Functional Clustering: Identifying Strongly Interactive Brain Regions in Neuroimaging Data

doi:10.1006/nimg.1997.0313

NeuroImage

Volume 7, Issue 2, February 1998, Pages 133-149

https://doi.org/10.1006/nimg.1997.0313 Get rights and content

Abstract

Brain imaging data are generally used to determine which brain regions are mostactivein an experimental paradigm or in a group of subjects. Theoretical considerations suggest that it would also be of interest to know which set of brain regions are mostinteractivein a given task or group of subjects. A subset of regions that are much more strongly interactive among themselves than with the rest of the brain is called here afunctional cluster.Functional clustering can be assessed by calculating for each subset of brain regions a measure, thecluster index,obtained by dividing the statistical dependence within the subset by that between the subset and rest of the brain. A cluster index value near 1 indicates a homogeneous system, while a high cluster index indicates that a subset of brain regions forms a distinct functional cluster. Within a functional cluster, individual brain regions are ranked at the center or at the periphery according to their statistical dependence with the rest of that cluster. The applicability of this approach has been tested on PET data obtained from normal and schizophrenic subjects performing a set of cognitive tasks. Analysis of the data reveals evidence of functional clustering. A comparative evaluation of which regions are more peripheral or more central suggests distinct differences between the two groups of subjects. We consider the applicability of this analysis to data obtained with imaging modalities offering higher temporal resolution than PET.

References (53)

M.S. Gazzaniga
Principles of human brain organization derived from split-brain studies
Neuron
(1995)
A.R. McIntosh et al.
Spatial pattern analysis of functional brain images using partial least squares
Neuroimage
(1996)
P. Arabie et al.
Clustering and Classification
(1996)
Bleuler, E. 1911, Dementia praecox or the group of schizophrenias, International University Press, New...
A.L. Blumenthal
The Process of Cognition
(1977)
H.-H. Bock
Probability models and hypothesis testing in partitioning cluster analysis
Clustering and Classification
(1996)
T.F. Budinger
Critical review of PET, SPECT, and neuroreceptor studies in schizophrenia
J. Neural Transm.
(1992)
J-L. Chandon et al.
Analyse Typologique. Theories et Applications
(1981)
W.C. Chang
On using principal components before separating a mixture of multivariate normal distributions
Appl. Stat.
(1983)
R.M. Cormack
A review of classification
J. R. Stat. Soc. A
(1971)

T.F. Cox et al.

Multidimensional Scaling

(1994)

G.M. Edelman

Neural Darwinism

(1987)

G.M. Edelman

The Remembered Present

(1989)

Edgington, E. S. 1980, Randomization Tests, Dekker, New...

A.K. Engel et al.

Interhemispheric synchronization of oscillatory neuronal responses in cat visual cortex

Science

(1991)

A.K. Engel et al.

Direct physiologic evidence for scene segmentation by temporal coding

Proc. Natl. Acad. Sci. USA

(1990)

B.S. Everitt

Cluster Analysis

(1993)

K.J. Friston

Functional and effective connectivity: A synthesis

Hum. Brain Mapp.

(1994)

K.J. Friston et al.

Spatial realignment and normalization of images

Hum. Brain Mapp.

(1995)

K.J. Friston et al.

Statistical parametric maps in functional neuroimaging: A general linear approach

Hum. Brain Mapp.

(1995)

K.J. Friston et al.

Schizophrenia: A disconnection syndrome

Clinical Neuroscience

(1995)

K.J. Friston et al.

Functional topography: Multidimensional scaling and functional connectivity in the brain

Cerebral Cortex

(1996)

K.J. Friston et al.

Characterizing the complexity of neural interactions

Hum. Brain Mapping

(1996)

N. Geschwind

Disconnection syndromes in animals and man

Brain

(1965)

G. Goldberg et al.

The cerebral localization of neuropsychological impairment in Alzheimer's disease: A SPECT study

J. Neurol.

(1989)

A.D. Gordon

Classification

(1980)

Cited by (202)

