Pigment dispersing factors (PDFs, or PDHs in crustaceans) form a structurally related group of neuropeptides found throughout the Ecdysozoa and were first discovered as pigmentary effector hormones in crustaceans. In insects PDFs fulfill crucial neuromodulatory roles, most notably as output regulators of the circadian system, underscoring their central position in physiological and behavioral organization of arthropods. Intriguingly, decapod crustaceans express multiple isoforms of PDH originating from separate genes, yet their differential functions are still to be determined. Here, we functionally define two PDH receptors in the crab Carcinus maenas and show them to be selectively activated by four PDH isoforms: PDHR 43673 was activated by PDH-1 and PDH-2 at low nanomolar doses whilst PDHR 41189 was activated by PDH-3 and an extended 20 residue e-PDH. Detailed examination of the anatomical distribution of all four peptides and their cognate receptors indicate that they likely perform different functions as secreted hormones and/or neuromodulators, with PDH-1 and its receptor 43,673 implicated in an authentic hormonal axis. PDH-2, PDH-3, and e-PDH were limited to non-neurohemal interneuronal sites in the CNS; PDHR 41189 was largely restricted to the nervous system suggesting a neuromodulatory function. Notably PDH-3 and e-PDH were without chromatophore dispersing activity. This is the first report which functionally defines a PDHR in an endocrine system in a crustacean and to indicate this and other putative roles of this physiologically pivotal peptide group in these organisms. Thus, our findings present opportunities to further examine the endocrine and circadian machinery in this important arthropod phylum.

Objective: To investigate the effect of sudden deprivation of sensory inputs from the periodontium on jaw kinematics and time-varying activation profile of the masseter muscle.

Methods: Fourteen (age range: 22-26 years; four men) healthy and natural dentate volunteers participated in a single experimental session. During the experiment, the participants were asked to eat six hard visco-elastic test food models, three each before and after an anesthetic intervention. The movements of the jaw in three dimensions and electromyographic (EMG) activity of the masseter muscle on the chewing side were recorded.

Results: The results of the study showed no significant differences in the number of chewing cycles (P = 0.233) and the duration of chewing sequence (P = 0.198) due to sudden deprivation of sensory inputs from the periodontium. However, there was a significant increase in the jaw opening velocity (P = 0.030) and a significant increase in the duration of occlusal phase (P = 0.004) during the anesthetized condition. The EMG activity of the jaw closing phase was significantly higher during the control condition [116.5 arbitrary units (AU)] than anesthetized condition (93.9 AU). The temporal profile of the masseter muscle showed a biphasic increase in the excitatory muscle drive in the control condition but this increase was virtually absent during the anesthetized condition.

Conclusion: Sudden deprivation of sensory inputs from the periodontium affects the jaw kinematics and jaw muscle activity, with a clear difference in the time-varying activation profile of the masseter muscle. The activation profile of the masseter muscle shows that periodontal mechanoreceptors contribute to approximately 20% of the EMG activity during the jaw closing phase.

Social memory, including the ability to recognize faces and voices, is essential for social relationships. It has a large heritable component, but the knowledge about the contributing genes is sparse. The genetic variation underlying inter-individual differences in social memory was investigated in an exploratory sample (n = 55), genotyped with a chip comprising approximately 200,000 single nucleotide polymorphisms (SNPs), and in a validation sample (n = 582), where 30 SNPs were targeted. In the exploratory study face identity recognition was measured. The validation study also measured vocal sound recognition, as well as recognition of faces and vocal sounds combined (multimodal condition). In the exploratory study, the 30 SNPs that were associated with face recognition at puncorrected < 0.001 and located in genes, were chosen for further study. In the validation study two of these SNPs showed significant associations with recognition of faces, vocal sounds, and multimodal stimuli: rs1800779 in the gene encoding nitric oxide synthase 3 (NOS3) and rs3807370 in the gene encoding the voltage-gated channel, subfamily H, member 2 (KCNH2), in strong linkage disequilibrium with each other. The uncommon alleles were associated with superior performance, and the effects were present for men only (p < 0.0002). The exploratory study also showed a weaker but significant association with (non-emotional) word recognition, an effect that was independent of the effect on face recognition. This study demonstrates evidence for an association between NOS3 and KCNH2SNPs and social memory.

