Integrative neural mechanisms for social communication of learned vocal behavior

Social cognitive dysfunction and altered functional connectivity and small-world properties of the social brain network in idiopathic generalized epilepsy.

Anatomical connectivity in children with developmental dyscalculia: A graph theory study.

In the last decade network theory has proved an effective tool for modeling and describing the complex topology emerging either from anatomical or functional brain connectivity patterns. From a graph theoretical perspective, the brain can be conceived as a networked system composed of nodes coincident with different brain sites and links which in the current view can either represent anatomical tracts between brain regions or measures of statistical dependencies between their electrical activity. One of the most intriguing, and now well known, examples of the application of network theory to neuroimaging data unveiled that the way brain regions are connected is typically neither regular nor random. Instead brain networks, like other real networked systems, tend to exhibit a complex structure theoretically consistent with the capability of processing information within regional clusters and avoiding excessive connections between clusters. While the simplest instantiation of this configuration showed similar characteristics to mathematically defined “small-world” networks, it has become clear that additional topological structures including hierarchical modularity can nuance our expectations of the brain's underlying architecture and dynamics. An important goal in these research endeavors is to identify how brain network organization can inform our understanding of the brain's intuitive need to balance the two competing principles of integration and segregation and how alterations in brain structure and dynamics can lead to alterations in human behavior and cognitive function. The present special issue collects a series of selected contributions related to the methodological and practical applications of network theory to anatomical and functional brain connectivity patterns. The contribution entitled “Voxel scale complex networks of functional connectivity in the rat brain: neurochemical state dependence of global and local topological properties” addresses the dependence of the functional connectivity estimated from the fMRI signals of the rat brain in response to alterations of the neurotransmitter system as induced by the administration of specific pharmaceutical drugs such as d-amphetamine, fluoxetine, and nicotine. The contribution entitled “A signal-processing-based approach to time-varying graph analysis for dynamic brain network identification” proposes a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed method is evaluated on event-related potential (ERP) data, which demonstrates the dynamic nature of functional connectivity. The contribution entitled “how the statistical validation of functional connectivity patterns can prevent erroneous definition of small-world properties of a brain connectivity network” addresses important methodological choices that are often made in the construction of functional brain network from EEG data, including the choice of statistical thresholds to determine the presence or absence of network links and the role of spatial correlations in determining graph properties. The contribution entitled “weighted phase lag index and graph analysis: preliminary investigation of functional connectivity during resting state in children” presents original results concerning the application of small-world parameters and betweenness centrality measures to characterize the topological structure of the functional network in the children's brain from noninvasive MEG recordings. The contribution entitled “source space analysis of event-related dynamic reorganization of brain networks” conducts a quantitative study of the dynamic reconfiguration of connectivity for event-related experiments at source space level, which provides a global and complete view of the stages of processing associated with the regional changes in activity. The contribution entitled “Redundancy as a Graph-Based Index of Frequency Specific MEG Functional Connectivity” focuses on quantifying the differential role that paths of different lengths potentially play in brain connectivity and demonstrates that a redundancy-based measure captures unique information not accessible via approaches that only examine the shortest paths through a network. The contribution entitled “A computationally efficient, exploratory approach to brain connectivity incorporating false discovery rate control, a priori knowledge, and group inference” proposes a multisubject, exploratory brain connectivity modeling approach. The proposed method allows for the incorporation of prior knowledge of connectivity and the determination of the dominant brain connectivity pattern among a group of subjects. Fabrizio De Vico Fallani Danielle Bassett Tianzi Jiang

Background: Facial Affect Recognition (FAR) is the capacity to understand and interpret facial expressions. It is previously shown that schizophrenia patients and siblings (albeit to a lesser extent) perform worse on FAR (Meijer et al, Psychol Med, 2012). The social brain network (SBN) is associated with emotion processing, and the interaction between grey and white matter within the network might explain poor FAR performance. Here, we investigate whether disturbed FAR can be explained by a differential association between the following modalities: cortical volume [CV] and white matter integrity (fractional anisotropy [FA], radial [RD], and axial diffusivity [AD]) within the SBN between patients, siblings, and controls.

