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The topological organization of brain networks, or connectomes, can be quantified using graph theory. Here, we investigated brain networks in higher dimensional spaces defined by up to ten node-level graph theoretical invariants. Nodal invariants are intrinsic nodal properties which reflect the topological characteristics of the nodes with respect to the whole network, including segregation (e.g., clustering coefficient) and centrality (e.g., betweenness centrality) measures. Using 100 healthy unrelated subjects from the Human Connectome Project, we generated multiple types of connectomes (structural/functional networks and binary/weighted networks) and embedded the corresponding network nodes (brain regions) into multidimensional graph spaces defined by the invariants. First, we observed that nodal invariants are correlated between them (i.e., they carry similar network information) at a whole-brain and subnetwork level. Second, we conducted a machine learning analysis to test whether brain regions embedded in multidimensional graph spaces can be accurately classified into higher order (association, subcortical and cerebellar) and lower order (visual, somatomotor, attention) areas. Brain regions of higher order and lower order brain circuits were classified with an 80-87% accuracy in a 10-dimensional (10D) space. 10D graph metrics performed better than 2D and 3D graph metrics, and non-linear Gaussian kernels performed better than linear kernels. This suggests a non-linear brain network information gain in a high-dimensional graph space. Finally, we quantified the inter-subject Euclidean distance of each brain region embedded in the multidimensional graph space. The inter-individual distance was largest for regions of the default mode and frontoparietal networks, providing a new avenue for subject-specific network coordinates in a multidimensional space. To conclude, we propose a new framework for quantifying connectome features in multidimensional spaces defined by graph invariants, providing a new avenue for subject-specific network coordinates and inter-individual distance analyses.

Idiopathic Normal Pressure Hydrocephalus (iNPH)—the leading cause of reversible dementia in aging—is characterized by ventriculomegaly and gait, cognitive and urinary impairments. Despite its high prevalence estimated at 6% among the elderlies, iNPH remains underdiagnosed and undertreated due to the lack of iNPH‐specific diagnostic markers and limited understanding of pathophysiological mechanisms. INPH diagnosis is also complicated by the frequent occurrence of comorbidities, the most common one being Alzheimer's disease (AD). Here we investigate the resting‐state functional magnetic resonance imaging dynamics of 26 iNPH patients before and after a CSF tap test, and of 48 normal older adults. Alzheimer's pathology was evaluated by CSF biomarkers. We show that the interactions between the default mode, and the executive‐control, salience and attention networks are impaired in iNPH, explain gait and executive disturbances in patients, and are not driven by AD‐pathology. In particular, AD molecular biomarkers are associated with functional changes distinct from iNPH functional alterations. Finally, we demonstrate a partial normalization of brain dynamics 24 hr after a CSF tap test, indicating functional plasticity mechanisms. We conclude that functional changes involving the default mode cross‐network interactions reflect iNPH pathophysiological mechanisms and track treatment response, possibly contributing to iNPH differential diagnosis and better clinical management., Idiopathic normal pressure hydrocephalus (iNPH), the leading cause of reversible dementia in ageing, is a prevalent neurological disorder characterised by gait, urinary, and cognitive impairments with ventriculomegaly. We show that iNPH patients have changes of brain functional dynamics which explain gait and executive deficits, are partially reverted by a CSF tap test, and are not driven by Alzheimer's disease or cerebrovascular comorbidities. Our results shed new light on the neurobiological substrates of iNPH and functional plasticity mechanisms following an invasive intervention, with possible implications for iNPH differential diagnosis and better clinical management.

Brain communication, defined as information transmission through white-matter connections, is at the foundation of the brain’s computational capacities that virtually subtend all aspects of behavior: from sensory perception shared across mammalian species, to complex cognitive functions in humans. How did communication strategies in macroscale brain networks adapted across evolution to accomplish increasingly complex functions? By applying a novel approach to measure information transmission in mouse, macaque and human brains, we found an evolutionary gradient from selective information processing, where brain regions share information through single polysynaptic pathways, to parallel information processing, where regions communicate through multiple parallel pathways. In humans, parallel processing acts as a major connector between unimodal and transmodal systems. Communication strategies are unique to individuals across different mammalian species, pointing at the individual-level specificity of information routing architecture. Our work provides compelling evidence that different communication strategies are tied to the evolutionary complexity of mammalian brain networks.

