Postoperative axonal changes in the contralateral hemisphere in children with medically refractory epilepsy: A longitudinal diffusion tensor imaging connectome analysis

Progressive remodeling of structural networks following surgery for operculo-insular epilepsy

Introduction

Operculo-insular epilepsy (OIE) is a rare condition amenable to surgery in well-selected cases. Despite the high rate of neurological complications associated with OIE surgery, most postoperative deficits recover fully and rapidly. We provide insights into this peculiar pattern of functional recovery by investigating the longitudinal reorganization of structural networks after surgery for OIE in 10 patients.

Methods

Structural T1 and diffusion-weighted MRIs were performed before surgery (t0) and at 6 months (t1) and 12 months (t2) postoperatively. These images were processed with an original, comprehensive structural connectivity pipeline. Using our method, we performed comparisons between the t0 and t1 timepoints and between the t1 and t2 timepoints to characterize the progressive structural remodeling.

Results

We found a widespread pattern of postoperative changes primarily in the surgical hemisphere, most of which consisted of reductions in connectivity strength (CS) and regional graph theoretic measures (rGTM) that reflect local connectivity. We also observed increases in CS and rGTMs predominantly in regions located near the resection cavity and in the contralateral healthy hemisphere. Finally, most structural changes arose in the first six months following surgery (i.e., between t0 and t1).

Discussion

To our knowledge, this study provides the first description of postoperative structural connectivity changes following surgery for OIE. The ipsilateral reductions in connectivity unveiled by our analysis may result from the reversal of seizure-related structural alterations following postoperative seizure control. Moreover, the strengthening of connections in peri-resection areas and in the contralateral hemisphere may be compatible with compensatory structural plasticity, a process that could contribute to the recovery of functions seen following operculo-insular resections for focal epilepsy.

Connectome-based approaches in pediatric epilepsy surgery: "State-of-the art" and future perspectives

Modern epilepsy science has overcome the traditional interpretation of a strict region-specific origin of epilepsy, highlighting the involvement of wider patterns of altered neuronal circuits. In selected cases, surgery may constitute a valuable option to achieve both seizure freedom and neurocognitive improvement. Although epilepsy is now considered as a brain network disease, the most relevant literature concerning the "connectome-based" epilepsy surgery mainly refers to adults, with a limited number of studies dedicated to the pediatric population. In this review, the Authors summarized the main current available knowledge on the relevance of WM surgical anatomy in epilepsy surgery, the post-surgical modifications of brain structural connectivity and the related clinical impact of such modifications within the pediatric context. In the last part, possible implications and future perspectives of this approach have been discussed, especially concerning the optimization of surgical strategies and the predictive value of the epilepsy network analysis for planning tailored approaches, with the final aim of improving case selection, presurgical planning, intraoperative management, and postoperative results.

Structural networking of the developing brain: from maturation to neurosurgical implications

Modern neuroscience agrees that neurological processing emerges from the multimodal interaction among multiple cortical and subcortical neuronal hubs, connected at short and long distance by white matter, to form a largely integrated and dynamic network, called the brain "connectome." The final architecture of these circuits results from a complex, continuous, and highly protracted development process of several axonal pathways that constitute the anatomical substrate of neuronal interactions. Awareness of the network organization of the central nervous system is crucial not only to understand the basis of children's neurological development, but also it may be of special interest to improve the quality of neurosurgical treatments of many pediatric diseases. Although there are a flourishing number of neuroimaging studies of the connectome, a comprehensive vision linking this research to neurosurgical practice is still lacking in the current pediatric literature. The goal of this review is to contribute to bridging this gap. In the first part, we summarize the main current knowledge concerning brain network maturation and its involvement in different aspects of normal neurocognitive development as well as in the pathophysiology of specific diseases. The final section is devoted to identifying possible implications of this knowledge in the neurosurgical field, especially in epilepsy and tumor surgery, and to discuss promising perspectives for future investigations.

