Faculty Bibliography

The proposed Neuronal α-Synuclein Disease Integrated Staging System (NSD-ISS) was recently applied to early Parkinson's disease (PD) cohorts. We applied this research framework to the BioFIND study cohort, which includes more moderately advanced PD participants with clinically established PD. Disease durations within each ISS stage were highly variable. Cognitive and non-motor anchors had little weight in determining staging. The analysis highlights strengths and limitations of NSD-ISS to guide further refinement of an integrated staging system.

Sentence production is the uniquely human ability to transform complex thoughts into strings of words. Despite the importance of this process, language production research has primarily focused on single words. It remains a largely untested assumption that the principles of word production generalize to more naturalistic utterances like sentences. Here, we investigate this using high-resolution neurosurgical recordings (ECoG) and an overt production experiment where ten patients produced six words in isolation (picture naming) and in sentences (scene description). We trained machine learning classifiers to identify the unique brain activity patterns for each word during picture naming, and used these patterns to decode which words patients were processing while they produced sentences. Our findings confirm that words share cortical representations across tasks, but reveal a division of labor within the language network. In sensorimotor cortex, words were consistently activated in the order in which they were said in the sentence. However, in prefrontal cortex, the order in which words were processed depended on the syntactic structure of the sentence. In non-canonical sentences (passives), we further observed a spatial code for syntactic roles, with subjects selectively encoded in inferior frontal gyrus (IFG) and objects selectively encoded in middle frontal gyrus (MFG). We suggest that these complex dynamics of prefrontal cortex may impose a subtle pressure on language evolution, potentially explaining why nearly all the world's languages position subjects before objects.

Despite decades of advancements in diagnostic MRI, 30-50% of temporal lobe epilepsy (TLE) patients remain categorized as "non-lesional" (i.e., MRI negative or MRI-) based on visual assessment by human experts. MRI- patients face diagnostic uncertainty and significant delays in treatment planning. Quantitative MRI studies have demonstrated that MRI- patients often exhibit a TLE-specific pattern of temporal and limbic atrophy that may be too subtle for the human eye to detect. This signature pattern could be successfully translated into clinical use via artificial intelligence (AI) advances in computer-aided MRI interpretation, thereby improving the detection of brain "lesional" patterns associated with TLE. Here, we tested this hypothesis by employing a three-dimensional convolutional neural network (3D CNN) applied to a dataset of 1,178 scans from 12 different centers. 3D CNN was able to differentiate TLE from healthy controls with high accuracy (85.9% ± 2.8), significantly outperforming support vector machines based on hippocampal (74.4% ± 2.6) and whole-brain (78.3% ± 3.3) volumes. Our analysis subsequently focused on a subset of patients who achieved sustained seizure freedom post-surgery as a gold standard for confirming TLE. Importantly, MRI- patients from this cohort were accurately identified as TLE 82.7% ± 0.9 of the time, an encouraging finding since clinically these were all patients considered to be MRI- (i.e., not radiographically different than controls). The saliency maps from the CNN revealed that limbic structures, particularly medial temporal, cingulate, and orbitofrontal areas, were most influential in classification, confirming the importance of the well-established TLE signature atrophy pattern for diagnosis. Indeed, the saliency maps were similar in MRI+ and MRI- TLE groups, suggesting that even when humans cannot distinguish more subtle levels of atrophy, these MRI- patients are on the same continuum common across all TLE patients. As such, AI can identify TLE lesional patterns and AI-aided diagnosis has the potential to greatly enhance the neuroimaging diagnosis of TLE and redefine the concept of "lesional" TLE.

Alzheimer's disease (AD) is characterized by the deposition of full-length and truncated amyloid beta (Aβ) species within brain parenchyma and cerebral vessels. However, Aβ truncated species remain understudied. Here, we present a protocol for culture and characterization of a mouse monoclonal antibody targeting N-terminally truncated proteoforms starting at position 4. We describe a detailed procedure for hybridoma culture, antibody collection, and isolation via affinity chromatography. We then describe steps for target validation via dot blot, as well as potential applications. For complete details on the use and execution of this protocol, please refer to Cabrera et al. and Rostagno et al.¹