We conducted a cross-sectional pilot study to explore the biological substrate of the Motoric Cognitive Risk (MCR) syndrome in a Memory Clinic cohort, using a multimodal imaging approach. Twenty participants were recruited and classified as MCR+/−. Amyloid- and tau-PET uptakes, temporal atrophy, white matter hyperintensities, lateral ventricular volume (LVV), and diffusion tensor parameters were compared between groups. No significant differences were found in imaging features related to Alzheimer’s disease or gross vascular damage. MCR+ patients had increased LVV and altered diffusion parameters in the superior corona radiata. Ventricular enlargement and microstructural damage of the surrounding white matter tracts could contribute to MCR pathophysiology.

Neuropsychological deficits and brain damage following SARS-CoV-2 infection are not well understood. 110 patients, with either severe, moderate or mild disease in the acute phase underwent neuropsychological and olfactory tests, as well as completed psychiatric and respiratory questionnaires at 223 ± 42 days post-infection. Additionally, a subgroup of 50 patients underwent functional magnetic resonance imaging. Patients in the severe group displayed poorer verbal episodic memory performances, and moderate patients had reduced mental flexibility. Neuroimaging revealed patterns of hypo and hyper functional connectivity in severe patients, while only hyperconnectivity patterns were observed for moderate. The default mode, somatosensory, dorsal attention and cerebellar networks were implicated. Partial least squares correlations analysis confirmed specific association between memory performances and brain functional connectivity. The severity of the infection in the acute phase is a predictor of neuropsychological post-COVID syndrome. SARS-CoV-2 infection causes long-term memory and executive dysfunctions, related to largescale functional brain connectivity alterations.

Idiopathic normal pressure hydrocephalus (iNPH) is a prevalent reversible neurological disorder characterized by impaired locomotion, cognition and urinary control with ventriculomegaly. Symptoms can be relieved with cerebrospinal fluid drainage, which makes iNPH the leading cause of reversible dementia. Because of a limited understanding of pathophysiological mechanisms, unspecific symptoms and the high prevalence of comorbidity (i.e. Alzheimer's disease), iNPH is largely underdiagnosed. For these reasons, there is an urgent need for developing noninvasive quantitative biomarkers for iNPH diagnosis and prognosis. Structural and functional changes of brain circuits in relation to symptoms and treatment response are expected to deliver major advances in this direction. We review structural and functional brain connectivity findings in iNPH and complement those findings with iNPH symptom meta-analyses in healthy populations. Our goal is to reinforce our conceptualization of iNPH as to brain network mechanisms and foster the development of new hypotheses for future research and treatment options.

Objective To assess whether gait, neuropsychological, and multimodal MRI parameters predict short-term symptom reversal after cerebrospinal fluid (CSF) tap test in idiopathic normal pressure hydrocephalus (iNPH). Methods Thirty patients (79.3 ± 5.9 years, 12 women) with a diagnosis of probable iNPH and 46 healthy controls (74.7 ± 5.4 years, 35 women) underwent comprehensive neuropsychological, quantitative gait, and multimodal MRI assessments of brain morphology, periventricular white-matter microstructure, cortical and subcortical blood perfusion, default mode network function, and white-matter lesion load. Responders were defined as an improvement of at least 10% in walking speed or timed up and go test 24 h after tap test. Univariate and multivariable tap test outcome prediction models were evaluated with logistic regression and linear support vector machine classification. Results Sixteen patients (53%) respondedpositively to tap test. None of the gait, neuropsychological, or neuroimaging parameters considered separately predicted outcome. A multivariable classifier achieved modest out-of-sample outcome prediction accuracy of 70% (p = .028); gait parameters, white-matter lesion load and periventricular microstructure were the main contributors. Conclusions Our negative findings show that short-term symptom reversal after tap test cannot be predicted from single gait, neuropsychological, or MRI parameters, thus supporting the use of tap test as prognostic procedure. However, multivariable approaches integrating non-invasive multimodal data are informative of outcome and may be included in patient-screening procedures. Their value in predicting shunting outcome should be further explored, particularly in relation to gait and white-matter parameters.

