Scopus list of publications for December 10, 2023
Cell-free RNA signatures predict Alzheimer’s disease
(2023) iScience, 26 (12), art. no. 108534, .
Cisterna-García, A.a b c , Beric, A.a c , Ali, M.a c , Pardo, J.A.b , Chen, H.-H.a c , Fernandez, M.V.a c , Norton, J.a c d , Gentsch, J.a c d , Bergmann, K.a c , Budde, J.a c , Perlmutter, J.S.e f g , Morris, J.C.d e h , Cruchaga, C.a c d e f i , Botia, J.A.b , Ibanez, L.a c e
a Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, MO, United States
b Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
c NeuroGenomics and Informatics Center, Washington University in Saint Louis School of Medicine, Saint Louis, MO, United States
d The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in Saint Louis, Saint Louis, MO, United States
e Department of Neurology, Washington University in Saint Louis School of Medicine, Saint Louis, MO, United States
f Hope Center for Neurological Disorders, Washington University in Saint Louis School of Medicine, Saint Louis, MO, United States
g Department of Radiology, Neuroscience, Physical Therapy, and Occupational Therapy, Washington University in Saint Louis School of Medicine, Saint Louis, MO, United States
h Department of Pathology and Immunology, Washington University in Saint Louis School of Medicine, Saint Louis, MO, United States
i Department of Genetics, Washington University in Saint Louis School of Medicine, Saint Louis, MO, United States
Abstract
There is a need for affordable, scalable, and specific blood-based biomarkers for Alzheimer’s disease that can be applied to a population level. We have developed and validated disease-specific cell-free transcriptomic blood-based biomarkers composed by a scalable number of transcripts that capture AD pathobiology even in the presymptomatic stages of the disease. Accuracies are in the range of the current CSF and plasma biomarkers, and specificities are high against other neurodegenerative diseases. © 2023 The Author(s)
Author Keywords
Biochemistry; Clinical finding; Disease; Molecular biology
Funding details
20762/FPI/18
National Institutes of HealthNIHK99/R00-AG062723, P01 AG026276, P01AG003991, P30 AG066444, R01AG044546, R01AG058501, RF1AG053303, RO1 NS075321, U01AG058922
U.S. Department of DefenseDOD
Michael J. Fox Foundation for Parkinson’s ResearchMJFF
Alzheimer’s Drug Discovery FoundationADDF
Biogen
BrightFocus FoundationBFF
Fundación SénecaFS
Hope Center for Neurological Disorders
Japan Science and Technology AgencyJST
Document Type: Article
Publication Stage: Final
Source: Scopus
Two Phase 3 Trials of Gantenerumab in Early Alzheimer’s Disease
(2023) New England Journal of Medicine, 389 (20), pp. 1862-1876.
Bateman, R.J.a , Smith, J.b , Donohue, M.C.e , Delmar, P.g , Abbas, R.g , Salloway, S.h , Wojtowicz, J.g , Blennow, K.i , Bittner, T.f g , Black, S.E.j , Klein, G.g , Boada, M.k , Grimmer, T.l , Tamaoka, A.m , Perry, R.J.c , Turner, R.S.n , Watson, D.o , Woodward, M.p , Thanasopoulou, A.g , Lane, C.b , Baudler, M.g , Fox, N.C.d , Cummings, J.L.q , Fontoura, P.g , Doody, R.S.f g
a The Department of Neurology, Washington University, School of Medicine, St. Louis, United States
b Roche Products, Welwyn Garden City, United Kingdom
c The Department of Brain Sciences, Faculty of Medicine, Imperial College London, United Kingdom
d The Dementia Research Centre, Department of Neurodegenerative Disease, The U.K. Dementia Research Institute, Queen Square Institute of Neurology, University College London, London, United Kingdom
e The Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, CA, United States
f Genentech, South San Francisco, CA, United States
g F. Hoffmann-La Roche, Basel, Switzerland
