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The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Giorgio Gost reports financial support was provided by Lazio Region. Giancarlo Ruocco reports financial support was provided by European Research Council. Viola Folli reports financial support was provided by D-TAILS srl., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background/Purpose: To identify brain hubs that are behaviorally relevant for neglect after stroke as well as to characterize their functional architecture of communication. Methods: Twenty acute right hemisphere damaged patients underwent neuropsychological and resting-state functional magnetic resonance imaging sessions. Spatial neglect was assessed by means of the Center of Cancellation on the Bells Cancellation Test. For each patient, resting-state functional connectivity matrices were derived by adopting a brain parcellation scheme consisting of 153 nodes. For every node, we extracted its betweenness centrality (BC) defined as the portion of all shortest paths in the connectome involving such node. Then, neglect hubs were identified as those regions showing a high correlation between their BC and neglect scores. Results: A first set of neglect hubs was identified in multiple systems including dorsal attention and ventral attention, default mode, and frontoparietal executive-control networks within the damaged hemisphere as well as in the posterior and anterior cingulate cortex. Such cortical regions exhibited a loss of BC and increased (i.e., less efficient) weighted shortest path length (WSPL) related to severe neglect. Conversely, a second group of neglect hubs found in visual and motor networks, in the undamaged hemisphere, exhibited a pathological increase of BC and reduction of WSPL associated with severe neglect. Conclusion: The topological reorganization of the brain in neglect patients might reflect a maladaptive shift in processing spatial information from higher level associative-control systems to lower level visual and sensory-motor processing areas after a right hemisphere lesion.

The brain is a dynamic system that generates a broad repertoire of perceptual, motor, and cognitive states by the integration and segregation of different functional domains represented in large-scale brain networks. However, the fundamental mechanisms underlying brain network integration remain elusive. Here, for the first time to our knowledge, we found that in the resting state the brain visits few synchronization modes defined as clusters of temporally aligned functional hubs. These modes alternate over time and their probability of switching leads to specific temporal loops among them. Notably, although each mode involves a small set of nodes, the brain integration seems highly vulnerable to a simulated attack on this temporal synchronization mechanism. In line with the hypothesis that the resting state represents a prior sculpted by the task activity, the observed synchronization modes might be interpreted as a temporal brain template needed to respond to task/environmental demands ., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: The study aim was to identify longitudinal abnormalities of functional connectivity and its relation with motor disability in early to moderately advanced stages of Parkinson's disease patients., Methods: 3.0T structural and resting-state functional MRI was performed in healthy subjects (n = 16) and Parkinson's disease patients (n = 16) with mean disease duration of 2.2 ± 1.2 years at baseline with a clinical follow-up of 1.5 ± 0.3 years. Resting-state fMRI analysis included region-to-region connectivity in correlation with UPDRS-III scores and computation of Global Efficiency and Degree Centrality., Results: At baseline, patients' connectivity increased between the cerebellum and somatomotor network, and decreased between motor regions (Rolandic operculum, precentral gyrus, supplementary motor area, postcentral gyrus) and cingulate connectivity. At 1.5 years follow-up, connectivity remained altered in the same regions identified at baseline. The cerebellum showed additional hyperconnectivity within itself and to the caudate nucleus, thalamus and amygdala compared to controls. These differences correlated with UPDRS-III scores. Seed-based connectivity revealed increased involvement of the default mode network with precentral gyrus in patients at follow-up investigation., Conclusion: Resting-state fMRI identified marked disturbances of the overall architecture of connectivity in Parkinson's disease. The noted alterations in cortical motor areas were associated with cerebellar hyperconnectivity in early to moderately advanced stages of Parkinson's disease suggesting ongoing attempts of recovery and compensatory mechanism for affected functions. The potential to identify connectivity alterations in regions related to both motor and attentional functions requires further evaluation as an objective marker to monitor disease progression, and medical, as well as surgical interventions., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Posteromedial cortex (PMC) is a highly segregated and dynamic core, which appears to play a critical role in internally/externally directed cognitive processes, including conscious awareness. Nevertheless, neuroimaging studies on acquired disorders of consciousness, have traditionally explored PMC as a homogenous and indivisible structure. We suggest that a fine-grained description of intrinsic PMC topology during coma, could expand our understanding about how this cortical hub contributes to consciousness generation and maintain, and could permit the identification of specific markers related to brain injury mechanism and useful for neurological prognostication. To explore this, we used a recently developed voxel-based unbiased approach, named functional connectivity density (CD). We compared 27 comatose patients (15 traumatic and 12 anoxic), to 14 age-matched healthy controls. The patients' outcome was assessed 3 months later using Coma Recovery Scale-Revised (CRS-R). A complex pattern of decreased and increased connections was observed, suggesting a network imbalance between internal/external processing systems, within PMC during coma. The number of PMC voxels with hypo-CD positive correlation showed a significant negative association with the CRS-R score, notwithstanding aetiology. Traumatic injury specifically appeared to be associated with a greater prevalence of hyper-connected (negative correlation) voxels, which was inversely associated with patient neurological outcome. A logistic regression model using the number of hypo-CD positive and hyper-CD negative correlations, accurately permitted patient's outcome prediction (AUC = 0.906, 95%IC = 0.795-1). These points might reflect adaptive plasticity mechanism and pave the way for innovative prognosis and therapeutics methods.

To study the functional connectivity in patients with severe acquired brain injury is very challenging for their high level of disability because of a prolonged period of coma, extended lesions, and several cognitive and behavioral disorders. In this article, we investigated in these patients the default mode network and somatomotor connectivity changes at rest longitudinally, in the subacute and late phase after brain injury. The aim of the study is to characterize such connectivity patterns and relate the observed changes to clinical and neuropsychological outcomes of these patients after a period of intensive neurorehabilitation. Our findings show within the default mode network a disruption of connectivity of medial pre-frontal regions and a significant change of amplitude of internal connections. Notably, strongest changes in functional connectivity significantly correlated to consistent clinical and cognitive recovery. This evidence seems to indicate that the reorganization of the Default Mode Network may represent a valid biomarker for the cognitive recovery in patients with severe acquired brain injury.

The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations., (Copyright © 2013 Elsevier Inc. All rights reserved.)

The Human Connectome Project (HCP) seeks to map the structural and functional connections between network elements in the human brain. Magnetoencephalography (MEG) provides a temporally rich source of information on brain network dynamics and represents one source of functional connectivity data to be provided by the HCP. High quality MEG data will be collected from 50 twin pairs both in the resting state and during performance of motor, working memory and language tasks. These data will be available to the general community. Additionally, using the cortical parcellation scheme common to all imaging modalities, the HCP will provide processing pipelines for calculating connection matrices as a function of time and frequency. Together with structural and functional data generated using magnetic resonance imaging methods, these data represent a unique opportunity to investigate brain network connectivity in a large cohort of normal adult human subjects. The analysis pipeline software and the dynamic connectivity matrices that it generates will all be made freely available to the research community., (Copyright © 2013 Elsevier Inc. All rights reserved.)