IU Indianapolis ScholarWorks :: Browsing by Author "Thompson, Paul M."

Browsing by Author "Thompson, Paul M."

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Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder
(Wiley, 2019-05) Salminen, Lauren E.; Morey, Rajendra A.; Riedel, Brandalyn C.; Jahanshad, Neda; Dennis, Emily L.; Thompson, Paul M.; Radiology and Imaging Sciences, School of Medicine
Posttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here, we used a novel machine learning method – evolving partitions to improve classification (EPIC) – to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat-exposed military veterans. METHODS: We used EPIC with repeated cross-validation (CV) to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n = 40) versus controls (n = 57), and classification of ELS within the PTSD (ELS+ n = 16; ELS− n = 24) and control groups (ELS+ n = 16; ELS− n = 41). Additional inputs included intracranial volume, age, sex, adult trauma, and depression. RESULTS: On average, EPIC classified PTSD with 69% accuracy (SD = 5%), and ELS with 64% accuracy in the PTSD group (SD = 10%), and 62% accuracy in controls (SD = 6%). EPIC selected unique sets of individual features that classified each group with 75–85% accuracy in post hoc analyses; combinations of regions marginally improved classification from the individual atlas-defined brain regions. Across analyses, surface area in the right posterior cingulate was the only variable that was repeatedly selected as an important feature for classification of PTSD and ELS. CONCLUSIONS: EPIC revealed unique patterns of features that distinguished PTSD and ELS in this sample of combat-exposed military veterans, which may represent distinct biotypes of stress-related neuropathology.
Age‐dependent white matter disruptions after military traumatic brain injury: Multivariate analysis results from ENIGMA brain injury
(Wiley, 2022) 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; Morey, Rajendra A.; Psychiatry, School of Medicine
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 < 0.05), which are spatially unconstrained by traditionally defined WM tracts. One component, occupying the most peripheral location, exhibited significantly stronger age‐dependent differences in Veterans with mTBI. We found NMF to be powerful and effective in detecting covarying patterns of FA associated with mTBI by applying standard parametric regression modeling. Our results highlight patterns of WM alteration that are differentially affected by TBI and mTBI in younger compared to older military Veterans.
Altered Cortical Brain Structure and Increased Risk for Disease Seen Decades After Perinatal Exposure to Maternal Smoking: A Study of 9000 Adults in the UK Biobank
(Oxford Academic, 2019-12-17) Salminen, Lauren E.; Wilcox, Rand R.; Zhu, Alyssa H.; Riedel, Brandalyn C.; Ching, Christopher R.K.; Rashid, Faisal; Thomopoulos, Sophia I.; Saremi, Arvin; Harrison, Marc B.; Ragothaman, Anjanibhargavi; Knight, Victoria; Boyle, Christina P.; Medland, Sarah E.; Thompson, Paul M.; Jahanshad, Neda; Radiology and Imaging Sciences, School of Medicine
Secondhand smoke exposure is a major public health risk that is especially harmful to the developing brain, but it is unclear if early exposure affects brain structure during middle age and older adulthood. Here we analyzed brain MRI data from the UK Biobank in a population-based sample of individuals (ages 44–80) who were exposed (n = 2510) or unexposed (n = 6079) to smoking around birth. We used robust statistical models, including quantile regressions, to test the effect of perinatal smoke exposure (PSE) on cortical surface area (SA), thickness, and subcortical volumes. We hypothesized that PSE would be associated with cortical disruption in primary sensory areas compared to unexposed (PSE−) adults. After adjusting for multiple comparisons, SA was significantly lower in the pericalcarine (PCAL), inferior parietal (IPL), and regions of the temporal and frontal cortex of PSE+ adults; these abnormalities were associated with increased risk for several diseases, including circulatory and endocrine conditions. Sensitivity analyses conducted in a hold-out group of healthy participants (exposed, n = 109, unexposed, n = 315) replicated the effect of PSE on SA in the PCAL and IPL. Collectively our results show a negative, long term effect of PSE on sensory cortices that may increase risk for disease later in life.
