Search

Your search keyword '"Kilian, M."' showing total 2,887 results
Start Over Author "Kilian, M."

Refine your results

more »

more »

more »

more »

more »

more »
2,887 results on '"Kilian, M."'

1. Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging

Author

Paschali, Magdalini, Jiang, Yu Hang, Siegel, Spencer, Gonzalez, Camila, Pohl, Kilian M., Chaudhari, Akshay, and Zhao, Qingyu

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

Recent advancements in medicine have confirmed that brain disorders often comprise multiple subtypes of mechanisms, developmental trajectories, or severity levels. Such heterogeneity is often associated with demographic aspects (e.g., sex) or disease-related contributors (e.g., genetics). Thus, the predictive power of machine learning models used for symptom prediction varies across subjects based on such factors. To model this heterogeneity, one can assign each training sample a factor-dependent weight, which modulates the subject's contribution to the overall objective loss function. To this end, we propose to model the subject weights as a linear combination of the eigenbases of a spectral population graph that captures the similarity of factors across subjects. In doing so, the learned weights smoothly vary across the graph, highlighting sub-cohorts with high and low predictability. Our proposed sample weighting scheme is evaluated on two tasks. First, we predict initiation of heavy alcohol drinking in young adulthood from imaging and neuropsychological measures from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). Next, we detect Dementia vs. Mild Cognitive Impairment (MCI) using imaging and demographic measurements in subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Compared to existing sample weighting schemes, our sample weights improve interpretability and highlight sub-cohorts with distinct characteristics and varying model accuracy.

Published
2024

2. SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis

Author

González, Camila, Miraoui, Yanis, Fan, Yiran, Adeli, Ehsan, and Pohl, Kilian M.

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

Deep learning can help uncover patterns in resting-state functional Magnetic Resonance Imaging (rs-fMRI) associated with psychiatric disorders and personal traits. Yet the problem of interpreting deep learning findings is rarely more evident than in fMRI analyses, as the data is sensitive to scanning effects and inherently difficult to visualize. We propose a simple approach to mitigate these challenges grounded on sparsification and self-supervision. Instead of extracting post-hoc feature attributions to uncover functional connections that are important to the target task, we identify a small subset of highly informative connections during training and occlude the rest. To this end, we jointly train a (1) sparse input mask, (2) variational autoencoder (VAE), and (3) downstream classifier in an end-to-end fashion. While we need a portion of labeled samples to train the classifier, we optimize the sparse mask and VAE with unlabeled data from additional acquisition sites, retaining only the input features that generalize well. We evaluate our method - Sparsely Reconstructed Graphs (SpaRG) - on the public ABIDE dataset for the task of sex classification, training with labeled cases from 18 sites and adapting the model to two additional out-of-distribution sites with a portion of unlabeled samples. For a relatively coarse parcellation (64 regions), SpaRG utilizes only 1% of the original connections while improving the classification accuracy across domains. Our code can be found at github.com/yanismiraoui/SpaRG.

Published
2024

3. Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning

Author

Wang, Yixin, Peng, Wei, Zhang, Yu, Adeli, Ehsan, Zhao, Qingyu, and Pohl, Kilian M.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Many longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. Doing so requires accurate encoding of their multidimensional relationship while accounting for individual variability over time. For this purpose, we propose an unsupervised learning model (called \underline{\textbf{Co}}ntrastive Learning-based \underline{\textbf{Gra}}ph Generalized \underline{\textbf{Ca}}nonical Correlation Analysis (CoGraCa)) that encodes their relationship via Graph Attention Networks and generalized Canonical Correlational Analysis. To create brain-cognition fingerprints reflecting unique neural and cognitive phenotype of each person, the model also relies on individualized and multimodal contrastive learning. We apply CoGraCa to longitudinal dataset of healthy individuals consisting of resting-state functional MRI and cognitive measures acquired at multiple visits for each participant. The generated fingerprints effectively capture significant individual differences and outperform current single-modal and CCA-based multimodal models in identifying sex and age. More importantly, our encoding provides interpretable interactions between those two modalities.

Published
2024

4. Evaluating the Quality of Brain MRI Generators

Author

Wu, Jiaqi, Peng, Wei, Li, Binxu, Zhang, Yu, and Pohl, Kilian M.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Deep learning models generating structural brain MRIs have the potential to significantly accelerate discovery of neuroscience studies. However, their use has been limited in part by the way their quality is evaluated. Most evaluations of generative models focus on metrics originally designed for natural images (such as structural similarity index and Frechet inception distance). As we show in a comparison of 6 state-of-the-art generative models trained and tested on over 3000 MRIs, these metrics are sensitive to the experimental setup and inadequately assess how well brain MRIs capture macrostructural properties of brain regions (i.e., anatomical plausibility). This shortcoming of the metrics results in inconclusive findings even when qualitative differences between the outputs of models are evident. We therefore propose a framework for evaluating models generating brain MRIs, which requires uniform processing of the real MRIs, standardizing the implementation of the models, and automatically segmenting the MRIs generated by the models. The segmentations are used for quantifying the plausibility of anatomy displayed in the MRIs. To ensure meaningful quantification, it is crucial that the segmentations are highly reliable. Our framework rigorously checks this reliability, a step often overlooked by prior work. Only 3 of the 6 generative models produced MRIs, of which at least 95% had highly reliable segmentations. More importantly, the assessment of each model by our framework is in line with qualitative assessments, reinforcing the validity of our approach., Comment: Accepted MICCAI 20224

Published
2024

5. Latent 3D Brain MRI Counterfactual

Author

Peng, Wei, Xia, Tian, Ribeiro, Fabio De Sousa, Bosschieter, Tomas, Adeli, Ehsan, Zhao, Qingyu, Glocker, Ben, and Pohl, Kilian M.

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

The number of samples in structural brain MRI studies is often too small to properly train deep learning models. Generative models show promise in addressing this issue by effectively learning the data distribution and generating high-fidelity MRI. However, they struggle to produce diverse, high-quality data outside the distribution defined by the training data. One way to address the issue is using causal models developed for 3D volume counterfactuals. However, accurately modeling causality in high-dimensional spaces is a challenge so that these models generally generate 3D brain MRIS of lower quality. To address these challenges, we propose a two-stage method that constructs a Structural Causal Model (SCM) within the latent space. In the first stage, we employ a VQ-VAE to learn a compact embedding of the MRI volume. Subsequently, we integrate our causal model into this latent space and execute a three-step counterfactual procedure using a closed-form Generalized Linear Model (GLM). Our experiments conducted on real-world high-resolution MRI data (1mm) demonstrate that our method can generate high-quality 3D MRI counterfactuals.

