Evaluation of denoising strategies for task-based functional connectivity: Equalizing residual motion artifacts between rest and cognitively demanding tasks.

In-scanner head motion represents a major confounding factor in functional connectivity studies and it raises particular concerns when motion correlates with the effect of interest. One such instance regards research focused on functional connectivity modulations induced by sustained cognitively demanding tasks. Indeed, cognitive engagement is generally associated with substantially lower in-scanner movement compared with unconstrained, or minimally constrained, conditions. Consequently, the reliability of condition-dependent changes in functional connectivity relies on effective denoising strategies. In this study, we evaluated the ability of common denoising pipelines to minimize and balance residual motion-related artifacts between resting-state and task conditions. Denoising pipelines-including realignment/tissue-based regression, PCA/ICA-based methods (aCompCor and ICA-AROMA, respectively), global signal regression, and censoring of motion-contaminated volumes-were evaluated according to a set of benchmarks designed to assess either residual artifacts or network identifiability. We found a marked heterogeneity in pipeline performance, with many approaches showing a differential efficacy between rest and task conditions. The most effective approaches included aCompCor, optimized to increase the noise prediction power of the extracted confounding signals, and global signal regression, although both strategies performed poorly in mitigating the spurious distance-dependent association between motion and connectivity. Censoring was the only approach that substantially reduced distance-dependent artifacts, yet this came at the great cost of reduced network identifiability. The implications of these findings for best practice in denoising task-based functional connectivity data, and more generally for resting-state data, are discussed.

A community detection analysis of malocclusion classes from orthodontics and upper airway data.

OBJECTIVE: The interaction between skeletal class and upper airway has been extensively studied. Nevertheless, this relationship has not been clearly elucidated, with the heterogeneity of results suggesting the existence of different patterns for patients' classification, which has been elusive so far, probably due to oversimplified approaches. Hence, a network analysis was applied to test whether different patterns in patients' grouping exist. SETTINGS AND SAMPLE POPULATION: Ninety young adult patients with no obvious signs of respiratory diseases and no previous adeno-tonsillectomy procedures, with thirty patients characterized as Class I (0 < ANB < 4); 30 Class II (ANB > 4); and 30 as Class III (ANB < 0). MATERIALS AND METHODS: A community detection approach was applied on a graph obtained from a previously analysed sample: thirty-two measurements (nineteen cephalometric and thirteen upper airways data) were considered. RESULTS: An airway-orthodontic complex network has been obtained by cross-correlating patients. Before entering the correlation, data were controlled for age and gender using linear regression and standardized. By including or not the upper airway measurements as independent variables, two different community structures were obtained. Each contained five modules, though with different patients' assignments. CONCLUSION: The community detection algorithm found the existence of more than the three classical skeletal classifications. These results support the development of alternative tools to classify subjects according to their craniofacial morphology. This approach could offer a powerful tool for implementing novel strategies for clinical and research in orthodontics.

Complexity and data mining in dental research: A network medicine perspective on interceptive orthodontics.

Procedures and models of computerized data analysis are becoming researchers' and practitioners' thinking partners by transforming the reasoning underlying biomedicine. Complexity theory, Network analysis and Artificial Intelligence are already approaching this discipline, intending to provide support for patient's diagnosis, prognosis and treatments. At the same time, due to the sparsity, noisiness and time-dependency of medical data, such procedures are raising many unprecedented problems related to the mismatch between the human mind's reasoning and the outputs of computational models. Thanks to these computational, non-anthropocentric models, a patient's clinical situation can be elucidated in the orthodontic discipline, and the growth outcome can be approximated. However, to have confidence in these procedures, orthodontists should be warned of the related benefits and risks. Here we want to present how these innovative approaches can derive better patients' characterization, also offering a different point of view about patient's classification, prognosis and treatment.

Sense of external agency is sustained by multisensory functional integration in the somatosensory cortex.

