Functional brain networks: great expectations, hard times and the big leap forward

Many physical and biological systems can be studied using complex network theory, a new statistical physics understanding of graph theory. The recent application of complex network theory to the study of functional brain networks has generated great enthusiasm as it allows addressing hitherto non-standard issues in the field, such as efficiency of brain functioning or vulnerability to damage. However, in spite of its high degree of generality, the theory was originally designed to describe systems profoundly different from the brain. We discuss some important caveats in the wholesale application of existing tools and concepts to a field they were not originally designed to describe. At the same time, we argue that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature. Finally, we propose that, rather than simply borrowing from an existing theory, functional neural networks can inspire a fundamental reformulation of complex network theory, to account for its exquisitely complex functioning mode

Olfactory fMRI Connectivity Analysis Based on Granger Causality with Application in Anosmia Assessment

In this work, we describe hubs organization within the olfactory network with Functional Magnetic Resonance Imaging (fMRI). Granger causality analyses were applied in the supposed regions of interest (ROIs) involved in olfactory tasks, as described in [1]. We aim to get deeper knowledge about the hierarchy of the regions within the olfactory network and to describe which of these regions, in terms of strength of the connectivity, impair in different types of anosmia

Functional Brain Networks: beyond the small-world paradigm

This contribution reviews the current state of art comprising the application of Complex Networks Theory to the analysis of functional brain networks. We briefly overview the main advances in this field during the last decade and we explain how graph analysis has increased our knowledge about how the brain behaves when performing a specific task or how it fails when a certain pathology arises. We also show the limitations of this kind of analysis, which have been a source of confusion and misunderstanding when interpreting the results obtained. Finally, we discuss about a possible direction to follow in the next years

Disparate connectivity for structural and functional networks is revealed when physical location of the connected nodes is considered

Macroscopic brain networks have been widely described with the manifold of metrics available using graph theory. However, most analyses do not incorporate information about the physical position of network nodes. Here, we provide a multimodal macroscopic network characterization while considering the physical positions of nodes. To do so, we examined anatomical and functional macroscopic brain networks in a sample of twenty healthy subjects. Anatomical networks are obtained with a graph based tractography algorithm from diffusion-weighted magnetic resonance images (DW-MRI). Anatomical con- nections identified via DW-MRI provided probabilistic constraints for determining the connectedness of 90 dif- ferent brain areas. Functional networks are derived from temporal linear correlations between blood-oxygenation level-dependent signals derived from the same brain areas. Rentian Scaling analysis, a technique adapted from very- large-scale integration circuits analyses, shows that func- tional networks are more random and less optimized than the anatomical networks. We also provide a new metric that allows quantifying the global connectivity arrange- ments for both structural and functional networks. While the functional networks show a higher contribution of inter-hemispheric connections, the anatomical networks highest connections are identified in a dorsal?ventral arrangement. These results indicate that anatomical and functional networks present different connectivity organi- zations that can only be identified when the physical locations of the nodes are included in the analysis

Changes in anatomical and functional connectivity related to lower hippocampal volume

Anatomical 3D T1 weighted images have been widely used to assess the volume of subcortical structures. It has been demonstrated that volume loss of hippocampi, entorhinal cortex and amygdala are early biomarkers for the diagnosis of cognitive impairments and AD1. In this work, we investigate AD biomarkers based on brain connectivity. SC-FC differences have been reported between controls and patients at risk for AD2. We will try to anticipate to MCI or AD diagnoses by differentiating in a sample of healthy subjects, i.e. with no cognitive impairment, other than related to ageing, using as criterion of separation the normalized hippocampal volume (NHV). The sample is formed by volunteers in the Valleca?s Initiative, a longitudinal study evaluating normal ageing in a cohort of more than 600 healthy elder people (70-85 years). The prevalence of AD in people older than 65 years is 13%3 suggesting that a certain number of those subjects will develop AD in the next years. The subjects with lower NHV are more prone to have AD than subjects with higher NHV. Thus they are more likely to manifest connectivity patterns that can be considered as AD biomarkers

Study of resting state cortico-cortical synchronization aimed to accurately discriminate Parkinson and essential tremor patients: A MEG source-space connectivity study