Published
2024

6. Multi-dimensional predictors of first drinking initiation and regular drinking onset in adolescence: A prospective longitudinal study

Author

Nguyen-Louie, Tam T, Thompson, Wesley K, Sullivan, Edith V, Pfefferbaum, Adolf, Gonzalez, Camila, Eberson-Shumate, Sonja C, Wade, Natasha E, Clark, Duncan B, Nagel, Bonnie J, Baker, Fiona C, Luna, Beatriz, Nooner, Kate B, de Zambotti, Massimiliano, Goldston, David B, Knutson, Brian, Pohl, Kilian M, and Tapert, Susan F

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Psychology, Paediatrics, Pharmacology and Pharmaceutical Sciences, Pediatric, Clinical Research, Basic Behavioral and Social Science, Prevention, Alcoholism, Alcohol Use and Health, Neurosciences, Underage Drinking, Substance Misuse, Behavioral and Social Science, Stroke, Cardiovascular, Oral and gastrointestinal, Good Health and Well Being, Humans, Adolescent, Male, Female, Longitudinal Studies, Prospective Studies, Binge Drinking, Adolescent Behavior, Alcohol Drinking, Risk Factors, Adolescent alcohol use onset, Regular drinking onset, Time-to-event models, NCANDA, Withdrawal, Binge drinking, Clinical Sciences, Cognitive Sciences, Biological psychology, Clinical and health psychology
Abstract

Early adolescent drinking onset is linked to myriad negative consequences. Using the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) baseline to year 8 data, this study (1) leveraged best subsets selection and Cox Proportional Hazards regressions to identify the most robust predictors of adolescent first and regular drinking onset, and (2) examined the clinical utility of drinking onset in forecasting later binge drinking and withdrawal effects. Baseline predictors included youth psychodevelopmental characteristics, cognition, brain structure, family, peer, and neighborhood domains. Participants (N=538) were alcohol-naïve at baseline. The strongest predictors of first and regular drinking onset were positive alcohol expectancies (Hazard Ratios [HRs]=1.67-1.87), easy home alcohol access (HRs=1.62-1.67), more parental solicitation (e.g., inquiring about activities; HRs=1.72-1.76), and less parental control and knowledge (HRs=.72-.73). Robust linear regressions showed earlier first and regular drinking onset predicted earlier transition into binge and regular binge drinking (βs=0.57-0.95). Zero-inflated Poisson regressions revealed that delayed first and regular drinking increased the likelihood (Incidence Rate Ratios [IRR]=1.62 and IRR=1.29, respectively) of never experiencing withdrawal. Findings identified behavioral and environmental factors predicting temporal paths to youthful drinking, dissociated first from regular drinking initiation, and revealed adverse sequelae of younger drinking initiation, supporting efforts to delay drinking onset.

Published
2024

7. Identifying high school risk factors that forecast heavy drinking onset in understudied young adults

Author

Zhao, Qingyu, Paschali, Magdalini, Dehoney, Joseph, Baker, Fiona C, de Zambotti, Massimiliano, De Bellis, Michael D, Goldston, David B, Nooner, Kate B, Clark, Duncan B, Luna, Beatriz, Nagel, Bonnie J, Brown, Sandra A, Tapert, Susan F, Eberson, Sonja, Thompson, Wesley K, Pfefferbaum, Adolf, Sullivan, Edith V, and Pohl, Kilian M

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical and Health Psychology, Neurosciences, Psychology, Pediatric, Health Disparities, Underage Drinking, Alcoholism, Alcohol Use and Health, Minority Health, Prevention, Clinical Research, Behavioral and Social Science, Social Determinants of Health, Substance Misuse, 2.3 Psychological, social and economic factors, Good Health and Well Being, Alcohol, Forecasting, Young adult, Adolescence, College, Humans, Risk Factors, Longitudinal Studies, Alcohol Drinking, Schools, Students, Adolescent, Adult, United States, Female, Male, Young Adult, Clinical Sciences, Cognitive Sciences, Biological psychology, Clinical and health psychology
Abstract

Heavy alcohol drinking is a major, preventable problem that adversely impacts the physical and mental health of US young adults. Studies seeking drinking risk factors typically focus on young adults who enrolled in 4-year residential college programs (4YCP) even though most high school graduates join the workforce, military, or community colleges. We examined 106 of these understudied young adults (USYA) and 453 4YCPs from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) by longitudinally following their drinking patterns for 8 years from adolescence to young adulthood. All participants were no-to-low drinkers during high school. Whereas 4YCP individuals were more likely to initiate heavy drinking during college years, USYA participants did so later. Using mental health metrics recorded during high school, machine learning forecasted individual-level risk for initiating heavy drinking after leaving high school. The risk factors differed between demographically matched USYA and 4YCP individuals and between sexes. Predictors for USYA drinkers were sexual abuse, physical abuse for girls, and extraversion for boys, whereas 4YCP drinkers were predicted by the ability to recognize facial emotion and, for boys, greater openness. Thus, alcohol prevention programs need to give special consideration to those joining the workforce, military, or community colleges, who make up the majority of this age group.

Published
2024

9. Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection

Author

Kim, Soopil, An, Sion, Chikontwe, Philip, Kang, Myeongkyun, Adeli, Ehsan, Pohl, Kilian M., and Park, Sang Hyun

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

Logical anomalies (LA) refer to data violating underlying logical constraints e.g., the quantity, arrangement, or composition of components within an image. Detecting accurately such anomalies requires models to reason about various component types through segmentation. However, curation of pixel-level annotations for semantic segmentation is both time-consuming and expensive. Although there are some prior few-shot or unsupervised co-part segmentation algorithms, they often fail on images with industrial object. These images have components with similar textures and shapes, and a precise differentiation proves challenging. In this study, we introduce a novel component segmentation model for LA detection that leverages a few labeled samples and unlabeled images sharing logical constraints. To ensure consistent segmentation across unlabeled images, we employ a histogram matching loss in conjunction with an entropy loss. As segmentation predictions play a crucial role, we propose to enhance both local and global sample validity detection by capturing key aspects from visual semantics via three memory banks: class histograms, component composition embeddings and patch-level representations. For effective LA detection, we propose an adaptive scaling strategy to standardize anomaly scores from different memory banks in inference. Extensive experiments on the public benchmark MVTec LOCO AD reveal our method achieves 98.1% AUROC in LA detection vs. 89.6% from competing methods., Comment: Accepted in AAAI2024

Published
2023

10. Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging

Author

Paschali, Magdalini, Jiang, Yu Hang, Siegel, Spencer, Gonzalez, Camila, Pohl, Kilian M., Chaudhari, Akshay, Zhao, Qingyu, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rekik, Islem, editor, Adeli, Ehsan, editor, Park, Sang Hyun, editor, and Cintas, Celia, editor

Published
2025
Full Text
View/download PDF

11. Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIs

Author

Peng, Wei, Bosschieter, Tomas, Ouyang, Jiahong, Paul, Robert, Adeli, Ehsan, Zhao, Qingyu, and Pohl, Kilian M.

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

Generative AI models hold great potential in creating synthetic brain MRIs that advance neuroimaging studies by, for example, enriching data diversity. However, the mainstay of AI research only focuses on optimizing the visual quality (such as signal-to-noise ratio) of the synthetic MRIs while lacking insights into their relevance to neuroscience. To gain these insights with respect to T1-weighted MRIs, we first propose a new generative model, BrainSynth, to synthesize metadata-conditioned (e.g., age- and sex-specific) MRIs that achieve state-of-the-art visual quality. We then extend our evaluation with a novel procedure to quantify anatomical plausibility, i.e., how well the synthetic MRIs capture macrostructural properties of brain regions, and how accurately they encode the effects of age and sex. Results indicate that more than half of the brain regions in our synthetic MRIs are anatomically accurate, i.e., with a small effect size between real and synthetic MRIs. Moreover, the anatomical plausibility varies across cortical regions according to their geometric complexity. As is, our synthetic MRIs can significantly improve the training of a Convolutional Neural Network to identify accelerated aging effects in an independent study. These results highlight the opportunities of using generative AI to aid neuroimaging research and point to areas for further improvement.

