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

The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes.

Alzheimer's research & therapy - 17 Feb 2024

Vipin Ashwati, Lee Bernett Teck Kwong, Kumar Dilip, Soo See Ann, Leow Yi Jin, Ghildiyal Smriti, Lee Faith Phemie Hui En, Hilal Saima, Kandiah Nagaendran

Abstract excerpt

BACKGROUND: The use of structural and perfusion brain imaging in combination with behavioural information in the prediction of cognitive syndromes using a data-driven approach remains to be explored. Here, we thus examined the contribution of brain structural and perfusion imaging and behavioural features to the existing classification of cognitive syndromes using a data-driven approach. METHODS: Study...

Read the complete abstract on PubMed

Topics

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

Share to Alzheimer Disease
Share to Cognition

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Cognitive Dysfunction

Share to Cross-Sectional Studies

Share to Dementia

Share to Gray Matter

Share to Magnetic Resonance Imaging

Share to Perfusion

Source

Open in PubMed Open full text

Identifiers

PMID: 38368378
DOI: 10.1186/s13195-024-01410-1
PMCID: PMC10874041

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes.PMID 38368378Read ↗

The role of perfusion, grey matter and behavioural phenotypes in the data-driven classification of cognitive syndromesSemantic proximity Read ↗

Multimodal Imaging and Logistic Weighted Cognitive Scores for Classification of MCI, AD, and FTD SubtypesSemantic proximity Read ↗

Frontoparietal network integrity supports cognitive function in pre-symptomatic frontotemporal dementia: Multimodal analysis of brain function, structure, and perfusion.Semantic proximity Read ↗

Brain-based ranking of cognitive domains to predict schizophreniaSemantic proximity Read ↗

Functional network phenotypes of mild behavioural impairment: cognitive effects moderated by amyloid.Semantic proximity Read ↗

Mapping behavioural, cognitive and affective transdiagnostic dimensions in frontotemporal dementiaSemantic proximity Read ↗

Neuroanatomical subtypes of Long COVID reveal distinct cognitive profiles alongside longitudinal brain changesSemantic proximity Read ↗

Cognitive Phenotypes for Biomarker Identification in Mental Illness: Forward and Reverse Translation.Semantic proximity Read ↗

Validation of the self-test computerised PROTECT Cognitive Test System for detection of dementia and Mild Cognitive ImpairmentSemantic proximity Read ↗

A Data-Driven Approach for Linking Epileptic Networks and Cognitive Profiles Using Stereo-EEGSemantic proximity Read ↗

Neuroanatomical correlates of behavioural phenotypes in behavioural variant of frontotemporal dementia.Semantic proximity Read ↗

Functional Impairment in Behavioral Variant Frontotemporal Dementia: Cognitive, Behavioral, Personality, and Brain Perfusion ContributionsSemantic proximity Read ↗

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