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Connectome-based machine learning models are vulnerable to subtle data manipulations: v1.0.0

Authors: Rosenblatt, Matthew;

Connectome-based machine learning models are vulnerable to subtle data manipulations: v1.0.0

Abstract

Code for the manuscript "Connectome-based machine learning models are vulnerable to subtle data manipulations." For the most updated version, please see https://github.com/mattrosenblatt7/trust_connectomes. Please see the README file for details about scripts and running the code. Essentially, the "minimal_code" folder contains code for which you can demonstrate enhancement attacks in your own data. The "paper_experiments" folder contains more detailed information about the experiments run in the manuscript. The specific data used in this study cannot be shared, but all four datasets used in this study are open-source: ABCD (NIMH Data Archive, https://nda.nih.gov/abcd), HCP (ConnectomeDB database, https://db.humanconnectome.org), PNC (dbGaP Study, accession code: phs000607.v3.p2, https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000607.v3.p2), and SLIM (INDI, http://fcon_1000.projects.nitrc.org/indi/retro/southwestuni_qiu_index.html). Data collection was approved by the relevant ethics review board for each of the four datasets.

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Keywords

trustworthy machine learning, fMRI, functional connectivity, predictive modeling

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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