Browsing by Author "Yu, Meichen"

Now showing 1 - 9 of 9
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    BrainAGE Estimation: Influence of Field Strength, Voxel Size, Race, and Ethnicity
    (medRxiv, 2023-12-05) Dempsey, Desarae A.; Deardorff, Rachael; Wu, Yu-Chien; Yu, Meichen; Apostolova, Liana G.; Brosch, Jared; Clark, David G.; Farlow, Martin R.; Gao, Sujuan; Wang, Sophia; Saykin, Andrew J.; Risacher, Shannon L.; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    The BrainAGE method is used to estimate biological brain age using structural neuroimaging. However, the stability of the model across different scan parameters and races/ethnicities has not been thoroughly investigated. Estimated brain age was compared within- and across- MRI field strength and across voxel sizes. Estimated brain age gap (BAG) was compared across demographically matched groups of different self-reported races and ethnicities in ADNI and IMAS cohorts. Longitudinal ComBat was used to correct for potential scanner effects. The brain age method was stable within field strength, but less stable across different field strengths. The method was stable across voxel sizes. There was a significant difference in BAG between races, but not ethnicities. Correction procedures are suggested to eliminate variation across scanner field strength while maintaining accurate brain age estimation. Further studies are warranted to determine the factors contributing to racial differences in BAG.
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    CYP1B1-RMDN2 Alzheimer's disease endophenotype locus identified for cerebral tau PET
    (Springer Nature, 2024-09-20) Nho, Kwangsik; Risacher, Shannon L.; Apostolova, Liana G.; Bice, Paula J.; Brosch, Jared R.; Deardorff, Rachael; Faber, Kelley; Farlow, Martin R.; Foroud, Tatiana; Gao, Sujuan; Rosewood, Thea; Kim, Jun Pyo; Nudelman, Kelly; Yu, Meichen; Aisen, Paul; Sperling, Reisa; Hooli, Basavaraj; Shcherbinin, Sergey; Svaldi, Diana; Jack, Clifford R., Jr.; Jagust, William J.; Landau, Susan; Vasanthakumar, Aparna; Waring, Jeffrey F.; Doré, Vincent; Laws, Simon M.; Masters, Colin L.; Porter, Tenielle; Rowe, Christopher C.; Villemagne, Victor L.; Dumitrescu, Logan; Hohman, Timothy J.; Libby, Julia B.; Mormino, Elizabeth; Buckley, Rachel F.; Johnson, Keith; Yang, Hyun-Sik; Petersen, Ronald C.; Ramanan, Vijay K.; Ertekin-Taner, Nilüfer; Vemuri, Prashanthi; Cohen, Ann D.; Fan, Kang-Hsien; Kamboh, M. Ilyas; Lopez, Oscar L.; Bennett, David A.; Ali, Muhammad; Benzinger, Tammie; Cruchaga, Carlos; Hobbs, Diana; De Jager, Philip L.; Fujita, Masashi; Jadhav, Vaishnavi; Lamb, Bruce T.; Tsai, Andy P.; Castanho, Isabel; Mill, Jonathan; Weiner, Michael W.; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Department of Defense Alzheimer’s Disease Neuroimaging Initiative (DoD-ADNI); Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Study (A4 Study) and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN); Australian Imaging, Biomarker & Lifestyle Study (AIBL); Saykin, Andrew J.; Radiology and Imaging Sciences, School of Medicine
    Determining the genetic architecture of Alzheimer's disease pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we perform a genome-wide association study of cortical tau quantified by positron emission tomography in 3046 participants from 12 independent studies. The CYP1B1-RMDN2 locus is associated with tau deposition. The most significant signal is at rs2113389, explaining 4.3% of the variation in cortical tau, while APOE4 rs429358 accounts for 3.6%. rs2113389 is associated with higher tau and faster cognitive decline. Additive effects, but no interactions, are observed between rs2113389 and diagnosis, APOE4, and amyloid beta positivity. CYP1B1 expression is upregulated in AD. rs2113389 is associated with higher CYP1B1 expression and methylation levels. Mouse model studies provide additional functional evidence for a relationship between CYP1B1 and tau deposition but not amyloid beta. These results provide insight into the genetic basis of cerebral tau deposition and support novel pathways for therapeutic development in AD.
