The functional properties of resting brain activity are poorly understood, but have generally been related to self-monitoring and introspective processes. Here we investigated how emotionally positive and negative information differentially influenced subsequent brain activity at rest. We acquired fMRI data in 15 participants during rest periods following fearful, joyful, and neutral movies. Several brain regions were more active during resting than during movie-watching, including posterior/anterior cingulate cortices (PCC, ACC), bilateral insula and inferior parietal lobules (IPL). Functional connectivity at different frequency bands was also assessed using a wavelet correlation approach and small-world network analysis. Resting activity in ACC and insula as well as their coupling were strongly enhanced by preceding emotions, while coupling between ventral-medial prefrontal cortex and amygdala was selectively reduced. These effects were more pronounced after fearful than joyful movies for higher frequency bands. Moreover, the initial suppression of resting activity in ACC and insula after emotional stimuli was followed by a gradual restoration over time. Emotions did not affect IPL average activity but increased its connectivity with other regions. These findings reveal specific neural circuits recruited during the recovery from emotional arousal and highlight the complex functional dynamics of default mode networks in emotionally salient contexts.

(123)I-ioflupane single photon emission computed tomography (SPECT) is a sensitive and well established imaging tool in Parkinson's disease (PD) and atypical parkinsonian syndromes (APS), yet a discrimination between PD and APS has been considered inconsistent at least based on visual inspection or simple region of interest analyses. We here reappraise this issue by applying advanced image analysis techniques to separate PD from the various APS. This study included 392 consecutive patients with degenerative parkinsonism undergoing (123)I-ioflupane SPECT at our institution over the last decade: 306 PD, 24 multiple system atrophy (MSA), 32 progressive supranuclear palsy (PSP) and 30 corticobasal degeneration (CBD) patients. Data analysis included voxel-wise univariate statistical parametric mapping and multivariate pattern recognition using linear discriminant classifiers. MSA and PSP showed less ioflupane uptake in the head of caudate nucleus relative to PD and CBD, yet there was no difference between MSA and PSP. CBD had higher uptake in both putamen relative to PD, MSA and PSP. Classification was significant for PD versus APS (AUC 0.69, p < 0.05) and between APS subtypes (MSA vs CBD AUC 0.80, p < 0.05; MSA vs PSP AUC 0.69 p < 0.05; CBD vs PSP AUC 0.69 p < 0.05). Both striatal and extra-striatal regions contain classification information, yet the combination of both regions does not significantly improve classification accuracy. PD, MSA, PSP and CBD have distinct patterns of dopaminergic depletion on (123)I-ioflupane SPECT. The high specificity of 84-90% for PD versus APS indicates that the classifier is particularly useful for confirming APS cases.

The 22q11.2 deletion syndrome (22q11DS) is associated with cognitive impairments and a 41% risk of developing schizophrenia. While several studies performed on patients with 22q11DS showed the presence of abnormal functional connectivity in this syndrome, how these alterations affect large-scale network organization is still unknown. Here we performed a network modularity analysis on whole-brain functional connectomes derived from the resting-state fMRI of 40 patients with 22q11DS and 41 healthy control participants, aged between 9 and 30 years old. We then split the sample at 18 years old to obtain two age subgroups and repeated the modularity analyses. We found alterations of modular communities affecting the visuo-spatial network and the anterior cingulate cortex (ACC) in both age groups. These results corroborate previous structural and functional studies in 22q11DS that showed early impairment of visuo-spatial processing regions. Furthermore, as ACC has been linked to the development of psychotic symptoms in 22q11DS, the early impairment of its functional connectivity provide further support that ACC alterations may provide potential biomarkers for an increased risk of schizophrenia. Finally, we found an abnormal modularity partition of the dorsolateral prefrontal cortex (DLPFC) only in adults with 22q11DS, suggesting the presence of an abnormal development of functional network communities during adolescence in 22q11DS.