Key scientific discoveries have resulted from genetic studies of Drosophila melanogaster, using a multitude of transgenic fly strains, the majority of which are constructed in a genetic background containing mutations in the white gene. Here we report that white mutant flies from w(1118) strain undergo retinal degeneration. We observed also that w(1118) mutants have progressive loss of climbing ability, shortened life span, as well as impaired resistance to various forms of stress. Retinal degeneration was abolished by transgenic expression of mini-white+ in the white null background w(1118). We conclude that beyond the classical eye-color phenotype, mutations in Drosophila white gene could impair several biological functions affecting parameters like mobility, life span and stress tolerance. Consequently, we suggest caution and attentiveness during the interpretation of old experiments employing white mutant flies and when planning new ones, especially within the research field of neurodegeneration and neuroprotection. We also encourage that the use of w(1118) strain as a wild-type control should be avoided.

The objective of this study is to introduce a new quantitative data-driven analysis (QDA) framework for the analysis of resting-state fMRI (R-fMRI) and use it to investigate the effect of adult age on resting-state functional connectivity (RFC). Whole-brain R-fMRI measurements were conducted on a 3T clinical MRI scanner in 227 healthy adult volunteers (N = 227, aged 18–76 years old, male/female = 99/128). With the proposed QDA framework we derived two types of voxel-wise RFC metrics: the connectivity strength index and connectivity density index utilizing the convolutions of the cross-correlation histogram with different kernels. Furthermore, we assessed the negative and positive portions of these metrics separately. With the QDA framework we found age-related declines of RFC metrics in the superior and middle frontal gyri, posterior cingulate cortex (PCC), right insula and inferior parietal lobule of the default mode network (DMN), which resembles previously reported results using other types of RFC data processing methods. Importantly, our new findings complement previously undocumented results in the following aspects: (1) the PCC and right insula are anti-correlated and tend to manifest simultaneously declines of both the negative and positive connectivity strength with subjects’ age; (2) separate assessment of the negative and positive RFC metrics provides enhanced sensitivity to the aging effect; and (3) the sensorimotor network depicts enhanced negative connectivity strength with the adult age. The proposed QDA framework can produce threshold-free and voxel-wise RFC metrics from R-fMRI data. The detected adult age effect is largely consistent with previously reported studies using different R-fMRI analysis approaches. Moreover, the separate assessment of the negative and positive contributions to the RFC metrics can enhance the RFC sensitivity and clarify some of the mixed results in the literature regarding to the DMN and sensorimotor network involvement in adult aging.

Previous research indicates elevated levels of clinically significant traits and symptoms of autism spectrum disorder and attention-deficit hyperactivity disorder (ADHD) in children with chronic pain, but associations with functioning and depression are yet unclear. The current study examined the relationships of autistic traits and ADHD symptoms with pain interference, depression, and health-related quality of life, as well as the mediating roles of insomnia and psychological inflexibility, in children with chronic pain (n = 146, 8-17 years, 102 girls) presenting at a tertiary pain clinic. Children completed measures of pain intensity, depression, pain interference, health-related quality of life, insomnia, and psychological inflexibility. Parents (n = 146, 111 mothers) completed measures to assess autistic traits and ADHD symptoms in their children. Children with clinically significant autistic traits and ADHD symptoms presented with significantly higher levels of depressive symptoms and pain interference, and significantly lower health-related quality of life, than did the other children. Autistic traits and ADHD symptoms contributed significantly to the prediction of pain interference and depressive symptoms, as well as health-related quality of life. Psychological inflexibility mediated the relationships between ADHD symptoms and autistic traits on the one hand and depression, pain interference, and health-related quality of life on the other, while insomnia mediated the relationships between ADHD symptoms and depression, pain interference, and health-related quality of life. All analyses were adjusted for demographics and pain intensity. Results suggest the utility of screening for neurodevelopmental disorders in children with chronic pain. Furthermore, the findings may indicate insomnia and skills related to psychological flexibility as potential treatment targets in interventions aiming at improving functioning and health-related quality of life in children with chronic pain and co-occurring symptoms of neurodevelopmental disorders.