"Sense of agency" (SoA), the feeling of control for events caused by one's own actions, is deceived by visuomotor incongruence. Sensorimotor networks are implicated in SoA, however little evidence exists on brain functionality during agency processing. Concurrently, it has been suggested that the brain's intrinsic resting-state (rs) activity has a preliminary influence on processing of agency cues. Here, we investigated the relation between performance in an agency attribution task and functional interactions among brain regions as derived by network analysis of rs functional magnetic resonance imaging. The action-effect delay was adaptively increased (range 90-1,620 ms) and behavioral measures correlated to indices of cognitive processes and appraised self-concepts. They were then regressed on local metrics of rs brain functional connectivity as to isolate the core areas enabling self-agency. Across subjects, the time window for self-agency was 90-625 ms, while the action-effect integration was impacted by self-evaluated personality traits. Neurally, the brain intrinsic organization sustaining consistency in self-agency attribution was characterized by high connectiveness in the secondary visual cortex, and regional segregation in the primary somatosensory area. Decreased connectiveness in the secondary visual area, regional segregation in the superior parietal lobule, and information control within a primary visual cortex-frontal eye fields network sustained self-agency over long-delayed effects. We thus demonstrate that self-agency is grounded on the intrinsic mode of brain function designed to organize information for visuomotor integration. Our observation is relevant for current models of psychopathology in clinical conditions in which both rs activity and sense of agency are altered.

Network Analysis of Gut Microbiome and Metabolome to Discover Microbiota-Linked Biomarkers in Patients Affected by Non-Small Cell Lung Cancer.

Several studies in recent times have linked gut microbiome (GM) diversity to the pathogenesis of cancer and its role in disease progression through immune response, inflammation and metabolism modulation. This study focused on the use of network analysis and weighted gene co-expression network analysis (WGCNA) to identify the biological interaction between the gut ecosystem and its metabolites that could impact the immunotherapy response in non-small cell lung cancer (NSCLC) patients undergoing second-line treatment with anti-PD1. Metabolomic data were merged with operational taxonomic units (OTUs) from 16S RNA-targeted metagenomics and classified by chemometric models. The traits considered for the analyses were: (i) condition: disease or control (CTRLs), and (ii) treatment: responder (R) or non-responder (NR). Network analysis indicated that indole and its derivatives, aldehydes and alcohols could play a signaling role in GM functionality. WGCNA generated, instead, strong correlations between short-chain fatty acids (SCFAs) and a healthy GM. Furthermore, commensal bacteria such as Akkermansia muciniphila, Rikenellaceae, Bacteroides, Peptostreptococcaceae, Mogibacteriaceae and Clostridiaceae were found to be more abundant in CTRLs than in NSCLC patients. Our preliminary study demonstrates that the discovery of microbiota-linked biomarkers could provide an indication on the road towards personalized management of NSCLC patients.

Scale-invariant rearrangement of resting state networks in the human brain under sustained stimulation.

Brain activity at rest is characterized by widely distributed and spatially specific patterns of synchronized low-frequency blood-oxygenation level-dependent (BOLD) fluctuations, which correspond to physiologically relevant brain networks. This network behaviour is known to persist also during task execution, yet the details underlying task-associated modulations of within- and between-network connectivity are largely unknown. In this study we exploited a multi-parametric and multi-scale approach to investigate how low-frequency fluctuations adapt to a sustained n-back working memory task. We found that the transition from the resting state to the task state involves a behaviourally relevant and scale-invariant modulation of synchronization patterns within both task-positive and default mode networks. Specifically, decreases of connectivity within networks are accompanied by increases of connectivity between networks. In spite of large and widespread changes of connectivity strength, the overall topology of brain networks is remarkably preserved. We show that these findings are strongly influenced by connectivity at rest, suggesting that the absolute change of connectivity (i.e., disregarding the baseline) may not be the most suitable metric to study dynamic modulations of functional connectivity. Our results indicate that a task can evoke scale-invariant, distributed changes of BOLD fluctuations, further confirming that low frequency BOLD oscillations show a specialized response and are tightly bound to task-evoked activation.