Altered brain regional homogeneity is associated with cognitive dysfunction in first-episode drug-naive major depressive disorder: A resting-state fMRI study
2023, Journal of Affective Disorders
Our study aimed to explore the abnormal spontaneous brain activity by regional homogeneity (ReHo) and its association with cognitive function to understand the neuropathology of major depressive disorder (MDD).
ReHo was used to investigate brain activities of 60 patients with first-episode drug-naive MDD and 60 healthy controls (HCs). Partial correlation analysis was conducted on altered ReHo values and the severity of symptoms and cognitive deficits. Moreover, support vector machine analysis was used to evaluate the accuracy of abnormal ReHo values in distinguishing patients with MDD from HCs.
Compared with HCs, patients with MDD showed significantly increased ReHo values in the right cerebellum crus2 and right thalamus and decreased ReHo values in the right angular gyrus (AG) and right precuneus (PCUN). The ReHo values in right cerebellum crus2 and right AG were positively associated with working memory and visual learning, respectively. Furthermore, the combination of ReHo values in the right cerebellum crus2 and right PCUN discriminated the patients with MDD from HCs with specificity, sensitivity, and accuracy of 0.9688, 0.6250, and 0.90, respectively.
The design of repeated cross-sectional surveys does not allow analyses of within individual changes.
Our study revealed that the pathophysiology mechanism of cognitive deficits in MDD may be related to abnormal spontaneous brain activity. Moreover, the combination of ReHo values in the right cerebellum crus2 and right PCUN can be used to discriminate patients with MDD from HCs effectively.
Fronto-temporal dysfunction in appetitive regulation of bulimia nervosa with affective disorders: A regional homogeneity and remote connectivity pattern analysis
2023, Journal of Affective Disorders
The aim of this study was to assess brain functional alterations in BN patients with affective disorders and their association with maladaptive eating behaviors.
A total of 42 BN patients with affective disorders (anxiety and depression) and 47 healthy controls (HCs) were enrolled in this study. The resting-state fMRI data were analyzed for functional changes as indicated by regional homogeneity based on Kendall's coefficient of concordance (KCC-ReHo) and seed-based functional connectivity (FC). A principal component analysis (PCA) model was used to identify the commonalities within the behavioral questionnaires from the BN group.
Patients in the BN group showed decreased ReHo in the bilateral middle frontal gyrus (MFG) and right supramarginal gyrus (SMG). Additionally, the BN group showed increased FC between the left MFG and the right inferior temporal gyrus (ITG); decreased FC between the right MFG and the bilateral insula and the left middle temporal gyrus (MTG); and decreased FC between the right SMG and the left superior temporal gyrus (STG) and right inferior frontal gyrus (IFG). In the FC-behavior association analysis, the second principal component (PC2) was negatively correlated with FC between the left MFG and the right ITG.
Based on a brain functional analysis (ReHo and FC), this study revealed significant aberrant changes in the frontal-temporal regions of BN patients with affective disorders. These regions, which serve as fronto-temporal circuitry, are associated with restraint and emotional eating behaviors. Our findings shed new light on the neural mechanisms underlying the condition.
Common and unique neural activities in subclinical depression and major depressive disorder indicate the development of brain impairments in different depressive stages
2022, Journal of Affective Disorders
Subclinical depression (SD) and major depressive disorder (MDD) can be considered as the early and late stages of depression, but the characteristics of intrinsic neural activity in different depressive stages are largely unknown.
Twenty-six SD, 36 MDD subjects and 33 well-matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Voxel-wise regional homogeneity (ReHo) was analyzed to explore the alterations of intrinsic neural activity, and machine learning classification based on ReHo features was performed to assess potential performance for diagnostic classification.
Common alterations of ReHo in both SD and MDD groups were found in the bilateral middle temporal gyrus and the left middle occipital gyrus. Opposite alterations in SD and MDD groups were found in the right superior cerebellum. Moreover, increased ReHo in the bilateral precuneus was only found in MDD, while increased ReHo in the right middle frontal gyrus and precentral gyrus were unique to SD. The distinct ReHo values correctly identified SD, MDD, and HC by linear support vector machine (SVM) with an accuracy of 77.89 %, which further verified the discrimination ability of altered ReHo in these brain regions.
The sample size is relatively small.
Common and unique ReHo alterations provided insights into the development of brain impairments in depression, and helped to understand the pathophysiology of SD and MDD.
ReSS: A tool for discovering relevant sets in complex systems
2021, SoftwareX
A complex system can be composed of inherent dynamical structures, i.e., relevant subsets of variables interacting tightly with one another and loosely with other subsets. In the literature, some effective methods to identify such relevant sets rely on the so-called Relevance Indexes (RIs), measuring subset relevance based on information theory principles. In this paper, we present ReSS, a collection of CUDA-based programs computing two of such RIs, either through an exhaustive search or a niching metaheuristic when the system dimension is too large. ReSS also includes a script that iteratively activates the search and identifies hierarchical relationships among the relevant subsets. The main purpose of ReSS is to establish a common and easy-to-use general RI-based platform for the analysis of complex systems and other possible applications.
Efficient search for informational cores in complex systems: Application to brain networks
2020, Neural Networks
An important step in understanding the nature of the brain is to identify “cores” in the brain network, where brain areas strongly interact with each other. Cores can be considered as essential sub-networks for brain functions. In the last few decades, an information-theoretic approach to identifying cores has been developed. In this approach, interactions between parts are measured by an information loss function, which quantifies how much information would be lost if interactions between parts were removed. Then, a core called a “complex” is defined as a subsystem wherein the amount of information loss is locally maximal. Although identifying complexes can be a novel and useful approach, its application is practically impossible because computation time grows exponentially with system size. Here we propose a fast and exact algorithm for finding complexes, called Hierarchical Partitioning for Complex search (HPC). HPC hierarchically partitions systems to narrow down candidates for complexes. The computation time of HPC is polynomial, enabling us to find complexes in large systems (up to several hundred) in a practical amount of time. We prove that HPC is exact when an information loss function satisfies a mathematical property, monotonicity. We show that mutual information is one such information loss function. We also show that a broad class of submodular functions can be considered as such information loss functions, indicating the expandability of our framework to the class. We applied HPC to electrocorticogram recordings from a monkey and demonstrated that HPC revealed temporally stable and characteristic complexes.
Major depressive disorder identification by referenced multiset canonical correlation analysis with clinical scores
2020, Medical Image Analysis
A novel method based on multiset canonical correlation analysis (mCCA) and linear discriminant analysis (LDA) is presented to identify the major depressive disorder (MDD). The new method comprises two parts, namely, the mCCA-rreg and sparse LDA models. The mCCA-rreg model extends the classical canonical correlation model to calculate functional connections by restricting the references to a reference space and adding a spatial regularization term. The reference space is used to ensure that the model extracts important components first from several datasets simultaneously by decreasing the importance of the components in which we are uninterested. The spatial regularization term helps in avoiding the multicollinearity and overfitting problems under the low signal-to-noise ratio circumstance. The sparse LDA model extends the classical LDA model to extract a small subset of discriminative classification features by fusing clinical scores. In the real data experiment, we extract two functional connection modes from 45 subjects by the mCCA-rreg model. Then, we construct classifiers to identify the patients with MDD based on the connections selected by the sparse LDA model. The best accuracy is higher than 95%. The results show that the mCCA-rreg model can retrieve the important components characterized by a preassigned reference space and exclude the noise or components of no interest. The sparse LDA model can extract discriminative classification features related to clinical scores.

