Disruption of structural brain networks in Primary Open Angle Glaucoma.

Primary open angle glaucoma (POAG) is one of the most common causes of permanent blindness in the world. Recent studies have originated the hypothesis that POAG could be considered as a central nervous system pathology which results in secondary visual involvement. The aim of this study is to assess possible structural whole brain connectivity alterations in POAG by combining multi-shell diffusion weighted imaging, multi-shell multi-tissue probabilistic tractography, graph theoretical measures and a newly designed disruption index, which evaluates the global reorganization of brain networks in group-wise comparisons. We found global differences in structural connectivity between Glaucoma patients and controls, as well as in local graph theoretical measures. These changes extended well beyond the primary visual pathway. Furthermore, group-wise and subject-wise disruption indices were found to be statistically different between glaucoma patients and controls, with a positive slope. Overall, our results support the hypothesis of a whole-brain structural reorganization in glaucoma which is specific to structural connectivity, possibly placing this disease within the recently defined groups of brain disconnection syndrome.

Time-resolved connectome of the five-factor model of personality.

The human brain is characterized by highly dynamic patterns of functional connectivity. However, it is unknown whether this time-variant 'connectome' is related to the individual differences in the behavioural and cognitive traits described in the five-factor model of personality. To answer this question, inter-network time-variant connectivity was computed in n = 818 healthy people via a dynamical conditional correlation model. Next, network dynamicity was quantified throughout an ad-hoc measure (T-index) and the generalizability of the multi-variate associations between personality traits and network dynamicity was assessed using a train/test split approach. Conscientiousness, reflecting enhanced cognitive and emotional control, was the sole trait linked to stationary connectivity across several circuits such as the default mode and prefronto-parietal network. The stationarity in the 'communication' across large-scale networks offers a mechanistic description of the capacity of conscientious people to 'protect' non-immediate goals against interference over-time. This study informs future research aiming at developing more realistic models of the brain dynamics mediating personality differences.

Neuroinflammation and Functional Connectivity in Alzheimer's Disease: Interactive Influences on Cognitive Performance.

Neuroinflammation is a key part of the etio-pathogenesis of Alzheimer's disease (AD). We tested the relationship between neuroinflammation and the disruption of functional connectivity in large-scale networks, and their joint influence on cognitive impairment. We combined [11C]PK11195 positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI) in 28 patients (12 females/16 males) with clinical diagnosis of probable AD or mild cognitive impairment with positive PET biomarker for amyloid, and 14 age-, sex-, and education-matched healthy controls (8 females/6 males). Source-based "inflammetry" was used to extract principal components of [11C]PK11195 PET signal variance across all participants. rs-fMRI data were preprocessed via independent component analyses to classify neuronal and non-neuronal signals. Multiple linear regression models identified sources of signal covariance between neuroinflammation and brain connectivity profiles, in relation to the diagnostic group (patients, controls) and cognitive status.Patients showed significantly higher [11C]PK11195 binding relative to controls, in a distributed spatial pattern including the hippocampus, frontal, and inferior temporal cortex. Patients with enhanced loading on this [11C]PK11195 binding distribution displayed diffuse abnormal functional connectivity. The expression of a stronger association between such abnormal connectivity and higher levels of neuroinflammation correlated with worse cognitive deficits.Our study suggests that neuroinflammation relates to the pathophysiological changes in network function that underlie cognitive deficits in Alzheimer's disease. Neuroinflammation, and its association with functionally-relevant reorganization of brain networks, is proposed as a target for emerging immunotherapeutic strategies aimed at preventing or slowing the emergence of dementia.SIGNIFICANCE STATEMENT Neuroinflammation is an important aspect of Alzheimer's disease (AD), but it was not known whether the influence of neuroinflammation on brain network function in humans was important for cognitive deficit. Our study provides clear evidence that in vivo neuroinflammation in AD impairs large-scale network connectivity; and that the link between neuro inflammation and functional network connectivity is relevant to cognitive impairment. We suggest that future studies should address how neuroinflammation relates to network function as AD progresses, and whether the neuroinflammation in AD is reversible, as the basis of immunotherapeutic strategies to slow the progression of AD.

The central autonomic network at rest: Uncovering functional MRI correlates of time-varying autonomic outflow.

