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Book ; Online: Collaborative Efforts for Understanding the Human Brain

Liew, Sook-Lei / Schmaal, Lianne / Jahanshad, Neda

2019

Keywords	Science: general issues ; Neurosciences ; Brain ; Neuroscience ; collaboration ; brain imaging ; MRI ; fMRI ; EEG ; Genetics ; imaging genetics ; Software
Size	1 electronic resource (192 pages)
Publisher	Frontiers Media SA
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT021231155
ISBN	9782889630295 ; 2889630293
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Introduction to the Special Issue: 2020 Pacific Rim New Horizons in Human Brain Imaging: Neuroimaging across the Lifespan.

Jahanshad, Neda / Zuo, Xi-Nian

Brain imaging and behavior

2022 Volume 15, Issue 6, Page(s) 2737–2740

MeSH term(s)	Brain/diagnostic imaging ; Humans ; Longevity ; Magnetic Resonance Imaging ; Neuroimaging
Language	English
Publishing date	2022-01-05
Publishing country	United States
Document type	Editorial
ZDB-ID	2377165-3
ISSN	1931-7565 ; 1931-7557
ISSN (online)	1931-7565
ISSN	1931-7557
DOI	10.1007/s11682-021-00606-5
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Article: Subclinical variations on ECG and their associations with structural brain aging networks.

Haddad, Elizabeth / Matloff, William / Park, Gilsoon / Liu, Mengting / Jahanshad, Neda / Kim, Ho Sung

medRxiv : the preprint server for health sciences

2024

Abstract: Impaired cardiac function is associated with cognitive impairment and brain imaging features of aging. Cardiac arrhythmias, including atrial fibrillation, are implicated in clinical and subclinical brain injuries. Even in the absence of a clinical ... ...

Abstract	Impaired cardiac function is associated with cognitive impairment and brain imaging features of aging. Cardiac arrhythmias, including atrial fibrillation, are implicated in clinical and subclinical brain injuries. Even in the absence of a clinical diagnosis, subclinical or prodromal substrates of arrhythmias, including an abnormally long or short P-wave duration (PWD), a measure associated with atrial abnormalities, have been associated with stroke and cognitive decline. However, the extent to which PWD has subclinical influences on overall aging patterns of the brain is not clearly understood. Here, using neuroimaging and ECG data from the UK Biobank, we use a novel regional "brain age" method to identify the brain aging networks associated with abnormal PWD. We find that PWD is inversely associated with accelerated brain aging in the sensorimotor, frontoparietal, ventral attention, and dorsal attention networks, even in the absence of overt cardiac diseases. These findings suggest that detrimental aging outcomes may result from subclinically abnormal PWD.
Language	English
Publishing date	2024-03-19
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.18.24304486
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Article ; Online: Harmonizing three-dimensional MRI using pseudo-warping field guided GAN.

Lin, Jiaying / Li, Zhuoshuo / Zeng, Youbing / Liu, Xiaobo / Li, Liang / Jahanshad, Neda / Ge, Xinting / Zhang, Dan / Lu, Minhua / Liu, Mengting

NeuroImage

2024 , Page(s) 120635

Abstract: In pursuit of cultivating automated models for magnetic resonance imaging (MRI) to aid in diagnostics, an escalating demand for extensive, multisite, and heterogeneous brain imaging datasets has emerged. This potentially introduces biased outcomes when ... ...

Abstract	In pursuit of cultivating automated models for magnetic resonance imaging (MRI) to aid in diagnostics, an escalating demand for extensive, multisite, and heterogeneous brain imaging datasets has emerged. This potentially introduces biased outcomes when directly applied for subsequent analysis. Researchers have endeavored to address this issue by pursuing the harmonization of MRIs. However, most existing image-based harmonization methods for MRI are tailored for 2D slices, which may introduce inter-slice variations when they are combined into a 3D volume. In this study, we aim to resolve inconsistencies between slices by introducing a pseudo-warping field. This field is created randomly and utilized to transform a slice into an artificially warped subsequent slice. The objective of this pseudo-warping field is to ensure that generators can consistently harmonize adjacent slices to another domain, without being affected by the varying content present in different slices. Furthermore, we construct unsupervised spatial and recycle loss to enhance the spatial accuracy and slice-wise consistency across the 3D images. The results demonstrate that our model effectively mitigates inter-slice variations and successfully preserves the anatomical details of the images during the harmonization process. Compared to generative harmonization models that employ 3D operators, our model exhibits greater computational efficiency and flexibility.
Language	English
Publishing date	2024-05-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	1147767-2
ISSN	1095-9572 ; 1053-8119
ISSN (online)	1095-9572
ISSN	1053-8119
DOI	10.1016/j.neuroimage.2024.120635
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Article: Neuroimaging Advances in Diagnosis and Differentiation of HIV, Comorbidities, and Aging in the cART Era.

