United States

Background
Chronic Traumatic Encephalopathy (CTE) is a progressive neurodegenerative disease associated with repetitive head trauma, predominantly affecting contact sports athletes and military personnel. Given the high prevalence of traumatic brain injuries (TBI) managed by neurosurgeons, it is crucial to understand CTE&#39;s pathophysiology, clinical presentation, and management strategies to enhance patient outcomes.
Methods
A systematic review and meta-analysis were conducted following PRISMA guidelines.
Searches were performed in PubMed, Cochrane Library, Embase, and Google Scholar for studies published from January 2010 to May 2024. Inclusion criteria encompassed studies on CTE epidemiology, pathophysiology, clinical presentation, diagnostics, and therapeutics. Data extraction and quality assessment were conducted independently by two reviewers, with discrepancies resolved by a third reviewer.

Results
Fourteen studies met the inclusion criteria, totaling 2729 observations and 799 events of CTE. The pooled proportion of CTE cases was 72.82% (95% CI: 48.17% to 88.53%) using a random effects model, with substantial heterogeneity (I2 = 98.6%). Subgroup analysis indicated high pooled proportions among athletes (71.43%) and military personnel (74.29%). Sensitivity analysis confirmed the robustness of the findings.
The pathophysiology of CTE involves the accumulation of hyperphosphorylated tau
protein and chronic neuroinflammation, leading to significant neuronal damage.
Clinically, CTE presents with cognitive impairment, mood disorders, and behavioral
changes, complicating its diagnosis due to overlapping symptoms with other
neurodegenerative diseases. Current treatments focus on symptomatic management, with emerging therapies targeting tau pathology and neuroinflammation showing promise.

Conclusion
This meta-analysis underscores the significant prevalence of CTE among individuals
exposed to repetitive head trauma, highlighting the need for standardized diagnostic
criteria and personalized management approaches. Neurosurgeons are pivotal in
advancing diagnostic protocols and therapeutic strategies to improve outcomes for
patients with CTE. Further research is needed to enhance understanding and
management of CTE, ultimately benefiting the broader TBI patient population frequently
encountered in neurosurgical

AMA Research Challenge 2024 

Chronic Traumatic Encephalopathy in Neurosurgical Practice: Insights from a Systematic Review and Meta-Analysis

Background
Chronic Traumatic Encephalopathy (CTE) is a progressive neurodegenerative disease associated with repetitive head trauma, predominantly affecting contact sports athletes and military personnel. Given the high prevalence of traumatic brain injuries (TBI) managed by neurosurgeons, it is crucial to understand CTE's pathophysiology, clinical presentation, and management strategies to enhance patient outcomes.
Methods
A systematic review and meta-analysis were conducted following PRISMA guidelines.
Searches were performed in PubMed, Cochrane Library, Embase, and Google Scholar for studies published from January 2010 to May 2024. Inclusion criteria encompassed studies on CTE epidemiology, pathophysiology, clinical presentation, diagnostics, and therapeutics. Data extraction and quality assessment were conducted independently by two reviewers, with discrepancies resolved by a third reviewer.

Results
Fourteen studies met the inclusion criteria, totaling 2729 observations and 799 events of CTE. The pooled proportion of CTE cases was 72.82% (95% CI: 48.17% to 88.53%) using a random effects model, with substantial heterogeneity (I2 = 98.6%). Subgroup analysis indicated high pooled proportions among athletes (71.43%) and military personnel (74.29%). Sensitivity analysis confirmed the robustness of the findings.
The pathophysiology of CTE involves the accumulation of hyperphosphorylated tau
protein and chronic neuroinflammation, leading to significant neuronal damage.
Clinically, CTE presents with cognitive impairment, mood disorders, and behavioral
changes, complicating its diagnosis due to overlapping symptoms with other
neurodegenerative diseases. Current treatments focus on symptomatic management, with emerging therapies targeting tau pathology and neuroinflammation showing promise.