h Butler Hospital and Warren Alpert Medical School, Brown University, Providence, RI, United States
i The Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, The Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
j The Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, The L.C. Campbell Cognitive Neurology Research Unit, Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
k The Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, The Networking Research Center on Neurodegenerative Diseases, Instituto de Salud Carlos III, Madrid, Spain
l The Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
m The Department of Neurology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
n The Department of Neurology, Georgetown University, School of Medicine, Washington, DC, United States
o The Alzheimer’s Research and Treatment Center, Wellington, FL, United States
p The Medical and Cognitive Research Unit, Heidelberg Repatriation Hospital, Austin Health, Melbourne, VIC, Australia
q The Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada, Las Vegas, Las Vegas, United States
Abstract
Background: Monoclonal antibodies that target amyloid-beta (Aβ) have the potential to slow cognitive and functional decline in persons with early Alzheimer’s disease. Gantenerumab is a subcutaneously administered, fully human, anti-Aβ IgG1 monoclonal antibody with highest affinity for aggregated Aβ that has been tested for the treatment of Alzheimer’s disease. Methods: We conducted two phase 3 trials (GRADUATE I and II) involving participants 50 to 90 years of age with mild cognitive impairment or mild dementia due to Alzheimer’s disease and evidence of amyloid plaques on positron-emission tomography (PET) or cerebrospinal fluid (CSF) testing. Participants were randomly assigned to receive gantenerumab or placebo every 2 weeks. The primary outcome was the change from baseline in the score on the Clinical Dementia Rating scale-Sum of Boxes (CDR-SB; range, 0 to 18, with higher scores indicating greater cognitive impairment) at week 116. Results: A total of 985 and 980 participants were enrolled in the GRADUATE I and II trials, respectively. The baseline CDR-SB score was 3.7 in the GRADUATE I trial and 3.6 in the GRADUATE II trial. The change from baseline in the CDR-SB score at week 116 was 3.35 with gantenerumab and 3.65 with placebo in the GRADUATE I trial (difference, -0.31; 95% confidence interval [CI], -0.66 to 0.05; P = 0.10) and was 2.82 with gantenerumab and 3.01 with placebo in the GRADUATE II trial (difference, -0.19; 95% CI, -0.55 to 0.17; P = 0.30). At week 116, the difference in the amyloid level on PET between the gantenerumab group and the placebo group was -66.44 and -56.46 centiloids in the GRADUATE I and II trials, respectively, and amyloid-negative status was attained in 28.0% and 26.8% of the participants receiving gantenerumab in the two trials. Across both trials, participants receiving gantenerumab had lower CSF levels of phosphorylated tau 181 and higher levels of Aβ42 than those receiving placebo; the accumulation of aggregated tau on PET was similar in the two groups. Amyloid-related imaging abnormalities with edema (ARIA-E) occurred in 24.9% of the participants receiving gantenerumab, and symptomatic ARIA-E occurred in 5.0%. Conclusions: Among persons with early Alzheimer’s disease, the use of gantenerumab led to a lower amyloid plaque burden than placebo at 116 weeks but was not associated with slower clinical decline. © 2023 Massachussetts Medical Society. All rights reserved.
Funding details
Roche
Document Type: Article
Publication Stage: Final
Source: Scopus
Plasma proteomic analysis on neuropathic pain in idiopathic peripheral neuropathy patients
(2023) Journal of the Peripheral Nervous System, .
