Paul Thompson
University of Southern California
H-index: 205
North America-United States
Description
Paul Thompson, With an exceptional h-index of 205 and a recent h-index of 124 (since 2020), a distinguished researcher at University of Southern California, specializes in the field of Brain Imaging, Neurology, Psychiatry, Neuroimaging, Genetics.
His recent articles reflect a diverse array of research interests and contributions to the field:
Cortical microstructural associations with CSF amyloid and pTau
Intracranial and subcortical volumes in adolescents with early‐onset psychosis: A multisite mega‐analysis from the ENIGMA consortium
Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model
A practical Alzheimer’s disease classifier via brain imaging-based deep learning on 85,721 samples
ENIGMA‐DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross‐diagnostic psychiatric research
ENIGMA‐anxiety working group: Rationale for and organization of large‐scale neuroimaging studies of anxiety disorders
The ENIGMA‐Epilepsy working group: Mapping disease from large data sets
Systematic evaluation of machine learning algorithms for neuroanatomically‐based age prediction in youth
Professor Information
University | University of Southern California |
---|---|
Position | Professor of Neurology & Psychiatry Imaging Genetics Center |
Citations(all) | 188262 |
Citations(since 2020) | 74205 |
Cited By | 152469 |
hIndex(all) | 205 |
hIndex(since 2020) | 124 |
i10Index(all) | 1427 |
i10Index(since 2020) | 990 |
University Profile Page | University of Southern California |
Research & Interests List
Brain Imaging
Neurology
Psychiatry
Neuroimaging
Genetics
Top articles of Paul Thompson
Cortical microstructural associations with CSF amyloid and pTau
Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer’s disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1–42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer’s Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1–42 and higher pTau181 were associated with cortical dMRI measures …
Authors
Talia M Nir,Julio E Villalón-Reina,Lauren E Salminen,Elizabeth Haddad,Hong Zheng,Sophia I Thomopoulos,Clifford R Jack Jr,Michael W Weiner,Paul M Thompson,Neda Jahanshad,Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Journal
Molecular Psychiatry
Published Date
2023/12/13
Intracranial and subcortical volumes in adolescents with early‐onset psychosis: A multisite mega‐analysis from the ENIGMA consortium
Early‐onset psychosis disorders are serious mental disorders arising before the age of 18 years. Here, we investigate the largest neuroimaging dataset, to date, of patients with early‐onset psychosis and healthy controls for differences in intracranial and subcortical brain volumes. The sample included 263 patients with early‐onset psychosis (mean age: 16.4 ± 1.4 years, mean illness duration: 1.5 ± 1.4 years, 39.2% female) and 359 healthy controls (mean age: 15.9 ± 1.7 years, 45.4% female) with magnetic resonance imaging data, pooled from 11 clinical cohorts. Patients were diagnosed with early‐onset schizophrenia (n = 183), affective psychosis (n = 39), or other psychotic disorders (n = 41). We used linear mixed‐effects models to investigate differences in intracranial and subcortical volumes across the patient sample, diagnostic subgroup and antipsychotic medication, relative to controls. We …
Authors