Published
2023

12. LSOR: Longitudinally-Consistent Self-Organized Representation Learning

Author

Ouyang, Jiahong, Zhao, Qingyu, Adeli, Ehsan, Peng, Wei, Zaharchuk, Greg, and Pohl, Kilian M.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs. One way to address this issue is by visualizing the high-dimensional latent spaces generated by deep learning via self-organizing maps (SOM). SOM separates the latent space into clusters and then maps the cluster centers to a discrete (typically 2D) grid preserving the high-dimensional relationship between clusters. However, learning SOM in a high-dimensional latent space tends to be unstable, especially in a self-supervision setting. Furthermore, the learned SOM grid does not necessarily capture clinically interesting information, such as brain age. To resolve these issues, we propose the first self-supervised SOM approach that derives a high-dimensional, interpretable representation stratified by brain age solely based on longitudinal brain MRIs (i.e., without demographic or cognitive information). Called Longitudinally-consistent Self-Organized Representation learning (LSOR), the method is stable during training as it relies on soft clustering (vs. the hard cluster assignments used by existing SOM). Furthermore, our approach generates a latent space stratified according to brain age by aligning trajectories inferred from longitudinal MRIs to the reference vector associated with the corresponding SOM cluster. When applied to longitudinal MRIs of the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=632), LSOR generates an interpretable latent space and achieves comparable or higher accuracy than the state-of-the-art representations with respect to the downstream tasks of classification (static vs. progressive mild cognitive impairment) and regression (determining ADAS-Cog score of all subjects). The code is available at https://github.com/ouyangjiahong/longitudinal-som-single-modality.

Published
2023

13. Brain structural covariance network features are robust markers of early heavy alcohol use

Author

Ottino‐González, Jonatan, Cupertino, Renata B, Cao, Zhipeng, Hahn, Sage, Pancholi, Devarshi, Albaugh, Matthew D, Brumback, Ty, Baker, Fiona C, Brown, Sandra A, Clark, Duncan B, de Zambotti, Massimiliano, Goldston, David B, Luna, Beatriz, Nagel, Bonnie J, Nooner, Kate B, Pohl, Kilian M, Tapert, Susan F, Thompson, Wesley K, Jernigan, Terry L, Conrod, Patricia, Mackey, Scott, and Garavan, Hugh

Subjects
Biological Psychology, Psychology, Neurosciences, Pediatric, Biomedical Imaging, Women's Health, Underage Drinking, Alcoholism, Alcohol Use and Health, Brain Disorders, Basic Behavioral and Social Science, Behavioral and Social Science, Substance Misuse, Clinical Research, Good Health and Well Being, Young Adult, Adolescent, Child, Humans, Adult, Alcoholism, Cross-Sectional Studies, Magnetic Resonance Imaging, Brain, Connectome, Alcohol, cortical thickness, early marker, graph theory, neurodevelopment, structural covariance networks, Medical and Health Sciences, Psychology and Cognitive Sciences, Substance Abuse, Public health, Clinical and health psychology
Abstract

Background and aimsRecently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.Design and settingCross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years).CasesCases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.MeasurementsGraph theory metrics of segregation and integration were used to summarize SCN.FindingsMirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.ConclusionStructural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

Published
2024

14. Imputing Brain Measurements Across Data Sets via Graph Neural Networks

Author

Wang, Yixin, Peng, Wei, Tapert, Susan F., Zhao, Qingyu, and Pohl, Kilian M.

Subjects
Computer Science - Machine Learning
Abstract

Publicly available data sets of structural MRIs might not contain specific measurements of brain Regions of Interests (ROIs) that are important for training machine learning models. For example, the curvature scores computed by Freesurfer are not released by the Adolescent Brain Cognitive Development (ABCD) Study. One can address this issue by simply reapplying Freesurfer to the data set. However, this approach is generally computationally and labor intensive (e.g., requiring quality control). An alternative is to impute the missing measurements via a deep learning approach. However, the state-of-the-art is designed to estimate randomly missing values rather than entire measurements. We therefore propose to re-frame the imputation problem as a prediction task on another (public) data set that contains the missing measurements and shares some ROI measurements with the data sets of interest. A deep learning model is then trained to predict the missing measurements from the shared ones and afterwards is applied to the other data sets. Our proposed algorithm models the dependencies between ROI measurements via a graph neural network (GNN) and accounts for demographic differences in brain measurements (e.g. sex) by feeding the graph encoding into a parallel architecture. The architecture simultaneously optimizes a graph decoder to impute values and a classifier in predicting demographic factors. We test the approach, called Demographic Aware Graph-based Imputation (DAGI), on imputing those missing Freesurfer measurements of ABCD (N=3760) by training the predictor on those publicly released by the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N=540)..., Comment: Accepted at the 6th workshop on PRedictive Intelligence in Medicine (PRIME 2023) - MICCAI 2023

Published
2023

15. An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment

Author

Nerrise, Favour, Zhao, Qingyu, Poston, Kathleen L., Pohl, Kilian M., and Adeli, Ehsan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Neurons and Cognition
Abstract

One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain function associated with gait impairment could be crucial in better understanding PD motor progression, thus advancing the development of more effective and personalized therapeutics. In this work, we present an explainable, geometric, weighted-graph attention neural network (xGW-GAT) to identify functional networks predictive of the progression of gait difficulties in individuals with PD. xGW-GAT predicts the multi-class gait impairment on the MDS Unified PD Rating Scale (MDS-UPDRS). Our computational- and data-efficient model represents functional connectomes as symmetric positive definite (SPD) matrices on a Riemannian manifold to explicitly encode pairwise interactions of entire connectomes, based on which we learn an attention mask yielding individual- and group-level explainability. Applied to our resting-state functional MRI (rs-fMRI) dataset of individuals with PD, xGW-GAT identifies functional connectivity patterns associated with gait impairment in PD and offers interpretable explanations of functional subnetworks associated with motor impairment. Our model successfully outperforms several existing methods while simultaneously revealing clinically-relevant connectivity patterns. The source code is available at https://github.com/favour-nerrise/xGW-GAT ., Comment: Accepted by the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). MICCAI Student-Author Registration (STAR) Award. 11 pages, 2 figures, 1 table, appendix. Source Code: https://github.com/favour-nerrise/xGW-GAT

Published
2023

16. Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model

Author

Peng, Wei, Adeli, Ehsan, Bosschieter, Tomas, Park, Sang Hyun, Zhao, Qingyu, and Pohl, Kilian M.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

As acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models. This challenge could be reduced by MRI synthesis, for which Generative Adversarial Networks (GANs) are popular. GANs, however, are commonly unstable and struggle with creating diverse and high-quality data. A more stable alternative is Diffusion Probabilistic Models (DPMs) with a fine-grained training strategy. To overcome their need for extensive computational resources, we propose a conditional DPM (cDPM) with a memory-efficient process that generates realistic-looking brain MRIs. To this end, we train a 2D cDPM to generate an MRI subvolume conditioned on another subset of slices from the same MRI. By generating slices using arbitrary combinations between condition and target slices, the model only requires limited computational resources to learn interdependencies between slices even if they are spatially far apart. After having learned these dependencies via an attention network, a new anatomy-consistent 3D brain MRI is generated by repeatedly applying the cDPM. Our experiments demonstrate that our method can generate high-quality 3D MRIs that share a similar distribution to real MRIs while still diversifying the training set. The code is available at https://github.com/xiaoiker/mask3DMRI_diffusion and also will be released as part of MONAI, at https://github.com/Project-MONAI/GenerativeModels.