Peripheral measures of autonomic nervous system (ANS) activity at rest have been extensively employed as putative biomarkers of autonomic cardiac control. However, a comprehensive characterization of the brain-based central autonomic network (CAN) sustaining cardiovascular oscillations at rest is missing, limiting the interpretability of these ANS measures as biomarkers of cardiac control. We evaluated combined cardiac and fMRI data from 34 healthy subjects from the Human Connectome Project to detect brain areas functionally linked to cardiovagal modulation at rest. Specifically, we combined voxel-wise fMRI analysis with instantaneous heartbeat and spectral estimates obtained from inhomogeneous linear point-process models. We found exclusively negative associations between cardiac parasympathetic activity at rest and a widespread network including bilateral anterior insulae, right dorsal middle and left posterior insula, right parietal operculum, bilateral medial dorsal and ventrolateral posterior thalamic nuclei, anterior and posterior mid-cingulate cortex, medial frontal gyrus/pre-supplementary motor area. Conversely, we found only positive associations between instantaneous heart rate and brain activity in areas including frontopolar cortex, dorsomedial prefrontal cortex, anterior, middle and posterior cingulate cortices, superior frontal gyrus, and precuneus. Taken together, our data suggests a much wider involvement of diverse brain areas in the CAN at rest than previously thought, which could reflect a differential (both spatially and directionally) CAN activation according to the underlying task. Our insight into CAN activity at rest also allows the investigation of its impairment in clinical populations in which task-based fMRI is difficult to obtain (e.g., comatose patients or infants).

Resting State Neural Correlates of Cardiac Sympathetic Dynamics in Healthy Subjects.

Recent advances in functional Magnetic Resonance Imaging (fMRI) research have uncovered the existence of the central autonomic network (CAN), which comprises brain regions whose activity correlates with autonomic nervous system dynamics. By exploiting the spectral paradigm of heartbeat dynamics, cortical and sub-cortical areas functionally linked to vagal activity have been identified. However, due to methodological limitations, functional neural correlates of cardiac sympathetic dynamics remain uncharacterized. To this extent, we exploit the high spatiotemporal resolution of fMRI data from the Human Connectome Project to study the CAN activity by correlating a recently proposed instantaneous characterization of sympathetic activity (the sympathetic activity index - SAI) from heartbeat dynamics. SAI estimates are embedded into the probabilistic modeling of inhomogeneous point-processes, and are derived from a combination of disentangling coefficients linked to the orthonormal Laguerre functions. By analyzing resting state recordings from 34 young healthy people, we obtain positive correlations between instantaneous SAI estimates and a number of brain regions including frontal pole, insular cortex, frontal and temporal gyri, lateral occipital cortex, paracingulate and cingulate gyri, precuneus and temporal fusiform cortices, as well as thalamus, caudate nucleus, putamen, brain-stem, hippocampus, amygdala, and nucleus accumbens. Our findings significantly extend current knowledge on the CAN, opening new avenues in the characterization of healthy and pathological states in humans.

Reduced betweenness centrality of a sensory-motor vestibular network in subclinical agoraphobia.

Agoraphobic patients feel dizzy in crowded open spaces and respond to this symptom with excessive fear and avoidance. These clinical features show great similitude with the newly defined syndrome of persistent postural perceptual dizziness (PPPD). Patients with PPPD show decreased activity and connectivity in regions of the vestibular cortex. Due to the great overlap between these two conditions, we hypothesized that individuals with sub-clinical agoraphobia would show reduction in the connectivity features of these regions. We selected a group of healthy individuals from the Human Connectome Project that self-reported agoraphobia episodes, and compared it with a control group. We accurately matched the two groups for psychological measures and personality traits in order to study the neural correlates of vestibular symptoms independently of possible psychiatric vulnerabilities. We found that the agoraphobia group showed reduced betweenness centrality of a network encompassing key regions of the vestibular cortex. Dysfunctions of the vestibular cortex may explain the dizziness symptom for a disorder previously labelled as psychogenic.

Structural connectome of the human vestibular, pre-motor, and navigation network.

