Cross-Sectional Comparison of Structural MRI Markers of Impairment in a Diverse Cohort of Older Adults
(2025) Human Brain Mapping, 46 (2), art. no. e70133, .
Wisch, J.K.a , Petersen, K.a , Millar, P.R.a , Abdelmoity, O.b , Babulal, G.M.a , Meeker, K.L.c , Braskie, M.N.d , Yaffe, K.e , Toga, A.W.d , O’Bryant, S.c , Ances, B.M.a , the HABS-HD Study Teamf
a Department of Neurology, Washington University in St. Louis, St. Louis, MO, United States
b Danforth Undergraduate Campus, Washington University in St. Louis, St. Louis, MO, United States
c Department of Family Medicine, Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, TX, United States
d Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
e Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
Abstract
Neurodegeneration is presumed to be the pathological process measure most proximal to clinical symptom onset in Alzheimer Disease (AD). Structural MRI is routinely collected in research and clinical trial settings. Several quantitative MRI-based measures of atrophy have been proposed, but their low correspondence with each other has been previously documented. The purpose of this study was to identify which commonly used structural MRI measure (hippocampal volume, cortical thickness in AD signature regions, or brain age gap [BAG]) had the best correspondence with the Clinical Dementia Rating (CDR) in an ethno-racially diverse sample. 2870 individuals recruited by the Healthy and Aging Brain Study—Health Disparities completed both structural MRI and CDR evaluation. Of these, 1887 individuals were matched on ethno-racial identity (Mexican American [MA], non-Hispanic Black [NHB], and non-Hispanic White [NHW]) and CDR (27% CDR > 0). We estimated brain age using two pipelines (DeepBrainNet, BrainAgeR) and then calculated BAG as the difference between the estimated brain age and chronological age. We also quantified their hippocampal volumes using HippoDeep and cortical thicknesses (both an AD-specific signature and average whole brain) using FreeSurfer. We used ordinal regression to evaluate associations between neuroimaging measures and CDR and to test whether these associations differed between ethno-racial groups. Higher BAG (pDeepBrainNet = 0.0002; pBrainAgeR = 0.00117) and lower hippocampal volume (p = 0.0015) and cortical thickness (p < 0.0001) were associated with worse clinical status (higher CDR). AD signature cortical thickness had the strongest relationship with CDR (AICDeepBrainNet = 2623, AICwhole cortex = 2588, AICBrainAgeR = 2533, AICHippocampus = 2293, AICSignature Cortical Thickness = 1903). The relationship between CDR and atrophy measures differed between ethno-racial groups for both BAG estimates and hippocampal volume, but not for cortical thickness. We interpret the lack of an interaction between ethno-racial identity and AD signature cortical thickness on CDR as evidence that cortical thickness effectively captures sources of disease-related atrophy that may differ across racial and ethnic groups. Cortical thickness had the strongest association with CDR. These results suggest that cortical thickness may be a more sensitive and generalizable marker of neurodegeneration than hippocampal volume or BAG in ethno-racially diverse cohorts. © 2025 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.
Author Keywords
Alzheimer disease; brain aging; clinical dementia rating; MRI
Funding details
National Institute on AgingNIA
National Institutes of HealthNIHR01AG070862, P41EB015922, R01AG054073, R01AG058533, K01AG083115, U19AG078109
National Institutes of HealthNIH
Document Type: Article
Publication Stage: Final
Source: Scopus
Differential gait features across Parkinson’s disease clinical subtypes
(2025) Clinical Biomechanics, 122, art. no. 106445, .
