Functional Brain Systems Research Articles

Towards personalized precision functional mapping in infancy

Abstract The precise network topology of functional brain systems is highly specific to individuals and undergoes dramatic changes during critical periods of development. Large amounts of high-quality resting state data are required to investigate these individual differences, but are difficult to obtain in early infancy. Using the template matching method, we generated a set of infant network templates to use as priors for individualized functional resting-state network mapping in two independent neonatal datasets with extended acquisition of resting-state functional MRI (fMRI) data. We show that template matching detects all major adult resting-state networks in individual infants and that the topology of these resting-state network maps is individual-specific. Interestingly, there was no plateau in within-subject network map similarity with up to 25 minutes of resting-state data, suggesting that the amount and/or quality of infant data required to achieve stable or high-precision network maps is higher than adults. These findings are a critical step towards personalized precision functional brain mapping in infants, which opens new avenues for clinical applicability of resting-state fMRI and potential for robust prediction of how early functional connectivity patterns relate to subsequent behavioral phenotypes and health outcomes.

Effect of animal behavior on EEG microstates in healthy children: An outdoor observation task

Analyzing physiological signals in the brain under outdoor conditions, like observing animal behavior, forms the normative basis for the outdoor task and provides new insights into the cognitive neuronal mechanisms of children’s functional brain systems. Here we investigated EEG data from a cohort of seventeen children (6–7 years old, 30-channel EEG) in the resting state and animal-observation state, using the microstate method combined with source-localization analysis to identify the changes in network-level functional interactions. Our study suggested that: while observing animal behavior, the parameters (global explained variance, occurrence, coverage, and duration) of microstates showed a regular trend, and the dynamic reorganization patterns of children’s brains were associated with verbal input networks and higher-order cognitive networks; the activity of the brain network in the frontal and temporal lobes of children increased, while the activity of the insula brain area decreased after observing the behavioral activities of animals. This study may be essential to understand the effects of animal behavior on changes in healthy children’s emotions and have important implications for education.

Individualized Functional Brain System Topologies and Major Depression: Relationships Among Patch Sizes and Clinical Profiles and Behavior

BackgroundNeuroimaging studies of major depression have typically been conducted using group-level approaches. However, given interindividual differences in brain systems, there is a need for individualized approaches to brain systems mapping and putative links toward diagnosis, symptoms, and behavior. MethodsWe used an iterative parcellation approach to map individualized brain systems in 328 participants from a multisite, placebo-controlled clinical trial. We hypothesized that participants with depression would show abnormalities in salience, control, default, and affective systems, which would be associated with higher levels of self-reported anhedonia, anxious arousal, and worse cognitive performance. Within hypothesized brain systems, we compared patch sizes (number of vertices) between depressed and healthy control groups. Within depressed groups, abnormal patches were correlated with hypothesized clinical and behavioral measures. ResultsSignificant group differences emerged in hypothesized patches of 1) the lateral salience system (parietal operculum; t326 = −3.11, p = .002) and 2) the control system (left medial posterior prefrontal cortex region; z = −3.63, p < .001), with significantly smaller patches in these regions in participants with depression than in healthy control participants. Results suggest that participants with depression with significantly smaller patch sizes in the lateral salience system and control system regions experience greater anxious arousal and cognitive deficits. ConclusionsThe findings imply that neural features mapped at the individual level may relate meaningfully to diagnosis, symptoms, and behavior. There is strong clinical relevance in taking an individualized brain systems approach to mapping neural functional connectivity because these associated region patch sizes may help advance our understanding of neural features linked to psychopathology and foster future patient-specific clinical decision making.

Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains

Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no studies have applied this framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of “bursty” dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.

Improved fMRI-based pain prediction using Bayesian group-wise functional registration.

In recent years, the field of neuroimaging has undergone a paradigm shift, moving away from the traditional brain mapping approach towards the development of integrated, multivariate brain models that can predict categories of mental events. However, large interindividual differences in both brain anatomy and functional localization after standard anatomical alignment remain a major limitation in performing this type of analysis, as it leads to feature misalignment across subjects in subsequent predictive models. This article addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subject's functional data to a common latent template map. Our proposed Bayesian functional group-wise registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. We achieve the probabilistic registration with inverse-consistency by utilizing the generalized Bayes framework with a loss function for the symmetric group-wise registration. It models the latent template with a Gaussian process, which helps capture spatial features in the template, producing a more precise estimation. We evaluate the method in simulation studies and apply it to data from an fMRI study of thermal pain, with the goal of using functional brain activity to predict physical pain. We find that the proposed approach allows for improved prediction of reported pain scores over conventional approaches. Received on 2 January 2017. Editorial decision on 8 June 2021.

Functional network disorganization and cognitive decline following fractionated whole-brain radiation in mice

Cognitive dysfunction following radiotherapy (RT) is one of the most common complications associated with RT delivered to the brain, but the precise mechanisms behind this dysfunction are not well understood, and to date, there are no preventative measures or effective treatments. To improve patient outcomes, a better understanding of the effects of radiation on the brain’s functional systems is required. Functional magnetic resonance imaging (fMRI) has shown promise in this regard, however, compared to neural activity, hemodynamic measures of brain function are slow and indirect. Understanding how RT acutely and chronically affects functional brain organization requires more direct examination of temporally evolving neural dynamics as they relate to cerebral hemodynamics for bridging with human studies. In order to adequately study the underlying mechanisms of RT-induced cognitive dysfunction, the development of clinically mimetic RT protocols in animal models is needed. To address these challenges, we developed a fractionated whole-brain RT protocol (3Gy/day for 10 days) and applied longitudinal wide field optical imaging (WFOI) of neural and hemodynamic brain activity at 1, 2, and 3 months post RT. At each time point, mice were subject to repeated behavioral testing across a variety of sensorimotor and cognitive domains. Disruptions in cortical neuronal and hemodynamic activity observed 1 month post RT were significantly worsened by 3 months. While broad changes were observed in functional brain organization post RT, brain regions most impacted by RT occurred within those overlapping with the mouse default mode network and other association areas similar to prior reports in human subjects. Further, significant cognitive deficits were observed following tests of novel object investigation and responses to auditory and contextual cues after fear conditioning. Our results fill a much-needed gap in understanding the effects of whole-brain RT on systems level brain organization and how RT affects neuronal versus hemodynamic signaling in the cortex. Having established a clinically-relevant injury model, future studies can examine therapeutic interventions designed to reduce neuroinflammation-based injury following RT. Given the overlap of sequelae that occur following RT with and without chemotherapy, these tools can also be easily incorporated to examine chemotherapy-related cognitive impairment.

An automated pipeline for obtaining labeled ICA-templates corresponding to functional brain systems.

The complexity of our actions and thinking is likely reflected in functional brain networks. Independent component analysis (ICA) is a popular data-driven method to compute group differences between such networks. A common way to investigate network differences is based on ICA maps which are generated from study-specific samples. However, this approach limits the generalizability and reproducibility of the results. Alternatively, network ICA templates can be used, but up to date, few such templates exist and are limited in terms of the functional systems they cover. Here, we propose a simple two-step procedure to obtain ICA-templates corresponding to functional brain systems of the researcher's choice: In step 1, the functional system of interest needs to be defined by means of a statistical parameter map (input), which one can generate with open-source software such as NeuroSynth or BrainMap. In step 2, that map is correlated to group-ICA maps provided by the Human Connectome Project (HCP), which is based on a large sample size and uses high quality and standardized acquisition procedures. The HCP-provided ICA-map with the highest correlation to the input map is then used as an ICA template representing the functional system of interest, for example, for subsequent analyses such as dual regression. We provide a toolbox to complete step 2 of the suggested procedure and demonstrate the usage of our pipeline by producing an ICA templates that corresponds to "motor function" and nine additional brain functional systems resulting in an ICA maps with excellent alignment with the gray matter/white matter boundaries of the brain. Our toolbox generates data in two different file formats: volumetric-based (NIFTI) and combined surface/volumetric files (CIFTI). Compared to 10 existing templates, our procedure output component maps with systematically stronger contribution of gray matter to the ICA z-values compared to white matter voxels in 9/10 cases by at least a factor of 2. The toolbox allows users to investigate functional networks of interest, which will enhance interpretability, reproducibility, and standardization of research investigating functional brain networks.

