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  1. Book ; Conference proceedings: Mathematics in brain imaging

    Thompson, Paul M.

    [workshop held ... from July 14 to 25 2008, Los Angeles, Calif.]

    (NeuroImage ; 45, Suppl. 1)

    2009  

    Author's details suppl. ed.: Paul M. Thompson
    Series title NeuroImage ; 45, Suppl. 1
    Collection
    Language English
    Size S221 S. : Ill., graph. Darst.
    Publisher Elsevier
    Publishing place San Diego, Calif
    Publishing country United States
    Document type Book ; Conference proceedings
    HBZ-ID HT015880924
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    Zs A 3664 -45,Suppl.1- not available for loan Zeitschriften-Lesesaal

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  2. Article ; Online: Correction/Erratum.

    Thompson, Paul M

    NeuroImage

    2020  Volume 223, Page(s) 117312

    Language English
    Publishing date 2020-09-03
    Publishing country United States
    Document type Journal Article ; Published Erratum
    ZDB-ID 1147767-2
    ISSN 1095-9572 ; 1053-8119
    ISSN (online) 1095-9572
    ISSN 1053-8119
    DOI 10.1016/j.neuroimage.2020.117312
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 3664: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 7: Show issues

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  3. Book: Mathematics in brain imaging

    Thompson, Paul M.

    (NeuroImage ; 23, Suppl. 1)

    2004  

    Author's details guest ed. Paul M. Thompson
    Series title NeuroImage ; 23, Suppl. 1
    Collection
    Keywords Bildgebendes Verfahren ; Gehirn ; Mathematik
    Subject Bildgebendes Diagnoseverfahren ; Diagnostik ; Bilddiagnostik ; Bildgebende Methode ; Medical Imaging ; Medizinische Bildgebung ; Bildgebende Diagnostik ; Bildgebende Verfahren ; Imaging ; Reine Mathematik ; Cerebrum ; Hirn ; Encephalon ; Enzephalon ; Hirngewebe ; Hirnmasse ; Gehirnmasse
    Language English
    Size S299 S. : Ill., graph. Darst.
    Publisher Elsevier
    Publishing place San Diego, Ca
    Publishing country United States
    Document type Book
    HBZ-ID HT014219776
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    Zs A 3664 -23,Suppl.1- not available for loan Zeitschriften-Lesesaal

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  4. Article: Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer's Disease Effect Detection.

    Dhinagar, Nikhil J / Thomopoulos, Sophia I / Laltoo, Emily / Thompson, Paul M

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Generative AI models have recently achieved mainstream attention with the advent of powerful approaches such as stable diffusion, DALL-E and MidJourney. The underlying breakthrough generative mechanism of denoising diffusion modeling can generate high ... ...

    Abstract Generative AI models have recently achieved mainstream attention with the advent of powerful approaches such as stable diffusion, DALL-E and MidJourney. The underlying breakthrough generative mechanism of denoising diffusion modeling can generate high quality synthetic images and can learn the underlying distribution of complex, high-dimensional data. Recent research has begun to extend these models to medical and specifically neuroimaging data. Typical neuroimaging tasks such as diagnostic classification and predictive modeling often rely on deep learning approaches based on convolutional neural networks (CNNs) and vision transformers (ViTs), with additional steps to help in interpreting the results. In our paper, we train conditional latent diffusion models (LDM) and denoising diffusion probabilistic models (DDPM) to provide insight into Alzheimer's disease (AD) effects on the brain's anatomy at the individual level. We first created diffusion models that could generate synthetic MRIs, by training them on real 3D T1-weighted MRI scans, and conditioning the generative process on the clinical diagnosis as a context variable. We conducted experiments to overcome limitations in training dataset size, compute time and memory resources, testing different model sizes, effects of pretraining, training duration, and latent diffusion models. We tested the sampling quality of the disease-conditioned diffusion using metrics to assess realism and diversity of the generated synthetic MRIs. We also evaluated the ability of diffusion models to conditionally sample MRI brains using a 3D CNN-based disease classifier relative to real MRIs. In our experiments, the diffusion models generated synthetic data that helped to train an AD classifier (using only 500 real training scans) -and boosted its performance by over 3% when tested on real MRI scans. Further, we used implicit classifier-free guidance to alter the conditioning of an encoded individual scan to its counterfactual (representing a healthy subject of the same age and sex) while preserving subject-specific image details. From this counterfactual image (where the same person appears healthy), a personalized disease map was generated to identify possible disease effects on the brain. Our approach efficiently generates realistic and diverse synthetic data, and may create interpretable AI-based maps for neuroscience research and clinical diagnostic applications.
    Language English
    Publishing date 2024-02-08
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.02.05.578983
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Large-Scale Neuroimaging of Mental Illness.

