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225 results on '"Cha, Jiook"'

1. AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer

Author

Kwon, Joonwoo, Kim, Sooyoung, Lin, Yuewei, Yoo, Shinjae, and Cha, Jiook

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

Neural style transfer (NST) has evolved significantly in recent years. Yet, despite its rapid progress and advancement, existing NST methods either struggle to transfer aesthetic information from a style effectively or suffer from high computational costs and inefficiencies in feature disentanglement due to using pre-trained models. This work proposes a lightweight but effective model, AesFA -- Aesthetic Feature-Aware NST. The primary idea is to decompose the image via its frequencies to better disentangle aesthetic styles from the reference image while training the entire model in an end-to-end manner to exclude pre-trained models at inference completely. To improve the network's ability to extract more distinct representations and further enhance the stylization quality, this work introduces a new aesthetic feature: contrastive loss. Extensive experiments and ablations show the approach not only outperforms recent NST methods in terms of stylization quality, but it also achieves faster inference. Codes are available at https://github.com/Sooyyoungg/AesFA., Comment: Accepted by AAAI 2024

Published
2023

2. SwiFT: Swin 4D fMRI Transformer

Author

Kim, Peter Yongho, Kwon, Junbeom, Joo, Sunghwan, Bae, Sangyoon, Lee, Donggyu, Jung, Yoonho, Yoo, Shinjae, Cha, Jiook, and Moon, Taesup

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI., Comment: NeurIPS 2023

Published
2023

4. White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

Author

Kim, Bo-Gyeom, Kim, Gakyung, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie, Anticevic, Alan, Arnold, Paul D., Balachander, Srinivas, Banaj, Nerisa, Bargalló, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Bertolín, Sara, Beucke, Jan Carl, Bollettini, Irene, Brem, Silvia, Brennan, Brian P., Buitelaar, Jan K., Calvo, Rosa, Castelo-Branco, Miguel, Cheng, Yuqi, Chhatkuli, Ritu Bhusal, Ciullo, Valentina, Coelho, Ana, Couto, Beatriz, Dallaspezia, Sara, Ely, Benjamin A., Ferreira, Sónia, Fontaine, Martine, Fouche, Jean-Paul, Grazioplene, Rachael, Gruner, Patricia, Hagen, Kristen, Hansen, Bjarne, Hanna, Gregory L., Hirano, Yoshiyuki, Höxter, Marcelo Q., Hough, Morgan, Hu, Hao, Huyser, Chaim, Ikuta, Toshikazu, Jahanshad, Neda, James, Anthony, Jaspers-Fayer, Fern, Kasprzak, Selina, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Kvale, Gerd, Kwon, Jun Soo, Lazaro, Luisa, Lee, Junhee, Lochner, Christine, Lu, Jin, Manrique, Daniela Rodriguez, Martínez-Zalacaín, Ignacio, Masuda, Yoshitada, Matsumoto, Koji, Maziero, Maria Paula, Menchón, Jose M., Minuzzi, Luciano, Moreira, Pedro Silva, Morgado, Pedro, Narayanaswamy, Janardhanan C., Narumoto, Jin, Ortiz, Ana E., Ota, Junko, Pariente, Jose C., Perriello, Chris, Picó-Pérez, Maria, Pittenger, Christopher, Poletti, Sara, Real, Eva, Reddy, Y. C. Janardhan, van Rooij, Daan, Sakai, Yuki, Sato, João Ricardo, Segalas, Cinto, Shavitt, Roseli G., Shen, Zonglin, Shimizu, Eiji, Shivakumar, Venkataram, Soreni, Noam, Soriano-Mas, Carles, Sousa, Nuno, Sousa, Mafalda Machado, Spalletta, Gianfranco, Stern, Emily R., Stewart, S. Evelyn, Szeszko, Philip R., Thomas, Rajat, Thomopoulos, Sophia I., Vecchio, Daniela, Venkatasubramanian, Ganesan, Vriend, Chris, Walitza, Susanne, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij, Xu, Jian, Yamada, Kei, Yun, Je-Yeon, Zarei, Mojtaba, Zhao, Qing, Zhu, Xi, Thompson, Paul M., Bruin, Willem B., van Wingen, Guido A., Piras, Federica, Piras, Fabrizio, Stein, Dan J., van den Heuvel, Odile A., Simpson, Helen Blair, Marsh, Rachel, and Cha, Jiook

Published
2024
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6. Correction: White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