Bilateral Transcranial Direct Current Stimulation Language Treatment Enhances Functional Connectivity in the Left Hemisphere: Preliminary Data from Aphasia.

Several studies have already shown that transcranial direct current stimulation (tDCS) is a useful tool for enhancing recovery in aphasia. However, no reports to date have investigated functional connectivity changes on cortical activity because of tDCS language treatment. Here, nine aphasic persons with articulatory disorders underwent an intensive language therapy in two different conditions: bilateral anodic stimulation over the left Broca's area and cathodic contralesional stimulation over the right homologue of Broca's area and a sham condition. The language treatment lasted 3 weeks (Monday to Friday, 15 sessions). In all patients, language measures were collected before (T0) and at the end of treatment (T15). Before and after each treatment condition (real vs. sham), each participant underwent a resting-state fMRI study. Results showed that, after real stimulation, patients exhibited the greatest recovery not only in terms of better accuracy in articulating the treated stimuli but also for untreated items on different tasks of the language test. Moreover, although after the sham condition connectivity changes were confined to the right brain hemisphere, real stimulation yielded to stronger functional connectivity increase in the left hemisphere. In conclusion, our data provide converging evidence from behavioral and functional imaging data that bilateral tDCS determines functional connectivity changes within the lesioned hemisphere, enhancing the language recovery process in stroke patients.

The thalamus and brainstem act as key hubs in alterations of human brain network connectivity induced by mild propofol sedation.

Despite their routine use during surgical procedures, no consensus has yet been reached on the precise mechanisms by which hypnotic anesthetic agents produce their effects. Molecular, animal and human studies have suggested disruption of thalamocortical communication as a key component of anesthetic action at the brain systems level. Here, we used the anesthetic agent, propofol, to modulate consciousness and to evaluate differences in the interactions of remote neural networks during altered consciousness. We investigated the effects of propofol, at a dose that produced mild sedation without loss of consciousness, on spontaneous cerebral activity of 15 healthy volunteers using functional magnetic resonance imaging (fMRI), exploiting oscillations (<0.1 Hz) in blood oxygenation level-dependent signal across functionally connected brain regions. We considered the data as a graph, or complex network of nodes and links, and used eigenvector centrality (EC) to characterize brain network properties. The EC mapping of fMRI data in healthy humans under propofol mild sedation demonstrated a decrease of centrality of the thalamus versus an increase of centrality within the pons of the brainstem, highlighting the important role of these two structures in regulating consciousness. Specifically, the decrease of thalamus centrality results from its disconnection from a widespread set of cortical and subcortical regions, while the increase of brainstem centrality may be a consequence of its increased influence, in the mildly sedated state, over a few highly central cortical regions key to the default mode network such as the posterior and anterior cingulate cortices.

Evidence of scale-free clusters of vegetation in tropical rainforests.

Tropical rainforests exhibit a rich repertoire of spatial patterns emerging from the intricate relationship between the microscopic interaction between species. In particular, the distribution of vegetation clusters can shed much light on the underlying process that regulates the ecosystem. Analyzing the distribution of vegetation clusters at different resolution scales, we show the first robust evidence of scale-invariant clusters of vegetation, suggesting the coexistence of multiple intertwined scales in the collective dynamics of tropical rainforests. We use field data and computational simulations to confirm our hypothesis, proposing a predictor that could be particularly interesting to monitor the ecological resilience of the world's "green lungs."

Network topology mapping of chemical compounds space.