    Ching, Christopher R K / Kang, Melody J Y / Thompson, Paul M

    Current topics in behavioral neurosciences

    2024  

    Abstract: Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness ... ...

    Abstract Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.
    Language English
    Publishing date 2024-03-31
    Publishing country Germany
    Document type Journal Article
    ISSN 1866-3370
    ISSN 1866-3370
    DOI 10.1007/7854_2024_462
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Predictive Modeling Of Alzheimer's Disease Prognosis Using Anatomical & Diffusion MRI.

    Goel, Nikita / Thomopoulos, Sophia I / Chattopadhyay, Tamoghna / Thompson, Paul M

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2023  Volume 2023, Page(s) 1–5

    Abstract: Mild cognitive impairment (MCI) is an intermediate stage between healthy aging and Alzheimer's disease (AD), and AD is a progressive neurodegenerative disorder that affects around 50 million people worldwide. As new AD treatments begin to be developed, ... ...

    Abstract Mild cognitive impairment (MCI) is an intermediate stage between healthy aging and Alzheimer's disease (AD), and AD is a progressive neurodegenerative disorder that affects around 50 million people worldwide. As new AD treatments begin to be developed, one key goal of AD research is to predict which individuals with MCI are most likely to progress to AD over a given interval (such as 2 years); if successful, these individuals could be preferentially enrolled in drug trials that aim to slow AD progression. Here we benchmarked a range of MCI-to-AD predictive models including linear regressions, support vector machines, and random forests, using predictors from anatomical and diffusion-weighted brain MRI, age, sex, APOE genotype and standardized clinical scores. In evaluations on 2,448 subjects (1,132 MCI, 883 healthy controls, 433 with dementia) from the ADNI study, models including PCA-compacted features achieved a balanced accuracy of 75.3% (using cortical features) and 89.7% using diffusion MRI measures on test set, suggesting the added prognostic value of microstructural metrics obtainable with diffusion MRI.
    MeSH term(s) Humans ; Alzheimer Disease/diagnostic imaging ; Magnetic Resonance Imaging ; Diffusion Magnetic Resonance Imaging ; Cognitive Dysfunction/diagnostic imaging ; Brain/diagnostic imaging
    Language English
    Publishing date 2023-12-08
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC40787.2023.10341001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection.

    Dhinagar, Nikhil J / Thomopoulos, Sophia I / Laltoo, Emily / Thompson, Paul M

    ArXiv

    2023  

    Abstract: Neuroimaging of large populations is valuable to identify factors that promote or resist brain disease, and to assist diagnosis, subtyping, and prognosis. Data-driven models such as convolutional neural networks (CNNs) have increasingly been applied to ... ...

    Abstract Neuroimaging of large populations is valuable to identify factors that promote or resist brain disease, and to assist diagnosis, subtyping, and prognosis. Data-driven models such as convolutional neural networks (CNNs) have increasingly been applied to brain images to perform diagnostic and prognostic tasks by learning robust features. Vision transformers (ViT) - a new class of deep learning architectures - have emerged in recent years as an alternative to CNNs for several computer vision applications. Here we tested variants of the ViT architecture for a range of desired neuroimaging downstream tasks based on difficulty, in this case for sex and Alzheimer's disease (AD) classification based on 3D brain MRI. In our experiments, two vision transformer architecture variants achieved an AUC of 0.987 for sex and 0.892 for AD classification, respectively. We independently evaluated our models on data from two benchmark AD datasets. We achieved a performance boost of 5% and 9-10% upon fine-tuning vision transformer models pre-trained on synthetic (generated by a latent diffusion model) and real MRI scans, respectively. Our main contributions include testing the effects of different ViT training strategies including pre-training, data augmentation and learning rate warm-ups followed by annealing, as pertaining to the neuroimaging domain. These techniques are essential for training ViT-like models for neuroimaging applications where training data is usually limited. We also analyzed the effect of the amount of training data utilized on the test-time performance of the ViT via data-model scaling curves.
    Language English
    Publishing date 2023-03-14
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection.

    Dhinagar, Nikhil J / Thomopoulos, Sophia I / Laltoo, Emily / Thompson, Paul M

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2023  Volume 2023, Page(s) 1–6

    Abstract: Neuroimaging of large populations is valuable to identify factors that promote or resist brain disease, and to assist diagnosis, subtyping, and prognosis. Data-driven models such as convolutional neural networks (CNNs) have increasingly been applied to ... ...

