Brain Decoding Using fMRI Images for Multiple Subjects through Deep Learning.

Substantial information related to human cerebral conditions can be decoded through various noninvasive evaluating techniques like fMRI. Exploration of the neuronal activity of the human brain can divulge the thoughts of a person like what the subject is perceiving, thinking, or visualizing. Furthermore, deep learning techniques can be used to decode the multifaceted patterns of the brain in response to external stimuli. Existing techniques are capable of exploring and classifying the thoughts of the human subject acquired by the fMRI imaging data. fMRI images are the volumetric imaging scans which are highly dimensional as well as require a lot of time for training when fed as an input in the deep learning network. However, the hassle for more efficient learning of highly dimensional high-level features in less training time and accurate interpretation of the brain voxels with less misclassification error is needed. In this research, we propose an improved CNN technique where features will be functionally aligned. The optimal features will be selected after dimensionality reduction. The highly dimensional feature vector will be transformed into low dimensional space for dimensionality reduction through autoadjusted weights and combination of best activation functions. Furthermore, we solve the problem of increased training time by using Swish activation function, making it denser and increasing efficiency of the model in less training time. Finally, the experimental results are evaluated and compared with other classifiers which demonstrated the supremacy of the proposed model in terms of accuracy.

Abnormal nodal and global network organization in resting state functional MRI from subjects with the 22q11 deletion syndrome.

The 22q11 deletion syndrome is a genetic disorder associated with a high risk of developing psychosis, and is therefore considered a neurodevelopmental model for studying the pathogenesis of schizophrenia. Studies have shown that localized abnormal functional brain connectivity is present in 22q11 deletion syndrome like in schizophrenia. However, it is less clear whether these abnormal cortical interactions lead to global or regional network disorganization as seen in schizophrenia. We analyzed from a graph-theory perspective fMRI data from 40 22q11 deletion syndrome patients and 67 healthy controls, and reconstructed functional networks from 105 brain regions. Between-group differences were examined by evaluating edge-wise strength and graph theoretical metrics of local (weighted degree, nodal efficiency, nodal local efficiency) and global topological properties (modularity, local and global efficiency). Connectivity strength was globally reduced in patients, driven by a large network comprising 147 reduced connections. The 22q11 deletion syndrome network presented with abnormal local topological properties, with decreased local efficiency and reductions in weighted degree particularly in hub nodes. We found evidence for abnormal integration but intact segregation of the 22q11 deletion syndrome network. Results suggest that 22q11 deletion syndrome patients present with similar aberrant local network organization as seen in schizophrenia, and this network configuration might represent a vulnerability factor to psychosis.

Topology-preserving smoothing of retinotopic maps.

Retinotopic mapping, i.e., the mapping between visual inputs on the retina and neuronal activations in cortical visual areas, is one of the central topics in visual neuroscience. For human observers, the mapping is obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Although it is well known from neurophysiology that the mapping is topological (i.e., the topology of neighborhood connectivity is preserved) within each visual area, retinotopic maps derived from the state-of-the-art methods are often not topological because of the low signal-to-noise ratio and spatial resolution of fMRI. The violation of topological condition is most severe in cortical regions corresponding to the neighborhood of the fovea (e.g., < 1 degree eccentricity in the Human Connectome Project (HCP) dataset), significantly impeding accurate analysis of retinotopic maps. This study aims to directly model the topological condition and generate topology-preserving and smooth retinotopic maps. Specifically, we adopted the Beltrami coefficient, a metric of quasiconformal mapping, to define the topological condition, developed a mathematical model to quantify topological smoothing as a constrained optimization problem, and elaborated an efficient numerical method to solve the problem. The method was then applied to V1, V2, and V3 simultaneously in the HCP dataset. Experiments with both simulated and real retinotopy data demonstrated that the proposed method could generate topological and smooth retinotopic maps.

Multidimensional analysis and detection of informative features in human brain white matter.

The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts "brain age." In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.

Extraction and Analysis of Dynamic Functional Connectome Patterns in Migraine Sufferers: A Resting-State fMRI Study.