Tiril P Gurholt,Vera Lonning,Stener Nerland,Kjetil N Jørgensen,Unn K Haukvik,Clara Alloza,Celso Arango,Claudia Barth,Carrie E Bearden,Michael Berk,Hannes Bohman,Orwa Dandash,Covadonga M Díaz‐Caneja,Carl T Edbom,Theo GM van Erp,Anne‐Kathrin J Fett,Sophia Frangou,Benjamin I Goldstein,Anahit Grigorian,Neda Jahanshad,Anthony C James,Joost Janssen,Cecilie Johannessen,Katherine H Karlsgodt,Matthew J Kempton,Peter Kochunov,Lydia Krabbendam,Marinos Kyriakopoulos,Mathias Lundberg,Bradley J MacIntosh,Bjørn Rishovd Rund,Runar E Smelror,Alysha Sultan,Christian K Tamnes,Sophia I Thomopoulos,Ariana Vajdi,Kirsten Wedervang‐Resell,Anne M Myhre,Ole A Andreassen,Paul M Thompson,Ingrid Agartz,ENIGMA‐EOP Working Group
Journal
Human brain mapping
Published Date
2022/1
Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model
Alzheimer’s disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomics – the study of gene expression – also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person’s genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated …
Authors
Jianfeng Wu,Yanxi Chen,Panwen Wang,Richard J Caselli,Paul M Thompson,Junwen Wang,Yalin Wang
Journal
Frontiers in radiology
Published Date
2022/1/21
A practical Alzheimer’s disease classifier via brain imaging-based deep learning on 85,721 samples
Beyond detecting brain lesions or tumors, comparatively little success has been attained in identifying brain disorders such as Alzheimer’s disease (AD), based on magnetic resonance imaging (MRI). Many machine learning algorithms to detect AD have been trained using limited training data, meaning they often generalize poorly when applied to scans from previously unseen scanners/populations. Therefore, we built a practical brain MRI-based AD diagnostic classifier using deep learning/transfer learning on a dataset of unprecedented size and diversity. A retrospective MRI dataset pooled from more than 217 sites/scanners constituted one of the largest brain MRI samples to date (85,721 scans from 50,876 participants) between January 2017 and August 2021. Next, a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, was built as a sex classifier with high generalization capability. The sex …
Authors
Bin Lu,Hui-Xian Li,Zhi-Kai Chang,Le Li,Ning-Xuan Chen,Zhi-Chen Zhu,Hui-Xia Zhou,Xue-Ying Li,Yu-Wei Wang,Shi-Xian Cui,Zhao-Yu Deng,Zhen Fan,Hong Yang,Xiao Chen,Paul M Thompson,Francisco Xavier Castellanos,Chao-Gan Yan
Journal
Journal of Big Data
Published Date
2022/10/13
ENIGMA‐DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross‐diagnostic psychiatric research
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 …
Authors
Peter Kochunov,L Elliot Hong,Emily L Dennis,Rajendra A Morey,David F Tate,Elisabeth A Wilde,Mark Logue,Sinead Kelly,Gary Donohoe,Pauline Favre,Josselin Houenou,Christopher RK Ching,Laurena Holleran,Ole A Andreassen,Laura S van Velzen,Lianne Schmaal,Julio E Villalón‐Reina,Carrie E Bearden,Fabrizio Piras,Gianfranco Spalletta,Odile A van den Heuvel,Dick J Veltman,Dan J Stein,Meghann C Ryan,Yunlong Tan,Theo GM van Erp,Jessica A Turner,Liz Haddad,Talia M Nir,David C Glahn,Paul M Thompson,Neda Jahanshad
Published Date
2022/1
ENIGMA‐anxiety working group: Rationale for and organization of large‐scale neuroimaging studies of anxiety disorders
Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA‐Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA‐Anxiety database contains information about more than 100 …