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    Enhanced amygdala-cingulate connectivity associates with better mood in both healthy and depressive individuals after sleep deprivation
    (National Academy of Science, 2023) Chai, Ya; Gehrman, Philip; Yu, Meichen; Mao, Tianxin; Deng, Yao; Rao, Joy; Shi, Hui; Quan, Peng; Xu, Jing; Zhang, Xiaocui; Lei, Hui; Fang, Zhuo; Xu, Sihua; Boland, Elaine; Goldschmied, Jennifer R.; Barilla, Holly; Goel, Namni; Basner, Mathias; Thase, Michael E.; Sheline, Yvette I.; Dinges, David F.; Detre, John A.; Zhang, Xiaochu; Rao, Hengyi; Radiology and Imaging Sciences, School of Medicine
    Sleep loss robustly disrupts mood and emotion regulation in healthy individuals but can have a transient antidepressant effect in a subset of patients with depression. The neural mechanisms underlying this paradoxical effect remain unclear. Previous studies suggest that the amygdala and dorsal nexus (DN) play key roles in depressive mood regulation. Here, we used functional MRI to examine associations between amygdala- and DN-related resting-state connectivity alterations and mood changes after one night of total sleep deprivation (TSD) in both healthy adults and patients with major depressive disorder using strictly controlled in-laboratory studies. Behavioral data showed that TSD increased negative mood in healthy participants but reduced depressive symptoms in 43% of patients. Imaging data showed that TSD enhanced both amygdala- and DN-related connectivity in healthy participants. Moreover, enhanced amygdala connectivity to the anterior cingulate cortex (ACC) after TSD associated with better mood in healthy participants and antidepressant effects in depressed patients. These findings support the key role of the amygdala-cingulate circuit in mood regulation in both healthy and depressed populations and suggest that rapid antidepressant treatment may target the enhancement of amygdala-ACC connectivity.
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    Functional connectomics in depression: insights into therapies
    (Elsevier, 2023) Chai, Ya; Sheline, Yvette I.; Oathes, Desmond J.; Balderston, Nicholas L.; Rao, Hengyi; Yu, Meichen; Radiology and Imaging Sciences, School of Medicine
    Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.
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    Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
    (Elsevier, 2023) Hu, Fengling; Chen, Andrew A.; Horng, Hannah; Bashyam, Vishnu; Davatzikos, Christos; Alexander-Bloch, Aaron; Li, Mingyao; Shou, Haochang; Satterthwaite, Theodore D.; Yu, Meichen; Shinohara, Russell T.; Radiology and Imaging Sciences, School of Medicine
    Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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    Integrating Imaging and Genetics Data for Improved Understanding and Detection of Alzheimer's Disease
    (2024-08) He, Bing; Janga, Sarath Chandra; Saykin, Andrew J.; Yu, Meichen; Yan, Jingwen
    Alzheimer’s disease (AD) is a progressive and irreversible brain disorder characterized by a slow and intricate progression, in which the initial pathological changes occur long before noticeable symptoms. AD is highly heritable and genetic factors play an essential role in AD development. Large scale genome-wide association studies have identified numerous SNPs related to AD. However, our understanding of the connections between genetics findings and altered brain phenotype is still limited. Brain imaging genetics, an emerging approach, aims to investigate the relationship between genetic variations and brain structure or function. It has great potential to provide insights into the underlying biological mechanisms and to enable the early detection of AD. Our study aimed to develop and apply novel computational approaches for more robust discovery of imaging genetics associations and for improved detection of AD in early stage. Specifically, we focused on addressing the heterogeneity problem inherent in integrating imaging and genetics data. In aim 1, we applied a novel biclustering method to associate genetic variations with functional brain connectivity altered in AD patients. In aim 2, we proposed novel strategy to integrate imaging and genetic data to serve as a new type of prior knowledge and investigated their role in guiding imaging genetics association. Finally, in aim 3, we proposed a multi-factorial pseudotime approach to integrate heterogeneous genotype and amyloid imaging data and examined its potential for staging and early detection of AD. Collectively, results from these objectives aimed to enhance our understanding and detection of AD, providing valuable information to inform therapeutic strategies to slow or halt disease progression.
