Modular structure of functional networks in olfactory memory

Meunier, David; Fonlupt, Pierre; Saive, Anne-Lise; Plailly, Jane; Ravel, Nadine; Royet, Jean-Pierre

doi:10.1016/j.neuroimage.2014.03.041

Cited by 71 publications

(57 citation statements)

References 108 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two of our studies recently further elucidated odor recognition memory by investigating the neural basis of this process as a function of task performance using event-related functional magnetic resonance imaging (fMRI; Royet et al, 2011; Meunier et al, 2014). Recognition memory performances were assessed using parameters from signal detection theory, which has widely dominated recognition memory theory since the 1950s (Swets, 1964; Lockhart and Murdock, 1970).…”

Section: Laboratory-based Approaches For Studying the Neural Bases Ofmentioning

confidence: 99%

“…These findings led to the hypothesis that a greater ease when performing the task results in less activation in the hippocampus. Recently, we explored the functional connectivity of the networks underpinning correct and incorrect olfactory memories using graph theory (Meunier et al, 2014). We found that among 36 regions of interest, the hippocampus, caudate nucleus, anterior cingulate and medial temporal gyrus were more frequently connected together during correct odor recognition and thus formed a specific module of this condition (Figure 3B).…”

Section: Laboratory-based Approaches For Studying the Neural Bases Ofmentioning

confidence: 99%

“…Other modules were also found during the CR, Miss or False alarm conditions. aCing, anterior cingulate; Caud, caudate nucleus; Hipp, hippocampus; IFg, Inferior frontal gyrus; Ins, insula; LOg, lateral orbital gyrus; MTg, medial temporal gyrus; pPC, posterior piriform cortex; Puta, putamen; Tha, thalamus (adapted from Meunier et al, 2014). …”

Section: Laboratory-based Approaches For Studying the Neural Bases Ofmentioning

confidence: 99%

See 2 more Smart Citations

A review on the neural bases of episodic odor memory: from laboratory-based to autobiographical approaches

Saive

Royet

Plailly

2014

Front. Behav. Neurosci.

Self Cite

View full text Add to dashboard Cite

Odors are powerful cues that trigger episodic memories. However, in light of the amount of behavioral data describing the characteristics of episodic odor memory, the paucity of information available on the neural substrates of this function is startling. Furthermore, the diversity of experimental paradigms complicates the identification of a generic episodic odor memory network. We conduct a systematic review of the literature depicting the current state of the neural correlates of episodic odor memory in healthy humans by placing a focus on the experimental approaches. Functional neuroimaging data are introduced by a brief characterization of the memory processes investigated. We present and discuss laboratory-based approaches, such as odor recognition and odor associative memory, and autobiographical approaches, such as the evaluation of odor familiarity and odor-evoked autobiographical memory. We then suggest the development of new laboratory-ecological approaches allowing for the controlled encoding and retrieval of specific multidimensional events that could open up new prospects for the comprehension of episodic odor memory and its neural underpinnings. While large conceptual differences distinguish experimental approaches, the overview of the functional neuroimaging findings suggests relatively stable neural correlates of episodic odor memory.

show abstract

Section: Laboratory-based Approaches For Studying the Neural Bases Ofmentioning

confidence: 99%

Section: Laboratory-based Approaches For Studying the Neural Bases Ofmentioning

confidence: 99%

Section: Laboratory-based Approaches For Studying the Neural Bases Ofmentioning

confidence: 99%

See 1 more Smart Citation

A review on the neural bases of episodic odor memory: from laboratory-based to autobiographical approaches

Saive

Royet

Plailly

2014

Front. Behav. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Functional connectivity has been studied in musicians (Pinho et al, 2014), creative writers (Lotze et al, 2014), functional network organization in chess experts (Duan et al, 2014), and olfactory memory (Meunier et al, 2014). These studies investigate network-based functional connectivity.…”

mentioning

confidence: 99%

Olfactory Network Differences in Master Sommeliers: Connectivity Analysis Using Granger Causality and Graph Theoretical Approach