View all citing articles on Scopus

^☆: P. ArabieL. J. HubertG. DeSoete

View full text

Regular ArticleFunctional Clustering: Identifying Strongly Interactive Brain Regions in Neuroimaging Data☆

Abstract

Neuron

Neuroimage

Clustering and Classification

The Process of Cognition

Probability models and hypothesis testing in partitioning cluster analysis

Clustering and Classification

Critical review of PET, SPECT, and neuroreceptor studies in schizophrenia

J. Neural Transm.

Analyse Typologique. Theories et Applications

On using principal components before separating a mixture of multivariate normal distributions

Appl. Stat.

A review of classification

J. R. Stat. Soc. A

Multidimensional Scaling

Neural Darwinism

The Remembered Present

Interhemispheric synchronization of oscillatory neuronal responses in cat visual cortex

Science

Direct physiologic evidence for scene segmentation by temporal coding

Proc. Natl. Acad. Sci. USA

Cluster Analysis

Functional and effective connectivity: A synthesis

Hum. Brain Mapp.

Spatial realignment and normalization of images

Hum. Brain Mapp.

Statistical parametric maps in functional neuroimaging: A general linear approach

Hum. Brain Mapp.

Schizophrenia: A disconnection syndrome

Clinical Neuroscience

Functional topography: Multidimensional scaling and functional connectivity in the brain

Cerebral Cortex

Characterizing the complexity of neural interactions

Hum. Brain Mapping

Disconnection syndromes in animals and man

Brain

The cerebral localization of neuropsychological impairment in Alzheimer's disease: A SPECT study

J. Neurol.

Classification

Regular Article
Functional Clustering: Identifying Strongly Interactive Brain Regions in Neuroimaging Data☆