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    Sex-specific frontal-striatal connectivity differences among adolescents with externalizing disorders
    (Elsevier, 2021) Chai, Ya; Chimelis-Santiago, José R.; Bixler, Kristy A.; Aalsma, Matthew; Yu, Meichen; Hulvershorn, Leslie A.; Psychiatry, School of Medicine
    Background: Sex-specific neurobiological underpinnings of impulsivity in youth with externalizing disorders have not been well studied. The only report of functional connectivity (FC) findings in this area demonstrated sex differences in fronto-subcortical connectivity in youth with attention-deficit/hyperactivity disorder (ADHD). Methods: The current study used functional magnetic resonance imaging(fMRI) to examine sex differences in resting-state seed-based FC, self-rated impulsivity, and their interactions in 11-12-year-old boys (n = 43) and girls (n = 43) with externalizing disorders. Generalized linear models controlling for pubertal development were used. Seeds were chosen in the ventral striatum, medial prefrontal cortex, middle frontal gyrus and amygdala. Results: Impulsivity scores were greater in boys than girls (p < 0.05). Boys showed greater positive connectivity within a ventromedial prefrontal-ventral striatal network. In addition, boys demonstrated weaker connectivity than girls within two medial-lateral prefrontal cortical networks. However, only boys showed greater medial-lateral prefrontal connectivity correlated with greater impulsivity. Conclusions: The findings provide evidence supporting sex differences in both ventral striatal-ventromedial prefrontal and medial-lateral prefrontal functional networks in youth with externalizing disorders. These important networks are thought to be implicated in impulse control. Medial-lateral prefrontal connectivity may represent a male-specific biomarker of impulsivity.
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    Spatial transcriptomic patterns underlying amyloid-β and tau pathology are associated with cognitive dysfunction in Alzheimer’s disease
    (Elsevier, 2024) Yu, Meichen; Risacher, Shannon L.; Nho, Kwangsik T.; Wen, Qiuting; Oblak, Adrian L.; Unverzagt, Frederick W.; Apostolova, Liana G.; Farlow, Martin R.; Brosch, Jared R.; Clark, David G.; Wang, Sophia; Deardorff, Rachael; Wu, Yu-Chien; Gao, Sujuan; Sporns, Olaf; Saykin, Andrew J.; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Radiology and Imaging Sciences, School of Medicine
    Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ and tau pathologies than others, gene expression may play a role. We study the association between brain-wide gene expression profiles and regional vulnerability to Aβ (gene-to-Aβ associations) and tau (gene-to-tau associations) pathologies by leveraging two large independent AD cohorts. We identify AD susceptibility genes and gene modules in a gene co-expression network with expression profiles specifically related to regional vulnerability to Aβ and tau pathologies in AD. In addition, we identify distinct biochemical pathways associated with the gene-to-Aβ and the gene-to-tau associations. These findings may explain the discordance between regional Aβ and tau pathologies. Finally, we propose an analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations.
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    The human connectome in Alzheimer disease - relationship to biomarkers and genetics
    (Springer Nature, 2021) Yu, Meichen; Sporns, Olaf; Saykin, Andrew J.; Radiology and Imaging Sciences, School of Medicine
    The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-β and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-β and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.