Involvement of White Matter Language Tracts in Glioma: Clinical Implications, Operative Management, and Functional Recovery After Injury

To achieve optimal survival and quality of life outcomes in patients with glioma, the extent of tumor resection must be maximized without causing injury to eloquent structures. Preservation of language function is of particular importance to patients and requires careful mapping to reveal the locations of cortical language hubs and their structural and functional connections. Within this language network, accurate mapping of eloquent white matter tracts is critical, given the high risk of permanent neurological impairment if they are injured during surgery. In this review, we start by describing the clinical implications of gliomas involving white matter language tracts. Next, we highlight the advantages and limitations of methods commonly used to identify these tracts during surgery including structural imaging techniques, functional imaging, non-invasive stimulation, and finally, awake craniotomy. We provide a rationale for combining these complementary techniques as part of a multimodal mapping paradigm to optimize postoperative language outcomes. Next, we review local and long-range adaptations that take place as the language network undergoes remodeling after tumor growth and surgical resection. We discuss the probable cellular mechanisms underlying this plasticity with emphasis on the white matter, which until recently was thought to have a limited role in adults. Finally, we provide an overview of emerging developments in targeting the glioma-neuronal network interface to achieve better disease control and promote recovery after injury.

Connectomic mapping of brain-spinal cord neural networks: future directions in assessing spinal cord injury at rest

Following spinal cord injury (SCI), the central nervous system undergoes significant reconstruction. The dynamic change in the interaction of the brain-spinal cord axis as well as in structure-function relations plays a vital role in the determination of neurological functions, which might have important clinical implications for the treatment and its efficacy evaluation of patients with SCI. Brain connectomes based on neuroimaging data is a relatively new field of research that maps the brain's large-scale structural and functional networks at rest. Importantly, increasing evidence shows that such resting-state signals can also be seen in the spinal cord. In the present review, we focus on the reconstruction of multi-level neural circuits after SCI. We also describe how the connectome concept could further our understanding of neuroplasticity after SCI. We propose that mapping the cortical-subcortical-spinal cord networks can provide novel insights into the pathologies of SCI.

Recovery of cortical atrophy in patients with temporal lobe epilepsy after successful anterior temporal lobectomy

The aims of this study were to investigate whether the cortical atrophy caused by temporal lobe epilepsy (TLE) was reversible after successful anterior temporal lobectomy (ATL) and to further observe whether possible changes are related to age at surgery and cognitive changes. Twelve patients with unilateral mesial TLE who received ATL and remained seizure free in one year follow-up were included. They underwent two MRI scans few days before and oneyear after surgery. Thirty age- and sex-matched healthy participants were recruited as controls. Group comparisons were used to test the differences in cortical thickness (CTh) between the pre-/postsurgical patients and controls. Longitudinal test was used to directly show postsurgical changes of the patients. Besides, the correlations between regional cortical volume (CVo) changes and age at surgery or cognitive changes were also tested. Compared with controls, the patients with TLE showed dispersed cortical thinning especially in the bilateral frontal lobes before surgery and no significant cortical thinning except for cortices near the resected areas after surgery. The longitudinal analysis showed CTh increment in the ipsilateral precentral and postcentral gyrus, cuneus and widespread in the contralateral cortex. In the volumetric analysis, the CVo changes in the contralateral hemisphere were negatively correlated with age at surgery and positively correlated with MoCA score changes. This study suggests that the cortical atrophy caused by TLE could recover after successful ATL. The recovery ability is greater in younger subjects and is positively related to cognitive recovery. These findings could serve as new clues that patients with TLE can benefit from timely and successful ATL.

Deep reasoning neural network analysis to predict language deficits from psychometry-driven DWI connectome of young children with persistent language concerns