Authors
Janna Marie Bas‐Hoogendam,Nynke A Groenewold,Moji Aghajani,Gabrielle F Freitag,Anita Harrewijn,Kevin Hilbert,Neda Jahanshad,Sophia I Thomopoulos,Paul M Thompson,Dick J Veltman,Anderson M Winkler,Ulrike Lueken,Daniel S Pine,Nic JA Van der Wee,Dan J Stein,ENIGMA‐Anxiety Working Group,Federica Agosta,Fredrik Åhs,Iseul An,Bianca AV Alberton,Carmen Andreescu,Takeshi Asami,Michal Assaf,Suzanne N Avery,L Nicholas,Balderston,Jacques P Barber,Marco Battaglia,Ali Bayram,Katja Beesdo‐Baum,Francesco Benedetti,Rachel Berta,Johannes Björkstrand,Jennifer Urbano Blackford,James R Blair,S Karina,Blair,Stephanie Boehme,Paolo Brambilla,Katie Burkhouse,Marta Cano,Elisa Canu,Elise M Cardinale,Narcis Cardoner,Jacqueline A Clauss,Camilla Cividini,Hugo D Critchley,Udo,Dannlowski,Jurgen Deckert,Tamer Demiralp,Gretchen J Diefenbach,Katharina Domschke,Alex Doruyter,Thomas Dresler,Angelika Erhardt,Andreas J Fallgatter,Lourdes Fañanás,Brandee,Feola,Courtney A Filippi,Massimo Filippi,Gregory A Fonzo,Erika E Forbes,Nathan A Fox,Mats Fredrikson,Tomas Furmark,Tian Ge,Andrew J Gerber,Savannah N Gosnell,Hans J Grabe,Dominik Grotegerd,Raquel E Gur,Ruben C Gur,Catherine J Harmer,Jennifer Harper,Alexandre Heeren,John Hettema,David Hofmann,Stefan G Hofmann,Andrea P Jackowski,Andreas,Jansen,Antonia N Kaczkurkin,Ellen Kingsley,Tilo Kircher,Milutin Kosti c,Benjamin Kreifelts,Axel Krug,Bart Larsen,Sang‐Hyuk Lee,Elisabeth J Leehr,Ellen Leibenluft,Christine Lochner,Eleonora Maggioni,Elena Makovac,Matteo Mancini,Gisele G Manfro,Kristoffer NT Månsson,Frances Meeten,Jarosław Michałowski,Barbara L Milrod,Andreas Mühlberger,R Lilianne,Ana Munjiza,Benson Mwangi,Michael Myers,c Igor Nenadi,Susanne Neufang,Jared A Nielsen,Hyuntaek Oh,Cristina Ottaviani,Pedro M Pan,Spiro P Pantazatos,P Martin,Paulus,Koraly Perez‐Edgar,Wenceslao Peñate,Michael T Perino,Jutta Peterburs,Bettina Pfleiderer,K Luan Phan,Sara Poletti,Daniel Porta‐Casteràs,Rebecca B Price,Jesus Pujol,Andrea,Reinecke,Francisco Rivero,Karin Roelofs,Isabelle Rosso,Philipp Saemann,Ramiro Salas,Giovanni A Salum,Theodore D Satterthwaite,Franklin Schneier,Koen RJ Schruers,Stefan M Schulz,Hanna Schwarzmeier,Fabian R Seeger,Jordan W Smoller,Jair C Soares,Rudolf Stark,Murray B Stein,Benjamin Straube,Thomas Straube,Jeffrey R Strawn,Benjamin Suarez‐Jimenez,Boris,Suchan
Published Date
2022/1
The ENIGMA‐Epilepsy working group: Mapping disease from large data sets
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 …
Authors
Sanjay M Sisodiya,Christopher D Whelan,Sean N Hatton,Khoa Huynh,Andre Altmann,Mina Ryten,Annamaria Vezzani,Maria Eugenia Caligiuri,Angelo Labate,Antonio Gambardella,Victoria Ives‐Deliperi,Stefano Meletti,Brent C Munsell,Leonardo Bonilha,Manuela Tondelli,Michael Rebsamen,Christian Rummel,Anna Elisabetta Vaudano,Roland Wiest,Akshara R Balachandra,Nuria Bargallo,Emanuele Bartolini,Andrea Bernasconi,Neda Bernasconi,Boris Bernhardt,Benoit Caldairou,Sarah JA Carr,Gianpiero L Cavalleri,Fernando Cendes,Luis Concha,Patricia M Desmond,Martin Domin,John S Duncan,Niels K Focke,Renzo Guerrini,Khalid Hamandi,Graeme D Jackson,Neda Jahanshad,Reetta Kälviäinen,Simon S Keller,Peter Kochunov,Magdalena A Kowalczyk,Barbara AK Kreilkamp,Patrick Kwan,Sara Lariviere,Matteo Lenge,Seymour M Lopez,Pascal Martin,Mario Mascalchi,Jose CV Moreira,Marcia E Morita‐Sherman,Heath R Pardoe,Jose C Pariente,Kotikalapudi Raviteja,Cristiane S Rocha,Raúl