We define bipartite and monopartite relational networks of chemical elements and compounds using two different datasets of inorganic chemical and material compounds, as well as study their topology. We discover that the connectivity between elements and compounds is distributed exponentially for materials, and with a fat tail for chemicals. Compounds networks show similar distribution of degrees, and feature a highly-connected club due to oxygen . Chemical compounds networks appear more modular than material ones, while the communities detected reveal different dominant elements specific to the topology. We successfully reproduce the connectivity of the empirical chemicals and materials networks by using a family of fitness models, where the fitness values are derived from the abundances of the elements in the aggregate compound data. Our results pave the way towards a relational network-based understanding of the inherent complexity of the vast chemical knowledge atlas, and our methodology can be applied to other systems with the ingredient-composite structure.

Symmetries and synchronization from whole-neural activity in C. elegans connectome: Integration of functional and structural networks.

Understanding the dynamical behavior of complex systems from their underlying network architectures is a long-standing question in complexity theory. Therefore, many metrics have been devised to extract network features like motifs, centrality, and modularity measures. It has previously been proposed that network symmetries are of particular importance since they are expected to underly the synchronization of a system's units, which is ubiquitously observed in nervous system activity patterns. However, perfectly symmetrical structures are difficult to assess in noisy measurements of biological systems, like neuronal connectomes. Here, we devise a principled method to infer network symmetries from combined connectome and neuronal activity data. Using nervous system-wide population activity recordings of the C.elegans backward locomotor system, we infer structures in the connectome called fibration symmetries, which can explain which group of neurons synchronize their activity. Our analysis suggests functional building blocks in the animal's motor periphery, providing new testable hypotheses on how descending interneuron circuits communicate with the motor periphery to control behavior. Our approach opens a new door to exploring the structure-function relations in other complex systems, like the nervous systems of larger animals.

Fibration symmetry-breaking supports functional transitions in a brain network engaged in language.

In his book 'A Beautiful Question' 1, physicist Frank Wilczek argues that symmetry is 'nature's deep design,' governing the behavior of the universe, from the smallest particles to the largest structures 1-4. While symmetry is a cornerstone of physics, it has not yet been found widespread applicability to describe biological systems 5, particularly the human brain. In this context, we study the human brain network engaged in language and explore the relationship between the structural connectivity (connectome or structural network) and the emergent synchronization of the mesoscopic regions of interest (functional network). We explain this relationship through a different kind of symmetry than physical symmetry, derived from the categorical notion of Grothendieck fibrations 6. This introduces a new understanding of the human brain by proposing a local symmetry theory of the connectome, which accounts for how the structure of the brain's network determines its coherent activity. Among the allowed patterns of structural connectivity, synchronization elicits different symmetry subsets according to the functional engagement of the brain. We show that the resting state is a particular realization of the cerebral synchronization pattern characterized by a fibration symmetry that is broken 7 in the transition from rest to language. Our findings suggest that the brain's network symmetry at the local level determines its coherent function, and we can understand this relationship from theoretical principles.

Fibration symmetry-breaking supports functional transitions in a brain network engaged in language.

In his book 'A Beautiful Question', physicist Frank Wilczek argues that symmetry is 'nature's deep design,' governing the behavior of the universe, from the smallest particles to the largest structures. While symmetry is a cornerstone of physics, it has not yet been found widespread applicability to describe biological systems, particularly the human brain. In this context, we study the human brain network engaged in language and explore the relationship between the structural connectivity (connectome or structural network) and the emergent synchronization of the mesoscopic regions of interest (functional network). We explain this relationship through a different kind of symmetry than physical symmetry, derived from the categorical notion of Grothendieck fibrations. This introduces a new understanding of the human brain by proposing a local symmetry theory of the connectome, which accounts for how the structure of the brain's network determines its coherent activity. Among the allowed patterns of structural connectivity, synchronization elicits different symmetry subsets according to the functional engagement of the brain. We show that the resting state is a particular realization of the cerebral synchronization pattern characterized by a fibration symmetry that is broken in the transition from rest to language. Our findings suggest that the brain's network symmetry at the local level determines its coherent function, and we can understand this relationship from theoretical principles.

