Whole-brain radiotherapy associated with structural changes resembling aging as determined by anatomic surface-based deep learning.

BACKGROUND: Brain metastases are the most common intracranial tumors in adults and are associated with significant morbidity and mortality. Whole-brain radiotherapy (WBRT) is used frequently in patients for palliation, but can result in neurocognitive deficits. While dose-dependent injury to individual areas such as the hippocampus has been demonstrated, global structural shape changes after WBRT remain to be studied. METHODS: We studied healthy controls and patients with brain metastases and examined MRI brain anatomic surface data before and after WBRT. We implemented a validated graph convolutional neural network model to estimate patient's "brain age". We further developed a mixed-effects linear model to compare the estimated age of the whole brain and substructures before and after WBRT. RESULTS: 4220 subjects were analyzed (4148 healthy controls and 72 patients). The median radiation dose was 30 Gy (range 25-37.5 Gy). The whole brain and substructures underwent structural change resembling rapid aging in radiated patients compared to healthy controls; the whole brain "aged" 9.32 times faster, the cortex 8.05 times faster, the subcortical structures 12.57 times faster, and the hippocampus 10.14 times faster. In a subset analysis, the hippocampus "aged" 8.88 times faster in patients after conventional WBRT versus after hippocampal avoidance (HA)-WBRT. CONCLUSIONS: Our findings suggest that WBRT causes the brain and its substructures to undergo structural changes at a pace up to 13x of the normal aging pace, where hippocampal avoidance offers focal structural protection. Correlating these structural imaging changes with neurocognitive outcomes following WBRT or HA-WBRT would benefit from future analysis.

A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction.

The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is dependent on prefrontal cortex and parietal cortex. In this work, we developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of Gf. Morphologic information of the cortical ribbons and subcortical structures was extracted from T1-weighted MRIs within two independent cohorts, the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62 years) of children and the Human Connectome Project (HCP; age: 28.81 ± 3.70 years). Prediction combining cortical and subcortical surfaces together yielded the highest accuracy of Gf for both ABCD (R = 0.314) and HCP datasets (R = 0.454), outperforming the state-of-the-art prediction of Gf from any other brain measures in the literature. Across both datasets, the morphology of the amygdala, hippocampus, and nucleus accumbens, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a significant reframing of the relationship between brain morphology and Gf to include systems involved with reward/aversion processing, judgment and decision-making, motivation, and emotion.

Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging.

Background: Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures. Methods: MRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject's age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD. Findings: Using an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala. Conclusion: Although similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.

Meningeal defects and focal cortical dysplasia: an unrecognized relationship? Illustrative case.

BACKGROUND: Focal cortical dysplasias (FCDs) are a heterogenous cluster of histopathologic entities classically associated with medically refractory epilepsy. Because there is substantial histopathologic variation among different types of FCD, there are likely multiple pathogenic mechanisms leading to these disorders. The meninges are known to play a role in cortical development, and disruption of meningeal-derived signaling pathways has been shown to impact neurodevelopment. To our knowledge, there has not yet been an investigation into whether genetic pathways regulating meningeal development may be involved in the development of FCD. OBSERVATIONS: The authors reported a patient with refractory epilepsy and evidence of FCD on imaging who received surgical intervention and was found to have an unusual dural anomaly overlying a region of type Ic FCD. To the authors' knowledge, this was the first report describing a lesion of this nature in the context of FCD. LESSONS: The dural anomaly exhibited by the patient presented what could be a potentially novel pathogenic mechanism of FCD. Resection of the cortical tissue underlying the dural anomaly resulted in improvement in seizure control. Although the pathogenesis is unclear, this case highlighted the importance of further investigation into the developmental origins of FCD, which may help elucidate whether a connection between meningeal development and FCD exists.

Patient-Specific Characteristics Associated with Favorable Response to Vagus Nerve Stimulation.

