Evidence map›Paper›PMID 41815437›Full record

ArticleMachine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco)2025

GAMing the Brain: Investigating the Cross-modal Relationships between Functional Connectivity and Structural Features using Generalized Additive Models.

Arunkumar Kannan, Brian Caffo, Archana Venkataraman

Abstract read
In one paragraph

Article in Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Arunkumar KannanDept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA.
Brian CaffoDept. of Biostatistics, Johns Hopkins University, Baltimore, USA.
Archana VenkataramanDept. of Electrical and Computer Engineering, Boston University, Boston, USA.

Funding

Statistical methods for structural and functional integration in multi-modal neuroimaging dataR01EB029977 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI CAFFO, BRIAN SCOTT · 2021 to 2024
$1.9M
A Modular Framework for Data-Driven Neurogenetics to Predict Complex and Multidimensional Autistic PhenotypesR01HD108790 · NICHD · JOHNS HOPKINS UNIVERSITY · PI Archana Venkataraman · 2022 to 2026
$1.9M
Automated Presurgical Language Mapping via Deep Learning for Multimodal Brain ConnectivityR21CA263804 · NCI · JOHNS HOPKINS UNIVERSITY · PI VANNORSDALL, TRACY DAWN · 2021 to 2022
$402k
NCI NIH HHS R21 CA263804NIBIB NIH HHS R01 EB029977NICHD NIH HHS R01 HD108790
6 · The paper itself

Abstract

Functional connectivity, reflecting synchronized brain activity across distinct regions, is crucial for understanding cognitive processes. Despite the recent interest in exploring the relationship between functional connectivity and structural brain features, understanding the precise link remains challenging. We propose a novel analysis method that integrates structural factors-such as anatomical morphology summaries, voxel intensity, diffusion-weighted information, and geographic distance to explain variation in functional connectivity. Our method employs generalized additive model (GAM), leveraging region-pair or vertex-pair information, while accommodating individual subject differences in both template and subject spaces. Furthermore, we assess repeatability via the so called discriminability of subjects under our approach, quantifying the probability of similarities between measurements for the same subject versus different subjects. Utilizing data from the Human Connectome Project, we analyze brain connectivity in twin pairs and non-twin pairs to evaluate the repeatability of model-based connectivity patterns estimated via GAMs. Our findings suggest that direct structure/function regression models enhances our understanding of functional connectivity variation, providing insights into underlying mechanisms and discriminability of brain connections.

Indexed as

DiscriminabilityExplainabilityfMRIFunctional ConnectivityGeneralized Additive ModelsStructural Features

Identifiers

PMID41815437
PMCPMC12974767

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.