20,222 results on '"Thompson, Paul"'
Search Results
152. Prevalence of statin intolerance: a meta-analysis
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Bytyçi, Ibadete, Penson, Peter E, Mikhailidis, Dimitri P, Wong, Nathan D, Hernandez, Adrian V, Sahebkar, Amirhossein, Thompson, Paul D, Mazidi, Mohsen, Rysz, Jacek, Pella, Daniel, Reiner, Željko, Toth, Peter P, and Banach, Maciej
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Heart Disease ,Cardiovascular ,Prevention ,Atherosclerosis ,Female ,Humans ,Hydroxymethylglutaryl-CoA Reductase Inhibitors ,Lipids ,Male ,Prevalence ,Randomized Controlled Trials as Topic ,Risk Factors ,Cardiovascular disease ,Risk factors ,Statin intolerance ,Cardiorespiratory Medicine and Haematology ,Clinical Sciences ,Cardiovascular System & Hematology - Abstract
AimsStatin intolerance (SI) represents a significant public health problem for which precise estimates of prevalence are needed. Statin intolerance remains an important clinical challenge, and it is associated with an increased risk of cardiovascular events. This meta-analysis estimates the overall prevalence of SI, the prevalence according to different diagnostic criteria and in different disease settings, and identifies possible risk factors/conditions that might increase the risk of SI.Methods and resultsWe searched several databases up to 31 May 2021, for studies that reported the prevalence of SI. The primary endpoint was overall prevalence and prevalence according to a range of diagnostic criteria [National Lipid Association (NLA), International Lipid Expert Panel (ILEP), and European Atherosclerosis Society (EAS)] and in different disease settings. The secondary endpoint was to identify possible risk factors for SI. A random-effects model was applied to estimate the overall pooled prevalence. A total of 176 studies [112 randomized controlled trials (RCTs); 64 cohort studies] with 4 143 517 patients were ultimately included in the analysis. The overall prevalence of SI was 9.1% (95% confidence interval 8.0-10%). The prevalence was similar when defined using NLA, ILEP, and EAS criteria [7.0% (6.0-8.0%), 6.7% (5.0-8.0%), 5.9% (4.0-7.0%), respectively]. The prevalence of SI in RCTs was significantly lower compared with cohort studies [4.9% (4.0-6.0%) vs. 17% (14-19%)]. The prevalence of SI in studies including both primary and secondary prevention patients was much higher than when primary or secondary prevention patients were analysed separately [18% (14-21%), 8.2% (6.0-10%), 9.1% (6.0-11%), respectively]. Statin lipid solubility did not affect the prevalence of SI [4.0% (2.0-5.0%) vs. 5.0% (4.0-6.0%)]. Age [odds ratio (OR) 1.33, P = 0.04], female gender (OR 1.47, P = 0.007), Asian and Black race (P 4 million patients, the prevalence of SI is low when diagnosed according to international definitions. These results support the concept that the prevalence of complete SI might often be overestimated and highlight the need for the careful assessment of patients with potential symptoms related to SI.
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- 2022
153. Obesity and brain structure in schizophrenia – ENIGMA study in 3021 individuals
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McWhinney, Sean R, Brosch, Katharina, Calhoun, Vince D, Crespo-Facorro, Benedicto, Crossley, Nicolas A, Dannlowski, Udo, Dickie, Erin, Dietze, Lorielle MF, Donohoe, Gary, Du Plessis, Stefan, Ehrlich, Stefan, Emsley, Robin, Furstova, Petra, Glahn, David C, Gonzalez- Valderrama, Alfonso, Grotegerd, Dominik, Holleran, Laurena, Kircher, Tilo TJ, Knytl, Pavel, Kolenic, Marian, Lencer, Rebekka, Nenadić, Igor, Opel, Nils, Pfarr, Julia-Katharina, Rodrigue, Amanda L, Rootes-Murdy, Kelly, Ross, Alex J, Sim, Kang, Škoch, Antonín, Spaniel, Filip, Stein, Frederike, Švancer, Patrik, Tordesillas-Gutiérrez, Diana, Undurraga, Juan, Vázquez-Bourgon, Javier, Voineskos, Aristotle, Walton, Esther, Weickert, Thomas W, Weickert, Cynthia Shannon, Thompson, Paul M, van Erp, Theo GM, Turner, Jessica A, and Hajek, Tomas
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Biomedical Imaging ,Obesity ,Neurosciences ,Serious Mental Illness ,Mental Health ,Schizophrenia ,Nutrition ,Clinical Research ,Brain Disorders ,2.1 Biological and endogenous factors ,Aetiology ,Mental health ,Humans ,Depressive Disorder ,Major ,Brain ,Magnetic Resonance Imaging ,Biological Sciences ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Psychiatry - Abstract
Schizophrenia is frequently associated with obesity, which is linked with neurostructural alterations. Yet, we do not understand how the brain correlates of obesity map onto the brain changes in schizophrenia. We obtained MRI-derived brain cortical and subcortical measures and body mass index (BMI) from 1260 individuals with schizophrenia and 1761 controls from 12 independent research sites within the ENIGMA-Schizophrenia Working Group. We jointly modeled the statistical effects of schizophrenia and BMI using mixed effects. BMI was additively associated with structure of many of the same brain regions as schizophrenia, but the cortical and subcortical alterations in schizophrenia were more widespread and pronounced. Both BMI and schizophrenia were primarily associated with changes in cortical thickness, with fewer correlates in surface area. While, BMI was negatively associated with cortical thickness, the significant associations between BMI and surface area or subcortical volumes were positive. Lastly, the brain correlates of obesity were replicated among large studies and closely resembled neurostructural changes in major depressive disorders. We confirmed widespread associations between BMI and brain structure in individuals with schizophrenia. People with both obesity and schizophrenia showed more pronounced brain alterations than people with only one of these conditions. Obesity appears to be a relevant factor which could account for heterogeneity of brain imaging findings and for differences in brain imaging outcomes among people with schizophrenia.
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- 2022
154. Multiscale neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology
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Park, Bo-yong, Kebets, Valeria, Larivière, Sara, Hettwer, Meike D, Paquola, Casey, van Rooij, Daan, Buitelaar, Jan, Franke, Barbara, Hoogman, Martine, Schmaal, Lianne, Veltman, Dick J, van den Heuvel, Odile A, Stein, Dan J, Andreassen, Ole A, Ching, Christopher RK, Turner, Jessica A, van Erp, Theo GM, Evans, Alan C, Dagher, Alain, Thomopoulos, Sophia I, Thompson, Paul M, Valk, Sofie L, Kirschner, Matthias, and Bernhardt, Boris C
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Biological Sciences ,Biomedical and Clinical Sciences ,Depression ,Neurosciences ,Behavioral and Social Science ,Serious Mental Illness ,Mental Health ,Brain Disorders ,Schizophrenia ,2.1 Biological and endogenous factors ,Aetiology ,Neurological ,Mental health ,Good Health and Well Being ,Autism Spectrum Disorder ,Connectome ,Dopamine ,Humans ,Neural Pathways ,Serotonin ,Biological sciences ,Biomedical and clinical sciences - Abstract
It is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we carried out a multiscale neural contextualization of shared alterations of cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression disorder, obsessive-compulsive disorder, bipolar disorder, and schizophrenia). Our framework cross-referenced shared morphological anomalies with respect to cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we identified a cortex-wide dimension of morphological changes that described a sensory-fugal pattern, with paralimbic regions showing the most consistent alterations across conditions. The shared disease dimension was closely related to cortical gradients of microstructure as well as neurotransmitter axes, specifically cortex-wide variations in serotonin and dopamine. Multiple sensitivity analyses confirmed robustness with respect to slight variations in analytical choices. Our findings embed shared effects of common psychiatric conditions on brain structure in multiple scales of brain organization, and may provide insights into neural mechanisms of transdiagnostic vulnerability.
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- 2022
155. Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals.
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McWhinney, Sean, Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, Del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick, Cannon, Dara, Dannlowski, Udo, Diaz-Zuluaga, Ana, Dietze, Lorielle, Elvsåshagen, Torbjørn, Fullerton, Janice, Goikolea, Jose, Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur, Ingvar, Martin, Kircher, Tilo, Krug, Axel, Kuplicki, Rayus, Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa, Mitchell, Philip, Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Overs, Bronwyn, Pfarr, Julia-Katharina, Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai, Roberts, Gloria, Ross, Alex, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter, Sim, Kang, Stein, Dan, Stein, Frederike, Temmingh, Henk, Thiel, Katharina, Thomopoulos, Sophia, van Haren, Neeltje, Van Gestel, Holly, Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi, Ching, Christopher, Andreassen, Ole, Thompson, Paul, Hajek, Tomas, Ophoff, Roel, and Eyler, Lisa
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bipolar disorders ,body mass index ,cortical thickness ,heterogeneity ,obesity ,surface area ,Bipolar Disorder ,Body Mass Index ,Cluster Analysis ,Humans ,Magnetic Resonance Imaging ,Obesity ,Temporal Lobe - Abstract
AIMS: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. METHODS: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. RESULTS: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. CONCLUSIONS: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.