Schizophrenia is characterized by a lack of integration between thought, emotion, and behavior. A disruption in the connectivity between brain processes may underlie this schism. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) were used to evaluate functional and anatomical brain connectivity in schizophrenia. In all, 29 chronic schizophrenia patients (11 females, age: mean=41.3, SD=9.28) and 29 controls (11 females, age: mean=41.1, SD=10.6) were recruited. Schizophrenia patients were assessed for severity of negative and positive symptoms and general cognitive abilities of attention/concentration and memory. Participants underwent a resting-fMRI scan and a DTI scan. For fMRI data, a hybrid independent components analysis was used to extract the group default mode network (DMN) and accompanying time-courses. Voxel-wise whole-brain multiple regressions with corresponding DMN time-courses was conducted for each subject. A t-test was conducted on resulting DMN correlation maps to look between-group differences. For DTI data, voxel-wise statistical analysis of the fractional anisotropy data was carried out to look for between-group differences. Voxel-wise correlations were conducted to investigate the relationship between brain connectivity and behavioral measures. Results revealed altered functional and anatomical connectivity in medial frontal and anterior cingulate gyri of schizophrenia patients. In addition, frontal connectivity in schizophrenia patients was positively associated with symptoms as well as with general cognitive ability measures. The present study shows convergent fMRI and DTI findings that are consistent with the disconnection hypothesis in schizophrenia, particularly in medial frontal regions, while adding some insight of the relationship between brain disconnectivity and behavior.

Numerous previous studies have classified brain regions related to social processing into the "social brain" regions. Recent genetic studies showed that gene expression has a crucial effect on both brain functions and behavioral social performance. However, studies still lack a clear understanding of the organization of the social gene expression (SocGene) network. This study aimed to distinguish the difference between the SocGene network and the social brain network (SBN) and further explored their deficits in schizophrenia (SCZ) patients. The SocGene network was constructed by generating the gene expression maps of six social neuropeptide receptors from the Allen Human Brain Atlas. Then, we recruited a general population sample of 37 participants and a clinical sample including 26 SCZ and 25 Healthy controls (HCs) successively to construct the resting-state SocGene and SBN at the individual level. The integration (global efficiency, GE) and segregation (local efficiency, LE) of these brain networks were calculated using the graphic analysis. Results showed that the GE and LE of the SocGene network were significantly higher than those of the SBN in both two cohorts. The SCZ patients showed significantly diminished LE of the two brain networks compared to HCs, especially in the SocGene network. These findings implied that the SocGene network strengthened the integration and segregation compared to the SBN. SCZ patients mainly exhibited deficits in the segregation of these two brain networks. The current findings provide a new perspective on combining genetic expression and brain function in understanding the psychopathology of social functioning.

Recently, there have been a large number of studies using resting state fMRI to characterize abnormal brain connectivity in patients with a variety of neurological, psychiatric, and developmental disorders. However, interpreting what the differences in resting state fMRI functional connectivity (rsfMRI-FC) actually reflect in terms of the underlying neural pathology has proved to be elusive because of the complexity of brain anatomical connectivity. The same is the case for task-based fMRI studies. In the last few years, several groups have used large-scale neural modeling to help provide some insight into the relationship between brain anatomical connectivity and the corresponding patterns of fMRI-FC. In this paper we review several efforts at using large-scale neural modeling to investigate the relationship between structural connectivity and functional/effective connectivity to determine how alterations in structural connectivity are manifested in altered patterns of functional/effective connectivity. Because the alterations made in the anatomical connectivity between specific brain regions in the model are known in detail, one can use the results of these simulations to determine the corresponding alterations in rsfMRI-FC. Many of these simulation studies found that structural connectivity changes do not necessarily result in matching changes in functional/effective connectivity in the areas of structural modification. Often, it was observed that increases in functional/effective connectivity in the altered brain did not necessarily correspond to increases in the strength of the anatomical connection weights. Note that increases in rsfMRI-FC in patients have been interpreted in some cases as resulting from neural plasticity. These results suggest that this interpretation can be mistaken. The relevance of these simulation findings to the use of functional/effective fMRI connectivity as biomarkers for brain disorders is also discussed.

Impaired insight into illness, associated with worse treatment outcome, is common in schizophrenia. Insight has been related to the self-reflective processing, centred on the medial frontal cortex. We hypothesized that anatomical and functional routes to and from the ventromedial prefrontal cortex (vmPFC) would differ in patients according to their degree of impaired insight. Forty-five schizophrenia patients and 19 healthy subjects performed a self-reflection task during fMRI, and underwent diffusion tensor imaging. Using dynamic causal modelling we observed increased effective connectivity from the posterior cingulate cortex (PCC), inferior parietal lobule (IPL), and dorsal mPFC (dmPFC) towards the vmPFC with poorer insight and decrease from vmPFC to the IPL. Stronger connectivity from the PCC to vmPFC during judgment of traits related to self was associated with poorer insight. We found small-scale significant changes in white matter integrity associated with clinical insight. Self-reflection may be influenced by synaptic changes that lead to the observed alterations in functional connectivity accompanied by the small-scale but measurable alterations in anatomical connections. Our findings may point to a neural compensatory response to an impairment of connectivity between self-processing regions. Similarly, the observed hyper-connectivity might be a primary deficit linked to inefficiency in the component cognitive processes that lead to impaired insight. We suggest that the stronger cognitive demands placed on patients with poor insight is reflected in increased effective connectivity during the task in this study.

Objective:Traumatic brain injury (TBI) is a prevalent cause of long-term morbidity in children and adolescents and can lead to persistent difficulties with social and behavioral function. TBI may impact brain structures that support social cognition, social perception, and day-to-day social interactions—termed the social brain network (SBN). We examined differences in links among the SBN and regions of interest from other neural networks thought to support social outcomes, i.e., the default mode network (DMN) and salience network (SN). Furthermore, we examined how differences in co-activation among the SBN and these other key networks were associated with ratings of social and day-to-day adaptive outcomes.Participants and Methods:Participants included children and adolescents with moderate to severe TBI (msTBI; n=11, Mage=11.78, 6 male), complicated-mild TBI (cmTBI; n=12, Mage=12.59, 9 male), and orthopedic injury (OI; n=22, Mage=11.69, 15 male). Participants underwent resting-state functional MRI on a 3Tesla Siemens Prisma scanner. Parents rated their child’s social and adaptive function on the Child Behavior Checklist (CBCL) and Adaptive Behavior Assessment System-Third Edition (ABAS-3). Resting-state connectivity was assessed using the CONN Toolbox, including preprocessing, denoising, and alignment to the participants’ processed T1 MPRAGE sequence followed by seed-to-voxel analysis using a SBN mask and targeted regions of interest within the DMN and SN. Individual-level r-to-z correlations were extracted from resulting clusters of co-activation with the SBN mask and exported into SPSSv28.0 for integration with behavioral data.Results:One-way ANOVAs used to examine group differences in social and adaptive outcome revealed significant group differences in CBCL Social Competence (F=4.49, p=.019) and all composite scores on the ABAS-3 (Fs=3.78 to 5.17, ps=.031 to .010). In each domain, children with msTBI were rated as having elevated difficulties relative to cmTBI or OI, whereas cmTBI and OI groups did not differ. Connectivity also differed significantly between groups, with children with OI demonstrating greater connectivity between the SBN and the anterior cingulate cortex of the SN (t=5.19, p(FDR)<.0001) and posterior cingulate cortex of the DMN (f=4.30, p(FDR)<.001) than children with msTBI. Children with cmTBI also showed greater connectivity between the SBN and left temporal pole of the DMN (t=7.45, p(FDR)<.000001) than children with msTBI. Degree of connectivity between the SBN and posterior cingulate was significantly positively correlated across all domains of adaptive function (rs=.451 to .504, ps=.010 to .003), whereas degree of connectivity between the SBN and left temporal pole was strongly positively related to Social Competence (a=.633, p=.006) and conceptual adaptive skills on the ABAS (A=.437, p=.037).Conclusions:Our findings provide insights into the neural substrates of social and adaptive morbidity after pediatric TBI, particularly msTBI, by linking alterations in connectivity among the SBN, DMN, and SN with measures of social and adaptive outcome. While the posterior cingulate was broadly associated with adaptive outcome, the temporal pole was particularly strongly associated with social competence. This may reflect the diverse functions and high degree of interconnectivity of the posterior cingulate, which contributes to various cognitive and attentional processes, relative to the strong amygdala/limbic connections of the temporal pole.

Tractography‐based parcellation of the human left inferior parietal lobule

Purpose: The aim of the study was to investigate development and evaluation of statistical models for network data, such as social networks, biological networks, and brain networks Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Statistical models for network data, encompassing social, biological, and brain networks, have enhanced our comprehension of these complex systems. These models reveal structural insights, dynamic patterns, and valuable applications across multiple disciplines, including epidemiology, genetics, and neuroscience. In essence, they provide indispensable tools for understanding intricate network dynamics. Unique Contribution to Theory, Practice and Policy: Principal component analysis (PCA), Deep learning and neural networks & Information theory may be used to anchor future studies on development and evaluation of statistical models for network data, such as social networks, biological networks, and brain networks. Create user-friendly software tools and packages for implementing advanced survival models, making them accessible to researchers and practitioners. By identifying trends and disparities over time, policymakers can design interventions and allocate resources more effectively.