Inflammatory bowel disease biomarkers revealed by the human gut microbiome network.

Inflammatory bowel diseases (IBDs) are complex medical conditions in which the gut microbiota is attacked by the immune system of genetically predisposed subjects when exposed to yet unclear environmental factors. The complexity of this class of diseases makes them suitable to be represented and studied with network science. In this paper, the metagenomic data of control, Crohn's disease, and ulcerative colitis subjects' gut microbiota were investigated by representing this data as correlation networks and co-expression networks. We obtained correlation networks by calculating Pearson's correlation between gene expression across subjects. A percolation-based procedure was used to threshold and binarize the adjacency matrices. In contrast, co-expression networks involved the construction of the bipartite subjects-genes networks and the monopartite genes-genes projection after binarization of the biadjacency matrix. Centrality measures and community detection were used on the so-built networks to mine data complexity and highlight possible biomarkers of the diseases. The main results were about the modules of Bacteroides, which were connected in the control subjects' correlation network, Faecalibacterium prausnitzii, where co-enzyme A became central in IBD correlation networks and Escherichia coli, whose module has different patterns of integration within the whole network in the different diagnoses.

Noise cleaning the precision matrix of short time series.

We present a comparison between various algorithms of inference of covariance and precision matrices in small data sets of real vectors of the typical length and dimension of human brain activity time series retrieved by functional magnetic resonance imaging (fMRI). Assuming a Gaussian model underlying the neural activity, the problem consists of denoising the empirically observed matrices to obtain a better estimator of the (unknown) true precision and covariance matrices. We consider several standard noise-cleaning algorithms and compare them on two types of data sets. The first type consists of synthetic time series sampled from a generative Gaussian model of which we can vary the fraction of dimensions per sample q and the strength of off-diagonal correlations. The second type consists of time series of fMRI brain activity of human subjects at rest. The reliability of each algorithm is assessed in terms of test-set likelihood and, in the case of synthetic data, of the distance from the true precision matrix. We observe that the so-called optimal rotationally invariant estimator, based on random matrix theory, leads to a significantly lower distance from the true precision matrix in synthetic data and higher test likelihood in natural fMRI data. We propose a variant of the optimal rotationally invariant estimator in which one of its parameters is optimzed by cross-validation. In the severe undersampling regime (large q) typical of fMRI series, it outperforms all the other estimators. We furthermore propose a simple algorithm based on an iterative likelihood gradient ascent, leading to very accurate estimations in weakly correlated synthetic data sets.

Information load dynamically modulates functional brain connectivity during narrative listening.

Narratives are paradigmatic examples of natural language, where nouns represent a proxy of information. Functional magnetic resonance imaging (fMRI) studies revealed the recruitment of temporal cortices during noun processing and the existence of a noun-specific network at rest. Yet, it is unclear whether, in narratives, changes in noun density influence the brain functional connectivity, so that the coupling between regions correlates with information load. We acquired fMRI activity in healthy individuals listening to a narrative with noun density changing over time and measured whole-network and node-specific degree and betweenness centrality. Network measures were correlated with information magnitude with a time-varying approach. Noun density correlated positively with the across-regions average number of connections and negatively with the average betweenness centrality, suggesting the pruning of peripheral connections as information decreased. Locally, the degree of the bilateral anterior superior temporal sulcus (aSTS) was positively associated with nouns. Importantly, aSTS connectivity cannot be explained by changes in other parts of speech (e.g., verbs) or syllable density. Our results indicate that the brain recalibrates its global connectivity as a function of the information conveyed by nouns in natural language. Also, using naturalistic stimulation and network metrics, we corroborate the role of aSTS in noun processing.

The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?

