Detecting subject-specific activations using fuzzy clustering

Inter-subject variability in evoked brain responses is attracting attention because it may reflect important variability in structure–function relationships over subjects. This variability could be a signature of degenerate (many-to-one) structure–function mappings in normal subjects or reflect chan...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:NeuroImage (Orlando, Fla.) Ročník 36; číslo 3; s. 594 - 605
Hlavní autori: Seghier, Mohamed L., Friston, Karl J., Price, Cathy J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Inc 01.07.2007
Elsevier Limited
Academic Press
Predmet:
Adult
Algorithms
Atypical activations
Classification
Cluster Analysis
Data analysis
Data Interpretation, Statistical
Data processing
Electroencephalography - statistics & numerical data
Evoked Potentials - physiology
Female
Functional magnetic resonance imaging
Fuzzy clustering
Fuzzy Logic
Fuzzy sets
Humans
Individuality
Inter-individual variability
Magnetic Resonance Imaging
Male
Outliers
Overt object naming
Reference Values
Second-level analysis
Statistics
Studies
Technical Note
D
RFX
Atypical activations
G
Second-level analysis
Outliers
FCM
FCP
Fuzzy clustering
U
Inter-individual variability
Functional magnetic resonance imaging
Overt object naming
ISSN:1053-8119, 1095-9572
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Inter-subject variability in evoked brain responses is attracting attention because it may reflect important variability in structure–function relationships over subjects. This variability could be a signature of degenerate (many-to-one) structure–function mappings in normal subjects or reflect changes that are disclosed by brain damage. In this paper, we describe a non-iterative fuzzy clustering algorithm (FCP: fuzzy clustering with fixed prototypes) for characterizing inter-subject variability in between-subject or second-level analyses of fMRI data. The approach identifies the contribution of each subject to response profiles in voxels surviving a classical F-statistic criterion. The output identifies subjects who drive activation in specific cortical regions (local effects) or in voxels distributed across neural systems (global effects). The sensitivity of the approach was assessed in 38 normal subjects performing an overt naming task. FCP revealed that several subjects had either abnormally high or abnormally low responses. FCP may be particularly useful for characterizing outlier responses in rare patients or heterogeneous populations. In these cases, atypical activations may not be detected by standard tests, under parametric assumptions. The advantage of using FCP is that it searches all voxels systematically and can identify atypical activation patterns in a quantitative and unsupervised manner.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2007.03.021