WashU weekly Neuroscience publications: January 12, 2024

Functional performance of patients with stroke during inpatient rehabilitation: a cross-sectional study of home and access visits
(2025) BMC Health Services Research, 25 (1), art. no. 34, . 

Krauss, M.J.^a , Somerville, E.^a , Poiter, C.^a , Bollinger, R.M.^a , Holden, B.M.^a , Blenden, G.^a , Kretzer, D.^b , Stark, S.L.^a

^a Program in Occupational Therapy, Washington University in St. Louis School of Medicine, Box 8505, 4444 Forest Park Ave, St. Louis, MO 63110, United States
^b The Rehabilitation Institute of St. Louis, St. Louis, MO, United States

Abstract
Background: Home visits prior to inpatient rehabilitation facility (IRF) discharge allow occupational therapists to observe functional abilities among patients with stroke and address barriers that impact daily activities at home. However, home visits prior to IRF discharge are not standard practice due to barriers of time and cost constraints. We explored whether an access visit (visiting the home without the patient) could serve as an alternative to a home visit (with the patient) to anticipate functional abilities at home. Methods: We used baseline data from a randomized controlled trial that occurred before and during the COVID-19 pandemic, which caused predischarge home visits to be modified to access visits without the participant. Participants had suffered a stroke and were treated in an IRF, aged ≥ 50, with plans to discharge home. International Classification of Functioning, Disability, and Health (ICF) qualifier scores were compared between participants’ home/access visits and IRF discharge. ICF scores were compared between predischarge home visits and IRF discharge and between access visits and IRF discharge using Wilcoxon signed-rank tests. Differences in ICF scores between home/access and IRF discharge were compared between home and access visits using linear regression models. Results: Among 99 participants (58% men, average 67 years old, 60% Black), 57 received a home visit and 42 received an access visit. Both groups had significantly worse ICF scores at the home/access visit compared to IRF discharge for most activities. Differences in scores between home visit and IRF were significantly greater than between access and IRF for bathing, upper and lower body dressing, bed/chair transfer, walking, and navigating stairs. The largest differences between home and access visits were for walking (β = 1.05 95% CI 0.46 to 1.64) and going up and down stairs (β = 0.87 95% CI 0.25 to 1.49). Conclusions: Participants with stroke had greater difficulty performing daily activities in both home and access visits than at the IRF, but observed differences were greater for home visits than access visits. While access visits may be beneficial to anticipate functional abilities in the home when home visits cannot occur, visiting the home to directly observe patients’ performance is ideal. Trial registration: Registered on 3/26/2018 at clinicaltrials.gov, NCT03485820. © The Author(s) 2025.

Author Keywords
Activities of daily living;  Home visits;  Occupational therapy;  Stroke

Document Type: Article
Publication Stage: Final
Source: Scopus

Integrative multiomics reveals common endotypes across PSEN1, PSEN2, and APP mutations in familial Alzheimer’s disease
(2025) Alzheimer’s Research and Therapy, 17 (1), art. no. 5, . 

Valdes, P.^a b , Caldwell, A.B.^a , Liu, Q.^c i , Fitzgerald, M.Q.^a b , Ramachandran, S.^a , Karch, C.M.^e , Xu, X.^k , Xu, J.^k , Xiong, C.^k , Weamer, E.^k , Wang, Q.^k , Wang, P.^k , Vöglein, J.^k , Thompson, S.^k , Taddei, K.^k , Stephens, S.^k , Sohrabi, H.^k , Snitz, B.^k , Smith, L.^k , Smith, J.^k , Sigurdson, W.^k , Shimada, H.^k , Shady, K.^k , Seyfried, N.T.^k , Senda, M.^k , Schofield, P.^k , Salloway, S.^k , Ringman, J.^k , Renton, A.^k , Preische, O.^k , Ping, L.^k , Perrin, R.^k , Patira, R.^k , O’Connor, A.^k , Obermüller, U.^k , Nuscher, B.^k , Norton, J.^k , Noble, J.^k , Niimi, Y.^k , Neimeyer, K.^k , Nagamatsu, A.^k , Nadkarni, N.^k , Mummery, C.^k , Mountz, J.^k , Morris, J.^k , Morenas-Rodriguez, E.^k , Mejia, A.^k , McDade, E.^k , McCullough, A.^k , Mawuenyega, K.^k , Masters, C.^k , Mason, N.S.^k , Martins, R.^k , Marsh, J.^k , Lopez, O.^k , Li, Y.^k , Levin, J.^k , Levey, A.^k , Laske, C.^k , Kuder-Buletta, E.^k , Koudelis, D.^k , Koeppe, R.^k , Klunk, W.^k , Keefe, S.^k , Kasuga, K.^k , Käser, S.^k , Jucker, M.^k , Johnson, E.^k , Jerome, G.^k , Jack, C.^k , Ishii, K.^k , Ikonomovic, S.^k , Ikeuchi, T.^k , Ihara, R.^k , Igor, Y.^k , Hornbeck, R.^k , Holtzman, D.^k , Hofmann, A.^k , Hoechst-Swisher, L.^k , Herries, E.^k , Hellm, C.^k , Hassenstab, J.^k , Häsler, L.^k , Haass, C.^k , Groves, A.^k , Grilo, M.^k , Gremminger, E.^k , Gray, J.^k , Graham, M.^k , Graff-Radford, N.^k , Gräber-Sultan, S.^k , Gordon, B.^k , Gonzalez, A.^k , Goldman, J.^k , Goldberg, S.^k , Goate, A.^k , Ghetti, B.^k , Gardener, S.^k , Fujii, H.^k , Joseph-Mathurin, N.^k , Franklin, E.^k , Fox, N.^k , Flores, S.^k , Fitzpatrick, C.^k , Feldman, B.^k , Farlow, M.^k , Fagan, A.^k , Esposito, B.^k , Egido, N.^k , Duong, D.^k , Douglas, J.^k , Donahue, T.^k , Dincer, A.^k , Diffenbacher, A.^k , Denner, D.^k , DeLaCruz, C.^k , Day, G.S.^k , Cruchaga, C.^k , Courtney, L.^k , Chui, H.^k , Chua, J.^k , Mendez, P.C.^k , Chhatwal, J.^k , Chen, C.^k , Cash, L.^k , Carter, K.^k , Buckles, V.^k , Buck, J.^k , Brosch, J.^k , Brooks, W.B.^k , Brandon, S.^k , Bodge, C.^k , Berman, S.^k , Benzinger, T.^k , Bechara, J.^k , Bateman, R.^k , Barthelemy, N.^k , Araki, A.^k , Allegri, R.^k , Adams, S.^k , Galasko, D.R.^c , Yuan, S.H.^c j , Wagner, S.L.^c d , Subramaniam, S.^a f g h , Dominantly Inherited Alzheimer Network (DIAN)^k

^a Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, United States
^b Bioengineering Graduate Program, University of California, San Diego, La Jolla, CA 92093, United States
^c Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, United States
^d VA San Diego Healthcare System, San Diego, CA 92161, United States
^e Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, MO 63110, United States
^f Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, United States
^g Department of Nanoengineering, University of California, San Diego, La Jolla, CA 92093, United States
^h Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, United States
ⁱ Present Address: Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA 92093, United States
^j Present Address: N. Bud Grossman Center for Memory Research and Care, Department of Neurology, University of Minnesota, GRECC, Minneapolis VA Health Care System, Minneapolis, MN 55417, United States

Abstract
Background: PSEN1, PSEN2, and APP mutations cause Alzheimer’s disease (AD) with an early age at onset (AAO) and progressive cognitive decline. PSEN1 mutations are more common and generally have an earlier AAO; however, certain PSEN1 mutations cause a later AAO, similar to those observed in PSEN2 and APP. Methods: We examined whether common disease endotypes exist across these mutations with a later AAO (~ 55 years) using hiPSC-derived neurons from familial Alzheimer’s disease (FAD) patients harboring mutations in PSEN1A79V, PSEN2N141I, and APPV717I and mechanistically characterized by integrating RNA-seq and ATAC-seq. Results: We identified common disease endotypes, such as dedifferentiation, dysregulation of synaptic signaling, repression of mitochondrial function and metabolism, and inflammation. We ascertained the master transcriptional regulators associated with these endotypes, including REST, ASCL1, and ZIC family members (activation), and NRF1 (repression). Conclusions: FAD mutations share common regulatory changes within endotypes with varying severity, resulting in reversion to a less-differentiated state. The regulatory mechanisms described offer potential targets for therapeutic interventions. © The Author(s) 2025.

Document Type: Article
Publication Stage: Final
Source: Scopus

Immunologic investigations into transgene directed immune-mediated myositis following delandistrogene moxeparvovec gene therapy
(2025) Scientific Reports, 15 (1), art. no. 4, . 

Potter, R.A.^a , Moeller, I.H.^a , Khan, S.^a , Haegel, H.^b , Hollenstein, A.^b , Steiner, G.^b , Wandel, C.^b , Murphy, A.P.^c , Asher, D.R.^a , Palatinsky, E.^a , Griffin, D.A.^a , Mason, S.^a , Iannaccone, S.T.^d , Zaidman, C.M.^e , Rodino-Klapac, L.R.^a

^a Sarepta Therapeutics, Inc., Cambridge, MA, United States
^b F. Hoffmann-La Roche Ltd, Basel, Switzerland
^c Roche Products Ltd, Welwyn Garden, United Kingdom
^d Departments of Pediatrics and Neurology, University of Texas Southwestern Medical Center and Children’s Health, Dallas, TX, United States
^e Department of Neurology, Washington University in St. Louis, St. Louis, MO, United States

Abstract
Delandistrogene moxeparvovec is an rAAVrh74 vector-based gene transfer therapy that delivers a transgene encoding delandistrogene moxeparvovec micro-dystrophin, an engineered, functional form of dystrophin shown to stabilize or slow disease progression in DMD. It is approved in the US and in other select countries. Two serious adverse event cases of immune-mediated myositis (IMM) were reported in the phase Ib ENDEAVOR trial (NCT04626674). We hypothesized that immune responses to the micro-dystrophin transgene product may have mediated these IMM events. An interferon-gamma ELISpot assay was used to detect T cell responses to delandistrogene moxeparvovec micro-dystrophin peptide pools. ELISpot analysis suggested that IMM resulted from T cell-mediated responses directed against specific micro-dystrophin peptides corresponding to exons 8 and 9 (Case 1) and exon 8 (Case 2) of the DMD gene. In silico epitope mapping based on the patients’ HLA-I alleles indicated greater probability for peptides derived from exons 8 and/or 9 to bind HLA-I, providing further evidence that peptides derived from corresponding micro-dystrophin regions may have higher immunogenic potential. Collectively, these data suggest that patients with DMD gene deletions involving exons 8 and/or 9 may be at increased risk of IMM following delandistrogene moxeparvovec micro-dystrophin gene therapy infusion. © The Author(s) 2024.

Author Keywords
AAV vector;  Delandistrogene moxeparvovec;  Duchenne muscular dystrophy;  Dystrophin;  Gene transfer therapy;  Immune-mediated myositis

Document Type: Article
Publication Stage: Final
Source: Scopus

Precocious Puberty in Children with Neurofibromatosis Type 1
(2025) Journal of Pediatrics, 278, art. no. 114440, . 

Katz, J.^a , Ratnam, S.^b , Listernick, R.H.^c , Habiby, R.L.^b , Gutmann, D.H.^d

^a Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States
^b Division of Endocrinology, Ann & Robert H. Lurie Children’s Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
^c Division of Academic General Pediatrics and Primary Care, Ann & Robert H. Lurie Children’s Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
^d Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States

Abstract
This multi-institutional, descriptive study of 19 children with neurofibromatosis 1 examines the link between optic pathway gliomas (OPGs) and central precocious puberty (CPP). We report that CPP can arise without OPG chiasmal involvement and that prior OPG chemotherapy does not prevent the development of CPP. © 2024 Elsevier Inc.

