Browsing by Author "Pereira, J. B."

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Open Access
Aberrant cerebral network topology and mild cognitive impairment in early Parkinson’s disease
(John Wiley & Sons, Inc., 2015-05-06) Pereira, J. B.; Aarsland, D.; Ginestet, C. E.; Lebedev, A. V.; Wahlund, L. O.; Simmons, A.; Volpe, G.; Westman, E.
The aim of this study was to assess whether mild cognitive impairment (MCI) is associatedwith disruption in large-scale structural networks in newly diagnosed, drug-na€ıve patients with Parkin-son’s disease (PD). Graph theoretical analyses were applied to 3T MRI data from 123 PD patients and 56controls from the Parkinson’s progression markers initiative (PPMI). Thirty-three patients were classifiedas having Parkinson’s disease with mild cognitive impairment (PD-MCI) using the Movement DisordersSociety Task Force criteria, while the remaining 90 PD patients were classified as cognitively normal (PD-CN). Global measures (clustering coefficient, characteristic path length, global efficiency, small-world-ness) and regional measures (regional clustering coefficient, regional efficiency, hubs) were assessed inthe structural networks that were constructed based on cortical thickness and subcortical volume data.PD-MCI patients showed a marked reduction in the average correlation strength between cortical andsubcortical regions compared with controls. These patients had a larger characteristic path length andreduced global efficiency in addition to a lower regional efficiency in frontal and parietal regions com-pared with PD-CN patients and controls. A reorganization of the highly connected regions in the networkwas observed in both groups of patients. This study shows that the earliest stages of cognitive decline inPD are associated with a disruption in the large-scale coordination of the brain network and with adecrease of the efficiency of parallel information processing.
Open Access
BRAPH: A graph theory software for the analysis of brain connectivity
(Public Library of Science, 2017) Mijalkov, M.; Kakaei, E.; Pereira, J. B.; Westman, E.; Volpe, G.
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 Parkinson’s patients with mild cognitive impairment. © 2017 Mijalkov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Open Access
Disrupted network topology in patients with stable and progressive mild cognitive impairment and alzheimer's disease
(Oxford University Press, 2016) Pereira, J. B.; Mijalkov, M.; Kakaei, E.; Mecocci, P.; Vellas, B.; Tsolaki, M.; Kłoszewska, I.; Soininen, H.; Spenger, C.; Lovestone, S.; Simmons, A.; Wahlund, L.-O.; Volpe, G.; Westman, E.
Recent findings suggest that Alzheimer's disease (AD) is a disconnection syndrome characterized by abnormalities in large-scale networks. However, the alterations that occur in network topology during the prodromal stages of AD, particularly in patients with stable mild cognitive impairment (MCI) and those that show a slow or faster progression to dementia, are still poorly understood. In this study, we used graph theory to assess the organization of structural MRI networks in stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients from 2 large multicenter cohorts: ADNI and AddNeuroMed. Our findings showed an abnormal global network organization in all patient groups, as reflected by an increased path length, reduced transitivity, and increased modularity compared with controls. In addition, lMCIc, eMCIc, and AD patients showed a decreased path length and mean clustering compared with the sMCI group. At the local level, there were nodal clustering decreases mostly in AD patients, while the nodal closeness centrality detected abnormalities across all patient groups, showing overlapping changes in the hippocampi and amygdala and nonoverlapping changes in parietal, entorhinal, and orbitofrontal regions. These findings suggest that the prodromal and clinical stages of AD are associated with an abnormal network topology.