Tachykinins (TKs) are ancient neuropeptides present throughout the bilaterians and are, with some exceptions, characterized by a conserved FX(1)GX(2)Ramide carboxy terminus among protostomes and FXGLMamide in deuterostomes. The best-known TK is the vertebrate substance P, which in mammals, together with other TKs, has been implicated in health and disease with important roles in pain, inflammation, cancer, depressive disorder, immune system, gut function, hematopoiesis, sensory processing, and hormone regulation. The invertebrate TKs are also known to have multiple functions in the central nervous system and intestine and these have been investigated in more detail in the fly Drosophila and some other arthropods. Here, we review the protostome and deuterostome organization and evolution of TK precursors, peptides and their receptors, as well as their functions, which appear to be partly conserved across Bilateria. We also outline the distribution of TKs in the brains of representative organisms. In Drosophila, recent studies have revealed roles of TKs in early olfactory processing, neuromodulation in circuits controlling locomotion and food search, nociception, aggression, metabolic stress, and hormone release. TK signaling also regulates lipid metabolism in the Drosophila intestine. In crustaceans, TK is an important neuromodulator in rhythm-generating motor circuits in the stomatogastric nervous system and a presynaptic modulator of photoreceptor cells. Several additional functional roles of invertebrate TKs can be inferred from their distribution in various brain circuits. In addition, there are a few interesting cases where invertebrate TKs are injected into prey animals as vasodilators from salivary glands or paralyzing agents from venom glands. In these cases, the peptides are produced in the glands of the predator with sequences mimicking the prey TKs. Lastly, the TK-signaling system appears to have duplicated in Panarthropoda (comprising arthropods, onychophores, and tardigrades) to give rise to a novel type of peptides, natalisins, with a distinct receptor. The distribution and functions of natalisins are distinct from the TKs. In general, it appears that TKs are widely distributed and act in circuits at short range as neuromodulators or cotransmitters.

Despite evidence of a fundamental role of DARPP-32 in integrating dopamine and glutamate signaling, studies examining gene coding for DARPP-32 in relation to neural and behavioral cor-relates in humans are scarce. Post mortem findings evidence genotype specific expressions of DARPP-32 in the dorsal frontal lobes. We therefore investigated the effects of genomic variation in DARPP-32 coding on frontal lobe volumes and episodic memory. Volumetric data from the dorsolateral (DLPFC), and visual cortices (VC) were obtained from 61 younger and older adults (♀54%). The major homozygote G, T or A genotypes in single nucleotide polymorphisms (SNPs: rs879606; rs907094; rs3764352), at the DARPP-32 regulating PPP1R1B gene influenced frontal gray matter volume and episodic memory (EM). Homozygous carriers of allelic variants with lower DARPP-32 expression had overall larger prefrontal volumes, in addition to greater EM recall accuracy. The SNPs did not influence VC volume. The genetic effects on DLPFC were greater in younger adults, and selective to this group for EM. Our findings suggest that genomic variation maps on to individual differences in frontal brain volumes, and cognitive functions. Larger DLPFC volumes were also related to better EM performance, suggesting that gene-related differences in frontal gray matter may contribute to individual differences in EM. These results need further replication from experimental and longitudinal reports to determine directions of causality.