Baudendistel, S.T.a , Rawson, K.S.a b , Lessov-Schlaggar, C.N.c , Maiti, B.b f , Kotzbauer, P.T.b , Perlmutter, J.S.a b d e f , Earhart, G.M.a b e , Campbell, M.C.b f
a Program in Physical Therapy, Washington University School of Medicine, CB 8502, 4444 Forest Park Ave., Suite 1101, St. Louis, MO 63108, United States
b Department of Neurology, Washington University School of Medicine, MSC 8111-29-9000, 660 S. Euclid Ave., St. Louis, MO 63110, United States
c Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave., St. Louis, MO 63110, United States
d Program in Occupational Therapy, Washington University School of Medicine, MSC 8505-66-1, 4444 Forest Park Ave., Suite 1101, St. Louis, MO 63108, United States
e Department of Neuroscience, Washington University School of Medicine, CB 8108, 660 S. Euclid Ave., St. Louis, MO 63110, United States
f Department of Radiology, Washington University School of Medicine, Campus Box 8225, 660 S. Euclid Ave, St. Louis, MO 63110, United States
Abstract
Background: Clinical subtypes in Parkinson’s disease including non-motor manifestations may be more beneficial than subtypes based upon motor manifestations alone. Inclusion of gait metrics may help identity targets for rehabilitation and potentially predict development of non-motor symptoms for individuals with Parkinson’s disease. This study aims to characterize gait differences across established multi-domain subtypes. Methods: “Motor Only”, “Psychiatric & Motor” and “Cognitive & Motor” clinical subtypes were established through motor, cognitive, and psychiatric assessment. Walking was assessed in the “OFF” medication state. Multivariate analysis of variance identified differences in gait domains across clinical subtypes. Findings: The “Motor Only” subtype exhibited the fastest velocity, longest step length, and least timing variability (swing, step, stance), compared to “Psychiatric & Motor” and “Cognitive & Motor” subtypes. Stance time differed across subtypes; “Psychiatric & Motor” subtype had the longest stance time, followed by “Cognitive & Motor”, then “Motor only”. The “Psychiatric & Motor” group had different asymmetry from the “Cognitive & Motor” subtype, as “Psychiatric & Motor” walked with longer steps on their less-affected side while the “Cognitive & Motor” subtype displayed the opposite pattern. No differences were observed for swing time, step velocity variability, step length variability, width measures, or other asymmetry measures. Interpretation: Cognitive and Psychiatric subtypes displayed worse gait performance than the “Motor only” group. Stance time and step length asymmetry were different between Psychiatric and Cognitive subtypes, indicating gait deficits may be related to distinct aspects of non-motor manifestations. Gait signatures may help clinicians distinguish between non-motor subtypes, guiding personalized treatment. © 2025 The Authors
Author Keywords
Gait; Neuropsychology; Parkinson’s disease; Walking
Funding details
American Parkinson Disease AssociationAPDA
National Institutes of HealthNIH
Foundation for Barnes-Jewish HospitalFBJH
Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNICHDT32-HE007434
Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNICHD
National Center for Medical Rehabilitation ResearchNCMRRR25HD105583
National Center for Medical Rehabilitation ResearchNCMRR
National Institute of Neurological Disorders and StrokeNINDSNS097799, NS075321, K23NS125107, NS097437
National Institute of Neurological Disorders and StrokeNINDS
UL1RR024992
Document Type: Article
Publication Stage: Final
Source: Scopus
Evaluating dimensionality reduction of comorbidities for predictive modeling in individuals with neurofibromatosis type 1
(2025) JAMIA Open, 8 (1), art. no. ooae157, .
Gupta, A.a , Hillis, E.a , Oh, I.Y.a , Morris, S.M.b , Abrams, Z.a , Foraker, R.E.a , Gutmann, D.H.c , Payne, P.R.O.a
a Institute for Informatics, Data Science and Biostatistics, Washington University, Saint Louis, MO 63110, United States
b Center for Autism Services, Science, and Innovation (CASSI), Kennedy Krieger Institute, Baltimore, MD 21205, United States
c Department of Neurology, School of Medicine, Washington University, Saint Louis, MO 63110, United States
Abstract
Objective: Dimensionality reduction techniques aim to enhance the performance of machine learning (ML) models by reducing noise and mitigating overfitting. We sought to compare the effect of different dimensionality reduction methods for comorbidity features extracted from electronic health records (EHRs) on the performance of ML models for predicting the development of various sub-phenotypes in children with Neurofibromatosis type 1 (NF1). Materials and Methods: EHR-derived data from pediatric subjects with a confirmed clinical diagnosis of NF1 were used to create 10 unique comorbidities code-derived feature sets by incorporating dimensionality reduction techniques using raw International Classification of Diseases codes, Clinical Classifications Software Refined, and Phecode mapping schemes. We compared the performance of logistic regression, XGBoost, and random forest models utilizing each feature set. Results: XGBoost-based predictive models were most successful at predicting NF1 sub-phenotypes. Overall, features based on domain knowledge-informed mapping schema performed better than unsupervised feature reduction methods. High-level features exhibited the worst performance across models and outcomes, suggesting excessive information loss with over-aggregation of features. Discussion: Model performance is significantly impacted by dimensionality reduction techniques and varies by specific ML algorithm and outcome being predicted. Automated methods using existing knowledge and ontology databases can effectively aggregate features extracted from EHRs. Conclusion: Dimensionality reduction through feature aggregation can enhance the performance of ML models, particularly in high-dimensional datasets with small sample sizes, commonly found in EHRs health applications. However, if not carefully optimized, it can lead to information loss and data oversimplification, potentially adversely affecting model performance. © 2025 The Author(s).
