Formation Of Brain Networks Research Articles

Simulation-based inference of developmental EEG maturation with the spectral graph model

The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.

Engineering an in vitro retinothalamic nerve model.

Understanding the retinogeniculate pathway in vitro can offer insights into its development and potential for future therapeutic applications. This study presents a Polydimethylsiloxane-based two-chamber system with axon guidance channels, designed to replicate unidirectional retinogeniculate signal transmission in vitro. Using embryonic rat retinas, we developed a model where retinal spheroids innervate thalamic targets through up to 6 mm long microfluidic channels. Using a combination of electrical stimulation and functional calcium imaging we assessed how channel length and electrical stimulation frequency affects thalamic target response. In the presented model we integrated up to 20 identical functional retinothalamic neural networks aligned on a single transparent microelectrode array, enhancing the robustness and quality of recorded functional data. We found that network integrity depends on channel length, with 0.5-2 mm channels maintaining over 90% morphological and 50% functional integrity. A reduced network integrity was recorded in longer channels. The results indicate a notable reduction in forward spike propagation in channels longer than 4 mm. Additionally, spike conduction fidelity decreased with increasing channel length. Yet, stimulation-induced thalamic target activity remained unaffected by channel length. Finally, the study found that a sustained thalamic calcium response could be elicited with stimulation frequencies up to 31 Hz, with higher frequencies leading to transient responses. In conclusion, this study presents a high-throughput platform that demonstrates how channel length affects retina to brain network formation and signal transmission in vitro.

Neurodevelopmental disorders and the role of PSD-95: Understanding pathways and pharmacological interventions

Neurodevelopmental disorders (NDDs) are often linked to disruption in brain development and present challenges for affected individuals in achieving their cognitive, emotional, and motor developmental milestones. NDDs encompass a spectrum of conditions, including autism spectrum disorder (ASD), schizophrenia (SCZ), attention-deficit hyperactivity disorder (ADHD), and epilepsy. The unequivocal diagnosis of an NDD is often challenging due to overlapping signs and symptoms across different conditions. Synaptic plasticity, the activity-driven modification of synaptic strength and efficacy, plays a crucial role in brain network formation and organization and is frequently altered in NDDs. Here, we explore the multifaceted roles of postsynaptic density-95 kDa (PSD-95) in NDDs. Psd-95 is a scaffolding protein belonging to the membrane-associated guanylate kinases (MAGUKs) family, located at the core of synapses, and is central to synaptic plasticity. Dysregulation of PSD-95 is linked to various neuropsychiatric disorders. In SCZ, decreased PSD-95 expression affects synaptic plasticity, thereby impacting learning and memory. Genes associated with ASD interact with PSD-95, and its removal in mice leads to ASD-like behavioral abnormalities. Furthermore, PSD-95 is implicated in ADHD, where its modulation influences neurotransmission. Medications used in NDD treatment, such as antipsychotic drugs and selective serotonin reuptake inhibitors (SSRIs), can alter PSD-95 levels, potentially influencing synapse formation. Alpha-2 adrenergic agonists might enhance synaptic integrity by impacting PSD-95. Alternative pharmacotherapies such as memantine, allopurinol, and ketamine, all influencing PSD-95 to a certain extent, hold promise in managing NDDs. Understanding the role of PSD-95 in these disorders can deepen our biological comprehension and pave the way for targeted therapies. Specifically, exploring how PSD-95 affects synaptic plasticity and dendritic spine development could uncover opportunities for repurposing drugs to treat NDDs associated with mutations in the DLG4 gene encoding PSD-95.

Towards a biologically annotated brain connectome.

The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and toconstruct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.

Very-long-chain fatty acids are crucial to neuronal polarity by providing sphingolipids to lipid rafts

Fatty acids have long been considered essential to brain development; however, the involvement of their synthesis in nervous system formation is unclear. We generate mice with knockout of GPSN2, an enzyme for synthesis of very-long-chain fatty acids (VLCFAs) and investigate the effects. Both GPSN2-/- and GPSN2+/- mice show abnormal neuronal networks as a result of impaired neuronal polarity determination. Lipidomics of GPSN2-/- embryos reveal that ceramide synthesis is specifically inhibited depending on FA length; namely, VLCFA-containing ceramide is reduced. We demonstrate that lipid rafts are highly enriched in growth cones and that GPSN2+/- neurons lose gangliosides in their membranes. Application of C24:0 ceramide, but not C16:0 ceramide or C24:0 phosphatidylcholine, to GPSN2+/- neurons rescues both neuronal polarity determination and lipid-raft density in the growth cone. Taken together, our results indicate that VLCFA synthesis contributes to physiological neuronal development in brain network formation, in particular neuronal polarity determination through the formation of lipid rafts.