This study investigated whether current state-of-the-art deep reasoning network analysis on psychometry-driven diffusion tractography connectome can accurately predict expressive and receptive language scores in a cohort of young children with persistent language concerns (n = 31, age: 4.25 ± 2.38 years). A dilated convolutional neural network combined with a relational network (dilated CNN + RN) was trained to reason the nonlinear relationship between "dilated CNN features of language network" and "clinically acquired language score". Three-fold cross-validation was then used to compare the Pearson correlation and mean absolute error (MAE) between dilated CNN + RN-predicted and actual language scores. The dilated CNN + RN outperformed other methods providing the most significant correlation between predicted and actual scores (i.e., Pearson's R/p-value: 1.00/<.001 and .99/<.001 for expressive and receptive language scores, respectively) and yielding MAE: 0.28 and 0.28 for the same scores. The strength of the relationship suggests elevated probability in the prediction of both expressive and receptive language scores (i.e., 1.00 and 1.00, respectively). Specifically, sparse connectivity not only within the right precentral gyrus but also involving the right caudate had the strongest relationship between deficit in both the expressive and receptive language domains. Subsequent subgroup analyses inferred that the effectiveness of the dilated CNN + RN-based prediction of language score(s) was independent of time interval (between MRI and language assessment) and age of MRI, suggesting that the dilated CNN + RN using psychometry-driven diffusion tractography connectome may be useful for prediction of the presence of language disorder, and possibly provide a better understanding of the neurological mechanisms of language deficits in young children.

Prediction of baseline expressive and receptive language function in children with focal epilepsy using diffusion tractography-based deep learning network

PURPOSE

Focal epilepsy is a risk factor for language impairment in children. We investigated whether the current state-of-the-art deep learning network on diffusion tractography connectome can accurately predict expressive and receptive language scores of children with epilepsy.

METHODS

We studied 37 children with a diagnosis of drug-resistant focal epilepsy (age: 11.8 ± 3.1 years) using 3 T MRI and diffusion tractography connectome: G = (S, Ω), where S is an adjacency matrix of edges representing the connectivity strength (number of white-matter tract streamlines) between each pair of brain regions, and Ω reflects a set of brain regions. A convolutional neural network (CNN) was trained to learn the nonlinear relationship between 'S (input)' and 'language score (output)'. Repeated hold-out validation was then employed to measure the Pearson correlation and mean absolute error (MAE) between CNN-predicted and actual language scores.

RESULTS

We found that CNN-predicted and actual scores were significantly correlated (i.e., Pearson's R/p-value: 0.82/<0.001 and 0.75/<0.001), yielding MAE: 7.77 and 7.40 for expressive and receptive scores, respectively. Specifically, sparse connectivity not only within the left cortico-cortical network but also involving the right subcortical structures was predictive of language impairment of expressive or receptive domain. Subsequent subgroup analyses inferred that the effectiveness of diffusion tractography-based prediction of language outcome was independent of clinical variables. Intrinsic diffusion tractography connectome properties may be useful for predicting the severity of baseline language dysfunction and possibly provide a better understanding of the biological mechanisms of epilepsy-related language impairment in children.

Deep Relational Reasoning for the Prediction of Language Impairment and Postoperative Seizure Outcome Using Preoperative DWI Connectome Data of Children With Focal Epilepsy

Prolonged seizures in children with focal epilepsy (FE) may impair language functions and often reoccur after surgical intervention. This study is aimed at developing a novel deep relational reasoning network to investigate whether conventional diffusion-weighted imaging connectome analysis can be improved when predicting expressive and receptive scores of preoperative language impairments and classifying postoperative seizure outcomes (seizure freedom or recurrence) in individual FE children. To deeply reason the dependencies of axonal connections that are sparsely distributed in the whole brain, this study proposes the "dilated CNN + RN", a dilated convolutional neural network (CNN) combined with a relation network (RN). The performance of the dilated CNN + RN was evaluated using whole brain connectome data from 51 FE children. It was found that when compared with other state-of-the-art algorithms, the dilated CNN + RN led to an average improvement of 90.2% and 97.3% in predicting expressive and receptive language scores, and 2.2% and 4% improvement in classifying seizure freedom and seizure recurrence, respectively. These improvements were independent of the prefixed connectome densities. Also, the dilated CNN + RN could provide an explainable artificial intelligence (AI) model by computing gradient-based regression/classification activation maps. This mapping analysis revealed left superior-medial frontal cortex, bilateral hippocampi, and cerebellum as crucial hubs, facilitating important connections that were most predictive of language function and seizure refractoriness after surgery.