Nir, Talia M / Haddad, Elizabeth / Thompson, Paul M / Jahanshad, Neda

Current topics in behavioral neurosciences

2021 Volume 50, Page(s) 105–143

Abstract: In the "cART era" of more widely available and accessible treatment, aging and HIV-related comorbidities, including symptoms of brain dysfunction, remain common among HIV-infected individuals on suppressive treatment. A better understanding of the ... ...

Abstract	In the "cART era" of more widely available and accessible treatment, aging and HIV-related comorbidities, including symptoms of brain dysfunction, remain common among HIV-infected individuals on suppressive treatment. A better understanding of the neurobiological consequences of HIV infection is essential for developing thorough treatment guidelines and for optimizing long-term neuropsychological outcomes and overall brain health. In this chapter, we first summarize magnetic resonance imaging (MRI) methods used in over two decades of neuroHIV research. These methods evaluate brain volumetric differences and circuitry disruptions in adults living with HIV, and help map clinical correlations with brain function and tissue microstructure. We then introduce and discuss aging and associated neurological complications in people living with HIV, and processes by which infection may contribute to the risk for late-onset dementias. We describe how new technologies and large-scale international collaborations are helping to disentangle the effect of genetic and environmental risk factors on brain aging and neurodegenerative diseases. We provide insights into how these advances, which are now at the forefront of Alzheimer's disease research, may advance the field of neuroHIV. We conclude with a summary of how we see the field of neuroHIV research advancing in the decades to come and highlight potential clinical implications.
MeSH term(s)	Adult ; Aging ; Brain/diagnostic imaging ; HIV Infections/complications ; HIV Infections/epidemiology ; Humans ; Neurodegenerative Diseases ; Neuroimaging
Language	English
Publishing date	2021-03-30
Publishing country	Germany
Document type	Journal Article
ISSN	1866-3370
ISSN	1866-3370
DOI	10.1007/7854_2021_221
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Article ; Online: Reply to: "Parkinson's Disease, Premature Mortality, and Amygdala".

Laansma, Max A / Bright, Joanna K / Jahanshad, Neda / Thompson, Paul M / van der Werf, Ysbrand D

Movement disorders : official journal of the Movement Disorder Society

2022 Volume 37, Issue 5, Page(s) 1111–1112

MeSH term(s)	Amygdala ; Humans ; Mortality, Premature ; Parkinson Disease
Language	English
Publishing date	2022-05-19
Publishing country	United States
Document type	Letter ; Comment
ZDB-ID	607633-6
ISSN	1531-8257 ; 0885-3185
ISSN (online)	1531-8257
ISSN	0885-3185
DOI	10.1002/mds.29004
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Article ; Online: The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks.

Cali, Ryan J / Bhatt, Ravi R / Thomopoulos, Sophia I / Gadewar, Shruti / Gari, Iyad Ba / Chattopadhyay, Tamoghna / Jahanshad, Neda / 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: In brain imaging research, it is becoming standard practice to remove the face from the individual's 3D structural MRI scan to ensure data privacy standards are met. Face removal - or 'defacing' - is being advocated for large, multi-site studies where ... ...