Lack of awareness of cognitive impairment (i.e. anosognosia) could be a key factor for distinguishing between neuropsychological post-COVID-19 condition phenotypes. In this context, the 2-fold aim of the present study was to (i) establish the prevalence of anosognosia for memory impairment, according to the severity of the infection in the acute phase and (ii) determine whether anosognosic patients with post-COVID syndrome have a different cognitive and psychiatric profile from nosognosic patients, with associated differences in brain functional connectivity. A battery of neuropsychological, psychiatric, olfactory, dyspnoea, fatigue and quality-of-life tests was administered 227.07 ± 42.69 days post-SARS-CoV-2 infection to 102 patients (mean age: 56.35 years, 65 men, no history of neurological, psychiatric, neuro-oncological or neurodevelopmental disorder prior to infection) who had experienced either a mild (not hospitalized; n = 45), moderate (conventional hospitalization; n = 34) or severe (hospitalization with intensive care unit stay and mechanical ventilation; n = 23) presentation in the acute phase. Patients were first divided into two groups according to the presence or absence of anosognosia for memory deficits (26 anosognosic patients and 76 nosognosic patients). Of these, 49 patients underwent an MRI. Structural images were visually analysed, and statistical intergroup analyses were then performed on behavioural and functional connectivity measures. Only 15.6% of patients who presented mild disease displayed anosognosia for memory dysfunction, compared with 32.4% of patients with moderate presentation and 34.8% of patients with severe disease. Compared with nosognosic patients, those with anosognosia for memory dysfunction performed significantly more poorly on objective cognitive and olfactory measures. By contrast, they gave significantly more positive subjective assessments of their quality of life, psychiatric status and fatigue. Interestingly, the proportion of patients exhibiting a lack of consciousness of olfactory deficits was significantly higher in the anosognosic group. Functional connectivity analyses revealed a significant decrease in connectivity, in the anosognosic group as compared with the nosognosic group, within and between the following networks: the left default mode, the bilateral somatosensory motor, the right executive control, the right salient ventral attention and the bilateral dorsal attention networks, as well as the right Lobules IV and V of the cerebellum. Lack of awareness of cognitive disorders and, to a broader extent, impairment of the self-monitoring brain system, may be a key factor for distinguishing between the clinical phenotypes of post-COVID syndrome with neuropsychological deficits.

Although shared behavioral and neural mechanisms between working memory (WM) and motor sequence learning (MSL) have been suggested, the additive and interactive effects of training have not been studied. This study aimed at investigating changes in brain functional connectivity (FC) induced by sequential (WM + MSL and MSL + WM) and combined (WM × MSL) training programs. 54 healthy subjects (27 women; mean age: 30.2 ± 8.6 years) allocated to three training groups underwent twenty-four 40-min training sessions over 6 weeks and four cognitive assessments including functional MRI. A double-baseline approach was applied to account for practice effects. Test performances were compared using linear mixed-effects models and t-tests. Resting state fMRI data were analysed using FSL. Processing speed, verbal WM and manual dexterity increased following training in all groups. MSL + WM training led to additive effects in processing speed and verbal WM. Increased FC was found after training in a network including the right angular gyrus, left superior temporal sulcus, right superior parietal gyrus, bilateral middle temporal gyri and left precentral gyrus. No difference in FC was found between double baselines. Results indicate distinct patterns of resting state FC modulation related to sequential and combined WM and MSL training suggesting a relevance of the order of training performance. These observations could provide new insight for the planning of effective training/rehabilitation.

The human auditory cortex comprises the supratemporal plane and large parts of the temporal and parietal convexities. We have investigated the relevant intrahemispheric cortico-cortical connections using in vivo DSI tractography combined with landmark-based registration, automatic cortical parcellation and whole-brain structural connection matrices in 20 right-handed male subjects. On the supratemporal plane, the pattern of connectivity was related to the architectonically defined early-stage auditory areas. It revealed a three-tier architecture characterized by a cascade of connections from the primary auditory cortex to six adjacent non-primary areas and from there to the superior temporal gyrus. Graph theory-driven analysis confirmed the cascade-like connectivity pattern and demonstrated a strong degree of segregation and hierarchy within early-stage auditory areas. Putative higher-order areas on the temporal and parietal convexities had more widely spread local connectivity and long-range connections with the prefrontal cortex; analysis of optimal community structure revealed five distinct modules in each hemisphere. The pattern of temporo-parieto-frontal connectivity was partially asymmetrical. In conclusion, the human early-stage auditory cortical connectivity, as revealed by in vivo DSI tractography, has strong similarities with that of non-human primates. The modular architecture and hemispheric asymmetry in higher-order regions is compatible with segregated processing streams and lateralization of cognitive functions.