Gray Matter Atrophy: The Impacts of Resective Surgery and Vagus Nerve Stimulation in Drug-Resistant Epilepsy

BACKGROUND

There is great concern for cognitive function after resective temporal lobe surgery for drug-resistant epilepsy. However, few studies have investigated postoperative anatomical changes, and the downstream effects of surgery are poorly understood. This study investigated volumetric changes after resective surgery and vagus nerve stimulation (VNS) for epilepsy.

METHODS

Preoperative and latest postoperative (mean, 28 months) structural T1 magnetic resonance imaging scans were retrospectively obtained for 43 patients: 27 temporal lobe resections (TLRs), 6 extratemporal lobe resections, and 10 VNS, undergoing surgery for drug-resistant epilepsy between 2012 and 2017. Automated volumetric analyses of predefined cortical gray matter and subcortical structures were performed. Preoperative and postoperative volumes were compared, and the effects of age, gender, operation type, resection laterality, selectivity, time since surgery, and seizure outcome on volumetric changes were analyzed.

RESULTS

After TLRs, there were reductions in contralateral hemispheric gray matter, temporal lobe, entorhinal cortex, parahippocampal, superior temporal, middle temporal, inferior temporal (P = 0.02), lingual, fusiform, precentral, paracentral, postcentral, pericalcarine gyri, and ipsilateral superior parietal gyrus. After VNS, there was bilateral atrophy in the thalamus, putamen, cerebellum, rostral anterior cingulate, posterior cingulate, medial orbitofrontal, paracentral, fusiform, and transverse temporal gyri. There was a significant effect of surgery type but no effect of age, gender, operation type, resection laterality, selectivity, time since surgery, and seizure outcome on contralateral hippocampal gray matter change.

CONCLUSION

This is the first study to demonstrate volumetric decreases in temporal and connected regions after TLRs and VNS. These results provide interesting insight into functional network changes.

Oscillatory and structural signatures of language plasticity in brain tumor patients: A longitudinal study

Recent evidence suggests that damage to the language network triggers its functional reorganization. Yet, the spectro‐temporal fingerprints of this plastic rearrangement and its relation to anatomical changes is less well understood. Here, we combined magnetoencephalographic recordings with a proxy measure of white matter to investigate oscillatory activity supporting language plasticity and its relation to structural reshaping. First, cortical dynamics were acquired in a group of healthy controls during object and action naming. Results showed segregated beta (13–28 Hz) power decreases in left ventral and dorsal pathways, in a time‐window associated to lexico‐semantic processing (~250–500 ms). Six patients with left tumors invading either ventral or dorsal regions performed the same naming task before and 3 months after surgery for tumor resection. When longitudinally comparing patients' responses we found beta compensation mimicking the category‐based segregation showed by controls, with ventral and dorsal damage leading to selective compensation for object and action naming, respectively. At the structural level, all patients showed preoperative changes in white matter tracts possibly linked to plasticity triggered by tumor growth. Furthermore, in some patients, structural changes were also evident after surgery and showed associations with longitudinal changes in beta power lateralization toward the contralesional hemisphere. Overall, our findings support the existence of anatomo‐functional dependencies in language reorganization and highlight the potential role of oscillatory markers in tracking longitudinal plasticity in brain tumor patients. By doing so, they provide valuable information for mapping preoperative and postoperative neural reshaping and plan surgical strategies to preserve language function and patient's quality of life.

Challenges of Epilepsy Surgery

Though frequently effective in the management of medically refractory seizures, epilepsy surgery presents numerous challenges. Selection of the appropriate candidate patients who are likely to benefit from surgery is critical to achieving seizure freedom and avoiding neurocognitive morbidity. Identifying the seizure focus and mapping epileptogenic networks involves an interdisciplinary team dedicated to formulating a safe and effective surgical plan. Various strategies can be employed either to eliminate the epileptic focus or to modulate network activity, including resection of the focus with open surgery or laser interstitial thermal therapy; modulation of epileptogenic firing patterns with responsive neurostimulation, deep brain stimulation, or vagus nerve stimulation; or non-invasive disconnection of epileptic circuits with focused ultrasound, which is also discussed in greater detail in the subsequent chapter in our series. We review several challenges of epilepsy surgery that must be thoughtfully addressed in order to ensure its success.