van Doormaal, P.T.C.a b c , Thomas, S.a , Ajroud-Driss, S.d , Cole, R.N.e , DeVine, L.R.e , Dimachkie, M.M.f , Geisler, S.g , Freeman, R.h , Simpson, D.M.i , Singleton, J.R.j , Smith, A.G.k , Stino, A.l , Höke, A.a
a Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
b Department of Neurology, Brain Center Rudolph Magnus, Utrecht Medical Center, Utrecht, Netherlands
c Department of Neurology, Tergooi Medical Center, Hilversum, Netherlands
d Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
e Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD, United States
f Department of Neurology, Kansas University Medical Center, Kansas City, MO, United States
g Department of Neurology, Washington University in St. Louis School of Medicine, St. Louis, MO, United States
h Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
i Department of Neurology, Icahn School of Medicine at Mount Sinai Medical Center, New York City, NY, United States
j Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, United States
k Department of Neurology, Virginia Commonwealth University, Richmond, VA, United States
l Department of Neurology, University of Michigan, Ann Arbor, MI, United States
Abstract
Background and Aims: Why only half of the idiopathic peripheral neuropathy (IPN) patients develop neuropathic pain remains unknown. By conducting a proteomics analysis on IPN patients, we aimed to discover proteins and new pathways that are associated with neuropathic pain. Methods: We conducted unbiased mass-spectrometry proteomics analysis on blood plasma from 31 IPN patients with severe neuropathic pain and 29 IPN patients with no pain, to investigate protein biomarkers and protein–protein interactions associated with neuropathic pain. Univariate modeling was done with linear mixed modeling (LMM) and corrected for multiple testing. Multivariate modeling was performed using elastic net analysis and validated with internal cross-validation and bootstrapping. Results: In the univariate analysis, 73 proteins showed a p-value <.05 and 12 proteins showed a p-value <.01. None were significant after Benjamini–Hochberg adjustment for multiple testing. Elastic net analysis created a model containing 12 proteins with reasonable discriminatory power to differentiate between painful and painless IPN (false-negative rate 0.10, false-positive rate 0.18, and an area under the curve 0.75). Eight of these 12 proteins were clustered into one interaction network, significantly enriched for the complement and coagulation pathway (Benjamini–Hochberg adjusted p-value =.0057), with complement component 3 (C3) as the central node. Bootstrap validation identified insulin-like growth factor-binding protein 2 (IGFBP2), complement factor H-related protein 4 (CFHR4), and ferritin light chain (FTL), as the most discriminatory proteins of the original 12 identified. Interpretation: This proteomics analysis suggests a role for the complement system in neuropathic pain in IPN. © 2023 The Authors. Journal of the Peripheral Nervous System published by Wiley Periodicals LLC on behalf of Peripheral Nerve Society.
Author Keywords
complement; neuropathy; pain; proteomics
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Spatial and amplitude dynamics of neurostimulation: Insights from the acute intrahippocampal kainate seizure mouse model
(2023) Epilepsia Open, .
Foutz, T.J.a , Rensing, N.a , Han, L.a , Durand, D.M.b , Wong, M.a
a Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
b Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
Abstract
Objective: Neurostimulation is an emerging treatment for patients with drug-resistant epilepsy, which is used to suppress, prevent, and terminate seizure activity. Unfortunately, after implantation and despite best clinical practice, most patients continue to have persistent seizures even after years of empirical optimization. The objective of this study is to determine optimal spatial and amplitude properties of neurostimulation in inhibiting epileptiform activity in an acute hippocampal seizure model. Methods: We performed high-throughput testing of high-frequency focal brain stimulation in the acute intrahippocampal kainic acid mouse model of status epilepticus. We evaluated combinations of six anatomic targets and three stimulus amplitudes. Results: We found that the spike-suppressive effects of high-frequency neurostimulation are highly dependent on the stimulation amplitude and location, with higher amplitude stimulation being significantly more effective. Epileptiform spiking activity was significantly reduced with ipsilateral 250 μA stimulation of the CA1 and CA3 hippocampal regions with 21.5% and 22.2% reductions, respectively. In contrast, we found that spiking frequency and amplitude significantly increased with stimulation of the ventral hippocampal commissure. We further found spatial differences with broader effects from CA1 versus CA3 stimulation. Significance: These findings demonstrate that the effects of therapeutic neurostimulation in an acute hippocampal seizure model are highly dependent on the location of stimulation and stimulus amplitude. We provide a platform to optimize the anti-seizure effects of neurostimulation, and demonstrate that an exploration of the large electrical parameter and location space can improve current modalities for treating epilepsy. Plain Language Summary: In this study, we tested how electrical pulses in the brain can help control seizures in mice. We found that the electrode’s placement and the stimulation amplitude had a large effect on outcomes. Some brain regions, notably nearby CA1 and CA3, responded positively with reduced seizure-like activities, while others showed increased activity. These findings emphasize that choosing the right spot for the electrode and adjusting the strength of electrical pulses are both crucial when considering neurostimulation treatments for epilepsy. © 2023 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.