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- 2022
156. Event‐based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross‐sectional data
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Lopez, Seymour M, Aksman, Leon M, Oxtoby, Neil P, Vos, Sjoerd B, Rao, Jun, Kaestner, Erik, Alhusaini, Saud, Alvim, Marina, Bender, Benjamin, Bernasconi, Andrea, Bernasconi, Neda, Bernhardt, Boris, Bonilha, Leonardo, Caciagli, Lorenzo, Caldairou, Benoit, Caligiuri, Maria Eugenia, Calvet, Angels, Cendes, Fernando, Concha, Luis, Conde‐Blanco, Estefania, Davoodi‐Bojd, Esmaeil, de Bézenac, Christophe, Delanty, Norman, Desmond, Patricia M, Devinsky, Orrin, Domin, Martin, Duncan, John S, Focke, Niels K, Foley, Sonya, Fortunato, Francesco, Galovic, Marian, Gambardella, Antonio, Gleichgerrcht, Ezequiel, Guerrini, Renzo, Hamandi, Khalid, Ives‐Deliperi, Victoria, Jackson, Graeme D, Jahanshad, Neda, Keller, Simon S, Kochunov, Peter, Kotikalapudi, Raviteja, Kreilkamp, Barbara AK, Labate, Angelo, Larivière, Sara, Lenge, Matteo, Lui, Elaine, Malpas, Charles, Martin, Pascal, Mascalchi, Mario, Medland, Sarah E, Meletti, Stefano, Morita‐Sherman, Marcia E, Owen, Thomas W, Richardson, Mark, Riva, Antonella, Rüber, Theodor, Sinclair, Ben, Soltanian‐Zadeh, Hamid, Stein, Dan J, Striano, Pasquale, Taylor, Peter N, Thomopoulos, Sophia I, Thompson, Paul M, Tondelli, Manuela, Vaudano, Anna Elisabetta, Vivash, Lucy, Wang, Yujiang, Weber, Bernd, Whelan, Christopher D, Wiest, Roland, Winston, Gavin P, Yasuda, Clarissa Lin, McDonald, Carrie R, Alexander, Daniel C, Sisodiya, Sanjay M, Altmann, Andre, Bargalló, Núria, Bartolini, Emanuele, O’Brien, Terence J, and Thomas, Rhys H
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Brain Disorders ,Epilepsy ,Neurodegenerative ,Neurosciences ,Clinical Research ,Biomedical Imaging ,Aetiology ,2.1 Biological and endogenous factors ,Neurological ,Good Health and Well Being ,Atrophy ,Biomarkers ,Cross-Sectional Studies ,Epilepsy ,Temporal Lobe ,Hippocampus ,Humans ,Magnetic Resonance Imaging ,Sclerosis ,disease progression ,duration of illness ,event-based model ,MTLE ,patient staging ,ENIGMA-Epilepsy Working Group ,Clinical Sciences ,Neurology & Neurosurgery - Abstract
ObjectiveRecent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features.MethodsWe extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1-weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1625 healthy controls from 25 centers. Features with a moderate case-control effect size (Cohen d ≥ .5) were used to train an event-based model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance.ResultsIn MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10-16 ), age at onset (ρ = -.18, p = 9.82 × 10-7 ), and ASM resistance (area under the curve = .59, p = .043, Mann-Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE-HS with mild or nondetectable abnormality on T1W MRI.SignificanceFrom cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features.
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- 2022
157. Maternal free fatty acid concentration during pregnancy is associated with newborn hypothalamic microstructure in humans
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Rasmussen, Jerod M, Thompson, Paul M, Gyllenhammer, Lauren E, Lindsay, Karen L, O'Connor, Thomas G, Koletzko, Berthold, Entringer, Sonja, Wadhwa, Pathik D, and Buss, Claudia
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Paediatrics ,Reproductive Medicine ,Biomedical and Clinical Sciences ,Prevention ,Biomedical Imaging ,Nutrition ,Perinatal Period - Conditions Originating in Perinatal Period ,Pediatric ,Clinical Research ,Reproductive health and childbirth ,Good Health and Well Being ,Child ,Child ,Preschool ,Fatty Acids ,Nonesterified ,Female ,Humans ,Hypothalamus ,Infant ,Infant ,Newborn ,Pediatric Obesity ,Pregnancy ,Prospective Studies ,Sexually Transmitted Diseases ,Endocrinology & Metabolism - Abstract
ObjectiveThis study tested the hypothesis, in a prospective cohort study design, that maternal saturated free fatty acid (sFFA) concentration during pregnancy is prospectively associated with offspring (newborn) hypothalamic (HTH) microstructure and to explore the functional relevance of this association with respect to early-childhood body fat percentage (BF%).MethodsIn N = 94 healthy newborns (born mean 39.3 [SD 1.5] weeks gestation), diffusion-weighted magnetic resonance imaging was performed shortly after birth (25.3 [12.5] postnatal days), and a subgroup (n = 37) underwent a dual-energy x-ray absorptiometry scan in early childhood (4.7 [SD 0.7] years). Maternal sFFA concentration during pregnancy was quantified in fasting blood samples via liquid chromatography-mass spectrometry. Infant HTH microstructural integrity was characterized using mean diffusivity (MD). Multiple linear regression was used to test the association between maternal sFFA and HTH MD, accounting for newborn sex, age at scan, mean white matter MD, and image quality. Multiple linear regression models also tested the association between HTH MD and early-childhood BF%, accounting for breastfeeding status.ResultsMaternal sFFA during pregnancy accounted for 8.3% of the variation in newborn HTH MD (β-std = 0.25; p = 0.006). Furthermore, newborn HTH MD prospectively accounted for 15% of the variation in early-childhood BF% (β-std = 0.32; p = 0.019).ConclusionsThese findings suggest that maternal overnutrition during pregnancy may influence the development of the fetal hypothalamus, which, in turn, may have clinical relevance for childhood obesity risk.
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- 2022
158. A federated learning architecture for secure and private neuroimaging analysis
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Stripelis, Dimitris, Gupta, Umang, Saleem, Hamza, Dhinagar, Nikhil, Ghai, Tanmay, Anastasiou, Chrysovalantis, Sánchez, Rafael, Steeg, Greg Ver, Ravi, Srivatsan, Naveed, Muhammad, Thompson, Paul M., and Ambite, José Luis
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- 2024
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159. Emotion detection for misinformation: A review
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Liu, Zhiwei, Zhang, Tianlin, Yang, Kailai, Thompson, Paul, Yu, Zeping, and Ananiadou, Sophia
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- 2024
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160. A systematic review of neuroimaging epigenetic research: calling for an increased focus on development
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Walton, Esther, Baltramonaityte, Vilte, Calhoun, Vince, Heijmans, Bastiaan T., Thompson, Paul M., and Cecil, Charlotte A. M.
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- 2023
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161. Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and Correntropy
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Wu, Jianfeng, Zhu, Wenhui, Su, Yi, Gui, Jie, Lepore, Natasha, Reiman, Eric M., Caselli, Richard J., Thompson, Paul M., Chen, Kewei, and Wang, Yalin
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Physics - Medical Physics ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Biomarker-assisted diagnosis and intervention in Alzheimer's disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM)., Comment: 10 pages, 5 figures, 17th International Symposium on Medical Information Processing and Analysis
