Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data published in Cerebral Cortex

Brain nodes. (Image taken from the article.)
Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data
Mite Mijalkov, Giovanni Volpe, Joana B Pereira
Anna Canal-Garcia, Emiliano Gómez-Ruiz, Mite Mijalkov, Yu-Wei Chang, Giovanni Volpe, Joana B Pereira, Alzheimer’s Disease Neuroimaging Initiative
Cerebral Cortex, bhab429 (2022)
doi: 10.1093/cercor/bhab429

The organization of the Alzheimer’s disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-β pathology (Aβ−) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aβ pathology (Aβ+) from the Alzheimer’s Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aβ-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aβ + patients and changes in multiplex brain hubs in both MCI Aβ + and AD Aβ + groups. These findings offer a new insight into the interplay between amyloid-β pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.

Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson’s Disease published in Cerebral Cortex

Differences between controls and PD participants in nodal network measures. (Image taken from the article.)
Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson’s Disease
Mite Mijalkov, Giovanni Volpe, Joana B Pereira
Cerebral Cortex, bhab237 (2021)
doi: 10.1093/cercor/bhab237

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

Dendritic spines are lost in clusters in patients with Alzheimer’s disease published in Scientific Report

Combined confocal microscopy picture showing a neuron with a soma free of PHF-tau.
Dendritic spines are lost in clusters in patients with Alzheimer’s disease
Mite Mijalkov, Giovanni Volpe, Isabel Fernaud-Espinosa, Javier DeFelipe, Joana B. Pereira, Paula Merino-Serrais
Sci. Rep. 11, 12350 (2021)
doi: 10.1038/s41598-021-91726-x
biorXiv: 10.1101/2020.10.20.346718

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a deterioration of neuronal connectivity. The pathological accumulation of tau protein in neurons is one of the hallmarks of AD and has been connected to the loss of dendritic spines of pyramidal cells, which are the major targets of cortical excitatory synapses and key elements in memory storage. However, the detailed mechanisms underlying the loss of dendritic spines in patients with AD are still unclear. Here, comparing dendrites with and without tau pathology of AD patients, we show that the presence of tau pathology determines the loss of dendritic spines in blocks, ruling out alternative models where spine loss occurs randomly. Since memory storage has been associated with synaptic clusters, the present results provide a new insight into the mechanisms by which tau drives synaptic damage in AD, paving the way to memory deficits by altering spine organization.

Age-related differences in network structure and dynamic synchrony of cognitive control on biorXiv

Gamma efficiency for older adults.
Age-related differences in network structure and dynamic synchrony of cognitive control
T. Hinault, M. Mijalkov, J.B. Pereira, Giovanni Volpe, A. Bakker, S.M. Courtney
NeuroImage 236, 118070 (2021)
biorXiv: 10.1101/2020.10.09.333567
doi: 10.1016/j.neuroimage.2021.118070

Cognitive trajectories vary greatly across older individuals, and the neural mechanisms underlying these differences remain poorly understood. Here, we propose a mechanistic framework of cognitive variability in older adults, linking the influence of white matter microstructure on fast and effective communications between brain regions. Using diffusion tensor imaging and electroencephalography, we show that individual differences in white matter network organization are associated with network clustering and efficiency in the alpha and high-gamma bands, and that functional network dynamics partly explain individual cognitive control performance in older adults. We show that older individuals with high versus low structural network clustering differ in task-related network dynamics and cognitive performance. These findings were corroborated by investigating magnetoencephalography networks in an independent dataset. This multimodal brain connectivity framework of individual differences provides a holistic account of how differences in white matter microstructure underlie age-related variability in dynamic network organization and cognitive performance.

Intercellular Communication Induces Glycolytic Synchronisation Waves published in PNAS

Intercellular communication induces glycolytic synchronization waves between individually oscillating cells

Intercellular communication induces glycolytic synchronization waves between individually oscillating cells
Martin Mojica-Benavides, David D. van Niekerk, Mite Mijalkov, Jacky L. Snoep, Bernhard Mehlig, Giovanni Volpe, Caroline B. Adiels & Mattias Goksör
PNAS 118(6), e2010075118 (2021)
doi: 10.1073/pnas.2010075118
arXiv: 1909.05187

Metabolic oscillations in single cells underlie the mechanisms behind cell synchronization and cell-cell communication. For example, glycolytic oscillations mediated by biochemical communication between cells may synchronize the pulsatile insulin secretion by pancreatic tissue, and a link between glycolytic synchronization anomalies and type-2 diabetes has been hypotesized. Cultures of yeast cells have provided an ideal model system to study synchronization and propagation waves of glycolytic oscillations in large populations. However, the mechanism by which synchronization occurs at individual cell-cell level and overcome local chemical concentrations and heterogenic biological clocks, is still an open question because of experimental limitations in sensitive and specific handling of single cells. Here, we show how the coupling of intercellular diffusion with the phase regulation of individual oscillating cells induce glycolytic synchronization waves. We directly measure the single-cell metabolic responses from yeast cells in a microfluidic environment and characterize a discretized cell-cell communication using graph theory. We corroborate our findings with simulations based on a kinetic detailed model for individual yeast cells. These findings can provide insight into the roles cellular synchronization play in biomedical applications, such as insulin secretion regulation at the cellular level.

