Favour Nerrise

All Publications

• GAMMA-PD: Graph-based Analysis of Multi-Modal Motor Impairment Assessments in Parkinson's Disease. Graphs in biomedical image analysis : 6th International Workshop, GRAIL 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceeding. GRAIL (Workshop) (6th : 2024 : Marrakesh, Morocco) Nerrise, F., Heiman, A. L., Adeli, E. 2025; 15182: 57-68

  Abstract

  The rapid advancement of medical technology has led to an exponential increase in multi-modal medical data, including imaging, genomics, and electronic health records (EHRs). Graph neural networks (GNNs) have been widely used to represent this data due to their prominent performance in capturing pairwise relationships. However, the heterogeneity and complexity of multi-modal medical data still pose significant challenges for standard GNNs, which struggle with learning higher-order, non-pairwise relationships. This paper proposes GAMMA-PD (Graph-based Analysis of Multi-modal Motor Impairment Assessments in Parkinson's Disease), a novel heterogeneous hypergraph fusion framework for multi-modal clinical data analysis. GAMMA-PD integrates imaging and non-imaging data into a "hypernetwork" (patient population graph) by preserving higher-order information and similarity between patient profiles and symptom subtypes. We also design a feature-based attention-weighted mechanism to interpret feature-level contributions towards downstream decision tasks. We evaluate our approach with clinical data from the Parkinson's Progression Markers Initiative (PPMI) and a private dataset. We demonstrate gains in predicting motor impairment symptoms in Parkinson's disease. Our end-to-end framework also learns associations between subsets of patient characteristics to generate clinically relevant explanations for disease and symptom profiles. The source code is available at https://github.com/favour-nerrise/GAMMA-PD.

  View details for DOI 10.1007/978-3-031-83243-7_6

  View details for PubMedID 40709078

• Data-Driven Discovery of Movement-Linked Heterogeneity in Neurodegenerative Diseases. Nature machine intelligence Endo, M., Nerrise, F., Zhao, Q., Sullivan, E. V., Fei-Fei, L., Henderson, V. W., Pohl, K. M., Poston, K. L., Adeli, E. 2024; 6 (9): 1034-1045

  Abstract

  Neurodegenerative diseases manifest different motor and cognitive signs and symptoms that are highly heterogeneous. Parsing these heterogeneities may lead to an improved understanding of underlying disease mechanisms; however current methods are dependent on clinical assessments and somewhat arbitrary choice of behavioral tests. Herein, we present a data-driven subtyping approach using video-captured human motion and brain functional connectivity (FC) from resting-state (rs)-fMRI. We applied our framework to a cohort of individuals at different stages of Parkinson's disease (PD). The process mapped the data to low-dimensional measures by projecting them onto a canonical correlation space that identified three PD subtypes: Subtype I was characterized by motor difficulties and poor visuospatial abilities; Subtype II exhibited difficulties in non-motor components of activities of daily living and motor complications (dyskinesias and motor fluctuations); and Subtype III was characterized by predominant tremor symptoms. We conducted a convergent validity analysis by comparing our approach to existing and widely used approaches. The compared approaches yielded subtypes that were adequately well-clustered in the motion-brain representation space we created to delineate subtypes. Our data-driven approach, contrary to other forms of subtyping, derived biomarkers predictive of motion impairment and subtype memberships that were captured objectively by digital videos.

  View details for DOI 10.1038/s42256-024-00882-y

  View details for PubMedID 40357335

  View details for PubMedCentralID PMC12068835

• Data-driven discovery of movement-linked heterogeneity in neurodegenerative diseases NATURE MACHINE INTELLIGENCE Endo, M., Nerrise, F., Zhao, Q., Sullivan, E. V., Fei-Fei, L., Henderson, V. W., Pohl, K. M., Poston, K. L., Adeli, E. 2024

  View details for DOI 10.1038/s42256-024-00882-y

  View details for Web of Science ID 001287474600001

• An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Nerrise, F., Zhao, Q., Poston, K. L., Pohl, K. M., Adeli, E. 2023; 14221: 723-733

  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.

  View details for DOI 10.1007/978-3-031-43895-0_68

  View details for PubMedID 37982132

  View details for PubMedCentralID PMC10657737

• An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment. ArXiv Nerrise, F., Zhao, Q., Poston, K. L., Pohl, K. M., Adeli, E. 2023

  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 explain-ability. 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.

  View details for PubMedID 37547656

  View details for PubMedCentralID PMC10402187

• User-Driven Support for Visualization Prototyping in D3 Bako, H. K., Varma, A., Faboro, A., Haider, M., Nerrise, F., Kenah, B., Dickerson, J. P., Battle, L., ACM ASSOC COMPUTING MACHINERY. 2023: 958-972

  View details for DOI 10.1145/3581641.3584041

  View details for Web of Science ID 001302573800066

• Predictive Agent-Based Modeling of Natural Disasters Using Machine Learning Nerrise, F., Assoc Advancement Artificial Intelligence ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2021: 15976-15977

  View details for Web of Science ID 000681269807185