Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Seemingly unrelated regression empowers detection of network failure in dementia.

Neurobiology of aging - 1 Jan 2015

Jahanshad Neda, Nir Talia M, Toga Arthur W, Jack Clifford R, Bernstein Matt A, Weiner Michael W, Thompson Paul M

Abstract excerpt

Brain connectivity is progressively disrupted in Alzheimer's disease (AD). Here, we used a seemingly unrelated regression (SUR) model to enhance the power to identify structural connections related to cognitive scores. We simultaneously solved regression equations with different predictors and us...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cognition
Share to Cognitive Dysfunction
Share to Diffusion Magnetic Resonance Imaging

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Educational Status

Share to Linear Models

Share to Nerve Net

Share to Neural Conduction

Share to Neuroimaging

Source

Open in PubMed Open full text

Identifiers

PMID: 25257986
DOI: 10.1016/j.neurobiolaging.2014.02.032
PMCID: PMC4276318

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026. Prepared record publication-25257986-2026-09-04.2. Manifest publication-graph-cohort-2026-09-04.2.

Source receipt: PubMed via Helena Literature Corpus, retrieved 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Seemingly unrelated regression empowers detection of network failure in dementia.PMID 25257986Read ↗

Exploratory Correlation of The Human Structural Connectome with Non-MRI Variables in Alzheimer’s DiseaseSemantic proximity Read ↗

Identifying vulnerable brain networks associated with Alzheimer's disease risk.Semantic proximity Read ↗

Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer’s diseaseSemantic proximity Read ↗

Structural Connectome Analysis using a Graph-based Deep Model for Prediction of Non-Imaging VariablesSemantic proximity Read ↗

Functional network collapse in neurodegenerative diseaseSemantic proximity Read ↗

Predicting phenotypes of elderly from resting state fMRISemantic proximity Read ↗

Virtual connectomic datasets in Alzheimer’s Disease and aging using whole-brain network dynamics modellingSemantic proximity Read ↗

Connectivity RegressionSemantic proximity Read ↗

Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized CognitionSemantic proximity Read ↗

Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivitySemantic proximity Read ↗

FMRI Functional Connectivity Evaluation in Alzheimer’s Stages: Linear and Non-Linear ApproachesSemantic proximity Read ↗

Whole-brain functional connectivity predicts regional tau PET in preclinical Alzheimer’s diseaseSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.