Published
2022

17. Anterior and posterior thalamic volumes differentially correlate with memory, attention, and motor processes in HIV infection and alcohol use disorder comorbidity

Author

Rosemary Fama, Stephanie A. Sassoon, Eva M. Müller-Oehring, Manojkumar Saranathan, Kilian M. Pohl, Natalie M. Zahr, Adolf Pfefferbaum, and Edith V. Sullivan

Subjects
Thalamic nuclei, HIV infection, Alcohol use disorder, Comorbidity, Memory, Motor processes, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

The thalamus, with its reciprocal connections to and from cortical, subcortical, and cerebellar regions, is a central active participant in multiple functional brain networks. Structural MRI studies measuring the entire thalamus without respect to its regional or nuclear divisions report volume shrinkage in diseases including HIV infection, alcohol use disorder (AUD), and their comorbidity (HIV+AUD). Here, we examined relations between thalamic subregions (anterior, ventral, medial, and posterior) and neuropsychological functions (attention/working memory, executive functioning, episodic memory, and motor skills). Volumes of thalamic subregions were derived from automatic segmentations of standard T1 weighted MRIs of 65 individuals with HIV, 189 with AUD, 80 with HIV+AUD comorbidity, and 141 healthy controls (CTRL). Total thalamic volume was smaller and cognitive and motor composite scores were lower in the three diagnostic groups relative to the CTRL group. The AUD and HIV+AUD groups had significantly smaller thalamic subregional volumes than the CTRL group. The HIV+AUD group had smaller anterior thalamic volume than the HIV-only group and smaller ventral thalamic volume than the AUD-only group. In the HIV+AUD group, memory scores correlated with anterior thalamic volumes, attention/working memory scores correlated with posterior and medial thalamic volumes, and motor skill scores correlated with posterior thalamic volumes. Exploratory analyses focused on the HIV+AUD group indicated that within the posterior thalamic region, the pulvinar and medial geniculate nuclei were related to attention/working memory scores, and the pulvinar was related to motor skills scores. This study is novel in locating volume deficits in specific thalamic subregions, in addition to the thalamus as a whole, in HIV, AUD, and their comorbidity and in identifying functional ramifications of these deficits. Taken together, this study highlights the relevance of thalamic subregional volume deficits to dissociable cognitive and motor processes.

Published
2024
Full Text
View/download PDF

18. Multi-dimensional predictors of first drinking initiation and regular drinking onset in adolescence: A prospective longitudinal study

Author

Tam T. Nguyen-Louie, Wesley K. Thompson, Edith V. Sullivan, Adolf Pfefferbaum, Camila Gonzalez, Sonja C. Eberson-Shumate, Natasha E. Wade, Duncan B. Clark, Bonnie J. Nagel, Fiona C. Baker, Beatriz Luna, Kate B. Nooner, Massimiliano de Zambotti, David B. Goldston, Brian Knutson, Kilian M. Pohl, and Susan F. Tapert

Subjects
Adolescent alcohol use onset, Regular drinking onset, Time-to-event models, NCANDA, Withdrawal, Binge drinking, Neurophysiology and neuropsychology, QP351-495
Abstract

Early adolescent drinking onset is linked to myriad negative consequences. Using the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) baseline to year 8 data, this study (1) leveraged best subsets selection and Cox Proportional Hazards regressions to identify the most robust predictors of adolescent first and regular drinking onset, and (2) examined the clinical utility of drinking onset in forecasting later binge drinking and withdrawal effects. Baseline predictors included youth psychodevelopmental characteristics, cognition, brain structure, family, peer, and neighborhood domains. Participants (N=538) were alcohol-naïve at baseline. The strongest predictors of first and regular drinking onset were positive alcohol expectancies (Hazard Ratios [HRs]=1.67–1.87), easy home alcohol access (HRs=1.62–1.67), more parental solicitation (e.g., inquiring about activities; HRs=1.72–1.76), and less parental control and knowledge (HRs=.72–.73). Robust linear regressions showed earlier first and regular drinking onset predicted earlier transition into binge and regular binge drinking (βs=0.57–0.95). Zero-inflated Poisson regressions revealed that delayed first and regular drinking increased the likelihood (Incidence Rate Ratios [IRR]=1.62 and IRR=1.29, respectively) of never experiencing withdrawal. Findings identified behavioral and environmental factors predicting temporal paths to youthful drinking, dissociated first from regular drinking initiation, and revealed adverse sequelae of younger drinking initiation, supporting efforts to delay drinking onset.

Published
2024
Full Text
View/download PDF

22. SOM2LM: Self-Organized Multi-Modal Longitudinal Maps

Author

Ouyang, Jiahong, Zhao, Qingyu, Adeli, Ehsan, Zaharchuk, Greg, Pohl, Kilian M., Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Linguraru, Marius George, editor, Dou, Qi, editor, Feragen, Aasa, editor, Giannarou, Stamatia, editor, Glocker, Ben, editor, Lekadir, Karim, editor, and Schnabel, Julia A., editor

Published
2024
Full Text
View/download PDF

23. Risk for depression tripled during the COVID-19 pandemic in emerging adults followed for the last 8 years

Author

Alzueta, Elisabet, Podhajsky, Simon, Zhao, Qingyu, Tapert, Susan F, Thompson, Wesley K, de Zambotti, Massimiliano, Yuksel, Dilara, Kiss, Orsolya, Wang, Rena, Volpe, Laila, Prouty, Devin, Colrain, Ian M, Clark, Duncan B, Goldston, David B, Nooner, Kate B, De Bellis, Michael D, Brown, Sandra A, Nagel, Bonnie J, Pfefferbaum, Adolf, Sullivan, Edith V, Baker, Fiona C, and Pohl, Kilian M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Clinical Research, Mental Health, Brain Disorders, Alcoholism, Alcohol Use and Health, Behavioral and Social Science, Prevention, Substance Misuse, Neurosciences, Depression, Sleep Research, Mental health, Good Health and Well Being, Adolescent, Adult, Humans, Female, COVID-19, Pandemics, SARS-CoV-2, Alcohol, COVID-19 pandemic, depression, emerging adults, longitudinal, sleep, Public Health and Health Services, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has significantly increased depression rates, particularly in emerging adults. The aim of this study was to examine longitudinal changes in depression risk before and during COVID-19 in a cohort of emerging adults in the U.S. and to determine whether prior drinking or sleep habits could predict the severity of depressive symptoms during the pandemic.MethodsParticipants were 525 emerging adults from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a five-site community sample including moderate-to-heavy drinkers. Poisson mixed-effect models evaluated changes in the Center for Epidemiological Studies Depression Scale (CES-D-10) from before to during COVID-19, also testing for sex and age interactions. Additional analyses examined whether alcohol use frequency or sleep duration measured in the last pre-COVID assessment predicted pandemic-related increase in depressive symptoms.ResultsThe prevalence of risk for clinical depression tripled due to a substantial and sustained increase in depressive symptoms during COVID-19 relative to pre-COVID years. Effects were strongest for younger women. Frequent alcohol use and short sleep duration during the closest pre-COVID visit predicted a greater increase in COVID-19 depressive symptoms.ConclusionsThe sharp increase in depression risk among emerging adults heralds a public health crisis with alarming implications for their social and emotional functioning as this generation matures. In addition to the heightened risk for younger women, the role of alcohol use and sleep behavior should be tracked through preventive care aiming to mitigate this looming mental health crisis.

Published
2023

24. Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV

Author

Mukerji, Shibani S, Petersen, Kalen J, Pohl, Kilian M, Dastgheyb, Raha M, Fox, Howard S, Bilder, Robert M, Brouillette, Marie-Josée, Gross, Alden L, Scott-Sheldon, Lori AJ, Paul, Robert H, and Gabuzda, Dana

Subjects
Mental Health, HIV/AIDS, Behavioral and Social Science, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Mental health, Good Health and Well Being, Humans, Cognition Disorders, Cognitive Dysfunction, Machine Learning, Phenotype, Cognition, HIV Infections, HIV, cognitive impairment, HIV-associated neurocognitive disorders, machine learning, deep learning, Biological Sciences, Medical and Health Sciences, Microbiology
Abstract

Cognitive disorders are prevalent in people with HIV (PWH) despite antiretroviral therapy. Given the heterogeneity of cognitive disorders in PWH in the current era and evidence that these disorders have different etiologies and risk factors, scientific rationale is growing for using data-driven models to identify biologically defined subtypes (biotypes) of these disorders. Here, we discuss the state of science using machine learning to understand cognitive phenotypes in PWH and their associated comorbidities, biological mechanisms, and risk factors. We also discuss methods, example applications, challenges, and what will be required from the field to successfully incorporate machine learning in research on cognitive disorders in PWH. These topics were discussed at the National Institute of Mental Health meeting on "Biotypes of CNS Complications in People Living with HIV" held in October 2021. These ongoing research initiatives seek to explain the heterogeneity of cognitive phenotypes in PWH and their associated biological mechanisms to facilitate clinical management and tailored interventions.