Author Keywords
neurofibromatosis;  NF1;  optic pathway glioma;  precocious puberty

Document Type: Article
Publication Stage: Final
Source: Scopus

Fate erasure logic of gene networks underlying direct neuronal conversion of somatic cells by microRNAs
(2025) Cell Reports, 44 (1), art. no. 115153, . 

Cates, K.^a c d , Yuan, L.^a c , Yang, Y.^a , Yoo, A.S.^a b

^a Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, United States
^b Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63110, United States
^c Program in Molecular Genetics and Genomics, Washington University School of Medicine, St. Louis, MO 63110, United States
^d Department of Genetics, Stanford University, Stanford, CA 94305, United States

Abstract
Neurogenic microRNAs 9/9∗ and 124 (miR-9/9∗-124) drive the direct reprogramming of human fibroblasts into neurons with the initiation of the fate erasure of fibroblasts. However, whether the miR-9/9∗-124 fate erasure logic extends to the neuronal conversion of other somatic cell types remains unknown. Here, we uncover that miR-9/9∗-124 induces neuronal conversion of multiple cell types: dura fibroblasts, astrocytes, smooth muscle cells, and pericytes. We reveal the cell-type-specific and pan-somatic gene network erasure induced by miR-9/9∗-124, including cell cycle, morphology, and proteostasis gene networks. Leveraging these pan-somatic gene networks, we predict upstream regulators that may antagonize somatic fate erasure. Among the predicted regulators, we identify TP53 (p53), whose inhibition is sufficient to enhance neuronal conversion even in post-mitotic cells. This study extends miR-9/9∗-124 reprogramming to alternate somatic cells, reveals the pan-somatic gene network fate erasure logic of miR-9/9∗-124, and shows a neurogenic role for p53 inhibition in the miR-9/9∗-124 signaling cascade. © 2024 The Author(s)

Author Keywords
cell fate;  CP: Molecular biology;  CP: Stem cell research;  direct conversion;  fate erasure;  microRNA;  neuronal reprogramming;  transcriptomics

Document Type: Article
Publication Stage: Final
Source: Scopus

Infiltrating plasma cells maintain glioblastoma stem cells through IgG-Tumor binding
(2025) Cancer Cell, 43 (1), pp. 122-143.e8. Cited 1 time.

Gao, J.^a b c , Gu, D.^a c d , Yang, K.^e , Zhang, J.^b c , Lin, Q.^a c , Yuan, W.^f , Zhu, X.^g , Dixit, D.^h , Gimple, R.C.ⁱ , You, H.^a c , Zhang, Q.^a c , Shi, Z.^b , Fan, X.^b c , Wu, Q.^h , Lu, C.^a b c , Cheng, Z.^b c , Li, D.^a c , Zhao, L.^h , Xue, B.^a , Zhu, Z.^j , Zhu, Z.^k , Yang, H.^l , Zhao, N.^m , Gao, W.^a c , Lu, Y.^a , Shao, J.^d , Cheng, C.ⁿ , Hao, D.^o , Yang, S.^c , Chen, Y.^c , Wang, X.^c , Kang, C.^p , Ji, J.^b c , Man, J.^q , Agnihotri, S.^r , Wang, Q.^c , Lin, F.^a , Qian, X.^c , Mack, S.C.^s , Hu, Z.^c , Li, C.^c , Taylor, M.D.^t , Li, Y.^g , Zhang, N.^u , Rich, J.N.^h , You, Y.^b c , Wang, X.^{a b c d v}