Encephalopathy is a neurological complication of COVID-19. The objective of this exploratory study is to investigate the link between systemic inflammation and brain microstructural changes (measured by diffusion-weighted imaging) in patients with COVID-19 encephalopathy. 20 patients with COVID-19 encephalopathy (age: 67.3 $$\pm$$ ± 10.0 years; 90% men) hospitalized in the Geneva University Hospitals for a SARS-CoV-2 infection between March and May 2020 were included in this retrospective cohort study. COVID-19 encephalopathy was diagnosed following a comprehensive neurobiological evaluation, excluding common causes of delirium, such as hypoxemic or metabolic encephalopathy. We investigated the correlation between systemic inflammation (measured by systemic C-reactive protein (CRP)) and brain microstructural changes in radiologically normal white matter (measured by apparent diffusion coefficient (ADC)) in nine spatially widespread regions of the white matter previously associated with delirium. Systemic inflammation (CRP = 60.8 ± 50.0 mg/L) was positively correlated with ADC values in the anterior corona radiata (p = 0.0089), genu of the corpus callosum (p = 0.0064) and external capsule (p = 0.0086) after adjusting for patients’ age. No statistically significant association between CRP and ADC was found in the other six white matter regions. Our findings indicate high risk of white matter abnormalities in COVID-19 encephalopathy patients with high peripheral inflammatory markers, suggesting aggressive imaging monitoring may be warranted in these patients. Future studies should clarify a possible specificity of the spatial patterns of CRP–white matter microstructure association in COVID-19 encephalopathy patients and disentangle the role of individual cytokines on brain inflammatory mechanisms.

Fluctuations in functional interactions between brain regions typically occur at the millisecond time scale. Conventional connectivity metrics are not adequately time-resolved to detect such fast fluctuations in functional connectivity. At the same time, attempts to use conventional metrics in a time-resolved manner usually come with the selection of sliding windows of fixed arbitrary length. In the current work, we evaluated the use of high temporal resolution metrics of functional connectivity in conjunction with non-negative tensor factorisation to detect fast fluctuations in connectivity and temporally evolving subnetworks. To this end, we used the phase difference derivative, wavelet coherence, and we also introduced a new metric, the instantaneous amplitude correlation. In order to deal with the inherently noisy nature of magnetoencephalography data and large datasets, we make use of recurrence plots and we used pair-wise orthogonalisation to avoid spurious estimates of functional connectivity due to signal leakage. Firstly, metrics were evaluated in the context of dynamically coupled neural mass models in the presence and absence of delays and also compared to conventional static metrics with fixed sliding windows. Simulations showed that these high temporal resolution metrics outperformed conventional static connectivity metrics. Secondly, the sensitivity of the metrics to fluctuations in connectivity was analysed in post-movement beta rebound magnetoencephalography data, which showed time locked sensorimotor subnetworks that modulated with the post-movement beta rebound. Finally, sensitivity of the metrics was evaluated in resting-state magnetoencephalography, showing similar spatial patterns across metrics, thereby indicating the robustness of the current analysis. The current methods can be applied in cognitive experiments that involve fast modulations in connectivity in relation to cognition. In addition, these methods could also be used as input to temporal graph analysis to further characterise the rapid fluctuation in brain network topology.

Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and fingerprinting, that is identifying individuals within a large group. Importantly, these brain signatures do not account for the underlying brain anatomy on which brain function takes place. Structure-function coupling based on graph signal processing (GSP) has recently revealed a meaningful spatial gradient from unimodal to transmodal regions, on average in healthy subjects during resting-state. Here, we explore the potential of GSP to introduce new imaging-based biomarkers to characterize tasks and individuals. We used multimodal magnetic resonance imaging of 100 unrelated healthy subjects from the Human Connectome Project both during rest and seven different tasks and adopted a support vector machine classification approach for both decoding and fingerprinting, with various cross-validation settings. We found that structurefunction coupling measures allow accurate classifications for both task decoding and fingerprinting. In particular, key information for fingerprinting is found in the more liberal portion of functional signals, that is the one decoupled from structure. A network mainly involving cortico-subcortical connections showed the strongest correlation with cognitive traits, assessed with partial least square analysis, corroborating its relevance for fingerprinting. By introducing a new perspective on GSP-based signal filtering and FC decomposition, these results show that brain structure-function coupling provides a new class of signatures of cognition and individual brain organization at rest and during tasks. Further, they provide insights on clarifying the role of low and high spatial frequencies of the structural connectome, leading to new understanding of where key structure-function information for characterizing individuals can be found across the structural connectome graph spectrum.Highlights-The relation of brain function with the underlying structural wiring is complex-We propose new structure-informed graph signal processing (GSP) of functional data-GSP-derived features allow accurate task decoding and individual fingerprinting-Functional connectivity from filtered data is more unique to subject and cognition-The role of structurally aligned and liberal graph frequencies is elucidated

Psychosis, characterized by hallucinations and delusions, is a common feature of psychiatric disease, especially schizophrenia. One prominent theory posits that psychosis is driven by abnormal sensorimotor predictions leading to the misattribution of self-related events. This misattribution has been linked to passivity experiences (PE), such as loss of agency and, more recently, to presence hallucinations (PH), defined as the conscious experience of the presence of an alien agent while no person is actually present. PH has been observed in schizophrenia, Parkinson’s disease, and neurological patients with brain lesions and, recently, the brain mechanisms of PH (PH-network) have been determined comprising bilateral posterior middle temporal gyrus (pMTG), inferior frontal gyrus (IFG), and ventral premotor cortex (vPMC). Given that the experience of an alien agent is a common feature of PE, we here analyzed the functional connectivity within the PH-network in psychotic patients with (N = 39) vs without PE (N = 26). We observed reduced fronto-temporal functional connectivity in patients with PE compared to patients without PE between the right pMTG and the right and left IFG of the PH-network. Moreover, when seeding from these altered regions, we observed specific alterations with brain regions commonly linked to auditory-verbal hallucinations (such as Heschl’s gyrus). The present connectivity findings within the PH-network extend the disconnection hypothesis for hallucinations to the specific case of PH and associates the PH-network with key brain regions for frequent psychotic symptoms such as auditory-verbal hallucinations, showing that PH are relevant to the study of the brain mechanisms of psychosis and PE.

Individual characterization of subjects based on their functional connectome (FC), termed “FC fingerprinting”, has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprints in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identification offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.

Background Idiopathic normal pressure hydrocephalus (iNPH) presents typical radiological signs that have been summarised in a semi-quantitative scale named the iNPH Radscale. However, the iNPH Radscale's predictive value for response to cerebrospinal fluid (CSF) tap test has never been studied. This study aims to investigate if the iNPH Radscale can predict locomotion improvement after CSF tap test. Methods A total of 100 patients with iNPH (age: 76.3 ± 7.9, gender: 36% female) were included in this retrospective study. Two raters, blinded to the response of the CSF tap test, evaluated the iNPH Radscale and its seven subitems (Evan's index, callosal angle, size of temporal horns, narrow high-convexity sulci, dilated Sylvian fissures, focally dilated sulci, and periventricular hypodensities). Locomotion improvement was assessed by the Timed Up and Go (TUG) performed before, and 24 h after, the CSF tap test. Results The iNPH Radscale (total score) was similar between the CSF tap test responders and non-responders (responders: 8.31 ± 1.96, non-responders: 9.18 ± 2.51, p = 0.128). However, the temporal horns score was smaller in the responders group (1.66 ± 0.57 versus 1.94 ± 0.24, p = 0.045), even after adjusting for age, gender, education level, white matter changes, and global cognition (β: -0.250, C.I. 95%: [−3.185; −0.161], p = 0.031). Conclusion The iNPH Radscale (total score) does not predict locomotion improvement after CSF tap test, while a smaller temporal horns score at baseline is associated with a positive tap test responder status.