Conclusion
This meta-analysis underscores the significant prevalence of CTE among individuals
exposed to repetitive head trauma, highlighting the need for standardized diagnostic
criteria and personalized management approaches. Neurosurgeons are pivotal in
advancing diagnostic protocols and therapeutic strategies to improve outcomes for
patients with CTE. Further research is needed to enhance understanding and
management of CTE, ultimately benefiting the broader TBI patient population frequently
encountered in neurosurgical

poster

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Introduction: Proximal junctional kyphosis (PJK) is a common complication following correction of adult spinal deformity (ASD) at the transition of fused and mobile motion segments. PJF represents PJK with vertebral fracture, soft-tissue disruption, and/or screw pullout at the upper instrumented vertebra (UIV) accompanied with neurological deficit. Coronal decompensation following long-segment fusion may contribute to the development of proximal junctional pathology. This study reports on the natural history of coronal imbalance in ASD patients that develop PJF after fusion of the thoracolumbar spine to the pelvis. 
Methods: A single-institution retrospective review was performed for 1180 ASD patients who underwent posterior instrumented fusion to the pelvis (2009-2021). Patients that developed PJF were grouped by the UIV: upper thoracic (UT, T2-T6, 10 patients), lower thoracic (LT, T8-T11, 35 patients), or lumbar spine (L, L1-L3, 8 patients). Radiographic parameters, including the major scoliotic curve (MSC), coronal and sagittal proximal junctional angle (cPJA/sPJA), and the coronal and sagittal C7 vertical angles (CVA/SVA), were recorded before primary surgery, before revision surgery for failure, and at both 6-week and final follow-up following revision. 
Results: From 1180 ASD patients, 54 (4.6%) developed PJF (59% female, mean age 65.0 ± 6.8 and BMI 30.3 ± 5.9). Mean follow-up after revision was 3.1 ± 2.7 years. The ΔMSC (UT –17.6 ± 24.7°, LT –11.5 ± 15.97°, L –5.7 ± 7.6°) was calculated as the difference in MSC before revision surgery and before primary surgery. The mean ΔcPJA (UT 0.5 ± 3.6°, LT 0.6 ± 24.7°, L 2.5 ± 8.1°), ΔsPJA (UT: 19.1 ± 11.0°, LT: 14.8° ± 4.0°, L: 16.0° ± 9.0°), ΔCVA (UT: -13.6 ± 35.5 mm, LT –8.1 ± 21.1 mm, L 3.3 ± 29.4 mm), and ΔSVA (UT: -42.8 ± 86.8 mm, LT: 15.9 ± 38.4 mm, L: 48.6 ± 58.5 mm) were also calculated in that manner. 
Conclusion: Proximal junctional pathology presents differently radiographically following PJF after instrumented fusion from the UT, LT, and L spine. In this study, patients with a UIV in the L spine develop more coronal tilt and sagittal imbalance after PJF than patients with an UIV in the UT and LT spine.

Coronal Imbalance in Adult Spinal Deformity Patients That Develop Proximal Junctional Failure

Background:
The clinical use of routine CT imaging following intracranial tumor resection is controversial, with variable use in clinical practice. Socioeconomic studies investigating the clinical utility and cost-effectiveness of this resource are lacking.

Methods:
An institutional retrospective chart review was conducted on patients who underwent intracranial tumor resection from 2014-2024, performed by 17 individual surgeons. Data analyzed included incidence of postoperative CT imaging prior or in addition to post-resection MRI, imaging indications, and resulting changes in clinical management. A cost analysis was estimated based on compensation and cost-transparency datasets. Chi-squared and ANOVA tests were performed to identify variables associated with clinically meaningful routine imaging.

Results:
2,082 patients (2,177 resections) were included. 460 (22.1%) patients obtained CT imaging postoperatively, of which 305 (63.2%) were routine and 155 (37.5%) were targeted; no routine scans (0%) effected change in management, while targeted 50 scans (32.5%) did. Cost analysis showed that total costs for all post-operative CTs in this cohort was $398,008, with only $40,184 being CTs with acute findings that changed management. This produced excess costs of $348,257, which could have been prevented had a targeted approach been used across all patients in our hospital system. Only skull base and frontal tumors, intraoperative complications, and prolonged operative duration were associated with clinically beneficial CTs. Finally, no patient experienced delayed detection of postoperative deficits without routine imaging.