Author

Kim, Bo-Gyeom, Kim, Gakyung, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie, Anticevic, Alan, Arnold, Paul D., Balachander, Srinivas, Banaj, Nerisa, Bargalló, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Bertolín, Sara, Beucke, Jan Carl, Bollettini, Irene, Brem, Silvia, Brennan, Brian P., Buitelaar, Jan K., Calvo, Rosa, Castelo-Branco, Miguel, Cheng, Yuqi, Chhatkuli, Ritu Bhusal, Ciullo, Valentina, Coelho, Ana, Couto, Beatriz, Dallaspezia, Sara, Ely, Benjamin A., Ferreira, Sónia, Fontaine, Martine, Fouche, Jean-Paul, Grazioplene, Rachael, Gruner, Patricia, Hagen, Kristen, Hansen, Bjarne, Hanna, Gregory L., Hirano, Yoshiyuki, Höxter, Marcelo Q., Hough, Morgan, Hu, Hao, Huyser, Chaim, Ikuta, Toshikazu, Jahanshad, Neda, James, Anthony, Jaspers-Fayer, Fern, Kasprzak, Selina, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Kvale, Gerd, Kwon, Jun Soo, Lazaro, Luisa, Lee, Junhee, Lochner, Christine, Lu, Jin, Manrique, Daniela Rodriguez, Martínez-Zalacaín, Ignacio, Masuda, Yoshitada, Matsumoto, Koji, Maziero, Maria Paula, Menchón, Jose M., Minuzzi, Luciano, Moreira, Pedro Silva, Morgado, Pedro, Narayanaswamy, Janardhanan C., Narumoto, Jin, Ortiz, Ana E., Ota, Junko, Pariente, Jose C., Perriello, Chris, Picó-Pérez, Maria, Pittenger, Christopher, Poletti, Sara, Real, Eva, Reddy, Y. C. Janardhan, van Rooij, Daan, Sakai, Yuki, Sato, João Ricardo, Segalas, Cinto, Shavitt, Roseli G., Shen, Zonglin, Shimizu, Eiji, Shivakumar, Venkataram, Soreni, Noam, Soriano-Mas, Carles, Sousa, Nuno, Sousa, Mafalda Machado, Spalletta, Gianfranco, Stern, Emily R., Stewart, S. Evelyn, Szeszko, Philip R., Thomas, Rajat, Thomopoulos, Sophia I., Vecchio, Daniela, Venkatasubramanian, Ganesan, Vriend, Chris, Walitza, Susanne, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij, Xu, Jian, Yamada, Kei, Yun, Je-Yeon, Zarei, Mojtaba, Zhao, Qing, Zhu, Xi, Thompson, Paul M., Bruin, Willem B., van Wingen, Guido A., Piras, Federica, Piras, Fabrizio, Stein, Dan J., van den Heuvel, Odile A., Simpson, Helen Blair, Marsh, Rachel, and Cha, Jiook

Published
2024
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7. Altered white matter microstructural organization in posttraumatic stress disorder across 3047 adults: results from the PGC-ENIGMA PTSD consortium

Author

Dennis, Emily L, Disner, Seth G, Fani, Negar, Salminen, Lauren E, Logue, Mark, Clarke, Emily K, Haswell, Courtney C, Averill, Christopher L, Baugh, Lee A, Bomyea, Jessica, Bruce, Steven E, Cha, Jiook, Choi, Kyle, Davenport, Nicholas D, Densmore, Maria, du Plessis, Stefan, Forster, Gina L, Frijling, Jessie L, Gonenc, Atilla, Gruber, Staci, Grupe, Daniel W, Guenette, Jeffrey P, Hayes, Jasmeet, Hofmann, David, Ipser, Jonathan, Jovanovic, Tanja, Kelly, Sinead, Kennis, Mitzy, Kinzel, Philipp, Koch, Saskia BJ, Koerte, Inga, Koopowitz, Sheri, Korgaonkar, Mayuresh, Krystal, John, Lebois, Lauren AM, Li, Gen, Magnotta, Vincent A, Manthey, Antje, May, Geoff J, Menefee, Deleene S, Nawijn, Laura, Nelson, Steven M, Neufeld, Richard WJ, Nitschke, Jack B, O’Doherty, Daniel, Peverill, Matthew, Ressler, Kerry J, Roos, Annerine, Sheridan, Margaret A, Sierk, Anika, Simmons, Alan, Simons, Raluca M, Simons, Jeffrey S, Stevens, Jennifer, Suarez-Jimenez, Benjamin, Sullivan, Danielle R, Théberge, Jean, Tran, Jana K, van den Heuvel, Leigh, van der Werff, Steven JA, van Rooij, Sanne JH, van Zuiden, Mirjam, Velez, Carmen, Verfaellie, Mieke, Vermeiren, Robert RJM, Wade, Benjamin SC, Wager, Tor, Walter, Henrik, Winternitz, Sherry, Wolff, Jonathan, York, Gerald, Zhu, Ye, Zhu, Xi, Abdallah, Chadi G, Bryant, Richard, Daniels, Judith K, Davidson, Richard J, Fercho, Kelene A, Franz, Carol, Geuze, Elbert, Gordon, Evan M, Kaufman, Milissa L, Kremen, William S, Lagopoulos, Jim, Lanius, Ruth A, Lyons, Michael J, McCauley, Stephen R, McGlinchey, Regina, McLaughlin, Katie A, Milberg, William, Neria, Yuval, Olff, Miranda, Seedat, Soraya, Shenton, Martha, Sponheim, Scott R, Stein, Dan J, Stein, Murray B, Straube, Thomas, Tate, David F, and van der Wee, Nic JA