Abstract	In brain imaging research, it is becoming standard practice to remove the face from the individual's 3D structural MRI scan to ensure data privacy standards are met. Face removal - or 'defacing' - is being advocated for large, multi-site studies where data is transferred across geographically diverse sites. Several methods have been developed to limit the loss of important brain data by accurately and precisely removing non-brain facial tissue. At the same time, deep learning methods such as convolutional neural networks (CNNs) are increasingly being used in medical imaging research for diagnostic classification and prognosis in neurological diseases. These neural networks train predictive models based on patterns in large numbers of images. Because of this, defacing scans could remove informative data. Here, we evaluated 4 popular defacing methods to identify the effects of defacing on 'brain age' prediction - a common benchmarking task of predicting a subject's chronological age from their 3D T1-weighted brain MRI. We compared brain-age calculations using defaced MRIs to those that were directly brain extracted, and those with both brain and face. Significant differences were present when comparing average per-subject error rates between algorithms in both the defaced brain data and the extracted facial tissue. Results also indicated brain age accuracy depends on defacing and the choice of algorithm. In a secondary analysis, we also examined how well comparable CNNs could predict chronological age from the facial region only (the extracted portion of the defaced image), as well as visualize areas of importance in facial tissue for predictive tasks using CNNs. We obtained better performance in age prediction when using the extracted face portion alone than images of the brain, suggesting the need for caution when defacing methods are used in medical image analysis.
MeSH term(s)	Neural Networks, Computer ; Algorithms ; Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging ; Neuroimaging
Language	English
Publishing date	2023-12-11
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC40787.2023.10340740
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks.

Cali, Ryan J / Bhatt, Ravi R / Thomopoulos, Sophia I / Gadewar, Shruti / Gari, Iyad Ba / Chattopadhyay, Tamoghna / Jahanshad, Neda / Thompson, Paul M

bioRxiv : the preprint server for biology

2023

Abstract	In brain imaging research, it is becoming standard practice to remove the face from the individual's 3D structural MRI scan to ensure data privacy standards are met. Face removal - or 'defacing' - is being advocated for large, multi-site studies where data is transferred across geographically diverse sites. Several methods have been developed to limit the loss of important brain data by accurately and precisely removing non-brain facial tissue. At the same time, deep learning methods such as convolutional neural networks (CNNs) are increasingly being used in medical imaging research for diagnostic classification and prognosis in neurological diseases. These neural networks train predictive models based on patterns in large numbers of images. Because of this, defacing scans could remove informative data. Here, we evaluated 4 popular defacing methods to identify the effects of defacing on 'brain age' prediction - a common benchmarking task of predicting a subject's chronological age from their 3D T1-weighted brain MRI. We compared brain-age calculations using defaced MRIs to those that were directly brain extracted, and those with both brain and face. Significant differences were present when comparing average per-subject error rates between algorithms in both the defaced brain data and the extracted facial tissue. Results also indicated brain age accuracy depends on defacing and the choice of algorithm. In a secondary analysis, we also examined how well comparable CNNs could predict chronological age from the facial region only (the extracted portion of the defaced image), as well as visualize areas of importance in facial tissue for predictive tasks using CNNs. We obtained better performance in age prediction when using the extracted face portion alone than images of the brain, suggesting the need for caution when defacing methods are used in medical image analysis.
Language	English
Publishing date	2023-04-29
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.04.28.538724
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes.

Jain, Pritesh R / Yates, Madison / de Celis, Carlos Rubin / Drineas, Petros / Jahanshad, Neda / Thompson, Paul / Paschou, Peristera

NeuroImage

2023 Volume 284, Page(s) 120466

Abstract: Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated ... ...

Abstract	Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes, with nine proteins and five metabolites replicated using independent exposure data. We found causal associations between accumbens volume and plasma protease c1 inhibitor as well as strong association between putamen volume and Agouti signaling protein. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.
MeSH term(s)	Humans ; Mendelian Randomization Analysis ; Genome-Wide Association Study/methods ; Multiomics ; Brain/diagnostic imaging ; Brain/pathology ; Biomarkers ; Magnetic Resonance Imaging/methods
Chemical Substances	Biomarkers
Language	English
Publishing date	2023-11-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	1147767-2
ISSN	1095-9572 ; 1053-8119
ISSN (online)	1095-9572
ISSN	1053-8119
DOI	10.1016/j.neuroimage.2023.120466
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Article: Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes.

Jain, Pritesh / Yates, Madison / de Celis, Carlos Rubin / Drineas, Petros / Jahanshad, Neda / Thompson, Paul / Paschou, Peristera

medRxiv : the preprint server for health sciences

2023

Abstract	Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2,994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes. We found causal associations between amygdala volume and granzyme A as well as association between accumbens volume and plasma protease c1 inhibitor. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.
Language	English
Publishing date	2023-04-03
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.03.30.23287968
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