Discussion:
A targeted approach to postoperative CT imaging after intracranial tumor resection based on neurological exam monitoring is superior to routine imaging use in terms of cost and clinical benefit. Routine imaging may serve useful for patients undergoing skull base or frontal resections, particularly following longer surgeries or intraoperative complications.

Economic Analysis and Clinical Utility of Routine Versus Targeted Use Of Postoperative Computed Tomography Following Intracranial Tumor Resection: A Decade-Long Multi-Surgeon Institutional Study

Background:
Decompressive craniectomy (DC) is a crucial intervention for managing severe brain injury. The optimal timing for subsequent cranioplasty (CP) to maximize benefits and minimize complications is debated. This meta-analysis aims to synthesize current evidence to ascertain the impact of early versus delayed cranioplasty on neurological postoperative outcomes and complication rates.

Methods: 
An exhaustive literature search was conducted across PubMed, Scopus, and Embase up to January 2024, focusing on studies that addressed the timing of cranioplasty after craniectomy. The inclusion criteria were specifically designed to capture cross-sectional and retrospective studies, and case series of four or more adult patients, which directly examined the timing of cranioplasty. Studies not focused on timing, including those comparing materials, reporting techniques, or presenting cases with fewer than four patients, were excluded. Data extraction emphasized study design, patient demographics, timing intervals, and related outcomes, ensuring a rigorous quality assessment using the Newcastle-Ottawa Scale. Statistical analyses were aimed at evaluating study heterogeneity and synthesizing data regarding timing and its impact on outcomes.

Results: 
From the initial screening to the final selection, 7 studies with a total of 1009 patients were analyzed. The meta-analysis elucidates a significant association between the timing of cranioplasty and both neurological outcomes and complication rates. Notably, cranioplasty performed within 90 days post-craniectomy was linked to improved neurological recovery and reduced complication rates compared to delayed cranioplasty. Moreover, an ultra-early timing (within 30 days) was associated with lower infection rates but an increased risk of hydrocephalus, underscoring the complexity of timing decisions.

Conclusion: This meta-analysis provides compelling evidence that the timing of cranioplasty significantly influences neurological recovery and the incidence of postoperative complications. Early cranioplasty, preferably within 90 days of craniectomy, appears to offer advantages in terms of outcome and safety. However, the decision regarding timing should be carefully tailored to individual patient conditions, considering the nuanced balance between benefits and risks. Further high-quality, prospective studies are needed to refine these findings and support evidence-based clinical decision-making in cranioplasty timing.

Impact of Cranioplasty Timing on Postoperative Outcomes

Background: Opioid dependence is a major public health concern. Genetic factors likely contribute to opioid addiction vulnerability. A number of studies have examined associations between specific gene variants and opioid dependence, but results have been mixed.
Methods: A systematic literature search was conducted through January 2024 to identify studies evaluating relationships between single nucleotide polymorphisms (SNPs) and opioid dependence. Pooled odds ratios (OR) and 95% confidence intervals (CI) were calculated using random-effects meta-analysis models. Between-study heterogeneity was assessed with I2 statistics.
Results: 66 studies involving 21,485 participants were included in the meta-analysis. Significant associations were found between opioid dependence and variants in the mu-opioid receptor (OPRM1) gene (OR=0.466, 95% CI 0.346-0.628, p=5.456x10-7), prodynorphin (PDYN) gene (OR=0.296, 95% CI 0.221-0.398, p<0.001), catechol-O-methyltransferase (COMT) gene (OR=0.438, 95% CI 0.383-0.501, p<0.001), and other genes. Effects were detected across multiple genetic models and ancestral groups.
Conclusion: This meta-analysis identified multiple single nucleotide polymorphisms associated with heightened opioid dependence susceptibility. The findings advance knowledge of genetic contributions to opioid addiction vulnerability across diverse populations.