Author Keywords
clinical research informatics; electronic health records; neurofibromatosis type 1; predictive modeling
Funding details
National Institute of Neurological Disorders and StrokeNINDS
National Institutes of HealthNIHR01NS131112
Document Type: Article
Publication Stage: Final
Source: Scopus
Association of rapid eye movement sleep latency with multimodal biomarkers of Alzheimer’s disease
(2025) Alzheimer’s and Dementia, 21 (2), art. no. e14495, .
Jin, J.a b , Chen, J.c , Cavaillès, C.d , Yaffe, K.d e , Winer, J.f , Stankeviciute, L.g , Lucey, B.P.h , Zhou, X.i , Gao, S.c , Peng, D.b j , Leng, Y.d
a Clinical Trial Research Center, China-Japan Friendship Hospital, Beijing, China
b Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
c Institute of Medical Technology, Peking University Health Science Center, Beijing, China
d Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, United States
e Department of Neurology, University of California, San Francisco, CA, United States
f Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, CA, United States
g Barcelonaβeta Brain Research Center (BBRC) Pasqual Maragall Foundation, Barcelona, Spain
h Department of Neurology, Washington University School of Medicine, St Louis, MO, United States
i Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
j Department of Neurology, China-Japan Friendship Hospital, Beijing, China
Abstract
INTRODUCTION: Sleep disturbances are associated with Alzheimer’s disease (AD) and Alzheimer’s disease and related dementias (ADRD), but the relationship between sleep architecture, particularly rapid eye movement (REM) sleep, and AD/ADRD biomarkers remains unclear. METHODS: We enrolled 128 adults (64 with Alzheimer’s disease, 41 with mild cognitive impairment [MCI], and 23 with normal cognition [NC]), mean age 70.8 ± 9.6 years, 56.9% female, from a tertiary hospital in China. Participants underwent overnight polysomnography (PSG), amyloid β (Aβ) positron emission tomography (PET), and plasma biomarker analysis: phosphorylated tau at threonine 181 (p-tau181), neurofilament light (NfL), and brain-derived neurotrophic factor (BDNF). RESULTS: After adjusting for demographics, apolipoprotein E (APOE) ε4 status, cognition, and comorbidities, the highest tertile of REM latency was associated with higher Aβ burden (β = 0.08, 95% confidence interval [CI]: 0.03 to 0.13, p = 0.002), elevated p-tau181 (β = 0.19, 95% CI: 0.02 to 0.13, p = 0.002), and reduced BDNF levels (β = -0.47, 95% CI: –0.68 to –0.13, p = 0.013), compared to the lowest tertile. DISCUSSION: Prolonged REM latency may serve as a novel marker or risk factor for AD/ADRD pathogenesis. Highlights: Rapid eye movement latency (REML) may be a potential marker for Alzheimer’s disease and Alzheimer’s disease and related dementias (AD/ADRD) pathogenesis. Prolonged REML was associated with higher amyloid beta (Aβ) burden, phosphorylated tau-181 (p-tau181), and lower brain-derived neurotrophic factor (BDNF) levels. Intervention trial is needed to determine if targeting REML can modify AD/ADRD risk. Slow-wave sleep was not associated with AD/ADRD biomarkers. © 2025 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.
