Editorial: Advanced physical methods in brain research

Bellotti, R.; Pascazio, Saverio

doi:10.1140/epjp/i2012-12145-4

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…These communities should be more strategic, high connected and informative, with a reduced number of expressed genes to handle and a stronger noise-free signal. Our research could be further developed according to this line of reasoning, most notably by continuing the biological validation of gene communities and investigating their relevance at the level of neuronal function, brain networks and behavior linked with brain disorders [ 52 ].…”

Section: Discussionmentioning

confidence: 99%

A complex network approach reveals a pivotal substructure of genes linked to schizophrenia

et al. 2018

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Research on brain disorders with a strong genetic component and complex heritability, such as schizophrenia, has led to the development of brain transcriptomics. This field seeks to gain a deeper understanding of gene expression, a key factor in exploring further research issues. Our study focused on how genes are associated amongst each other. In this perspective, we have developed a novel data-driven strategy for characterizing genetic modules, i.e., clusters of strongly interacting genes. The aim was to uncover a pivotal community of genes linked to a target gene for schizophrenia. Our approach combined network topological properties with information theory to highlight the presence of a pivotal community, for a specific gene, and to simultaneously assess the information content of partitions with the Shannon’s entropy based on betweenness. We analyzed the publicly available BrainCloud dataset containing post-mortem gene expression data and focused on the Dopamine D2 receptor, encoded by the DRD2 gene. We used four different community detection algorithms to evaluate the consistence of our approach. A pivotal DRD2 community emerged for all the procedures applied, with a considerable reduction in size, compared to the initial network. The stability of the results was confirmed by a Dice index ≥80% within a range of tested parameters. The detected community was also the most informative, as it represented an optimization of the Shannon entropy. Lastly, we verified the strength of connection of the DRD2 community, which was stronger than any other randomly selected community and even more so than the Weighted Gene Co-expression Network Analysis module, commonly considered the standard approach for such studies. This finding substantiates the conclusion that the detected community represents a more connected and informative cluster of genes for the DRD2 community, and therefore better elucidates the behavior of this module of strongly related DRD2 genes. Because this gene plays a relevant role in Schizophrenia, this finding of a more specific DRD2 community will improve the understanding of the genetic factors related with this disorder.

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Section: Discussionmentioning

confidence: 99%

A complex network approach reveals a pivotal substructure of genes linked to schizophrenia

et al. 2018

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“…In particular, it was shown that the hippocampus volume and the volume from other subcortical structures at MCI were well correlated to a worse progression to AD, with accuracy of about 65% in the prediction from MCI to AD [ 13 ]. Particularly important is the choice of the hippocampal segmentation protocol [ 14 ]. Rather than assuming that specific brain regions are going to be affected by AD, other authors achieved a better accuracy in the prediction from MCI to AD (achieving values of about 80% accuracy) by performing a blind approach including multiple regions of interest [ 15 – 17 ].…”

Section: Introductionmentioning

confidence: 99%

Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer’s disease

et al. 2017

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Alzheimer’s disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex): HC (healthy control participants), ncMCI (mild cognitive impairment not converting to AD), cMCI (mild cognitive impairment converting to AD) and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i) considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii) Eight and seven nodes were significant for the comparisons AD-ncMCI and AD-cMCI, respectively; six nodes, among them, were significant in both comparisons and these nodes form a connected brain region (corresponding to hippocampus, amygdala, Parahippocampal Gyrus, Planum Polare, Frontal Orbital Cortex, Temporal Pole and subcallosal cortex) showing reduced strength of connectivity in the MCI stages; (iii) The connectivity maps of cMCI and ncMCI subjects significantly differ from the connectome of healthy subjects in three regions all corresponding to Frontal Orbital Cortex.

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“…In general the comparison of different segmentation algorithms is arduous, due to the fact that most studies have different data sources or validation techniques. Also, when dealing specifically with hippocampal segmentation the differences in segmentation protocols represent a particularly limiting constraint (Bellotti and Pascazio 2012, Nestor et al 2013. Although in recent years a considerable effort has been invested in the creation of a unified segmentation protocol (Frisoni andJack 2011, Frisoni et al 2015), consensus has not been reached.…”

Section: Introductionmentioning

confidence: 99%

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool

et al. 2015

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In this study we present a novel fully automated Hippocampal Unified Multi-Atlas-Networks (HUMAN) algorithm for the segmentation of the hippocampus in structural magnetic resonance imaging. In multi-atlas approaches atlas selection is of crucial importance for the accuracy of the segmentation. Here we present an optimized method based on the definition of a small peri-hippocampal region to target the atlas learning with linear and non-linear embedded manifolds. All atlases were co-registered to a data driven template resulting in a computationally efficient method that requires only one test registration. The optimal atlases identified were used to train dedicated artificial neural networks whose labels were then propagated and fused to obtain the final segmentation. To quantify data heterogeneity and protocol inherent effects, HUMAN was tested on two independent data sets provided by the Alzheimer's Disease Neuroimaging Initiative and the Open Access

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Editorial: Advanced physical methods in brain research

Cited by 13 publications

References 7 publications

A complex network approach reveals a pivotal substructure of genes linked to schizophrenia

A complex network approach reveals a pivotal substructure of genes linked to schizophrenia

Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer’s disease

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool

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