Single Nucleotide Polymorphisms Associated with Risk of Opioid Dependence

Introduction: Stroke patients often experience motor deficits in the limbs and do not reach satisfactory recovery. Deep brain stimulation (DBS) can target intrinsic pathways that project to vital cortical areas. One structure of interest is the ventro-oralis posterior (VOP)/ventro-intermedium (VIM) of the motor thalamus. The VOP/VIM is a thalamic relay with outgoing excitatory connectivity to the primary motor cortex (M1) and could be a potential target for DBS to facilitate motor function after stroke. We hypothesized that VOP/VIM stimulation may increase the excitability of M1 and strengthen corticospinal motor output and control.
Methods: Five non-human primates (NHP) were implanted with DBS electrodes into the ventral lateral motor thalamus, the internal capsule (IC), and intracortical microelectrode arrays over M1. This setup was replicated in six patients undergoing implantation of DBS electrodes targeting the VOP/VIM for essential tremor with the addition of a macroarray placed over M1. Intraoperatively, direct cortical stimulation (DCS) was paired with VOP/VIM stimulation. Additionally, a single patient with a traumatic brain injury (TBI) with motor deficits was implanted. When the TBI patient returned for DBS programming, he was asked to modulate their force along a trace and the error of deviation was measured with and without DBS while performing weighted and unweighted arm exercises.
Results: In the NHPs, a significantly increased amplitude of evoked potentials (EP) and single unit spike counts recorded in M1 with VOP/VIM stimulation was observed. IC stimulation with VOP/VIM stimulation increased the amplitude of motor evoked potentials (MEP) and kinematics of hand muscles. In the intraoperative human studies, when VOP/VIM was stimulated, similar amplification in M1 EPs and MEPs recorded in upper extremity muscles were observed. Stimulation also increased the height the TBI patient was able to reach in the arm exercises as well as reduced the error during force modulation tasks.
Conclusion: This study documented that direct electrical stimulation of the motor thalamus strengthens motor output, both in NHPs and humans. These outcomes support DBS of the motor thalamus as a potential therapeutic approach to treat post-stroke motor deficits.


Strengthening Motor Output with Direct Motor Thalamus Stimulation. Evidence For A Novel Deep Brain Neuromodulation Modality For Stroke.

Background: This systematic review and meta-analysis comprehensively compares
and analyzes the strengths and weaknesses of three neuromodulating treatment
options—electroconvulsive therapy (ECT), transcranial magnetic stimulation (rTMS),
and psychedelics—for pain management in individuals with Fibromyalgia.

Methods: A systematic literature search began May 31, 2023 and ended July 31,
2023 and was conducted across multiple databases, including PubMed, Google
Scholar, ScienceDirect, EMBASE, Wiley Online Library, Cochrane Library, and
EBSCO, using relevant keywords and controlled vocabulary terms. Key terms for
search included: “Fibromyalgia,” “Neuromodulation therapy,” “Electroconvulsive
Therapy,” “ECT,” “Transcranial Magnetic Stimulation.” Studies published between
2004-2023 were included in the review. The inclusion criteria encompassed
randomized controlled trials, qualitative studies, surveys, prospective cohort studies,
and case-control studies evaluating the effectiveness of ECT, TMS, and psychedelics
as neurocognitive treatments for Fibromyalgia pain management. The exclusion
criteria included animal trials, articles written in a language other than English, case
reports, and case studies.

Results: The search yielded ten studies with a total of 541 subjects, meeting the
inclusion criteria. There was an overall 2.7 mean pain decrease in pain rating scale
after therapy.

Conclusion: This review on neuromodulating Fibromyalgia pain treatments
recognizes marked improvement in pain perception in Fibromyalgia patients
receiving neuromodulating treatment.

