scholarly journals Toward Establishing Internal Validity for Correlated Gene Expression Measures in Imaging Genomics of Functional Networks: Why Distance Corrections and External Face Validity Alone Fall Short. Reply to “Distance Is Not Everything in Imaging Genomics of Functional Networks: Reply to a Commentary on Correlated Gene Expression Supports Synchronous Activity in Brain Networks”

2020 ◽  
Vol 14 ◽  
Author(s):  
Spiro P. Pantazatos ◽  
Mike F. Schmidt
Keyword(s):  
Gene Expression ◽  
Brain Networks ◽  
Internal Validity ◽  
Face Validity ◽  
Functional Networks ◽  
Synchronous Activity ◽  
Imaging Genomics ◽  
Short Reply ◽  
External Face

scholarly journals Towards establishing internal validity for correlated gene expression measures in imaging genomics of functional networks: why distance corrections and external face validity alone fall short. Reply to “Distance is not everything in imaging genomics of functional networks: reply to a commentary on Correlated gene expression supports synchronous activity in brain networks”

2019 ◽  
Author(s):  
Spiro P. Pantazatos ◽  
Mike Schmidt
Keyword(s):  
Gene Expression ◽  
False Positive ◽  
Cluster Size ◽  
False Positive Rate ◽  
Internal Validity ◽  
Face Validity ◽  
Spatial Proximity ◽  
Functional Networks ◽  
Imaging Genomics ◽  
External Face

AbstractThe primary claim of the Richiardi et. al. 2015 Science article1 is that a measure of correlated gene expression, significant strength fraction (SSF), is related to resting state fMRI (rsfMRI) networks. However, there is still debate about this claim and whether spatial proximity, in the form of contiguous clusters, accounts entirely, or only partially, for SSF2,3. Here, thirteen distributed networks were simulated by combining 34 contiguous clusters randomly placed throughout cortex, with resulting edge distance distributions similar to rsfMRI networks. Cluster size was modulated (6-15mm radius) to test its influence on SSF false positive rate (SSF-FPR) among the simulated ‘noise’ networks. The contribution of rsfMRI networks on SSF-FPR was examined by comparing simulations using: 1) all cortical samples 2) all samples with non-rsfMRI cluster centers and 3) only non-rsfMRI samples. Results show that SSF-FPR is influenced only by cluster size (r>0.9, p<0.001), not by rsfMRI samples. Simulations using 14mm radius clusters most resembled rsfMRI networks. When thresholding at p<10-4, the SSF-FPR was 0.47. Genes that maximize SF have high global spatial autocorrelation. In conclusion, SSF is unrelated to rsfMRI networks. The main conclusion of Richiardi et. al. 2015 is based on a finding that is ∼50% likely to be a false positive, not less than 0.01% as originally reported in the article1. We discuss why distance corrections alone and external face validity are insufficient to establish a trustworthy relationship between correlated gene expression measures and rsfMRI networks, and propose more rigorous approaches to preclude common pitfalls in related studies.

scholarly journals Correlated gene expression supports synchronous activity in brain networks

Science ◽  
2015 ◽  
Vol 348 (6240) ◽  
pp. 1241-1244 ◽  
Author(s):  
J. Richiardi ◽  
A. Altmann ◽  
A.-C. Milazzo ◽  
C. Chang ◽  
M. M. Chakravarty ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brain Networks ◽  
Synchronous Activity

Deep Learning for the Automated Classification of Functional Brain Networks in fMRI (Preprint)

2021 ◽  
Author(s):  
Lukman Ismael ◽  
Pejman Rasti ◽  
Florian Bernard ◽  
Philippe Menei ◽  
Aram Ter Minassian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Brain Networks ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Functional Networks ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Deep Learning Model

BACKGROUND The functional MRI (fMRI) is an essential tool for the presurgical planning of brain tumor removal, allowing the identification of functional brain networks in order to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rsfMRI). However, this technique is not routinely used because of the necessity to have a expert reviewer to identify manually each functional networks. OBJECTIVE We aimed to automatize the detection of brain functional networks in rsfMRI data using deep learning and machine learning algorithms METHODS We used the rsfMRI data of 82 healthy patients to test the diagnostic performance of our proposed end-to-end deep learning model to the reference functional networks identified manually by 2 expert reviewers. RESULTS Experiment results show the best performance of 86% correct recognition rate obtained from the proposed deep learning architecture which shows its superiority over other machine learning algorithms that were equally tested for this classification task. CONCLUSIONS The proposed end-to-end deep learning model was the most performant machine learning algorithm. The use of this model to automatize the functional networks detection in rsfMRI may allow to broaden the use of the rsfMRI, allowing the presurgical identification of these networks and thus help to preserve the patient’s neurological status. CLINICALTRIAL Comité de protection des personnes Ouest II, decision reference CPP 2012-25)

scholarly journals Psychological resilience correlates with EEG source-space brain network flexibility

2019 ◽  
Vol 3 (2) ◽  
pp. 539-550 ◽  
Author(s):  
Véronique Paban ◽  
Julien Modolo ◽  
Ahmad Mheich ◽  
Mahmoud Hassan
Keyword(s):  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Personality Testing ◽  
Functional Networks ◽  
Psychological Resilience ◽  
Full Spectrum ◽  
Sliding Windows ◽  
Resilience Scale ◽  
Network Flexibility

We aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience. To tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-item Connor-Davidson Resilience Scale (CD-RISC). By using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha, and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time. The results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha, and beta bands. This study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks.

scholarly journals Morphological, Structural, and Functional Networks Highlight the Role of the Cortical-Subcortical Circuit in Individuals With Subjective Cognitive Decline

2021 ◽  
Vol 13 ◽  
Author(s):  
Xiaowen Xu ◽  
Tao Wang ◽  
Weikai Li ◽  
Hai Li ◽  
Boyan Xu ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Cognitive Decline ◽  
Brain Networks ◽  
Subjective Cognitive Decline ◽  
Support Vector ◽  
Functional Networks ◽  
Resonance Imaging ◽  
Potential Biomarkers

Subjective cognitive decline (SCD) is considered the earliest stage of the clinical manifestations of the continuous progression of Alzheimer’s Disease (AD). Previous studies have suggested that multimodal brain networks play an important role in the early diagnosis and mechanisms underlying SCD. However, most of the previous studies focused on a single modality, and lacked correlation analysis between different modal biomarkers and brain regions. In order to further explore the specific characteristic of the multimodal brain networks in the stage of SCD, 22 individuals with SCD and 20 matched healthy controls (HCs) were recruited in the present study. We constructed the individual morphological, structural and functional brain networks based on 3D-T1 structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. A t-test was used to select the connections with significant difference, and a multi-kernel support vector machine (MK-SVM) was applied to combine the selected multimodal connections to distinguish SCD from HCs. Moreover, we further identified the consensus connections of brain networks as the most discriminative features to explore the pathological mechanisms and potential biomarkers associated with SCD. Our results shown that the combination of three modal connections using MK-SVM achieved the best classification performance, with an accuracy of 92.68%, sensitivity of 95.00%, and specificity of 90.48%. Furthermore, the consensus connections and hub nodes based on the morphological, structural, and functional networks identified in our study exhibited abnormal cortical-subcortical connections in individuals with SCD. In addition, the functional networks presented more discriminative connections and hubs in the cortical-subcortical regions, and were found to perform better in distinguishing SCD from HCs. Therefore, our findings highlight the role of the cortical-subcortical circuit in individuals with SCD from the perspective of a multimodal brain network, providing potential biomarkers for the diagnosis and prediction of the preclinical stage of AD.

scholarly journals Altered Weibull Degree Distribution in Resting-State Functional Brain Networks Is Associated With Cognitive Decline in Mild Cognitive Impairment

2021 ◽  
Vol 12 ◽  
Author(s):  
Yifei Zhang ◽  
Xiaodan Chen ◽  
Xinyuan Liang ◽  
Zhijiang Wang ◽  
Teng Xie ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Human Brain ◽  
Cognitive Performance ◽  
Power Law ◽  
Resting State ◽  
Brain Networks ◽  
Functional Networks ◽  
Whole Brain ◽  
Connectivity Degree

The topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is altered in Alzheimer's disease (AD). Here we employed resting-state functional MRI and graph theory approaches to investigate the fitting of degree distributions of the whole-brain functional networks and seven subnetworks in healthy subjects and individuals with amnestic mild cognitive impairment (aMCI), i.e., the prodromal stage of AD, and whether they are altered and correlated with cognitive performance in patients. Forty-one elderly cognitively healthy controls and 30 aMCI subjects were included. We constructed functional connectivity matrices among brain voxels and examined nodal degree distributions that were fitted by maximum likelihood estimation. In the whole-brain networks and all functional subnetworks, the connectivity degree distributions were fitted better by the Weibull distribution [f(x)~x(β−1)e(−λxβ)] than power law or power law with exponential cutoff. Compared with the healthy control group, the aMCI group showed lower Weibull β parameters (shape factor) in both the whole-brain networks and all seven subnetworks (false-discovery rate-corrected, p &lt; 0.05). These decreases of the Weibull β parameters in the whole-brain networks and all subnetworks except for ventral attention were associated with reduced cognitive performance in individuals with aMCI. Thus, we provided a short-tailed model to capture intrinsic connectivity structure of the human brain functional networks in health and disease.

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