statistic of 0.897.SignificanceThis study demonstrates that preoperative cortical volume loss in both frontal and extrafrontal regions can be predictive of seizure outcome after frontal lobectomy, and models can be developed with excellent predictive capabilities using preoperative MRI data. Automated quantitative MRI analysis can be quickly and reliably performed in patients with frontal lobe epilepsy, and further studies may be developed for integration into preoperative risk stratification.
| S-EPMC8896091 | biostudies-literature
Project description:ObjectiveAnterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug-resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL.MethodsWe analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure-free and non-seizure-free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave-one-patient-out cross-validation scheme within the MC data set and then tested using the out-of-sample CC data set. Finally, we compared the predictive performance of normal scalp EEG-derived features against MRI abnormalities.ResultsWe found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10-25 Hz range. The Naïve Bayes classification based on those features predicted 1-year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10-25 Hz range provided better predictability than other combinations and (b) normal scalp EEG-derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score).SignificanceThese results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug-resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.
| S-EPMC9283304 | biostudies-literature
Project description:At 3 time points, this study examined long-term psychosocial life outcomes of individuals who underwent anterior temporal lobectomy in comparison with individuals with temporal lobe epilepsy who were medically managed.To examine seizure frequency, employment, driving, independent living, financial independence, mental health, and quality of life at each follow-up assessment, as well as predictors of outcomes.All participants were diagnosed with medically intractable complex partial seizures of temporal lobe origin with or without secondary generalization. A structured clinical interview was used at all 3 time points. Information was obtained regarding seizure frequency, antiepilepsy medications, employment, driving status, financial assistance, and independent living. Additionally, questions regarding quality of life, satisfaction with surgery, and the presence of depression or anxiety were included. Participants were, on average, 17 years postsurgery.Surgery resulted in significantly improved and sustained seizure outcomes. At the first, second, and third follow-ups, 67%, 72%, and 67% of participants in the surgery group remained seizure-free in the year before the follow-up interview. At each follow-up, 97%, 84%, and 84% reported that they would undergo surgery again. Seizure freedom predicted driving outcomes at all 3 time points, but was not a significant predictor for employment, independent living, or financial independence. Psychosocial life outcomes in the surgical group were improved and maintained over time in comparison with the medically managed group.This systematic long-term investigation provides strong support for the positive impact of anterior temporal lobectomy on psychosocial life outcomes including driving, employment, independent living, and financial independence.
| S-EPMC4162125 | biostudies-literature
Project description:ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance.
| S-EPMC6953962 | biostudies-literature
Project description:Many recent literature studies have revealed interesting dynamics patterns of functional brain networks derived from fMRI data. However, it has been rarely explored how functional networks spatially overlap (or interact) and how such connectome-scale network interactions temporally evolve. To explore these unanswered questions, this paper presents a novel framework for spatio-temporal modeling of connectome-scale functional brain network interactions via two main effective computational methodologies. First, to integrate, pool and compare brain networks across individuals and their cognitive states under task performances, we designed a novel group-wise dictionary learning scheme to derive connectome-scale consistent brain network templates that can be used to define the common reference space of brain network interactions. Second, the temporal dynamics of spatial network interactions is modeled by a weighted time-evolving graph, and then a data-driven unsupervised learning algorithm based on the dynamic behavioral mixed-membership model (DBMM) is adopted to identify behavioral patterns of brain networks during the temporal evolution process of spatial overlaps/interactions. Experimental results on the Human Connectome Project (HCP) task fMRI data showed that our methods can reveal meaningful, diverse behavior patterns of connectome-scale network interactions. In particular, those networks' behavior patterns are distinct across HCP tasks such as motor, working memory, language and social tasks, and their dynamics well correspond to the temporal changes of specific task designs. In general, our framework offers a new approach to characterizing human brain function by quantitative description for the temporal evolution of spatial overlaps/interactions of connectome-scale brain networks in a standard reference space.
| S-EPMC5943195 | biostudies-literature
Project description:Network studies have demonstrated that a small set of highly connected regions may play a central role in global information integration, together forming a rich club organization. Given that generalized tonic-clonic seizure (GTCS) has been conceptualized as a network disorder, we hypothesized that the rich club disturbances may be related to the network abnormalities of GTCS. Here, we used graph theoretical analysis to investigate the rich club organization of both structural and functional connectome in patients with GTCS (n = 50) and healthy controls (n = 60). We further measured the level of global efficiency and clustering in rich club and non-rich club organization. We, respectively, identified a small number of highly connected hubs as rich club organization from structural and functional networks. Patients were found to exhibit significantly reduced rich club connectivity among the central hubs. Meanwhile, both structural and functional network showed changed levels of global efficiency and clustering of rich club organization in GTCS. Furthermore, in patients, lower levels of rich club connectivity were found to be correlated with longer duration of illness and seizure frequency. Together, these findings suggest that GTCS is characterized by a selective disruption of rich club organization due to the long-term injurious effects of epileptic actions on the central hub regions, which potentially contribute to a reduced level of brain integration capacity among different functional domains and an added effect of illness on a preexisting vulnerability. Our findings emphasize a central role for abnormal rich club organization in the pathophysiological mechanism underlying GTCS. Hum Brain Mapp 37:4487-4499, 2016. © 2016 Wiley Periodicals, Inc.
| S-EPMC6867540 | biostudies-literature
Project description:How does the human brain's structural scaffold give rise to its intricate functional dynamics? This is a central question in translational neuroscience that is particularly relevant to epilepsy, a disorder affecting over 50 million subjects worldwide. Treatment for medication-resistant focal epilepsy is often structural-through surgery or laser ablation-but structural targets, particularly in patients without clear lesions, are largely based on functional mapping via intracranial EEG. Unfortunately, the relationship between structural and functional connectivity in the seizing brain is poorly understood. In this study, we quantify structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy. We use high angular resolution diffusion imaging (HARDI) tractography to construct structural connectivity networks and correlate these networks with time-varying broadband and frequency-specific functional networks derived from coregistered intracranial EEG. Across all frequency bands, we find significant increases in structure-function coupling from pre-ictal to ictal periods. We demonstrate that short-range structural connections are primarily responsible for this increase in coupling. Finally, we find that spatiotemporal patterns of structure-function coupling are highly stereotyped for each patient. These results suggest that seizures harness the underlying structural connectome as they propagate. Mapping the relationship between structural and functional connectivity in epilepsy may inform new therapies to halt seizure spread, and pave the way for targeted patient-specific interventions.
| S-EPMC6598625 | biostudies-literature