    Abstract Neuroimaging of large populations is valuable to identify factors that promote or resist brain disease, and to assist diagnosis, subtyping, and prognosis. Data-driven models such as convolutional neural networks (CNNs) have increasingly been applied to brain images to perform diagnostic and prognostic tasks by learning robust features. Vision transformers (ViT) - a new class of deep learning architectures - have emerged in recent years as an alternative to CNNs for several computer vision applications. Here we tested variants of the ViT architecture for a range of desired neuroimaging downstream tasks based on difficulty, in this case for sex and Alzheimer's disease (AD) classification based on 3D brain MRI. In our experiments, two vision transformer architecture variants achieved an AUC of 0.987 for sex and 0.892 for AD classification, respectively. We independently evaluated our models on data from two benchmark AD datasets. We achieved a performance boost of 5% and 9-10% upon fine-tuning vision transformer models pre-trained on synthetic (generated by a latent diffusion model) and real MRI scans, respectively. Our main contributions include testing the effects of different ViT training strategies including pre-training, data augmentation and learning rate warm-ups followed by annealing, as pertaining to the neuroimaging domain. These techniques are essential for training ViT-like models for neuroimaging applications where training data is usually limited. We also analyzed the effect of the amount of training data utilized on the test-time performance of the ViT via data-model scaling curves.Clinical Relevance- The models evaluated in this work could be trained on neuroimaging data to assist in diagnosis, subtyping and prognosis of Alzheimer's disease.
    MeSH term(s) Humans ; Alzheimer Disease/diagnostic imaging ; Magnetic Resonance Imaging ; Neuroimaging ; Benchmarking ; Brain/diagnostic imaging
    Language English
    Publishing date 2023-12-22
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC40787.2023.10341190
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Significant heterogeneity in structural asymmetry of the habenula in the human brain: A systematic review and meta-analysis.

    Abuduaini, Yilamujiang / Pu, Yi / Thompson, Paul M / Kong, Xiang-Zhen

    Human brain mapping

    2023  Volume 44, Issue 10, Page(s) 4165–4182

    Abstract: Understanding the evolutionarily conserved feature of functional laterality in the habenula has been attracting attention due to its potential role in human cognition and neuropsychiatric disorders. Deciphering the structure of the human habenula remains ...

    Abstract Understanding the evolutionarily conserved feature of functional laterality in the habenula has been attracting attention due to its potential role in human cognition and neuropsychiatric disorders. Deciphering the structure of the human habenula remains to be challenging, which resulted in inconsistent findings for brain disorders. Here, we present a large-scale meta-analysis of the left-right differences in the habenular volume in the human brain to provide a clearer picture of the habenular asymmetry. We searched PubMed, Web of Science, and Google Scholar for articles that reported volume data of the bilateral habenula in the human brain, and assessed the left-right differences. We also assessed the potential effects of several moderating variables including the mean age of the participants, magnetic field strengths of the scanners and different disorders by using meta-regression and subgroup analysis. In total 52 datasets (N = 1427) were identified and showed significant heterogeneity in the left-right differences and the unilateral volume per se. Moderator analyses suggested that such heterogeneity was mainly due to different MRI scanners and segmentation approaches used. While inversed asymmetry patterns were suggested in patients with depression (leftward) and schizophrenia (rightward), no significant disorder-related differences relative to healthy controls were found in either the left-right asymmetry or the unilateral volume. This study provides useful data for future studies of brain imaging and methodological developments related to precision habenula measurements, and also helps to further understand potential roles of the habenula in various disorders.
    MeSH term(s) Humans ; Habenula/diagnostic imaging ; Cognition ; Magnetic Resonance Imaging ; Functional Laterality
    Language English
    Publishing date 2023-05-17
    Publishing country United States
    Document type Meta-Analysis ; Systematic Review ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1197207-5
    ISSN 1097-0193 ; 1065-9471
    ISSN (online) 1097-0193
    ISSN 1065-9471
    DOI 10.1002/hbm.26337
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Cracking the brain's genetic code.

    Thompson, Paul M

    Proceedings of the National Academy of Sciences of the United States of America

    2015  Volume 112, Issue 50, Page(s) 15269–15270

    MeSH term(s) Forecasting ; Research ; Science
    Language English
    Publishing date 2015-12-15
    Publishing country United States
    Document type Comment ; Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.1520702112
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

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    Zs.A 1148: Show issues Location:
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