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Traditional Chinese medicine (TCM) is a systematic medicine. It provides alternative strategies for the treatment of depression with its clinical experience, comprehensive diagnosis, and treatment theory. Chinese herbal medicine (CHM) is the major form of TCM prescription, and numerous CHMs have been demonstrated to possess remarkable antidepressant-like properties. A diversity of mechanisms have been implicated in CHM-associated antidepressant property. This paper reviewed the neuroplastic mechanisms underlying the antidepressant actions of CHM, finding that CHM repairs neuroplasticity by improving neurogenesis, neurotrophic factors, synaptic spine morphology, cell signaling, glutamatergic system, monoamine neurotransmitters, and neural apoptosis. CHM thereby exerts an antidepressant effect, attempting to offer a better understanding of the mechanisms implicated in TCM-related antidepressant-like efficacy and laying a foundation for the scientific evaluation and development of TCM in treating depression.

Fractional calculus and fractional-order calculus are arranged in lineage as regards the mathematical models with complexity-theoretical tenets capable of capturing the subtle molecular dynamics by the integration of power-law convolution kernels into time- and space-related derivatives emerging in equations concerning the Magnetic Resonance Imaging (MRI) phenomena to which the fractional models of diffusion and relaxation are applied. Endowed with an intricate level of complexity and a unique physical and structural scaffolding at molecular and cellular levels with numerous synapses forming elaborate neural networks which entail in-depth probing and computing of patterns and signatures in individual cells and neurons, human brain as a heterogeneous medium is constituted of tissues with cells of different sizes and shapes, distributed across an extra-cellular space. Characterization of the unique brain cells is sought after to unravel the connections between different cells and tissues for accurate, reliable, robust and optimal models and computing. Accordingly, Diffusion Magnetic Resonance Imaging (DMRI), as a noninvasive and experimental imaging technique with clinical and research applications, provides a measure related to the diffusion characteristics of water in biological tissues, particularly in the brain tissues. Compatible with these aspects and beyond the diffusion coefficients’ measurement, DMRI technique aims to exceed the spatial resolution of the MRI images and draw inferences from the microstructural properties of the related medium. Thus, novel tools become essential for the description of the biological (organelles, membranes, macromolecules and so on) and neurological (axons, dendrites, neurons and so forth) tissues’ complexity. Mathematical model-based computational analyses with multifaceted methods to extract information from the DMRI with SpinDoctor into neuronal dynamics can provide quantitative parametric instruments in order to reflect the tissue properties focusing on the precise link between the tissue microstructure and signals acquired by employing advanced medical imaging technologies. Coalesced with accurate neuron geometry models as well as numerical DMRI simulations, a novel extended and multifaceted predictive mathematical model based on SpinDoctor and Bloch–Torrey partial differential equation (BTPDE) with the Caputo fractional-order derivative (FOD) with three-parameter $(\alpha ,\beta ,\delta )$ Mittag-Leffler function (MLF) has been proposed and developed in our study by extending for the application on Brain Neuron Spin Unit dataset with the relevant multi-stage application-related steps. The feedforward neural networks (FFNNs) with BFGS Quasi-Newton equation, as one of the artificial neural network (ANN) algorithms, are applied on BTPDE with Caputo fractional-order derivative for the neurons and their algorithmic complexity is computed by building a BTPDE with Caputo FOD Neuron model based on different fractional orders. The fractional-order degree of the proposed and developed model is applied in relation to their corresponding complexity degrees. Consequently, experimentation and observations from the simulation-driven FFNN (with BFGS Quasi-Newton equation) learning scheme applied to the Bloch–Torrey PDE–Caputo FOD with MLF Neuron model (named as FFNN–BTPDE–CFODMLF Neuron model) proposed in this study, are made. Thus, by investigating whether the mathematical models based on the accurate neuron geometry models obtained can be optimized by comparing the errors in order to define the order parameter and identify to what optimal extent the errors are in relation to the prediction results with a particular focus on the neuron model, we have been able to estimate and predict brain microstructure through DMRI, accentuating mathematical and medical contributions based on the exploitation and corroboration of powerful modeling as well as computational capabilities.

Two experiments are reported in which participants perceived different physical quantities: size and speed. The perceptual tasks were performed in the context of motor performance problems. Participants perceived the size of objects in order to grasp the objects single handed or with both hands. Likewise, participants perceived the speed of a moving treadmill in order to control walking or running at that speed. In both experiments, the perceptual tasks were repeatedly performed by the participants while the to-be-perceived quantity was gradually varied from small to large objects (Experiment 1) and from low to high speeds (Experiment 2). Hysteresis with negative sign was found when participants were not allowed to execute the motor component, that is, when the execution stage was decoupled from the planning stage. No such effect was found in the control condition, when participants were allowed to execute the motor action. Using a Lotka–Volterra–Haken model for two competing neural populations, it is argued that the observations are consistent with the notion that the repetitions induce an adaptation effect of the perceptual system via top-down regulation. Moreover, the amount of synaptic modulation involved in the adaptation is estimated from participant data.

"What comes first: the chicken or the egg?" Eyes and vision were a great concern for Darwin. Recently, religious fundamentalists have started to attack evolution on the grounds that this is a chicken and egg problem. How could eyes improve without the brain module to use the new information that eye provides? But how could the brain evolve a neural circuit to process data not available to it until a new eye capability emerges? We argue that neural plasticity in the brain allows it to make use of essentially any useful information the eye can produce. And it does so easily within the animal's lifetime. Richard Gregory suggested something like this 40 years ago. Our work resolves a problem with his otherwise-insightful work.

High and increasing prevalence of neurodevelopmental disorders place enormous personal and economic burdens on society. Given the growing realization that the roots of neurodevelopmental disorders often lie in early childhood, there is an urgent need to identify childhood risk factors. Neurodevelopment is marked by periods of heightened experience-dependent neuroplasticity wherein neural circuitry is optimized by the environment. If these critical periods are disrupted, development of normal brain function can be permanently altered, leading to neurodevelopmental disorders. Here, we aim to systematically identify human variants in neuroplasticity-related genes that confer risk for neurodevelopmental disorders. Historically, this knowledge has been limited by a lack of techniques to identify genes related to neurodevelopmental plasticity in a high-throughput manner and a lack of methods to systematically identify mutations in these genes that confer risk for neurodevelopmental disorders. Using an integrative genomics approach, we determined loss-of-function (LOF) variants in putative plasticity genes, identified from transcriptional profiles of brain from mice with elevated plasticity, that were associated with neurodevelopmental disorders. From five shared differentially expressed genes found in two mouse models of juvenile-like elevated plasticity (juvenile wild-type or adult Lynx1^-/- relative to adult wild-type) that were also genotyped in the Mount Sinai BioMe Biobank we identified multiple associations between LOF genes and increased risk for neurodevelopmental disorders across 10,510 patients linked to the Mount Sinai Electronic Medical Records (EMR), including epilepsy and schizophrenia. This work demonstrates a novel approach to identify neurodevelopmental risk genes and points toward a promising avenue to discover new drug targets to address the unmet therapeutic needs of neurodevelopmental disease.

Meditation is a family of practices that cultivate specific psychological states or mental capacities for heightened self-awareness and insight. Neuroscientists have found that regular and sustained meditation has significant structural and functional effects on the brain. Structural and functional neuroimaging have linked meditation to improved regulation of the default network of attention, controlled focus, and sustained attention. Studies have also linked meditation practice with the cultivation of positive emotion. In part because of the evidence that it causes neurobiological changes, support is growing for research on the potential use of meditation for therapies promoting cognitive and affective processing, which should ultimately lift physiological and psychological well-being. Thus far, research outcomes are largely positive and only rarely accompanied by adverse side effects, suggesting that meditation is a promising therapy — and may even help prevent some psychopathologies. Future research should continue to examine the potential healing and restorative effects of meditation.

Autism has been viewed as a highly heritable neurobiological condition of mysterious but presumably genetic origin, which involves lifelong neurocognitive, perceptual and emotional deficits. This conceptual framing has led to a focus on searching for underlying genetic causes of differences in the autistic brain, particularly in anatomical structure, that are presumed to be hardwired into the system.

More recently, it is becoming clear that genes alone do not create autism. The more inclusive emerging view is that genes and environment interact to influence epigenetics, cell signaling and physiology. This formulation goes beyond the idea that a preconceptional or prenatal gene-environment interaction definitively causes a person's autism, and instead emphasizes that the interplay of all of these levels develops over time contingent upon exposures, experiences and lifestyle choices, and that the behaviors are actively produced by living cells that function differently, rather than simply being caused by static brain wiring that is different from the start.

From this vantage point it is important to look freshly at how we think about the brain, and what it is that the brain does to create autistic behaviors. A multi-scaled, whole-body, dynamical approach to the processes of signal generation in the brain and how these processes are shaped by ongoing dynamic interplays of multiple modulators offers previously unappreciated opportunities for improvement of brain function.