Independent component analysis (ICA) is a suitable method for decomposing functional magnetic resonance imaging (fMRI) activity into spatially independent patterns. Practice has revealed that low-pass filtering prior to ICA may improve ICA results by reducing noise and possibly by increasing source smoothness, which may enhance source independence; however, it eliminates useful information in high frequency features and it amplifies low signal fluctuations leading to independence loss. On the other hand, high-pass filtering may increase the independence by preserving spatial information, but its denoising properties are weak. Thus, such filtering strategies did not lead to simultaneous enhancements in independence and noise reduction; therefore, band-pass filtering or more sophisticated filtering methods are expected to be more appropriate. We used advanced wavelet filtering procedures, such as wavelet-based methods relying upon hard and soft coefficient thresholding and non-stationary Gaussian modelling based on geometrical prior information, to denoise artificial and real fMRI data. We compared the performance of these methods with the performance of traditional Gaussian smoothing techniques. First, we demonstrated both analytically and empirically the consistent performance increase of spatial filtering prior to ICA using spatial correlation and statistical sensitivity as quality measures. Second, all filtering methods were computationally efficient. Finally, denoising using low-pass filters was needed to improve ICA, suggesting that noise reduction may have a more significant effect on the component independence than the preservation of information contained within high frequencies.

Alzheimer's disease (AD) is characterized by amyloidosis of brain tissues. This phenomenon is studied with genetically-modified mouse models. We propose a method to quantify amyloidosis in whole 5xFAD mouse brains, a model of AD. We use optical projection tomography (OPT) and a random forest voxel classifier to segment and measure amyloid plaques. We validate our method in a preliminary cross-sectional study, where we measure 6136 ± 1637, 8477 ± 3438, and 17267 ± 4241 plaques (AVG ± SD) at 11, 17, and 31 weeks. Overall, this method can be used in the evaluation of new treatments against AD.

Electroencephalography (EEG) of brain activity can be represented in terms of dynamically changing topographies (microstates). Notably, spontaneous brain activity recorded at rest can be characterized by four distinctive topographies. Despite their well-established role during resting state, their implication in the generation of motor behavior is debated. Evidence of such a functional role of spontaneous brain activity would provide support for the design of novel and sensitive biomarkers in neurological disorders. Here we examined whether and to what extent intrinsic brain activity contributes and plays a functional role during natural motor behaviors. For this we first extracted subject-specific EEG microstates and muscle synergies during reaching-and-grasping movements in healthy volunteers. We show that, in every subject, well-known resting-state microstates persist during movement execution with similar topographies and temporal characteristics, but are supplemented by novel task-related microstates. We then show that the subject-specific microstates' dynamical organization correlates with the activation of muscle synergies and can be used to decode individual grasping movements with high accuracy. These findings provide first evidence that spontaneous brain activity encodes detailed information about motor control, offering as such the prospect of a novel tool for the definition of subject-specific biomarkers of brain plasticity and recovery in neuro-motor disorders.

Patterns of brain structural connectivity (SC) and functional connectivity (FC) are known to be related. In SC-FC comparisons, FC has classically been evaluated from correlations between functional time series, and more recently from partial correlations or their unnormalized version encoded in the precision matrix. The latter FC metrics yield more meaningful comparisons to SC because they capture 'direct' statistical dependencies, that is, discarding the effects of mediators, but their use has been limited because of estimation issues. With the rise of high-quality and large neuroimaging datasets, we revisit the relevance of different FC metrics in the context of SC-FC comparisons. Using data from 100 unrelated Human Connectome Project subjects, we first explore the amount of functional data required to reliably estimate various FC metrics. We find that precision-based FC yields a better match to SC than correlation-based FC when using 5 minutes of functional data or more. Finally, using a linear model linking SC and FC, we show that the SC-FC match can be used to further interrogate various aspects of brain structure and function such as the timescales of functional dynamics in different resting-state networks or the intensity of anatomical self-connections.