An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification
(Frontiers Media, 2023-10-26) Suh, Erica H.; Lee, Garam; Jung, Sang-Hyuk; Wen, Zixuan; Bao, Jingxuan; Nho, Kwangsik; Huang, Heng; Davatzikos, Christos; Saykin, Andrew J.; Thompson, Paul M.; Shen, Li; Kim, Dokyoon; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
Introduction: Stratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors. Methods: Using whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores. Results: adORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature. Discussion: Compared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.
Associations of Sex, Race, and Apolipoprotein E Alleles With Multiple Domains of Cognition Among Older Adults
(American Medical Association, 2023) Walters, Skylar; Contreras, Alex G.; Eissman, Jaclyn M.; Mukherjee, Shubhabrata; Lee, Michael L.; Choi, Seo-Eun; Scollard, Phoebe; Trittschuh, Emily H.; Mez, Jesse B.; Bush, William S.; Kunkle, Brian W.; Naj, Adam C.; Peterson, Amalia; Gifford, Katherine A.; Cuccaro, Michael L.; Cruchaga, Carlos; Pericak-Vance, Margaret A.; Farrer, Lindsay A.; Wang, Li-San; Haines, Jonathan L.; Jefferson, Angela L.; Kukull, Walter A.; Keene, C. Dirk; Saykin, Andrew J.; Thompson, Paul M.; Martin, Eden R.; Bennett, David A.; Barnes, Lisa L.; Schneider, Julie A.; Crane, Paul K.; Hohman, Timothy J.; Dumitrescu, Logan; Alzheimer’s Disease Neuroimaging Initiative; Alzheimer’s Disease Genetics Consortium; Alzheimer’s Disease Sequencing Project; Radiology and Imaging Sciences, School of Medicine
Importance: Sex differences are established in associations between apolipoprotein E (APOE) ε4 and cognitive impairment in Alzheimer disease (AD). However, it is unclear whether sex-specific cognitive consequences of APOE are consistent across races and extend to the APOE ε2 allele. Objective: To investigate whether sex and race modify APOE ε4 and ε2 associations with cognition. Design, setting, and participants: This genetic association study included longitudinal cognitive data from 4 AD and cognitive aging cohorts. Participants were older than 60 years and self-identified as non-Hispanic White or non-Hispanic Black (hereafter, White and Black). Data were previously collected across multiple US locations from 1994 to 2018. Secondary analyses began December 2021 and ended September 2022. Main outcomes and measures: Harmonized composite scores for memory, executive function, and language were generated using psychometric approaches. Linear regression assessed interactions between APOE ε4 or APOE ε2 and sex on baseline cognitive scores, while linear mixed-effect models assessed interactions on cognitive trajectories. The intersectional effect of race was modeled using an APOE × sex × race interaction term, assessing whether APOE × sex interactions differed by race. Models were adjusted for age at baseline and corrected for multiple comparisons. Results: Of 32 427 participants who met inclusion criteria, there were 19 007 females (59%), 4453 Black individuals (14%), and 27 974 White individuals (86%); the mean (SD) age at baseline was 74 years (7.9). At baseline, 6048 individuals (19%) had AD, 4398 (14%) were APOE ε2 carriers, and 12 538 (38%) were APOE ε4 carriers. Participants missing APOE status were excluded (n = 9266). For APOE ε4, a robust sex interaction was observed on baseline memory (β = -0.071, SE = 0.014; P = 9.6 × 10-7), whereby the APOE ε4 negative effect was stronger in females compared with males and did not significantly differ among races. Contrastingly, despite the large sample size, no APOE ε2 × sex interactions on cognition were observed among all participants. When testing for intersectional effects of sex, APOE ε2, and race, an interaction was revealed on baseline executive function among individuals who were cognitively unimpaired (β = -0.165, SE = 0.066; P = .01), whereby the APOE ε2 protective effect was female-specific among White individuals but male-specific among Black individuals. Conclusions and relevance: In this study, while race did not modify sex differences in APOE ε4, the APOE ε2 protective effect could vary by race and sex. Although female sex enhanced ε4-associated risk, there was no comparable sex difference in ε2, suggesting biological pathways underlying ε4-associated risk are distinct from ε2 and likely intersect with age-related changes in sex biology.
