Paper Citation Record · LEDGER

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification

As of 18 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2412.16197.

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

pith.paper-citation-record.v1
2412.16197 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:12:54.755086Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T12:17:54.168604Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T12:20:22.600435Z

Reference resolution

25 of 25 outbound references displayed

verified exact1
verified fuzzy14
unresolved9
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 88dd88fb-09c7-413a-bf6b-740777fc0202 · outbound

This paper cites Prediction of post traumatic epilepsy using mri-based imaging markers.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Prediction of post traumatic epilepsy using mri-based imaging markers

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.364224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 869a5519-c556-486e-bbfb-4492c3eb89ff · outbound

This paper cites Momentum Contrast for Unsupervised Visual Representation Learning.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Momentum Contrast for Unsupervised Visual Representation Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T14:12:54.666099Z

Source-reported events for the cited work

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Observation e24f44c9-9c72-4604-81ce-1920d7941b3a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Adam: A Method for Stochastic Optimization

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T14:12:54.670494Z

Source-reported events for the cited work

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Observation 633e9017-12a4-4706-a21f-d20368300bf0 · outbound

This paper cites Meta-learning Transferable Representations with a Single Target Domain.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Meta-learning Transferable Representations with a Single Target Domain

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:12:55.087046Z

Source-reported events for the cited work

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Observation 9bc51d10-e6d9-4962-a8ae-6f8163d411f9 · outbound

This paper cites Brainlm: A foundation model for brain activity recordings.bioRxiv, pp.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Brainlm: A foundation model for brain activity recordings.bioRxiv, pp

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.326390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d6f2b317-9c0b-478c-a1f5-8076072f4243 · outbound

This paper cites Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity mri.Cerebral cortex, 28(9):3095–3114,.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity mri.Cerebral cortex, 28(9):3095–3114,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.313823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0d264d1e-bac9-44e3-923e-4af8ca047c51 · outbound

This paper cites Contrastive functional connectivity graph learning for population-based fmri classification.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Contrastive functional connectivity graph learning for population-based fmri classification

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.297292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d783a6da-6d92-4a9b-9bbf-3c8c9ca84e1d · outbound

This paper cites doi: 10.1109/tmi.2024.3392988.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification doi: 10.1109/tmi.2024.3392988

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T14:12:54.709987Z

Source-reported events for the cited work

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Observation e7d4bd01-9da9-4a4f-9739-1c56e93467c3 · outbound

This paper cites When does self-supervision help graph convolutional networks? In international conference on machine learning, pp.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification When does self-supervision help graph convolutional networks? In international conference on machine learning, pp

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.284300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dca7086d-931d-4366-9aa6-93e28693af3f · outbound

This paper cites This allows us to assess the robustness and generalization capability of MeTSK under data-scarce conditions—settings that are common in clinical practice.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification This allows us to assess the robustness and generalization capability of MeTSK under data-scarce conditions—settings that are common in clinical practice

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.267533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0ad058ab-8014-4eae-a890-27b1f474c376 · outbound

This paper cites (2024) 0.5 F1 Score 40% for testing 42 Harvard-Oxford 69Li et al.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification (2024) 0.5 F1 Score 40% for testing 42 Harvard-Oxford 69Li et al

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.234950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e5b059b7-70db-4bf1-b9ab-4f7e85ccacea · outbound

This paper cites (2021) 0.675 Accuracy 10-fold CV 40 Schaefer 100MeTSK (Ours) 0.6831 AUC 5-fold CV 40 AAL 116 OASIS-3 Han et al.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification (2021) 0.675 Accuracy 10-fold CV 40 Schaefer 100MeTSK (Ours) 0.6831 AUC 5-fold CV 40 AAL 116 OASIS-3 Han et al

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.216569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 898f5df7-b735-4204-a082-0ab4e6e7875a · outbound

This paper cites an unresolved cited work.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-11T14:12:55.200270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7aea5197-b1f6-43ff-a715-144f579e647c · outbound

This paper cites Fluctuations are expected, as the loss is computed on a small (39-subject) randomly sampled meta-validation set.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Fluctuations are expected, as the loss is computed on a small (39-subject) randomly sampled meta-validation set

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.186530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eb0c0435-fb87-41e3-b181-753dd694ebb1 · outbound

This paper cites Notably, the pre-training datasets for these foundation models already included the HCP data, so aligning the training data required only fine- tuning on the ADHD-Peking dataset.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Notably, the pre-training datasets for these foundation models already included the HCP data, so aligning the training data required only fine- tuning on the ADHD-Peking dataset

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.173479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d8d141c8-40a7-47c1-acfc-86576ec15bf3 · outbound

This paper cites 2005.1521516.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification 2005.1521516

Reference 2005

Resolution: malformed identifier; no resolver link, observed 2026-08-11T14:12:54.718658Z

Source-reported events for the cited work

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Observation 0d1d0e92-05c0-4a7b-a975-a1db35aefdf8 · outbound

This paper cites Large scale fine-grained categorization and domain-specific transfer learning,.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Large scale fine-grained categorization and domain-specific transfer learning,

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.352727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 47af3e69-dcfa-4490-b114-237b7c243cbf · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Semi-Supervised Classification with Graph Convolutional Networks

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-11T14:12:54.675092Z

Source-reported events for the cited work

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Observation 0dba54fe-0631-47e3-bf53-12fdc8b561ff · outbound

This paper cites Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-11T14:12:54.679454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5783f16-35d8-4cf6-a85e-5aa64d0bfadc · outbound

This paper cites (2022) 0.667 AUC 10-fold CV 42 Brainnetome (Fan et al.,.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification (2022) 0.667 AUC 10-fold CV 42 Brainnetome (Fan et al.,

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.251081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fa28aeaf-6f26-4e90-8ad3-9e83da431dc3 · outbound

This paper cites Bootstrap your own latent: A new approach to self-supervised Learning.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Bootstrap your own latent: A new approach to self-supervised Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T14:12:54.661495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da815db0-8829-45a7-8bb8-040423c23f9f · outbound

This paper cites Prediction of posttrau- matic epilepsy using machine learning.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Prediction of posttrau- matic epilepsy using machine learning

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.375748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1c6d004f-2ca0-4deb-b072-61df03ee0d60 · outbound

This paper cites Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease.MedRxiv, pp.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease.MedRxiv, pp

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:12:55.339171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c9426981-3fc0-4916-bf00-4be6e64f403f · outbound

This paper cites Xi Sheryl Zhang, Fengyi Tang, Hiroko H Dodge, Jiayu Zhou, and Fei Wang.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification Xi Sheryl Zhang, Fengyi Tang, Hiroko H Dodge, Jiayu Zhou, and Fei Wang

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T14:12:54.722488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6b590c5-6e69-4be2-b716-591999b10a46 · outbound

This paper cites The Data Addition Dilemma.

Generalizable Representation Learning for fMRI-based Neurological Disorder Identification The Data Addition Dilemma

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T14:12:54.700280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation ed54fe39-be64-4815-9f8e-0873603771a8 · inbound

Predicting Post-Traumatic Epilepsy from Clinical Records using Large Language Model Embeddings cites this paper.

Predicting Post-Traumatic Epilepsy from Clinical Records using Large Language Model Embeddings Generalizable Representation Learning for fMRI-based Neurological Disorder Identification

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:20:22.601781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.