Motor tremor-related syndromes like essential tremor (ET) and Parkinson's disease (PD) have a common symptomatology in early stages: the presence of tremor. Even when both diseases have a different aetiology and, thus, different prognosis and treatment, the symptoms in early stages are quite similar. This usually leads to misdiagnosis, with the associated risks and limitations. A PD patient with an ET treatment will continue developing the disease, loosing an important window of action. On the other hand, an ET patient with a PD treatment will suffer strong side effects. A correct diagnosis is in both cases mandatory for the well-being of the patients. In this experiment we tried to find a biomarker based in magneto-physiological data that allows clinicians a faster and easier diagnosis of ET and PD patients, saving time and money to both patients and hospitals

Study of resting state cortico-cortical synchronization aimed to accurately discriminate Parkinson and essential tremor patients: A MEG signal-space connectivity study

Motor tremor-related syndromes like essential tremor (ET) and Parkinson?s disease (PD) have a common symptomatology in early stages: the presence of tremor. Even when both diseases have a different aetiology and, thus, different prognosis and treatment, the symptoms in early stages are quite similar. This usually leads to misdiagnosis, with the associated risks and limitations. A PD patient with an ET treatment will continue developing the disease, loosing an important window of action. On the other hand, an ET patient with a PD treatment will suffer strong side effects. A correct diagnosis is in both cases mandatory for the well-being of the patients. In this experiment we tried to find a biomarker based in magneto-physiological data that allows clinicians a faster and easier diagnosis of ET and PD patients, saving time and money to both patients and hospitals

Anatomo-functional organization in brain networks

There are several studies focused on comparing rsFC networks with their structural substrate \cite{hagmann2008, honey2010}. However an accurate description of how anatomo-­functional connections are organized, both at physical and topological levels, is still to be defined. Here we present an approach to quantify the anatomo-functional organization and discuss its consistency

Functional and structural brain connectivity of young binge drinkers: a follow-up study

Adolescence is a period of ongoing brain maturation characterized by hierarchical changes in the functional and structural networks. For this reason, the young brain is particularly vulnerable to the toxic effects of alcohol. Nowadays, binge drinking is a pattern of alcohol consumption increasingly prevalent among adolescents. The aim of the present study is to evaluate the evolution of the functional and anatomical connectivity of the Default Mode Network (DMN) in young binge drinkers along two years. Magnetoencephalography signal during eyes closed resting state as well as Diffusion Tensor Imaging (DTI) were acquired twice within a 2-year interval from 39 undergraduate students (22 controls, 17 binge drinkers) with neither personal nor family history of alcoholism. The group comparison showed that, after maintaining a binge drinking pattern along at least two years, binge drinkers displayed an increased brain connectivity of the DMN in comparison with the control group. On the other hand, the structural connectivity did not show significant differences neither between groups nor over the time. These findings point out that a continued pattern of binge drinking leads to functional alterations in the normal brain maturation process, even before anatomical changes can be detected.This study was supported by the projects SPI/2010 and SPI/2010/051 from Spanish Ministry of Health and Social Politics (National Plan on Drugs). Funding was also provided from the SFRH/BPD/109750/2015 Postdoctoral Fellowship of the Portuguese Foundation for Science and Technology as well as the Research Center on Psychology (UID/PSI/01662/2013)S

Alpha-band hypersynchronization in progressive mild cognitive impairment. A magnetoencephalography study

People with mild cognitive impairment (MCI) show a high risk to develop Alzheimer?s disease (AD; Petersen et al., 2001). Nonetheless, there is a lack of studies about how functional connectivity patterns may distinguish between progressive (pMCI) and stable (sMCI) MCI patients. To examine whether there were differences in functional connectivity between groups, MEG eyes-closed recordings from 30 sMCI and 19 pMCI subjects were compared. The average conversion time of pMCI was 1 year, so they were considered as fast converters. To this end, functional connectivity in different frequency bands was assessed with phase locking value in source space. Then the significant differences between both groups were correlated with neuropsychological scores and entorhinal, parahippocampal, and hippocampal volumes. Both groups did not differ in age, gender, or educational level. pMCI patients obtained lower scores in episodic and semantic memory and also in executive functioning. At the structural level, there were no differences in hippocampal volume, although some were found in left entorhinal volume between both groups. Additionally, pMCI patients exhibit a higher synchronization in the alpha band between the right anterior cingulate and temporo-occipital regions than sMCI subjects. This hypersynchronization was inversely correlated with cognitive performance, both hippocampal volumes, and left entorhinal volume. The increase in phase synchro- nization between the right anterior cingulate and temporo-occipital areas may be predictive of conversion from MCI to AD