Cognitive Architecture of Cognitive Activity: Modeling and Psychophysiological Assessment

Abstract—The main approaches to modeling human cognitive activity and the underlying neural mechanisms are described. The systematization of cognitive architectures is given, and such popular models as ACT-R, SOAR, CLARION and CHREST is overviewed with examples of their practical application in psychology and neurophysiology. The use of the developed models of cognitive functions makes it possible to predict the effectiveness of perception and selection of information, which knowledge and procedures are required for the optimal solution of the problem, the expected error rate while task performing, and what functional brain system is used to organize behavior. Improvement and addition of existing models of cognitive architecture is considered as a prospect for the development of cognitive neuroscience, understanding the patterns of intelligence formation and the development of artificial intelligence.

Task-driven approach to artificial intelligence

The paper considers the task-driven approach to artificial intelligence. It is shown that, on the one hand, it generalizes such approaches as the agent-based approach and general artificial intelligence, and, on the other hand, accurately reflects the cognitive processes and purposeful behavior described in the physiological Theory of functional brain systems.

Нейропсихологический подход в коррекции речевой слуховой агнозии у детей дошкольного возраста

The study aims to substantiate the relevance of using a neuropsychological approach in the formation of auditory gnosis in preschool children with speech disorders in modern speech therapy practice. The paper summarises the results of Russian research in the field of neuroscience on the structure and functioning of brain systems that ensure the perception and implementation of speech activities; determines the goals, objectives, stages of a pedagogical experimental work to overcome speech auditory agnosia in preschool age. The authors analysed the ontogenesis and symptoms of auditory gnosis disorders in children, justified the need to overcome these disorders in preschool age in game activities. The scientific novelty of the study lies in the fact that the relationship between auditory disorders and the main modern causes of systemic speech disorders in preschool children has been determined, the use of a neuropsychological approach in auditory gnosis formation has been tested, which is reflected in the authors’ complex of special speech therapy games. The multifunctionality of the games of this complex is realised in the normalisation of auditory gnosis simultaneously with finger and articulatory praxis, which increases the effectiveness of corrective action. The results of the experimental work convincingly prove the effectiveness of a neuropsychological approach in the correction of speech auditory agnosia in preschool children.

Joint-embeddings reveal functional differences in default-mode network architecture between marmosets and humans

The default-mode network (DMN) is a distributed functional brain system integral for social and higher-order cognition in humans with implications in a myriad of neuropsychological disorders. In this study, we compared the functional architecture of the DMN between humans and marmosets to assess their similarities and differences using joint gradients. This approach permits simultaneous large-scale mapping of functional systems across the cortex of humans and marmosets, revealing evidence of putative homologies between them. In doing so, we find that the DMN architecture of the marmoset exhibits differences along its anterolateral-posterior axis. Specifically, the anterolateral node of the DMN (dorsolateral prefrontal cortex) displayed weak connections and inconsistent connection topographies as compared to its posterior DMN-nodes (posterior cingulate and posterior parietal cortices). We also present evidence that the marmoset medial prefrontal cortex and temporal lobe areas correspond to other macroscopical distributed functional systems that are not part of the DMN. Given the importance of the marmoset as a pre-clinical primate model for higher-order cognitive functioning and the DMN's relevance to cognition, our results suggest that the marmoset may lack the capacity to integrate neural information to subserve cortical dynamics that are necessary for supporting diverse cognitive demands.

A variational Bayesian approach to identifying whole-brain directed networks with fMRI data

The brain is a high-dimensional directed network system, as it consists of many regions as network nodes that exert influence on each other. The directed influence exerted by one region on another is referred to as directed connectivity. We aim to reveal whole-brain directed networks based on resting-state functional magnetic resonance imaging (fMRI) data of many subjects. However, it is both statistically and computationally challenging to produce scientifically meaningful estimates of whole-brain directed networks. To address the statistical modeling challenge, we assume modular brain networks which reflect functional specialization and functional integration of the brain. We address the computational challenge by developing a variational Bayesian method to estimate the new model. We apply our method to resting-state fMRI data of many subjects and identify modules and directed connections in whole-brain directed networks. The identified modules are accordant with functional brain systems specialized for different functions. We also detect directed connections between functionally specialized modules, which is not attainable by existing network methods, based on functional connectivity. In summary, this paper presents a new computationally efficient and flexible method for directed network studies of the brain as well as new scientific findings regarding the functional organization of the human brain.

Hierarchical functional system development supports executive function.

In this perspective, we describe how developmental improvements in youth executive function (EF) are supported by hierarchically organized maturational changes in functional brain systems. We first highlight evidence that functional brain systems are embedded within a hierarchical sensorimotor-association axis of cortical organization. We then review data showing that functional system developmental profiles vary along this axis: systems near the associative end become more functionally segregated, while those in the middle become more integrative. Developmental changes that strengthen the hierarchical organization of the cortex may support EF by facilitating top-down information flow and balancing within- and between-system communication. We propose a central role for attention and frontoparietal control systems in the maturation of healthy EF and suggest that reduced functional system differentiation across the sensorimotor-association axis contributes to transdiagnostic EF deficits.

BAYESIAN FUNCTIONAL REGISTRATION OF FMRI ACTIVATION MAPS.

Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large inter-individual differences in both brain anatomy and functional localization after anatomical alignment remain a major limitation in conducting group analyses and performing population level inference. This paper addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subjects functional data to a common reference map. Our proposed Bayesian functional registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. It combines intensity-based and feature-based information into an integrated framework, and allows inference to be performed on the transformation via the posterior samples. We evaluate the method in a simulation study and apply it to data from a study of thermal pain. We find that the proposed approach provides increased sensitivity for group-level inference.

Examining the Role of Socioeconomic Status and Maternal Sensitivity in Predicting Functional Brain Network Connectivity in 5-Month-Old Infants.

Infancy is a sensitive period of human brain development that is plastically shaped by environmental factors. Both proximal factors, such as sensitive parenting, and distal factors, such as socioeconomic status (SES), are known predictors of individual differences in structural and functional brain systems across the lifespan, yet it is unclear how these familial and contextual factors work together to shape functional brain development during infancy, particularly during the first months of life. In the current study, we examined pre-registered hypotheses regarding the interplay between these factors to assess how maternal sensitivity, within the broader context of socioeconomic variation, relates to the development of functional connectivity in long-range cortical brain networks. Specifically, we measured resting-state functional connectivity in three cortical brain networks (fronto-parietal network, default mode network, homologous-interhemispheric connectivity) using functional near-infrared spectroscopy (fNIRS), and examined the associations between maternal sensitivity, SES, and functional connectivity in a sample of 5-month-old infants and their mothers (N = 50 dyads). Results showed that all three networks were detectable during a passive viewing task, and that maternal sensitivity was positively associated with functional connectivity in the default mode network, such that infants with more sensitive mothers exhibited enhanced functional connectivity in this network. Contrary to hypotheses, we did not observe any associations of SES with functional connectivity in the brain networks assessed in this study. This suggests that at 5 months of age, maternal sensitivity is an important proximal environmental factor associated with individual differences in functional connectivity in a long-range cortical brain network implicated in a host of emotional and social-cognitive brain processes.

Combined analysis of cytoarchitectonic, molecular and transcriptomic patterns reveal differences in brain organization across human functional brain systems

Brain areas show specific cellular, molecular, and gene expression patterns that are linked to function, but their precise relationships are largely unknown. To unravel these structure-function relationships, a combined analysis of 53 neurotransmitter receptor genes, receptor densities of six transmitter systems and cytoarchitectonic data of the auditory, somatosensory, visual, motor systems was conducted. Besides covariation of areal gene expression with receptor density, the study reveals specific gene expression patterns in functional systems, which are most prominent for the inhibitory GABAA and excitatory glutamatergic NMDA receptors. Furthermore, gene expression-receptor relationships changed in a systematic manner according to information flow from primary to higher associative areas. The findings shed new light on the relationship of anatomical, functional, and molecular and transcriptomic principles of cortical segregation towards a more comprehensive understanding of human brain organization.

К когнитивной модели познания

The physiology of cognitive activity of the human body is considered: reflexes, instincts, brain afferentations. The afferent fields are laid in human ontogenesis, and their development goes along the path of narrowing the circle of afferentations, the allocation of the “leading” and the location of the others in the reserve fund. The vertex of the cognitive knowledge of the material world is conceptual principles and fundamental natural generalizings, which are polycerent functional brain systems. Cognitive subject activity has in its composition two components — the natural and the social, forming the inextricable unity of natural and social starts of the living mental process of self-regulation in all forms of the activity of the subject. At the highest stages of personality development, the mental still does not cease to be natural and does not become “purely” social. Proportion of natural and social, ensuring the fundamental role of the subject in their mental development, the child's brain receives in the womb. External impacts and influences are valid only indirectly through the internal conditions of the subject in accordance with the cognitive principle of determinism — “external (inter) only through the internal (intra)”. The most important property of a person is self-consciousness of itself in the system of other people and events of society, their activity and their development and self-development. There are two aspects of the manifestation of subjectivity in education: the formation of the subject and personal position of the individual in the development of the sociocultural world and determine its place in it. The subject position of the individual to the environment and people and the subject of the activities of the teachings and mastering (engineering) profession (general scientific and professional education engineer).

Minimum spanning tree hierarchical clustering algorithm: A new Pythagorean fuzzy similarity measure for the analysis of functional brain networks

Clustering structures are one of the most important aspects of complex networks. Minimum spanning tree (MST), the tree that connects all vertices with minimum total weight, can be considered as a fundamental unit of original weighted graphs. There are different types of algorithms that identify clusters in a network, but the existing theories and algorithms for searching trees have not been investigated for uncertain scenarios. This paper tackles the situations where network parameters may be uncertain. Rather, we permit the parameters to take the form of Pythagorean fuzzy numbers (PFNs). Moreover, to represent qualitative aspects of uncertainty, the use of linguistic variables (LVs) has effective means for experts in expressing their views. The current study proposes a graph theory-based agglomerative hierarchical clustering technique for Pythagorean fuzzy sets. We first define generalized Pythagorean fuzzy numbers (GPFNs) and LR-type PFNs. Then we compute the PF distance between two GPFNs and also LR-type PFNs, and formulate the expressions for PF similarity measure. This paper mainly examines the use of PF distance and similarity measures in a minimum spanning tree agglomerative hierarchical clustering method by considering PFLVs. Since the structural and functional systems of brain are characterized by complex networks, we apply the proposed algorithm on a functional brain network to prove its practicality and efficiency. We discuss the hierarchical clustering consequences of the proposed algorithm in the shape of a dendrogram. Finally, we compare the clustering results obtained by different similarity measures.

Dynamics of psychovegetative disorders in participants in modern wars

Twenty modem war participants who have had a mild brain trauma in Afganistan war were engaged in 7,000,30 year follow-up study. The basic vegetative state, regulatory brain system function, emotional state (MMPY, Scmiscek-Litman test, Spilberger questionnaire) were investigated in the follow-up study. The increase of nonspecific brain structure dysfunction clinical characteristics: suchas vegetative dysfunction with elevation of sympathetic activity and emotional shift to depressive and hypochondric disturbances was found.

Controllability of Structural Brain Networks and the Waxing and Waning of Negative Affect in Daily Life

BackgroundThe waxing and waning of negative affect in daily life is normative, reflecting an adaptive capacity to respond flexibly to changing circumstances. However, understanding of the brain structure correlates of affective variability in naturalistic settings has been limited. Using network control theory, we examine facets of brain structure that may enable negative affect variability in daily life. MethodsWe used diffusion-weighted imaging data from 95 young adults (age [in years]: mean = 20.19, SD = 1.80; 56 women) to construct structural connectivity networks that map white matter fiber connections between 200 cortical and 14 subcortical regions. We applied network control theory to these structural networks to estimate the degree to which each brain region’s pattern of structural connectivity facilitates the spread of activity to other brain systems. We examined how the average controllability of functional brain systems relates to negative affect variability, computed by taking the standard deviation of negative affect self-reports collected via smartphone-based experience sampling twice per day over 28 days as participants went about their daily lives. ResultsWe found that high average controllability of the cingulo-insular system is associated with increased negative affect variability. We also found that greater negative affect variability is related to the presence of more depressive symptoms, yet average controllability of the cingulo-insular system was not associated with depressive symptoms. ConclusionsOur results highlight the role that brain structure plays in affective dynamics as observed in the context of daily life, suggesting that average controllability of the cingulo-insular system promotes normative negative affect variability.