Activity-dependent structural plasticity

Precision Functional Mapping to Advance Developmental Psychiatry Research

Many psychiatric conditions have their roots in early development. Individual differences in prenatal brain function (which is influenced by a combination of genetic risk and the prenatal environment) likely interact with individual differences in postnatal experience, resulting in substantial variation in brain functional organization and development in infancy. Neuroimaging has been a powerful tool for understanding typical and atypical brain function and holds promise for uncovering the neurodevelopmental basis of psychiatric illness; however, its clinical utility has been relatively limited thus far. A substantial challenge in this endeavor is the traditional approach of averaging brain data across groups despite individuals varying in their brain organization, which likely obscures important clinically relevant individual variation. Precision functional mapping (PFM) is a neuroimaging technique that allows the capture of individual-specific and highly reliable functional brain properties. Here, we discuss how PFM, through its focus on individuals, has provided novel insights for understanding brain organization across the life span and its promise in elucidating the neural basis of psychiatric disorders. We first summarize the extant literature on PFM in normative populations, followed by its limited utilization in studying psychiatric conditions in adults. We conclude by discussing the potential for infant PFM in advancing developmental precision psychiatry applications, given that many psychiatric disorders start during early infancy and are associated with changes in individual-specific functional neuroanatomy. By exploring the intersection of PFM, development, and psychiatric research, this article underscores the importance of individualized approaches in unraveling the complexities of brain function and improving clinical outcomes across development.

Can a nature walk change your brain? Investigating hippocampal brain plasticity after one hour in a forest

In cities, the incidence of mental disorders is higher, while visits to nature have been reported to benefit mental health and brain function. However, there is a lack of knowledge about how exposure to natural and urban environments affects brain structure. To explore the causal relationship between exposure to these environments and the hippocampal formation, 60 participants were sent on a one hour walk in either a natural (forest) or an urban environment (busy street), and high-resolution hippocampal imaging was performed before and after the walks. We found that the participants who walked in the forest had an increase in subiculum volume, a hippocampal subfield involved in stress response inhibition, while no change was observed after the urban walk. However, this result did not withstand Bonferroni correction for multiple comparisons. Furthermore, the increase in subiculum volume after the forest walk was associated with a decrease in self-reported rumination. These results indicate that visits to nature can lead to observable alterations in brain structure, with potential benefits for mental health and implications for public health and urban planning policies.

Synaptic reorganization of synchronized neuronal networks with synaptic weight and structural plasticity

Abnormally strong neural synchronization may impair brain function, as observed in several brain disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, in particular, during (de)synchronization processes and how they are affected by external perturbation. To investigate the impact of different types of plasticity mechanisms, we combine a network of excitatory integrate-and-fire neurons with different synaptic weight and/or structural plasticity mechanisms: (i) only spike-timing-dependent plasticity (STDP), (ii) only homeostatic structural plasticity (hSP), i.e., without weight-dependent pruning and without STDP, (iii) a combination of STDP and hSP, i.e., without weight-dependent pruning, and (iv) a combination of STDP and structural plasticity (SP) that includes hSP and weight-dependent pruning. To accommodate the diverse time scales of neuronal firing, STDP, and SP, we introduce a simple stochastic SP model, enabling detailed numerical analyses. With tools from network theory, we reveal that structural reorganization may remarkably enhance the network's level of synchrony. When weaker contacts are preferentially eliminated by weight-dependent pruning, synchrony is achieved with significantly sparser connections than in randomly structured networks in the STDP-only model. In particular, the strengthening of contacts from neurons with higher natural firing rates to those with lower rates and the weakening of contacts in the opposite direction, followed by selective removal of weak contacts, allows for strong synchrony with fewer connections. This activity-led network reorganization results in the emergence of degree-frequency, degree-degree correlations, and a mixture of degree assortativity. We compare the stimulation-induced desynchronization of synchronized states in the STDP-only model (i) with the desynchronization of models (iii) and (iv). The latter require stimuli of significantly higher intensity to achieve long-term desynchronization. These findings may inform future pre-clinical and clinical studies with invasive or non-invasive stimulus modalities aiming at inducing long-lasting relief of symptoms, e.g., in Parkinson's disease.

Neuronal activity induces symmetry breaking in neurodegenerative disease spreading

Dynamical systems on networks typically involve several dynamical processes evolving at different timescales. For instance, in Alzheimer's disease, the spread of toxic protein throughout the brain not only disrupts neuronal activity but is also influenced by neuronal activity itself, establishing a feedback loop between the fast neuronal activity and the slow protein spreading. Motivated by the case of Alzheimer's disease, we study the multiple-timescale dynamics of a heterodimer spreading process on an adaptive network of Kuramoto oscillators. Using a minimal two-node model, we establish that heterogeneous oscillatory activity facilitates toxic outbreaks and induces symmetry breaking in the spreading patterns. We then extend the model formulation to larger networks and perform numerical simulations of the slow-fast dynamics on common network motifs and on the brain connectome. The simulations corroborate the findings from the minimal model, underscoring the significance of multiple-timescale dynamics in the modeling of neurodegenerative diseases.

Sensory Stimulation-dependent Npas4 Expression in the Olfactory Bulb during Early Postnatal Development

The development of the olfactory system is influenced by sensory inputs, and it maintains neuronal generation and plasticity throughout the lifespan. The olfactory bulb contains a higher proportion of interneurons than other brain regions, particularly during the early postnatal period of neurogenesis. Although the relationship between sensory stimulation and olfactory bulb development during the postnatal period has been well studied, the molecular mechanisms have yet to be identified. In this study, we used western blotting and immunohistochemistry to analyze the expression of the transcription factor Npas4, a neuron-specific immediate-early gene that acts as a developmental regulator in many brain regions. We found that Npas4 is highly expressed in olfactory bulb interneurons during the early postnatal stages and gradually decreases toward the late postnatal stages. Npas4 expression was observed in all olfactory bulb layers, including the rostral migratory stream, where newborn neurons are generated and migrate to the olfactory bulb. Under sensory deprivation, the olfactory bulb size and the number of olfactory bulb interneurons were reduced. Furthermore, Npas4 expression and the expression of putative Npas4 downstream molecules were decreased. Collectively, these findings indicate that Npas4 expression induced by sensory input plays a role in the formation of neural circuits with excitatory mitral/tufted cells by regulating the survival of olfactory bulb interneurons during the early stages of postnatal development.

Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism

Memory formation is usually associated with Hebbian learning and synaptic plasticity, which changes the synaptic strengths but omits structural changes. A recent study suggests that structural plasticity can also lead to silent memory engrams, reproducing a conditioned learning paradigm with neuron ensembles. However, this study is limited by its way of synapse formation, enabling the formation of only one memory engram. Overcoming this, our model allows the formation of many engrams simultaneously while retaining high neurophysiological accuracy, e.g., as found in cortical columns. We achieve this by substituting the random synapse formation with the Model of Structural Plasticity. As a homeostatic model, neurons regulate their activity by growing and pruning synaptic elements based on their current activity. Utilizing synapse formation based on the Euclidean distance between the neurons with a scalable algorithm allows us to easily simulate 4 million neurons with 343 memory engrams. These engrams do not interfere with one another by default, yet we can change the simulation parameters to form long-reaching associations. Our model's analysis shows that homeostatic engram formation requires a certain spatiotemporal order of events. It predicts that synaptic pruning precedes and enables synaptic engram formation and that it does not occur as a mere compensatory response to enduring synapse potentiation as in Hebbian plasticity with synaptic scaling. Our model paves the way for simulations addressing further inquiries, ranging from memory chains and hierarchies to complex memory systems comprising areas with different learning mechanisms.

An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control

Mean-field models have been developed to replicate key features of epileptic seizure dynamics. However, the precise mechanisms and the role of the brain area responsible for seizure onset and propagation remain incompletely understood. In this study, we employ computational methods within The Virtual Brain framework and the Epileptor model to explore how the location and connectivity of an Epileptogenic Zone (EZ) in a mouse brain are related to focal seizures (seizures that start in one brain area and may or may not remain localized), with a specific focus on the hippocampal region known for its association with epileptic seizures. We then devise computational strategies to confine seizures (prevent widespread propagation), simulating medical-like treatments such as tissue resection and the application of an anti-seizure drugs or neurostimulation to suppress hyperexcitability. Through selectively removing (blocking) specific connections informed by the structural connectome and graph network measurements or by locally reducing outgoing connection weights of EZ areas, we demonstrate that seizures can be kept constrained around the EZ region. We successfully identified the minimal connections necessary to prevent widespread seizures, with a particular focus on minimizing surgical or medical intervention while simultaneously preserving the original structural connectivity and maximizing brain functionality.

Fractal Phototherapy in Maximizing Retina and Brain Plasticity

The neuroplasticity potential is reduced with aging and impairs during neurodegenerative diseases and brain and visual system injuries. This limits the brain's capacity to repair the structure and dynamics of its activity after lesions. Maximization of neuroplasticity is necessary to provide the maximal CNS response to therapeutic intervention and adaptive reorganization of neuronal networks in patients with degenerative pathology and traumatic injury to restore the functional activity of the brain and retina.Considering the fractal geometry and dynamics of the healthy brain and the loss of fractality in neurodegenerative pathology, we suggest that the application of self-similar visual signals with a fractal temporal structure in the stimulation therapy can reactivate the adaptive neuroplasticity and enhance the effectiveness of neurorehabilitation. This proposition was tested in the recent studies. Patients with glaucoma had a statistically significant positive effect of fractal photic therapy on light sensitivity and the perimetric MD index, which shows that methods of fractal stimulation can be a novel nonpharmacological approach to neuroprotective therapy and neurorehabilitation. In healthy rabbits, it was demonstrated that a long-term course of photostimulation with fractal signals does not harm the electroretinogram (ERG) and retina structure. Rabbits with modeled retinal atrophy showed better dynamics of the ERG restoration during daily stimulation therapy for a week in comparison with the controls. Positive changes in the retinal function can indirectly suggest the activation of its adaptive plasticity and the high potential of stimulation therapy with fractal visual stimuli in a nonpharmacological neurorehabilitation, which requires further study.

Changes in brain perfusion with training-related visuomotor improvement in MS

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. A better understanding of the mechanisms supporting brain plasticity in MS would help to develop targeted interventions to promote recovery. A total of 29 MS patients and 19 healthy volunteers underwent clinical assessment and multi-modal MRI acquisition [fMRI during serial reaction time task (SRT), DWI, T1w structural scans and ASL of resting perfusion] at baseline and after 4-weeks of SRT training. Reduction of functional hyperactivation was observed in MS patients following the training, shown by the stronger reduction of the BOLD response during task execution compared to healthy volunteers. The functional reorganization was accompanied by a positive correlation between improvements in task accuracy and the change in resting perfusion after 4 weeks' training in right angular and supramarginal gyri in MS patients. No longitudinal changes in WM and GM measures and no correlation between task performance improvements and brain structure were observed in MS patients. Our results highlight a potential role for CBF as an early marker of plasticity, in terms of functional (cortical reorganization) and behavioral (performance improvement) changes in MS patients that may help to guide future interventions that exploit preserved plasticity mechanisms.

Phase transitions in an adaptive network with the global order parameter adaptation

We consider an adaptive network of Kuramoto oscillators with purely dyadic coupling, where the adaption is proportional to the degree of the global order parameter. We find only the continuous transition to synchronization via the pitchfork bifurcation, an abrupt synchronization (desynchronization) transition via the pitchfork (saddle-node) bifurcation resulting in the bistable region R_{1}. This is a smooth continuous transition to a weakly synchronized state via the pitchfork bifurcation followed by a subsequent abrupt transition to a strongly synchronized state via a second saddle-node bifurcation along with an abrupt desynchronization transition via the first saddle-node bifurcation resulting in the bistable region R_{2} between the weak and strong synchronization. The transition goes from the bistable region R_{1} to the bistable region R_{2}, and transition from the incoherent state to the bistable region R_{2} as a function of the coupling strength for various ranges of the degree of the global order parameter and the adaptive coupling strength. We also find that the phase-lag parameter enlarges the spread of the weakly synchronized state and the bistable states R_{1} and R_{2} to a large region of the parameter space. We also derive the low-dimensional evolution equations for the global order parameters using the Ott-Antonsen ansatz. Further, we also deduce the pitchfork, first and second saddle-node bifurcation conditions, which is in agreement with the simulation results.

Complex Oxides for Brain-Inspired Computing: A Review

The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

Emotion regulation and reactivity are associated with cortical thickness in early to mid-childhood

This study explored the neural correlates of emotion regulation and emotional reactivity in early to mid-childhood. A sample of 96 children (70% White, mid-to-high socioeconomic status) aged 3-8 years provided structural neuroimaging data and caregivers reported on emotion regulation and emotional reactivity. The amygdala, insula, inferior frontal gyrus, anterior cingulate cortex, and medial orbitofrontal cortex were explored as a priori regions of interest (ROIs). ROI analyses revealed that emotion regulation was positively associated with cortical thickness in the insula, whereas emotional reactivity was negatively associated with cortical thickness in the inferior frontal gyrus. Exploratory whole-brain analyses suggested positive associations between emotion regulation and both left superior temporal thickness and right inferior temporal thickness, as well as negative associations between emotional reactivity and left superior temporal thickness. There were no significant associations between emotional regulation or reactivity and amygdala volume or cortical surface area. These findings support the notion that surface area and cortical thickness are distinct measures of brain maturation. In sum, these findings suggest that children may rely on a wider set of neural regions for emotion regulation and reactivity than adults, which is consistent with theories of interactive specialization across the life span.

Experience-dependent structural plasticity of pyramidal neurons in the developing sensory cortices

Sensory experience regulates the structural and functional wiring of neuronal circuits, during development and throughout adulthood. Here, we review current knowledge of how experience affects structural plasticity of pyramidal neurons in the sensory cortices. We discuss the pros and cons of existing labeling approaches, as well as what structural parameters are most plastic. We further discuss how recent advances in sparse labeling of specific neuronal subtypes, as well as development of techniques that allow fast, high resolution imaging in large fields, would enable future studies to address currently unanswered questions in the field of structural plasticity.

Functional and spatial rewiring principles jointly regulate context-sensitive computation

Adaptive rewiring provides a basic principle of self-organizing connectivity in evolving neural network topology. By selectively adding connections to regions with intense signal flow and deleting underutilized connections, adaptive rewiring generates optimized brain-like, i.e. modular, small-world, and rich club connectivity structures. Besides topology, neural self-organization also follows spatial optimization principles, such as minimizing the neural wiring distance and topographic alignment of neural pathways. We simulated the interplay of these spatial principles and adaptive rewiring in evolving neural networks with weighted and directed connections. The neural traffic flow within the network is represented by the equivalent of diffusion dynamics for directed edges: consensus and advection. We observe a constructive synergy between adaptive and spatial rewiring, which contributes to network connectedness. In particular, wiring distance minimization facilitates adaptive rewiring in creating convergent-divergent units. These units support the flow of neural information and enable context-sensitive information processing in the sensory cortex and elsewhere. Convergent-divergent units consist of convergent hub nodes, which collect inputs from pools of nodes and project these signals via a densely interconnected set of intermediate nodes onto divergent hub nodes, which broadcast their output back to the network. Convergent-divergent units vary in the degree to which their intermediate nodes are isolated from the rest of the network. This degree, and hence the context-sensitivity of the network's processing style, is parametrically determined in the evolving network model by the relative prominence of spatial versus adaptive rewiring.

Early retinal deprivation crossmodally alters nascent subplate circuits and activity in the auditory cortex during the precritical period

Sensory perturbation in one modality results in the adaptive reorganization of neural pathways within the spared modalities, a phenomenon known as "crossmodal plasticity," which has been examined during or after the classic "critical period." Because peripheral perturbations can alter the auditory cortex (ACX) activity and functional connectivity of the ACX subplate neurons (SPNs) even before the critical period, called the precritical period, we investigated if retinal deprivation at birth crossmodally alters the ACX activity and SPN circuits during the precritical period. We deprived newborn mice of visual inputs after birth by performing bilateral enucleation. We performed in vivo widefield imaging in the ACX of awake pups during the first two postnatal weeks to investigate cortical activity. We found that enucleation alters spontaneous and sound-evoked activities in the ACX in an age-dependent manner. Next, we performed whole-cell patch clamp recording combined with laser scanning photostimulation in ACX slices to investigate circuit changes in SPNs. We found that enucleation alters the intracortical inhibitory circuits impinging on SPNs, shifting the excitation-inhibition balance toward excitation and this shift persists after ear opening. Together, our results indicate that crossmodal functional changes exist in the developing sensory cortices at early ages before the onset of the classic critical period.

Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches.

Early retinal deprivation crossmodally alters nascent subplate circuits and activity in the auditory cortex during the precritical period

Sensory perturbation in one modality results in adaptive reorganization of neural pathways within the spared modalities, a phenomenon known as "crossmodal plasticity", which has been examined during or after the classic 'critical period'. Because peripheral perturbations can alter auditory cortex (ACX) activity and functional connectivity of the ACX subplate neurons (SPNs) even before the classic critical period, called the precritical period, we investigated if retinal deprivation at birth crossmodally alters ACX activity and SPN circuits during the precritical period. We deprived newborn mice of visual inputs after birth by performing bilateral enucleation. We performed in vivo imaging in the ACX of awake pups during the first two postnatal weeks to investigate cortical activity. We found that enucleation alters spontaneous and sound-evoked activity in the ACX in an age-dependent manner. Next, we performed whole-cell patch clamp recording combined with laser scanning photostimulation in ACX slices to investigate circuit changes in SPNs. We found that enucleation alters the intracortical inhibitory circuits impinging on SPNs shifting the excitation-inhibition balance towards excitation and this shift persists after ear opening. Together, our results indicate that crossmodal functional changes exist in the developing sensory cortices at early ages before the onset of the classic critical period.

Effect of mobility on collective phase dynamics of nonlocally coupled oscillators with a phase lag

Nonlocally coupled oscillators with a phase lag self-organize into various patterns, such as global synchronization, the twisted state, and the chimera state. In this paper, we consider nonlocally coupled oscillators that move on a ring by randomly exchanging their positions with the neighbors and investigate the combined effects of phase lag and mobility on the collective phase dynamics. Spanning the whole range of phase lag and mobility, we show that mobility promotes synchronization for an attractive coupling, whereas it destroys coherence for a repulsive coupling. The transition behaviors are discussed in terms of the timescales of synchronization and diffusion of the oscillators. We also find a novel spatiotemporal pattern at the border between coherent and incoherent states.

On the Criticality of Adaptive Boolean Network Robots

Systems poised at a dynamical critical regime, between order and disorder, have been shown capable of exhibiting complex dynamics that balance robustness to external perturbations and rich repertoires of responses to inputs. This property has been exploited in artificial network classifiers, and preliminary results have also been attained in the context of robots controlled by Boolean networks. In this work, we investigate the role of dynamical criticality in robots undergoing online adaptation, i.e., robots that adapt some of their internal parameters to improve a performance metric over time during their activity. We study the behavior of robots controlled by random Boolean networks, which are either adapted in their coupling with robot sensors and actuators or in their structure or both. We observe that robots controlled by critical random Boolean networks have higher average and maximum performance than that of robots controlled by ordered and disordered nets. Notably, in general, adaptation by change of couplings produces robots with slightly higher performance than those adapted by changing their structure. Moreover, we observe that when adapted in their structure, ordered networks tend to move to the critical dynamical regime. These results provide further support to the conjecture that critical regimes favor adaptation and indicate the advantage of calibrating robot control systems at dynamical critical states.

Studying the structure of the nucleus of the trochlear nerve in mice through 7 days of readaptation to earth gravity after spaceflight

The negative effect of hypogravity on the human organism is manifested to a greater extent after the astronauts return to the conditions of habitual gravity. In this work, to elucidate the causes underlying atypical nystagmus, arising after the flight, we studied structural changes in the motoneurons of the trochlear nerve after a 7-day readaptation of mice to the conditions of Earth's gravity. It is known, that motoneurons of the trochlear nerve innervate the muscle that controls the movement of the eyes in the vertical direction. We showed that the number of axodendritic synapses and some other morphological parameters of motoneurons changed by microgravity can return to their original state in 7 days. However, according to some parameters, motoneurons retain a "memory" of the action of microgravity and do not completely restore the structure. The volume of the soma, the shape of the nuclei, the number and orientation of dendrites do not return to pre-flight parameters. The number of dendrites after 7 days of adaptation remained increased, and the proportion of dendrites in the ventrolateral direction became 2.5 times greater than in motoneurons after space flight. The increased number of mitochondria after space flight became even more significant after readaptation. Microgravity-induced plastic changes retain to some extent "memory traces" after readaptation to Earth's gravity. It can be assumed that the restoration of the function of the trochlear nuclei (overcoming nystagmus) is carried out not only by reversible restoration of the structure of neurons, but partially using those mechanisms that are formed in weightlessness.

Dynamics of phase oscillator networks with synaptic weight and structural plasticity

We study the dynamics of Kuramoto oscillator networks with two distinct adaptation processes, one varying the coupling strengths and the other altering the network structure. Such systems model certain networks of oscillatory neurons where the neuronal dynamics, synaptic weights, and network structure interact with and shape each other. We model synaptic weight adaptation with spike-timing-dependent plasticity (STDP) that runs on a longer time scale than neuronal spiking. Structural changes that include addition and elimination of contacts occur at yet a longer time scale than the weight adaptations. First, we study the steady-state dynamics of Kuramoto networks that are bistable and can settle in synchronized or desynchronized states. To compare the impact of adding structural plasticity, we contrast the network with only STDP to one with a combination of STDP and structural plasticity. We show that the inclusion of structural plasticity optimizes the synchronized state of a network by allowing for synchronization with fewer links than a network with STDP alone. With non-identical units in the network, the addition of structural plasticity leads to the emergence of correlations between the oscillators' natural frequencies and node degrees. In the desynchronized regime, the structural plasticity decreases the number of contacts, leading to a sparse network. In this way, adding structural plasticity strengthens both synchronized and desynchronized states of a network. Second, we use desynchronizing coordinated reset stimulation and synchronizing periodic stimulation to induce desynchronized and synchronized states, respectively. Our findings indicate that a network with a combination of STDP and structural plasticity may require stronger and longer stimulation to switch between the states than a network with STDP only.

Percentage amplitude of fluctuation and structural covariance changes of subjective cognitive decline in patients: A multimodal imaging study

Back ground

Subjective cognitive decline (SCD) may be the first clinical sign of Alzheimer’s disease (AD). The possible neural mechanisms of SCD are not well known. This study aimed to compare percent amplitude of fluctuation (PerAF) and structural covariance patterns in patients with SCD and healthy controls (HCs).

Methods

We enrolled 53 patients with SCD and 65 HCs. Resting-state functional magnetic resonance imaging (MRI) data and T1-weighted anatomical brain 3.0-T MRI scans were collected. The PerAF approach was applied to distinguish altered brain functions between the two groups. A whole-brain voxel-based morphometry analysis was performed, and all significant regions were selected as regions of interest (ROIs) for the structural covariance analysis. Statistical analysis was performed using two-sample t-tests, and multiple regressions were applied to examine the relationships between neuroimaging findings and clinical symptoms.

Results

Functional MRI results revealed significantly increased PerAF including the right hippocampus (HIPP) and right thalamus (THA) in patients with SCD relative to HCs. Gray matter volume (GMV) results demonstrated decreased GMV in the bilateral ventrolateral prefrontal cortex (vlPFC) and right insula in patients with SCD relative to HCs. Taking these three areas including the bilateral vlPFC and right insula as ROIs, differences were observed in the structural covariance of the ROIs with several regions between the two groups. Additionally, significant correlations were observed between neuroimaging findings and clinical symptoms.

Conclusion

Our study investigated the abnormal PerAF and structural covariance patterns in patients with SCD, which might provide new insights into the pathological mechanisms of SCD.

Neurons as hierarchies of quantum reference frames

Conceptual and mathematical models of neurons have lagged behind empirical understanding for decades. Here we extend previous work in modeling biological systems with fully scale-independent quantum information-theoretic tools to develop a uniform, scalable representation of synapses, dendritic and axonal processes, neurons, and local networks of neurons. In this representation, hierarchies of quantum reference frames act as hierarchical active-inference systems. The resulting model enables specific predictions of correlations between synaptic activity, dendritic remodeling, and trophic reward. We summarize how the model may be generalized to nonneural cells and tissues in developmental and regenerative contexts.

Theoretical modeling of dendrite growth from conductive wire electro-polymerization

Electropolymerization is a bottom-up materials engineering process of micro/nano-scale that utilizes electrical signals to deposit conducting dendrites morphologies by a redox reaction in the liquid phase. It resembles synaptogenesis in the brain, in which the electrical stimulation in the brain causes the formation of synapses from the cellular neural composites. The strategy has been recently explored for neuromorphic engineering by establishing link between the electrical signals and the dendrites’ shapes. Since the geometry of these structures determines their electrochemical properties, understanding the mechanisms that regulate polymer assembly under electrically programmed conditions is an important aspect. In this manuscript, we simulate this phenomenon using mesoscale simulations, taking into account the important features of spatial–temporal potential mapping based on the time-varying signal, the motion of charged particles in the liquid due to the electric field, and the attachment of particles on the electrode. The study helps in visualizing the motion of the charged particles in different electrical conditions, which is not possible to probe experimentally. Consistent with the experiments, the higher AC frequency of electrical activities favors linear wire-like growth, while lower frequency leads to more dense and fractal dendrites’ growth, and voltage offset leads to asymmetrical growth. We find that dendrites' shape and growth process systematically depend on particle concentration and random scattering. We discover that the different dendrites’ architectures are associated with different Laplace and diffusion fields, which govern the monomers’ trajectory and subsequent dendrites’ growth. Such unconventional engineering routes could have a variety of applications from neuromorphic engineering to bottom-up computing strategies.

Understanding Language Reorganization With Neuroimaging: How Language Adapts to Different Focal Lesions and Insights Into Clinical Applications

When the language-dominant hemisphere is damaged by a focal lesion, the brain may reorganize the language network through functional and structural changes known as adaptive plasticity. Adaptive plasticity is documented for triggers including ischemic, tumoral, and epileptic focal lesions, with effects in clinical practice. Many questions remain regarding language plasticity. Different lesions may induce different patterns of reorganization depending on pathologic features, location in the brain, and timing of onset. Neuroimaging provides insights into language plasticity due to its non-invasiveness, ability to image the whole brain, and large-scale implementation. This review provides an overview of language plasticity on MRI with insights for patient care. First, we describe the structural and functional language network as depicted by neuroimaging. Second, we explore language reorganization triggered by stroke, brain tumors, and epileptic lesions and analyze applications in clinical diagnosis and treatment planning. By comparing different focal lesions, we investigate determinants of language plasticity including lesion location and timing of onset, longitudinal evolution of reorganization, and the relationship between structural and functional changes.

Structure and function differences in the prelimbic cortex to basolateral amygdala circuit mediate trait vulnerability in a novel model of acute social defeat stress in male mice

Stressful life events are ubiquitous and well-known to negatively impact mental health. However, in both humans and animal models, there is large individual variability in how individuals respond to stress, with some but not all experiencing long-term adverse consequences. While there is growing understanding of the neurobiological underpinnings of the stress response, much less is known about how neurocircuits shaped by lifetime experiences are activated during an initial stressor and contribute to this selective vulnerability versus resilience. We developed a model of acute social defeat stress (ASDS) that allows classification of male mice into "susceptible" (socially avoidant) versus "resilient" (expressing control-level social approach) one hour after exposure to six minutes of social stress. Using circuit tracing and high-resolution confocal imaging, we explored differences in activation and dendritic spine density and morphology in the prelimbic cortex to basolateral amygdala (PL→BLA) circuit in resilient versus susceptible mice. Susceptible mice had greater PL→BLA recruitment during ASDS and activated PL→BLA neurons from susceptible mice had more and larger mushroom spines compared to resilient mice. We hypothesized identified structure/function differences indicate an overactive PL→BLA response in susceptible mice and used an intersectional chemogenetic approach to inhibit the PL→BLA circuit during or prior to ASDS. We found in both cases that this blocked ASDS-induced social avoidance. Overall, we show PL→BLA structure/function differences mediate divergent behavioral responses to ASDS in male mice. These results support PL→BLA circuit overactivity during stress as a biomarker of trait vulnerability and potential target for prevention of stress-induced psychopathology.

Adaptive rewiring in nonuniform coupled oscillators

Structural plasticity of the brain can be represented in a highly simplified form as adaptive rewiring, the relay of connections according to the spontaneous dynamic synchronization in network activity. Adaptive rewiring, over time, leads from initial random networks to brain-like complex networks, that is, networks with modular small-world structures and a rich-club effect. Adaptive rewiring has only been studied, however, in networks of identical oscillators with uniform or random coupling strengths. To implement information-processing functions (e.g., stimulus selection or memory storage), it is necessary to consider symmetry-breaking perturbations of oscillator amplitudes and coupling strengths. We studied whether nonuniformities in amplitude or connection strength could operate in tandem with adaptive rewiring. Throughout network evolution, either amplitude or connection strength of a subset of oscillators was kept different from the rest. In these extreme conditions, subsets might become isolated from the rest of the network or otherwise interfere with the development of network complexity. However, whereas these subsets form distinctive structural and functional communities, they generally maintain connectivity with the rest of the network and allow the development of network complexity. Pathological development was observed only in a small proportion of the models. These results suggest that adaptive rewiring can robustly operate alongside information processing in biological and artificial neural networks.

Spike-Timing-Dependent Plasticity Mediated by Dopamine and its Role in Parkinson's Disease Pathophysiology

Parkinson's disease (PD) is a multi-systemic neurodegenerative brain disorder. Motor symptoms of PD are linked to the significant dopamine (DA) loss in substantia nigra pars compacta (SNc) followed by basal ganglia (BG) circuit dysfunction. Increasing experimental and computational evidence indicates that (synaptic) plasticity plays a key role in the emergence of PD-related pathological changes following DA loss. Spike-timing-dependent plasticity (STDP) mediated by DA provides a mechanistic model for synaptic plasticity to modify synaptic connections within the BG according to the neuronal activity. To shed light on how DA-mediated STDP can shape neuronal activity and synaptic connectivity in the PD condition, we reviewed experimental and computational findings addressing the modulatory effect of DA on STDP as well as other plasticity mechanisms and discussed their potential role in PD pathophysiology and related network dynamics and connectivity. In particular, reshaping of STDP profiles together with other plasticity-mediated processes following DA loss may abnormally modify synaptic connections in competing pathways of the BG. The cascade of plasticity-induced maladaptive or compensatory changes can impair the excitation-inhibition balance towards the BG output nuclei, leading to the emergence of pathological activity-connectivity patterns in PD. Pre-clinical, clinical as well as computational studies reviewed here provide an understanding of the impact of synaptic plasticity and other plasticity mechanisms on PD pathophysiology, especially PD-related network activity and connectivity, after DA loss. This review may provide further insights into the abnormal structure-function relationship within the BG contributing to the emergence of pathological states in PD. Specifically, this review is intended to provide detailed information for the development of computational network models for PD, serving as testbeds for the development and optimization of invasive and non-invasive brain stimulation techniques. Computationally derived hypotheses may accelerate the development of therapeutic stimulation techniques and potentially reduce the number of related animal experiments.

Computational benefits of structural plasticity, illustrated in songbirds

The plasticity of nervous systems allows animals to quickly adapt to a changing environment. In particular, the structural plasticity of brain networks is often critical to the development of the central nervous system and the acquisition of complex behaviors. As an example, structural plasticity is central to the development of song-related brain circuits and may be critical for song acquisition in juvenile songbirds. Here, we review current evidences for structural plasticity and their significance from a computational point of view. We start by reviewing evidence for structural plasticity across species and categorizing them along the spatial axes as well as the along the time course during development. We introduce the vocal learning circuitry in zebra finches, as a useful example of structural plasticity, and use this specific case to explore the possible contributions of structural plasticity to computational models. Finally, we discuss current modeling studies incorporating structural plasticity and unexplored questions which are raised by such models.

Integrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods

The anatomical architecture of the brain constrains the dynamics of interactions between various regions. On a microscopic scale, neural plasticity regulates the connections between individual neurons. This microstructural adaptation facilitates coordinated dynamics of populations of neurons (mesoscopic scale) and brain regions (macroscopic scale). However, the mechanisms acting on multiple timescales that govern the reciprocal relationship between neural network structure and its intrinsic dynamics are not well understood. Studies empirically investigating such relationships on the whole-brain level rely on macroscopic measurements of structural and functional connectivity estimated from various neuroimaging modalities such as Diffusion-weighted Magnetic Resonance Imaging (dMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), and functional Magnetic Resonance Imaging (fMRI). dMRI measures the anisotropy of water diffusion along axonal fibers, from which structural connections are estimated. EEG and MEG signals measure electrical activity and magnetic fields induced by the electrical activity, respectively, from various brain regions with a high temporal resolution (but limited spatial coverage), whereas fMRI measures regional activations indirectly via blood oxygen level-dependent (BOLD) signals with a high spatial resolution (but limited temporal resolution). There are several studies in the neuroimaging literature reporting statistical associations between macroscopic structural and functional connectivity. On the other hand, models of large-scale oscillatory dynamics conditioned on network structure (such as the one estimated from dMRI connectivity) provide a platform to probe into the structure-dynamics relationship at the mesoscopic level. Such investigations promise to uncover the theoretical underpinnings of the interplay between network structure and dynamics and could be complementary to the macroscopic level inquiries. In this article, we review theoretical and empirical studies that attempt to elucidate the coupling between brain structure and dynamics. Special attention is given to various clinically relevant dimensions of brain connectivity such as the topological features and neural synchronization, and their applicability for a given modality, spatial or temporal scale of analysis is discussed. Our review provides a summary of the progress made along this line of research and identifies challenges and promising future directions for multi-modal neuroimaging analyses.

Time Course of Homeostatic Structural Plasticity in Response to Optogenetic Stimulation in Mouse Anterior Cingulate Cortex

Plasticity is the mechanistic basis of development, aging, learning, and memory, both in healthy and pathological brains. Structural plasticity is rarely accounted for in computational network models due to a lack of insight into the underlying neuronal mechanisms and processes. Little is known about how the rewiring of networks is dynamically regulated. To inform such models, we characterized the time course of neural activity, the expression of synaptic proteins, and neural morphology employing an in vivo optogenetic mouse model. We stimulated pyramidal neurons in the anterior cingulate cortex of mice and harvested their brains at 1.5 h, 24 h, and $48\,\mathrm{h}$ after stimulation. Stimulus-induced cortical hyperactivity persisted up to 1.5 h and decayed to baseline after $24\,\mathrm{h}$ indicated by c-Fos expression. The synaptic proteins VGLUT1 and PSD-95, in contrast, were upregulated at $24\,\mathrm{h}$ and downregulated at $48\,\mathrm{h}$, respectively. Spine density and spine head volume were also increased at $24\,\mathrm{h}$ and decreased at $48\,\mathrm{h}$. This specific sequence of events reflects a continuous joint evolution of activity and connectivity that is characteristic of the model of homeostatic structural plasticity. Our computer simulations thus corroborate the observed empirical evidence from our animal experiments.

Long-Term Desynchronization by Coordinated Reset Stimulation in a Neural Network Model With Synaptic and Structural Plasticity

Several brain disorders are characterized by abnormal neuronal synchronization. To specifically counteract abnormal neuronal synchrony and, hence, related symptoms, coordinated reset (CR) stimulation was computationally developed. In principle, successive epochs of synchronizing and desynchronizing stimulation may reversibly move neural networks with plastic synapses back and forth between stable regimes with synchronized and desynchronized firing. Computationally derived predictions have been verified in pre-clinical and clinical studies, paving the way for novel therapies. However, as yet, computational models were not able to reproduce the clinically observed increase of desynchronizing effects of regularly administered CR stimulation intermingled by long stimulation-free epochs. We show that this clinically important phenomenon can be computationally reproduced by taking into account structural plasticity (SP), a mechanism that deletes or generates synapses in order to homeostatically adapt the firing rates of neurons to a set point-like target firing rate in the course of days to months. If we assume that CR stimulation favorably reduces the target firing rate of SP, the desynchronizing effects of CR stimulation increase after long stimulation-free epochs, in accordance with clinically observed phenomena. Our study highlights the pivotal role of stimulation- and dosing-induced modulation of homeostatic set points in therapeutic processes.

Multibranch Formal Neuron: An Internally Nonlinear Learning Unit

The transformation of synaptic input into action potential in nerve cells is strongly influenced by the morphology of the dendritic arbor as well as the synaptic efficacy map. The multiplicity of dendritic branches strikingly enables a single cell to act as a highly nonlinear processing element. Studies have also found functional synaptic clustering whereby synapses that encode a common sensory feature are spatially clustered together on the branches. Motivated by these findings, here we introduce a multibranch formal model of the neuron that can integrate synaptic inputs nonlinearly through collective action of its dendritic branches and yields synaptic clustering. An analysis in support of its use as a computational building block is offered. Also offered is an accompanying gradient descent-based learning algorithm. The model unit spans a wide spectrum of nonlinearities, including the parity problem, and can outperform the multilayer perceptron in generalizing to unseen data. The occurrence of synaptic clustering boosts the generalization efficiency of the unit, which may also be the answer for the puzzling ubiquity of synaptic clustering in the real neurons. Our theoretical analysis is backed up by simulations. The study could pave the way to new artificial neural networks.

The immediate impact of transcranial magnetic stimulation on brain structure: Short-term neuroplasticity following one session of cTBS

Recent evidence demonstrates that activation-dependent neuroplasticity on a structural level can occur in a short time (2 hour or less) in the human brain. However, the exact time scale of structural plasticity in the human brain remains unclear. Using voxel-based morphometry (VBM), we investigated changes in grey matter (GM) after one session of continuous theta-burst stimulation (cTBS) delivered to the anterior temporal lobe (ATL). Twenty-five participants received cTBS over the left ATL or the occipital pole as a control site outside of the scanner, followed by structural and functional imaging. During functional imaging, participants performed a semantic association task and a number judgment task as a control task. VBM results revealed decreased GM in the left ATL and right cerebellum after the ATL stimulation compared to the control stimulation. In addition, cTBS over the left ATL induced slower semantic reaction times, reduced regional activity at the target site, and altered functional connectivity between the left and right ATL during semantic processing. Furthermore, the decreased ATL GM density was associated with the interhemispheric ATL-connectivity changes after the ATL stimulation. These results demonstrate that structural alterations caused by one session of cTBS are mirrored in the functional reorganizations in the semantic representation system, showing the rapid dynamics of cortical plasticity. Our findings support fast adapting neuronal plasticity such as synaptic morphology changes. Our results suggest that TBS is able to produce powerful changes in regional synaptic activity in the adult human brain.

Decline in Motor Function during the COVID-19 Pandemic Restrictions and Its Recovery in a Child with Cerebral Palsy: A Case Report

Children with cerebral palsy (CP) experience various restrictions owing to their underdeveloped mobility. Home confinement due to the coronavirus disease 2019 pandemic may further increase these restrictions. We report the case of a 7-year-old boy with CP (Gross Motor Function Classification System level IV) whose motor function declined during the period when physical therapy was discontinued due to lockdown, approximately four months. At the end of the home confinement, the patient's ability to maintain a sitting posture and weight-bearing capacity of the lower extremities decreased. His Gross Motor Function Measure total score also decreased from 34.5% to 31.9%. After resuming physical therapy, the patient recovered the function status seen before the discontinuation of physical therapy, but this took almost twice as long as the confinement period. We reaffirm that frequent physical therapy is crucial for maintaining motor function in non-ambulatory children with CP. As a countermeasure for the future, urgent efforts are needed for the development of telerehabilitation.

The dendrite arbor of Purkinje cells is altered following to tail regeneration in the leopard gecko

Purkinje cells of the cerebellum have a complex arborized arrangement of dendrites and are amongst the most distinctive cell types of the nervous system. Although the neuromorphology of Purkinje cells has been well described for some mammals and teleost fish, for most vertebrates less is known. Here we used a modified Golgi-Cox method to investigate the neuromorphology of Purkinje cells from the lizard Eublepharis macularius, the leopard gecko. Using Sholl and Branch Structure Analyses, we sought to investigate whether the neuromorphology of gecko Purkinje cells was altered is response to tail loss and regeneration. Tail loss is an evolved mechanism commonly used by geckos to escape predation. Loss of the tail represents a significant and sudden change in body length and mass, which is only partially recovered as the tail is regenerated. We predicted that tail loss and regeneration would induce a quantifiable change in Purkinje cell dendrite arborization. Post hoc comparisons of Sholl analyses data showed that geckos with regenerated tails have significant changes in dendrite diameter and the number of dendrite intersections in regions corresponding to the position of parallel fiber synapses. We propose that the neuromorphological alterations observed in gecko Purkinje cells represent a compensatory response to tail regrowth, and perhaps a role in motor learning.

Contralesional Structural Plasticity in Different Molecular Pathologic Subtypes of Insular Glioma

Neuroplasticity may preserve neurologic function in insular glioma, thereby improving prognosis following resection. However, the anatomic and molecular bases of this phenomenon are not known. To address this gap in knowledge, the present study investigated contralesional compensation in different molecular pathologic subtypes of insular glioma by high-resolution three-dimensional T1-weighted structural magnetic resonance imaging. A total of 52 patients with insular glioma were examined. We compared the gray matter volume (GMV) of the contralesional insula according to histological grade [low-grade glioma (LGG) and high-grade glioma (HGG)] and molecular pathology status [isocitrate dehydrogenase (IDH) mutation, telomerase reverse-transcriptase (TERT) promoter mutation, and 1p19q codeletion] by voxel-based morphometry (VBM). A cluster of 320 voxels in contralesional insula with higher GMV was observed in glioma with IDH mutation as compared to IDH wild-type tumors by region of interest-based VBM analysis (family-wise error-corrected at p < 0.05). The GMV of the entire contralesional insula was also larger in insular glioma patients with IDH mutation than in patients with wild-type IDH. However, there was no association between histological grade, TERT promoter mutation, or 1p19q codeletion and GMV in the contralesional insula. Thus, IDH mutation is associated with greater structural compensation in insular glioma. These findings may be useful for predicting neurocognitive and functional outcomes in patients undergoing resection surgery.

Default mode network activity in depression subtypes

Depression continues to carry a major disease burden worldwide, with limitations on the success of traditional pharmacological or psychological treatments. Recent approaches have therefore focused upon the neurobiological underpinnings of depression, and on the "individualization" of depression symptom profiles. One such model of depression has divided the standard diagnostic criteria into four "depression subtypes", with neurological and behavioral pathways. At the same time, attention has been focused upon the region of the brain known as the "default mode network" (DMN) and its role in attention and problem-solving. However, to date, no review has been published of the links between the DMN and the four subtypes of depression. By searching the literature studies from the last 20 years, 62 relevant papers were identified, and their findings are described for the association they demonstrate between aspects of the DMN and the four depression subtypes. It is apparent from this review that there are potential positive clinical and therapeutic outcomes from focusing upon DMN activation and connectivity, via psychological therapies, transcranial magnetic stimulation, and some emerging pharmacological models.

Microgravity effects on the human brain and behavior: Dysfunction and adaptive plasticity

Emerging plans for travel to Mars and other deep space destinations make it critical for us to understand how spaceflight affects the human brain and behavior. Research over the past decade has demonstrated two co-occurring patterns of spaceflight effects on the brain and behavior: dysfunction and adaptive plasticity. Evidence indicates the spaceflight environment induces adverse effects on the brain, including intracranial fluid shifts, gray matter changes, and white matter declines. Past work also suggests that the spaceflight environment induces adaptive neural effects such as sensory reweighting and neural compensation. Here, we introduce a new conceptual framework to synthesize spaceflight effects on the brain, Spaceflight Perturbation Adaptation Coupled with Dysfunction (SPACeD). We review the literature implicating neurobehavioral dysfunction and adaptation in response to spaceflight and microgravity analogues, and we consider pre-, during-, and post-flight factors that may interact with these processes. We draw several instructive parallels with the aging literature which also suggests co-occurring neurobehavioral dysfunction and adaptive processes. We close with recommendations for future spaceflight research, including: 1) increased efforts to distinguish between dysfunctional versus adaptive effects by testing brain-behavioral correlations, and 2) greater focus on tracking recovery time courses.

Double Duty: Mitotic Kinesins and Their Post-Mitotic Functions in Neurons

Neurons, regarded as post-mitotic cells, are characterized by their extensive dendritic and axonal arborization. This unique architecture imposes challenges to how to supply materials required at distal neuronal components. Kinesins are molecular motor proteins that mediate the active delivery of cellular materials along the microtubule cytoskeleton for facilitating the local biochemical and structural changes at the synapse. Recent studies have made intriguing observations that some kinesins that function during neuronal mitosis also have a critical role in post-mitotic neurons. However, we know very little about the function and regulation of such kinesins. Here, we summarize the known cellular and biochemical functions of mitotic kinesins in post-mitotic neurons.

Structural plasticity on an accelerated analog neuromorphic hardware system

In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their neural connectivity and synaptic capacity depend on their specific design choices, but is always intrinsically limited. Here, we present a strategy to achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- and postsynaptic partners while keeping the neuronal fan-in constant and the connectome sparse. In particular, we implemented this algorithm on the analog neuromorphic system BrainScaleS-2. It was executed on a custom embedded digital processor located on chip, accompanying the mixed-signal substrate of spiking neurons and synapse circuits. We evaluated our implementation in a simple supervised learning scenario, showing its ability to optimize the network topology with respect to the nature of its training data, as well as its overall computational efficiency.

Transcriptome Analysis of Deafness: Intracellular Signal Transduction Signaling Pathways Regulate Neuroplastic Changes in the Auditory Cortex

HYPOTHESIS AND BACKGROUND

Hearing loss leads to synaptic changes in auditory neurons and their networks, and functions as a consequence of the interplay between genes and proteins. However, cellular and molecular mechanisms leading to deafness-induced plasticity in the auditory cortex (AC) remain unclear. Here, we examined the changes in gene expression and key signaling pathways that regulate differentially expressed genes (DEGs) in the AC following auditory deafferentation using RNA-sequencing (RNA-Seq) analysis.

METHODS

Cochlear ablation-induced bilaterally deafened Sprague-Dawley rats were maintained for 12 weeks and their ACs were harvested. RNA-seq analysis was performed on each sample to identify which genes were expressed. This information was then used for comparative analysis of DEGs between samples. The statistical significance of DEGs was determined by fold change (|FC| > 1.5) and independent t test (p < 0.05).

RESULTS

RNA-seq analysis identified 72 DEGs, of which 19 were upregulated and 53 were down-regulated after bilateral deafening in the ACs. Gene ontology (GO) analysis revealed the potential involvement of mitogen-activated protein kinase, tumor necrosis factor, and cyclic adenosine 3',5'-monophosphate (e.g., Bdnf, Gli1, and c-Fos) signaling pathways in regulating changes in the expression of the genes listed herein. The DEGs of interest-including c-Fos, Arc, Ntf3, and Gli1-from the RNA-seq analysis were consistent with result of quantitative reverse transcriptase polymerase chain reaction.

CONCLUSION

RNA-seq analysis revealed that auditory deprivation in adult rats elicited changes in gene expression, transcription factor levels, and their complex interaction at specific gene promoters in the AC. Particularly, activation of intracellular signal transduction signaling pathways may be key to neuronal plasticity in deafness.

A Nexus Model of Restricted Interests in Autism Spectrum Disorder

Restricted interests (RIs) in autism spectrum disorder (ASD) are clinically impairing interests of unusual focus or intensity. They are a subtype of restricted and repetitive behaviors which are one of two diagnostic criteria for the disorder. Despite the near ubiquity of RIs in ASD, the neural basis for their development is not well understood. However, recent cognitive neuroscience findings from nonclinical samples and from individuals with ASD shed light on neural mechanisms that may explain the emergence of RIs. We propose the nexus model of RIs in ASD, a novel conceptualization of this symptom domain that suggests that RIs may reflect a co-opting of brain systems that typically serve to integrate complex attention, memory, semantic, and social communication functions during development. The nexus model of RIs hypothesizes that when social communicative development is compromised, brain functions typically located within the lateral surface of cortex may expand into social processing brain systems and alter cortical representations of various cognitive functions during development. These changes, in turn, promote the development of RIs as an alternative process mediated by these brain networks. The nexus model of RIs makes testable predictions about reciprocal relations between the impaired development of social communication and the emergence of RIs in ASD and suggests novel avenues for treatment development.

Adaptive rewiring evolves brain-like structure in weighted networks

Activity-dependent plasticity refers to a range of mechanisms for adaptively reshaping neuronal connections. We model their common principle in terms of adaptive rewiring of network connectivity, while representing neural activity by diffusion on the network: Where diffusion is intensive, shortcut connections are established, while underused connections are pruned. In binary networks, this process is known to steer initially random networks robustly to high levels of structural complexity, reflecting the global characteristics of brain anatomy: modular or centralized small world topologies. We investigate whether this result extends to more realistic, weighted networks. Both normally- and lognormally-distributed weighted networks evolve either modular or centralized topologies. Which of these prevails depends on a single control parameter, representing global homeostatic or normalizing regulation mechanisms. Intermediate control parameter values exhibit the greatest levels of network complexity, incorporating both modular and centralized tendencies. The simulation results allow us to propose diffusion based adaptive rewiring as a parsimonious model for activity-dependent reshaping of brain connectivity structure.

Identifying prognostic markers in autism spectrum disorder using eye tracking

While many children with autism spectrum disorder are now detected at young ages given the rise in screening and general awareness, little is known regarding the prognosis of early detected children. The brain is shaped by experience-dependent mechanisms; thus, what a child pays attention to plays a pivotal role in shaping brain development. Eye tracking can provide an index of a child's visual attention and, as such, holds promise as a technology for revealing prognostic markers. In this, 49 children aged 1-3 years with autism spectrum disorder participated in an eye-tracking test, the GeoPref Test, that revealed preference for social versus nonsocial images. Next, children participated in a comprehensive test battery 5-9 years following the initial GeoPref Test. Statistical tests examined whether early age eye tracking predicted later school-age outcomes in symptom severity, social functioning, adaptive behavior, joint attention, and IQ. Results indicated that toddlers with higher preference for geometric images demonstrated greater symptom severity and fewer gaze shifts at school age. This relationship was not found in relation to IQ or adaptive behavior. Overall, the GeoPref Test holds promise as a symptom severity prognostic tool; further development of eye-tracking paradigms may enhance prognostic power and prove valuable in validating treatment progress.

Modality-specific overlaps in brain structure and function in obsessive-compulsive disorder: Multimodal meta-analysis of case-control MRI studies

Neuroimaging research has shown that patients with obsessive-compulsive disorder (OCD) may present brain structural and functional alterations, but the results across imaging modalities and task paradigms are difficult to reconcile. Are the same brain systems that are structurally different in OCD patients also involved in executive function and emotional processing? To answer this, we conducted separate meta-analyses of voxel-based morphometry studies, executive function functional magnetic resonance imaging (fMRI) studies, and emotional processing fMRI studies. Next, with a multimodal approach (conjunction analysis), we identified the common alterations across meta-analyses. Patients presented increased gray matter volume and hyperactivation in the putamen, but the putamen subregions affected differed depending on the psychological process. Left posterior/dorsal putamen showed hyperactivation during executive processing tasks, while predominantly right anterior/ventral putamen showed hyperactivation during emotional processing tasks. Interestingly, age was significantly associated with increased right putamen volume. Finally, the left dorsolateral prefrontal cortex was hyperactive in both functional domains. Our findings highlight task-specific correlates of brain structure and function in OCD and help integrate a growing literature.

A conceptual framework for plasticity in the developing brain

In this chapter, we highlight the various definitions of early brain plasticity commonly used in the scientific literature. We then present a conceptual framework of early brain plasticity that focuses on plasticity at the level of the synapse (synaptic plasticity) and the level of the network (connectivity). The proposed framework is organized around three main domains through which current theories and principles of early brain plasticity can be integrated: (1) the mechanisms of plasticity and constraints at the synaptic level and network connectivity, (2) the importance of temporal considerations related to the development of the immature brain, and (3) the functions early brain plasticity serve. We then apply this framework to discuss some clinical disorders caused by and/or associated with impaired plasticity mechanisms. We propose that a careful examination of the relationship between mechanisms, constraints, and functions of early brain plasticity in health and disease may provide an integrative understanding of the current theories and principles generated by experimental and observational studies.