Author Keywords
Alzheimer’s disease; amyloid beta; biomarker; polysomnography; rapid eye movement latency; sleep
Funding details
Eisai
National Institutes of HealthNIH
Ministry of Science and Technology of the People’s Republic of ChinaMOST2021ZD0201902
Ministry of Science and Technology of the People’s Republic of ChinaMOST
2020ZD10
Document Type: Article
Publication Stage: Final
Source: Scopus
Clinical and neuropathological associations of plasma Aβ42/Aβ40, p-tau217 and neurofilament light in sporadic frontotemporal dementia spectrum disorders
(2025) Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring, 17 (1), art. no. e70078, .
Rajbanshi, B.a , Prufer Q C Araujo, I.a , VandeVrede, L.a , Ljubenkov, P.A.a , Staffaroni, A.M.a , Heuer, H.W.a , Lario Lago, A.a , Ramos, E.M.b , Petrucelli, L.c , Gendron, T.c , Dage, J.L.d , Seeley, W.W.a , Grinberg, L.T.a , Spina, S.a , Bateman, R.J.e , Rosen, H.J.a , Boeve, B.F.f , Boxer, A.L.a , Rojas, J.C.a , for the ALLFTD Consortiumg
a Weill Institute for Neurosciences, Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States
b Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, United States
c Department of Neuroscience, Mayo Clinic, Jacksonville, FL, United States
d Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, United States
e Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
f Department of Neurology, Mayo Clinic, Rochester, MN, United States
Abstract
INTRODUCTION: Plasma amyloid beta42/amyloid beta40 (Aβ42/Aβ40) and phosphorylated tau217 (p-tau217) identify individuals with primary Alzheimer’s disease (AD). They may detect AD co-pathology in the setting of other primary neurodegenerative diseases, but this has not been systematically studied. METHODS: We compared the clinical, neuroimaging, and neuropathological associations of plasma Aβ42/Aβ40 (mass spectrometry), p-tau217 (electrochemiluminescence), and neurofilament light ([NfL], single molecule array [Simoa]), as markers of AD co-pathology, in a sporadic frontotemporal dementia (FTD) cohort (n = 620). RESULTS: Aβ42/Aβ40 showed no clinicopathological associations. High p-tau217 was present in amnestic dementia (AmD) presumed to be due to FTD, logopenic primary progressive aphasia (lvPPA), and APOEε4 carriers, and correlated with worse baseline and longitudinal clinical scores, lower hippocampal volumes, and more severe AD co-pathology (Braak Stage). NfL was elevated in all FTD phenotypes, and correlated with clinical scores and frontotemporal brain volumes. DISCUSSION: Plasma p-tau217 has clinical, neuroimaging, and neuropathological correlates in sporadic FTD and may identify FTD cases with AD co-pathology. Highlights: Alzheimer’s disease (AD) features could be identified with plasma phosphorylated tau217 (p-tau217) in frontotemporal lobar degeneration (FTLD). Plasma p-tau217 is a better discriminator of AD co-pathology and AD-associated features in FTLD than plasma amyloid beta42/amyloid beta40 (Aβ42/Aβ40) and neurofilament light (NfL). In FTLD, plasma p-tau217, but not Aβ42/Aβ40 or neurofilament light, has phenotypical, neurocognitive, and neuroimaging correlates suggestive of AD co-pathology. © 2025 The Author(s). Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer’s Association.
Author Keywords
Alzheimer’s disease; fluid biomarkers; frontotemporal dementia; plasma amyloid; plasma neurofilament; plasma tau
Funding details
Alzheimer’s AssociationAA
John Douglas French Alzheimer’s FoundationJDFAF
National Institutes of HealthNIH
Larry L. Hillblom FoundationLLHF
National Institute on AgingNIA
AlzOut
U24 AG021886
National Center for Advancing Translational SciencesNCATSU54 NS092089, U01 AG045390
National Center for Advancing Translational SciencesNCATS
Rainwater Charitable FoundationRCFK23AG059888
Rainwater Charitable FoundationRCF
National Institute of Neurological Disorders and StrokeNINDSAG063911
National Institute of Neurological Disorders and StrokeNINDS
Document Type: Article
Publication Stage: Final
Source: Scopus
SARM1 Inhibition Maintains Axonal Integrity After Rat Sciatic Nerve Transection and Repair
(2025) Journal of Hand Surgery, .