Subjects
Biological Psychology, Psychology, Neurosciences, Mental Health, Brain Disorders, Biomedical Imaging, Post-Traumatic Stress Disorder (PTSD), Clinical Research, Anxiety Disorders, Mental health, Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, Anisotropy, Brain, Diffusion Tensor Imaging, Female, Humans, Male, Middle Aged, Stress Disorders, Post-Traumatic, White Matter, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1426 individuals with PTSD and 1621 controls (2174 males/873 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen's d = -0.11, p = 0.0055). The tapetum connects the left and right hippocampus, for which structure and function have been consistently implicated in PTSD. Results were consistent even after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.

Published
2021

10. GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI

Author

Shin, Hoo-Chang, Ihsani, Alvin, Xu, Ziyue, Mandava, Swetha, Sreenivas, Sharath Turuvekere, Forster, Christopher, Cha, Jiook, and Initiative, Alzheimer's Disease Neuroimaging

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

Positron Emission Tomography (PET) is now regarded as the gold standard for the diagnosis of Alzheimer's Disease (AD). However, PET imaging can be prohibitive in terms of cost and planning, and is also among the imaging techniques with the highest dosage of radiation. Magnetic Resonance Imaging (MRI), in contrast, is more widely available and provides more flexibility when setting the desired image resolution. Unfortunately, the diagnosis of AD using MRI is difficult due to the very subtle physiological differences between healthy and AD subjects visible on MRI. As a result, many attempts have been made to synthesize PET images from MR images using generative adversarial networks (GANs) in the interest of enabling the diagnosis of AD from MR. Existing work on PET synthesis from MRI has largely focused on Conditional GANs, where MR images are used to generate PET images and subsequently used for AD diagnosis. There is no end-to-end training goal. This paper proposes an alternative approach to the aforementioned, where AD diagnosis is incorporated in the GAN training objective to achieve the best AD classification performance. Different GAN lossesare fine-tuned based on the discriminator performance, and the overall training is stabilized. The proposed network architecture and training regime show state-of-the-art performance for three- and four- class AD classification tasks., Comment: Accepted for publication at the MICCAI 2020 conference

Published
2020

11. GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis

Author

Shin, Hoo-Chang, Ihsani, Alvin, Mandava, Swetha, Sreenivas, Sharath Turuvekere, Forster, Christopher, Cha, Jiook, and Initiative, Alzheimer's Disease Neuroimaging

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

Synthesizing medical images, such as PET, is a challenging task due to the fact that the intensity range is much wider and denser than those in photographs and digital renderings and are often heavily biased toward zero. Above all, intensity values in PET have absolute significance, and are used to compute parameters that are reproducible across the population. Yet, usually much manual adjustment has to be made in pre-/post- processing when synthesizing PET images, because its intensity ranges can vary a lot, e.g., between -100 to 1000 in floating point values. To overcome these challenges, we adopt the Bidirectional Encoder Representations from Transformers (BERT) algorithm that has had great success in natural language processing (NLP), where wide-range floating point intensity values are represented as integers ranging between 0 to 10000 that resemble a dictionary of natural language vocabularies. BERT is then trained to predict a proportion of masked values images, where its "next sentence prediction (NSP)" acts as GAN discriminator. Our proposed approach, is able to generate PET images from MRI images in wide intensity range, with no manual adjustments in pre-/post- processing. It is a method that can scale and ready to deploy.