Artificial intelligence (AI) models and procedures hold remarkable predictive efficiency in the medical domain through their ability to discover hidden, non-obvious clinical patterns in data. However, due to the sparsity, noise, and time-dependency of medical data, AI procedures are raising unprecedented issues related to the mismatch between doctors' mentalreasoning and the statistical answers provided by algorithms. Electronic systems can reproduce or even amplify noise hidden in the data, especially when the diagnosis of the subjects in the training data set is inaccurate or incomplete. In this paper we describe the conditions that need to be met for AI instruments to be truly useful in the orthodontic domain. We report some examples of computational procedures that are capable of extracting orthodontic knowledge through ever deeper patient representation. To have confidence in these procedures, orthodontic practitioners should recognize the benefits, shortcomings, and unintended consequences of AI models, as algorithms that learn from human decisions likewise learn mistakes and biases.

Anatomical connectivity mapping: a new tool to assess brain disconnection in Alzheimer's disease.

Previous studies suggest that the clinical manifestations of Alzheimer's disease (AD) are not only associated with regional gray matter damage but also with abnormal functional integration of different brain regions by disconnection mechanisms. A measure of anatomical connectivity (anatomical connectivity mapping or ACM) can be obtained by initiating diffusion tractography streamlines from all parenchymal voxels and then counting the number of streamlines passing through each voxel of the brain. In order to assess the potential of this parameter for the study of disconnection in AD, we computed it in a group of patients with AD (N=9), in 16 patients with amnestic mild cognitive impairment (a-MCI, which is considered the prodromal stage of AD) and in 12 healthy volunteers. All subjects had an MRI scan at 3T, and diffusion MRI data were analyzed to obtain fractional anisotropy (FA) and ACM. Two types of ACM maps, absolute count (ac-ACM) and normalized by brain size count (nc-ACM), were obtained. No between group differences in FA surviving correction for multiple comparison were found, while areas of both decreased (in the supramarginal gyrus) and increased (in the putamen) ACM were found in patients with AD. Similar results were obtained with ac-ACM and nc-ACM. ACM of the supramarginal gyrus was strongly associated with measures of short-term memory in healthy subjects. This study shows that ACM provides information that is complementary to that offered by FA and appears to be more sensitive than FA to brain changes in patients with AD. The increased ACM in the putamen was unexpected. Given the nature of ACM, an increase of this parameter may reflect a change in any of the areas connected to it. One intriguing possibility is that this increase of ACM in AD patients might reflect processes of brain plasticity driven by cholinesterase inhibitors.

Modeling the contribution of neuron-astrocyte cross talk to slow blood oxygenation level-dependent signal oscillations.

A consistent and prominent feature of brain functional magnetic resonance imaging (fMRI) data is the presence of low-frequency (<0.1 Hz) fluctuations of the blood oxygenation level-dependent (BOLD) signal that are thought to reflect spontaneous neuronal activity. In this report we provide modeling evidence that cyclic physiological activation of astroglial cells produces similar BOLD oscillations through a mechanism mediated by intracellular Ca(2+) signaling. Specifically, neurotransmission induces pulses of Ca(2+) concentration in astrocytes, resulting in increased cerebral perfusion and neuroactive transmitter release by these cells (i.e., gliotransmission), which in turn stimulates neuronal activity. Noticeably, the level of neuron-astrocyte cross talk regulates the periodic behavior of the Ca(2+) wave-induced BOLD fluctuations. Our results suggest that the spontaneous ongoing activity of neuroglial networks is a potential source of the observed slow fMRI signal oscillations.

Emergent spatial patterns of coexistence in species-rich plant communities.

Statistical physics has proved essential to analyze multiagent environments. Motivated by the empirical observation of various nonequilibrium features in Barro Colorado and other ecological systems, we analyze a plant-species abundance model of neutral competition, presenting analytical evidence of scale-invariant plant clusters and nontrivial emergent modular correlations. Such first theoretical confirmation of a scale-invariant region, based on percolation processes, reproduces the key features in natural rainforest ecosystems and can confer the most stable equilibrium for ecosystems with vast biodiversity.