The implementation of synaptic plasticity in neural simulation or neuromorphic hardware is usually very resource-intensive, often requiring a compromise between efficiency and flexibility. A versatile, but computationally-expensive plasticity mechanism is provided by the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm. Building upon Bayesian statistics, and having clear links to biological plasticity processes, the BCPNN learning rule has been applied in many fields, ranging from data classification, associative memory, reward-based learning, probabilistic inference to cortical attractor memory networks. In the spike-based version of this learning rule the pre-, postsynaptic and coincident activity is traced in three low-pass-filtering stages, requiring a total of eight state variables, whose dynamics are typically simulated with the fixed step size Euler method. We derive analytic solutions allowing an efficient event-driven implementation of this learning rule. Further speedup is achieved by first rewriting the model which reduces the number of basic arithmetic operations per update to one half, and second by using look-up tables for the frequently calculated exponential decay. Ultimately, in a typical use case, the simulation using our approach is more than one order of magnitude faster than with the fixed step size Euler method. Aiming for a small memory footprint per BCPNN synapse, we also evaluate the use of fixed-point numbers for the state variables, and assess the number of bits required to achieve same or better accuracy than with the conventional explicit Euler method. All of this will allow a real-time simulation of a reduced cortex model based on BCPNN in high performance computing. More important, with the analytic solution at hand and due to the reduced memory bandwidth, the learning rule can be efficiently implemented in dedicated or existing digital neuromorphic hardware.

The Bayesian Confidence Propagation Neural Network (BCPNN) has been implemented in a way that allows mapping to neural and synaptic processes in the human cortex and has been used extensively in detailed spiking models of cortical associative memory function and recently also for machine learning applications. In conventional digital implementations of BCPNN, the von Neumann bottleneck is a major challenge with synaptic storage and access to it as the dominant cost. The memristor is a non-volatile device ideal for artificial synapses that fuses computation and storage and thus fundamentally overcomes the von Neumann bottleneck. While the implementation of other neural networks like Spiking Neural Network (SNN) and even Convolutional Neural Network (CNN) on memristor has been studied, the implementation of BCPNN has not. In this paper, the BCPNN learning rule is mapped to a memristor model and implemented with a memristor-based architecture. The implementation of the BCPNN learning rule is a mixed-signal design with the main computation and storage happening in the analog domain. In particular, the nonlinear dopant drift phenomenon of the memristor is exploited to simulate the exponential decay of the synaptic state variables in the BCPNN learning rule. The consistency between the memristor-based solution and the BCPNN learning rule is simulated and verified in Matlab, with a correlation coefficient as high as 0.99. The analog circuit is designed and implemented in the SPICE simulation environment, demonstrating a good emulation effect for the BCPNN learning rule with a correlation coefficient as high as 0.98. This work focuses on demonstrating the feasibility of mapping the BCPNN learning rule to in-circuit computation in memristor. The feasibility of the memristor-based implementation is evaluated and validated in the paper, to pave the way for a more efficient BCPNN implementation, toward a real-time brain emulation engine.

Simulation of large scale biologically plausible spiking neural networks, e.g., Bayesian Confidence Propagation Neural Network (BCPNN), usually requires high-performance supercomputers with dedicated accelerators, such as GPUs, FPGAs, or even Application-Specific Integrated Circuits (ASICs). Almost all of these computers are based on the von Neumann architecture that separates storage and computation. In all these solutions, memory access is the dominant cost even for highly customized computation and memory architecture, such as ASICs. In this paper, we propose an optimization technique that can make the BCPNN simulation memory access friendly by avoiding a dual-access pattern. The BCPNN synaptic traces and weights are organized as matrices accessed both row-wise and column-wise. Accessing data stored in DRAM with a dual-access pattern is extremely expensive. A post-synaptic history buffer and an approximation function thus are introduced to eliminate the troublesome column update. The error analysis combining theoretical analysis and experiments suggests that the probability of introducing intolerable errors by such optimization can be bounded to a very small number, which makes it almost negligible. Derivation and validation of such a bound is the core contribution of this paper. Experiments on a GPU platform shows that compared to the previously reported baseline simulation strategy, the proposed optimization technique reduces the storage requirement by 33%, the global memory access demand by more than 27% and DRAM access rate by more than 5%; the latency of updating synaptic traces decreases by roughly 50%. Compared with the other similar optimization technique reported in the literature, our method clearly shows considerably better results. Although the BCPNN is used as the targeted neural network model, the proposed optimization method can be applied to other artificial neural network models based on a Hebbian learning rule.

Perceptual correlates of Turkish word stress and their contribution to lexical access were studied using the mismatch negativity (MMN) component in event-related potentials (ERPs). The MMN was expected to indicate if segmentally identical Turkish words were distinguished on the sole basis of prosodic features such as fundamental frequency (f0), spectral emphasis (SE) and duration. The salience of these features in lexical access was expected to be reflected in the amplitude of MMN responses. In a multi-deviant oddball paradigm, neural responses to changes in f0, SE, and duration individually, as well as to all three features combined, were recorded for words and pseudowords presented to 14 native speakers of Turkish. The word and pseudoword contrast was used to differentiate language-related effects from acoustic-change effects on the neural responses. First and in line with previous findings, the overall MMN was maximal over frontal and central scalp locations. Second, changes in prosodic features elicited neural responses both in words and pseudowords, confirming the brain’s automatic response to any change in auditory input. However, there were processing differences between the prosodic features, most significantly in f0: While f0 manipulation elicited a slightly right-lateralized frontally-maximal MMN in words, it elicited a frontal P3a in pseudowords. Considering that P3a is associated with involuntary allocation of attention to salient changes, the manipulations of f0 in the absence of lexical processing lead to an intentional evaluation of pitch change. f0 is therefore claimed to be lexically specified in Turkish. Rather than combined features, individual prosodic features differentiate language-related effects from acoustic-change effects. The present study confirms that segmentally identical words can be distinguished on the basis of prosodic information alone, and establishes the salience of f0 in lexical access.

Like that of many other Germanic languages, the stress system of Swedish has mainly undergone phonological analysis. Recently, however, researchers have begun to recognize the central role of morphology in these systems. Similar to the lexical specification of tonal accent, the Swedish stress system is claimed to be morphologically determined and morphemes are thus categorized as prosodically specified and prosodically unspecified. Prosodically specified morphemes bear stress information as part of their lexical representations and are classified as tonic (i.e., lexically stressed), pretonic and posttonic, whereas prosodically unspecified morphemes receive stress through a phonological rule that is right-edge oriented, but is sensitive to prosodic specification at that edge. The presence of prosodic specification is inferred from vowel quality and vowel quantity; if stress moves elsewhere, vowel quality and quantity change radically in phonologically stressed morphemes, whereas traces of stress remain in lexically stressed morphemes. The present study is the first to investigate whether stress is a lexical property of Swedish morphemes by comparing mismatch negativity (MMN) responses to vowel quality and quantity changes in phonologically stressed and lexically stressed words. In a passive oddball paradigm, 15 native speakers of Swedish were presented with standards and deviants, which differed from the standards in formant frequency and duration. Given that vowel quality and quantity changes are associated with morphological derivations only in phonologically stressed words, MMN responses are expected to be greater in phonologically stressed words than in lexically stressed words that lack such an association. The results indicated that the processing differences between phonologically and lexically stressed words were reflected in the amplitude and topography of MMN responses. Confirming the expectation, MMN amplitude was greater for the phonologically stressed word than for the lexically stressed word and showed a more widespread topographic distribution. The brain did not only detect vowel quality and quantity changes but also used them to activate memory traces associated with derivations. The present study therefore implies that morphology is directly involved in the Swedish stress system and that changes in phonological shape due to stress shift cue upcoming stress and potential addition of a morpheme.