OBJECTIVE: The expansion in treatments for medically refractory epilepsy heightens the importance of identifying patients who are likely to benefit from vagus nerve stimulation (VNS). Here, we identify predictors with a positive VNS response. METHODS: We present a retrospective analysis of 158 patients with medically refractory epilepsy. Patients were categorized as VNS responders or nonresponders. Baseline characteristics and time to VNS response were recorded. Univariate and multivariate Cox regression were used to identify predictors of response. Recursive partitioning analysis was used to identify likely VNS responders. RESULTS: Eighty-nine (56.3%) patients achieved ≥50% seizure frequency reduction. Left-hand dominance (hazard ratio [HR] 1.703, P = 0.038), age at epilepsy onset ≥15 years (HR 2.029, P = 0.005), duration of epilepsy ≥8 years (HR 1.968, P = 0.007) and age at implantation ≥35 years (HR 1.809, P = 0.020), and baseline seizure frequency <5/month (HR 1.569, P = 0.044) were significant univariate predictors of VNS response. Following multivariate Cox regression, left-hand dominance, age at epilepsy onset ≥15 years, and duration of epilepsy ≥8 years remained significant. With recursive partitioning analysis, patients with either age at epilepsy onset ≥15 years, left-hand dominance, or baseline seizure frequency <5/month were stratified into Group A and had a 73.9% responder rate; the remaining patients stratified into Group B had a 43.8% responder rate. CONCLUSIONS: Patients with age at epilepsy onset ≥15 years, left-hand dominance, or baseline seizure frequency <5/month are ideal candidates for VNS.

Geometric deep learning on brain shape predicts sex and age.

The complex relationship between the shape and function of the human brain remains elusive despite extensive studies of cortical folding over many decades. The analysis of cortical gyrification presents an opportunity to advance our knowledge about this relationship, and better understand the etiology of a variety of pathologies involving diverse degrees of cortical folding abnormalities. Hypothesis-driven surface-based approaches have been shown to be particularly efficient in their ability to accurately describe unique features of the folded sheet topology of the cortical ribbon. However, the utility of these approaches has been blunted by their reliance on manually defined features aiming to capture the relevant geometric properties of cortical folding. In this paper, we propose an entirely novel, data-driven deep-learning based method to analyze the brain's shape that eliminates this reliance on manual feature definition. This method builds on the emerging field of geometric deep-learning and uses traditional convolutional neural network architecture uniquely adapted to the surface representation of the cortical ribbon. This method is a complete departure from prior brain MRI CNN investigations, all of which have relied on three dimensional MRI data and interpreted features of the MRI signal for prediction. MRI data from 6410 healthy subjects obtained from 11 publicly available data repositories were used for analysis. Ages ranged from 6 to 89 years. Both inner and outer cortical surfaces were extracted using Freesurfer and then registered into MNI space. For purposes of method development, both a classification and regression challenge were introduced for network learning including sex and age prediction, respectively. Two independent graph convolutional neural networks (gCNNs) were trained, the first of which to predict subject's self-identified sex, the second of which to predict subject's age. Class Activation Maps (CAM) and Regression Activation Maps (RAM) were constructed respectively to map the topographic distribution of the most influential brain regions involved in the decision process for each gCNN. Using this approach, the gCNN was able to predict a subject's sex with an average accuracy of 87.99 % and achieved a Person's coefficient of correlation of 0.93 with an average absolute error 4.58 years when predicting a subject's age. We believe this shape-based convolutional classifier offers a novel, data-driven approach to define biomedically relevant features from the brain at both the population and single subject levels and therefore lays a critical foundation for future precision medicine applications.

Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes.

We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.

Completion Corpus Callosotomy with Stereotactic Radiosurgery for Drug-Resistant, Intractable Epilepsy.

BACKGROUND: Stereotactic radiosurgery (SRS) offers a noninvasive technique for division of the corpus callosum, which can confer improved seizure control to patients suffering from frequent atonic seizures due to rapid interhemispheric generalization. This noninvasive approach is well-suited for use in a palliative intervention for improved seizure control in this patient population. To our knowledge, this is the first report of radiosurgical completion corpus callosotomy in an adult in the United States. CASE DESCRIPTION: A 20-year-old ambidextrous nonverbal man with a history of refractory generalized epilepsy status post open anterior corpus callosotomy at age 10 years, Lennox-Gastaut syndrome, and autism presented after 2 years of incremental, progressive deterioration in seizure control and behavior including 1 year. The family decided to pursue SRS corpus callosotomy. Under general anesthesia, a volume of interest encompassing a full midsagittal plane of the corpus callosum was defined to deliver 60 Gy to the 50% isodose line fully encompassing the target. Gamma Knife was used with 2 isocenters at 90° and 1 at 110° and isodose lines of 60, 20, and 12 Gy. Treatment was carried out without difficulty or complications while the patient remained under close monitoring. The patient was discharged the next day with a 2-week taper of dexamethasone. CONCLUSIONS: Eight months postradiosurgical corpus callosotomy, the patient is free of atonic seizures and is ambulatory. In carefully selected cases and with protective radiosurgical planning, SRS for completion corpus callosotomy represents an effective option for refractory seizure control.

Connectivity strength, time lag structure and the epilepsy network in resting-state fMRI.

The relationship between the epilepsy network, intrinsic brain networks and hypersynchrony in epilepsy remains incompletely understood. To converge upon a synthesized understanding of these features, we studied two elements of functional connectivity in epilepsy: correlation and time lag structure using resting state fMRI data from both SEEG-defined epileptic brain regions and whole-brain fMRI analysis. Functional connectivity (FC) was analyzed in 15 patients with epilepsy and 36 controls. Correlation strength and time lag were selected to investigate the magnitude of and temporal interdependency across brain regions. Zone-based analysis was carried out investigating directed correlation strength and time lag between both SEEG-defined nodes of the epilepsy network and between the epileptogenic zone and all other brain regions. Findings were compared between patients and controls and against a functional atlas. FC analysis on the nodal and whole brain levels identifies consistent patterns of altered correlation strength and altered time lag architecture in epilepsy patients compared to controls. These patterns include 1) broadly distributed increased strength of correlation between the seizure onset node and the remainder of the brain, 2) decreased time lag within the seizure onset node, and 3) globally increased time lag throughout all regions of the brain not involved in seizure onset or propagation. Comparing the topographic distribution of findings against a functional atlas, all resting state networks were involved to a variable degree. These local and whole brain findings presented here lead us to propose the network steal hypothesis as a possible mechanistic explanation for the non-seizure clinical manifestations of epilepsy.

Resting state connectivity in neocortical epilepsy: The epilepsy network as a patient-specific biomarker.

OBJECTIVE: Localization related epilepsy (LRE) is increasingly accepted as a network disorder. To better understand the network specific characteristics of LRE, we defined individual epilepsy networks and compared them across patients. METHODS: The epilepsy network was defined in the slow cortical potential frequency band in 10 patients using intracranial EEG data obtained during interictal periods. Cortical regions were included in the epilepsy network if their connectivity pattern was similar to the connectivity pattern of the seizure onset electrode contact. Patients were subdivided into frontal, temporal, and posterior quadrant cohorts according to the anatomic location of seizure onset. Jaccard similarity was calculated within each cohort to assess for similarity of the epilepsy network between patients within each cohort. RESULTS: All patients exhibited an epilepsy network in the slow cortical potential frequency band. The topographic distribution of this correlated network activity was found to be unique at the single subject level. CONCLUSIONS: The epilepsy network was unique at the single patient level, even between patients with similar seizure onset locations. SIGNIFICANCE: We demonstrated that the epilepsy network is patient-specific. This is in keeping with our current understanding of brain networks and identifies the patient-specific epilepsy network as a possible biomarker in LRE.

Clinical Integration of Quantitative Susceptibility Mapping Magnetic Resonance Imaging into Neurosurgical Practice.

OBJECTIVE: To introduce quantitative susceptibility mapping (QSM), a novel magnetic resonance imaging sequence, to the field of neurosurgery. METHODS: QSM is introduced both in its historical context and by providing a brief overview of the physics behind the technique tailored to a neurosurgical audience. Its application to clinical neurosurgery is then highlighted using case examples. RESULTS: QSM offers a quantitative assessment of susceptibility (previously considered as an artifact) via a single, straightforward gradient echo acquisition. QSM differs from standard susceptibility weighted imaging in its ability to both quantify and precisely localize susceptibility effects. Clinical applications of QSM are wide reaching and include precise localization of the deep nuclei for deep brain stimulation electrode placement, differentiation between blood products and calcification within brain lesions, and enhanced sensitivity of cerebral micrometastasis identification. CONCLUSIONS: We present this diverse range of QSM's clinical applications to neurosurgical care via case examples. QSM can be obtained in all patients able to undergo magnetic resonance imaging and is easily integratable into busy neuroradiology programs because of its short acquisition time and straightforward, automated offline postprocessing workflow. Clinical integration of QSM may help clinicians better identify and characterize neurosurgical lesions, thereby improving patient care.

Epilepsy Surgery in Children Under Three Years of Age.

Researchers from the University Medical Centre Schleswig-Holstein, Epilepsy Centre Kork, University of Freiburg, University Children's Hospital Heidelberg, Goethe University, and University Children's Hospital Zürich conducted a study to evaluate seizure occurrence and cognitive development following epilepsy surgery in children under 3 years of age.

Integration of resting state functional MRI into clinical practice - A large single institution experience.

Functional magnetic resonance imaging (fMRI) is an important tool for pre-surgical evaluation of eloquent cortex. Classic task-based paradigms require patient participation and individual imaging sequence acquisitions for each functional domain that is being assessed. Resting state fMRI (rs-fMRI), however, enables functional localization without patient participation and can evaluate numerous functional domains with a single imaging session. To date, post-processing of this resting state data has been resource intensive, which limits its widespread application for routine clinical use. Through a novel automated algorithm and advanced imaging IT structure, we report the clinical application and the large-scale integration of rs-fMRI into routine neurosurgical practice. One hundred and ninety one consecutive patients underwent a 3T rs-fMRI, 83 of whom also underwent both motor and language task-based fMRI. Data were processed using a novel, automated, multi-layer perceptron algorithm and integrated into stereotactic navigation using a streamlined IT imaging pipeline. One hundred eighty-five studies were performed for intracranial neoplasm, 14 for refractory epilepsy and 33 for vascular malformations or other neurological disorders. Failure rate of rs-fMRI of 13% was significantly better than that for task-based fMRI (38.5%,) (p <0.001). In conclusion, at Washington University in St. Louis, rs-fMRI has become an integral part of standard imaging for neurosurgical planning. Resting state fMRI can be used in all patients, and due to its lower failure rate than task-based fMRI, it is useful for patients who are unable to cooperate with task-based studies.

Anatomic consistencies across epilepsies: a stereotactic-EEG informed high-resolution structural connectivity study.

See Bernasconi (doi:10.1093/brain/awx229) for a scientific commentary on this article. Drug-resistant localization-related epilepsies are now recognized as network diseases. However, the exact relationship between the organization of the epileptogenic network and brain anatomy overall remains incompletely understood. To better understand this relationship, we studied structural connectivity obtained from diffusion weighted imaging in patients with epilepsy using both stereo-electroencephalography (SEEG)-determined epileptic brain regions and whole-brain analysis. High resolution structural connectivity analysis was applied in 15 patients with drug-resistant localization-related epilepsies and 36 healthy control subjects to study structural connectivity changes in epilepsy. Two different methods of structural connectivity analysis were carried out using diffusion weighted imaging, one focusing on the relationship between epileptic regions determined by SEEG investigations and one blinded to epileptic regions looking at whole-brain connectivity. First, we performed zone-based analysis comparing structural connectivity findings in patients and controls within and between SEEG-defined zones of interest. Next, we performed whole-brain structural connectivity analysis in all subjects and compared findings to the same SEEG-defined zones of interest. Finally, structural connectivity findings were correlated against clinical features. Zone-based analysis revealed no significant decreased structural connectivity within nodes of the epilepsy network at the group level, but did demonstrate significant structural connectivity differences between nodes of the epileptogenic network (regions involved in seizures generation and propagation) and the remaining of the brain in patients compared to controls. Whole-brain analyses showed a total of 133 clusters of significantly decreased structural connectivity across all patients. One cluster of significantly increased structural connectivity was identified in a single patient. Clusters of decreased structural connectivity showed topographical preference for both the salience and default mode networks despite clinical heterogeneity within our patient sample. Correlation analysis did not reveal any significant findings regarding either the effect of age at disease onset, disease duration or post-surgical outcome on structural connectivity. Taken together, this work demonstrates that structural connectivity disintegration targets distributed functional networks while sparing the epilepsy network.

The impact of high grade glial neoplasms on human cortical electrophysiology.

OBJECTIVE: The brain's functional architecture of interconnected network-related oscillatory patterns in discrete cortical regions has been well established with functional magnetic resonance imaging (fMRI) studies or direct cortical electrophysiology from electrodes placed on the surface of the brain, or electrocorticography (ECoG). These resting state networks exhibit a robust functional architecture that persists through all stages of sleep and under anesthesia. While the stability of these networks provides a fundamental understanding of the organization of the brain, understanding how these regions can be perturbed is also critical in defining the brain's ability to adapt while learning and recovering from injury. METHODS: Patients undergoing an awake craniotomy for resection of a tumor were studied as a unique model of an evolving injury to help define how the cortical physiology and the associated networks were altered by the presence of an invasive brain tumor. RESULTS: This study demonstrates that there is a distinct pattern of alteration of cortical physiology in the setting of a malignant glioma. These changes lead to a physiologic sequestration and progressive synaptic homogeneity suggesting that a de-learning phenomenon occurs within the tumoral tissue compared to its surroundings. SIGNIFICANCE: These findings provide insight into how the brain accommodates a region of "defunctionalized" cortex. Additionally, these findings may have important implications for emerging techniques in brain mapping using endogenous cortical physiology.

Whole-Brain High-Resolution Structural Connectome: Inter-Subject Validation and Application to the Anatomical Segmentation of the Striatum.

The present study describes extraction of high-resolution structural connectome (HRSC) in 99 healthy subjects, acquired and made available by the Human Connectome Project. Single subject connectomes were then registered to the common surface space to allow assessment of inter-individual reproducibility of this novel technique using a leave-one-out approach. The anatomic relevance of the surface-based connectome was examined via a clustering algorithm, which identified anatomic subdivisions within the striatum. The connectivity of these striatal subdivisions were then mapped on the cortical and other subcortical surfaces. Findings demonstrate that HRSC analysis is robust across individuals and accurately models the actual underlying brain networks related to the striatum. This suggests that this method has the potential to model and characterize the healthy whole-brain structural network at high anatomic resolution.

Resting state functional connectivity magnetic resonance imaging integrated with intraoperative neuronavigation for functional mapping after aborted awake craniotomy.

BACKGROUND: Awake craniotomy is currently the gold standard for aggressive tumor resections in eloquent cortex. However, a significant subset of patients is unable to tolerate this procedure, particularly the very young or old or those with psychiatric comorbidities, cardiopulmonary comorbidities, or obesity, among other conditions. In these cases, typical alternative procedures include biopsy alone or subtotal resection, both of which are associated with diminished surgical outcomes. CASE DESCRIPTION: Here, we report the successful use of a preoperatively obtained resting state functional connectivity magnetic resonance imaging (MRI) integrated with intraoperative neuronavigation software in order to perform functional cortical mapping in the setting of an aborted awake craniotomy due to loss of airway. CONCLUSION: Resting state functional connectivity MRI integrated with intraoperative neuronavigation software can provide an alternative option for functional cortical mapping in the setting of an aborted awake craniotomy.

Minimally Invasive Neurosurgery for Epilepsy Using Stereotactic MRI Guidance.

Medically refractory epilepsy is associated with significant morbidity and mortality. Surgery is a safe and effective option for some patients, however the opportunity exists to develop less invasive and more effective surgical options. To this end, multiple minimally invasive, image-guided techniques have been applied to the treatment of epilepsy. These techniques can be divided into thermoablative and disconnective techniques. Each has been described in the treatment of epilepsy only in small case series. Larger series and longer follow up periods will determine each option's place in the surgical armamentarium for the treatment of refractory epilepsy but early results are promising.

Congenital Acute Myeloid Leukemia with Unique Translocation t(11;19)(q23;p13.3).

Congenital leukemia is rarely encountered in clinical practice, even in tertiary children's hospitals. Leukemia may cause significant coagulopathy, putting the patient at risk of intracranial hemorrhage. In this case, the authors present a female infant with a unique mixed phenotypic congenital acute myeloid leukemia showing mixed-lineage leukemia (MLL) rearrangement and severe coagulopathy resulting in a large subdural hematoma. Despite the fatal outcome in this case, neurosurgical treatment of patients with acute myeloid leukemia should be considered if coagulopathy and the clinical scenario allow.

Laser ablation as treatment strategy for medically refractory dominant insular epilepsy: therapeutic and functional considerations.

Since its introduction to neurosurgery in 2008, laser ablative techniques have been largely confined to the management of unresectable tumors. Application of this technology for the management of focal epilepsy in the adult population has not been fully explored. Given that nearly 1,000,000 Americans live with medically refractory epilepsy and current surgical techniques only address a fraction of epileptic pathologies, additional therapeutic options are needed. We report the successful treatment of dominant insular epilepsy in a 53-year-old male with minimally invasive laser ablation complicated by mild verbal and memory deficits. We also report neuropsychological test data on this patient before surgery and at 8 months after the ablation procedure. This account represents the first reported successful patient outcome of laser ablation as an effective treatment option for medically refractory post-stroke epilepsy in an adult.