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- 2021
162. Friend or foe?
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Thompson, Paul
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- 2014
163. Light-heavy integration at the JRTC (Joint Readiness Training Center): Offensive operations
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Thompson, Paul E., Jr, 1stSgt
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JOINT READINESS TRAINING CENTER ,JOINT FORCES - United States - Training ,ARMORED WARFARE - Study and Teaching - Abstract
illus
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- 2001
164. Toward a tool for evaluating corpus-based word lists for use in english language teaching contexts
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Alzeer, Sarah and Thompson, Paul
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- 2024
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165. Contrasting association pattern of plasma low-density lipoprotein with white matter integrity in APOE4 carriers versus non-carriers
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Ye, Zhenyao, Pan, Yezhi, McCoy, Rozalina G., Bi, Chuan, Mo, Chen, Feng, Li, Yu, Jiaao, Lu, Tong, Liu, Song, Carson Smith, J., Duan, Minxi, Gao, Si, Ma, Yizhou, Chen, Chixiang, Mitchell, Braxton D., Thompson, Paul M., Elliot Hong, L., Kochunov, Peter, Ma, Tianzhou, and Chen, Shuo
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- 2024
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166. Age‐dependent white matter disruptions after military traumatic brain injury: Multivariate analysis results from ENIGMA brain injury
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Bouchard, Heather C, Sun, Delin, Dennis, Emily L, Newsome, Mary R, Disner, Seth G, Elman, Jeremy, Silva, Annelise, Velez, Carmen, Irimia, Andrei, Davenport, Nicholas D, Sponheim, Scott R, Franz, Carol E, Kremen, William S, Coleman, Michael J, Williams, M Wright, Geuze, Elbert, Koerte, Inga K, Shenton, Martha E, Adamson, Maheen M, Coimbra, Raul, Grant, Gerald, Shutter, Lori, George, Mark S, Zafonte, Ross D, McAllister, Thomas W, Stein, Murray B, Thompson, Paul M, Wilde, Elisabeth A, Tate, David F, Sotiras, Aristeidis, and Morey, Rajendra A
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Clinical and Health Psychology ,Psychology ,Neurosciences ,Traumatic Head and Spine Injury ,Biomedical Imaging ,Brain Disorders ,Mental Health ,Clinical Research ,Physical Injury - Accidents and Adverse Effects ,Traumatic Brain Injury (TBI) ,Mental health ,Brain ,Brain Concussion ,Brain Injuries ,Brain Injuries ,Traumatic ,Humans ,Military Personnel ,Multivariate Analysis ,Stress Disorders ,Post-Traumatic ,Veterans ,White Matter ,diffusion MRI ,ENIGMA ,military ,mTBI ,nonnegative matrix factorization ,traumatic brain injury ,Cognitive Sciences ,Experimental Psychology ,Biological psychology ,Cognitive and computational psychology - Abstract
Mild Traumatic brain injury (mTBI) is a signature wound in military personnel, and repetitive mTBI has been linked to age-related neurogenerative disorders that affect white matter (WM) in the brain. However, findings of injury to specific WM tracts have been variable and inconsistent. This may be due to the heterogeneity of mechanisms, etiology, and comorbid disorders related to mTBI. Non-negative matrix factorization (NMF) is a data-driven approach that detects covarying patterns (components) within high-dimensional data. We applied NMF to diffusion imaging data from military Veterans with and without a self-reported TBI history. NMF identified 12 independent components derived from fractional anisotropy (FA) in a large dataset (n = 1,475) gathered through the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) Military Brain Injury working group. Regressions were used to examine TBI- and mTBI-related associations in NMF-derived components while adjusting for age, sex, post-traumatic stress disorder, depression, and data acquisition site/scanner. We found significantly stronger age-dependent effects of lower FA in Veterans with TBI than Veterans without in four components (q
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- 2022
167. Interpretable deep clustering survival machines for Alzheimer’s disease subtype discovery
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Hou, Bojian, Wen, Zixuan, Bao, Jingxuan, Zhang, Richard, Tong, Boning, Yang, Shu, Wen, Junhao, Cui, Yuhan, Moore, Jason H., Saykin, Andrew J., Huang, Heng, Thompson, Paul M., Ritchie, Marylyn D., Davatzikos, Christos, and Shen, Li
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- 2024
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168. Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy
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Park, Bo-yong, Larivière, Sara, Rodríguez-Cruces, Raul, Royer, Jessica, Tavakol, Shahin, Wang, Yezhou, Caciagli, Lorenzo, Caligiuri, Maria Eugenia, Gambardella, Antonio, Concha, Luis, Keller, Simon S, Cendes, Fernando, Alvim, Marina KM, Yasuda, Clarissa, Bonilha, Leonardo, Gleichgerrcht, Ezequiel, Focke, Niels K, Kreilkamp, Barbara AK, Domin, Martin, von Podewils, Felix, Langner, Soenke, Rummel, Christian, Rebsamen, Michael, Wiest, Roland, Martin, Pascal, Kotikalapudi, Raviteja, Bender, Benjamin, O’Brien, Terence J, Law, Meng, Sinclair, Benjamin, Vivash, Lucy, Kwan, Patrick, Desmond, Patricia M, Malpas, Charles B, Lui, Elaine, Alhusaini, Saud, Doherty, Colin P, Cavalleri, Gianpiero L, Delanty, Norman, Kälviäinen, Reetta, Jackson, Graeme D, Kowalczyk, Magdalena, Mascalchi, Mario, Semmelroch, Mira, Thomas, Rhys H, Soltanian-Zadeh, Hamid, Davoodi-Bojd, Esmaeil, Zhang, Junsong, Lenge, Matteo, Guerrini, Renzo, Bartolini, Emanuele, Hamandi, Khalid, Foley, Sonya, Weber, Bernd, Depondt, Chantal, Absil, Julie, Carr, Sarah JA, Abela, Eugenio, Richardson, Mark P, Devinsky, Orrin, Severino, Mariasavina, Striano, Pasquale, Parodi, Costanza, Tortora, Domenico, Hatton, Sean N, Vos, Sjoerd B, Duncan, John S, Galovic, Marian, Whelan, Christopher D, Bargalló, Núria, Pariente, Jose, Conde-Blanco, Estefania, Vaudano, Anna Elisabetta, Tondelli, Manuela, Meletti, Stefano, Kong, Xiang‐Zhen, Francks, Clyde, Fisher, Simon E, Caldairou, Benoit, Ryten, Mina, Labate, Angelo, Sisodiya, Sanjay M, Thompson, Paul M, McDonald, Carrie R, Bernasconi, Andrea, Bernasconi, Neda, and Bernhardt, Boris C
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Epilepsy ,Neurodegenerative ,Clinical Research ,Brain Disorders ,Neurosciences ,Aetiology ,2.1 Biological and endogenous factors ,Neurological ,Adult ,Atrophy ,Connectome ,Epilepsy ,Temporal Lobe ,Hippocampus ,Humans ,Magnetic Resonance Imaging ,temporal lobe epilepsy ,asymmetry ,cortical thickness ,multi-site ,gradients ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Neurology & Neurosurgery - Abstract
Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.
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- 2022
169. Chronic Stroke Sensorimotor Impairment Is Related to Smaller Hippocampal Volumes: An ENIGMA Analysis
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Zavaliangos‐Petropulu, Artemis, Lo, Bethany, Donnelly, Miranda R, Schweighofer, Nicolas, Lohse, Keith, Jahanshad, Neda, Barisano, Giuseppe, Banaj, Nerisa, Borich, Michael R, Boyd, Lara A, Buetefisch, Cathrin M, Byblow, Winston D, Cassidy, Jessica M, Charalambous, Charalambos C, Conforto, Adriana B, DiCarlo, Julie A, Dula, Adrienne N, Egorova‐Brumley, Natalia, Etherton, Mark R, Feng, Wuwei, Fercho, Kelene A, Geranmayeh, Fatemeh, Hanlon, Colleen A, Hayward, Kathryn S, Hordacre, Brenton, Kautz, Steven A, Khlif, Mohamed Salah, Kim, Hosung, Kuceyeski, Amy, Lin, David J, Liu, Jingchun, Lotze, Martin, MacIntosh, Bradley J, Margetis, John L, Mohamed, Feroze B, Piras, Fabrizio, Ramos‐Murguialday, Ander, Revill, Kate P, Roberts, Pamela S, Robertson, Andrew D, Schambra, Heidi M, Seo, Na Jin, Shiroishi, Mark S, Stinear, Cathy M, Soekadar, Surjo R, Spalletta, Gianfranco, Taga, Myriam, Tang, Wai Kwong, Thielman, Gregory T, Vecchio, Daniela, Ward, Nick S, Westlye, Lars T, Werden, Emilio, Winstein, Carolee, Wittenberg, George F, Wolf, Steven L, Wong, Kristin A, Yu, Chunshui, Brodtmann, Amy, Cramer, Steven C, Thompson, Paul M, and Liew, Sook‐Lei
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Neurosciences ,Stroke ,Aging ,Brain Disorders ,Cross-Sectional Studies ,Female ,Hippocampus ,Humans ,Male ,Quality of Life ,Recovery of Function ,Stroke Rehabilitation ,Upper Extremity ,hippocampus ,MRI ,sensorimotor impairment ,stroke ,Cardiorespiratory Medicine and Haematology - Abstract
Background Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is vulnerable to poststroke secondary degeneration and is involved in sensorimotor behavior but has not been widely studied within the context of poststroke upper-limb sensorimotor impairment. We investigated associations between non-lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke, hypothesizing that smaller ipsilesional hippocampal volumes would be associated with greater sensorimotor impairment. Methods and Results Cross-sectional T1-weighted magnetic resonance images of the brain were pooled from 357 participants with chronic stroke from 18 research cohorts of the ENIGMA (Enhancing NeuoImaging Genetics through Meta-Analysis) Stroke Recovery Working Group. Sensorimotor impairment was estimated from the FMA-UE (Fugl-Meyer Assessment of Upper Extremity). Robust mixed-effects linear models were used to test associations between poststroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni-corrected, P
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- 2022
170. Cross disorder comparisons of brain structure in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 deletion syndrome: A review of ENIGMA findings
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Cheon, Eun‐Jin, Bearden, Carrie E, Sun, Daqiang, Ching, Christopher RK, Andreassen, Ole A, Schmaal, Lianne, Veltman, Dick J, Thomopoulos, Sophia I, Kochunov, Peter, Jahanshad, Neda, Thompson, Paul M, Turner, Jessica A, and van Erp, Theo GM
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Depression ,Neurosciences ,Schizophrenia ,Genetics ,Clinical Research ,Biomedical Imaging ,Serious Mental Illness ,Brain Disorders ,Bipolar Disorder ,Mental Health ,Aetiology ,2.3 Psychological ,social and economic factors ,2.1 Biological and endogenous factors ,Mental health ,Brain ,Depressive Disorder ,Major ,DiGeorge Syndrome ,Diffusion Tensor Imaging ,Humans ,Magnetic Resonance Imaging ,bipolar disorder ,ENIGMA ,major depressive disorder ,schizophrenia ,velocardiofacial ,Clinical Sciences ,Cognitive Sciences - Abstract
This review compares the main brain abnormalities in schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and 22q11.2 Deletion Syndrome (22q11DS) determined by ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium investigations. We obtained ranked effect sizes for subcortical volumes, regional cortical thickness, cortical surface area, and diffusion tensor imaging abnormalities, comparing each of these disorders relative to healthy controls. In addition, the studies report on significant associations between brain imaging metrics and disorder-related factors such as symptom severity and treatments. Visual comparison of effect size profiles shows that effect sizes are generally in the same direction and scale in severity with the disorders (in the order SZ > BD > MDD). The effect sizes for 22q11DS, a rare genetic syndrome that increases the risk for psychiatric disorders, appear to be much larger than for either of the complex psychiatric disorders. This is consistent with the idea of generally larger effects on the brain of rare compared to common genetic variants. Cortical thickness and surface area effect sizes for 22q11DS with psychosis compared to 22q11DS without psychosis are more similar to those of SZ and BD than those of MDD; a pattern not observed for subcortical brain structures and fractional anisotropy effect sizes. The observed similarities in effect size profiles for cortical measures across the psychiatric disorders mimic those observed for shared genetic variance between these disorders reported based on family and genetic studies and are consistent with shared genetic risk for SZ and BD and structural brain phenotypes.
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- 2022
171. Is Neuroscience FAIR? A Call for Collaborative Standardisation of Neuroscience Data.
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Poline, Jean-Baptiste, Kennedy, David N, Sommer, Friedrich T, Ascoli, Giorgio A, Van Essen, David C, Ferguson, Adam R, Grethe, Jeffrey S, Hawrylycz, Michael J, Thompson, Paul M, Poldrack, Russell A, Ghosh, Satrajit S, Keator, David B, Athey, Thomas L, Vogelstein, Joshua T, Mayberg, Helen S, and Martone, Maryann E
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Data Collection ,Neurosciences ,International Neuroinformatics Coordinating Facility ,Interoperability ,Neuroscience ,Standards ,Networking and Information Technology R&D (NITRD) ,Standards ,International Neuroinformatics Coordinating Facility ,Biochemistry and Cell Biology ,Neurology & Neurosurgery - Abstract
In this perspective article, we consider the critical issue of data and other research object standardisation and, specifically, how international collaboration, and organizations such as the International Neuroinformatics Coordinating Facility (INCF) can encourage that emerging neuroscience data be Findable, Accessible, Interoperable, and Reusable (FAIR). As neuroscientists engaged in the sharing and integration of multi-modal and multiscale data, we see the current insufficiency of standards as a major impediment in the Interoperability and Reusability of research results. We call for increased international collaborative standardisation of neuroscience data to foster integration and efficient reuse of research objects.
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- 2022
172. Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption
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Stripelis, Dimitris, Saleem, Hamza, Ghai, Tanmay, Dhinagar, Nikhil, Gupta, Umang, Anastasiou, Chrysovalantis, Steeg, Greg Ver, Ravi, Srivatsan, Naveed, Muhammad, Thompson, Paul M., and Ambite, Jose Luis
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Computer Science - Cryptography and Security ,Computer Science - Machine Learning - Abstract
Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location. This results in an improved generalizability of models and efficient scaling of computation as more sources and larger datasets are added to the federation. Nevertheless, recent membership attacks show that private or sensitive personal data can sometimes be leaked or inferred when model parameters or summary statistics are shared with a central site, requiring improved security solutions. In this work, we propose a framework for secure FL using fully-homomorphic encryption (FHE). Specifically, we use the CKKS construction, an approximate, floating point compatible scheme that benefits from ciphertext packing and rescaling. In our evaluation on large-scale brain MRI datasets, we use our proposed secure FL framework to train a deep learning model to predict a person's age from distributed MRI scans, a common benchmarking task, and demonstrate that there is no degradation in the learning performance between the encrypted and non-encrypted federated models., Comment: 9 pages, 3 figures, 1 algorithm
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- 2021
173. Deep Learning–Based Automated Imaging Classification of ADPKD
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Steinman, Theodore, Wei, Jesse, Czarnecki, Peter, Pedrosa, Ivan, Braun, William, Nurko, Saul, Remer, Erick, Chapman, Arlene, Martin, Diego, Rahbari-Oskoui, Frederic, Mittal, Pardeep, Torres, Vicente, Hogan, Marie C., El-Zoghby, Ziad, Harris, Peter, Glockner, James, King, Bernard, Jr., Perrone, Ronald, Halin, Neil, Miskulin, Dana, Schrier, Robert, Brosnahan, Godela, Gitomer, Berenice, Kelleher, Cass, Masoumi, Amirali, Patel, Nayana, Winklhofer, Franz, Grantham, Jared, Yu, Alan, Wang, Connie, Wetzel, Louis, Moore, Charity G., Bost, James E., Bae, Kyongtae, Abebe, Kaleab Z., Miller, J. Philip, Thompson, Paul A., Briggs, Josephine, Flessner, Michael, Meyers, Catherine M., Star, Robert, Shayman, James, Henrich, William, Greene, Tom, Leonard, Mary, McCullough, Peter, Moe, Sharon, Rocco, Michael, Wendler, David, Kim, Youngwoo, Bu, Seonah, Tao, Cheng, and Bae, Kyongtae T.
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- 2024
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174. A multi-state, student-level analysis of the effects of the four-day school week on student achievement and growth
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Morton, Emily, Thompson, Paul N., and Kuhfeld, Megan
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- 2024
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175. The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration
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Lu, Bin, Chen, Xiao, Xavier Castellanos, Francisco, Thompson, Paul M., Zuo, Xi-Nian, Zang, Yu-Feng, and Yan, Chao-Gan
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- 2024
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176. Targeting the GPI transamidase subunit GPAA1 abrogates the CD24 immune checkpoint in ovarian cancer
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Mishra, Alok K., Ye, Tianyi, Banday, Shahid, Thakare, Ritesh P., Su, Chinh Tran-To, Pham, Ngoc N.H., Ali, Amjad, Kulshreshtha, Ankur, Chowdhury, Shreya Roy, Simone, Tessa M., Hu, Kai, Zhu, Lihua Julie, Eisenhaber, Birgit, Deibler, Sara K., Simin, Karl, Thompson, Paul R., Kelliher, Michelle A., Eisenhaber, Frank, Malonia, Sunil K., and Green, Michael R.
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- 2024
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177. Incorporation of palladium into pyrite: Insights from X-ray absorption spectroscopy analysis and modelling
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Filimonova, Olga N., Snigireva, Irina I., Thompson, Paul, and Wermeille, Didier
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- 2024
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178. Incidence of ocular surface squamous neoplasia in pterygium specimens
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Yang, Yelin, Bachour, Kenan, Tong, Maya, Khair, Diana, Gaffar, Judy, Robert, Marie-Claude, Thompson, Paul, Racine, Louis, Segal, Laura, and Harissi-Dagher, Mona
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- 2024
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179. Light/heavy integration at the Joint Readiness Training Center
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Thompson, Paul E., Jr, Sgt1C
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JOINT READINESS TRAINING CENTER ,COMBAT - Study and Teaching ,JOINT FORCES - United States - Training - Abstract
illus chart tab bibliog
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- 1998
180. The legend of George F. Kennan
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Thompson, Paul B., CDR
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SECURITY, NATIONAL - United States - Study and Teaching ,NATIONAL WAR COLLEGE - History ,THOUGHT AND THINKING - Abstract
bibliog illus
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- 2006
181. Mapping and identifying service models for community-based services for children with intellectual disabilities and behaviours that challenge in England
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Taylor, Emma L., Thompson, Paul A., Manktelow, Nicholas, Flynn, Samantha, Gillespie, David, Bradshaw, Jill, Gore, Nick, Liew, Ashley, Lovell, Mark, Sutton, Kate, Richards, Caroline, Petrou, Stavros, Langdon, Peter E., Grant, Gemma, Cooper, Vivien, Seers, Kate, and Hastings, Richard P.
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- 2023
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182. Knockout or inhibition of USP30 protects dopaminergic neurons in a Parkinson’s disease mouse model
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Fang, Tracy-Shi Zhang, Sun, Yu, Pearce, Andrew C., Eleuteri, Simona, Kemp, Mark, Luckhurst, Christopher A., Williams, Rachel, Mills, Ross, Almond, Sarah, Burzynski, Laura, Márkus, Nóra M., Lelliott, Christopher J., Karp, Natasha A., Adams, David J., Jackson, Stephen P., Zhao, Jin-Feng, Ganley, Ian G., Thompson, Paul W., Balmus, Gabriel, and Simon, David K.
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- 2023
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183. Developing and validating a mid-frequency word list for chemistry: a corpus-based approach using big data
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Xodabande, Ismail, Atai, Mahmood Reza, Hashemi, Mohammad R., and Thompson, Paul
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- 2023
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184. The Queensland Twin Adolescent Brain Project, a longitudinal study of adolescent brain development
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Strike, Lachlan T., Hansell, Narelle K., Chuang, Kai-Hsiang, Miller, Jessica L., de Zubicaray, Greig I., Thompson, Paul M., McMahon, Katie L., and Wright, Margaret J.
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- 2023
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185. Fetal programming of human energy homeostasis brain networks: Issues and considerations
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Rasmussen, Jerod M, Thompson, Paul M, Entringer, Sonja, Buss, Claudia, and Wadhwa, Pathik D
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Reproductive Medicine ,Biomedical and Clinical Sciences ,Pediatric ,Neurosciences ,Obesity ,Nutrition ,Prevention ,Underpinning research ,1.1 Normal biological development and functioning ,Animals ,Brain ,Child ,Female ,Fetal Development ,Homeostasis ,Humans ,Pediatric Obesity ,Placenta ,Pregnancy ,Prenatal Exposure Delayed Effects ,brain circuitry ,childhood obesity ,energy balance homeostasis ,fetal programming ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Endocrinology & Metabolism ,Biomedical and clinical sciences ,Health sciences ,Psychology - Abstract
In this paper, we present a transdisciplinary framework and testable hypotheses regarding the process of fetal programming of energy homeostasis brain circuitry. Our model proposes that key aspects of energy homeostasis brain circuitry already are functional by the time of birth (with substantial interindividual variation); that this phenotypic variation at birth is an important determinant of subsequent susceptibility for energy imbalance and childhood obesity risk; and that this brain circuitry exhibits developmental plasticity, in that it is influenced by conditions during intrauterine life, particularly maternal-placental-fetal endocrine, immune/inflammatory, and metabolic processes and their upstream determinants. We review evidence that supports the scientific premise for each element of this formulation, identify future research directions, particularly recent advances that may facilitate a better quantification of the ontogeny of energy homeostasis brain networks, highlight animal and in vitro-based approaches that may better address the determinants of interindividual variation in energy homeostasis brain networks, and discuss the implications of this formulation for the development of strategies targeted towards the primary prevention of childhood obesity.
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- 2022
186. Cortical and subcortical neuroanatomical signatures of schizotypy in 3004 individuals assessed in a worldwide ENIGMA study
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Kirschner, Matthias, Hodzic-Santor, Benazir, Antoniades, Mathilde, Nenadic, Igor, Kircher, Tilo, Krug, Axel, Meller, Tina, Grotegerd, Dominik, Fornito, Alex, Arnatkeviciute, Aurina, Bellgrove, Mark A, Tiego, Jeggan, Dannlowski, Udo, Koch, Katharina, Hülsmann, Carina, Kugel, Harald, Enneking, Verena, Klug, Melissa, Leehr, Elisabeth J, Böhnlein, Joscha, Gruber, Marius, Mehler, David, DeRosse, Pamela, Moyett, Ashley, Baune, Bernhard T, Green, Melissa, Quidé, Yann, Pantelis, Christos, Chan, Raymond, Wang, Yi, Ettinger, Ulrich, Debbané, Martin, Derome, Melodie, Gaser, Christian, Besteher, Bianca, Diederen, Kelly, Spencer, Tom J, Fletcher, Paul, Rössler, Wulf, Smigielski, Lukasz, Kumari, Veena, Premkumar, Preethi, Park, Haeme RP, Wiebels, Kristina, Lemmers-Jansen, Imke, Gilleen, James, Allen, Paul, Kozhuharova, Petya, Marsman, Jan-Bernard, Lebedeva, Irina, Tomyshev, Alexander, Mukhorina, Anna, Kaiser, Stefan, Fett, Anne-Kathrin, Sommer, Iris, Schuite-Koops, Sanne, Paquola, Casey, Larivière, Sara, Bernhardt, Boris, Dagher, Alain, Grant, Phillip, van Erp, Theo GM, Turner, Jessica A, Thompson, Paul M, Aleman, André, and Modinos, Gemma
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Biological Psychology ,Biomedical and Clinical Sciences ,Psychology ,Brain Disorders ,Neurosciences ,Serious Mental Illness ,Schizophrenia ,Clinical Research ,Mental Health ,Mental health ,Good Health and Well Being ,Bipolar Disorder ,Female ,Humans ,Magnetic Resonance Imaging ,Male ,Psychotic Disorders ,Schizotypal Personality Disorder ,Biological Sciences ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Psychiatry ,Clinical sciences ,Biological psychology ,Clinical and health psychology - Abstract
Neuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3004 unmedicated healthy individuals (12-68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores (r = 0.067, pFDR = 0.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ (r = 0.285, pspin = 0.024), but not BD (r = 0.166, pspin = 0.205) or MDD (r = -0.274, pspin = 0.073). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho = -0.690, pspin = 0.006), BD (rho = -0.672, pspin = 0.009), and MDD (rho = -0.692, pspin = 0.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype.
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- 2022
187. ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research.
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Kochunov, Peter, Hong, L, Dennis, Emily, Morey, Rajendra, Tate, David, Wilde, Elisabeth, Logue, Mark, Kelly, Sinead, Donohoe, Gary, Favre, Pauline, Houenou, Josselin, Ching, Christopher, Holleran, Laurena, Andreassen, Ole, van Velzen, Laura, Schmaal, Lianne, Villalón-Reina, Julio, Piras, Fabrizio, Spalletta, Gianfranco, van den Heuvel, Odile, Veltman, Dick, Stein, Dan, Ryan, Meghann, Tan, Yunlong, Turner, Jessica, Haddad, Liz, Nir, Talia, Glahn, David, Thompson, Paul, Jahanshad, Neda, Bearden, Carrie, and Van Erp, Theodorus
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DTI ,ENIGMA ,RVI ,big data ,cross-disorder ,white matter deficit patterns ,Biomedical Research ,Diffusion Tensor Imaging ,Humans ,Mental Disorders ,Multicenter Studies as Topic ,Psychiatry ,White Matter - Abstract
The ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individuals brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features.
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- 2022
188. Translating ENIGMA schizophrenia findings using the regional vulnerability index: Association with cognition, symptoms, and disease trajectory.
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Kochunov, Peter, Fan, Fengmei, Ryan, Meghann, Hatch, Kathryn, Tan, Shuping, Jahanshad, Neda, Thompson, Paul, Turner, Jessica, Chen, Shuo, Du, Xiaoming, Adhikari, Bhim, Bruce, Heather, Hare, Stephanie, Goldwaser, Eric, Kvarta, Mark, Huang, Junchao, Tong, Jinghui, Cui, Yimin, Cao, Baopeng, Tan, Yunlong, Hong, L, and Van Erp, Theodorus
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ENIGMA ,gray matter ,regional vulnerability index ,schizophrenia ,white matter ,Adolescent ,Adult ,Aged ,Cerebral Cortex ,Chronic Disease ,Cognitive Dysfunction ,Diffusion Tensor Imaging ,Disease Progression ,Gray Matter ,Humans ,Magnetic Resonance Imaging ,Middle Aged ,Neuroimaging ,Schizophrenia ,White Matter ,Young Adult - Abstract
Patients with schizophrenia have patterns of brain deficits including reduced cortical thickness, subcortical gray matter volumes, and cerebral white matter integrity. We proposed the regional vulnerability index (RVI) to translate the results of Enhancing Neuro Imaging Genetics Meta-Analysis studies to the individual level. We calculated RVIs for cortical, subcortical, and white matter measurements and a multimodality RVI. We evaluated RVI as a measure sensitive to schizophrenia-specific neuroanatomical deficits and symptoms and studied the timeline of deficit formations in: early (≤5 years since diagnosis, N = 45, age = 28.8 ± 8.5); intermediate (6-20 years, N = 30, age 43.3 ± 8.6); and chronic (21+ years, N = 44, age = 52.5 ± 5.2) patients and healthy controls (N = 76, age = 38.6 ± 12.4). All RVIs were significantly elevated in patients compared to controls, with the multimodal RVI showing the largest effect size, followed by cortical, white matter and subcortical RVIs (d = 1.57, 1.23, 1.09, and 0.61, all p
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- 2022
189. The ENIGMA‐Epilepsy working group: Mapping disease from large data sets
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Sisodiya, Sanjay M, Whelan, Christopher D, Hatton, Sean N, Huynh, Khoa, Altmann, Andre, Ryten, Mina, Vezzani, Annamaria, Caligiuri, Maria Eugenia, Labate, Angelo, Gambardella, Antonio, Ives‐Deliperi, Victoria, Meletti, Stefano, Munsell, Brent C, Bonilha, Leonardo, Tondelli, Manuela, Rebsamen, Michael, Rummel, Christian, Vaudano, Anna Elisabetta, Wiest, Roland, Balachandra, Akshara R, Bargalló, Núria, Bartolini, Emanuele, Bernasconi, Andrea, Bernasconi, Neda, Bernhardt, Boris, Caldairou, Benoit, Carr, Sarah JA, Cavalleri, Gianpiero L, Cendes, Fernando, Concha, Luis, Desmond, Patricia M, Domin, Martin, Duncan, John S, Focke, Niels K, Guerrini, Renzo, Hamandi, Khalid, Jackson, Graeme D, Jahanshad, Neda, Kälviäinen, Reetta, Keller, Simon S, Kochunov, Peter, Kowalczyk, Magdalena A, Kreilkamp, Barbara AK, Kwan, Patrick, Lariviere, Sara, Lenge, Matteo, Lopez, Seymour M, Martin, Pascal, Mascalchi, Mario, Moreira, José CV, Morita‐Sherman, Marcia E, Pardoe, Heath R, Pariente, Jose C, Raviteja, Kotikalapudi, Rocha, Cristiane S, Rodríguez‐Cruces, Raúl, Seeck, Margitta, Semmelroch, Mira KHG, Sinclair, Benjamin, Soltanian‐Zadeh, Hamid, Stein, Dan J, Striano, Pasquale, Taylor, Peter N, Thomas, Rhys H, Thomopoulos, Sophia I, Velakoulis, Dennis, Vivash, Lucy, Weber, Bernd, Yasuda, Clarissa Lin, Zhang, Junsong, Thompson, Paul M, McDonald, Carrie R, Abela, Eugenio, Absil, Julie, Adams, Sophia, Alhusaini, Saud, Alvim, Marina, Balestrini, Simona, Bender, Benjamin, Bergo, Felipe, Bernardes, Tauana, Calvo, Anna, Carreno, Mar, Cherubini, Andrea, David, Philippe, Davoodi‐Bojd, Esmaeil, Delanty, Norman, Depondt, Chantal, Devinsky, Orrin, Doherty, Colin, França, Wendy Caroline, Franceschet, Leticia, Hibar, Derrek P, Ishikawa, Akari, Kaestner, Erik, Langner, Soenke, Liu, Min, Mirandola, Laura, Naylor, Jillian, and Nazem‐Zadeh, Mohammad‐reza
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Brain Disorders ,Biomedical Imaging ,Neurosciences ,Epilepsy ,Neurodegenerative ,2.1 Biological and endogenous factors ,Aetiology ,Neurological ,covariance ,deep learning ,DTI ,event-based modeling ,gene expression ,genetics ,imaging ,MRI ,quantitative ,rsfMRI ,ENIGMA Consortium Epilepsy Working Group ,Cognitive Sciences ,Experimental Psychology - Abstract
Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy.
