ArticleHuman brain mapping2014
Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification.
Article in Human brain mapping, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Discriminative analysis of Parkinson's disease based on whole-brain functional connectivity.PloS one · 2015Trial
- Integrating Time and Frequency Domain Features of fMRI Time Series for Alzheimer's Disease Classification Using Graph Neural Networks.Interdisciplinary sciences, computational life sciences · 2026Article
- Article
- Brain Network Classification for Accurate Detection of Alzheimer's Disease via Manifold Harmonic Discriminant Analysis.IEEE transactions on neural networks and learning systems · 2024Article
- Adaptive spatial-temporal neural network for ADHD identification using functional fMRI.Frontiers in neuroscience · 2024Article
- Sparsity-guided multiple functional connectivity patterns for classification of schizophrenia via convolutional network.Human brain mapping · 2023Article
- Characterizing Topological Properties of Brain Functional Networks Using Multi-Threshold Derivative for End-Stage Renal Disease with Mild Cognitive Impairment.Brain sciences · 2023Article
- Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification.Journal of personalized medicine · 2023Article
- Brain Functional Connectivity Analysis via Graphical Deep Learning.IEEE transactions on bio-medical engineering · 2022Article
- Individual-specific networks for prediction modelling - A scoping review of methods.BMC medical research methodology · 2022Article
- Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification.Frontiers in neuroscience · 2022Article
- Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network.Frontiers in neuroscience · 2022Article
- Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data.Frontiers in neuroscience · 2022Article
- Deep learning for sex classification in resting-state and task functional brain networks from the UK Biobank.NeuroImage · 2021Article
- Identifying and characterizing different stages toward Alzheimer's disease using ordered core features and machine learning.Heliyon · 2021Article
- Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison.Brain sciences · 2021Article
- Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets.Brain imaging and behavior · 2020Article
- Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network.IEEE transactions on bio-medical engineering · 2020Article
- Effective differentiation of mild cognitive impairment by functional brain graph analysis and computerized testing.PloS one · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Recently, brain connectivity networks have been used for classification of Alzheimer's disease and mild cognitive impairment (MCI) from normal controls (NC). In typical connectivity-networks-based classification approaches, local measures of connectivity networks are first extracted from each region-of-interest as network features, which are then concatenated into a vector for subsequent feature selection and classification. However, some useful structural information of network, especially global topological information, may be lost in this type of approaches. To address this issue, in this article, we propose a connectivity-networks-based classification framework to identify accurately the MCI patients from NC. The core of the proposed method involves the use of a new graph-kernel-based approach to measure directly the topological similarity between connectivity networks. We evaluate our method on functional connectivity networks of 12 MCI and 25 NC subjects. The experimental results show that our proposed method achieves a classification accuracy of 91.9%, a sensitivity of 100.0%, a balanced accuracy of 94.0%, and an area under receiver operating characteristic curve of 0.94, demonstrating a great potential in MCI classification, based on connectivity networks. Further connectivity analysis indicates that the connectivity of the selected brain regions is different between MCI patients and NC, that is, MCI patients show reduced functional connectivity compared with NC, in line with the findings reported in the existing studies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.