Pediatric Traumatic Brain Injury: Impact on the Developing Brain

Traumatic brain injury (TBI) is a serious public health concern impacting millions of children and adolescents each year. Experiencing a brain injury during key critical periods of brain development can affect the normal formation of brain networks that are responsible for a range of complex neurocognitive outcomes. In addition, there are multiple pre- and postinjury factors that influence the trajectory of recovery and outcomes. In this review, we will focus on the current state of the literature within pediatric TBI; systematically review the available research on developmental aspects of TBI in children, focusing on the pathophysiology of the injury and its impact on the developing brain; and highlight knowledge gaps for further exploration.

Archetypal Analysis for neuronal clique detection in low-rate calcium fluorescence imaging.

Archetypal analysis (AA) is a versatile data analysis method to cluster distinct features within a data set. Here, we demonstrate a framework showing the power of AA to spatio-temporally resolve events in calcium imaging, an imaging modality commonly used in neurobiology and neuroscience to capture neuronal communication patterns. After validation of our AA-based approach on synthetic data sets, we were able to characterize neuronal communication patterns in recorded calcium waves. Clinical relevance- Transient calcium events play an essential role in brain cell communication, growth, and network formation, as well as in neurodegeneration. To reliably interpret calcium events from personalized medicine data, where patterns may differ from patient to patient, appropriate image processing and signal analysis methods need to be developed for optimal network characterization.

Epilepsy Research Now in 3D: Harnessing the Power of Brain Organoids in Epilepsy.

Brain organoids represent a powerful tool for studying human neurological diseases, particularly those that affect brain growth and structure. However, many diseases manifest with clear evidence of physiological and network abnormality in the absence of anatomical changes, raising the question of whether organoids possess sufficient neural network complexity to model these conditions. Here, we explore the network-level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex network dynamics reminiscent of intact brain preparations. We demonstrate highly abnormal and epileptiform-like activity in organoids derived from induced pluripotent stem cells from individuals with Rett syndrome, accompanied by transcriptomic differences revealed by single-cell analyses. We also rescue key physiological activities with an unconventional neuroregulatory drug, pifithrin-α. Together, these findings provide an essential foundation for the utilization of brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.

This Month in Anesthesiology

This Month in Anesthesiology

Identification of neural oscillations and epileptiform changes in human brain organoids.

Brain organoids represent a powerful tool for studying human neurological diseases, particularly those that affect brain growth and structure. However, many diseases manifest with clear evidence of physiological and network abnormality in the absence of anatomical changes, raising the question of whether organoids possess sufficient neural network complexity to model these conditions. Here, we explore the network-level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex network dynamics reminiscent of intact brain preparations. We demonstrate highly abnormal and epileptiform-like activity in organoids derived from induced pluripotent stem cells from individuals with Rett syndrome, accompanied by transcriptomic differences revealed by single-cell analyses. We also rescue key physiological activities with an unconventional neuroregulatory drug, pifithrin-α. Together, these findings provide an essential foundation for the utilization of brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.

A generative network model of neurodevelopmental diversity in structural brain organization

The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis—from genes to cognition.

Energy constraints on brain network formation

Energy constraints are a fundamental limitation of the brain, which is physically embedded in a restricted space. The collective dynamics of neurons through connections enable the brain to achieve rich functionality, but building connections and maintaining activity come at a high cost. The effects of reducing these costs can be found in the characteristic structures of the brain network. Nevertheless, the mechanism by which energy constraints affect the organization and formation of the neuronal network in the brain is unclear. Here, it is shown that a simple model based on cost minimization can reproduce structures characteristic of the brain network. With reference to the behavior of neurons in real brains, the cost function was introduced in an activity-dependent form correlating the activity cost and the wiring cost as a simple ratio. Cost reduction of this ratio resulted in strengthening connections, especially at highly activated nodes, and induced the formation of large clusters. Regarding these network features, statistical similarity was confirmed by comparison to connectome datasets from various real brains. The findings indicate that these networks share an efficient structure maintained with low costs, both for activity and for wiring. These results imply the crucial role of energy constraints in regulating the network activity and structure of the brain.