Published
2023

25. Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome

Author

Li, Yueting, Wei, Qingyue, Adeli, Ehsan, Pohl, Kilian M., and Zhao, Qingyu

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The white-matter (micro-)structural architecture of the brain promotes synchrony among neuronal populations, giving rise to richly patterned functional connections. A fundamental problem for systems neuroscience is determining the best way to relate structural and functional networks quantified by diffusion tensor imaging and resting-state functional MRI. As one of the state-of-the-art approaches for network analysis, graph convolutional networks (GCN) have been separately used to analyze functional and structural networks, but have not been applied to explore inter-network relationships. In this work, we propose to couple the two networks of an individual by adding inter-network edges between corresponding brain regions, so that the joint structure-function graph can be directly analyzed by a single GCN. The weights of inter-network edges are learnable, reflecting non-uniform structure-function coupling strength across the brain. We apply our Joint-GCN to predict age and sex of 662 participants from the public dataset of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) based on their functional and micro-structural white-matter networks. Our results support that the proposed Joint-GCN outperforms existing multi-modal graph learning approaches for analyzing structural and functional networks.

Published
2022

26. The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research

Author

Qingyu Zhao, Kate B. Nooner, Susan F. Tapert, Ehsan Adeli, Kilian M. Pohl, Amy Kuceyeski, and Mert R. Sabuncu

Subjects
Brain-behavior mapping, Heterogeneity, Machine Learning, Neuroimaging, Psychiatry, RC435-571
Abstract

Despite the advantage of neuroimaging-based machine learning (ML) models as pivotal tools for investigating brain-behavior relationships in neuropsychiatric studies, these data-driven predictive approaches have yet to yield substantial, clinically actionable insights for mental health care. A notable impediment lies in the inadequate accommodation of most ML research to the natural heterogeneity within large samples. Although commonly thought of as individual-level analyses, many ML algorithms are unimodal and homogeneous and thus incapable of capturing the potentially heterogeneous relationships between biology and psychopathology. We review the current landscape of computational research targeting population heterogeneity and argue that there is a need to expand from brain subtyping and behavioral phenotyping to analyses that focus on heterogeneity at the relational level. To this end, we review and suggest several existing ML models with the capacity to discern how external environmental and sociodemographic factors moderate the brain-behavior mapping function in a data-driven fashion. These heterogeneous ML models hold promise for enhancing the discovery of individualized brain-behavior associations and advancing precision psychiatry.

Published
2025
Full Text
View/download PDF

27. Multiple Instance Neuroimage Transformer

Author

Singla, Ayush, Zhao, Qingyu, Do, Daniel K., Zhou, Yuyin, Pohl, Kilian M., and Adeli, Ehsan

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

For the first time, we propose using a multiple instance learning based convolution-free transformer model, called Multiple Instance Neuroimage Transformer (MINiT), for the classification of T1weighted (T1w) MRIs. We first present several variants of transformer models adopted for neuroimages. These models extract non-overlapping 3D blocks from the input volume and perform multi-headed self-attention on a sequence of their linear projections. MINiT, on the other hand, treats each of the non-overlapping 3D blocks of the input MRI as its own instance, splitting it further into non-overlapping 3D patches, on which multi-headed self-attention is computed. As a proof-of-concept, we evaluate the efficacy of our model by training it to identify sex from T1w-MRIs of two public datasets: Adolescent Brain Cognitive Development (ABCD) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). The learned attention maps highlight voxels contributing to identifying sex differences in brain morphometry. The code is available at https://github.com/singlaayush/MINIT.

Published
2022
Full Text
View/download PDF

28. Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing

Author

Paschali, Magdalini, Zhao, Qingyu, Adeli, Ehsan, and Pohl, Kilian M.

Subjects
Computer Science - Machine Learning
Abstract

A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i.e., whether certain factors (e.g., related to life events) are associated with an outcome (e.g., depression). In recent years, deep learning has become a potential alternative approach for conducting such analyses by predicting an outcome from a collection of factors and identifying the most "informative" ones driving the prediction. However, this approach has had limited impact as its findings are not linked to statistical significance of factors supporting hypotheses. In this article, we proposed a flexible and scalable approach based on the concept of permutation testing that integrates hypothesis testing into the data-driven deep learning analysis. We apply our approach to the yearly self-reported assessments of 621 adolescent participants of the National Consortium of Alcohol and Neurodevelopment in Adolescence (NCANDA) to predict negative valence, a symptom of major depressive disorder according to the NIMH Research Domain Criteria (RDoC). Our method successfully identifies categories of risk factors that further explain the symptom., Comment: Accepted at the 5th workshop on PRedictive Intelligence in Medicine (PRIME 2022) - MICCAI 2022

Published
2022

29. A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models

Author

Vento, Anthony, Zhao, Qingyu, Paul, Robert, Pohl, Kilian M., and Adeli, Ehsan

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

Translating machine learning algorithms into clinical applications requires addressing challenges related to interpretability, such as accounting for the effect of confounding variables (or metadata). Confounding variables affect the relationship between input training data and target outputs. When we train a model on such data, confounding variables will bias the distribution of the learned features. A recent promising solution, MetaData Normalization (MDN), estimates the linear relationship between the metadata and each feature based on a non-trainable closed-form solution. However, this estimation is confined by the sample size of a mini-batch and thereby may cause the approach to be unstable during training. In this paper, we extend the MDN method by applying a Penalty approach (referred to as PDMN). We cast the problem into a bi-level nested optimization problem. We then approximate this optimization problem using a penalty method so that the linear parameters within the MDN layer are trainable and learned on all samples. This enables PMDN to be plugged into any architectures, even those unfit to run batch-level operations, such as transformers and recurrent models. We show improvement in model accuracy and greater independence from confounders using PMDN over MDN in a synthetic experiment and a multi-label, multi-site dataset of magnetic resonance images (MRIs).