Late human development is characterized by the maturation of high-level functional processes, which rely on reshaping of white matter connections, as well as synaptic density. However, the relationship between the whole-brain dynamics and the underlying white matter networks in neurodevelopment is largely unknown. In this study, we focused on how the structural connectome shapes the emerging dynamics of cerebral development between the ages of 6 and 33 years, using functional and diffusion magnetic resonance imaging combined into a spatiotemporal connectivity framework. We defined two new measures of brain dynamics, namely the system diversity and the spatiotemporal diversity, which quantify the level of integration/segregation between functional systems and the level of temporal self-similarity of the functional patterns of brain dynamics, respectively. We observed a global increase in system diversity and a global decrease and local refinement in spatiotemporal diversity values with age. In support of these findings, we further found an increase in the usage of long-range and inter-system white matter connectivity and a decrease in the usage of short-range connectivity with age. These findings suggest that dynamic functional patterns in the brain progressively become more integrative and temporally self-similar with age. These functional changes are supported by a greater involvement of long-range and inter-system axonal pathways., AUTHOR SUMMARY, Maturation in human development is represented by changes in both functional dynamics and structural connectivity in the human brain. By constructing a spatiotemporal connectome for a cohort of 81 subjects ranging from 6 to 33 years of age, we demonstrate how these changes can be studied in a unified framework. We do so by defining two new measures of brain dynamics, namely the spatiotemporal diversity, mapping the level of temporal self-similarity of the functional patterns of brain dynamics, and system diversity, quantifying the level of integration/segregation between functional systems. These measures, we argue, represent a novel way of looking at brain dynamics constraints by structural connectivity. Using these measures, we show that dynamic functional patterns in the brain progressively become more integrative and temporally self-similar with age.

Over the past decades, network neuroscience has played a fundamental role in the understanding of large-scale brain connectivity architecture. Brains, and more generally nervous systems, can be modeled as sets of elements (neurons, assemblies, or cortical chunks) that dynamically interact through a highly structured and adaptive neurocircuitry. An interesting property of neural networks is that elements rich in connections are central to the network organization and tend to interconnect strongly with each other, forming so-called rich clubs. The ubiquity of rich-club organization across different species and scales of investigation suggests that this topology could be a distinctive feature of biological systems with information processing capabilities. This review surveys recent neuroimaging, computational, and cross-species comparative literature to offer an insight into the function and origin of rich-club architecture in nervous systems, discussing its relevance to human cognition and behavior, and vulnerability to brain disorders.

Stochastic resonance is a phenomenon in which noise enhances the response of a system to an input signal. The brain is an example of a system that has to detect and transmit signals in a noisy environment, suggesting that it is a good candidate to take advantage of stochastic resonance. In this work, we aim to identify the optimal levels of noise that promote signal transmission through a simple network model of the human brain. Specifically, using a dynamic model implemented on an anatomical brain network (connectome), we investigate the similarity between an input signal and a signal that has traveled across the network while the system is subject to different noise levels. We find that non-zero levels of noise enhance the similarity between the input signal and the signal that has traveled through the system. The optimal noise level is not unique; rather, there is a set of parameter values at which the information is transmitted with greater precision, this set corresponds to the parameter values that place the system in a critical regime. The multiplicity of critical points in our model allows it to adapt to different noise situations and remain at criticality.

The study of brain dynamics enables us to characterize the time-varying functional connectivity among distinct neural groups. However, current methods suffer from the absence of structural connectivity information. We propose to integrate infra-slow neural oscillations and anatomical-connectivity maps, as derived from functional and diffusion MRI, in a multilayer-graph framework that captures transient networks of spatio-temporal connectivity. These networks group anatomically wired and temporary synchronized brain regions and encode the propagation of functional activity on the structural connectome. In a group of 71 healthy subjects, we find that these transient networks demonstrate power-law spatial and temporal size, globally organize into well-known functional systems and describe wave-like trajectories of activation across anatomically connected regions. Within the transient networks, activity propagates through polysynaptic paths that include selective ensembles of structural connections and differ from the structural shortest paths. In the light of the communication-through-coherence principle, the identified spatio-temporal networks could encode communication channels' selection and neural assemblies, which deserves further attention. This work contributes to the understanding of brain structure-function relationships by considering the time-varying nature of resting-state interactions on the axonal scaffold, and it offers a convenient framework to study large-scale communication mechanisms and functional dynamics.

Aim Impulsive behaviours, which are frequent in young people suffering from psychosis have been linked to risky and violent behaviours and participate to the burden of psychotic illness. Given that morphological brain correlates of impulsivity in schizophrenia have been poorly investigated especially in young adults, the aim of this study was to investigate the relationship between impulsivity and cortical thickness in early psychosis (EP) patients. Method A total of 17 male subjects in the early phase of psychosis were recruited. Impulsivity was assessed with the Lecrubier Impulsivity Rating Scale. Mean cortical thickness was extracted from magnetic resonance imaging brain scans, using surface-based methods. Results Mean cortical thickness in the frontal lobe correlated positively with mean impulsivity in EP male patients. Conclusion Our results suggest that psychotic subjects exhibiting higher impulsivity have larger frontal cortical thickness, which may pave the way towards the identification of patients with a higher risk to display impulsive behaviours.

Background Psychosis is often depicted as a disruption of the self-model. Patients suffering from psychosis report many symptoms relating to deficiencies in the minimal self, including loss of the sense of control over their actions (Sense of Agency) as well as numerous disturbances of body representation (e.g. Body Ownership). Positive symptoms of psychosis such as passivity symptoms and auditory hallucinations (termed first-rank symptoms) are characterized by a diminished demarcation of self-other boundaries, causing misattribution of self-generated actions to external sources. It has been suggested that this deficiency in self-monitoring in schizophrenia is due to abnormal sensorimotor prediction mechanisms, causing a loss of agency for actions and thoughts. While the neurobiological underpinnings of schizophrenia are yet unclear, many studies have reported aberrant neural connectivity in schizophrenia patients which may impact sensorimotor prediction and integration. Methods Recently, we have shown that introducing sensorimotor conflict (SMC) can induce psychosis like symptoms in healthy patients. Employing a master-slave robotic system we induced a conflict by introducing a delay between the participants’ movements and the haptic feedback. The SMC caused a feeling of a Presence (FoP) which is a first rank symptom of psychosis. The FoP is also found in neurological patients with lesions in cortical regions of the temporoparietal cortex, insular cortex and fronto-parietal cortex related to abnormal bodily self-representation. Here, we tested if SMC may cause misattribution of auditory stimuli and if this is related to neural connectivity. We tested first episode psychosis patients (N=31) with and without first rank symptoms (related to the sense of control over actions and thoughts) as well as healthy participants (N=20) on an auditory attribution task while inducing conflict with the master-slave robot. Results We found that when a SMC was introduced patients with first rank symptoms showed a decrease in their ability to judge if the auditory stimuli were in their own voice or the voice of another person. Resting state functional connectivity analysis indicated that the first rank patients had reduced connectivity in the network related to the SMC, but not in other control regions. Furthermore, the reduced functional connectivity correlated with the rates of auditory misattribution. Conclusions Our results show that induction of SMC can cause auditory misattributions, and that this is related to reduced cortical communication in regions related to sensorimotor body representation. These findings connect two influential theories of psychosis linking cognitive theories of sensorimotor prediction error in schizophrenia with systems level theories of neural disconnectivity. Understanding these neurocognitive mechanisms underlying the disruption of the self-model in psychosis may allow novel approaches in early diagnosis and treatment of these conditions.

Emerging evidence indicates that a disruption in brain network organization may play an important role in the pathophysiology of schizophrenia. The neuroimaging fingerprint reflecting the pathophysiology of first-episode schizophrenia remains to be identified. Here, we aimed at characterizing the connectome organization of first-episode medication-naïve patients with schizophrenia. A cross-sectional structural and functional neuroimaging study using two independent samples (principal dataset including 42 medication-naïve, previously untreated patients and 48 healthy controls; replication dataset including 39 first-episode patients [10 untreated patients] and 66 healthy controls) was performed. Brain network architecture was assessed by means of white matter fiber integrity measures derived from diffusion-weighted imaging (DWI) and by means of structural-functional (SC-FC) coupling measured by combining DWI and resting-state functional magnetic resonance imaging. Connectome rich club organization was found to be significantly disrupted in medication-naïve patients as compared with healthy controls (P = .012, uncorrected), with rich club connection strength (P = .032, uncorrected) and SC-FC coupling (P < .001, corrected for false discovery rate) decreased in patients. Similar results were found in the replication dataset. Our findings suggest that a disruption of rich club organization and functional dynamics may reflect an early feature of schizophrenia pathophysiology. These findings add to our understanding of the neuropathological mechanisms of schizophrenia and provide new insights into the early stages of the disorder.