Sachar, R.a , Lee, T.Y.a , DiAntonio, A.b , Dy, C.J.a , Wever, J.a , Milbrandt, J.b , Brogan, D.M.a
a Department of Orthopedic Surgery, Washington University in St. Louis, St. Louis, MO, United States
b Department of Genetics, Washington University in St. Louis, St. Louis, MO, United States
Abstract
Purpose: Sterile alpha and TlR motif containing-1 (SARM1) protein has been demonstrated to play a critical role in the initiation of Wallerian degeneration after nerve injury. The goal of this study was to assess whether blockade of SARM1 activity inhibits Wallerian degeneration following nerve transection, potentially promoting more rapid recovery of axonal function. Methods: An adeno-associated virus plasmid encoded with a dominant-negative SARM1 protein fused with green fluorescent protein to impair SARM1 function, was injected into 24 juvenile rats to create a SARM1 dominant-negative (SARM1-DN) phenotype. Twenty-four control rats were injected with a control plasmid expressing only green fluorescent protein. Three weeks after transfection, the rats underwent unilateral sciatic nerve transection and repair. Walking track analysis and nonsurvival surgeries were performed at 2 days, 2 weeks, or 6 weeks to assess muscle strength and compound nerve action potential. Histomorphologic and electrodiagnostic studies were evaluated with mixed-effect analysis. Results: Histomorphologic analysis showed maintenance of axons in the SARM1-DN animals at 2 weeks, with significantly improved compound nerve action potential amplitude. Muscle testing demonstrated greater gastrocnemius strength in SARM1 DN muscles at 2 days and 2 weeks compared to controls, although this was not maintained at 6 weeks. Conclusion: Inhibition of SARM1 resulted in early increases in number and myelination of axons and action potential after sciatic nerve transection and repair in SARM1-DN rats. Clinical relevance: SARM1 inhibition may offer the potential to delay Wallerian degeneration following nerve transection and enable earlier functional recovery of motor strength. © 2024 American Society for Surgery of the Hand
Author Keywords
Gene therapy; peripheral nerve; regeneration; SARM1; Wallerian degeneration
Funding details
American Society for Surgery of the HandASSH
American College of SurgeonsACS
Orthopaedic Research and Education FoundationOREF
National Institute of Arthritis and Musculoskeletal and Skin DiseasesNIAMSK08AR080260-01
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Using Artificial Intelligence to Identify Three Presenting Phenotypes of Chiari Type-1 Malformation and Syringomyelia
(2024) Neurosurgery, .
Gupta, V.P.a , Xu, Z.b c , Greenberg, J.K.a c , Strahle, J.M.a , Haller, G.a c , Meehan, T.a , Roberts, A.a , Limbrick, D.D.d , Lu, C.b c
a Department of Neurosurgery, Washington University, School of Medicine in St. Louis, St. Louis, MO, United States
b Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, United States
c AI for Health Institute, Washington University in St. Louis, St. Louis, MO, United States
d Department of Neurosurgery, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
Abstract
BACKGROUND AND OBJECTIVES:Chiari type-1 malformation (CM1) and syringomyelia (SM) are common related pediatric neurosurgical conditions with heterogeneous clinical and radiological presentations that offer challenges related to diagnosis and management. Artificial intelligence (AI) techniques have been used in other fields of medicine to identify different phenotypic clusters that guide clinical care. In this study, we use a novel, combined data-driven and clinician input feature selection process and AI clustering to differentiate presenting phenotypes of CM1 + SM.METHODS:A total of 1340 patients with CM1 + SM in the Park Reeves Syringomyelia Research Consortium registry were split a priori into internal and external cohorts by site of enrollment. The internal cohort was used for feature selection and clustering. Features with high Laplacian scores were identified from preselected groups of clinically relevant variables. An expert clinician survey further identified features for inclusion that were not selected by the data-driven process.RESULTS:The feature selection process identified 33 features (28 from the data-driven process and 5 from the clinician survey) from an initial pool of 582 variables that were incorporated into the final model. A K-modes clustering algorithm was used to identify an optimum of 3 clusters in the internal cohort. An identical process was performed independently in the external cohort with similar results. Cluster 1 was defined by older CM1 diagnosis age, small syringes, lower tonsil position, more headaches, and fewer other comorbidities. Cluster 2 was defined by younger CM1 diagnosis age, more bulbar symptoms and hydrocephalus, small syringes, more congenital medical issues, and more previous neurosurgical procedures. Cluster 3 was defined by largest syringes, highest prevalence of spine deformity, fewer headaches, less tonsillar ectopia, and more motor deficits.CONCLUSION:This is the first study that uses an AI clustering algorithm combining a data-driven feature selection process with clinical expertise to identify different presenting phenotypes of CM1 + SM. © Congress of Neurological Surgeons 2024. All rights reserved.