Published
2020

19. GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-Tuning for Alzheimer’s Disease Diagnosis from MRI

Author

Alzheimer’s Disease Neuroimaging Initiative, Shin, Hoo-Chang, Ihsani, Alvin, Xu, Ziyue, Mandava, Swetha, Sreenivas, Sharath Turuvekere, Forster, Christopher, Cha, Jiook, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Martel, Anne L., editor, Abolmaesumi, Purang, editor, Stoyanov, Danail, editor, Mateus, Diana, editor, Zuluaga, Maria A., editor, Zhou, S. Kevin, editor, Racoceanu, Daniel, editor, and Joskowicz, Leo, editor

Published
2020
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22. Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning

Author

Watkins, William, Wang, Heehwan, Bae, Sangyoon, Tseng, Huan-Hsin, Cha, Jiook, Chen, Samuel Yen-Chi, Yoo, Shinjae, Watkins, William, Wang, Heehwan, Bae, Sangyoon, Tseng, Huan-Hsin, Cha, Jiook, Chen, Samuel Yen-Chi, and Yoo, Shinjae

Abstract

The utility of machine learning has rapidly expanded in the last two decades and presents an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation of Teacher Ensembles (PATE) to enable federated learning in which multiple teacher models are trained on disjoint datasets. This study is the first to apply PATE to an ensemble of quantum neural networks (QNN) to pave a new way of ensuring privacy in quantum machine learning (QML) models.

Published
2024

26. White matter diffusion estimates in obsessive-compulsive disorder across 1,653 individuals: Machine learning findings from the ENIGMA OCD Working Group

Author

Cha, Jiook, Kim, Bogyeom; https://orcid.org/0000-0003-2685-877X, Kim, Gakyung, Thompson, Paul; https://orcid.org/0000-0002-3365-1980, Bruin, Willem B; https://orcid.org/0000-0002-0672-8903, Wingen, Guido van; https://orcid.org/0000-0003-3076-5891, Piras, Federica, Piras, Fabrizio; https://orcid.org/0000-0003-3566-5494, Stein, Dan; https://orcid.org/0000-0001-7218-7810, Heuvel, Odile van den; https://orcid.org/0000-0002-9804-7653, Simpson, H, Marsh, Rachel; https://orcid.org/0000-0003-2439-6305, Abe, Yoshinari; https://orcid.org/0000-0001-8348-0801, Alonso, Pino; https://orcid.org/0000-0002-5779-9111, Ameis, Stephanie; https://orcid.org/0000-0002-7282-6077, Anticevic, Alan; https://orcid.org/0000-0002-4324-0536, Arnold, Paul; https://orcid.org/0000-0003-2496-4624, Balachander, Srinivas, Banaj, Nerisa, Bargallo, Nuria, Batistuzzo, Marcelo; https://orcid.org/0000-0003-1347-8241, Benedetti, Francesco; https://orcid.org/0000-0003-4949-856X, Triquell, Sara Bertolin; https://orcid.org/0000-0003-1468-7862, Beucke, Jan, Bollettini, Irene; https://orcid.org/0000-0003-3666-3538, Brem, Silvia; https://orcid.org/0000-0002-8031-1305, Brennan, Brian; https://orcid.org/0000-0001-5468-6552, Buitelaar, Jan; https://orcid.org/0000-0001-8288-7757, Calvo-Escalona, Rosa; https://orcid.org/0000-0001-9572-4228, Cheng, Yuqi, Walitza, Susanne; https://orcid.org/0000-0002-8161-8683, et al, Cha, Jiook, Kim, Bogyeom; https://orcid.org/0000-0003-2685-877X, Kim, Gakyung, Thompson, Paul; https://orcid.org/0000-0002-3365-1980, Bruin, Willem B; https://orcid.org/0000-0002-0672-8903, Wingen, Guido van; https://orcid.org/0000-0003-3076-5891, Piras, Federica, Piras, Fabrizio; https://orcid.org/0000-0003-3566-5494, Stein, Dan; https://orcid.org/0000-0001-7218-7810, Heuvel, Odile van den; https://orcid.org/0000-0002-9804-7653, Simpson, H, Marsh, Rachel; https://orcid.org/0000-0003-2439-6305, Abe, Yoshinari; https://orcid.org/0000-0001-8348-0801, Alonso, Pino; https://orcid.org/0000-0002-5779-9111, Ameis, Stephanie; https://orcid.org/0000-0002-7282-6077, Anticevic, Alan; https://orcid.org/0000-0002-4324-0536, Arnold, Paul; https://orcid.org/0000-0003-2496-4624, Balachander, Srinivas, Banaj, Nerisa, Bargallo, Nuria, Batistuzzo, Marcelo; https://orcid.org/0000-0003-1347-8241, Benedetti, Francesco; https://orcid.org/0000-0003-4949-856X, Triquell, Sara Bertolin; https://orcid.org/0000-0003-1468-7862, Beucke, Jan, Bollettini, Irene; https://orcid.org/0000-0003-3666-3538, Brem, Silvia; https://orcid.org/0000-0002-8031-1305, Brennan, Brian; https://orcid.org/0000-0001-5468-6552, Buitelaar, Jan; https://orcid.org/0000-0001-8288-7757, Calvo-Escalona, Rosa; https://orcid.org/0000-0001-9572-4228, Cheng, Yuqi, Walitza, Susanne; https://orcid.org/0000-0002-8161-8683, and et al

