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Our study showed that brain dysfunction in cognition network was related to errant eating behavior and obesity. Brain networks are important for obesity as they are regulated by hormones including leptin and ghrelin that control appetite 5 , 6. We found shared brain networks correlated with obesity and eating behavior. The identified shared brain networks might be important image biomarkers for obesity-related research. We propose that future research on obesity and abnormal eating behaviors should consider brain network alterations.
Our study was performed in full accordance with the local IRB guidelines. Informed consent was obtained from all subjects.
The phase encoding of rs-fMRI and T1-weighted structural images was from anterior to posterior direction. Eight underweight participants with BMI less than The remaining participants were classified into 41 HW and 58 non-HW subjects.
Finally, 41 HW and 41 non-HW subjects were considered for the study. Detailed participant information is given in Table 1. Skull was removed using BET.
Magnetic field bias was corrected and brain tissues were classified into white or gray matter and cerebrospinal fluid with FAST. Spatial smoothing was applied with full width at half maximum value of 6 mm. Intensity normalization of time series 4D data was applied with a value of 10, A high-pass filter with cutoff second was applied.
We did not apply band-pass filtering and only kept high frequency signals based on recent findings 48 , Functional EPI images were registered onto the T1-weighted structural image and T1-weighted structural image was subsequently registered onto the Montreal Neurological Institute standard space. The 4D dataset was resampled to 3 mm isotropic resolution. An ICA approach was applied to generate spatial maps automatically, called independent components ICs The process of determining number of IC was driven by data.
Each spatial map was a collection of voxels sharing similar patterns of brain activity. Obtained ICs were used as regressors to estimate participant-specific time series The generated ICs contain functionally interpretable networks as well as uninteresting signals.
Establishing such connection to reference RSNs allowed standardized interpretation of results The process ensured that only functionally interpretable ICs were kept after the cross correlation procedure. Network construction Connectivity analysis requires the regions of interest ROIs to investigate correlation between different regions. Each IC was represented as a node in a graph.
Each edge was defined as the correlation of the time series between two different nodes. We adopted the weighted and undirected network model. Edge values were entered into the matrix as elements and the matrix was referred to the correlation matrix.
Soft thresholding was applied to weights to avoid binarizing the correlation matrix using the following equation; , where rij means the edge value between the node i and j 51 , Connectivity analysis There are several network centrality measures such as degree-, betweenness-, eigenvector-, and closeness-centrality Degree centrality is defined as the sum of all edge weights connected to a given node Betweenness centrality is defined as the number of shortest paths between any two nodes that run through that node Eigenvector centrality of node i is defined as the ith element in the eigenvector corresponding to the largest eigenvalue of the correlation matrix 52 , It considers neighborhood nodes as well the given node itself 52 , Thus it is a locally weighted centrality measure.
Edges with high node centrality contribute more to the network in using eigenvector centrality 52 , Closeness centrality is defined as the inverse of the average shortest path length from one node to all other nodes Degree centrality is one of the sensitive network measures among measures such as betweenness-, eigenvector-, and closeness centrality We adopted degree centrality because it is a simple and sensitive local parameter to describe the brain network 30 , Degree centrality was computed as weighted degree in the weighted network model.
The weighted degree value is the sum of all edge weights connected to a given node A node i. Degree values for each node i. Statistical analysis Differences between HW and non-HW groups were assessed performing permutation tests 10, times randomly assigning participants to HW and non-HW groups to avoid multiple comparison issue The null distribution was constructed from the permutation tests. Higher d values lead to lower p-values.
The identified ICs and the associated degree values were correlated with clinical scores. The significance of the linear regression between clinical scores and degree values were quantified with r- and p-values. Additional Information.
Examples include the economies of Singapore , Norway , Vietnam and China —all of which feature large state-owned enterprise sectors operating alongside large private sectors. The French economy featured a large state sector from until , mixing a substantial amount of state-owned enterprises and nationalized firms with private enterprise.
In the s, the central government concentrated its ownership in strategic sectors of the economy, but local and provincial level state-owned enterprises continue to operate in almost every industry including information technology, automobiles, machinery and hospitality.
The latest round of state-owned enterprise reform initiated in stressed increased dividend payouts of state enterprises to the central government and "mixed ownership reform" which includes partial private investment into state-owned firms. As a result, many nominally private-sector firms are actually partially state-owned by various levels of government and state institutional investors; and many state-owned enterprises are partially privately owned resulting in a "mixed ownership" economy.
It can include capitalist economies with indicative macroeconomic planning policies and socialist planned economies that introduced market forces into their economies, such as in Hungary. Dirigisme was an economic policy initiated under Charles de Gaulle in France, designating an economy where the government exerts strong directive influence through indicative economic planning.
In the period of Dirigisme, the French state used indicative economic planning to supplement market forces for guiding its market economy. It involved state control of industries such as transportation, energy and telecommunication infrastructures as well as various incentives for private corporations to merge or engage in certain projects.
Under its influence France experienced what is called "Thirty Glorious Years" of profound economic growth.