The aim of this study is to characterize modules and hubs within the multimodal vestibular system and, particularly, to test the centrality of posterior peri-sylvian regions. Structural connectivity matrices from 50 unrelated healthy right-handed subjects from the Human Connectome Project (HCP) database were analyzed using multishell diffusion-weighted data, probabilistic tractography (constrained spherical-deconvolution informed filtering of tractograms) in combination with subject-specific grey matter parcellations. Network nodes included parcellated regions within the vestibular, pre-motor and navigation system. Module calculation produced two and three modules in the right and left hemisphere, respectively. On the right, regions were grouped into a vestibular and pre-motor module, and into a visual-navigation module. On the left this last module was split into an inferior and superior component. In the thalamus, a region comprising the mediodorsal and anterior complex, and lateral and inferior pulvinar, was included in the ipsilateral navigation module, while the remaining thalamus was clustered with the ipsilateral vestibular pre-motor module. Hubs were located bilaterally in regions encompassing the inferior parietal cortex and the precuneus. This analysis revealed a dorso-lateral path within the multi-modal vestibular system related to vestibular / motor control, and a ventro-medial path related to spatial orientation / navigation. Posterior peri-sylvian regions may represent the main hubs of the whole modular network.

Functional connectome of the five-factor model of personality.

A key objective of the emerging field of personality neuroscience is to link the great variety of the enduring dispositions of human behaviour with reliable markers of brain function. This can be achieved by analyzing large sets of data with methods that model whole-brain connectivity patterns. To meet these expectations, we exploited a large repository of personality and neuroimaging measures made publicly available via the Human Connectome Project. Using connectomic analyses based on graph theory, we computed global and local indices of functional connectivity (e.g., nodal strength, efficiency, clustering, betweenness centrality) and related these metrics to the five-factor-model (FFM) personality traits (i.e., neuroticism, extraversion, openness, agreeableness, and conscientiousness). The maximal information coefficient was used to assess for linear and non-linear statistical dependencies across the graph 'nodes', which were defined as distinct brain circuits identified via independent component analysis. Multi-variate regression models and 'train/test' machine-learning approaches were also used to examine the associations between FFM traits and connectomic indices as well as to test for the generalizability of the main findings, whilst accounting for age and sex differences. Conscientiousness was the sole FFM trait linked to measures of higher functional connectivity in the fronto-parietal and default mode networks. This might provide a mechanistic explanation of the behavioural observation that conscientious people are reliable and efficient in goal-setting or planning. Our study provides new inputs to understanding the neurological basis of personality and contributes to the development of more realistic models of the brain dynamics that mediate personality differences.

The neuroanatomical and neurochemical basis of apathy and impulsivity in frontotemporal lobar degeneration.

Apathy and impulsivity are common and often coexistent consequences of frontotemporal lobar degeneration (FTLD). They increase patient morbidity and carer distress, but remain under-estimated and poorly treated. Recent trans-diagnostic approaches that span the spectrum of clinical presentations of FTLD and parkinsonism, indicate that apathy and impulsivity can be fractionated into multiple neuroanatomical and pharmacological systems. These include ventral/dorsal fronto-striatal circuits for reward-sensitivity, response-inhibition, and decision-making; moderated by noradrenaline, dopamine, and serotonin. Improved assessment tools, formal models of cognition and behavior, combined with brain imaging and psycho-pharmacology, are creating new therapeutic targets and establishing principles for stratification in future clinical trials.

Dynamical brain connectivity estimation using GARCH models: An application to personality neuroscience.

It has recently become evident that the functional connectome of the human brain is a dynamical entity whose time evolution carries important information underpinning physiological brain function as well as its disease-related aberrations. While simple sliding window approaches have had some success in estimating dynamical brain connectivity in a functional MRI (fMRI) context, these methods suffer from limitations related to the arbitrary choice of window length and limited time resolution. Recently, Generalized autoregressive conditional heteroscedastic (GARCH) models have been employed to generate dynamical covariance models which can be applied to fMRI. Here, we employ a GARCH-based method (dynamic conditional correlation - DCC) to estimate dynamical brain connectivity in the Human Connectome Project (HCP) dataset and study how the dynamic functional connectivity behaviors related to personality as described by the five-factor model. Openness, a trait related to curiosity and creativity, is the only trait associated with significant differences in the amount of time-variability (but not in absolute median connectivity) of several inter-network functional connections in the human brain. The DCC method offers a novel window to extract dynamical information which can aid in elucidating the neurophysiological underpinning of phenomena to which conventional static brain connectivity estimates are insensitive.