Author Keywords
brain stimulation; epilepsy; neuromodulation; seizures; status epilepticus
Funding details
National Institutes of HealthNIHP50HD103525
National Institute of General Medical SciencesNIGMS
American Epilepsy SocietyAES
Washington University in St. LouisWUSTL
Document Type: Article
Publication Stage: Article in Press
Source: Scopus
Risk of pre-term birth as a function of sleep quality and obesity: prospective analysis in a large Prematurity Research Cohort
(2023) SLEEP Advances, 4 (1), art. no. zpad043, .
Sutcliffe, S.a , Zhao, P.b , Pilz, L.K.c d , Oakes, M.e , Frolova, A.I.f , Herzog, E.D.g , England, S.K.h
a Division of Public Health Sciences, Department of Surgery, Alvin J. Siteman Cancer Center, The Department of Obstetrics and Gynecology, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
b Division of Clinical Research, Department of Obstetrics and Gynecology, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
c Department of Anesthesiology, Intensive Care Medicine, The Experimental and Clinical Research Center, Charite Universitatsmedizin Berlin, Berlin, Germany
d Freie Universität Berlin, Humboldt Universität zu Berlin, Berlin, Germany
e Department of Obstetrics and Gynecology, MemorialCare Miller Children’s and Women’s Hospital, Long Beach, CA, United States
f Division of Maternal-Fetal Medicine and Ultrasound, Department of Obstetrics and Gynecology, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
g Department of Biology, Washington University in St. Louis, St. Louis, MO, United States
h Department of Obstetrics and Gynecology, Center for Reproductive Health Sciences, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
Abstract
Study Objective: To investigate whether poor sleep quality is associated with pre-term birth (PTB) risk, overall and independent of sleep apnea and habitual snoring. Methods: We used longitudinal data from the Washington University Prematurity Research Cohort to investigate the association between poor sleep quality (defined as a Pittsburgh Sleep Quality Index > 5) and PTB, overall and independent of sleep apnea and snoring (defined by the Berlin questionnaire and prior sleep clinic attendance). Associations were investigated for sleep quality early and throughout pregnancy. Stratified analyses were performed by factors previously shown to modify associations between sleep and PTB (race, pre-pregnancy obesity). Results: Of the 976 eligible participants, 50.1% experienced poor sleep quality early in pregnancy (<20 completed weeks) and 14.2% delivered pre-term (n = 50 without and 89 with poor sleep quality). In multivariable-adjusted analyses, poor sleep quality early in pregnancy was associated with increased PTB risk (hazard ratio [HR] = 1.48, 95% confidence interval [CI] = 1.02–2.14). This association persisted after further adjustment for sleep apnea and snoring (HR = 1.50, 95% CI = 1.02–2.20) and in analyses stratified by race. It varied, however, by pre-pregnancy obesity. Among individuals without obesity, no association was observed between poor sleep and PTB (HR = 1.08, 95% CI = 0.65–1.79), whereas among those with obesity, a positive association was observed (HR = 2.94, 95% CI = 1.52–5.69, p-interaction = .05). This association was limited to individuals with obesity who experienced poor sleep both earlier and later in pregnancy (HR = 3.94, 95% CI = 1.56–9.99). Conclusion: Our findings suggest that improving sleep quality early in pregnancy may be important for PTB prevention, particularly among individuals with obesity. © The Author(s) 2023. Published by Oxford University Press on behalf of Sleep Research Society.
Author Keywords
cohort study; obesity; pregnancy; sleep
Funding details
March of Dimes FoundationMDF
University of WashingtonUW
Washington University School of Medicine in St. LouisWUSM
St. Louis Children’s HospitalSLCH
Document Type: Article
Publication Stage: Final
Source: Scopus
Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods
(2023) Neuropsychopharmacology, .
Yang, J.a , Huggins, A.A.b c , Sun, D.b c d , Baird, C.L.b c , Haswell, C.C.b c , Frijling, J.L.e , Olff, M.e f , van Zuiden, M.e , Koch, S.B.J.e g , Nawijn, L.e , Veltman, D.J.e , Suarez-Jimenez, B.h , Zhu, X.i j , Neria, Y.i j , Hudson, A.R.k , Mueller, S.C.k , Baker, J.T.l m , Lebois, L.A.M.l n , Kaufman, M.L.l o , Qi, R.p , Lu, G.M.p , Říha, P.q r , Rektor, I.r , Dennis, E.L.s t , Ching, C.R.K.u , Thomopoulos, S.I.u , Salminen, L.E.u , Jahanshad, N.u , Thompson, P.M.u , Stein, D.J.v , Koopowitz, S.M.v , Ipser, J.C.v , Seedat, S.w , du Plessis, S.w , van den Heuvel, L.L.w , Wang, L.x y , Zhu, Y.x y , Li, G.x y , Sierk, A.z , Manthey, A.z , Walter, H.z , Daniels, J.K.aa , Schmahl, C.ab , Herzog, J.I.ab , Liberzon, I.ac , King, A.ad , Angstadt, M.ad , Davenport, N.D.ae af , Sponheim, S.R.ae af , Disner, S.G.ae af , Straube, T.ag , Hofmann, D.ag , Grupe, D.W.ah , Nitschke, J.B.ai , Davidson, R.J.ah ai aj , Larson, C.L.ak , deRoon-Cassini, T.A.al am , Blackford, J.U.an ao , Olatunji, B.O.ap , Gordon, E.M.a , May, G.aq ar as at , Nelson, S.M.aq ar as at , Abdallah, C.G.au av , Levy, I.aw ax ay az ba , Harpaz-Rotem, I.au ay ba , Krystal, J.H.au ba , Morey, R.A.b c , Sotiras, A.a bb
a Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
b Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
c Mid-Atlantic Mental Illness Research Education and Clinical Center, Durham VA Medical Center, Durham, NC, United States
d Department of Psychology, The Education University of Hong Kong, Hong Kong
e Department of Psychiatry, Amsterdam University Medical Center, Amsterdam, Netherlands
f ARQ National Psychotrauma Centre, Diemen, Netherlands
g Donders Institute for Brain, Cognition and Behavior, Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, Netherlands
h Del Monte Institute for Neuroscience, University of Rochester Medical Center, Rochester, NY, United States
i Department of Psychiatry, Columbia University Medical Center, New York, NY, United States
j New York State Psychiatric Institute, New York, NY, United States
k Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
l Department of Psychiatry, Harvard Medical School, Boston, MA, United States
m Institute for Technology in Psychiatry, McLean Hospital, Harvard University, Belmont, MA, United States
n Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, United States
o Division of Women’s Mental Health, McLean Hospital, Belmont, MA, United States
p Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Jiangsu, China
q First Department of Neurology, St. Anne’s University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
r CEITEC-Central European Institute of Technology, Multimodal and Functional Neuroimaging Research Group, Masaryk University, Brno, Czech Republic
s Department of Neurology, University of Utah, Salt Lake City, UT, United States
t George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, United States
u Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, United States
v Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
w Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
x Laboratory for Traumatic Stress Studies, Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
y Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
z University Medical Centre Charité, Berlin, Germany
aa Department of Clinical Psychology, University of Groningen, Groningen, Netherlands
ab Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
ac Department of Psychiatry and Behavioral Science, Texas A&M University, College Station, TX, United States
ad Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
ae Minneapolis VA Health Care System, Minneapolis, MN, United States
af Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States
ag Institute of Medical Psychology and Systems Neuroscience, University of Münster, Münster, Germany
ah Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, United States
ai Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
aj Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
ak Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
al Division of Trauma and Acute Care Surgery, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, United States
am Comprehensive Injury Center, Medical College of Wisconsin, Milwaukee, WI, United States
an Munroe-Meyer Institute, University of Nebraska Medical Center, Omaha, NE, United States
ao Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
ap Department of Psychology, Vanderbilt University, Nashville, TN, United States
aq Veterans Integrated Service Network-17 Center of Excellence for Research on Returning War Veterans, Waco, TX, United States
ar Department of Psychology and Neuroscience, Baylor University, Waco, TX, United States
as Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, United States
at Department of Psychiatry and Behavioral Science, Texas A&M University Health Science Center, Bryan, TX, United States
au Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
av Department of Psychiatry of Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
aw Department of Comparative Medicine, Yale University, New Haven, CT, United States
ax Department of Neuroscience, Yale University, New Haven, CT, United States
ay Department of Psychology, Yale University, New Haven, CT, United States
az Wu Tsai Institute, Yale University, New Haven, CT, United States
ba Division of Clinical Neuroscience, National Center for PTSD, West Haven, CT, United States
bb Institute for Informatics, Data Science & Biostatistics, Washington University in St. Louis, St. Louis, MO, United States
Abstract
Posttraumatic stress disorder (PTSD) is associated with lower cortical thickness (CT) in prefrontal, cingulate, and insular cortices in diverse trauma-affected samples. However, some studies have failed to detect differences between PTSD patients and healthy controls or reported that PTSD is associated with greater CT. Using data-driven dimensionality reduction, we sought to conduct a well-powered study to identify vulnerable networks without regard to neuroanatomic boundaries. Moreover, this approach enabled us to avoid the excessive burden of multiple comparison correction that plagues vertex-wise methods. We derived structural covariance networks (SCNs) by applying non-negative matrix factorization (NMF) to CT data from 961 PTSD patients and 1124 trauma-exposed controls without PTSD. We used regression analyses to investigate associations between CT within SCNs and PTSD diagnosis (with and without accounting for the potential confounding effect of trauma type) and symptom severity in the full sample. We performed additional regression analyses in subsets of the data to examine associations between SCNs and comorbid depression, childhood trauma severity, and alcohol abuse. NMF identified 20 unbiased SCNs, which aligned closely with functionally defined brain networks. PTSD diagnosis was most strongly associated with diminished CT in SCNs that encompassed the bilateral superior frontal cortex, motor cortex, insular cortex, orbitofrontal cortex, medial occipital cortex, anterior cingulate cortex, and posterior cingulate cortex. CT in these networks was significantly negatively correlated with PTSD symptom severity. Collectively, these findings suggest that PTSD diagnosis is associated with widespread reductions in CT, particularly within prefrontal regulatory regions and broader emotion and sensory processing cortical regions. © 2023, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
Funding details
110614, K01 MH118428-01
AZV NV18-7 04-00559, BE2022705
National Science FoundationNSF
National Institutes of HealthNIHMJFF 14848, R01 AG059874, R01 MH111671, R01 MH116147, R01 MH117601, R01 MH129742, U54 EB020403
U.S. Department of DefenseDODP41 EB015922, R56 AG058854, W81XWH-12-2-0012
National Institute of Mental HealthNIMHR01-MH043454, T32-MH018931
National Institute on Alcohol Abuse and AlcoholismNIAAAP50
National Institute of Child Health and Human DevelopmentNICHDP30-HD003352, R01 MH106574, VA CSR&D 1IK2CX001680
Congressionally Directed Medical Research ProgramsCDMRPW81XWH-08–2–0038
U.S. Department of Veterans AffairsVA
Dana FoundationDF
Scoliosis Research SocietySRS1K2RX002922, VA RR&D 1K1RX002325
California Department of Fish and GameDFGC06
Institute for Clinical and Translational Research, University of Wisconsin, MadisonUW ICTR
National Center for Advancing Translational SciencesNCATSR01AG067103
National Alliance for Research on Schizophrenia and DepressionNARSAD01J05415, 27040, R01 MH105355, RO1 MH111671
National Center for PTSD, U.S. Department of Veterans AffairsNCPTSDR01 MH105535, R21 MH102634
National Research FoundationNRF
South African Medical Research CouncilSAMRCMRC-RFA-IFSP-01-2013
Department of Science and Technology, Ministry of Science and Technology, IndiaDST
Deutsche ForschungsgemeinschaftDFGDA 1222/4-1, R01 MH113574, VA RR&D 1IK2RX000709, VA RR&D I01RX000622, WA 1539/8-2
National Natural Science Foundation of ChinaNSFC31971020, U21A20364
ZonMw40-00812-98-10041
Chinese Academy of SciencesCAS
Ministry of EducationMOE16JJD190006
Ministerstvo Zdravotnictví Ceské RepublikyMZCR
Universiteit GentK01 MH118467, R01 MH119227, R21 MH112956
Natural Science Foundation of Jiangsu ProvinceBK20221554
Bijzonder Onderzoeksfonds UGentBOF
National Office for Philosophy and Social SciencesNPOPSS20ZDA079
National Treasury
Document Type: Article
Publication Stage: Article in Press
Source: Scopus