Automated and manual hippocampal segmentation techniques: Comparison of results, reproducibility and clinical applicability
(Elsevier, 2019) Hurtz, Sona; Chow, Nicole; Watson, Amity E.; Somme, Johanne H.; Goukasian, Naira; Hwang, Kristy S.; Morra, John; Elashoff, David; Gao, Sujuan; Petersen, Ronald C.; Aisen, Paul S.; Thompson, Paul M.; Apostolova, Liana G.; Biostatistics, School of Public Health
BACKGROUND: Imaging techniques used to measure hippocampal atrophy are key to understanding the clinical progression of Alzheimer's disease (AD). Various semi-automated hippocampal segmentation techniques are available and require human expert input to learn how to accurately segment new data. Our goal was to compare 1) the performance of our automated hippocampal segmentation technique relative to manual segmentations, and 2) the performance of our automated technique when provided with a training set from two different raters. We also explored the ability of hippocampal volumes obtained using manual and automated hippocampal segmentations to predict conversion from MCI to AD. METHODS: We analyzed 161 1.5 T T1-weighted brain magnetic resonance images (MRI) from the ADCS Donepezil/Vitamin E clinical study. All subjects carried a diagnosis of mild cognitive impairment (MCI). Three different segmentation outputs (one produced by manual tracing and two produced by a semi-automated algorithm trained with training sets developed by two raters) were compared using single measure intraclass correlation statistics (smICC). The radial distance method was used to assess each segmentation technique's ability to detect hippocampal atrophy in 3D. We then compared how well each segmentation method detected baseline hippocampal differences between MCI subjects who remained stable (MCInc) and those who converted to AD (MCIc) during the trial. Our statistical maps were corrected for multiple comparisons using permutation-based statistics with a threshold of p < .01. RESULTS: Our smICC analyses showed significant agreement between the manual and automated hippocampal segmentations from rater 1 [right smICC = 0.78 (95%CI 0.72-0.84); left smICC = 0.79 (95%CI 0.72-0.85)], the manual segmentations from rater 1 versus the automated segmentations from rater 2 [right smICC = 0.78 (95%CI 0.7-0.84); left smICC = 0.78 (95%CI 0.71-0.84)], and the automated segmentations of rater 1 versus rater 2 [right smICC = 0.97 (95%CI 0.96-0.98); left smICC = 0.97 (95%CI 0.96-0.98)]. All three segmentation methods detected significant CA1 and subicular atrophy in MCIc compared to MCInc at baseline (manual: right pcorrected = 0.0112, left pcorrected = 0.0006; automated rater 1: right pcorrected = 0.0318, left pcorrected = 0.0302; automated rater 2: right pcorrected = 0.0029, left pcorrected = 0.0166). CONCLUSIONS: The hippocampal volumes obtained with a fast semi-automated segmentation method were highly comparable to the ones obtained with the labor-intensive manual segmentation method. The AdaBoost automated hippocampal segmentation technique is highly reliable allowing the efficient analysis of large data sets.