Dynamic inter-network connectivity in the human brain.

Recently, the field of functional brain connectivity has shifted its attention on studying how functional connectivity (FC) between remote regions changes over time. It is becoming increasingly evident that the human "connectome" is a dynamical entity whose variations are effected over very short timescales and reflect crucial mechanisms which underline the physiological functioning of the brain. In this study, we employ ad-hoc statistical and surrogate data generation methods to quantify whether and which brain networks displayed dynamic behaviors in a very large sample of healthy subjects provided by the Human Connectome Project (HCP). Our findings provided evidences that there are specific pairs of networks and specific networks within the healthy brain that are more likely to display dynamic behaviors. This new set of findings supports the notion that studying the time-variant connectivity in the brain could reveal useful and important properties about brain functioning in health and disease.

Resting-state brain correlates of cardiovascular complexity.

While estimates of complex heartbeat dynamics have provided effective prognostic and diagnostic markers for a wide range of pathologies, brain correlates of complex cardiac measures in general and of complex sympatho-vagal dynamics in particular are still unknown. In this study we combine resting state functional Magnetic Resonance Imaging (fMRI) and physiological signal acquisition from 34 healthy subjects selected from the Human Connectome Project (HCP) repository with inhomogeneous point-process approximate and sample heartbeat entropy measures (ipApEn and ipSampEn) to investigate brain areas involved in complex cardiovascular control. Our results show that activity in the Temporal Gyrus, Frontal Orbital Cortex, Temporal Fusiform and Opercular cortices, Planum Temporale, and Paracingulate cortex are negatively correlated with ipApEn dynamics. Activity in the same cortical areas as well as in the Temporal Fusiform cortex are negatively correlated with ipSampEn dynamics. No significant positive correlations were found. These pioneering results suggest that cardiovascular complexity at rest is linked to a few specific cortical brain structures, including crucial areas connected with parasympathetic outflow. This corroborates the hypothesis of a multidimensional central network which controls nonlinear cardiac dynamics under a predominantly vagally-driven tone.

Resting-state brain correlates of instantaneous autonomic outflow.

A prominent pathway of brain-heart interaction is represented by autonomic nervous system (ANS) heartbeat modulation. While within-brain resting state networks have been the object of intense functional Magnetic Resonance Imaging (fMRI) research, technological and methodological limitations have hampered research on the central correlates of cardiovascular control dynamics. Here we combine the high temporal and spatial resolution as well as data volume afforded by the Human Connectome Project with a probabilistic model of heartbeat dynamics to characterize central correlates of sympathetic and parasympathetic ANS activity at rest. We demonstrate an involvement of a number of brain regions such as the Insular cortex, Frontal Gyrus, Lateral Occipital Cortex, Paracingulate and Cingulate Gyrus and Precuneous Cortex, as well as subcortical structures (Thalamus, Putamen, Pallidum, Brain-Stem, Hippocampus, Amygdala, and Right Caudate) in the modulation of ANS-mediated cardiovascular control, possibly indicating a broader definition of the central autonomic network (CAN). Our findings provide a basis for an informed neurobiological interpretation of the numerous studies which employ HRV-related measures as standalone biomarkers in health and disease.

Estimating directed brain-brain and brain-heart connectivity through globally conditioned Granger causality approaches.

While a large body of research has focused on the study of within-brain physiological networks (i.e. brain connectivity) as well as their disease-related aberration, few investigators have focused on estimating the directionality of these brain-brain interaction which, given the complexity of brain networks, should be properly conditioned in order to avoid the high number of false positives commonly encountered when using bivariate approaches to brain connectivity estimation. Additionally, the constituents of a number of brain subnetworks, and in particular of the central autonomic network (CAN), are still not completely determined. In this study we present and validate a global conditioning approach to reconstructing directed networks using complex synthetic networks of nonlinear oscillators. We then employ our framework, along with a probabilistic model for heartbeat generation, to characterize the directed functional connectome of the human brain and to establish which parts of this connectome effect the directed central modulation of peripheral autonomic cardiovascular control. We demonstrate the effectiveness of our conditioning approach and unveil a top-down directed influence of the default mode network on the salience network, which in turn is seen to be the strongest modulator of directed autonomic cardiovascular control.

Simultaneous estimation of the in-mean and in-variance causal connectomes of the human brain.

In recent years, the study of the human connectome (i.e. of statistical relationships between non spatially contiguous neurophysiological events in the human brain) has been enormously fuelled by technological advances in high-field functional magnetic resonance imaging (fMRI) as well as by coordinated world wide data-collection efforts like the Human Connectome Project (HCP). In this context, Granger Causality (GC) approaches have recently been employed to incorporate information about the directionality of the influence exerted by a brain region on another. However, while fluctuations in the Blood Oxygenation Level Dependent (BOLD) signal at rest also contain important information about the physiological processes that underlie neurovascular coupling and associations between disjoint brain regions, so far all connectivity estimation frameworks have focused on central tendencies, hence completely disregarding so-called in-variance causality (i.e. the directed influence of the volatility of one signal on the volatility of another). In this paper, we develop a framework for simultaneous estimation of both in-mean and in-variance causality in complex networks. We validate our approach using synthetic data from complex ensembles of coupled nonlinear oscillators, and successively employ HCP data to provide the very first estimate of the in-variance connectome of the human brain.

Effects of naltrexone are influenced by childhood adversity during negative emotional processing in addiction recovery.

Naltrexone is an opioid receptor antagonist used in the management of alcohol dependence. Although the endogenous opioid system has been implicated in emotion regulation, the effects of mu-opioid receptor blockade on brain systems underlying negative emotional processing are not clear in addiction. Individuals meeting criteria for alcohol dependence alone (n=18, alcohol) and in combination with cocaine and/or opioid dependence (n=21, alcohol/drugs) and healthy individuals without a history of alcohol or drug dependence (n=21) were recruited. Participants were alcohol and drug abstinent before entered into this double-blind, placebo-controlled, randomized, crossover study. Functional magnetic resonance imaging was used to investigate brain response while viewing aversive and neutral images relative to baseline on 50 mg of naltrexone and placebo. We found that naltrexone modulated task-related activation in the medial prefrontal cortex and functional connectivity between the anterior cingulate cortex and the hippocampus as a function of childhood adversity (for aversive versus neutral images) in all groups. Furthermore, there was a group-by-treatment-by-condition interaction in the right amygdala, which was mainly driven by a normalization of response for aversive relative to neutral images under naltrexone in the alcohol/drugs group. We conclude that early childhood adversity is one environmental factor that influences pharmacological response to naltrexone. Pharmacotherapy with naltrexone may also have some ameliorative effects on negative emotional processing in combined alcohol and drug dependence, possibly due to alterations in endogenous opioid transmission or the kappa-opioid receptor antagonist actions of naltrexone.

Chronic subjective dizziness: Analysis of underlying personality factors.

BACKGROUND: Chronic subjective dizziness (CSD) is characterized by persistent dizziness, unsteadiness, and hypersensitivity to one's own motion or exposure to complex visual stimuli. CSD may be triggered, in predisposed individuals with specific personality traits, by acute vestibular diseases. CSD is also thought to arise from failure to re-establish normal balance strategies after resolution of acute vestibular events which may be modulated by diathesis to develop anxiety and depression. OBJECTIVE: To confirm the role of personality traits linked to anxiety and depression (i.e., neuroticism, introversion, low openness) as predisposing factors for CSD and to evaluate how individual differences in these personality traits are associated with CSD severity. METHODS: We compared 19 CSD patients with 24 individuals who had suffered from periferal vestibular disorders (PVD) (i.e., Benign Paroxysmal Postural Vertigo or Vestibular Neuritis) but had not developed CSD as well as with 25 healthy controls (HC) in terms of personality traits, assessed via the NEO-PI-R questionnaire. RESULTS: CSD patients, relative to PVD patients and HCs, scored higher on the anxiety facet of neuroticism. Total neuroticism scores were also significantly associated with dizziness severity in CSD patients but not PVD patients. CONCLUSIONS: Pre-existing anxiety-related personality traits may promote and sustain the initial etiophatogenetic mechanisms linked with the development of CSD. Targeting anxiety-related mechanisms in CSD may be therefore a promising way to reduce the disability associated with CSD.