Generative network models of altered structural brain connectivity in schizophrenia

Alterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms remain largely unknown. Generative network models have recently been introduced as a tool to test the biological underpinnings of altered brain network formation. We evaluated different generative network models in healthy controls (n=152), schizophrenia patients (n=66), and their unaffected first-degree relatives (n=32), and we identified spatial and topological factors contributing to network formation. We further investigated how these factors relate to cognition and to polygenic risk for schizophrenia. Our data show that among the four tested classes of generative network models, structural brain networks were optimally accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation across study groups. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. Further exploratory analyses point to potential associations of the model parameter reflecting spatial constraints with the polygenic risk for schizophrenia and cognitive performance. Our results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to regular brain network formation as well as altered connectomes in schizophrenia and healthy individuals at familial risk for schizophrenia. On an exploratory level, our data further point to the potential relevance of spatial constraints for the genetic risk for schizophrenia and general cognitive functioning, thereby encouraging future studies in following up on these observations to gain further insights into the biological basis and behavioral relevance of model parameters.

The neural basis of attentional alterations in prenatally protein malnourished rats.

Protein malnutrition during gestation alters brain development and produces specific behavioral and cognitive changes that persist into adulthood and increase the risks of neuropsychiatric disorders. Given evidence for the role of the prefrontal cortex in such diseases, it is significant that studies in humans and animal models have shown that prenatal protein malnutrition specifically affects functions associated with prefrontal cortex. However, the neural basis underlying these changes is unclear. In the current study, prenatally malnourished and control rats performed a sustained attention task with an unpredictable distractor, a task that depends on intact prefrontal cortical function. Radiolabeled 2-deoxyglucose was used to measure neural and brain network activity during the task. Results confirmed that adult prenatally malnourished rats were more distractible than controls and exhibited lower functional activity in prefrontal cortices. Thus, prefrontal activity was a predictor of task performance in controls but not prenatally malnourished animals. Instead, prenatally malnourished animals relied on different brain networks involving limbic structures such as the hippocampus. These results provide evidence that protein reduction during brain development has more wide-reaching effects on brain networks than previously appreciated, resulting in the formation of brain networks that may reflect compensatory responses in prenatally malnourished brains.

Cultural Exaptation and Cultural Neural Reuse: A Mechanism for the Emergence of Modern Culture and Behavior

On the basis of recent advancements in both neuroscience and archaeology, we propose a plausible biocultural mechanism at the basis of cultural evolution. The proposed mechanism, which relies on the notions of cultural exaptation and cultural neural reuse, may account for the asynchronous, discontinuous, and patchy emergence of innovations around the globe. Cultural exaptation refers to the reuse of previously devised cultural features for new purposes. Cultural neural reuse refers to cases in which exposure to cultural practices induces the formation, activation, and stabilization of new functional and/or structural brain networks during the individual lifespan. The invention of writing is interpreted as a case of cultural exaptation of previous devices to record information, in use since at least the Early Later Stone Age and the beginning of the Upper Paleolithic (44,000 years before present). The measurable changes in brain structure and functioning caused by learning to read are proposed as an exemplar case of cultural neural reuse. It is argued that repeated cycles of cultural exaptation, development of appropriate strategies of cultural transmission, and ensuing cultural neural reuse represent the fundamental mechanism that has regulated the cultural evolution of our lineage. A general predictive model of when and under which circumstances the proposed mechanism should be expected to occur is proposed, and the relationship of our mechanism with gene-culture coevolutionary models is discussed.

Modeling brain dynamic state changes with adaptive mixture independent component analysis

There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting the fluidity of human cognition and behavior. This non-stationarity may involve ever-changing formation and dissolution of active cortical sources and brain networks. However, unsupervised approaches to identify and model these changes in brain dynamics as continuous transitions between quasi-stable brain states using unlabeled, noninvasive recordings of brain activity have been limited. This study explores the use of adaptive mixture independent component analysis (AMICA) to model multichannel electroencephalographic (EEG) data with a set of ICA models, each of which decomposes an adaptively learned portion of the data into statistically independent sources. We first show that AMICA can segment simulated quasi-stationary EEG data and accurately identify ground-truth sources and source model transitions. Next, we demonstrate that AMICA decomposition, applied to 6–13 channel scalp recordings from the CAP Sleep Database, can characterize sleep stage dynamics, allowing 75% accuracy in identifying transitions between six sleep stages without use of EEG power spectra. Finally, applied to 30-channel data from subjects in a driving simulator, AMICA identifies models that account for EEG during faster and slower response to driving challenges, respectively. We show changes in relative probabilities of these models allow effective prediction of subject response speed and moment-by-moment characterization of state changes within single trials. AMICA thus provides a generic unsupervised approach to identifying and modeling changes in EEG dynamics. Applied to continuous, unlabeled multichannel data, AMICA may likely be used to detect and study any changes in cognitive states.

Genetic and Molecular Approaches to Study Neuronal Migration in the Developing Cerebral Cortex.

The migration of neuronal cells in the developing cerebral cortex is essential for proper development of the brain and brain networks. Disturbances in this process, due to genetic abnormalities or exogenous factors, leads to aberrant brain formation, brain network formation, and brain function. In the last decade, there has been extensive research in the field of neuronal migration. In this review, we describe different methods and approaches to assess and study neuronal migration in the developing cerebral cortex. First, we discuss several genetic methods, techniques and genetic models that have been used to study neuronal migration in the developing cortex. Second, we describe several molecular approaches to study aberrant neuronal migration in the cortex which can be used to elucidate the underlying mechanisms of neuronal migration. Finally, we describe model systems to investigate and assess the potential toxicity effect of prenatal exposure to environmental chemicals on proper brain formation and neuronal migration.

The emergence of functional architecture during early brain development

Early human brain development constitutes a sequence of intricate processes resulting in the ontogeny of functionally operative neural circuits. Developmental trajectories of early brain network formation are genetically programmed and can be modified by epigenetic and environmental influences. Such alterations may exert profound effects on neurodevelopment, potentially persisting throughout the lifespan. This review focuses on the critical period of fetal and early postnatal brain development. Here we collate findings from neuroimaging studies, with a particular focus on functional MRI research that interrogated early brain network development in both health and high-risk or disease states. First, we will provide an overview of the developmental processes that take place from the embryonic period through early infancy in order to contextualize brain network formation. Second, functional brain network development in the typically developing brain will be discussed. Third, we will touch on prenatal and perinatal risk factors that may interfere with the trajectories of functional brain wiring, including prenatal substance exposure, maternal mental illness and preterm birth. Collectively, studies have revealed the blueprint of adult human brain organization to be present in the neonatal brain. Distinct attributes of human brain architecture have even been detected in the developing fetal brain from as early as 24 postconceptional weeks. During postnatal brain development, the brain's wiring pattern is further sculpted and modulated to become the full facsimile of the adult human brain, with functional brain network refinement being more rigorous than structural brain network maturation. Advances in neuroimaging techniques have paved the way towards a comprehensive understanding of the maturational pathways of brain network development and of how early developmental adversity may affect these trajectories. Such insights are fundamental for our understanding of human brain functioning, for early identification of infants at risk, as well as for future neuroprotective strategies.

Functional connectivity and network analysis of midbrain and brainstem nuclei

There is limited understanding of how monoamine-producing nuclei within midbrain and brainstem contribute to the formation and functional dynamics of brain networks across the human neocortex. We used resting state fMRI in 154 healthy participants to elucidate patterns of functional connectivity and network organization between cortical/subcortical regions and midbrain/brainstem nuclei.By means of univariate functional connectivity and graph-based analysis, we show that dopaminergic midbrain centers and the serotonergic dorsal raphe nucleus (DRN) are functionally integrated with the default mode network (DMN), whereas the remaining serotonergic raphe nuclei and the noradrenergic locus coeruleus are functionally integrated with the executive-control network (ECN). The majority of midbrain/brainstem nuclei show a high level of connectedness to other network modules classifying these nuclei as “connector” hubs. The additionally applied probabilistic independent component analysis (PICA) broadly corresponded with the results of the GT analysis, describing similar functionally-relevant cortical networks. Since monoaminergic neurotransmission is essential to neocortical function, and represents an important target for pharmacotherapy, our novel findings contribute to a comprehensive understanding of the functional organization of the human brain.

A multi-layer network approach to MEG connectivity analysis

Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia.