Integrative neuroscience: linking levels of analyses

Exploring undiscovered public knowledge in neuroscience

In this essay, I argue that the combination of research synthesis and philosophical methods can fill an important methodological gap in neuroscience. While experimental research and formal modelling have seen their methods progressively increase in rigour and sophistication over the years, the task of analysing and synthesizing the vast literature reporting new results and models has lagged behind. The problem is aggravated because neuroscience has grown and expanded into a vast mosaic of related but partially independent subfields, each with their own literatures. This fragmentation not only makes it difficult to see the full picture emerging from neuroscience research but also limits progress in individual subfields. The current neuroscience literature has the perfect conditions to create what the information scientist Don Swanson called "undiscovered public knowledge"-knowledge that exists in the mutual implications of different published pieces of information but that is nonetheless undiscovered because those pieces have not been explicitly connected. Current methods for rigorous research synthesis, such as systematic reviews and meta-analyses, mostly focus on combining similar studies and are not suited for exploring undiscovered public knowledge. To that aim, they need to be adapted and supplemented. I argue that successful exploration of the hidden implications in the neuroscience literature will require the combination of these adapted research synthesis methods with philosophical methods for rigorous (and creative) analysis and synthesis.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Understanding the neural bases of bodily self-consciousness: recent achievements and main challenges

The last two decades have seen a surge of interest in the mechanisms underpinning bodily self-consciousness (BSC). Studies showed that BSC relies on several bodily experiences (i.e., self-location, body ownership, agency, first-person perspective) and multisensory integration. The aim of this literature review is to summarize new insights and novel developments into the understanding of the neural bases of BSC, such as the contribution of the interoceptive signals to the neural mechanisms of BSC, and the overlap with the neural bases of conscious experience in general and of higher-level forms of self (i.e., the cognitive self). We also identify the main challenges and propose future perspectives that need to be conducted to progress into the understanding of the neural mechanisms of BSC. In particular, we point the lack of crosstalk and cross-fertilization between subdisciplines of integrative neuroscience to better understand BSC, especially the lack of research in animal models to decipher the neural networks and systems of neurotransmitters underpinning BSC. We highlight the need for more causal evidence that specific brain areas are instrumental in generating BSC and the need for studies tapping into interindividual differences in the phenomenal experience of BSC and their underlying mechanisms.

A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics

Recent studies aiming to facilitate mathematical skill development in primary school children have explored the electrophysiological characteristics associated with different levels of arithmetic achievement. The present work introduces an alternative EEG signal characterization using graph metrics and, based on such features, a classification analysis using a decision tree model. This proposal aims to identify group differences in brain connectivity networks with respect to mathematical skills in elementary school children. The methods of analysis utilized were signal-processing (EEG artifact removal, Laplacian filtering, and magnitude square coherence measurement) and the characterization (Graph metrics) and classification (Decision Tree) of EEG signals recorded during performance of a numerical comparison task. Our results suggest that the analysis of quantitative EEG frequency-band parameters can be used successfully to discriminate several levels of arithmetic achievement. Specifically, the most significant results showed an accuracy of 80.00% (α band), 78.33% (δ band), and 76.67% (θ band) in differentiating high-skilled participants from low-skilled ones, averaged-skilled subjects from all others, and averaged-skilled participants from low-skilled ones, respectively. The use of a decision tree tool during the classification stage allows the identification of several brain areas that seem to be more specialized in numerical processing.

Cingulate-basal ganglia-thalamo-cortical aspects of catatonia and implications for treatment

The catatonic syndrome is an example of a multifactorial neurobehavioral disorder that causes much morbidity and mortality but also has the potential to unlock the mystery of how motivation and movement interact to produce behavior. In this chapter, an attempt is made to understand better the catatonic syndrome through the lens of neurobiology and neuropathophysiology updated by recent studies in molecular biology, genomics, inflammasomics, neuroimaging, neural network theory, and neuropsychopathology. This will result in a neurostructural model for the catatonic syndrome that centers on paralimbic regions including the anterior and midcingulate cortices, as they interface with striatal and thalamic nodes in the salience decision-making network. Examination of neurologic disorders like the abulic syndrome, which includes in its extreme catatonic form, akinetic mutism, will identify the cingulate cortex and paralimbic neighbors as regions of interest. This exploration has the potential to unlock mysteries of the brain cascade from motivation to movement and to clarify catatonia therapeutics. Such a synthesis may also help us discern meaning inherent in this complex neurobehavioral syndrome.

Inverse modulation of motor neuron cellular and synaptic properties can maintain the same motor output

Although often examined in isolation, a single neuromodulator typically has multiple cellular and synaptic effects. Here, we have examined the interaction of the cellular and synaptic effects of 5-HT in the lamprey spinal cord. 5-HT reduces the amplitude of glutamatergic synaptic inputs and the slow post-spike afterhyperpolarization (sAHP) in motor neurons. We examined the interaction between these effects using ventral root activity evoked by stimulation of the spinal cord. While 5-HT reduced excitatory glutamatergic synaptic inputs in motor neurons to approximately 60% of control, ventral root activity was not significantly affected. The reduction of the sAHP by 5-HT increased motor neuron excitability by reducing spike frequency adaptation, an effect that could in principle have opposed the reduction of the excitatory synaptic input. Support for this was sought by reducing the amplitude of the sAHP by applying the toxin apamin before 5-HT application. In these experiments, 5-HT reduced the ventral root response, presumably because the reduction of the synaptic input now dominated. This was supported by computer simulations that showed that the motor output could be maintained over a wide range of synaptic input values if they were matched by changes in postsynaptic excitability. The effects of 5-HT on ventral root responses were altered by spinal cord lesions: 5-HT significantly increased ventral root responses in animals that recovered good locomotor function, consistent with a lesion-induced reduction in the synaptic effects of 5-HT, which thus biases its effects to the increase in motor neuron excitability.

Soul, butterfly, mythological nymph: psyche in philosophy and neuroscience

The term "psyche" and its derivatives - including "Psychology" and "Psychiatry" - are rooted in classical philosophy and in mythology. Over the centuries, psyche has been the subject of discourse and contemplation, and of fable; it has also come to signify, in entomology, the order of Lepidoptera. In the current surge of research on brain and mind, there is a gradual transition from the psyche (or the "soul") to the specified descriptors defined by the fields of Behavioral, Cognitive and Integrative Neuroscience.

The use of resting state data in an integrative approach to studying neurocognitive ageing - Commentary on Campbell and Schacter (2016)

This is a commentary on Campbell and Schacter (2016), 'Ageing and the Resting State: Is Cognition Obsolete?'. Campbell and Schacter argue that resting state data have a limited ability to contribute to the study of neurocognitive ageing and that the field should focus more on results from carefully controlled experimental designs. In this commentary, we argue for a different perspective on future research directions in neurocognitive ageing. Specifically for the need to use a more integrative approach; combining rest and task data as well as information from different modalities to obtain a better understanding of the neural mechanisms that underlie healthy cognitive ageing. Potential benefits of this integrative approach are illustrated with a number of examples. In addition, we discuss some of the advantages of using resting state data as part of this integrative approach.

Novel plasticity rule can explain the development of sensorimotor intelligence

Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher-level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no system-specific modifications of the DEP rule. They rather arise from the underlying mechanism of spontaneous symmetry breaking, which is due to the tight brain body environment coupling. The new synaptic rule is biologically plausible and would be an interesting target for neurobiological investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.

Visual hallucinations in Parkinson's disease: theoretical models

One of the most challenging tasks in neuroscience is to be able to meaningfully connect information across the different levels of investigation, from molecular or structural biology to the resulting behavior and cognition. Visual hallucinations are a frequent occurrence in Parkinson's disease and significantly contribute to the burden of the disease. Because of the widespread pathological processes implicated in visual hallucinations in Parkinson's disease, a final common mechanism that explains their manifestation will require an integrative approach, in which consideration is taken across all complementary levels of analysis. This review considers the leading hypothetical frameworks for visual hallucinations in Parkinson's disease, summarizing the key aspects of each in an attempt to highlight the aspects of the condition that such a unifying hypothesis must explain. These competing hypotheses include implications of dream imagery intrusion, deficits in reality monitoring, and impairments in visual perception and attention.

Megascience efforts and the brain

Several recent megascale neuroscience efforts in the U.S. and Europe are concerned with developing infrastructure for tools, modeling, or neuroinformatics. It may seem surprising that they are not instead focused directly on gaining fundamental new insights into brain function.

The metastable brain

Neural ensembles oscillate across a broad range of frequencies and are transiently coupled or "bound" together when people attend to a stimulus, perceive, think, and act. This is a dynamic, self-assembling process, with parts of the brain engaging and disengaging in time. But how is it done? The theory of Coordination Dynamics proposes a mechanism called metastability, a subtle blend of integration and segregation. Tendencies for brain regions to express their individual autonomy and specialized functions (segregation, modularity) coexist with tendencies to couple and coordinate globally for multiple functions (integration). Although metastability has garnered increasing attention, it has yet to be demonstrated and treated within a fully spatiotemporal perspective. Here, we illustrate metastability in continuous neural and behavioral recordings, and we discuss theory and experiments at multiple scales, suggesting that metastable dynamics underlie the real-time coordination necessary for the brain's dynamic cognitive, behavioral, and social functions.

Time to get rid of the 'Modular' in neuropsychology: a unified theory of anosognosia as aberrant predictive coding

Cognitive neuroscience, being more inclusive and ambitious in scope than cognitive neuropsychology, seems to have taken the place of the latter within the modern neurosciences. Nevertheless, recent advances in the neurosciences afford neuropsychology with epistemic possibilities that simply did not exist even 15 years ago. Human lesion studies still have an important role to play in shaping such possibilities, particularly when combined with other methods of enquiry. I first outline theoretical and methodological advances within the neurosciences that can inform and shape the rebirth of a dynamic, non-modular neuropsychology. I then use an influential computational theory of brain function, the free energy principle, to suggest an unified account of anosognosia for hemiplegia as a research example of the potential for transition from a modular, cognitive neuropsychology to a dynamic, computational and even restorative neuropsychology. These and many other adjectives that can flexibly, take the place of 'cognitive' next to 'neuropsychology' will hopefully designate the much needed rebirth and demarcation of a field, neuropsychology itself, that has somehow lost its place within the modern neurosciences and yet seems to have a unique and important role to play in the future understanding of the brain.

Dynamic systems approaches and levels of analysis in the nervous system

Various analyses are applied to physiological signals. While epistemological diversity is necessary to address effects at different levels, there is often a sense of competition between analyses rather than integration. This is evidenced by the differences in the criteria needed to claim understanding in different approaches. In the nervous system, neuronal analyses that attempt to explain network outputs in cellular and synaptic terms are rightly criticized as being insufficient to explain global effects, emergent or otherwise, while higher-level statistical and mathematical analyses can provide quantitative descriptions of outputs but can only hypothesize on their underlying mechanisms. The major gap in neuroscience is arguably our inability to translate what should be seen as complementary effects between levels. We thus ultimately need approaches that allow us to bridge between different spatial and temporal levels. Analytical approaches derived from critical phenomena in the physical sciences are increasingly being applied to physiological systems, including the nervous system, and claim to provide novel insight into physiological mechanisms and opportunities for their control. Analyses of criticality have suggested several important insights that should be considered in cellular analyses. However, there is a mismatch between lower-level neurophysiological approaches and statistical phenomenological analyses that assume that lower-level effects can be abstracted away, which means that these effects are unknown or inaccessible to experimentalists. As a result experimental designs often generate data that is insufficient for analyses of criticality. This review considers the relevance of insights from analyses of criticality to neuronal network analyses, and highlights that to move the analyses forward and close the gap between the theoretical and neurobiological levels, it is necessary to consider that effects at each level are complementary rather than in competition.

Neuronal network analyses: premises, promises and uncertainties

Neuronal networks assemble the cellular components needed for sensory, motor and cognitive functions. Any rational intervention in the nervous system will thus require an understanding of network function. Obtaining this understanding is widely considered to be one of the major tasks facing neuroscience today. Network analyses have been performed for some years in relatively simple systems. In addition to the direct insights these systems have provided, they also illustrate some of the difficulties of understanding network function. Nevertheless, in more complex systems (including human), claims are made that the cellular bases of behaviour are, or will shortly be, understood. While the discussion is necessarily limited, this issue will examine these claims and highlight some traditional and novel aspects of network analyses and their difficulties. This introduction discusses the criteria that need to be satisfied for network understanding, and how they relate to traditional and novel approaches being applied to addressing network function.

Development of motor rhythms in zebrafish embryos

The nervous system can generate rhythms of various frequencies; on the low-frequency side, we have the circuits regulating circadian rhythms with a 24-h period, while on the high-frequency side we have the motor circuits that underlie flight in a hummingbird. Given the ubiquitous nature of rhythms, it is surprising that we know very little of the cellular and molecular mechanisms that produce them in the embryos and of their potential role during the development of neuronal circuits. Recently, zebrafish has been developed as a vertebrate model to study the genetics of neural development. Zebrafish offer several advantages to the study of nervous system development including optical and electrophysiological analysis of neuronal activity even at the earliest embryonic stages. This unique combination of physiology and genetics in the same animal model has led to insights into the development of neuronal networks. This chapter reviews work on the development of zebrafish motor rhythms and speculates on birth and maturation of the circuits that produce them.

Coordination of rhythmic motor activity by gradients of synaptic strength in a neural circuit that couples modular neural oscillators

Synchronization of distributed neural circuits is required for many behavioral tasks, but the mechanisms that coordinate these circuits are largely unknown. The modular local circuits that control crayfish swimmerets are distributed in four segments of the CNS, but when the swimmeret system is active their outputs are synchronized with a stable intersegmental phase difference of 0.25, an example of metachronal synchronization (Izhikevich, 2007). In each module, coordinating neurons encode detailed information about each cycle of the module's motor output as bursts of spikes, and their axons conduct this information to targets in other segments. This information is both necessary and sufficient for normal intersegmental coordination. In a comprehensive set of recordings, we mapped the synaptic connections of two types of coordinating neurons onto their common target neurons in other segments. Both types of coordinating axons caused large, brief EPSPs in their targets. The shape indices of these EPSPs are tuned to transmit the information from each axon precisely. In each target neuron's own module, these bursts of EPSPs modified the phase of the module's motor output. Each axon made its strongest synapse onto the target neuron in the nearest neighboring segment. Its synapses onto homologous targets in more remote segments were progressively weaker. Each target neuron decodes information from several coordinating axons, and the strengths of their synapses differ systematically. These differences in synaptic strength weight information from each segment differently, which might account for features of the system's characteristic metachronal synchronization.

Spontaneous neuronal burst discharges as dependent and independent variables in the maturation of cerebral cortex tissue cultured in vitro: a review of activity-dependent studies in live 'model' systems for the development of intrinsically generated bioelectric slow-wave sleep patterns

A survey is presented of recent experiments which utilize spontaneous neuronal spike trains as dependent and/or independent variables in developing cerebral cortex cultures when synaptic transmission is interfered with for varying periods of time. Special attention is given to current difficulties in selecting suitable preparations for carrying out biologically relevant developmental studies, and in applying spike-train analysis methods with sufficient resolution to detect activity-dependent age and treatment effects. A hierarchy of synchronized nested burst discharges which approximate early slow-wave sleep patterns in the intact organism is established as a stable basis for isolated cortex function. The complexity of reported long- and short-term homeostatic responses to experimental interference with synaptic transmission is reviewed, and the crucial role played by intrinsically generated bioelectric activity in the maturation of cortical networks is emphasized.

Development and validation of a neural population model based on the dynamics of a discontinuous membrane potential neuron model

The major goal of this study was to develop a population density based model derived from statistical mechanics based on the dynamics of a discontinuous membrane potential neuron model. A secondary goal was to validate this model by comparing results from a direct simulation approach on the one hand and our population based approach on the other hand. Comparisons between the two approaches in the case of a synaptically uncoupled and a synaptically coupled neural population produced satisfactory qualitative agreement in terms of firing rate and mean membrane potential. Reasonable quantitative agreement was also obtained for these variables in performed simulations. The results of this work based on the dynamics of a discontinuous membrane potential neuron model provide a basis to simulate phenomenologically large-scale neuronal networks with a reasonably short computing time.

Biological pattern generation: the cellular and computational logic of networks in motion

In 1900, Ramón y Cajal advanced the neuron doctrine, defining the neuron as the fundamental signaling unit of the nervous system. Over a century later, neurobiologists address the circuit doctrine: the logic of the core units of neuronal circuitry that control animal behavior. These are circuits that can be called into action for perceptual, conceptual, and motor tasks, and we now need to understand whether there are coherent and overriding principles that govern the design and function of these modules. The discovery of central motor programs has provided crucial insight into the logic of one prototypic set of neural circuits: those that generate motor patterns. In this review, I discuss the mode of operation of these pattern generator networks and consider the neural mechanisms through which they are selected and activated. In addition, I will outline the utility of computational models in analysis of the dynamic actions of these motor networks.

A Multiple-Levels-of-Analysis Perspective on Resilience: Implications for the Developing Brain, Neural Plasticity, and Preventive Interventions

Resilient functioning, the attainment of unexpected competence despite significant adversity, is among the most intriguing and adaptive phenomena of human development. Although growing attention has been paid to discovering the processes through which individuals at high risk do not develop maladaptively, the empirical study of resilience has focused predominantly on detecting the psychosocial determinants of the phenomenon. For the field of resilience to grow in ways that are commensurate with the complexity inherent to the construct, efforts to understand underlying processes will be facilitated by the increased implementation of interdisciplinary research designed within a developmental psychopathology framework. Research of this nature would entail a consideration of psychological, biological, and environmental-contextual processes from which pathways to resilience might eventuate (known as equifinality), as well as those that result in diverse outcomes among individuals who have achieved resilient functioning (know as multifinality). The possible relation between the mechanisms of neural plasticity and resilience and specific suggestions concerning research questions needed to examine this association are discussed. Examples from developmental neuroscience and molecular genetics are provided to illustrate the potential of incorporating biology into the study of resilience. The importance of adopting a multiple-levels-of-analysis perspective for designing and evaluating interventions aimed at fostering resilient outcomes in persons facing significant adversity is emphasized.