Estimating brain wiring patterns is critical to better understand the brain organization and function. Anatomical brain connectivity models axonal pathways, while the functional brain connectivity characterizes the statistical dependencies and correlation between the activities of various brain regions. The synchronization of brain activity can be inferred through the variation of blood-oxygen-level dependent (BOLD) signal from functional MRI (fMRI) and the neural connections can be estimated using tractography from diffusion MRI (dMRI). Functional connections between brain regions are supported by anatomical connections, and the synchronization of brain activities arises through sharing of information in the form of electro-chemical signals on axon pathways. Jointly modeling fMRI and dMRI data may improve the accuracy in constructing anatomical connectivity as well as functional connectivity. Such an approach may lead to novel multimodal biomarkers potentially able to better capture functional and anatomical connectivity variations. We present a novel brain network model which jointly models the dMRI and fMRI data to improve the anatomical connectivity estimation and extract the anatomical subnetworks associated with specific functional modes by constraining the anatomical connections as structural supports to the functional connections. The key idea is similar to a multi-commodity flow optimization problem that minimizes the cost or maximizes the efficiency for flow configuration and simultaneously fulfills the supply-demand constraint for each commodity. In the proposed network, the nodes represent the grey matter (GM) regions providing brain functionality, and the links represent white matter (WM) fiber bundles connecting those regions and delivering information. The commodities can be thought of as the information corresponding to brain activity patterns as obtained for instance by independent component analysis (ICA) of fMRI data. The concept of information flow is introduced and used to model the propagation of information between GM areas through WM fiber bundles. The link capacity , i.e., ability to transfer information, is characterized by the relative strength of fiber bundles, e.g., fiber count gathered from the tractography of dMRI data. The node information demand is considered to be proportional to the correlation between neural activity at various cortical areas involved in a particular functional mode (e.g. visual, motor, etc.). These two properties lead to the link capacity and node demand constraints in the proposed model. Moreover, the information flow of a link cannot exceed the demand from either end node. This is captured by the feasibility constraints . Two different cost functions are considered in the optimization formulation in this paper. The first cost function, the reciprocal of fiber strength represents the unit cost for information passing through the link. In the second cost function, a min-max (minimizing the maximal link load) approach is used to balance the usage of each link. Optimizing the first cost function selects the pathway with strongest fiber strength for information propagation. In the second case, the optimization procedure finds all the possible propagation pathways and allocates the flow proportionally to their strength. Additionally, a penalty term is incorporated with both the cost functions to capture the possible missing and weak anatomical connections. With this set of constraints and the proposed cost functions, solving the network optimization problem recovers missing and weak anatomical connections supported by the functional information and provides the functional-associated anatomical subnetworks. Feasibility is demonstrated using realistic diffusion and functional MRI phantom data. It is shown that the proposed model recovers the maximum number of true connections, with fewest number of false connections when compared with the connectivity derived from a joint probabilistic model using the expectation-maximization (EM) algorithm presented in a prior work. We also apply the proposed method to data provided by the Human Connectome Project (HCP).

Graph-theoretical methods have proven valuable for investigating alterations in both anatomical and functional brain connectivity networks during Alzheimer's disease (AD). Recent studies suggest that representing brain networks in a suitable geometric space can better capture their connectivity structure. This study introduces a novel approach to characterize brain connectivity changes using low-dimensional, informative representations of networks in a latent geometric space. Specifically, the networks are embedded in a polar representation of the hyperbolic plane, the hyperbolic disk. Here, we used a geometric score, entirely based on the computation of distances between nodes in the latent space, to measure the effect of a perturbation on the nodes. Precisely, the score is a local measure of distortion in the geometric neighborhood of a node following a perturbation. The method is applied to a brain network dataset of patients with AD and healthy participants, derived from diffusion-weighted (DWI) and functional magnetic resonance (fMRI) imaging scans. We show that, compared with standard graph measures, our method more accurately identifies the brain regions most affected by neurodegeneration. Notably, the abnormalities detected in memory-related and frontal areas are robust across multiple brain parcellation scales. Finally, our findings suggest that the geometric perturbation score could serve as a potential biomarker for characterizing the progression of the disease.

Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship.

Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer’s Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer’s disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer’s disease. Our experimental results reveal new insights into Alzheimer’s disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases.