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5. Learning ratio performance on a brief visual learning and memory test moderates cognitive training gains in Double Decision task in healthy older adults

Author

Hardcastle, Cheshire, Kraft, Jessica N., Hausman, Hanna K., O’Shea, Andrew, Albizu, Alejandro, Evangelista, Nicole D., Boutzoukas, Emanuel M., Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., Porges, Eric, DeKosky, Steven T., Hishaw, Georg A., Wu, Samuel S., Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., and Woods, Adam J.

Published
2024
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7. DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability

Author

Stolte, Skylar E., Volle, Kyle, Indahlastari, Aprinda, Albizu, Alejandro, Woods, Adam J., Brink, Kevin, Hale, Matthew, and Fang, Ruogu

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model's training data. DL models that perform well on in-domain test data could struggle on OOD data. Overcoming this discrepancy is essential to the reliable deployment of DL. Proper model calibration decreases the number of spurious connections that are made between model features and class outputs. Hence, calibrated DL can improve OOD generalization by only learning features that are truly indicative of the respective classes. Previous work proposed domain-aware model calibration (DOMINO) to improve DL calibration, but it lacks designs for model generalizability to OOD data. In this work, we propose DOMINO++, a dual-guidance and dynamic domain-aware loss regularization focused on OOD generalizability. DOMINO++ integrates expert-guided and data-guided knowledge in its regularization. Unlike DOMINO which imposed a fixed scaling and regularization rate, DOMINO++ designs a dynamic scaling factor and an adaptive regularization rate. Comprehensive evaluations compare DOMINO++ with DOMINO and the baseline model for head tissue segmentation from magnetic resonance images (MRIs) on OOD data. The OOD data consists of synthetic noisy and rotated datasets, as well as real data using a different MRI scanner from a separate site. DOMINO++'s superior performance demonstrates its potential to improve the trustworthy deployment of DL on real clinical data., Comment: 12 pages, 5 figures, 5 tables, Accepted by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2023

Published
2023

8. The impact of a tDCS and cognitive training intervention on task-based functional connectivity

Author

Kraft, Jessica N., Indahlastari, Aprinda, Boutzoukas, Emanuel M., Hausman, Hanna K., Hardcastle, Cheshire, Albizu, Alejandro, O’Shea, Andrew, Evangelista, Nicole D., Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., DeKosky, Steven T., Hishaw, Georg A., Wu, Samuel, Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., Porges, Eric, and Woods, Adam J.

Published
2024
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10. DOMINO: Domain-aware Loss for Deep Learning Calibration

Author

Stolte, Skylar E., Volle, Kyle, Indahlastari, Aprinda, Albizu, Alejandro, Woods, Adam J., Brink, Kevin, Hale, Matthew, and Fang, Ruogu

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered. Uncalibrated models are potentially dangerous in high-risk applications since the user does not know when they will fail. Therefore, this paper proposes a novel domain-aware loss function to calibrate deep learning models. The proposed loss function applies a class-wise penalty based on the similarity between classes within a given target domain. Thus, the approach improves the calibration while also ensuring that the model makes less risky errors even when incorrect. The code for this software is available at https://github.com/lab-smile/DOMINO., Comment: 7 pages, 1 figure, 1 table, accepted by the Software Impacts journal

Published
2023

12. DOMINO: Domain-aware Model Calibration in Medical Image Segmentation

Author

Stolte, Skylar E., Volle, Kyle, Indahlastari, Aprinda, Albizu, Alejandro, Woods, Adam J., Brink, Kevin, Hale, Matthew, and Fang, Ruogu

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applications. Unfortunately, modern deep neural networks are poorly calibrated, compromising trustworthiness and reliability. Medical image segmentation particularly suffers from this due to the natural uncertainty of tissue boundaries. This is exasperated by their loss functions, which favor overconfidence in the majority classes. We address these challenges with DOMINO, a domain-aware model calibration method that leverages the semantic confusability and hierarchical similarity between class labels. Our experiments demonstrate that our DOMINO-calibrated deep neural networks outperform non-calibrated models and state-of-the-art morphometric methods in head image segmentation. Our results show that our method can consistently achieve better calibration, higher accuracy, and faster inference times than these methods, especially on rarer classes. This performance is attributed to our domain-aware regularization to inform semantic model calibration. These findings show the importance of semantic ties between class labels in building confidence in deep learning models. The framework has the potential to improve the trustworthiness and reliability of generic medical image segmentation models. The code for this article is available at: https://github.com/lab-smile/DOMINO., Comment: 10 pages, 6 figures, 3 tables. Accepted by International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 Oral Talk

Published
2022

13. Connecting memory and functional brain networks in older adults: a resting-state fMRI study

Author

Waner, Jori L., Hausman, Hanna K., Kraft, Jessica N., Hardcastle, Cheshire, Evangelista, Nicole D., O’Shea, Andrew, Albizu, Alejandro, Boutzoukas, Emanuel M., Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., DeKosky, Steven T., Hishaw, Georg A., Wu, Samuel S., Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., Porges, Eric C., and Woods, Adam J.

Published
2023
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15. Task-based functional connectivity of the Useful Field of View (UFOV) fMRI task

Author

Kraft, Jessica N., Hausman, Hanna K., Hardcastle, Cheshire, Albizu, Alejandro, O’Shea, Andrew, Evangelista, Nicole D., Boutzoukas, Emanuel M., Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., DeKosky, Steven, Hishaw, Georg A., Wu, Samuel, Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., Porges, Eric, and Woods, Adam J.

Published
2023
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17. Higher white matter hyperintensity load adversely affects pre-post proximal cognitive training performance in healthy older adults

Author

Boutzoukas, Emanuel M., O’Shea, Andrew, Kraft, Jessica N., Hardcastle, Cheshire, Evangelista, Nicole D., Hausman, Hanna K., Albizu, Alejandro, Van Etten, Emily J., Bharadwaj, Pradyumna K., Smith, Samantha G., Song, Hyun, Porges, Eric C., Hishaw, Alex, DeKosky, Steven T., Wu, Samuel S., Marsiske, Michael, Alexander, Gene E., Cohen, Ronald, and Woods, Adam J.

Published
2022
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18. Proximal improvement and higher-order resting state network change after multidomain cognitive training intervention in healthy older adults

Author

Hardcastle, Cheshire, Hausman, Hanna K., Kraft, Jessica N., Albizu, Alejandro, O’Shea, Andrew, Boutzoukas, Emanuel M., Evangelista, Nicole D., Langer, Kailey, Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., Porges, Eric, DeKosky, Steven T., Hishaw, Georg A., Wu, Samuel S., Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., and Woods, Adam J.

Published
2022
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19. Cingulo-opercular and frontoparietal control network connectivity and executive functioning in older adults

Author

Hausman, Hanna K., Hardcastle, Cheshire, Albizu, Alejandro, Kraft, Jessica N., Evangelista, Nicole D., Boutzoukas, Emanuel M., Langer, Kailey, O’Shea, Andrew, Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., Porges, Eric, DeKosky, Steven T., Hishaw, Georg A., Wu, Samuel, Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., and Woods, Adam J.

Published
2022
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20. The association between head motion during functional magnetic resonance imaging and executive functioning in older adults

Author

Hausman, Hanna K., Hardcastle, Cheshire, Kraft, Jessica N., Evangelista, Nicole D., Boutzoukas, Emanuel M., O'Shea, Andrew, Albizu, Alejandro, Langer, Kailey, Van Etten, Emily J., Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., Porges, Eric, Hishaw, Georg A., Wu, Samuel, DeKosky, Steven, Alexander, Gene E., Marsiske, Michael, Cohen, Ronald, and Woods, Adam J.

Published
2022
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21. Higher-order resting state network association with the useful field of view task in older adults

Author

Hardcastle, Cheshire, Hausman, Hanna K., Kraft, Jessica N., Albizu, Alejandro, Evangelista, Nicole D., Boutzoukas, Emanuel M., O’Shea, Andrew, Langer, Kailey, Van Van Etten, Emily, Bharadwaj, Pradyumna K., Song, Hyun, Smith, Samantha G., Porges, Eric, DeKosky, Steven T., Hishaw, Georg A., Wu, Samuel S., Marsiske, Michael, Cohen, Ronald, Alexander, Gene E., and Woods, Adam J.

Published
2022
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22. Frequency drift in MR spectroscopy at 3T

Author

Hui, Steve C.N., Mikkelsen, Mark, Zöllner, Helge J., Ahluwalia, Vishwadeep, Alcauter, Sarael, Baltusis, Laima, Barany, Deborah A., Barlow, Laura R., Becker, Robert, Berman, Jeffrey I., Berrington, Adam, Bhattacharyya, Pallab K., Blicher, Jakob Udby, Bogner, Wolfgang, Brown, Mark S., Calhoun, Vince D., Castillo, Ryan, Cecil, Kim M., Choi, Yeo Bi, Chu, Winnie C.W., Clarke, William T., Craven, Alexander R., Cuypers, Koen, Dacko, Michael, de la Fuente-Sandoval, Camilo, Desmond, Patricia, Domagalik, Aleksandra, Dumont, Julien, Duncan, Niall W., Dydak, Ulrike, Dyke, Katherine, Edmondson, David A., Ende, Gabriele, Ersland, Lars, Evans, C. John, Fermin, Alan S.R., Ferretti, Antonio, Fillmer, Ariane, Gong, Tao, Greenhouse, Ian, Grist, James T., Gu, Meng, Harris, Ashley D., Hat, Katarzyna, Heba, Stefanie, Heckova, Eva, Hegarty, John P., II, Heise, Kirstin-Friederike, Honda, Shiori, Jacobson, Aaron, Jansen, Jacobus F.A., Jenkins, Christopher W., Johnston, Stephen J., Juchem, Christoph, Kangarlu, Alayar, Kerr, Adam B., Landheer, Karl, Lange, Thomas, Lee, Phil, Levendovszky, Swati Rane, Limperopoulos, Catherine, Liu, Feng, Lloyd, William, Lythgoe, David J., Machizawa, Maro G., MacMillan, Erin L., Maddock, Richard J., Manzhurtsev, Andrei V., Martinez-Gudino, María L., Miller, Jack J., Mirzakhanian, Heline, Moreno-Ortega, Marta, Mullins, Paul G., Nakajima, Shinichiro, Near, Jamie, Noeske, Ralph, Nordhøy, Wibeke, Oeltzschner, Georg, Osorio-Duran, Raul, Otaduy, Maria C.G., Pasaye, Erick H., Peeters, Ronald, Peltier, Scott J., Pilatus, Ulrich, Polomac, Nenad, Porges, Eric C., Pradhan, Subechhya, Prisciandaro, James Joseph, Puts, Nicolaas A, Rae, Caroline D., Reyes-Madrigal, Francisco, Roberts, Timothy P.L., Robertson, Caroline E., Rosenberg, Jens T., Rotaru, Diana-Georgiana, O'Gorman Tuura, Ruth L, Saleh, Muhammad G., Sandberg, Kristian, Sangill, Ryan, Schembri, Keith, Schrantee, Anouk, Semenova, Natalia A., Singel, Debra, Sitnikov, Rouslan, Smith, Jolinda, Song, Yulu, Stark, Craig, Stoffers, Diederick, Swinnen, Stephan P., Tain, Rongwen, Tanase, Costin, Tapper, Sofie, Tegenthoff, Martin, Thiel, Thomas, Thioux, Marc, Truong, Peter, van Dijk, Pim, Vella, Nolan, Vidyasagar, Rishma, Vovk, Andrej, Wang, Guangbin, Westlye, Lars T., Wilbur, Timothy K., Willoughby, William R., Wilson, Martin, Wittsack, Hans-Jörg, Woods, Adam J., Wu, Yen-Chien, Xu, Junqian, Lopez, Maria Yanez, Yeung, David K.W., Zhao, Qun, Zhou, Xiaopeng, Zupan, Gasper, and Edden, Richard A.E.

Published
2021
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31. Guidelines for TMS/tES clinical services and research through the COVID-19 pandemic

Author

Bikson, Marom, Hanlon, Colleen A., Woods, Adam J., Gillick, Bernadette T., Charvet, Leigh, Lamm, Claus, Madeo, Graziella, Holczer, Adrienn, Almeida, Jorge, Antal, Andrea, Ay, Mohammad Reza, Baeken, Chris, Blumberger, Daniel M., Campanella, Salvatore, Camprodon, Joan A., Christiansen, Lasse, Loo, Colleen, Crinion, Jennifer T., Fitzgerald, Paul, Gallimberti, Luigi, Ghobadi-Azbari, Peyman, Ghodratitoostani, Iman, Grabner, Roland H., Hartwigsen, Gesa, Hirata, Akimasa, Kirton, Adam, Knotkova, Helena, Krupitsky, Evgeny, Marangolo, Paola, Nakamura-Palacios, Ester M., Potok, Weronika, Praharaj, Samir K., Ruff, Christian C., Schlaug, Gottfried, Siebner, Hartwig R., Stagg, Charlotte J., Thielscher, Axel, Wenderoth, Nicole, Yuan, Ti-Fei, Zhang, Xiaochu, and Ekhtiari, Hamed

Published
2020
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35. Frontal–temporal regional differences in brain energy metabolism and mitochondrial function using 31P MRS in older adults.

Author

Lopez, Francesca V., O'Shea, Andrew, Huo, Zhiguang, DeKosky, Steven T., Trouard, Theodore P., Alexander, Gene E., Woods, Adam J., and Bowers, Dawn

Subjects
BRAIN metabolism, OLDER people, ENERGY metabolism, NUCLEAR magnetic resonance spectroscopy, PHOSPHATE metabolism, REGIONAL differences
Abstract

Aging is a major risk for cognitive decline and transition to dementia. One well-known age-related change involves decreased brain efficiency and energy production, mediated in part by changes in mitochondrial function. Damaged or dysfunctional mitochondria have been implicated in the pathogenesis of age-related neurodegenerative conditions like Alzheimer's disease (AD). The aim of the current study was to investigate mitochondrial function over frontal and temporal regions in a sample of 70 cognitively normal older adults with subjective memory complaints and a first-degree family history of AD. We hypothesized cerebral mitochondrial function and energy metabolism would be greater in temporal as compared to frontal regions based on the high energy consumption in the temporal lobes (i.e., hippocampus). To test this hypothesis, we used phosphorous (31P) magnetic resonance spectroscopy (MRS) which is a non-invasive and powerful method for investigating in vivo mitochondrial function via high energy phosphates and phospholipid metabolism ratios. We used a single voxel method (left temporal and bilateral prefrontal) to achieve optimal sensitivity. Results of separate repeated measures analyses of variance showed 31P MRS ratios of static energy, energy reserve, energy consumption, energy demand, and phospholipid membrane metabolism were greater in the left temporal than bilateral prefrontal voxels. Our findings that all 31P MRS ratios were greater in temporal than bifrontal regions support our hypothesis. Future studies are needed to determine whether findings are related to cognition in older adults. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Associations between physical exercise type, fluid intelligence, executive function, and processing speed in the oldest-old (85 +)

Author

Ho, Brian Duy, primary, Gullett, Joseph M., additional, Anton, Stephen, additional, Franchetti, Mary Kathryn, additional, Bharadwaj, Pradyumna K., additional, Raichlen, David A., additional, Alexander, Gene E., additional, Rundek, Tatjana, additional, Levin, Bonnie, additional, Visscher, Kristina, additional, Woods, Adam J., additional, and Cohen, Ronald A., additional

Published
2023
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47. The longitudinal impact of the COVID-19 pandemic on health behaviors, psychosocial factors, and cognitive functioning in older adults

Author

Hausman, Hanna K., primary, Dai, Yunfeng, additional, O’Shea, Andrew, additional, Dominguez, Vanessa, additional, Fillingim, Matthew, additional, Calfee, Kristin, additional, Carballo, Daniela, additional, Hernandez, Cindy, additional, Perryman, Sean, additional, Kraft, Jessica N., additional, Evangelista, Nicole D., additional, Van Etten, Emily J., additional, Smith, Samantha G., additional, Bharadwaj, Pradyumna K., additional, Song, Hyun, additional, Porges, Eric, additional, DeKosky, Steven T., additional, Hishaw, Georg A., additional, Marsiske, Michael, additional, Cohen, Ronald, additional, Alexander, Gene E., additional, Wu, Samuel S., additional, and Woods, Adam J., additional

Published
2022
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49. Frontal adenosine triphosphate markers from 31P MRS are associated with cognitive performance in healthy older adults: preliminary findings.

Author

Lopez, Francesca V., O'Shea, Andrew, Rosenberg, Jens T., Leeuwenburgh, Christiaan, Anton, Stephen, Bowers, Dawn, and Woods, Adam J.

Subjects
ADENOSINE triphosphate, ENERGY density, BRAIN, RELIABILITY (Personality trait), EXPERIMENTAL design, ACTIVE aging, MITOCHONDRIAL pathology, OPERATIVE surgery, NUCLEAR magnetic resonance spectroscopy, ACQUISITION of data, COGNITIVE aging, RESEARCH funding, DESCRIPTIVE statistics, COGNITIVE testing, PHOSPHATES, OLD age
Abstract

Aging is associated with declines in mitochondrial efficiency and energy production which directly impacts the availability of adenosine triphosphate (ATP), which contains high energy phosphates critical for a variety of cellular functions. Previous phosphorous magnetic resonance spectroscopy (31P MRS) studies demonstrate cerebral ATP declines with age. The purpose of this study was to explore the functional relationships of frontal and posterior ATP levels with cognition in healthy aging. Here, we measured frontal and posterior ATP levels using 31P MRS at 3 Tesla (3 T) and assessed cognition using the Montreal Cognitive Assessment (MoCA) in 30 healthy older adults. We found that greater frontal, but not posterior, ATP levels were significantly associated with better MoCA performance. This relationship remained significant after controlling for age, sex, years of education, and brain atrophy. In conclusion, our findings indicate that cognition is related to ATP in the frontal cortex. These preliminary findings may have important implications in the search for non-invasive markers of in vivo mitochondrial function and the impact of ATP availability on cognition. Future studies are needed to confirm the functional significance of regional ATP and cognition across the lifespan. [ABSTRACT FROM AUTHOR]

Published
2023
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