Author Keywords
Artificial intelligence; Chiari malformation; Clustering; Syringomyelia
Funding details
Neurosurgery Research and Education FoundationNREF
National Institute of Neurological Disorders and StrokeNINDSP01NS131131-01 5758
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Case Report: Targeted treatment by fluoxetine/norfluoxetine of a KCNC2 variant causing developmental and epileptic encephalopathy
(2024) Frontiers in Pharmacology, 15, art. no. 1528541, .
Li, P.a , Butler, A.a , Zhou, Y.b , Magleby, K.L.c , Gurnett, C.A.d , Salkoff, L.a
a Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
b Department of Anesthesiology, Washington University in St. Louis, St. Louis, MO, United States
c Department of Physiology and Biophysics, University of Miami Miller School of Medicine, Miami, FL, United States
d Department of Neurology, Division of Pediatric and Developmental Neurology at Washington University in St. Louis, St. Louis, MO, United States
Abstract
The Kv3.2 subfamily of voltage activated potassium channels encoded by the KCNC2 gene is abundantly expressed in neurons that fire trains of fast action potentials that are a major source of cortical inhibition. Gain-of-function (GOF) de novo pathogenic variants in KCNC1 and KCNC2, encoding Kv3.1 and Kv3.2 respectively, cause several types of epilepsy including developmental and epileptic encephalopathy (DEE). Fluoxetine (Prozac) is a known inhibitor of the Kv3.1 current and was reported to improve seizure control in a single patient with a KCNC1 GOF variant. Here, we describe fluoxetine treatment of two siblings with a de novo KCNC2 V473A variant associated with DEE, which resulted in improved seizure control, ability to wean antiepileptic medications, and improved development. The KCNC2 V437A variant showed GOF activity as demonstrated by HEK293 cells expressing variant subunits activating at more hyperpolarized potentials than WT channels. Fluoxetine reduced currents equally for both Kv3.2 WT and Kv3.2-V473A variant channels, with an IC50 of ∼12 µM. Further analysis of this repurposed drug showed that norfluoxetine, a long-lasting metabolite of fluoxetine which is produced in the liver and accumulates in the brain, was more effective than fluoxetine itself in selectively inhibiting the dominant pathogenic channel activity of the pathogenic allele. Norfluoxetine showed 7-fold greater selectivity in inhibiting Kv3.2 variant currents (IC50 of ∼0.4 µM) compared to WT currents (IC50 of ∼2.9 µM). Combined with a previous report of improved outcomes for a KCNC1 variant, our results suggest that fluoxetine or its metabolite, norfluoxetine, may be beneficial for patients with GOF variants in KCNC2 and other neuronal potassium channels. Copyright © 2025 Li, Butler, Zhou, Magleby, Gurnett and Salkoff.
Author Keywords
autism; epilepsy; fluoxetine; KCNC2 electrophysiology; Kv3.2; norfluoxetine 4-fluoro-7-nitro-2,1,3-benzoxadiazole; potassium channel; Prozac
Funding details
University of WashingtonUW
Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNICHD
National Institutes of HealthNIHP50HD103525
National Institutes of HealthNIH
Institute of Clinical and Translational SciencesICTSUL1TR002345
Institute of Clinical and Translational SciencesICTS
Document Type: Article
Publication Stage: Final
Source: Scopus