A Systematic Review Comparing the Strengths and Weaknesses of Neuromodulating
Treatments for Pain Management in Fibromyalgia: ECT, TMS, and Psychedelics


Neuropathology holds the potential to advance our understanding of Alzheimer's disease and related dementias. Artificial intelligence (AI) models through well-labeled datasets can effectively perform complex tasks at or above human performance. Recent studies have analyzed neuropathological biomarkers like amyloid beta plaques and neurofibrillary tangles in whole slide images through AI models. However, appropriately labeled images are needed to detect which brain region the neuropathological biomarkers originate from for analytical purposes. The study aims to develop automated detection of gray matter regions in WSIs for downstream testing of established AI pipelines in neuropathology.The study involves manual annotations of gray matter regions on Bielschowsky silver stained WSIs from the Emory ADRC digital cohort. These annotations are used to train UNET semantic segmentation models for automated detection of gray matter regions on a larger cohort of non-annotated WSIs. Co-registration to immunohistochemically stained WSIs (amyloid beta, tau, TDP-43) is used to define gray matter annotations across stains. Established AI pipelines are then deployed in these regions to assess improvement on original results. 300 gray matter masks have been generated and an initial classifier has been trained using the YOLOv8 (You Only Look Once) model developed by Ultralytics. While the project is ongoing, our initial results have demonstrated the feasibility of developing an automated gray matter segmentation model to further understanding of biomarkers associated with clinical care.


Artificial Intelligence Powered Neuropathology: Automated Gray Matter Segmentation For Biomarker Analysis

Migraine is a common, disabling medical condition affecting over 40 million people in the United States. Cervical pain has been reported to be more prevalent among migraine sufferers than nausea (Calhoun et al., 2010), yet there is limited research describing the characteristics of the neck pain experienced by persons with migraine. We designed a 10-question survey to analyze the types and characteristics of neck pain experienced by patients with episodic migraine (EM, defined as 14 or fewer headache days per month.)
Subjects were recruited via patient portal message at the New England Institute of Neurology and Headache (NEINH, Stamford, CT) or through school newsletter at the Frank H Netter MD School of Medicine at Quinnipiac University (North Haven, CT). Subjects who met the inclusion criteria (diagnosed with EM, age 18-55, self-report of cervical pain with migraines) and had no history of cervicogenic headache or cervical trauma were interviewed. 
The majority of patients (58%) described their neck pain using the terms “tight or stiff”. The VAS score rating for those whose neck pain began before the migraine (M = 6.7, SD = 2.1) was significantly higher than those whose pain began during the headache (M = 4.2, SD = 1.3, p < 0.05). Patients with aura rated the neck pain (M = 6.7, SD = 1.9) significantly higher than those who do not have aura (M = 5.2, SD = 2.0, p <0.05). There was a strong, positive correlation between age and how much the neck pain contributed to overall disability (Pearson = 0.648, p<0.001). 
Our study demonstrates a small portion of the complex interplay between neck pain and migraine. The significantly higher VAS rating by those whose neck pain begins before their migraine may suggest the pain as a trigger. The difference in VAS scores based on aura may also suggest some relationship between neck pain and aura. Though we attempted to eliminate confounding neck pain that may arise due to age, the significant positive correlation may suggest that we set the age cut off too high. We recognize that our study had a small sample size and our survey only captured a small snapshot of the interplay that exists between neck pain and migraine. The study should be expanded to investigate if similar relationships between neck pain and migraine exist with a larger, more diverse sample size.

Incidence and Characteristics of Neck Pain in Episodic Migraine

Background
The International Mouse Phenotyping Consortium (IMPC) is a database that characterizes the function of over 20,000 protein-encoding genes in mice through knockout. Each knockout gene and its associated phenotype can be accessed through their website. For this project, we used the IMPC to explore the Atlastin GTPase 3 gene (Atl3), a gene related to Hereditary Sensory Neuropathy Type 1F (HSN1F). HSN1F is a rare neurological genetic disorder, characterized by the loss of sensation to the hands and feet due to a mutation in Atl3. When Atl3 is knocked out in mice, the IMPC revealed seven phenotypes: enlarged prostate gland, abnormal liver morphology, small adrenal glands, small kidney, enlarged lymph nodes, cataract, and enlarged urinary bladder.