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- 2022
190. Cerebral blood flow and cardiovascular risk effects on resting brain regional homogeneity
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Adhikari, Bhim M, Hong, L Elliot, Zhao, Zhiwei, Wang, Danny JJ, Thompson, Paul M, Jahanshad, Neda, Zhu, Alyssa H, Holiga, Stefan, Turner, Jessica A, van Erp, Theo GM, Calhoun, Vince D, Hatch, Kathryn S, Bruce, Heather, Hare, Stephanie M, Chiappelli, Joshua, Goldwaser, Eric L, Kvarta, Mark D, Ma, Yizhou, Du, Xiaoming, Nichols, Thomas E, Shuldiner, Alan R, Mitchell, Braxton D, Dukart, Juergen, Chen, Shuo, and Kochunov, Peter
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Arterial-spin labeling ,Correlation ,Local functional connectivity ,Multivariate mediation analysis ,Resting state functional MRI ,Regional Homegeneity ,Cerebral Blood Flow ,Mediation Analysis ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Neurology & Neurosurgery ,Biomedical and clinical sciences ,Health sciences - Abstract
Regional homogeneity (ReHo) is a measure of local functional brain connectivity that has been reported to be altered in a wide range of neuropsychiatric disorders. Computed from brain resting-state functional MRI time series, ReHo is also sensitive to fluctuations in cerebral blood flow (CBF) that in turn may be influenced by cerebrovascular health. We accessed cerebrovascular health with Framingham cardiovascular risk score (FCVRS). We hypothesize that ReHo signal may be influenced by regional CBF; and that these associations can be summarized as FCVRS→CBF→ReHo. We used three independent samples to test this hypothesis. A test-retest sample of N = 30 healthy volunteers was used for test-retest evaluation of CBF effects on ReHo. Amish Connectome Project (ACP) sample (N = 204, healthy individuals) was used to evaluate association between FCVRS and ReHo and testing if the association diminishes given CBF. The UKBB sample (N = 6,285, healthy participants) was used to replicate the effects of FCVRS on ReHo. We observed strong CBF→ReHo links (p
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- 2022
191. White matter microstructure differences in individuals with dependence on cocaine, methamphetamine, and nicotine: Findings from the ENIGMA-Addiction working group
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Ottino-González, Jonatan, Uhlmann, Anne, Hahn, Sage, Cao, Zhipeng, Cupertino, Renata B, Schwab, Nathan, Allgaier, Nicholas, Alia-Klein, Nelly, Ekhtiari, Hamed, Fouche, Jean-Paul, Goldstein, Rita Z, Li, Chiang-Shan R, Lochner, Christine, London, Edythe D, Luijten, Maartje, Masjoodi, Sadegh, Momenan, Reza, Oghabian, Mohammad Ali, Roos, Annerine, Stein, Dan J, Stein, Elliot A, Veltman, Dick J, Verdejo-García, Antonio, Zhang, Sheng, Zhao, Min, Zhong, Na, Jahanshad, Neda, Thompson, Paul M, Conrod, Patricia, Mackey, Scott, and Garavan, Hugh
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Pharmacology and Pharmaceutical Sciences ,Biomedical and Clinical Sciences ,Brain Disorders ,Substance Misuse ,Drug Abuse (NIDA only) ,Clinical Research ,Methamphetamine ,Neurosciences ,Mental health ,Good Health and Well Being ,Cocaine ,Diffusion Tensor Imaging ,Humans ,Nicotine ,White Matter ,Addiction ,DTI ,FA ,Myelin ,Machine learning ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Substance Abuse ,Biochemistry and cell biology ,Pharmacology and pharmaceutical sciences ,Epidemiology - Abstract
BackgroundNicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention.MethodsEleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence.ResultsThe cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p
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- 2022
192. The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke
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Liew, Sook‐Lei, Zavaliangos‐Petropulu, Artemis, Jahanshad, Neda, Lang, Catherine E, Hayward, Kathryn S, Lohse, Keith R, Juliano, Julia M, Assogna, Francesca, Baugh, Lee A, Bhattacharya, Anup K, Bigjahan, Bavrina, Borich, Michael R, Boyd, Lara A, Brodtmann, Amy, Buetefisch, Cathrin M, Byblow, Winston D, Cassidy, Jessica M, Conforto, Adriana B, Craddock, R Cameron, Dimyan, Michael A, Dula, Adrienne N, Ermer, Elsa, Etherton, Mark R, Fercho, Kelene A, Gregory, Chris M, Hadidchi, Shahram, Holguin, Jess A, Hwang, Darryl H, Jung, Simon, Kautz, Steven A, Khlif, Mohamed Salah, Khoshab, Nima, Kim, Bokkyu, Kim, Hosung, Kuceyeski, Amy, Lotze, Martin, MacIntosh, Bradley J, Margetis, John L, Mohamed, Feroze B, Piras, Fabrizio, Ramos‐Murguialday, Ander, Richard, Geneviève, Roberts, Pamela, Robertson, Andrew D, Rondina, Jane M, Rost, Natalia S, Sanossian, Nerses, Schweighofer, Nicolas, Seo, Na Jin, Shiroishi, Mark S, Soekadar, Surjo R, Spalletta, Gianfranco, Stinear, Cathy M, Suri, Anisha, Tang, Wai Kwong W, Thielman, Gregory T, Vecchio, Daniela, Villringer, Arno, Ward, Nick S, Werden, Emilio, Westlye, Lars T, Winstein, Carolee, Wittenberg, George F, Wong, Kristin A, Yu, Chunshui, Cramer, Steven C, and Thompson, Paul M
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Stroke ,Brain Disorders ,Biomedical Imaging ,Neurosciences ,Behavioral and Social Science ,Humans ,Magnetic Resonance Imaging ,Multicenter Studies as Topic ,Neuroimaging ,Stroke Rehabilitation ,big data ,lesions ,MRI ,neuroinformatics ,stroke ,Cognitive Sciences ,Experimental Psychology - Abstract
The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.
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- 2022
193. Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression
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Larivière, Sara, Royer, Jessica, Rodríguez-Cruces, Raúl, Paquola, Casey, Caligiuri, Maria Eugenia, Gambardella, Antonio, Concha, Luis, Keller, Simon S, Cendes, Fernando, Yasuda, Clarissa L, Bonilha, Leonardo, Gleichgerrcht, Ezequiel, Focke, Niels K, Domin, Martin, von Podewills, Felix, Langner, Soenke, Rummel, Christian, Wiest, Roland, Martin, Pascal, Kotikalapudi, Raviteja, O’Brien, Terence J, Sinclair, Benjamin, Vivash, Lucy, Desmond, Patricia M, Lui, Elaine, Vaudano, Anna Elisabetta, Meletti, Stefano, Tondelli, Manuela, Alhusaini, Saud, Doherty, Colin P, Cavalleri, Gianpiero L, Delanty, Norman, Kälviäinen, Reetta, Jackson, Graeme D, Kowalczyk, Magdalena, Mascalchi, Mario, Semmelroch, Mira, Thomas, Rhys H, Soltanian-Zadeh, Hamid, Davoodi-Bojd, Esmaeil, Zhang, Junsong, Winston, Gavin P, Griffin, Aoife, Singh, Aditi, Tiwari, Vijay K, Kreilkamp, Barbara AK, Lenge, Matteo, Guerrini, Renzo, Hamandi, Khalid, Foley, Sonya, Rüber, Theodor, Weber, Bernd, Depondt, Chantal, Absil, Julie, Carr, Sarah JA, Abela, Eugenio, Richardson, Mark P, Devinsky, Orrin, Severino, Mariasavina, Striano, Pasquale, Tortora, Domenico, Kaestner, Erik, Hatton, Sean N, Vos, Sjoerd B, Caciagli, Lorenzo, Duncan, John S, Whelan, Christopher D, Thompson, Paul M, Sisodiya, Sanjay M, Bernasconi, Andrea, Labate, Angelo, McDonald, Carrie R, Bernasconi, Neda, and Bernhardt, Boris C
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Neurodegenerative ,Genetics ,Neurosciences ,Brain Disorders ,Epilepsy ,Aetiology ,2.1 Biological and endogenous factors ,Neurological ,Adult ,Connectome ,Epilepsy ,Generalized ,Epilepsy ,Temporal Lobe ,Gene Expression ,Humans ,Immunoglobulin E ,Magnetic Resonance Imaging ,Nerve Net - Abstract
Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.
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- 2022
194. Intelligence, educational attainment, and brain structure in those at familial high‐risk for schizophrenia or bipolar disorder
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Zwarte, Sonja MC, Brouwer, Rachel M, Agartz, Ingrid, Alda, Martin, Alonso‐Lana, Silvia, Bearden, Carrie E, Bertolino, Alessandro, Bonvino, Aurora, Bramon, Elvira, Buimer, Elizabeth EL, Cahn, Wiepke, Canales‐Rodríguez, Erick J, Cannon, Dara M, Cannon, Tyrone D, Caseras, Xavier, Castro‐Fornieles, Josefina, Chen, Qiang, Chung, Yoonho, De la Serna, Elena, Bonnin, Caterina Mar, Demro, Caroline, Di Giorgio, Annabella, Doucet, Gaelle E, Eker, Mehmet Cagdas, Erk, Susanne, Fatjó‐Vilas, Mar, Fears, Scott C, Foley, Sonya F, Frangou, Sophia, Fullerton, Janice M, Glahn, David C, Goghari, Vina M, Goikolea, Jose M, Goldman, Aaron L, Gonul, Ali Saffet, Gruber, Oliver, Hajek, Tomas, Hawkins, Emma L, Heinz, Andreas, Ongun, Ceren Hidiroglu, Hillegers, Manon HJ, Houenou, Josselin, Pol, Hilleke E Hulshoff, Hultman, Christina M, Ingvar, Martin, Johansson, Viktoria, Jönsson, Erik G, Kane, Fergus, Kempton, Matthew J, Koenis, Marinka MG, Kopecek, Miloslav, Krämer, Bernd, Lawrie, Stephen M, Lenroot, Rhoshel K, Marcelis, Machteld, Mattay, Venkata S, McDonald, Colm, Meyer‐Lindenberg, Andreas, Michielse, Stijn, Mitchell, Philip B, Moreno, Dolores, Murray, Robin M, Mwangi, Benson, Nabulsi, Leila, Newport, Jason, Olman, Cheryl A, Os, Jim, Overs, Bronwyn J, Ozerdem, Aysegul, Pergola, Giulio, Picchioni, Marco M, Piguet, Camille, Pomarol‐Clotet, Edith, Radua, Joaquim, Ramsay, Ian S, Richter, Anja, Roberts, Gloria, Salvador, Raymond, Aydogan, Aybala Saricicek, Sarró, Salvador, Schofield, Peter R, Simsek, Esma M, Simsek, Fatma, Soares, Jair C, Sponheim, Scott R, Sugranyes, Gisela, Toulopoulou, Timothea, Tronchin, Giulia, Vieta, Eduard, Walter, Henrik, Weinberger, Daniel R, Whalley, Heather C, Wu, Mon‐Ju, Yalin, Nefize, Andreassen, Ole A, Ching, Christopher RK, Thomopoulos, Sophia I, Erp, Theo GM, Jahanshad, Neda, and Thompson, Paul M
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Biological Psychology ,Biomedical and Clinical Sciences ,Psychology ,Brain Disorders ,Bipolar Disorder ,Clinical Research ,Serious Mental Illness ,Neurosciences ,Schizophrenia ,Mental Health ,Aetiology ,2.3 Psychological ,social and economic factors ,2.1 Biological and endogenous factors ,Mental health ,Cognitive Dysfunction ,Educational Status ,Family ,Genetic Predisposition to Disease ,Humans ,Intelligence ,Magnetic Resonance Imaging ,Neuroimaging ,bipolar disorder ,education ,intelligence ,neuroimaging ,relatives ,schizophrenia ,Cognitive Sciences ,Experimental Psychology ,Biological psychology ,Cognitive and computational psychology - Abstract
First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.
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- 2022
195. In vivo hippocampal subfield volumes in bipolar disorder—A mega‐analysis from The Enhancing Neuro Imaging Genetics through Meta‐Analysis Bipolar Disorder Working Group
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Haukvik, Unn K, Gurholt, Tiril P, Nerland, Stener, Elvsåshagen, Torbjørn, Akudjedu, Theophilus N, Alda, Martin, Alnæs, Dag, Alonso‐Lana, Silvia, Bauer, Jochen, Baune, Bernhard T, Benedetti, Francesco, Berk, Michael, Bettella, Francesco, Bøen, Erlend, Bonnín, Caterina M, Brambilla, Paolo, Canales‐Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Dandash, Orwa, Dannlowski, Udo, Delvecchio, Giuseppe, Díaz‐Zuluaga, Ana M, Erp, Theo GM, Fatjó‐Vilas, Mar, Foley, Sonya F, Förster, Katharina, Fullerton, Janice M, Goikolea, José M, Grotegerd, Dominik, Gruber, Oliver, Haarman, Bartholomeus CM, Haatveit, Beathe, Hajek, Tomas, Hallahan, Brian, Harris, Mathew, Hawkins, Emma L, Howells, Fleur M, Hülsmann, Carina, Jahanshad, Neda, Jørgensen, Kjetil N, Kircher, Tilo, Krämer, Bernd, Krug, Axel, Kuplicki, Rayus, Lagerberg, Trine V, Lancaster, Thomas M, Lenroot, Rhoshel K, Lonning, Vera, López‐Jaramillo, Carlos, Malt, Ulrik F, McDonald, Colm, McIntosh, Andrew M, McPhilemy, Genevieve, Meer, Dennis, Melle, Ingrid, Melloni, Elisa MT, Mitchell, Philip B, Nabulsi, Leila, Nenadić, Igor, Oertel, Viola, Oldani, Lucio, Opel, Nils, Otaduy, Maria CG, Overs, Bronwyn J, Pineda‐Zapata, Julian A, Pomarol‐Clotet, Edith, Radua, Joaquim, Rauer, Lisa, Redlich, Ronny, Repple, Jonathan, Rive, Maria M, Roberts, Gloria, Ruhe, Henricus G, Salminen, Lauren E, Salvador, Raymond, Sarró, Salvador, Savitz, Jonathan, Schene, Aart H, Sim, Kang, Soeiro‐de‐Souza, Marcio G, Stäblein, Michael, Stein, Dan J, Stein, Frederike, Tamnes, Christian K, Temmingh, Henk S, Thomopoulos, Sophia I, Veltman, Dick J, Vieta, Eduard, Waltemate, Lena, Westlye, Lars T, Whalley, Heather C, Sämann, Philipp G, Thompson, Paul M, Ching, Christopher RK, Andreassen, Ole A, Agartz, Ingrid, and Group, ENIGMA Bipolar Disorder Working
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Biological Psychology ,Pharmacology and Pharmaceutical Sciences ,Biomedical and Clinical Sciences ,Psychology ,Brain Disorders ,Mental Health ,Serious Mental Illness ,Neurosciences ,Biomedical Imaging ,Bipolar Disorder ,Mental health ,Genetics ,Hippocampus ,Humans ,Magnetic Resonance Imaging ,Neuroimaging ,ENIGMA Bipolar Disorder Working Group ,bipolar disorder subtype ,hippocampus ,large-scale ,lithium ,psychosis ,structural brain MRI ,Cognitive Sciences ,Experimental Psychology ,Biological psychology ,Cognitive and computational psychology - Abstract
The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.
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- 2022
196. Local molecular and global connectomic contributions to cross-disorder cortical abnormalities
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Hansen, Justine Y, Shafiei, Golia, Vogel, Jacob W, Smart, Kelly, Bearden, Carrie E, Hoogman, Martine, Franke, Barbara, van Rooij, Daan, Buitelaar, Jan, McDonald, Carrie R, Sisodiya, Sanjay M, Schmaal, Lianne, Veltman, Dick J, van den Heuvel, Odile A, Stein, Dan J, van Erp, Theo GM, Ching, Christopher RK, Andreassen, Ole A, Hajek, Tomas, Opel, Nils, Modinos, Gemma, Aleman, André, van der Werf, Ysbrand, Jahanshad, Neda, Thomopoulos, Sophia I, Thompson, Paul M, Carson, Richard E, Dagher, Alain, and Misic, Bratislav
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Neurosciences ,Pediatric ,Brain Disorders ,Mental Health ,Aetiology ,Underpinning research ,2.1 Biological and endogenous factors ,1.1 Normal biological development and functioning ,Mental health ,Neurological ,Brain ,Brain Diseases ,Connectome ,Humans ,Magnetic Resonance Imaging ,Neural Pathways - Abstract
Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.
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- 2022
197. Predicting alcohol dependence from multi‐site brain structural measures
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Hahn, Sage, Mackey, Scott, Cousijn, Janna, Foxe, John J, Heinz, Andreas, Hester, Robert, Hutchinson, Kent, Kiefer, Falk, Korucuoglu, Ozlem, Lett, Tristram, Li, Chiang‐Shan R, London, Edythe, Lorenzetti, Valentina, Maartje, Luijten, Momenan, Reza, Orr, Catherine, Paulus, Martin, Schmaal, Lianne, Sinha, Rajita, Sjoerds, Zsuzsika, Stein, Dan J, Stein, Elliot, Holst, Ruth J, Veltman, Dick, Walter, Henrik, Wiers, Reinout W, Yucel, Murat, Thompson, Paul M, Conrod, Patricia, Allgaier, Nicholas, and Garavan, Hugh
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Biological Psychology ,Psychology ,Brain Disorders ,Neurosciences ,Substance Misuse ,Alcoholism ,Alcohol Use and Health ,Neurological ,Good Health and Well Being ,Alcoholism ,Cerebral Cortex ,Humans ,Machine Learning ,Magnetic Resonance Imaging ,Multicenter Studies as Topic ,Neuroimaging ,Putamen ,Reproducibility of Results ,addiction ,alcohol dependence ,genetic algorithm ,machine learning ,multi-site ,prediction ,structural MRI ,Cognitive Sciences ,Experimental Psychology ,Biological psychology ,Cognitive and computational psychology - Abstract
To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored in a mega-analysis of previously published datasets from 2,034 AD and comparison participants spanning 27 sites curated by the ENIGMA Addiction Working Group. Data were grouped into a training set used for internal validation including 1,652 participants (692 AD, 24 sites), and a test set used for external validation with 382 participants (146 AD, 3 sites). An exploratory data analysis was first conducted, followed by an evolutionary search based feature selection to site generalizable and high performing subsets of brain measurements. Exploratory data analysis revealed that inclusion of case- and control-only sites led to the inadvertent learning of site-effects. Cross validation methods that do not properly account for site can drastically overestimate results. Evolutionary-based feature selection leveraging leave-one-site-out cross-validation, to combat unintentional learning, identified cortical thickness in the left superior frontal gyrus and right lateral orbitofrontal cortex, cortical surface area in the right transverse temporal gyrus, and left putamen volume as final features. Ridge regression restricted to these features yielded a test-set area under the receiver operating characteristic curve of 0.768. These findings evaluate strategies for handling multi-site data with varied underlying class distributions and identify potential biomarkers for individuals with current AD.
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- 2022
198. Mapping brain asymmetry in health and disease through the ENIGMA consortium
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Kong, Xiang‐Zhen, Postema, Merel C, Guadalupe, Tulio, de Kovel, Carolien, Boedhoe, Premika SW, Hoogman, Martine, Mathias, Samuel R, van Rooij, Daan, Schijven, Dick, Glahn, David C, Medland, Sarah E, Jahanshad, Neda, Thomopoulos, Sophia I, Turner, Jessica A, Buitelaar, Jan, van Erp, Theo GM, Franke, Barbara, Fisher, Simon E, van den Heuvel, Odile A, Schmaal, Lianne, Thompson, Paul M, and Francks, Clyde
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Biological Psychology ,Psychology ,Brain Disorders ,Mental Health ,Neurosciences ,Clinical Research ,Mental health ,Neurological ,Autism Spectrum Disorder ,Cerebral Cortex ,Depressive Disorder ,Major ,Gray Matter ,Humans ,Magnetic Resonance Imaging ,Multicenter Studies as Topic ,Neuroimaging ,Obsessive-Compulsive Disorder ,autism spectrum disorder ,brain asymmetry ,brain laterality ,major depressive disorder ,mega-analysis ,meta-analysis ,obsessive-compulsive disorder ,structural imaging ,Cognitive Sciences ,Experimental Psychology ,Biological psychology ,Cognitive and computational psychology - Abstract
Left-right asymmetry of the human brain is one of its cardinal features, and also a complex, multivariate trait. Decades of research have suggested that brain asymmetry may be altered in psychiatric disorders. However, findings have been inconsistent and often based on small sample sizes. There are also open questions surrounding which structures are asymmetrical on average in the healthy population, and how variability in brain asymmetry relates to basic biological variables such as age and sex. Over the last 4 years, the ENIGMA-Laterality Working Group has published six studies of gray matter morphological asymmetry based on total sample sizes from roughly 3,500 to 17,000 individuals, which were between one and two orders of magnitude larger than those published in previous decades. A population-level mapping of average asymmetry was achieved, including an intriguing fronto-occipital gradient of cortical thickness asymmetry in healthy brains. ENIGMA's multi-dataset approach also supported an empirical illustration of reproducibility of hemispheric differences across datasets. Effect sizes were estimated for gray matter asymmetry based on large, international, samples in relation to age, sex, handedness, and brain volume, as well as for three psychiatric disorders: autism spectrum disorder was associated with subtly reduced asymmetry of cortical thickness at regions spread widely over the cortex; pediatric obsessive-compulsive disorder was associated with altered subcortical asymmetry; major depressive disorder was not significantly associated with changes of asymmetry. Ongoing studies are examining brain asymmetry in other disorders. Moreover, a groundwork has been laid for possibly identifying shared genetic contributions to brain asymmetry and disorders.
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- 2022
199. An overview of the first 5 years of the ENIGMA obsessive–compulsive disorder working group: The power of worldwide collaboration
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van den Heuvel, Odile A, Boedhoe, Premika SW, Bertolin, Sara, Bruin, Willem B, Francks, Clyde, Ivanov, Iliyan, Jahanshad, Neda, Kong, Xiang‐Zhen, Kwon, Jun Soo, O'Neill, Joseph, Paus, Tomas, Patel, Yash, Piras, Fabrizio, Schmaal, Lianne, Soriano‐Mas, Carles, Spalletta, Gianfranco, van Wingen, Guido A, Yun, Je‐Yeon, Vriend, Chris, Simpson, H Blair, van Rooij, Daan, Hoexter, Marcelo Q, Hoogman, Martine, Buitelaar, Jan K, Arnold, Paul, Beucke, Jan C, Benedetti, Francesco, Bollettini, Irene, Bose, Anushree, Brennan, Brian P, De Nadai, Alessandro S, Fitzgerald, Kate, Gruner, Patricia, Grünblatt, Edna, Hirano, Yoshiyuki, Huyser, Chaim, James, Anthony, Koch, Kathrin, Kvale, Gerd, Lazaro, Luisa, Lochner, Christine, Marsh, Rachel, Mataix‐Cols, David, Morgado, Pedro, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika, Pittenger, Christopher, Reddy, YC Janardhan, Sato, João R, Soreni, Noam, Stewart, S Evelyn, Taylor, Stephan F, Tolin, David, Thomopoulos, Sophia I, Veltman, Dick J, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Thompson, Paul M, Stein, Dan J, Abe, Yoshinari, Alonso, Pino, Assogna, Francesca, Banaj, Nerisa, Batistuzzo, Marcelo C, Brem, Silvia, Ciullo, Valentina, Feusner, Jamie, Martínez‐Zalacaín, Ignacio, Menchón, José M, Miguel, Euripedes C, Piacentini, John, Piras, Federica, Sakai, Yuki, Wolters, Lidewij, and Yamada, Kei
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Biological Psychology ,Psychology ,Brain Disorders ,Clinical Research ,Serious Mental Illness ,Pediatric ,Neurosciences ,Mental Health ,Mental health ,Neurological ,Cerebral Cortex ,Humans ,Machine Learning ,Multicenter Studies as Topic ,Neuroimaging ,Obsessive-Compulsive Disorder ,cortical thickness ,ENIGMA ,mega-analysis ,meta-analysis ,MRI ,obsessive-compulsive disorder ,surface area ,volume ,ENIGMA-OCD working group ,Cognitive Sciences ,Experimental Psychology ,Biological psychology ,Cognitive and computational psychology - Abstract
Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.
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- 2022
200. FreeSurfer‐based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts
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Sämann, Philipp G, Iglesias, Juan Eugenio, Gutman, Boris, Grotegerd, Dominik, Leenings, Ramona, Flint, Claas, Dannlowski, Udo, Clarke‐Rubright, Emily K, Morey, Rajendra A, Erp, Theo GM, Whelan, Christopher D, Han, Laura KM, Velzen, Laura S, Cao, Bo, Augustinack, Jean C, Thompson, Paul M, Jahanshad, Neda, and Schmaal, Lianne
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Biological Psychology ,Biomedical and Clinical Sciences ,Psychology ,Mental Health ,Behavioral and Social Science ,Neurosciences ,Aging ,Brain Disorders ,Biomedical Imaging ,Neurodegenerative ,Bioengineering ,Neurological ,Mental health ,Hippocampus ,Humans ,Image Processing ,Computer-Assisted ,Magnetic Resonance Imaging ,Multicenter Studies as Topic ,Neuroimaging ,Quality Control ,ENIGMA ,FreeSurfer ,MRI ,hippocampal subfields ,hippocampal subregions ,hippocampus ,quality control ,segmentation ,Cognitive Sciences ,Experimental Psychology ,Biological psychology ,Cognitive and computational psychology - Abstract
Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013-12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi-)genetics. Finally, we highlight points where FreeSurfer-based hippocampal subfield studies may be optimized.
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- 2022
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