Neuroinflammatory and morphological changes in late-life depression: the NIMROD study.

We studied neuroinflammation in individuals with late-life depression, as a risk factor for dementia, using [11C]PK11195 positron emission tomography (PET). Five older participants with major depression and 13 controls underwent PET and multimodal 3T magnetic resonance imaging (MRI), with blood taken to measure C-reactive protein (CRP). We found significantly higher CRP levels in those with late-life depression and raised [11C]PK11195 binding compared with controls in brain regions associated with depression, including subgenual anterior cingulate cortex, and significant hippocampal subfield atrophy in cornu ammonis 1 and subiculum. Our findings suggest neuroinflammation requires further investigation in late-life depression, both as a possible aetiological factor and a potential therapeutic target.

Characterizing structural neural networks in de novo Parkinson disease patients using diffusion tensor imaging.

Parkinson disease (PD) can be considered as a brain multisystemic disease arising from dysfunction in several neural networks. The principal aim of this study was to assess whether large-scale structural topological network changes are detectable in PD patients who have not been exposed yet to dopaminergic therapy (de novo patients). Twenty-one drug-naïve PD patients and thirty healthy controls underwent a 3T structural MRI. Next, Diffusion Tensor Imaging (DTI) and graph theoretic analyses to compute individual structural white-matter (WM) networks were combined. Centrality (degree, eigenvector centrality), segregation (clustering coefficient), and integration measures (efficiency, path length) were assessed in subject-specific structural networks. Moreover, Network-based statistic (NBS) was used to identify whether and which subnetworks were significantly different between PD and control participants. De novo PD patients showed decreased clustering coefficient and strength in specific brain regions such as putamen, pallidum, amygdala, and olfactory cortex compared with healthy controls. Moreover, NBS analyses demonstrated that two specific subnetworks of reduced connectivity characterized the WM structural organization of PD patients. In particular, several key pathways in the limbic system, basal ganglia, and sensorimotor circuits showed reduced patterns of communications when comparing PD patients to controls. This study shows that PD is characterized by a disruption in the structural connectivity of several motor and non-motor regions. These findings provide support to the presence of disconnectivity mechanisms in motor (basal ganglia) as well as in non-motor (e.g., limbic, olfactory) circuits at an early disease stage of PD. Hum Brain Mapp 37:4500-4510, 2016. © 2016 Wiley Periodicals, Inc.

Increased functional connectivity within mesocortical networks in open people.

Openness is a personality trait reflecting absorption in sensory experience, preference for novelty, and creativity, and is thus considered a driving force of human evolution. At the brain level, a relation between openness and dopaminergic circuits has been proposed, although evidence to support this hypothesis is lacking. Recent behavioral research has also found that people with mania, a psychopathological condition linked to dopaminergic dysfunctions, may display high levels of openness. However, whether openness is related to dopaminergic circuits has not been determined thus far. We addressed this issue via three functional magnetic resonance imaging (fMRI) experiments in n=46 healthy volunteers. In the first experiment participants lied at rest in the scanner while in the other two experiments they performed active tasks that included the presentation of pleasant odors and pictures of food. Individual differences in openness and other personality traits were assessed via the NEO-PI-R questionnaire (NEO-Personality Inventory-Revised), a widely employed measure of the five-factor model personality traits. Correlation between fMRI and personality data was analyzed via state-of-art methods assessing resting-state and task-related functional connectivity within specific brain networks. Openness was positively associated with the functional connectivity between the right substantia nigra/ventral tegmental area, the major source of dopaminergic inputs in the brain, and the ipsilateral dorsolateral prefrontal cortex (DLPFC), a key region in encoding, maintaining, and updating information that is relevant for adaptive behaviors. Of note, the same connectivity pattern was consistently found across all of the three fMRI experiments. Given the critical role of dopaminergic signal in gating information in DLPFC, the increased functional connectivity within mesocortical networks in open people may explain why these individuals display a wide "mental permeability" to salient stimuli and an increased absorption in sensory experience.