et al. 2017

View full text Add to dashboard Cite

Previous studies investigating the differences in olfactory processing and judgments between trained sommeliers and controls have shown increased activations in brain regions involving higher level cognitive processes in sommeliers. However, there is little information about the influence of expertise on causal connectivity and topological properties of the connectivity networks between these regions. Therefore, the current study focuses on addressing these questions in a functional magnetic resonance imaging (fMRI) study of olfactory perception in Master Sommeliers. fMRI data were acquired from Master Sommeliers and control participants during different olfactory and nonolfactory tasks. Mean time series were extracted from 90 different regions of interest (ROIs; based on Automated Anatomical Labeling atlas). The underlying neuronal variables were extracted using blind hemodynamic deconvolution and then input into a dynamic multivariate autoregressive model to obtain connectivity between every pair of ROIs as a function of time. These connectivity values were then statistically compared to obtain paths that were significantly different between the two groups. The obtained connectivity matrices were further studied using graph theoretical methods. In sommeliers, significantly greater connectivity was observed in connections involving the precuneus, caudate, putamen, and several frontal and temporal regions. The controls showed increased connectivity from the left hippocampus to the frontal regions. Furthermore, the sommeliers exhibited significantly higher small-world topology than the controls. These findings are significant, given that learning about neuroplasticity in adulthood in these regions may then have added clinical importance in diseases such as Alzheimer's and Parkinson's where early neurodegeneration is isolated to regions important in smell.

show abstract

“…As seen from Table 2, the t value of node ORBinf.L, CAU.L, MTG.L, PUT, PAL and PCG.R are less than 0 in patients, which indicates that activation level of these regions are less than those in normal group, and the functional connection strength of patient is relatively small. The value of node PoCG.R, IPL.L, PCUN.R, PreCG and PCG.L are greater than 0, this shows that the activation of this part of the patient's [26][27][28][29][30] region is higher than the normal, and the functional connection strength is relatively large. As can be seen from Table 2, the Precuneus's statistical value is large, which shows that the degree of activation in the region has a greater degree of weakening of patient.…”

Section: Discussionmentioning

confidence: 99%

Modularization Analysis of Brain Functional Network Using Fuzzy C-means Algorithm and Correlation in Resting State

Zhuqing¹,

Huan²,

Zhao³

2017

J Health Med Inform

View full text Add to dashboard Cite

Aiming to study the local functional structure of brain function network in resting state, the Fuzzy C-means (FCM) algorithm is adapted to modular the brain functional network. Then the nodes in module are connected into a network using correlation between the time series extracted from Functional Magnetic Resonance Imaging (fMRI) data. Afterwards node degree, clustering coefficient and shortest path length are used to analyse the functional characteristics of networks. Finally, the differences in activation between patients and normal controls' brain regions are compared through Amplitude of Low Frequency Fluctuation (ALFF). Experimental results demonstrate that, the shortest path length of the patient is smaller than that of the normal human, so the information transmission rate increases. Clustering coefficient is higher than the normal, and the degree of grouping of network is enhanced. The correlation between the patient nodes is generally greater than the normal, and there is a weakened situation in the local area. It also found that the proportion of region for the activation level higher than the average of the whole brain in normal with more than the patient. In particular, the activation level of the Precentral gyrus (PreCG) and other regions in patient has a large degree decline. And the activation level in left Caudate nucleus (CAU.L), the lenticular nucleus, Putamen (PUT) and the lenticular nucleus, Palladium (PAL) and other regions is increased for patient. The research results verify the feasibility of modularization analysis of brain functional network using algorithm and correlation in resting state. The main idea of the k-means is the minimization of an objective function, which is normally chosen to be the total distance between all patterns from their respective cluster centres. Its solution relies on an iterative scheme, which starts with arbitrarily chosen initial cluster memberships or centres. The distribution of objects among clusters and the updating of cluster centres are the two main steps of the k-means algorithm [15]. The algorithm alternates between these two steps until the value of the objective function cannot be reduced anymore. Modularization Analysis of Brain Functional Network Using Fuzzy C-means Algorithm and Correlation in Resting StateZhuqingFCM Clustering is a soft version of k-means, where each data point has a fuzzy degree of belonging to each cluster [16]. The FCM algorithm for vector set is a clustering technique that aims to partitioning a set of measured vector xi (i=1,2,...,n) into Gi (i=1,2,...,c) clusters, the main result is the minimization of an objective function J(U,G) with respect to a fuzzy partition matrix U and a set of prototypes G through cluster centre of each cluster.Where d (xk-Gi) represents a universal distance function. Corresponding to the fuzzy partition, elements value of U= [uij]c×n is allowed 0 to 1. However, with the normalization rule, the membership of cluster is equal to oneWhen the Euclidean distance is chosen as the non-similar...

show abstract

Modular structure of functional networks in olfactory memory

Cited by 71 publications

References 108 publications

A review on the neural bases of episodic odor memory: from laboratory-based to autobiographical approaches

A review on the neural bases of episodic odor memory: from laboratory-based to autobiographical approaches

Olfactory Network Differences in Master Sommeliers: Connectivity Analysis Using Granger Causality and Graph Theoretical Approach

Modularization Analysis of Brain Functional Network Using Fuzzy C-means Algorithm and Correlation in Resting State

Contact Info

Product

Resources

About