Different patterns of white matter changes after successful surgery of mesial temporal lobe epilepsy

Objectives

To explore the dynamic changes of white matters following anterior temporal lobectomy (ATL) in mesial temporal lobe epilepsy (MTLE) patients who achieved seizure-free at two-year follow-up.

Methods

Diffusion tensor imaging (DTI) was obtained in ten MTLE patients at five serial time points: before surgery, three months, six months, 12 months and 24 months after surgery, as well as in 11 age- and sex-matched healthy controls at one time point. Regions with significant postoperative fractional anisotropy (FA) changes and their dynamic changes were confirmed by comparing all preoperative and postoperative data using Tract-Based Spatial Statistics (TBSS).

Results

After successful ATL, significant FA changes were found in widespread ipsilateral and contralateral white matter regions (P <.05, FWE correction). Ipsilateral external capsule, cingulum, superior corona radiate, body of corpus callosum, inferior longitudinal fasciculus, optic radiation and contralateral inferior cerebellar peduncle, inferior longitudinal fasciculus showed significant FA decrease at three months after surgery, without further changes. Ipsilateral superior cerebellar peduncle and contralateral corpus callosum, anterior corona radiate, external capsule, optic radiation showed significant FA decrease at three months follow up but increase later. Ipsilateral cerebral peduncle and contralateral middle cerebellar peduncle showed significant FA decrease at three months follow up, with further decrease after that. While ipsilateral posterior limb of internal capsule, retrolenticular part of internal capsule and contralateral posterior corona radiate showed significant FA increase after surgery.

Conclusions

FA changes after successful ATL presented as four distinct patterns, reflecting different structural adaptions following epilepsy surgery. Some FA increases indicated the reversibility of preoperative diffusion abnormalities and the possibility of structural reorganization, especially in the contralateral hemisphere.

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Widespread white matter abnormalities existed in mesial temporal lobe epilepsy.

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We explored longitudinal DTI changes at five serial time points before and after anterior temporal lobectomy.

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We found four distinct patterns of diffusion changes, reflecting different structural adaptions following epilepsy surgery.

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Structural reorganization did occur after surgery, especially in contralateral hemisphere.

Mindfulness training induces structural connectome changes in insula networks

Although mindfulness meditation is known to provide a wealth of psychological benefits, the neural mechanisms involved in these effects remain to be well characterized. A central question is whether the observed benefits of mindfulness training derive from specific changes in the structural brain connectome that do not result from alternative forms of experimental intervention. Measures of whole-brain and node-level structural connectome changes induced by mindfulness training were compared with those induced by cognitive and physical fitness training within a large, multi-group intervention protocol (n = 86). Whole-brain analyses examined global graph-theoretical changes in structural network topology. A hypothesis-driven approach was taken to investigate connectivity changes within the insula, which was predicted here to mediate interoceptive awareness skills that have been shown to improve through mindfulness training. No global changes were observed in whole-brain network topology. However, node-level results confirmed a priori hypotheses, demonstrating significant increases in mean connection strength in right insula across all of its connections. Present findings suggest that mindfulness strengthens interoception, operationalized here as the mean insula connection strength within the overall connectome. This finding further elucidates the neural mechanisms of mindfulness meditation and motivates new perspectives about the unique benefits of mindfulness training compared to contemporary cognitive and physical fitness interventions.

The impact of epilepsy surgery on the structural connectome and its relation to outcome

Background

Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome.

Methods

We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network. We then applied graph theory and machine learning to investigate the properties of change between the preoperative and predicted postoperative networks.

Results

Temporal lobe surgery had a modest impact on global network efficiency, despite the disruption caused. This was due to alternative shortest paths in the network leading to widespread increases in betweenness centrality post-surgery. Measurements of network change could retrospectively predict seizure outcomes with 79% accuracy and 65% specificity, which is twice as high as the empirical distribution. Fifteen connections which changed due to surgery were identified as useful for prediction of outcome, eight of which connected to the ipsilateral temporal pole.

Conclusion

Our results suggest that the use of network change metrics may have clinical value for predicting seizure outcome. This approach could be used to prospectively predict outcomes given a suggested resection mask using preoperative data only.