Paper Citation Record · LEDGER

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks

As of 15 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2501.01510.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.01510 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:31:27.457556Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:31:27.324912Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-10T22:31:27.525209Z

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c92fe7d9-26e7-45e1-9193-73323c6d1a69 · outbound

This paper cites Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks

Reference 1

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Observation 702628b1-bb76-413a-8151-75bf286bdbb8 · outbound

This paper cites A single layer of VNN is formed by concate- nating a coVariance filter with a pointwise non-linear activa- tion function σ(·) (e.g., ReLU, tanh) that satisfies σ(u) = [σ(u1),.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks A single layer of VNN is formed by concate- nating a coVariance filter with a pointwise non-linear activa- tion function σ(·) (e.g., ReLU, tanh) that satisfies σ(u) = [σ(u1),

Reference 2

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Observation bfcde79a-e75e-4cac-ae65-b23fa37c4022 · outbound

This paper cites an unresolved cited work.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Unresolved cited work

Reference 3

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Observation 75be4e48-7f27-42d0-bc38-e08875329b6c · outbound

This paper cites VNN was oblivious to the identity or any information about the disease.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks VNN was oblivious to the identity or any information about the disease

Reference 4

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Observation 1a0ec8c9-b200-440a-ab64-1539c9879273 · outbound

This paper cites 3 illustrate that the distributions of ∆-Age estimates can be substantially overlapping across different diseases.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks 3 illustrate that the distributions of ∆-Age estimates can be substantially overlapping across different diseases

Reference 5

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Observation ad58d7b7-f5cc-4930-98b0-532d98e862c3 · outbound

This paper cites an unresolved cited work.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Unresolved cited work

Reference 6

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Observation 74577f38-4f86-4a8a-a71e-d1d808947f0f · outbound

This paper cites The hallmarks of aging,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks The hallmarks of aging,

Reference 7

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Observation 3a7aad80-8297-4b26-aca2-9c62bb1f3e51 · outbound

This paper cites Machine learning for brain age prediction: Introduction to methods and clinical applica- tions,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Machine learning for brain age prediction: Introduction to methods and clinical applica- tions,

Reference 8

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Observation 2a44f27c-f09b-45d4-aa56-33f94987c552 · outbound

This paper cites Metabolomics of human brain aging and age-related neurodegenerative diseases,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Metabolomics of human brain aging and age-related neurodegenerative diseases,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:31:27.784491Z

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Observation 1efb25e6-dbe4-4240-a2e1-7c9b3ba166b0 · outbound

This paper cites Predicting age using neu- roimaging: Innovative brain ageing biomarkers,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Predicting age using neu- roimaging: Innovative brain ageing biomarkers,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:31:27.774454Z

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Observation 07b05164-d789-4bd9-a1d3-f872f1d61d42 · outbound

This paper cites Ten years of brainage as a neuroimaging biomarker of brain aging: What insights have we gained?,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Ten years of brainage as a neuroimaging biomarker of brain aging: What insights have we gained?,

Reference 11

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Observation 48e4613b-0b30-4d46-bdd6-406de548a28e · outbound

This paper cites MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals world- wide,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals world- wide,

Reference 12

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Observation 8a577eb8-7a9f-4510-ba92-663ded0a699d · outbound

This paper cites Accurate brain age predic- tion with lightweight deep neural networks,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Accurate brain age predic- tion with lightweight deep neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:31:27.744465Z

Source-reported events for the cited work

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Observation 5fafd0aa-e2e3-4912-98bd-704c73c9e0c7 · outbound

This paper cites Improved prediction of brain age using multimodal neu- roimaging data,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Improved prediction of brain age using multimodal neu- roimaging data,

Reference 14

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Observation 6128368f-b373-4772-8734-abdcf0c79a73 · outbound

This paper cites A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility,

Reference 15

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Observation 6b1aaf7c-ea85-49f0-9284-ace2b19b1a7b · outbound

This paper cites Explainable brain age prediction using co- variance neural networks,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Explainable brain age prediction using co- variance neural networks,

Reference 16

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Observation 1f3983b2-c4f5-4a8f-9ada-540af162e04e · outbound

This paper cites coVariance neural networks,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks coVariance neural networks,

Reference 17

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Observation 7793a940-7601-4e58-8869-8f108f674fd6 · outbound

This paper cites Fair CoVariance Neural Networks.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Fair CoVariance Neural Networks

Reference 18

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Observation 776a6322-9a79-4e41-94db-2c756ade1708 · outbound

This paper cites Sparse Covariance Neural Networks.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Sparse Covariance Neural Networks

Reference 19

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Observation b969de5d-97b6-4fb5-8fe2-1319eb5a580b · outbound

This paper cites Spa- tiotemporal covariance neural networks,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Spa- tiotemporal covariance neural networks,

Reference 20

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Observation 10a4b9d1-0137-4930-a04e-746dc8267861 · outbound

This paper cites Transferability of covariance neural networks,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Transferability of covariance neural networks,

Reference 21

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Observation 17c274b9-9398-4763-8c4e-0d4476a1627f · outbound

This paper cites Graph signal processing: Overview, challenges, and applications,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Graph signal processing: Overview, challenges, and applications,

Reference 22

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Observation 32f28a1b-3ecf-4048-b1a4-fa1efc1848cf · outbound

This paper cites Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks

Reference 23

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Observation 9b768698-5a27-4996-b2da-0b880193f171 · outbound

This paper cites Predicting brain age using transferable coVari- ance neural networks,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Predicting brain age using transferable coVari- ance neural networks,

Reference 24

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Observation 688aaf11-8aae-4676-b0a8-fbab69e33340 · outbound

This paper cites Stability properties of graph neural networks,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Stability properties of graph neural networks,

Reference 25

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Observation d67573ff-17a5-493e-b459-cea21a2d258e · outbound

This paper cites The alzheimer’s disease neuroimaging initiative (adni): Mri meth- ods,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks The alzheimer’s disease neuroimaging initiative (adni): Mri meth- ods,

Reference 26

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Observation e5067803-2820-40b7-afae-825e557ed086 · outbound

This paper cites CAT–a computational anatomy toolbox for the analysis of structural mri data,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks CAT–a computational anatomy toolbox for the analysis of structural mri data,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:31:27.641807Z

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Observation 43af2337-876d-4806-b619-43237e9a14ee · outbound

This paper cites The neurogenetics of atypical parkinsonian disorders,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks The neurogenetics of atypical parkinsonian disorders,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:31:27.631423Z

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Observation dd1ca2bf-085b-48dd-9d83-e3689b7c25dc · outbound

This paper cites OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease,

Reference 29

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Observation 9970f904-4158-4b5a-8a4e-b71253aac98f · outbound

This paper cites Optuna: A next-generation hyperpa- rameter optimization framework,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Optuna: A next-generation hyperpa- rameter optimization framework,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:31:27.610866Z

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Observation 8442a0a3-3a2b-41cf-b6e5-e1a7a24639ef · outbound

This paper cites Bias-adjustment in neuroimaging-based brain age frameworks: A robust scheme,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Bias-adjustment in neuroimaging-based brain age frameworks: A robust scheme,

Reference 31

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Observation 4ba8b356-24cc-481c-bbe1-eef0c8a98e6d · outbound

This paper cites Temporoparietal mr imaging measures of atrophy in subjects with mild cognitive impair- ment that predict subsequent diagnosis of alzheimer disease,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Temporoparietal mr imaging measures of atrophy in subjects with mild cognitive impair- ment that predict subsequent diagnosis of alzheimer disease,

Reference 32

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Observation 2c2f9664-0785-41d0-9f73-3d0e6abecf53 · outbound

This paper cites Structural brain imaging in frontotem- poral dementia,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Structural brain imaging in frontotem- poral dementia,

Reference 33

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Observation 49efd37c-7121-40b0-bf6a-aa167bb9b43d · outbound

This paper cites Diagnostic approach to atypical parkinsonian syndromes,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Diagnostic approach to atypical parkinsonian syndromes,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:31:27.573460Z

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Observation 5e215a47-4b5c-4bd5-a051-a8afc6d1a6d4 · outbound

This paper cites Explainable deep learning for personalized age prediction with brain morphology,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Explainable deep learning for personalized age prediction with brain morphology,

Reference 35

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Observation ef8722ac-bed3-4d78-85e5-ec163f2fa6e3 · outbound

This paper cites Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:31:27.552080Z

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Observation 1caee340-3854-44ef-8879-b61ad08c3fcd · outbound

This paper cites Towards the interpretability of deep learning models for multi-modal neu- roimaging: Finding structural changes of the ageing brain,.

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks Towards the interpretability of deep learning models for multi-modal neu- roimaging: Finding structural changes of the ageing brain,

Reference 37

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Pith citing papers

Observation c92fe7d9-26e7-45e1-9193-73323c6d1a69 · inbound