Abstract

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1,336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) “OCD vs. healthy controls'' (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) “unmedicated OCD vs. healthy controls” (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) “medicated OCD vs. unmedicated OCD” (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6–79.1 in adults; 35.9–63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classificatio

Published
2023

27. Anticipation of high arousal aversive and positive movie clips engages common and distinct neural substrates

Author

Greenberg, Tsafrir, Carlson, Joshua M, Rubin, Denis, Cha, Jiook, and Mujica-Parodi, Lilianne

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Basic Behavioral and Social Science, Mental Health, Clinical Research, Behavioral and Social Science, Neurosciences, Mental health, Affect, Anticipation, Psychological, Arousal, Attention, Brain, Brain Mapping, Cues, Echo-Planar Imaging, Emotions, Female, Humans, Male, Motion Pictures, Nerve Net, Photic Stimulation, Young Adult, expectation, valence, uncertainty, fMRI, films, Cognitive Sciences, Experimental Psychology, Biological psychology, Clinical and health psychology
Abstract

The neural correlates of anxious anticipation have been primarily studied with aversive and neutral stimuli. In this study, we examined the effect of valence on anticipation by using high arousal aversive and positive stimuli and a condition of uncertainty (i.e. either positive or aversive). The task consisted of predetermined cues warning participants of upcoming aversive, positive, 'uncertain' (either aversive or positive) and neutral movie clips. Anticipation of all affective clips engaged common regions including the anterior insula, dorsal anterior cingulate cortex, thalamus, caudate, inferior parietal and prefrontal cortex that are associated with emotional experience, sustained attention and appraisal. In contrast, the nucleus accumbens and medial prefrontal cortex, regions implicated in reward processing, were selectively engaged during anticipation of positive clips (depicting sexually explicit content) and the mid-insula, which has been linked to processing aversive stimuli, was selectively engaged during anticipation of aversive clips (depicting graphic medical procedures); these three areas were also activated during anticipation of 'uncertain' clips reflecting a broad preparatory response for both aversive and positive stimuli. These results suggest that a common circuitry is recruited in anticipation of affective clips regardless of valence, with additional areas preferentially engaged depending on whether expected stimuli are negative or positive.

Published
2015

30. 23. HOW GENES AND ENVIRONMENTS MODIFY INTERGENERATIONAL RISK FOR DEPRESSION – USING POLYGENIC SCORES TO TRANSLATE BETWEEN RODENTS AND HUMANS

Author

van Dijk, Milenna, primary, Malave, Lauren, additional, Pokhvisneva, Irina, additional, Patel, Sachin, additional, Mariani, Madeline Butler, additional, Kashyap, Pratik, additional, Desai, Karan, additional, Cha, Jiook, additional, Gameroff, Marc, additional, Posner, Jonathan E., additional, Boldrini, Maura, additional, Weissman, Myrna, additional, Talati, Ardesheer, additional, Anacker, Christoph, additional, and Silveira, Patricia Pelufo, additional

Published
2023
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34. Dentate Gyrus Microstructure Is Associated With Resilience After Exposure to Maternal Stress Across Two Human Cohorts