Press release on joint research on intercellular communication mechanism by Biological Physics Lab and Soft Matter Lab

The article Intercellular Communication Induces Glycolytic Synchronisation Waves published in PNAS has been featured in the News of the Faculty of Science of Gothenburg University.

Here the links to the press releases:
Swedish: Forskare har knäckt koden för cellkommunikation
English: Researchers have broken the code for cell communication

Delayed correlations improve the reconstruction of the brain connectome published on PLoS ONE

Example of a weighted small-world structural network.

Delayed correlations improve the reconstruction of the brain connectome
Mite Mijalkov, Joana B. Pereira & Giovanni Volpe
PLoS ONE 15(2), e0228334 (2020)
doi: https://doi.org/10.1371/journal.pone.0228334

The brain works as a large-scale complex network, known as the connectome. The strength of the connections between two brain regions in the connectome is commonly estimated by calculating the correlations between their patterns of activation. This approach relies on the assumption that the activation of connected regions occurs together and at the same time. However, there are delays between the activation of connected regions due to excitatory and inhibitory connections. Here, we propose a method to harvest this additional information and reconstruct the structural brain connectome using delayed correlations. This delayed-correlation method correctly identifies 70% to 80% of connections of simulated brain networks, compared to only 5% to 25% of connections detected by the standard methods; this result is robust against changes in the network parameters (small-worldness, excitatory vs. inhibitory connection ratio, weight distribution) and network activation dynamics. The delayed-correlation method predicts more accurately both the global network properties (characteristic path length, global efficiency, clustering coefficient, transitivity) and the nodal network properties (nodal degree, nodal clustering, nodal global efficiency), particularly at lower network densities. We obtain similar results in networks derived from animal and human data. These results suggest that the use of delayed correlations improves the reconstruction of the structural brain connectome and open new possibilities for the analysis of the brain connectome, as well as for other types of networks.

Mite Mijalkov defended his PhD Thesis. Congrats!

Mite Mijalkov defended his PhD Thesis on 24 April 2018 in the Physics Department seminar room (SA240).

Assoc. Prof. Hande Toffoli (Middle-East Technical University), Prof. Tayfun Ozcelik (Bilkent University), Assoc. Prof. Alpan Bek (Middle-East Technical University), Assist. Prof. Seymur Cahangirov (Bilkent Unievrsity) and Assist. Prof. Giovanni Volpe (Bilkent University) will be the thesis committee members.

Thesis title: Graph Theory Study of Complex Networks in the Brain

Thesis abstract: The brain is a large-scale, intricate web of neurons, known as the connectome. By representing the brain as a network i.e. a set of nodes connected by edges, one can study its organization by using concepts from graph theory to evaluate various measures. We have developed BRAPH – BRain Analysis using graPHtheory, a MatLab, object-oriented freeware that facilitates the connectivity analysis of brain networks. BRAPH provides user-friendly interfaces that guide the user through the various steps of the connectivity analysis, such as, calculating adjacency matrices, evaluating global and local measures, performing group comparisons by non-parametric permutations and assessing the communities in a network. Furthermore, using graph theory, we showed that structural MRI undirected networks of stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients show abnormal organization. This is indicated, at global level, by decreases in clustering and transitivity accompanied by increases in path length and modularity and, at nodal level, by changes in nodal clustering and closeness centrality in patient groups when compared to controls. In samples that do not exhibit differences in the undirected analysis, we propose the usage of directed networks to assess any topological changes due to a neurodegenerative disease. We demonstrate that such changes can be identified in Alzheimer’s and Parkinson’s patients by using directed networks built by delayed correlation coefficients. Finally, we put forward a method that improves the reconstruction of the brain connectome by utilizing the delays in the dynamic behavior of the neurons. We show that this delayed correlationmethod correctly identifies 70% to 80% of the real connections in simulated networks and performs well in the identification of their global and nodal properties.

Name of the PhD programme: Material Science and Nanotechnology Graduate Program
Thesis Advisor  Giovanni Volpe, Department of Physics, Bilkent University

Place: Physics Department seminar room (SA240), Bilkent University
Time: 24 April, 2018, 11:00

BRAPH published in Plos ONE

BRAPH: A graph theory software for the analysis of brain connectivity

BRAPH: A graph theory software for the analysis of brain connectivity
Mite Mijalkov, Ehsan Kakaei, Joana B. Pereira, Eric Westman & Giovanni Volpe
PLoS ONE 12(8), e0178798 (2017)
DOI: 10.1371/journal.pone.0178798
bioRxiv: 106625

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkin- son’s patients with mild cognitive impairment.