ImageNomer: description of a functional connectivity and omics analysis tool and case study identifying a race confound

• URL: http://arxiv.org/abs/2302.00767v2
• Date: Wed, 11 Oct 2023 18:23:59 GMT
• Title: ImageNomer: description of a functional connectivity and omics analysis tool and case study identifying a race confound
• Authors: Anton Orlichenko, Grant Daly, Ziyu Zhou, Anqi Liu, Hui Shen, Hong-Wen Deng, Yu-Ping Wang
• Abstract summary: ImageNomer is a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores. This work casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.
• Score: 11.948945216339197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer, we find a clear potential for confounding effects of race. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15\% of WRAT score variation, this predictive ability disappears when controlling for race. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.

Related papers

• Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction [54.23208041792073]
  Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review. A key challenge in the ASQP task is the scarcity of labeled data, which limits the performance of existing methods. We propose a self-training framework with a pseudo-label scorer, wherein a scorer assesses the match between reviews and their pseudo-labels.
  arXiv  Detail & Related papers  (2024-06-26T05:30:21Z)
• Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
  We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces. We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain. We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
  arXiv  Detail & Related papers  (2024-01-05T19:45:12Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• Learnable Graph Matching: A Practical Paradigm for Data Association [74.28753343714858]
  We propose a general learnable graph matching method to address these issues. Our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet.
  arXiv  Detail & Related papers  (2023-03-27T17:39:00Z)
• AU-aware graph convolutional network for Macro- and Micro-expression spotting [44.507747407072685]
  We propose a graph convolutional-based network, called Action-Unit-aWare Graph Convolutional Network (AUW-GCN) To inject prior information and to cope with the problem of small datasets, AU-related statistics are encoded into the network. Our results outperform baseline methods consistently and achieve new SOTA performance in two benchmark datasets.
  arXiv  Detail & Related papers  (2023-03-16T07:00:36Z)
• Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data [44.64190826937705]
  We present scalable algorithms for detecting label errors and outlier data based on the relational graph structure of data. We also introduce a visualization tool that provides contextual information of a data point in the feature-embedded space. Our approach achieves state-of-the-art detection performance on all tasks considered and demonstrates its effectiveness in large-scale real-world datasets.
  arXiv  Detail & Related papers  (2023-01-29T02:09:13Z)
• Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition [5.600003119721707]
  Human activity recognition (HAR) through wearable devices has received much interest due to its numerous applications in fitness tracking, wellness screening, and supported living. Traditional deep learning (DL) has set a state of the art performance for HAR domain. We propose an approach based on Graph Neural Networks (GNNs) for structuring the input representation and exploiting the relations among the samples.
  arXiv  Detail & Related papers  (2022-06-10T03:04:23Z)
• Interactive Visual Pattern Search on Graph Data via Graph Representation Learning [20.795511688640296]
  We propose a visual analytics system GraphQ to support human-in-the-loop, example-based, subgraph pattern search. To support fast, interactive queries, we use graph neural networks (GNNs) to encode a graph as fixed-length latent vector representation. We also propose a novel GNN for node-alignment called NeuroAlign to facilitate easy validation and interpretation of the query results.
  arXiv  Detail & Related papers  (2022-02-18T22:30:28Z)
• Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction [123.20238648121445]
  We propose a new self-supervised learning framework, Graph Information Aided Node feature exTraction (GIANT) GIANT makes use of the eXtreme Multi-label Classification (XMC) formalism, which is crucial for fine-tuning the language model based on graph information. We demonstrate the superior performance of GIANT over the standard GNN pipeline on Open Graph Benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-29T19:55:12Z)
• Coarse-to-Fine Object Tracking Using Deep Features and Correlation Filters [2.3526458707956643]
  This paper presents a novel deep learning tracking algorithm. We exploit the generalization ability of deep features to coarsely estimate target translation. Then, we capitalize on the discriminative power of correlation filters to precisely localize the tracked object.
  arXiv  Detail & Related papers  (2020-12-23T16:43:21Z)
• ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection [101.56529337489417]
  We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of human, action, object> in images. We argue that multi-level consistencies among objects, actions and interactions are strong cues for generating semantic representations of rare or previously unseen HOIs. Our model takes visual features of candidate human-object pairs and word embeddings of HOI labels as inputs, maps them into visual-semantic joint embedding space and obtains detection results by measuring their similarities.
  arXiv  Detail & Related papers  (2020-08-14T09:11:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.