^a National Health Commission Key Laboratory of Antibody Techniques, Department of Cell Biology, Jiangsu Provincial Key Laboratory of Human Functional Genomics, School of Basic Medical Sciences, Nanjing Medical University, Jiangsu, Nanjing, 211166, China
^b Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Jiangsu, Nanjing, 210029, China
^c Institute for Brain Tumors, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Jiangsu, Nanjing, 210029, China
^d The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Jiangsu, Wuxi, 214000, China
^e Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH 44195, United States
^f Department of Pathology, The Yancheng Clinical College of Xuzhou Medical University, The First People’s Hospital of Yancheng, Jiangsu, Yancheng, 224005, China
^g National Resource Center for Mutant Mice and MOE Key Laboratory of Model Animal for Disease Study, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Model Animal Research Center, Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210061, China
^h Department of Neurology, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15213, United States
ⁱ Department of Medicine, Washington University School of Medicine, Washington University in St Louis, St. Louis, MO 63110, United States
^j University of Science and Technology of China, Anhui, Hefei, 230026, China
^k Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY 10032, United States
^l Department of Neurosurgery, Huashan Hospital, Shanghai Key laboratory of Brain Function Restoration and Neural Regeneration, Shanghai Clinical Medical Center of Neurosurgery, Fudan University, Shanghai, 200032, China
^m China Exposomics Institute, 781 Cai Lun Road, Shanghai, 200120, China
ⁿ Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui, Hefei, 230001, China
^o Department of Pathology, NHC Key Laboratory of Etiology and Epidemiology, Harbin Medical University, Harbin, 150081, China
^p Laboratory of Neuro-oncology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China
^q State Key Laboratory of Proteomics, National Center of Biomedical analysis, Beijing, 100850, China
^r Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, United States
^s Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, United States
^t Department of Pediatrics – Hematology/Oncology and Neurosurgery, Baylor College of Medicine, Houston, TX 77004, United States
^u Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangdong Provincial Key Laboratory of Brain Function and Disease, Guangdong Translational Medicine Innovation Platform, Guangdong, Guangzhou, 510080, China
^v Jiangsu Cancer Hospital, Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu, Nanjing, 210009, China

Abstract
Glioblastoma is a highly aggressive primary brain tumor with glioblastoma stem cells (GSCs) enforcing the intra-tumoral hierarchy. Plasma cells (PCs) are critical effectors of the B-lineage immune system, but their roles in glioblastoma remain largely unexplored. Here, we leverage single-cell RNA and B cell receptor sequencing of tumor-infiltrating B-lineage cells and reveal that PCs are aberrantly enriched in the glioblastoma-infiltrating B-lineage population, experience low level of somatic hypermutation, and are associated with poor prognosis. PCs secrete immunoglobulin G (IgG), which stimulates GSC proliferation via the IgG-FcγRIIA-AKT-mTOR axis. Disruption of IgG-FcγRIIA paracrine communication inhibits GSC proliferation and self-renewal. Glioblastoma-infiltrating PCs are recruited to GSC niches via CCL2-CCR2 chemokine program. GSCs further derive pro-proliferative signals from broadly utilized monoclonal antibody-based immune checkpoint inhibitors via FcγRIIA signaling. Our data generate an atlas of B-lineage cells in glioblastoma with a framework for combinatorial targeting of both tumor cell-intrinsic and microenvironmental dependencies. © 2024 Elsevier Inc.

Author Keywords
FcγRIIA;  glioblastoma;  glioblastoma stem cell;  plasma cell;  single-cell B cell receptor-seq;  single-cell RNA-seq

Document Type: Article
Publication Stage: Final
Source: Scopus

Aperiodic (1/f) Neural Activity Robustly Tracks Symptom Severity Changes in Treatment-Resistant Depression
(2025) Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, . 

Hacker, C.^a b , Mocchi, M.M.^a , Xiao, J.^a , Metzger, B.^a , Adkinson, J.^a , Pascuzzi, B.^a , Mathura, R.^a , Oswalt, D.^c , Watrous, A.^a , Bartoli, E.^a , Allawala, A.^d , Pirtle, V.^a , Fan, X.^a , Danstrom, I.^a , Shofty, B.^a , Banks, G.^a , Zhang, Y.^a , Armenta-Salas, M.^e , Mirpour, K.^e , Provenza, N.^a , Mathew, S.^f , Cohn, J.F.^g , Borton, D.^d h , Goodman, W.^f , Pouratian, N.^e , Sheth, S.A.^a , Bijanki, K.R.^a

^a Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, United States
^b Department of Neurosurgery, Washington University in St. Louis, St. Louis, Missouri, United States
^c Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, United States
^d Department of Biomedical Engineering, Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States
^e Department of Neurosurgery, University of Texas Southwestern, Dallas, Texas, United States
^f Department of Psychiatry, Baylor College of Medicine, Houston, Texas, United States
^g Department of Psychology, University of Pittsburgh, Pittsburgh, PA, United States
^h Department of Veterans Affairs, Center for Neurorestoration and Neurotechnology, Brown University, Providence, Rhode Island, United States

Abstract
Background: A reliable physiological biomarker for major depressive disorder is essential for developing and optimizing neuromodulatory treatment paradigms. In this study, we investigated a passive electrophysiologic biomarker that tracks changes in depressive symptom severity on the order of minutes to hours. Methods: We analyzed brief recordings from intracranial electrodes implanted deep in the brain during a clinical trial of deep brain stimulation for treatment-resistant depression in 5 human participants (nfemale = 3, nmale = 2). This surgical setting allowed for precise temporal and spatial sensitivity in the ventromedial prefrontal cortex, a challenging area to measure. We focused on the aperiodic slope of the power spectral density, a metric that reflects the balance of activity across all frequency bands and may serve as a proxy for excitatory/inhibitory balance in the brain. Results: Our findings demonstrated that shifts in aperiodic slope correlated with depression severity, with flatter (less negative) slopes indicating reduced depression severity. This significant correlation was observed in all 5 participants, particularly in the ventromedial prefrontal cortex. Conclusions: This biomarker offers a new way to track patient responses to major depressive disorder treatment, thus paving the way for individualized therapies in both intracranial and noninvasive monitoring contexts. © 2024 Society of Biological Psychiatry

Author Keywords
Biomarker;  DBS;  Electrophysiology;  Intracranial;  MDD;  Slope

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Spatial proteomic differences in chronic traumatic encephalopathy, Alzheimer’s disease, and primary age-related tauopathy hippocampi
(2025) Alzheimer’s and Dementia, . 

Richardson, T.E.^a , Orr, M.E.^b c , Orr, T.C.^b , Rohde, S.K.^{a d e f g} , Ehrenberg, A.J.^h i j , Thorn, E.L.^a k , Christie, T.D.^a k , Flores-Almazan, V.^a k , Afzal, R.^a k , De Sanctis, C.^a k , Maldonado-Díaz, C.^a , Hiya, S.^a , Canbeldek, L.^a , Kulumani Mahadevan, L.S.^a , Slocum, C.^a , Samanamud, J.^a , Clare, K.^a , Scibetta, N.^a , Yokoda, R.T.^l , Koenigsberg, D.^{a k m n o p} , Marx, G.A.^{a k m n o p q} , Kauffman, J.^{a k m n o p} , Goldstein, A.^a k , Selmanovic, E.^m p , Drummond, E.^r , Wisniewski, T.^s t u , White, C.L., III^v , Goate, A.M.^m o w , Crary, J.F.^{a k m n o p} , Farrell, K.^{a k m n o p} , Alosco, M.L.^x y , Mez, J.^x y , McKee, A.C.^x y z aa , Stein, T.D.^x y z aa , Bieniek, K.F.^ab ac , Kautz, T.F.^ac , Daoud, E.V.^v , Walker, J.M.^a k m ac

^a Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^b Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^c St. Louis VA Medical Center, St. Louis, MO, United States
^d Department of Pathology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^e Department of Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^f Department of Human Genetics, Genomics of Neurodegenerative Diseases and Aging, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^g Department of Neurology, Alzheimer Center Amsterdam, Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^h Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, United States
ⁱ Helen Wills Neuroscience Institute, University of California, Berkeley, CA, United States
^j Innovative Genomics Institute, University of California, Berkeley, CA, United States
^k Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^l Department of Pathology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY, United States
^m Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
ⁿ Department of Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^o Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^p Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^q Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^r Brain & Mind Center and School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
^s Department of Pathology, New York University Grossman School of Medicine, New York, NY, United States
^t Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
^u Center for Cognitive Neurology, Department of Neurology, New York University Grossman School of Medicine, New York, NY, United States
^v Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, United States
^w Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^x Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
^y Boston University Alzheimer’s Disease Research Center and BU CTE Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
^z VA Boston Healthcare System, Boston, MA, United States
^aa VA Bedford Healthcare System, Bedford, MA, United States
^ab Department of Pathology & Laboratory Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
^ac Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States

Abstract
INTRODUCTION: Alzheimer’s disease (AD), primary age-related tauopathy (PART), and chronic traumatic encephalopathy (CTE) all feature hyperphosphorylated tau (p-tau)–immunoreactive neurofibrillary degeneration, but differ in neuroanatomical distribution and progression of neurofibrillary degeneration and amyloid beta (Aβ) deposition. METHODS: We used Nanostring GeoMx Digital Spatial Profiling to compare the expression of 70 proteins in neurofibrillary tangle (NFT)-bearing and non–NFT-bearing neurons in hippocampal CA1, CA2, and CA4 subregions and entorhinal cortex of cases with autopsy-confirmed AD (n = 8), PART (n = 7), and CTE (n = 5). RESULTS: There were numerous subregion-specific differences related to Aβ processing, autophagy/proteostasis, inflammation, gliosis, oxidative stress, neuronal/synaptic integrity, and p-tau epitopes among these different disorders. DISCUSSION: These results suggest that there are subregion-specific proteomic differences among the neurons of these disorders, which appear to be influenced to a large degree by the presence of hippocampal Aβ. These proteomic differences may play a role in the differing hippocampal p-tau distribution and pathogenesis of these disorders. Highlights: Alzheimer’s disease neuropathologic change (ADNC), possible primary age-related tauopathy (PART), definite PART, and chronic traumatic encephalopathy (CTE) can be differentiated based on the proteomic composition of their neurofibrillary tangle (NFT)- and non–NFT-bearing neurons. The proteome of these NFT- and non–NFT-bearing neurons is largely correlated with the presence or absence of amyloid beta (Aβ). Neurons in CTE and definite PART (Aβ-independent pathologies) share numerous proteomic similarities that distinguish them from ADNC and possible PART (Aβ-positive pathologies). © 2024 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.

Author Keywords
aging;  Alzheimer’s disease neuropathologic change;  autophagy;  chronic traumatic encephalopathy;  cognitive reserve;  neurodegeneration;  oxidative stress;  primary age-related tauopathy;  resilience;  resistance;  synapse loss;  synapses;  tauopathy

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

Polarization-driven dynamic laser speckle analysis for brain neoplasms differentiation
(2024) Light: Advanced Manufacturing, 5 (4), pp. 509-522. 

Abbasian, V.^a b , Rad, V.F.^c d , Shamshiripour, P.^d e , Ahmadvand, D.^d , Darafsheh, A.^a

^a Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
^b Imaging Science Program, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States
^c Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran
^d Department of Molecular Imaging, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
^e Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran

Abstract
Early diagnosis of brain tumors is often hindered by non-specific symptoms, particularly in eloquent brain regions where open surgery for tissue sampling is unfeasible. This limitation increases the risk of misdiagnosis due to tumor heterogeneity in stereotactic biopsies. Label-free diagnostic methods, including intraoperative probes and cellular origin analysis techniques, hold promise for improving diagnostic accuracy. Polarimetry offers valuable information on the polarization properties of biomedical samples, yet it may not fully reveal specific structural characteristics. The interpretative scope of polarimetric data is sometimes constrained by the limitations of existing decomposition methods. On the other hand, dynamic laser speckle analysis (DLSA), a burgeoning technique, not only does not account for the polarimetric attributes but also is known for tracking only the temporal activity of the dynamic samples. This study bridges these gaps by synergizing conventional polarimetric imaging with DLSA for an in-depth examination of sample polarization properties. The effectiveness of our system is shown by analyzing the collection of polarimetric images of various tissue samples, utilizing a variety of adapted numerical and graphical statistical post-processing methods. © The Author(s) 2024.

Author Keywords
Brain neoplasm;  Cancerous tissue;  Dynamic laser speckle analysis;  Label-free diagnostics;  Polarimetric imaging

Document Type: Article
Publication Stage: Final
Source: Scopus

Explainable Artificial Intelligence for Mental Disorder Screening: A Computational Design Science Approach
(2024) Journal of Management Information Systems, 41 (4), pp. 958-981. 

Tutun, S.^a , Topuz, K.^b , Tosyali, A.^c , Bhattacherjee, A.^d , Li, G.^e

^a John M. Olin Business School, Washington University in St. Louis, St. Louis, MO, United States
^b Collins College of Business, The University of Tulsa, Tulsa, OK, United States
^c Saunders College of Business, Rochester Institute of Technology, Rochester, NY, United States
^d Muma College of Business, University of South Florida, Tampa, FL, United States
^e Bosch Center for Artificial Intelligence, Sunnyvale, CA, United States

Abstract
Mental disorders affect nearly one billion people globally, 94% of whom are undiagnosed and untreated due to an acute shortage of trained clinicians. In response to this crisis, this study introduces mental disorder scan (MDscan), a novel artifact for screening ten mental disorders using data from the SCL-90-R mental disorder screening instrument, an explainable artificial intelligence approach, and our own ShapRadiation algorithm. MDscan converts 90 mental health indicators for each patient into an easily interpretable diagnostic image for mental disorders, similar to radiological images, and explains which indicators contributed to that prediction, increasing clinicians’ ability to screen more patients in less time. A field evaluation with clinical data shows that MDscan has high classification accuracy, with average F1 scores between 0.77 and 0.94, compared against prerecorded ground truth. Furthermore, unlike traditional black-box models, MDscan’s transparency and explainability can help enhance trust in artificial intelligence (AI) applications for clinical use. © 2025 Taylor & Francis Group, LLC.

Author Keywords
algorithmic diagnosis;  computational approach;  Explainable AI;  field experiments;  health screening;  image generation;  mental health

Document Type: Article
Publication Stage: Final
Source: Scopus

Preferred fixation position and gaze location: Two factors modulating the composite face effect
(2024) Journal of Vision, 24 (13), pp. 1-18. 

Chakravarthula, P.N.^a b , Soni, A.K.^a c , Eckstein, M.P.^a

^a Department of Psychological and Brain Science, University of California, Santa Barbara, CA, United States
^b Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, United States
^c Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States

Abstract
Humans consistently land their first saccade to a face at a preferred fixation location (PFL). Humans also typically process faces as wholes, as evidenced by perceptual effects such as the composite face effect (CFE). However, not known is whether an individual’s tendency to process faces as wholes varies with their gaze patterns on the face. Here, we investigated variation of the CFE with the PFL. We compared the strength of the CFE for two groups of observers who were screened to have their PFLs either higher up, closer to the eyes, or lower on the face, closer to the tip of the nose. During the task, observers maintained their gaze at either their own group’s mean PFL or at the other group’s mean PFL. We found that the top half of the face elicits a stronger CFE than the bottom half. Further, the strength of the CFE was modulated by the distance of the PFL from the eyes, such that individuals with a PFL closer to the eyes had a stronger CFE than those with a PFL closer to the mouth. Finally, the top-half CFE for both upper-lookers and lower-lookers was abolished when they fixated at a non-preferred location on the face. Our findings show that the CFE relies on internal face representations shaped by the long-term use of a consistent oculomotor strategy to view faces. © 2024 The Authors

Author Keywords
composite face effect;  eye movements;  individual differences

Document Type: Article
Publication Stage: Final
Source: Scopus