Methods
The National Organization for Rare Disorders (NORD) was used to identify the disease of interest and its associated gene, Atl3. Using the IMPC database, seven phenotypes associated with the Atl3 knockout mice were identified and compiled into a well-known analytical tool, Microsoft Excel. The genes that showed that phenotype when knocked out in mice were compiled for each phenotype. Using variations of a common algorithm and conditional formatting in Microsoft Excel, the specific number of times a gene showed up and the number of phenotypes each gene was associated with were sorted visually with a specific color. Further analysis, totaling the number of times each specific gene appeared in multiple times was formatted graphically.

Results
The seven phenotypes associated with the Atl3 knockout mice were enlarged prostate gland, abnormal liver morphology, small adrenal glands, small kidney, enlarged lymph nodes, cataract, and enlarged urinary bladder. Our analysis revealed four genes, dynein cytoplasmic 1 light intermediate chain 1 (dync1li1), piggyBac transposable element derived 1 (Pgbd1), MAS-related GPR, member B2 (Mrgprb2), and ADNP homeobox 2 (Adnp2), that when knocked out in mice shared five of the seven phenotypes associated with the Atl3 knockout mouse.

Conclusion
Through the IMPC database, we identified dync1li1, Pgbd1, Mrgprb2, and Adnp2 as genes that potentially have an interaction or could be involved in pathways with Atl3 because they share five of the seven phenotypes. Based on our data, further research is recommended into these genes and how they relate to Atl3 as it shares these phenotypes. Utilizing currently existing tools such as the IMPC, genes and their interactions can be identified to further find genes that may have not been extensively studied yet that mimic the loss of Atl3.

Investigating Atl3-Related Genes Utilizing Data from the International Mouse Phenotyping Consortium

Background: Brain metastases, secondary brain tumors originating from cancers elsewhere in the body, can present with focal neurologic deficits without systemic symptoms and are usually evaluated using magnetic resonance imaging (MRI). Multimodal imaging methods are usually used to determine the primary cancer source. Radiomic analysis involves extracting quantitative features from medical images using data-characterization algorithms. Recent advances in deep learning and radiomics, which quantitatively analyze imaging data, offer the potential for improved prediction of primary tumor origin through MR imaging analysis. Using longitudinal MRI data, we developed and validated a mixed-effects neural network model for predicting the primary origin of brain metastases.

Methods: We used the open-source dataset from 5 different institutions made available by Ocaña-Tienda et al. (2023) to build the model. We extracted 2446 radiomic features, out of which 60 features were retained after feature selection using wrapper- and filter-based techniques. For the nested mixed-effects neural network model, we incorporated a dual-layer structure with ReLU activation for the fixed effects and unique embeddings for each cluster, leading to a nested structure. The model was compiled using an Adam optimizer and trained across several epochs with a steadily declining learning rate facilitated by exponential decay. Regularization was implicitly managed through embedding and learning rate adjustments. Model performance was evaluated using receiver operating characteristic curve (ROC) analysis.

Results: Our mixed-effects model demonstrated high accuracy (90.5%), precision (92.7%), recall (87.8%), and F1 score (90.2%), with an area under the ROC (AUC-ROC) curve of 0.944 in identifying the primary tumor site. The nested mixed-effects model was superior to the fixed-effects model with an AUC-ROC of 0.719.

Conclusion: Our mixed-effects neural network model accurately predicts the primary origin of brain metastases with high internal and external validity. Using advanced radiomic feature selection and deep learning techniques. Our model achieved high-performance metrics, thus providing a promising tool for enhancing diagnostic capability and tailoring patient-specific management strategies. We are exploring it's clinical utility further by adding more data from a tertiary cancer hospital. We have also deployed a patented prototype software for research use to study the effect of integrating this model in clinical workflows to optimize patient care.

Mixed-effects deep-learning based quantitative radiomic analysis of longitudinal magnetic resonance imaging of brain metastases predict primary tumor origin

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Chronic Traumatic Encephalopathy in Neurosurgical Practice: Insights from a Systematic Review and Meta-Analysis

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