Author

van Dijk, Milenna T., primary, Talati, Ardesheer, additional, Kashyap, Pratik, additional, Desai, Karan, additional, Kelsall, Nora C., additional, Gameroff, Marc J., additional, Aw, Natalie, additional, Abraham, Eyal, additional, Cullen, Breda, additional, Cha, Jiook, additional, Anacker, Christoph, additional, Weissman, Myrna M., additional, and Posner, Jonathan, additional

Published
2023
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39. BROAD DEPRESSION POLYGENIC RISK SCORE PREDICTS DENTATE GYRUS MORPHOLOGY, DEPRESSIVE SYMPTOMS AND SUICIDE ATTEMPTS IN OFFSPRING AT HIGH FAMILY RISK FOR DEPRESSION

Author

van Dijk, Milenna, primary, Pokhvisneva, Irina, additional, Patel, Sachin, additional, Cha, Jiook, additional, Gameroff, Marc, additional, Posner, Jonathan E., additional, Meaney, Michael J., additional, Weissman, Myrna M., additional, Silveira, Patricia Pelufo, additional, and Talati, Ardesheer, additional

Published
2022
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41. P227. Polygenic Risk for Depression is Associated With Depressive Symptoms and Suicide Attempts and Interacts With Adverse Childhood Environments to Predict Decreased Dentate Gyrus Structure in Offspring at Family Risk for Depression

Author

van Dijk, Milenna, primary, Pokhvisneva, Irina, additional, Patel, Sachin, additional, Cha, Jiook, additional, Gameroff, Marc J., additional, Posner, Jonathan, additional, Meaney, Michael J., additional, Weissman, Myrna M., additional, Silveira, Patricia Pelufo, additional, and Talati, Ardesheer, additional

Published
2022
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44. Maternal age at birth and child attention‐deficit hyperactivity disorder: causal association or familial confounding?

Author

Baker, Brennan H., Joo, Yoonjung Yoonie, Park, Junghoon, Cha, Jiook, Baccarelli, Andrea A., and Posner, Jonathan

Subjects
RISK factors of attention-deficit hyperactivity disorder, MATERNAL exposure, CONFIDENCE intervals, AGE distribution, CROSS-sectional method, REGRESSION analysis, ATTENTION-deficit hyperactivity disorder, RISK assessment, CHILD Behavior Checklist, SOCIOECONOMIC factors, PSYCHOLOGICAL tests, MATERNAL age, FACTOR analysis, DESCRIPTIVE statistics, LOGISTIC regression analysis, ODDS ratio, CHILDREN
Abstract

Background: Causal explanations for the association of young motherhood with increased risk for child attention‐deficit hyperactivity disorder (ADHD) remain unclear. Methods: The ABCD Study recruited 11,878 youth from 22 sites across the United States between June 1, 2016 and October 15, 2018. This cross‐sectional analysis of 8,514 children aged 8–11 years excluded 2,260 twins/triplets, 265 adopted children, and 839 younger siblings. We examined associations of maternal age with ADHD clinical range diagnoses based on the Child Behavior Checklist and NIH Toolbox Flanker Attention Scores using mixed logistic and linear regression models, respectively. We conducted confounding and causal mediation analyses using genotype array, demographic, socioeconomic, and prenatal environment data to investigate which genetic and environmental variables may explain the association between young maternal age and child ADHD. Results: In crude models, each 10‐year increase in maternal age was associated with 32% decreased odds of ADHD clinical range diagnosis (OR = 0.68; 95% CI [0.59, 0.78]) and 1.09‐points increased NIH Flanker Attention Scores (β = 1.09; 95% CI [0.76, 1.41]), indicating better child visual selective attention. However, adjustment for confounders weakened these associations. The strongest confounders were family income, caregiver education, and ADHD polygenic risk score for ADHD clinical range diagnoses, and family income, caregiver education, and race/ethnicity for NIH Flanker Attention Scores. Breastfeeding duration, prenatal alcohol exposure, and prenatal tobacco exposure were responsible for up to 18%, 6%, and 4% mediation, respectively. Conclusions: Socioeconomic disadvantages were likely the primary explanation for the association of young maternal age with child ADHD, although genetics and modifiable environmental factors also played a role. Public policies aimed at reducing the burden of ADHD associated with young motherhood should target socioeconomic inequalities and support young pregnant women by advocating for reduced prenatal tobacco exposure and healthy breastfeeding practices after childbirth. [ABSTRACT FROM AUTHOR]

Published
2023
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