Published
2022

30. GaitForeMer: Self-Supervised Pre-Training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation

Author

Endo, Mark, Poston, Kathleen L., Sullivan, Edith V., Fei-Fei, Li, Pohl, Kilian M., and Adeli, Ehsan

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

Parkinson's disease (PD) is a neurological disorder that has a variety of observable motor-related symptoms such as slow movement, tremor, muscular rigidity, and impaired posture. PD is typically diagnosed by evaluating the severity of motor impairments according to scoring systems such as the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Automated severity prediction using video recordings of individuals provides a promising route for non-intrusive monitoring of motor impairments. However, the limited size of PD gait data hinders model ability and clinical potential. Because of this clinical data scarcity and inspired by the recent advances in self-supervised large-scale language models like GPT-3, we use human motion forecasting as an effective self-supervised pre-training task for the estimation of motor impairment severity. We introduce GaitForeMer, Gait Forecasting and impairment estimation transforMer, which is first pre-trained on public datasets to forecast gait movements and then applied to clinical data to predict MDS-UPDRS gait impairment severity. Our method outperforms previous approaches that rely solely on clinical data by a large margin, achieving an F1 score of 0.76, precision of 0.79, and recall of 0.75. Using GaitForeMer, we show how public human movement data repositories can assist clinical use cases through learning universal motion representations. The code is available at https://github.com/markendo/GaitForeMer ., Comment: Accepted as a conference paper at MICCAI (Medical Image Computing and Computer Assisted Intervention) 2022

Published
2022

31. Prior test experience confounds longitudinal tracking of adolescent cognitive and motor development

Author

Sullivan, Edith V, Thompson, Wesley K, Brumback, Ty, Prouty, Devin, Tapert, Susan F, Brown, Sandra A, De Bellis, Michael D, Nooner, Kate B, Baker, Fiona C, Colrain, Ian M, Clark, Duncan B, Nagel, Bonnie J, Pohl, Kilian M, and Pfefferbaum, Adolf

Subjects
Epidemiology, Public Health, Health Sciences, Pediatric, Basic Behavioral and Social Science, Clinical Research, Mind and Body, Neurosciences, Brain Disorders, Mental Health, Behavioral and Social Science, Underpinning research, 1.2 Psychological and socioeconomic processes, Neurological, Mental health, Adolescent, Adult, Child, Cognition, Cross-Sectional Studies, Emotions, Female, Humans, Longitudinal Studies, Male, Neuropsychological Tests, Young Adult, Longitudinal, Practice effects, Development, Motor, Public Health and Health Services, General & Internal Medicine, Public health
Abstract

BackgroundAccurate measurement of trajectories in longitudinal studies, considered the gold standard method for tracking functional growth during adolescence, decline in aging, and change after head injury, is subject to confounding by testing experience.MethodsWe measured change in cognitive and motor abilities over four test sessions (baseline and three annual assessments) in 154 male and 165 female participants (baseline age 12-21 years) from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. At each of the four test sessions, these participants were given a test battery using computerized administration and traditional pencil and paper tests that yielded accuracy and speed measures for multiple component cognitive (Abstraction, Attention, Emotion, Episodic memory, Working memory, and General Ability) and motor (Ataxia and Speed) functions. The analysis aim was to dissociate neurodevelopment from testing experience by using an adaptation of the twice-minus-once tested method, which calculated the difference between longitudinal change (comprising developmental plus practice effects) and practice-free initial cross-sectional performance for each consecutive pairs of test sessions. Accordingly, the first set of analyses quantified the effects of learning (i.e., prior test experience) on accuracy and after speed domain scores. Then developmental effects were  determined for each domain for accuracy and speed having removed the measured learning effects.ResultsThe greatest gains in performance occurred between the first and second sessions, especially in younger participants, regardless of sex, but practice gains continued to accrue thereafter for several functions. For all 8 accuracy composite scores, the developmental effect after accounting for learning was significant across age and was adequately described by linear fits. The learning-adjusted developmental effects for speed were adequately described by linear fits for Abstraction, Emotion, Episodic Memory, General Ability, and Motor scores, although a nonlinear fit was better for Attention, Working Memory, and Average Speed scores.ConclusionThus, what appeared as accelerated cognitive and motor development was, in most cases, attributable to learning. Recognition of the substantial influence of prior testing experience is critical for accurate characterization of normal development and for developing norms for clinical neuropsychological investigations of conditions affecting the brain.

Published
2022

32. Identifying high school risk factors that forecast heavy drinking onset in understudied young adults

Author

Qingyu Zhao, Magdalini Paschali, Joseph Dehoney, Fiona C. Baker, Massimiliano de Zambotti, Michael D. De Bellis, David B. Goldston, Kate B. Nooner, Duncan B. Clark, Beatriz Luna, Bonnie J. Nagel, Sandra A. Brown, Susan F. Tapert, Sonja Eberson, Wesley K. Thompson, Adolf Pfefferbaum, Edith V. Sullivan, and Kilian M. Pohl

Subjects
Alcohol, Forecasting, Young adult, Adolescence, College, Neurophysiology and neuropsychology, QP351-495
Abstract

Heavy alcohol drinking is a major, preventable problem that adversely impacts the physical and mental health of US young adults. Studies seeking drinking risk factors typically focus on young adults who enrolled in 4-year residential college programs (4YCP) even though most high school graduates join the workforce, military, or community colleges. We examined 106 of these understudied young adults (USYA) and 453 4YCPs from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) by longitudinally following their drinking patterns for 8 years from adolescence to young adulthood. All participants were no-to-low drinkers during high school. Whereas 4YCP individuals were more likely to initiate heavy drinking during college years, USYA participants did so later. Using mental health metrics recorded during high school, machine learning forecasted individual-level risk for initiating heavy drinking after leaving high school. The risk factors differed between demographically matched USYA and 4YCP individuals and between sexes. Predictors for USYA drinkers were sexual abuse, physical abuse for girls, and extraversion for boys, whereas 4YCP drinkers were predicted by the ability to recognize facial emotion and, for boys, greater openness. Thus, alcohol prevention programs need to give special consideration to those joining the workforce, military, or community colleges, who make up the majority of this age group.

Published
2024
Full Text
View/download PDF

34. Multi-dimensional predictors of first drinking initiation and regular drinking onset in adolescence: A prospective longitudinal study

Author

Nguyen-Louie, Tam T., Thompson, Wesley K., Sullivan, Edith V., Pfefferbaum, Adolf, Gonzalez, Camila, Eberson-Shumate, Sonja C., Wade, Natasha E., Clark, Duncan B., Nagel, Bonnie J., Baker, Fiona C., Luna, Beatriz, Nooner, Kate B., de Zambotti, Massimiliano, Goldston, David B., Knutson, Brian, Pohl, Kilian M., and Tapert, Susan F.

Published
2024
Full Text
View/download PDF

39. Did the acute impact of the COVID-19 pandemic on drinking or nicotine use persist? Evidence from a cohort of emerging adults followed for up to nine years

Author

Pelham, William E, Yuksel, Dilara, Tapert, Susan F, Baker, Fiona C, Pohl, Kilian M, Thompson, Wesley K, Podhajsky, Simon, Reuter, Chase, Zhao, Qingyu, Eberson-Shumate, Sonja C, Clark, Duncan B, Goldston, David B, Nooner, Kate B, and Brown, Sandra A

Subjects
Biological Psychology, Psychology, Coronaviruses, Alcoholism, Alcohol Use and Health, Coronaviruses Disparities and At-Risk Populations, Neurosciences, Clinical Research, Prevention, Basic Behavioral and Social Science, Pediatric, Underage Drinking, Health Disparities, Tobacco, Behavioral and Social Science, Substance Misuse, 2.4 Surveillance and distribution, Good Health and Well Being, Adolescent, Adult, Alcohol Drinking, Binge Drinking, COVID-19, Female, Humans, Longitudinal Studies, Male, Nicotine, Pandemics, Young Adult, Alcohol, Emerging adulthood, Public Health and Health Services, Substance Abuse, Public health, Biological psychology, Clinical and health psychology
Abstract

ObjectiveThis study examined the impact of the COVID-19 pandemic on drinking and nicotine use through June of 2021 in a community-based sample of young adults.MethodData were from 348 individuals (49% female) enrolled in a long-term longitudinal study with an accelerated longitudinal design: the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) Study. Individuals completed pre-pandemic assessments biannually from 2016 to early 2020, then completed up to three web-based, during-pandemic surveys in June 2020, December 2020, and June 2021. Assessments when individuals were 18.8-22.4 years old (N = 1,458) were used to compare drinking and nicotine use pre-pandemic vs. at each of the three during-pandemic timepoints, adjusting for the age-related increases expected over time.ResultsCompared to pre-pandemic, participants were less likely to report past-month drinking in June or December 2020, but there was an increase in drinking days among drinkers in June 2020. By June 2021, both the prevalence of past-month drinking and number of drinking days among drinks were similar to pre-pandemic levels. On average, there were no statistically significant differences between pre-pandemic and during-pandemic time points for binge drinking, typical drinking quantity, or nicotine use. Young adults who reported an adverse financial impact of the pandemic showed increased nicotine use while their peers showed stable or decreased nicotine use.ConclusionInitial effects of the pandemic on alcohol use faded by June 2021, and on average there was little effect of the pandemic on nicotine use.

Published
2022

40. Value of intravenous thrombolysis in endovascular treatment for large-vessel anterior circulation stroke: individual participant data meta-analysis of six randomised trials

Author

Zi, Wenjie, Nogueira, Raul, Yang, Qingwu, Liu, Jianmin, Yang, Pengfei, Zhang, Yongwei, Yan, Bernard, Mitchell, Peter, Miao, Zhong Rong, Majoie, Charles B., Roos, Yvo B., Suzuki, Kentaro, Kimura, Kazumi, Matsumaru, Yuji, Fischer, Urs, Gralla, Jan, Cavalcante, Fabiano W., Kappelhof, Manon, Treurniet, Kilian M., Kaesmacher, Johannes, Zhang, Lei, Bush, Steven, Nieboer, Daan, Lingsma, Hester F., Rothwell, Peter, Saver, Jeffrey, Fiehler, Jens, Li, Fengli, Huang, Jiacheng, Song, Jiaxing, Hong, Bo, Chen, Wenhuo, Peng, Ya, Han, Hongxing, Zhang, Liyong, Li, Zifu, Xing, Pengfei, Shen, Hongjian, Zhang, Ping, Zhang, Xiaoxi, Davis, Stephen, Nguyen, Huy-Thang, Donnan, Geoffrey, Huo, Xiaochuan, Nan, Guangxian, Bivard, Andrew, Ma, Henry, Vu, Dang Lu, Campbell, Bruce, Rinkel, Leon A., Emmer, Bart J., Coutinho, Jonathan M., LeCouffe, Natalie E., Dippel, Diederik W., van der Lugt, Aad, van Zwam, Wim H., van Oostenbrugge, Robert J., Uyttenboogaart, Maarten, Costalat, Vincent, Lycklama, Geert, Hofmeijer, Jeannette, van Norden, Anouk, Otsuka, Toshiaki, Takeuchi, Masataka, Morimoto, Masafumi, Kanazawa, Ryuzaburo, Takayama, Yohei, Kamiya, Yuki, Shigeta, Keigo, Okubo, Seiji, Hayakawa, Mikito, Strbian, Daniel, Eker, Omer, Cognard, Christophe, Meinel, Thomas, Dobrocky, Tomas, Jung, Simon, Piechowiak, Eike, Marnat, Gaultier, Sibon, Igor, Bourcier, Romain, de Gaalon, Solene, Papagiannaki, Chrysanthi, Lefebvre, Margaux, Majoie, Charles B, Cavalcante, Fabiano, Treurniet, Kilian M, Miao, Zhongrong, Rinkel, Leon A, Wang, Shouchun, Coutinho, Jonathan M, Emmer, Bart J, LeCouffe, Natalie E, Lingsma, Hester F, Nogueira, Raul G, and Roos, Yvo B

Published
2023
Full Text
View/download PDF

43. Frontal cortical volume deficits as enduring evidence of childhood abuse in community adults with AUD and HIV infection comorbidity

Author

Stephanie A. Sassoon, Rosemary Fama, Kilian M. Pohl, Adolf Pfefferbaum, and Edith V. Sullivan

Subjects
abuse, Trauma, Alcohol use disorder, HIV, Brain structure, MRI, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429, Neurophysiology and neuropsychology, QP351-495
Abstract

Background: Childhood abuse is an underappreciated source of stress, associated with adverse mental and physical health consequences. Childhood abuse has been directly associated with risky behavior thereby increasing the likelihood of alcohol misuse and risk of HIV infection, conditions associated with brain structural and functional deficits. Here, we examined the neural and behavioral correlates of childhood trauma history in alcohol use disorder (AUD), HIV infection (HIV), and their comorbidity (AUD+HIV). Methods: Occurrence of childhood trauma was evaluated by retrospective interview. Cortical (frontal, temporal, parietal, and occipital), subcortical (hippocampus, amygdala), and regional frontal volumes were derived from structural MRI, adjusted for intracranial volume and age. Test scores of executive functioning, attention/working memory, verbal/visual learning, verbal/visual memory, and motor speed functional domains were standardized on age and education of a laboratory control group. Results: History of childhood abuse was associated with smaller frontal lobe volumes regardless of diagnosis. For frontal subregional volumes, history of childhood abuse was selectively associated with smaller orbitofrontal and supplementary motor volumes. In participants with a child abuse history, poorer verbal/visual memory performance was associated with smaller orbitofrontal and frontal middle volumes, whereas in those without childhood abuse, poorer verbal/visual memory performance was associated with smaller orbitofrontal, frontal superior, and supplemental motor volumes. Conclusions: Taken together, these results comport with and extend the findings that childhood abuse is associated with brain and behavioral sequelae in AUD, HIV, and AUD+HIV comorbidity. Further, these findings suggest that sequelae of abuse in childhood may be best conceptualized as a spectrum disorder as significant deficits may be present in those who may not meet criteria for a formal trauma-related diagnosis yet may be suffering enduring stress effects on brain structural and functional health.

Published
2024
Full Text
View/download PDF

44. The Pandemic's Toll on Young Adolescents: Prevention and Intervention Targets to Preserve Their Mental Health

Author

Kiss, Orsolya, Alzueta, Elisabet, Yuksel, Dilara, Pohl, Kilian M, de Zambotti, Massimiliano, Műller-Oehring, Eva M, Prouty, Devin, Durley, Ingrid, Pelham, William E, McCabe, Connor J, Gonzalez, Marybel R, Brown, Sandra A, Wade, Natasha E, Marshall, Andrew T, Sowell, Elizabeth R, Breslin, Florence J, Lisdahl, Krista M, Dick, Anthony S, Sheth, Chandni S, McCandliss, Bruce D, Guillaume, Mathieu, Van Rinsveld, Amandine M, Dowling, Gayathri J, Tapert, Susan F, and Baker, Fiona C

Subjects
Biomedical and Clinical Sciences, Clinical and Health Psychology, Health Services and Systems, Health Sciences, Psychology, Paediatrics, Violence Research, Clinical Research, Depression, Pediatric, Basic Behavioral and Social Science, Behavioral and Social Science, Prevention, Mind and Body, Mental Health, Aetiology, 2.3 Psychological, social and economic factors, Mental health, Good Health and Well Being, Adolescent, COVID-19, Female, Humans, Longitudinal Studies, Pandemics, SARS-CoV-2, Adolescents, Children, Mental-health, Pandemic, Sleep, Sex differences, Medical and Health Sciences, Education, Psychology and Cognitive Sciences, Public Health, Biomedical and clinical sciences, Health sciences
Abstract

PurposeAdolescence is characterized by dramatic physical, social, and emotional changes, making teens particularly vulnerable to the mental health effects of the COVID-19 pandemic. This longitudinal study identifies young adolescents who are most vulnerable to the psychological toll of the pandemic and provides insights to inform strategies to help adolescents cope better in times of crisis.MethodsA data-driven approach was applied to a longitudinal, demographically diverse cohort of more than 3,000 young adolescents (11-14 years) participating in the ongoing Adolescent Brain Cognitive Development Study in the United States, including multiple prepandemic visits and three assessments during the COVID-19 pandemic (May-August 2020). We fitted machine learning models and provided a comprehensive list of predictors of psychological distress in individuals.ResultsPositive affect, stress, anxiety, and depressive symptoms were accurately detected with our classifiers. Female sex and prepandemic internalizing symptoms and sleep problems were strong predictors of psychological distress. Parent- and youth-reported pandemic-related psychosocial factors, including poorer quality and functioning of family relationships, more screen time, and witnessing discrimination in relation to the pandemic further predicted youth distress. However, better social support, regular physical activities, coping strategies, and healthy behaviors predicted better emotional well-being.DiscussionFindings highlight the importance of social connectedness and healthy behaviors, such as sleep and physical activity, as buffering factors against the deleterious effects of the pandemic on adolescents' mental health. They also point to the need for greater attention toward coping strategies that help the most vulnerable adolescents, particularly girls and those with prepandemic psychological problems.

Published
2022

45. Longitudinal Correlation Analysis for Decoding Multi-Modal Brain Development

Author

Zhao, Qingyu, Adeli, Ehsan, and Pohl, Kilian M.

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Starting from childhood, the human brain restructures and rewires throughout life. Characterizing such complex brain development requires effective analysis of longitudinal and multi-modal neuroimaging data. Here, we propose such an analysis approach named Longitudinal Correlation Analysis (LCA). LCA couples the data of two modalities by first reducing the input from each modality to a latent representation based on autoencoders. A self-supervised strategy then relates the two latent spaces by jointly disentangling two directions, one in each space, such that the longitudinal changes in latent representations along those directions are maximally correlated between modalities. We applied LCA to analyze the longitudinal T1-weighted and diffusion-weighted MRIs of 679 youths from the National Consortium on Alcohol and Neurodevelopment in Adolescence. Unlike existing approaches that focus on either cross-sectional or single-modal modeling, LCA successfully unraveled coupled macrostructural and microstructural brain development from morphological and diffusivity features extracted from the data. A retesting of LCA on raw 3D image volumes of those subjects successfully replicated the findings from the feature-based analysis. Lastly, the developmental effects revealed by LCA were inline with the current understanding of maturational patterns of the adolescent brain.

Published
2021

46. Association between computed tomography perfusion and the effect of intravenous alteplase prior to endovascular treatment in acute ischemic stroke

Author

Hoving, Jan W., van Voorst, Henk, Peerlings, Daan, Daems, Jasper D., Koopman, Miou S., Wouters, Anke, Kappelhof, Manon, LeCouffe, Natalie E., Treurniet, Kilian M., Bruggeman, Agnetha A. E., Rinkel, Leon A., van den Wijngaard, Ido R., Coutinho, Jonathan M., van der Lugt, Aad, Marquering, Henk A., Roos, Yvo B. W. E. M., Majoie, Charles B. L. M., and Emmer, Bart J.

Published
2023
Full Text
View/download PDF

49. Earlier Bedtime and Effective Coping Skills Predict a Return to Low-Risk of Depression in Young Adults during the COVID-19 Pandemic

Author

Zhao, Qingyu, Wang, Kevin, Kiss, Orsolya, Yuksel, Dilara, de Zambotti, Massimiliano, Clark, Duncan B, Goldston, David B, Nooner, Kate B, Brown, Sandra A, Tapert, Susan F, Thompson, Wesley K, Nagel, Bonnie J, Pfefferbaum, Adolf, Sullivan, Edith V, Pohl, Kilian M, and Baker, Fiona C

Subjects
Biomedical and Clinical Sciences, Clinical and Health Psychology, Clinical Sciences, Health Sciences, Psychology, Mental Health, Pediatric, Prevention, Behavioral and Social Science, Depression, Brain Disorders, 2.3 Psychological, social and economic factors, Aetiology, Mental health, Good Health and Well Being, Adaptation, Psychological, Adolescent, Adult, COVID-19, Female, Humans, Male, Pandemics, Risk Factors, Young Adult, young adults, depressive symptoms, sleep, coping, Toxicology
Abstract

To determine the persistent effects of the pandemic on mental health in young adults, we categorized depressive symptom trajectories and sought factors that promoted a reduction in depressive symptoms in high-risk individuals. Specifically, longitudinal analysis investigated changes in the risk for depression before and during the pandemic until December 2021 in 399 young adults (57% female; age range: 22.8 ± 2.6 years) in the United States (U.S.) participating in the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. The Center for Epidemiologic Studies Depression Scale (CES-D-10) was administered multiple times before and during the pandemic. A score ≥10 identified individuals at high-risk for depression. Self-reported sleep behavior, substance use, and coping skills at the start of the pandemic were assessed as predictors for returning to low-risk levels while controlling for demographic factors. The analysis identified four trajectory groups regarding depression risk, with 38% being at low-risk pre-pandemic through 2021, 14% showing persistent high-risk pre-pandemic through 2021, and the remainder converting to high-risk either in June 2020 (30%) or later (18%). Of those who became high-risk in June 2020, 51% were no longer at high-risk in 2021. Logistic regression revealed that earlier bedtime and, for the older participants (mid to late twenties), better coping skills were associated with this declining risk. Results indicate divergence in trajectories of depressive symptoms, with a considerable number of young adults developing persistent depressive symptoms. Healthy sleep behavior and specific coping skills have the potential to promote remittance from depressive symptoms in the context of the pandemic.

Published
2022

50. Growth Trajectories of Cognitive and Motor Control in Adolescence: How Much Is Development and How Much Is Practice?

Author

Lannoy, Séverine, Pfefferbaum, Adolf, Le Berre, Anne-Pascale, Thompson, Wesley K, Brumback, Ty, Schulte, Tilman, Pohl, Kilian M, De Bellis, Michael D, Nooner, Kate B, Baker, Fiona C, Prouty, Devin, Colrain, Ian M, Nagel, Bonnie J, Brown, Sandra A, Clark, Duncan B, Tapert, Susan F, Sullivan, Edith V, and Müller-Oehring, Eva M

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Paediatrics, Psychology, Behavioral and Social Science, Clinical Research, Underage Drinking, Brain Disorders, Pediatric, Alcoholism, Alcohol Use and Health, Substance Misuse, Mental health, Good Health and Well Being, Adolescent, Adolescent Development, Alcohol Drinking, Cognition, Executive Function, Female, Humans, Longitudinal Studies, Male, executive control, development, learning, alcohol, adolescence, Neurosciences, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

ObjectiveExecutive control continues to develop throughout adolescence and is vulnerable to alcohol use. Although longitudinal assessment is ideal for tracking executive function development and onset of alcohol use, prior testing experience must be distinguished from developmental trajectories.MethodWe used the Stroop Match-to-Sample task to examine the improvement of processing speed and specific cognitive and motor control over 4 years in 445 adolescents. The twice-minus-once-tested method was used and expanded to four test sessions to delineate prior experience (i.e., learning) from development. A General Additive Model evaluated the predictive value of age and sex on executive function development and potential influences of alcohol use on development.ResultsResults revealed strong learning between the first two assessments. Adolescents significantly improved their speed processing over 4 years. Compared with boys, girls enhanced ability to control cognitive interference and motor reactions. Finally, the influence of alcohol use initiation was tested over 4 years for development in 110 no/low, 110 moderate/heavy age- and sex-matched drinkers; alcohol effects were not detected in the matched groups.ConclusionsEstimation of learning effects is crucial for examining developmental changes longitudinally. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Published
2022

Catalog

Books, media, physical & digital resources
See catalog results