Theoretical advances in the neurosciences are leading to the development of an increasing number of proposed interventions for the enhancement of functional recovery after brain damage. Integration of these novel approaches in clinical practice depends on the availability of reliable, simple, and sensitive biomarkers of impairment level and extent of recovery, to enable an informed clinical-decision process. However, the neuropsychological tests currently in use do not tap into the complex neural re-organization process that occurs after brain insult and its modulation by treatment. Here we show that topographical analysis of resting-state electroencephalography (rsEEG) patterns using singular value decomposition (SVD) could be used to capture these processes. In two groups of subacute stroke patients, we show reliable detection of deviant neurophysiological patterns over repeated measurement sessions on separate days. These patterns generalized across patients groups. Additionally, they maintained a significant association with ipsilesional attention bias, discriminating patients with spatial neglect of different severity levels. The sensitivity and reliability of these rsEEG topographical analyses support their use as a tool for monitoring natural and treatment-induced recovery in the rehabilitation process.

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

Background: Schizophrenia is currently considered a neurodevelopmental disorder of connectivity. Still few studies have investigated how brain networks develop in children and adolescents who are at risk for developing psychosis. 22q11.2 Deletion Syndrome (22q11DS) offers a unique opportunity to investigate the pathogenesis of schizophrenia from a neurodevelopmental perspective. Structural covariance (SC) is a powerful approach to explore morphometric relations between brain regions that can furthermore detect biomarkers of psychosis, both in 22q11DS and in the general population. Methods: Here we implement a state-of-the-art sliding-window approach to characterize maturation of SC network architecture in a large longitudinal cohort of patients with 22q11DS (110 with 221 visits) and healthy controls (117 with 211 visits). We furthermore propose a new clustering-based approach to group regions according to trajectories of structural connectivity maturation. We correlate measures of SC with development of working memory, a core executive function that is highly affected in both idiopathic psychosis and 22q11DS. Finally, in 22q11DS we explore correlations between SC dysconnectivity and severity of internalizing psychopathology. Results: In HCs network architecture underwent a quadratic developmental trajectory maturing up to mid-adolescence. Late-childhood maturation was particularly evident for fronto-parietal cortices, while Default-Mode-Network-related regions showed a more protracted linear development. Working memory performance was positively correlated with network segregation and fronto-parietal connectivity. In 22q11DS, we demonstrate aberrant maturation of SC with disturbed architecture selectively emerging during adolescence and correlating more severe internalizing psychopathology. Patients also presented a lack of typical network development during late-childhood, that was particularly prominent for frontal connectivity. Conclusions: Our results suggest that SC maturation may underlie critical cognitive development occurring during late-childhood in healthy controls. Aberrant trajectories of SC maturation may reflect core developmental features of 22q11DS, including disturbed cognitive maturation during childhood and predisposition to internalizing psychopathology and psychosis during adolescence.

The corpus callosum is the largest white matter pathway connecting homologous structures of the two cerebral hemispheres. Remarkably, children and adults with developmental absence of the corpus callosum (callosal dysgenesis, CD) show typical interhemispheric integration, which is classically impaired in adult split-brain patients, for whom the corpus callosum is surgically severed. Tovar-Moll and colleagues (2014) proposed alternative neural pathways involved in the preservation of interhemispheric transfer. In a sample of six adults with CD, they revealed two homotopic bundles crossing the midline via the anterior and posterior commissures and connecting parietal cortices, and the microstructural properties of these aberrant bundles were associated with functional connectivity of these regions. The aberrant bundles were specific to CD and not visualised in healthy brains. We extended this study in a developmental cohort of 20 children with CD and 29 typically developing controls (TDC). The two anomalous white-matter bundles were visualised using tractography. Associations between structural properties of these bundles and their regional functional connectivity were explored. The proposed atypical bundles were observed in 30% of our CD cohort crossing via the anterior commissure, and in 30% crossing via the posterior commissure (also observed in 6.9% of TDC). However, the structural property measures of these bundles were not associated with parietal functional connectivity, bringing into question their role and implication for interhemispheric functional connectivity in CD. It is possible that very early disruption of embryological callosal development enhances neuroplasticity and facilitates the formation of these proposed alternative neural pathways, but further evidence is needed.

Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could significantly hinder our understanding of clinical pathways connecting individual symptoms. Network analysis techniques have emerged as alternative approaches that could help shed light on the complex dynamics of early psychopathology. The present study attempts to address the two main limitations that have in our opinion hindered the application of network approaches in the clinical setting. Firstly, we show that a multi-layer network analysis approach, can move beyond a static view of psychopathology, by providing an intuitive characterization of the role of specific symptoms in contributing to clinical trajectories over time. Secondly, we show that a Graph-Signal-Processing approach, can exploit knowledge of longitudinal interactions between symptoms, to predict clinical trajectories at the level of the individual. We test our approaches in two independent samples of individuals with genetic and clinical vulnerability for developing psychosis. Novel network approaches can allow to embrace the dynamic complexity of early psychopathology and help pave the way towards a more a personalized approach to clinical care.

Sleep favors the reactivation and consolidation of newly acquired memories. Yet, how our brain selects the noteworthy information to be reprocessed during sleep remains largely unknown. From an evolutionary perspective, individuals must retain information that promotes survival, such as avoiding dangers, finding food, or obtaining praise or money. Here, we test whether neural representations of rewarded (compared to non-rewarded) events have priority for reactivation during sleep. Using functional MRI and a brain decoding approach, we show that patterns of brain activity observed during waking behavior spontaneously reemerge during slow-wave sleep. Critically, we report a privileged reactivation of neural patterns previously associated with a rewarded task (i.e., winning at a complex game). Moreover, during sleep, activity in task-related brain regions correlates with better subsequent memory performance. Our study uncovers a neural mechanism whereby rewarded life experiences are preferentially replayed and consolidated while we sleep.

Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time- and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a "rule-in" procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting.

Children born very preterm (VPT; <32 weeks' gestation) are at high risk of neurodevelopmental and behavioural difficulties associated with atypical brain maturation, including socio-emotional difficulties. The analysis of large-scale brain network dynamics during rest allows us to investigate brain functional connectivity and its association with behavioural outcomes.

Despite a lack of scientific consensus on the definition of emotions, they are generally considered to involve several modifications in the mind, body, and behavior. Although psychology theories emphasized multi-componential characteristics of emotions, little is known about the nature and neural architecture of such components in the brain. We used a multivariate data-driven approach to decompose a wide range of emotions into functional core processes and identify their neural organization. Twenty participants watched 40 emotional clips and rated 119 emotional moments in terms of 32 component features defined by a previously validated componential model. Results show how different emotions emerge from coordinated activity across a set of brain networks coding for component processes associated with valuation appraisal, hedonic experience, novelty, goal-relevance, approach/avoidance tendencies, and social concerns. Our study goes beyond previous research that focused on categorical or dimensional emotions, by highlighting how novel methodology combined with theory-driven modeling may provide new foundations for emotion neuroscience and unveil the functional architecture of human affective experiences.

Moving from association to causal analysis of neuroimaging data is crucial to advance our understanding of brain function. The arrow-of-time (AoT), that is, the known asymmetric nature of the passage of time, is the bedrock of causal structures shaping physical phenomena. However, almost all current time series metrics do not exploit this asymmetry, probably due to the difficulty to account for it in modeling frameworks. Here, we introduce an AoT-sensitive metric that captures the intensity of causal effects in multivariate time series, and apply it to high-resolution functional neuroimaging data. We find that causal effects underlying brain function are more distinctively localized in space and time than functional activity or connectivity, thereby allowing us to trace neural pathways recruited in different conditions. Overall, we provide a mapping of the causal brain that challenges the association paradigm of brain function.

Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.

Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity.

The neural endophenotype associated with 22q11.2 deletion syndrome (22q11DS) includes deviant cortical development and alterations in brain connectivity. Resting-state functional magnetic resonance imaging (fMRI) findings also reported disconnectivity within the default mode network (DMN). In this study, we explored the relationship between functional and structural DMN connectivity and their changes with age in patients with 22q11DS in comparison to control participants. Given previous evidence of an association between DMN disconnectivity and the manifestation of psychotic symptoms, we further investigated this relationship in our group of patients with 22q11DS.