332 citations · 365 across the 11 of their papers we have counts for
13 papers
Transferable conditional brain charts link cortico-subcortical structure to Alzheimer's disease progression
Nitin Sharma, Sicheng Dai, Francesco A. Mallus +29
Normative models chart how brain measures vary across the lifespan, expressing each individual as a deviation from a healthy reference. However, most regional models of cortical th…
Charting the velocity of brain growth and development
Johanna M. M. Bayer, Augustijn A. A. de Boer, Barbora Rehak-Bucova +30
Brain charts have emerged as a highly useful approach for understanding brain development and aging on the basis of brain imaging and have shown substantial utility in describing t…
Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfaces
Hao-Chun Yang, Sicheng Dai, Saige Rutherford +4
Unsupervised anomaly detection in brain imaging is challenging. In this paper, we propose self-supervised masked mesh learning for unsupervised anomaly detection on 3D cortical sur…
Unveiling Normative Trajectories of Lifespan Brain Maturation Using Quantitative MRI
Xinjie Chen, Mario Ocampo-Pineda, Po-Jui Lu +17
Background: Brain maturation and aging involve significant microstructural changes, resulting in functional and cognitive alterations. Quantitative MRI (qMRI) can measure this evol…
To which reference class do you belong? Measuring racial fairness of reference classes with normative modeling
Saige Rutherford, Thomas Wolfers, Charlotte Fraza +4
Reference classes in healthcare establish healthy norms, such as pediatric growth charts of height and weight, and are used to chart deviations from these norms which represent pot…
Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping
Hao-Chun Yang, Ole Andreassen, Lars Tjelta Westlye +3
The detection of heterogeneous mental disorders based on brain readouts remains challenging due to the complexity of symptoms and the absence of reliable biomarkers. This paper int…
PROMISSING: Pruning Missing Values in Neural Networks
Seyed Mostafa Kia, Nastaran Mohammadian Rad, Daniel van Opstal +5
While data are the primary fuel for machine learning models, they often suffer from missing values, especially when collected in real-world scenarios. However, many off-the-shelf m…
Hierarchical Bayesian Regression for Multi-Site Normative Modeling of Neuroimaging Data
Seyed Mostafa Kia, Hester Huijsdens, Richard Dinga +6
Clinical neuroimaging has recently witnessed explosive growth in data availability which brings studying heterogeneity in clinical cohorts to the spotlight. Normative modeling is a…
Analyzing Brain Circuits in Population Neuroscience: A Case to Be a Bayesian
Danilo Bzdok, Dorothea L. Floris, Andre F. Marquand
Functional connectivity fingerprints are among today's best choices to obtain a faithful sampling of an individual's brain and cognition in health and disease. Here we make a case…
Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data
Seyed Mostafa Kia, Andre F. Marquand
Normative modeling has recently been introduced as a promising approach for modeling variation of neuroimaging measures across individuals in order to derive biomarkers of psychiat…
Scalable Multi-Task Gaussian Process Tensor Regression for Normative Modeling of Structured Variation in Neuroimaging Data
Seyed Mostafa Kia, Christian F. Beckmann, Andre F. Marquand
Most brain disorders are very heterogeneous in terms of their underlying biology and developing analysis methods to model such heterogeneity is a major challenge. A promising appro…
Normative Modeling of Neuroimaging Data using Scalable Multi-Task Gaussian Processes
Seyed Mostafa Kia, Andre Marquand
Normative modeling has recently been proposed as an alternative for the case-control approach in modeling heterogeneity within clinical cohorts. Normative modeling is based on sing…
Connectopic mapping with resting-state fMRI
Koen V. Haak, Andre F. Marquand, Christian F. Beckmann
Brain regions are often topographically connected: nearby locations within one brain area connect with nearby locations in another area. Mapping these connection topographies, or '…