BACKGROUND:Clinical evaluation of distal symmetric polyneuropathy (DSP), which can include small fiber neuropathy (SFN), differs among neurologists, neuromuscular specialists, and internists. The American Academy of Neurology (AAN) 2009 Practice Parameter guides evaluation of DSP, but there are no guidelines or AAN practice parameters for the evaluation of SFN. OBJECTIVE:Determine how neurologists evaluate and test for SFN in their clinical practice and compare responses between neuromuscular (NM) and non-neuromuscular specialists (non-NM). DESIGN/METHODS/METHODS:Eight hundred randomly selected AAN members, which included 400 members who indicated NM medicine to be a primary or secondary specialty, were selected to answer a survey about SFN. Respondents answered a survey instrument with a list of 44 serum tests and procedures for different neuropathy clinical scenarios. RESULTS:The survey response rate was 29.3% (234/798), with 48.8% (N = 114) indicating that their primary specialty was neuromuscular. For an initial evaluation of distal symmetric SFN, respondents ordered a mean of 12 tests (SD 5.8) with a range of 0-26 tests. There was no statistically significant difference between the mean number of tests ordered by neuromuscular versus non-neuromuscular specialists. The 5 most common overall responses were complete blood count (87%), vitamin B12 (86%), basic metabolic panel (84%), thyroid stimulating hormone (78%), and hemoglobin A1c (77%). For a secondary evaluation of etiologies of distal symmetric SFN, 52% of non-neuromuscular specialists (95% CI, 42%-61%) versus 35% of neuromuscular specialists (95% CI, 26%-45%) would order a paraneoplastic panel. There was significant disparity in ordering a skin biopsy for intraepidermal nerve fiber density, with 65% of neuromuscular specialists (95% CI, 55%-74%) indicating that they would order this test compared with 38% of non-neuromuscular specialists (95% CI, 29%-48%). CONCLUSIONS:The recommended studies with the highest yield for finding a cause of DSP were not universally ordered. There is variability in approaches to diagnosing SFN and searching for a possible etiology. The development of an AAN practice parameter for SFN may help promote consistent practice among neurologists of all subspecialties.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Iatrogenic cerebrovascular injury can cause intracranial hemorrhage and pseudoaneurysm formation, putting patients at high risk for postoperative bleeding. No consensus for management exists. This study describes endovascular treatment of these acute injuries with flow diverter stents. METHODS:Electronic medical records were retrospectively reviewed for injury type and etiology, timing of diagnosis, and endovascular management, including antiplatelet regimens, embolization results, and clinical outcome. RESULTS:Six patients were included. Three suffered an injury to the internal carotid artery, 1 suffered an injury to the left anterior cerebral artery, 1 suffered an injury to the right posterior cerebral artery, and 1 suffered an injury to the basilar artery. Four of the 6 injuries occurred during attempted tumor resection, 1 occurred during cerebrospinal fluid leak repair, and 1 occurred during an ophthalmic artery aneurysm clipping. All injuries resulted in pseudoaneurysm formation. Four were immediately detected on angiography; 2 were initially negative on imaging. Five were treated with a pipeline embolization device, and 1 was treated with a Silk Vista Baby. Two were treated with 2 pipeline embolization devices telescopically overlapped across the pseudoaneurysm. All devices deployed successfully. No pseudoaneurysm recurrence or rebleeding occurred. No parent artery occlusion or stenosis was observed, and complete pseudoaneurysm occlusion was observed in 4 patients (in 2 patients, follow-up imaging could not be obtained). CONCLUSION/CONCLUSIONS:With proper antiplatelet regimens, flow diverter stents can be used safely to successfully treat complex acute iatrogenic injuries. Early repeat angiogram is needed when immediate postinjury imaging does not discover the point of vessel injury.

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

Gliomas are a major cause of cancer-related deaths in adolescents and young adults (AYAs; ages 15-39 years). Different molecular alterations drive gliomas in children and adults, leading to distinct biology and clinical consequences, but the implications of pediatric- versus adult-type alterations in AYAs are unknown. Our population-based analysis of 1,456 clinically and molecularly characterized gliomas in patients aged 0-39 years addresses this gap. Pediatric-type alterations were found in 31% of AYA gliomas and conferred superior outcomes compared to adult-type alterations. AYA low-grade gliomas with specific RAS-MAPK alterations exhibited senescence, tended to arise in different locations and were associated with superior outcomes compared to gliomas in children, suggesting different cellular origins. Hemispheric IDH-mutant, BRAF p.V600E and FGFR-altered gliomas were associated with the risk of malignant transformation, having worse outcomes with increased age. These insights into gliomagenesis may provide a rationale for earlier intervention for certain tumors to disrupt the typical behavior, leading to improved outcomes.

The International League Against Epilepsy (ILAE) has updated the operational classification of epileptic seizures, building upon the framework established in 2017. This revision, informed by the implementation experience, involved a working group appointed by the ILAE Executive Committee. Comprising 37 members from all ILAE regions, the group utilized a modified Delphi process, requiring a consensus threshold of more than two thirds for any proposal. Following public comments, the Executive Committee appointed seven additional experts to the revision task force to address and incorporate the issues raised, as appropriate. The updated classification maintains four main seizure classes: Focal, Generalized, Unknown (whether focal or generalized), and Unclassified. Taxonomic rules distinguish classifiers, which are considered to reflect biological classes and directly impact clinical management, from descriptors, which indicate other important seizure characteristics. Focal seizures and those of unknown origin are further classified by the patient's state of consciousness (impaired or preserved) during the seizure, defined operationally through clinical assessment of awareness and responsiveness. If the state of consciousness is undetermined, the seizure is classified under the parent term, that is, the main seizure class (focal seizure or seizure of unknown origin). Generalized seizures are grouped into absence seizures, generalized tonic-clonic seizures, and other generalized seizures, now including recognition of negative myoclonus as a seizure type. Seizures are described in the basic version as with or without observable manifestations, whereas an expanded version utilizes the chronological sequence of seizure semiology. This updated classification comprises four main classes and 21 seizure types. Special emphasis was placed on ensuring translatability into languages beyond English. Its aim is to establish a common language for all health care professionals involved in epilepsy care, from resource-limited areas to highly specialized centers, and to provide accessible terms for patients and caregivers.

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.