Migraine seriously affects the physical and mental health of patients because of its recurrence and the hypersensitivity to the environment that it causes. However, the pathogenesis and pathophysiology of migraine are not fully understood. We addressed this issue in the present study using an autodynamic functional connectome model (A-DFCM) with twice-clustering to compare dynamic functional connectome patterns (DFCPs) from resting-state functional magnetic resonance imaging data from migraine patients and normal control subjects. We used automatic localization of segment points to improve the efficiency of the model, and intergroup differences and network metrics were analyzed to identify the neural mechanisms of migraine. Using the A-DFCM model, we identified 17 DFCPs-including 1 that was specific and 16 that were general-based on intergroup differences. The specific DFCP was closely associated with neuronal dysfunction in migraine, whereas the general DFCPs showed that the 2 groups had similar functional topology as well as differences in the brain resting state. An analysis of network metrics revealed the critical brain regions in the specific DFCP; these were not only distributed in brain areas related to pain such as Brodmann area 1/2/3, basal ganglia, and thalamus but also located in regions that have been implicated in migraine symptoms such as the occipital lobe. An analysis of the dissimilarities in general DFCPs between the 2 groups identified 6 brain areas belonging to the so-called pain matrix. Our findings provide insight into the neural mechanisms of migraine while also identifying neuroimaging biomarkers that can aid in the diagnosis or monitoring of migraine patients.

Stochastic and vibrational resonance in complex networks of neurons.

The concept of resonance in nonlinear systems is crucial and traditionally refers to a specific realization of maximum response provoked by a particular external perturbation. Depending on the system and the nature of perturbation, many different resonance types have been identified in various fields of science. A prominent example is in neuroscience where it has been widely accepted that a neural system may exhibit resonances at microscopic, mesoscopic and macroscopic scales and benefit from such resonances in various tasks. In this context, the two well-known forms are stochastic and vibrational resonance phenomena which manifest that detection and propagation of a feeble information signal in neural structures can be enhanced by additional perturbations via these two resonance mechanisms. Given the importance of network architecture in proper functioning of the nervous system, we here present a review of recent studies on stochastic and vibrational resonance phenomena in neuronal media, focusing mainly on their emergence in complex networks of neurons as well as in simple network structures that represent local behaviours of neuron communities. From this perspective, we aim to provide a secure guide by including theoretical and experimental approaches that analyse in detail possible reasons and necessary conditions for the appearance of stochastic resonance and vibrational resonance in neural systems. This article is part of the theme issue 'Vibrational and stochastic resonance in driven nonlinear systems (part 2)'.

Statistical power: Implications for planning MEG studies.

Statistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated "experiments" using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the "signal condition", but not in the "noise condition", and detected significant differences at sensor level with classical paired t-tests across subjects, using amplitude, squared amplitude, and global field power (GFP) measures. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the most detectable effects originate from source locations that are closest to the sensors and oriented tangentially with respect to the head surface. In addition, cross-subject variability in orientation also affected group-level detectability, boosting detection in regions where this variability was small and hindering detection in regions where it was large. Incidentally, we observed a considerable covariation of source position, orientation, and their cross-subject variability in individual brain anatomical space, making it difficult to assess the impact of each of these variables independently of one another. We thus also performed simulations where we controlled spatial properties independently of individual anatomy. These additional simulations confirmed the strong impact of distance and orientation and further showed that orientation variability across subjects affects detectability, whereas position variability does not. Importantly, our study indicates that strict unequivocal recommendations as to the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies and should be adapted according to the brain regions under study.

A neuroimaging biomarker for sustained experimental and clinical pain.

Sustained pain is a major characteristic of clinical pain disorders, but it is difficult to assess in isolation from co-occurring cognitive and emotional features in patients. In this study, we developed a functional magnetic resonance imaging signature based on whole-brain functional connectivity that tracks experimentally induced tonic pain intensity and tested its sensitivity, specificity and generalizability to clinical pain across six studies (total n = 334). The signature displayed high sensitivity and specificity to tonic pain across three independent studies of orofacial tonic pain and aversive taste. It also predicted clinical pain severity and classified patients versus controls in two independent studies of clinical low back pain. Tonic and clinical pain showed similar network-level representations, particularly in somatomotor, frontoparietal and dorsal attention networks. These patterns were distinct from representations of experimental phasic pain. This study identified a brain biomarker for sustained pain with high potential for clinical translation.

Assessment of Effective Network Connectivity among MEG None Contaminated Epileptic Transitory Events.

Characterizing epileptogenic zones EZ (sources responsible of excessive discharges) would assist a neurologist during epilepsy diagnosis. Locating efficiently these abnormal sources among magnetoencephalography (MEG) biomarker is obtained by several inverse problem techniques. These techniques present different assumptions and particular epileptic network connectivity. Here, we proposed to evaluate performances of distributed inverse problem in defining EZ. First, we applied an advanced technique based on Singular Value Decomposition (SVD) to recover only pure transitory activities (interictal epileptiform discharges). We evaluated our technique's robustness in separation between transitory and ripples versus frequency range, transitory shapes, and signal to noise ratio on simulated data (depicting both epileptic biomarkers and respecting time series and spectral properties of realistic data). We validated our technique on MEG signal using detector precision on 5 patients. Then, we applied four methods of inverse problem to define cortical areas and neural generators of excessive discharges. We computed network connectivity of each technique. Then, we confronted obtained noninvasive networks to intracerebral EEG transitory network connectivity using nodes in common, connection strength, distance metrics between concordant nodes of MEG and IEEG, and average propagation delay. Coherent Maximum Entropy on the Mean (cMEM) proved a high matching between MEG network connectivity and IEEG based on distance between active sources, followed by Exact low-resolution brain electromagnetic tomography (eLORETA), Dynamical Statistical Parametric Mapping (dSPM), and Minimum norm estimation (MNE). Clinical performance was interesting for entire methods providing in an average of 73.5% of active sources detected in depth and seen in MEG, and vice versa, about 77.15% of active sources were detected from MEG and seen in IEEG. Investigated problem techniques succeed at least in finding one part of seizure onset zone. dSPM and eLORETA depict the highest connection strength among all techniques. Propagation delay varies in this range [18, 25]ms, knowing that eLORETA ensures the lowest propagation delay (18 ms) and the closet one to IEEG propagation delay.

Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks.

Brain networks offers a new insight about connections between function and anatomical regions of human brain. We present results from brain networks built from functional magnetic resonance images during finger tapping paradigm. Pearson voxel-voxel correlation in time and frequency domains were performed for all subjects. Besides this standard framework we have implemented a new approach consisting in filtering the data with respect to the fMRI paradigm (finger tapping) in order to obtain a better understanding of the network involved in the execution of the task. The main topological graph measures have been compared in both cases: voxel-voxel correlation and voxel-paradigm filtering plus voxel-voxel correlation. With the standard voxel-voxel correlation a clearly free-scale network was obtained. On the other hand, when we prefiltered the paradigm we obtained two different kind of networks: 1) free-scale; 2) random-like. To our best knowledge, this behaviour is reported here for first time for brain networks. We suggest that paradigm signal prefiltering can provide more infomation about the brain networks.

Association of Prenatal Acetaminophen Exposure Measured in Meconium With Risk of Attention-Deficit/Hyperactivity Disorder Mediated by Frontoparietal Network Brain Connectivity.

Importance: Despite evidence of an association between prenatal acetaminophen exposure and attention-deficit/hyperactivity disorder (ADHD) in offspring, the drug is not contraindicated during pregnancy, possibly because prior studies have relied on maternal self-report, failed to quantify acetaminophen dose, and lacked mechanistic insight. Objective: To examine the association between prenatal acetaminophen exposure measured in meconium (hereinafter referred to as meconium acetaminophen) and ADHD in children aged 6 to 7 years, along with the potential for mediation by functional brain connectivity. Design, Setting, and Participants: This prospective birth cohort study from the Centre Hospitalier Université de Sherbrooke in Sherbrooke, Québec, Canada, included 394 eligible children, of whom 345 had meconium samples collected at delivery and information on ADHD diagnosis. Mothers were enrolled from September 25, 2007, to September 10, 2009, at their first prenatal care visit or delivery and were followed up when children were aged 6 to 7 years. When children were aged 9 to 11 years, resting-state brain connectivity was assessed with magnetic resonance imaging. Data for the present study were collected from September 25, 2007, to January 18, 2020, and analyzed from January 7, 2019, to January 22, 2020. Exposures: Acetaminophen levels measured in meconium. Main Outcomes and Measures: Physician diagnosis of ADHD was determined at follow-up when children were aged 6 to 7 years or from medical records. Resting-state brain connectivity was assessed with magnetic resonance imaging; attention problems and hyperactivity were assessed with the Behavioral Assessment System for Children Parent Report Scale. Associations between meconium acetaminophen levels and outcomes were estimated with linear and logistic regressions weighted on the inverse probability of treatment to account for potential confounders. Causal mediation analysis was used to test for mediation of the association between prenatal acetaminophen exposure and hyperactivity by resting-state brain connectivity. Results: Among the 345 children included in the analysis (177 boys [51.3%]; mean [SD] age, 6.58 [0.54] years), acetaminophen was detected in 199 meconium samples (57.7%), and ADHD was diagnosed in 33 children (9.6%). Compared with no acetaminophen, detection of acetaminophen in meconium was associated with increased odds of ADHD (odds ratio [OR], 2.43; 95% CI, 1.41-4.21). A dose-response association was detected; each doubling of exposure increased the odds of ADHD by 10% (OR, 1.10; 95% CI, 1.02-1.19). Children with acetaminophen detected in meconium showed increased negative connectivity between frontoparietal and default mode network nodes to clusters in the sensorimotor cortices, which mediated an indirect effect on increased child hyperactivity (14%; 95% CI, 1%-26%). Conclusions and Relevance: Together with the multitude of other cohort studies showing adverse neurodevelopment associated with prenatal acetaminophen exposure, this work suggests caution should be used in administering acetaminophen during pregnancy. Research into alternative pain management strategies for pregnant women could be beneficial.

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder.

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

Measuring graphical strength within the connectome: A neuroanatomic, parcellation-based study.

INTRODUCTION: Graph theory is a promising mathematical tool to study the connectome. However, little research has been undertaken to correlate graph metrics to functional properties of the brain. In this study, we report a unique association between the strength of cortical regions and their function. METHODS: Eight structural graphs were constructed within DSI Studio using publicly available imaging data derived from the Human Connectome Project. Whole-brain fiber tractography was performed to quantify the strength of each cortical region comprising our atlas. RESULTS: Rank-order analysis revealed 27 distinct areas with high average strength, several of which are associated with eloquent cortical functions. Area 4 localizes to the primary motor cortex and is important for fine motor control. Areas 2, 3a and 3b localize to the primary sensory cortex and are involved in primary sensory processing. Areas V1-V4 in the occipital pole are involved in primary visual processing. Several language areas, including area 44, were also found to have high average strength. CONCLUSIONS: Regions of average high strength tend to localize to eloquent areas of the brain, such as the primary sensorimotor cortex, primary visual cortex, and Broca's area. Future studies will examine the dynamic effects of neurologic disease on this metric.

Distance-based analysis of variance for brain connectivity.

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures, including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder-related changes. Unfortunately, classical statistical testing procedures are not well suited to high-dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low-dimensional features, a process that might mask the salient features of high-dimensional distributions. In this work, we consider a general framework for two-sample testing of complex structures by studying generalized within-group and between-group variances based on distances between complex and potentially high-dimensional observations. We derive an asymptotic approximation to the null distribution of the ANOVA test statistic, and conduct simulation studies with scalar and graph outcomes to study finite sample properties of the test. Finally, we apply our test to our motivating study of structural connectivity in autism spectrum disorder.

Probing neural networks for dynamic switches of communication pathways.

Dynamic communication and routing play important roles in the human brain in order to facilitate flexibility in task solving and thought processes. Here, we present a network perturbation methodology that allows investigating dynamic switching between different network pathways based on phase offsets between two external oscillatory drivers. We apply this method in a computational model of the human connectome with delay-coupled neural masses. To analyze dynamic switching of pathways, we define four new metrics that measure dynamic network response properties for pairs of stimulated nodes. Evaluating these metrics for all network pathways, we found a broad spectrum of pathways with distinct dynamic properties and switching behaviors. We show that network pathways can have characteristic timescales and thus specific preferences for the phase lag between the regions they connect. Specifically, we identified pairs of network nodes whose connecting paths can either be (1) insensitive to the phase relationship between the node pair, (2) turned on and off via changes in the phase relationship between the node pair, or (3) switched between via changes in the phase relationship between the node pair. Regarding the latter, we found that 33% of node pairs can switch their communication from one pathway to another depending on their phase offsets. This reveals a potential mechanistic role that phase offsets and coupling delays might play for the dynamic information routing via communication pathways in the brain.

Detecting associations between intact connectomes and clinical covariates using recursive partitioning object-oriented data analysis.

Many neuroscientists are interested in how connectomes (graphical representations of functional connectivity between areas of the brain) change in relation to covariates. In statistics, changes like this are analyzed using regression, where the outcomes or dependent variables are regressed onto the covariates. However, when the outcome is a complex object, such as connectome graphs, classical regression models cannot be used. The regression approach developed here to work with complex graph outcomes combines recursive partitioning with the Gibbs distribution. We will only discuss the application to connectomes, but the method is generally applicable to any graphical outcome. The method, called Gibbs-RPart, partitions the covariate space into a set of nonoverlapping regions such that the connectomes within regions are more similar than they are to the connectomes in other regions. This paper extends the object-oriented data analysis paradigm for graph-valued data based on the Gibbs distribution, which we have applied previously to hypothesis testing to compare populations of connectomes from distinct groups (see the work of La Rosa et al).

Group-representative functional network estimation from multi-subject fMRI data via MRF-based image segmentation.

BACKGROUND AND OBJECTIVE: There has been growing interest in using functional connectivity patterns, determined from fMRI data to characterize groups of individuals exhibiting common traits. However, the present challenge lies in efficient and accurate identification of distinct patterns observed consistently across multiple subjects. Existing approaches either impose strong assumptions, require aligning images before processing, or require data-intensive machine learning algorithms with manually labeled training datasets. In this paper, we propose a more principled and flexible approach to address this. METHODS: Our approach redefines the problem of estimating the group-representative functional network as an image segmentation problem. After employing an improved clustering-based ICA scheme to pre-process the dataset of individual functional network images, we use a maximum a posteriori-Markov random field (MAP-MRF) framework to solve the image segmentation problem. In this framework, we propose a probabilistic model of the individual pixels of the fMRI data, with the model involving a latent group-representative functional network image. Given an observed dataset, we apply a novel and efficient variational Bayes algorithm to recover the associated latent group image. Our methodology seeks to overcome limitations in more traditional schemes by exploiting spatial relationships underlying the connectivity maps and accounting for uncertainty in the estimation process. RESULTS: We validate our approach using synthetic, simulated and real data. First, we generate datasets from the proposed forward model with subject-specific binary masking and measurement noise, as well as from a variant of the model without measurement noise. We use both datasets to evaluate our model, along with two algorithms: coordinate-ascent algorithm and variational Bayes algorithm. We conclude that our proposed model with variational Bayes outperforms other competitors, even under model-misspecification. Using variational Bayes offers a significant improvement in performance, with almost no additional computational overhead. We next test our approach on simulated fMRI data. We show our approach is robust to initialization and can recover a solution close to the ground truth. Finally, we apply our proposed methodology along with baselines to a real dataset of fMRI recordings of individuals from two groups, a control group and a group suffering from depression, with recordings made while individuals were subjected to musical stimuli. Our methodology is able to identify group differences that are less clear under competing methods. CONCLUSIONS: Our model-based approach demonstrates the advantage of probabilistic models and modern algorithms that account for uncertainty in accurate identification of group-representative connectivity maps. The variational Bayes methodology yields highly accurate results without increasing the computational load compared to traditional methods. In addition, it is robust to model misspecification, and increases the ability to avoid local optima in the solution.

Investigation of an emotion perception test using functional magnetic resonance imaging.

BACKGROUND AND OBJECTIVE: Patients with mood disorders are known to have an emotion recognition deficit in facial emotion processing. Emotion perception involves two systems of cognitive and affective processes associated with brain activation in the fusiform gyrus and prefrontal cortices. To overcome the limitations of existing emotion perception tests, we designed an emotion perception index to assess the individuals' mood status. METHODS: We selected 66 emotional faces (22 pleasant, 22 unpleasant, and 22 neutral) for the emotion perception test and recruited 40 healthy participants to verify the test. The participants completed a demographic data questionnaire and were administered the Beck Depressive Inventory (BDI). They were also scanned to assess the brain functional connectivity (FC) between seeds of the fusiform gyrus and other brain regions using resting-state functional magnetic resonance imaging (rs-fMRI). After rs-fMRI scanning, the participants were administered the emotion perception test on a computer. RESULTS: In response to 108 questions regarding emotional face differentiation, the study group showed an average correct-answer rate of 90.7 ±â€¯6.4% and a mean reaction time of 1.4 ±â€¯0.4 s. We created an emotion perception index from the calculation of correct rate, number of correct responses, and reaction time in response to 108 questions; the mean of the emotion perception index in the study group was 3.8 ±â€¯0.2. The emotion perception index was positively correlated with the BDI scores (r = 0.4, p = 0.01); further, it was positively correlated with the FC from the fusiform gyrus to the left superior frontal gyrus (FDRq < 0.01), left medial frontal gyrus (FDRq < 0.01), left frontal precentral gyrus (FDRq = 0.02), left insula (FDRq < 0.01), and left occipital cuneus (FDRq = 0.01). The FC from the fusiform gyrus to the left insula was positively correlated with the BDI scores (r = 0.59, p < 0.001). CONCLUSIONS: The emotion perception index designed in this study may correctly indicate the mood status of individuals. In addition, the emotion perception test was associated with brain FC from the fusiform gyrus to the frontal and insular cortices.

Networks of random trees as a model of neuronal connectivity.

We provide an analysis of a randomly grown 2-d network which models the morphological growth of dendritic and axonal arbors. From the stochastic geometry of this model we derive a dynamic graph of potential synaptic connections. We estimate standard network parameters such as degree distribution, average shortest path length and clustering coefficient, considering all these parameters as functions of time. Our results show that even a simple model with just a few parameters is capable of representing a wide spectra of architecture, capturing properties of well-known models, such as random graphs or small world networks, depending on the time of the network development. The introduced model allows not only rather straightforward simulations but it is also amenable to a rigorous analysis. This provides a base for further study of formation of synaptic connections on such networks and their dynamics due to plasticity.

Spatiotemporal ontogeny of brain wiring.

The wiring of vertebrate and invertebrate brains provides the anatomical skeleton for cognition and behavior. Connections among brain regions are characterized by heterogeneous strength that is parsimoniously described by the wiring cost and homophily principles. Moreover, brains exhibit a characteristic global network topology, including modules and hubs. However, the mechanisms resulting in the observed interregional wiring principles and network topology of brains are unknown. Here, with the aid of computational modeling, we demonstrate that a mechanism based on heterochronous and spatially ordered neurodevelopmental gradients, without the involvement of activity-dependent plasticity or axonal guidance cues, can reconstruct a large part of the wiring principles (on average, 83%) and global network topology (on average, 80%) of diverse adult brain connectomes, including fly and human connectomes. In sum, space and time are key components of a parsimonious, plausible neurodevelopmental mechanism of brain wiring with a potential universal scope, encompassing vertebrate and invertebrate brains.