Common genetic variants influence human subcortical brain structures
(Nature Publishing Group, 2015-04-09) Hibar, Derrek P.; Stein, Jason L.; Renteria, Miguel E.; Arias-Vasquez, Alejandro; Desrivières, Sylvane; Jahanshad, Neda; Toro, Roberto; Wittfeld, Katharina; Abramovic, Lucija; Andersson, Micael; Aribisala, Benjamin S.; Armstrong, Nicola J.; Bernard, Manon; Bohlken, Marc M.; Boks, Marco P.; Bralten, Janita; Brown, Andrew A.; Chakravarty, M. Mallar; Chen, Qiang; Ching, Christopher R. K.; Cuellar-Partida, Gabriel; den Braber, Anouk; Giddaluru, Sudheer; Goldman, Aaron L.; Grimm, Oliver; Guadalupe, Tulio; Hass, Johanna; Woldehawariat, Girma; Holmes, Avram J.; Hoogman, Martine; Janowitz, Deborah; Jia, Tianye; Kim, Sungeun; Klein, Marieke; Kraemer, Bernd; Lee, Phil H.; Olde Loohuis, Loes M.; Luciano, Michelle; Macare, Christine; Mather, Karen A.; Mattheisen, Manuel; Milaneschi, Yuri; Nho, Kwangsik; Papmeyer, Martina; Ramasamy, Adaikalavan; Risacher, Shannon L.; Roiz-Santiañez, Roberto; Rose, Emma J.; Salami, Alireza; Sämann, Philipp G.; Schmaal, Lianne; Schork, Andrew J.; Shin, Jean; Strike, Lachlan T.; Teumer, Alexander; van Donkelaar, Marjolein M. J.; van Eijk, Kristel R.; Walters, Raymond K.; Westlye, Lars T.; Whelan, Christopher D.; Winkler, Anderson M.; Zwiers, Marcel P.; Alhusaini, Saud; Athanasiu, Lavinia; Ehrlich, Stefan; Hakobjan, Marina M. H.; Hartberg, Cecilie B.; Haukvik, Unn K.; Heister, Angelien J. G. A. M.; Hoehn, David; Kasperaviciute, Dalia; Liewald, David C. M.; Lopez, Lorna M.; Makkinje, Remco R. R.; Matarin, Mar; Naber, Marlies A. M.; McKay, D. Reese; Needham, Margaret; Nugent, Allison C.; Pütz, Benno; Royle, Natalie A.; Shen, Li; Sprooten, Emma; Trabzuni, Daniah; van der Marel, Saskia S. L.; van Hulzen, Kimm J. E.; Walton, Esther; Wolf, Christiane; Almasy, Laura; Ames, David; Arepalli, Sampath; Assareh, Amelia A.; Bastin, Mark E.; Brodaty, Henry; Bulayeva, Kazima B.; Carless, Melanie A.; Cichon, Sven; Corvin, Aiden; Curran, Joanne E.; Czisch, Michael; de Zubicaray, Greig I.; Dillman, Allissa; Duggirala, Ravi; Dyer, Thomas D.; Erk, Susanne; Fedko, Iryna O.; Ferrucci, Luigi; Foroud, Tatiana M.; Fox, Peter T.; Fukunaga, Masaki; Gibbs, J. Raphael; Göring, Harald H. H.; Green, Robert C.; Guelfi, Sebastian; Hansell, Narelle K.; Hartman, Catharina A.; Hegenscheid, Katrin; Heinz, Andreas; Hernandez, Dena G.; Heslenfeld, Dirk J.; Hoekstra, Pieter J.; Holsboer, Florian; Homuth, Georg; Hottenga, Jouke-Jan; Ikeda, Masashi; Jack, Clifford R.; Jenkinson, Mark; Johnson, Robert; Kanai, Ryota; Keil, Maria; Kent, Jack W.; Kochunov, Peter; Kwok, John B.; Lawrie, Stephen M.; Liu, Xinmin; Longo, Dan L.; McMahon, Katie L.; Meisenzahl, Eva; Melle, Ingrid; Mohnke, Sebastian; Montgomery, Grant W.; Mostert, Jeanette C.; Mühleisen, Thomas W.; Nalls, Michael A.; Nichols, Thomas E.; Nilsson, Lars G.; Nöthen, Markus M.; Ohi, Kazutaka; Olvera, Rene L.; Perez-Iglesias, Rocio; Pike, G. Bruce; Potkin, Steven G.; Reinvang, Ivar; Reppermund, Simone; Rietschel, Marcella; Romanczuk-Seiferth, Nina; Rosen, Glenn D.; Rujescu, Dan; Schnell, Knut; Schofield, Peter R.; Smith, Colin; Steen, Vidar M.; Sussmann, Jessika E.; Thalamuthu, Anbupalam; Toga, Arthur W.; Traynor, Bryan J.; Troncoso, Juan; Turner, Jessica A.; Valdés Hernández, Maria C.; van ’t Ent, Dennis; van der Brug, Marcel; van der Wee, Nic J. A.; van Tol, Marie-Jose; Veltman, Dick J.; Wassink, Thomas H.; Westman, Eric; Zielke, Ronald H.; Zonderman, Alan B.; Ashbrook, David G.; Hager, Reinmar; Lu, Lu; McMahon, Francis J.; Morris, Derek W.; Williams, Robert W.; Brunner, Han G.; Buckner, Randy L.; Buitelaar, Jan K.; Cahn, Wiepke; Calhoun, Vince D.; Cavalleri, Gianpiero L.; Crespo-Facorro, Benedicto; Dale, Anders M.; Davies, Gareth E.; Delanty, Norman; Depondt, Chantal; Djurovic, Srdjan; Drevets, Wayne C.; Espeseth, Thomas; Gollub, Randy L.; Ho, Beng-Choon; Hoffmann, Wolfgang; Hosten, Norbert; Kahn, René S.; Le Hellard, Stephanie; Meyer-Lindenberg, Andreas; Müller-Myhsok, Bertram; Nauck, Matthias; Nyberg, Lars; Pandolfo, Massimo; Penninx, Brenda W. J. H.; Roffman, Joshua L.; Sisodiya, Sanjay M.; Smoller, Jordan W.; van Bokhoven, Hans; van Haren, Neeltje E. M.; Völzke, Henry; Walter, Henrik; Weiner, Michael W.; Wen, Wei; White, Tonya; Agartz, Ingrid; Andreassen, Ole A.; Blangero, John; Boomsma, Dorret I.; Brouwer, Rachel M.; Cannon, Dara M.; Cookson, Mark R.; de Geus, Eco J. C.; Deary, Ian J.; Donohoe, Gary; Fernández, Guillén; Fisher, Simon E.; Francks, Clyde; Glahn, David C.; Grabe, Hans J.; Gruber, Oliver; Hardy, John; Hashimoto, Ryota; Hulshoff Pol, Hilleke E.; Jönsson, Erik G.; Kloszewska, Iwona; Lovestone, Simon; Mattay, Venkata S.; Mecocci, Patrizia; McDonald, Colm; McIntosh, Andrew M.; Ophoff, Roel A.; Paus, Tomas; Pausova, Zdenka; Ryten, Mina; Sachdev, Perminder S.; Saykin, Andrew J.; Simmons, Andy; Singleton, Andrew; Soininen, Hilkka; Wardlaw, Joanna M.; Weale, Michael E.; Weinberger, Daniel R.; Adams, Hieab H. H.; Launer, Lenore J.; Seiler, Stephan; Schmidt, Reinhold; Chauhan, Ganesh; Satizabal, Claudia L.; Becker, James T.; Yanek, Lisa; van der Lee, Sven J.; Ebling, Maritza; Fischl, Bruce; Longstreth, W. T.; Greve, Douglas; Schmidt, Helena; Nyquist, Paul; Vinke, Louis N.; van Duijn, Cornelia M.; Xue, Luting; Mazoyer, Bernard; Bis, Joshua C.; Gudnason, Vilmundur; Seshadri, Sudha; Ikram, M. Arfan; Martin, Nicholas G.; Wright, Margaret J.; Schumann, Gunter; Franke, Barbara; Thompson, Paul M.; Medland, Sarah E.; Department of Radiology and Imaging Sciences, IU School of Medicine
The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270
Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years
(Wiley, 2022-01) Frangou, Sophia; Modabbernia, Amirhossein; Williams, Steven C.R.; Papachristou, Efstathios; Doucet, Gaelle E.; Agartz, Ingrid; Aghajani, Moji; Akudjedu, Theophilus N.; Albajes-Eizagirre, Anton; Alnæs, Dag; Alpert, Kathryn I.; Andersson, Micael; Andreasen, Nancy C.; Andreassen, Ole A.; Asherson, Philip; Banaschewski, Tobias; Bargallo, Nuria; Baumeister, Sarah; Baur-Streubel, Ramona; Bertolino, Alessandro; Bonvino, Aurora; Boomsma, Dorret I.; Borgwardt, Stefan; Bourque, Josiane; Brandeis, Daniel; Breier, Alan; Brodaty, Henry; Brouwer, Rachel M.; Buitelaar, Jan K.; Busatto, Geraldo F.; Buckner, Randy L.; Calhoun, Vincent; Canales-Rodríguez, Erick J.; Cannon, Dara M.; Caseras, Xavier; Castellanos, Francisco X.; Cervenka, Simon; Chaim-Avancini, Tiffany M.; Ching, Christopher R.K.; Chubar, Victoria; Clark, Vincent P.; Conrod, Patricia; Conzelmann, Annette; Crespo-Facorro, Benedicto; Crivello, Fabrice; Crone, Eveline A.; Dale, Anders M.; Dannlowski, Udo; Davey, Christopher; de Geus, Eco J.C.; de Haan, Lieuwe; de Zubicaray, Greig I.; den Braber, Anouk; Dickie, Erin W.; Di Giorgio, Annabella; Doan, Nhat Trung; Dørum, Erlend S.; Ehrlich, Stefan; Erk, Susanne; Espeseth, Thomas; Fatouros-Bergman, Helena; Fisher, Simon E.; Fouche, Jean-Paul; Franke, Barbara; Frodl, Thomas; Fuentes-Claramonte, Paola; Glahn, David C.; Gotlib, Ian H.; Grabe, Hans-Jörgen; Grimm, Oliver; Groenewold, Nynke A.; Grotegerd, Dominik; Gruber, Oliver; Gruner, Patricia; Gur, Rachel E.; Gur, Ruben C.; Hahn, Tim; Harrison, Ben J.; Hartman, Catharine A.; Hatton, Sean N.; Heinz, Andreas; Heslenfeld, Dirk J.; Hibar, Derrek P.; Hickie, Ian B.; Ho, Beng-Choon; Hoekstra, Pieter J.; Hohmann, Sarah; Holmes, Avram J.; Hoogman, Martine; Hosten, Norbert; Howells, Fleur M.; Hulshoff Pol, Hilleke E.; Huyser, Chaim; Jahanshad, Neda; James, Anthony; Jernigan, Terry L.; Jiang, Jiyang; Jönsson, Erik G.; Joska, John A.; Kahn, Rene; Kalnin, Andrew; Kanai, Ryota; Klein, Marieke; Klyushnik, Tatyana P.; Koenders, Laura; Koops, Sanne; Krämer, Bernd; Kuntsi, Jonna; Lagopoulos, Jim; Lázaro, Luisa; Lebedeva, Irina; Lee, Won Hee; Lesch, Klaus-Peter; Lochner, Christine; Machielsen, Marise W.J.; Maingault, Sophie; Martin, Nicholas G.; Martínez-Zalacaín, Ignacio; Mataix-Cols, David; Mazoyer, Bernard; McDonald, Colm; McDonald, Brenna C.; McIntosh, Andrew M.; McMahon, Katie L.; McPhilemy, Genevieve; Meinert, Susanne; Menchón, José M.; Medland, Sarah E.; Meyer-Lindenberg, Andreas; Naaijen, Jilly; Najt, Pablo; Nakao, Tomohiro; Nordvik, Jan E.; Nyberg, Lars; Oosterlaan, Jaap; Ortiz-García de la Foz, Víctor; Paloyelis, Yannis; Pauli, Paul; Pergola, Giulio; Pomarol-Clotet, Edith; Portella, Maria J.; Potkin, Steven G.; Radua, Joaquim; Reif, Andreas; Rinker, Daniel A.; Roffman, Joshua L.; Rosa, Pedro G.P.; Sacchet, Matthew D.; Sachdev, Perminder S.; Salvador, Raymond; Sánchez-Juan, Pascual; Sarró, Salvador; Satterthwaite, Theodore D.; Saykin, Andrew J.; Serpa, Mauricio H.; Schmaal, Lianne; Schnell, Knut; Schumann, Gunter; Sim, Kang; Smoller, Jordan W.; Sommer, Iris; Soriano-Mas, Carles; Stein, Dan J.; Strike, Lachlan T.; Swagerman, Suzanne C.; Tamnes, Christian K.; Temmingh, Henk S.; Thomopoulos, Sophia I.; Tomyshev, Alexander S.; Tordesillas-Gutiérrez, Diana; Trollor, Julian N.; Turner, Jessica A.; Uhlmann, Anne; van den Heuvel, Odile A.; van den Meer, Dennis; van der Wee, Nic J.A.; van Haren, Neeltje E.M.; van't Ent, Dennis; van Erp, Theo G.M.; Veer, Ilya M.; Veltman, Dick J.; Voineskos, Aristotle; Völzke, Henry; Walter, Henrik; Walton, Esther; Wang, Lei; Wang, Yang; Wassink, Thomas H.; Weber, Bernd; Wen, Wei; West, John D.; Westlye, Lars T.; Whalley, Heather; Wierenga, Lara M.; Wittfeld, Katharina; Wolf, Daniel H.; Worker, Amanda; Wright, Margaret J.; Yang, Kun; Yoncheva, Yulyia; Zanetti, Marcus V.; Ziegler, Georg C.; Karolinska Schizophrenia Project (KaSP); Thompson, Paul M.; Dima, Danai; Radiology and Imaging Sciences, School of Medicine
Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
Distance-weighted Sinkhorn loss for Alzheimer's disease classification
(Elsevier, 2024-02-12) Wang, Zexuan; Zhan, Qipeng; Tong, Boning; Yang, Shu; Hou, Bojian; Huang, Heng; Saykin, Andrew J.; Thompson, Paul M.; Davatzikos, Christos; Shen, Li; Radiology and Imaging Sciences, School of Medicine
Traditional loss functions such as cross-entropy loss often quantify the penalty for each mis-classified training sample without adequately considering its distance from the ground truth class distribution in the feature space. Intuitively, the larger this distance is, the higher the penalty should be. With this observation, we propose a penalty called distance-weighted Sinkhorn (DWS) loss. For each mis-classified training sample (with predicted label A and true label B), its contribution to the DWS loss positively correlates to the distance the training sample needs to travel to reach the ground truth distribution of all the A samples. We apply the DWS framework with a neural network to classify different stages of Alzheimer's disease. Our empirical results demonstrate that the DWS framework outperforms the traditional neural network loss functions and is comparable or better to traditional machine learning methods, highlighting its potential in biomedical informatics and data science.
ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide
(Elsevier, 2017-01-15) Thompson, Paul M.; Andreassen, Ole A.; Arias-Vasquez, Alejandro; Bearden, Carrie E.; Boedhoe, Premika S.; Brouwer, Rachel M.; Buckner, Randy L.; Buitelaar, Jan K.; Bulayeva, Kazima B.; Cannon, Dara M.; Cohen, Ronald A.; Conrod, Patricia J.; Dale, Anders M.; Deary, Ian J.; Dennis, Emily L.; de Reus, Marcel A.; Desrivieres, Sylvane; Dima, Danai; Donohoe, Gary; Fisher, Simon E.; Fouche, Jean-Paul; Francks, Clyde; Frangou, Sophia; Franke, Barbara; Ganjgahi, Habib; Garavan, Hugh; Glahn, David C.; Grabe, Hans J.; Guadalupe, Tulio; Gutman, Boris A.; Hashimoto, Ryota; Hibar, Derrek P.; Holland, Dominic; Hoogman, Martine; Pol, Hilleke E. Hulshoff; Hosten, Norbert; Jahanshad, Neda; Kelly, Sinead; Kochunov, Peter; Kremen, William S.; Lee, Phil H.; Mackey, Scott; Martin, Nicholas G.; Mazoyer, Bernard; McDonald, Colm; Medland, Sarah E.; Morey, Rajendra A.; Nichols, Thomas E.; Paus, Tomas; Pausova, Zdenka; Schmaal, Lianne; Schumann, Gunter; Shen, Li; Sisodiya, Sanjay M.; Smit, Dirk J.A.; Smoller, Jordan W.; Stein, Dan J.; Stein, Jason L.; Toro, Roberto; Turner, Jessica A.; Heuvel, Martijn P. van den; Heuvel, Odile L. van den; Erp, Theo G.M. van; Rooij, Daan van; Veltman, Dick J.; Walter, Henrik; Wang, Yalin; Wardlaw, Joanna M.; Whelan, Christopher D.; Wright, Margaret J.; Ye, Jieping; ENIGMA Consortium; Radiology and Imaging Sciences, School of Medicine
In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) – a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date – of schizophrenia and major depression – ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens – now numbering over 30,000 MRI scans – have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants – and genetic variants in general – may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures – from tens of thousands of people – that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.

Browsing by Author "Thompson, Paul M."

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