Rodríguez‐Cruces,Margitta Seeck,Mira KHG Semmelroch,Benjamin Sinclair,Hamid Soltanian‐Zadeh,Dan J Stein,Pasquale Striano,Peter N Taylor,Rhys H Thomas,Sophia I Thomopoulos,Dennis Velakoulis,Lucy Vivash,Bernd Weber,Clarissa Lin Yasuda,Junsong Zhang,Paul M Thompson,Carrie R McDonald,ENIGMA Consortium Epilepsy Working Group,Eugenio Abela,Julie Absil,Sophia Adams,Saud Alhusaini,Marina Alvim,Simona Balestrini,Benjamin Bender,Felipe Bergo,Tauana Bernardes,Anna Calvo,Mar Carreno,Andrea Cherubini,Philippe David,Esmaeil Davoodi‐Bojd,Norman Delanty,Chantal Depondt,Orrin Devinsky,Colin Doherty,Wendy Caroline França,Leticia Franceschet,Derrek P Hibar,Akari Ishikawa,Erik Kaestner,Soenke Langner,Min Liu,Laura Mirandola,Jillian Naylor,Mohammad‐reza Nazem‐Zadeh,Terence J O'Brien,Letícia F Ribeiro,Mark Richardson,Felix Rosenow,Mariasavina Severino,Chen Shuai,Domenico Tortora,Felix von Podewils,Sjoerd B Vos,Jan Wagner,Guohao Zhang
Published Date
2022/1
Systematic evaluation of machine learning algorithms for neuroanatomically‐based age prediction in youth
Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain‐age). The choice of the ML approach in estimating brain‐age in youth is important because age‐related brain changes in this age‐group are dynamic. However, the comparative performance of the available ML algorithms has not been systematically appraised. To address this gap, the present study evaluated the accuracy (mean absolute error [MAE]) and computational efficiency of 21 machine learning algorithms using sMRI data from 2105 typically developing individuals aged 5–22 years from five cohorts. The trained models were then tested in two independent holdout datasets, one comprising 4078 individuals aged 9–10 years and another comprising 594 individuals aged 5–21 years. The algorithms encompassed …
Authors
Amirhossein Modabbernia,Heather C Whalley,David C Glahn,Paul M Thompson,Rene S Kahn,Sophia Frangou
Published Date
2022/12/1
Professor FAQs
What is Paul Thompson's h-index at University of Southern California?
The h-index of Paul Thompson has been 124 since 2020 and 205 in total.
What are Paul Thompson's top articles?
The articles with the titles of
Cortical microstructural associations with CSF amyloid and pTau
Intracranial and subcortical volumes in adolescents with early‐onset psychosis: A multisite mega‐analysis from the ENIGMA consortium
Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model
A practical Alzheimer’s disease classifier via brain imaging-based deep learning on 85,721 samples
ENIGMA‐DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross‐diagnostic psychiatric research
ENIGMA‐anxiety working group: Rationale for and organization of large‐scale neuroimaging studies of anxiety disorders
The ENIGMA‐Epilepsy working group: Mapping disease from large data sets
Systematic evaluation of machine learning algorithms for neuroanatomically‐based age prediction in youth
...
are the top articles of Paul Thompson at University of Southern California.
What are Paul Thompson's research interests?
The research interests of Paul Thompson are: Brain Imaging, Neurology, Psychiatry, Neuroimaging, Genetics
What is Paul Thompson's total number of citations?
Paul Thompson has 188,262 citations in total.
What are the co-authors of Paul Thompson?
The co-authors of Paul Thompson are Elizabeth Sowell, Neda Jahanshad, Greig de Zubicaray, Katie McMahon, Jason L Stein, Alex Leow MDPhD.