Probabilistic, Recurrent, Fuzzy Neural Network for Processing Noisy Time-Series Data.

The rapidly increasing volumes of data and the need for big data analytics have emphasized the need for algorithms that can accommodate incomplete or noisy data. The concept of recurrency is an important aspect of signal processing, providing greater robustness and accuracy in many situations, such as biological signal processing. Probabilistic fuzzy neural networks (PFNN) have shown potential in dealing with uncertainties associated with both stochastic and nonstochastic noise simultaneously. Previous research work on this topic has addressed either the fuzzy-neural aspects or alternatively the probabilistic aspects, but currently a probabilistic fuzzy neural algorithm with recurrent feedback does not exist. In this article, a PFNN with a recurrent probabilistic generation module (designated PFNN-R) is proposed to enhance and extend the ability of the PFNN to accommodate noisy data. A back-propagation-based mechanism, which is used to shape the distribution of the probabilistic density function of the fuzzy membership, is also developed. The objective of the work was to develop an approach that provides an enhanced capability to accommodate various types of noisy data. We apply the algorithm to a number of benchmark problems and demonstrate through simulation results that the proposed technique incorporating recurrency advances the ability of PFNNs to model time-series data with high intensity, random noise.

Variations in the oral microbiome are associated with depression in young adults.

A growing body of evidence supports an important role for alterations in the brain-gut-microbiome axis in the aetiology of depression and other psychiatric disorders. The potential role of the oral microbiome in mental health has received little attention, even though it is one of the most diverse microbiomes in the body and oral dysbiosis has been linked to systemic diseases with an underlying inflammatory aetiology. This study examines the structure and composition of the salivary microbiome for the first time in young adults who met the DSM-IV criteria for depression (n = 40) and matched controls (n = 43) using 16S rRNA gene-based next generation sequencing. Subtle but significant differences in alpha and beta diversity of the salivary microbiome were observed, with clear separation of depressed and healthy control cohorts into distinct clusters. A total of 21 bacterial taxa were found to be differentially abundant in the depressed cohort, including increased Neisseria spp. and Prevotella nigrescens, while 19 taxa had a decreased abundance. In this preliminary study we have shown that the composition of the oral microbiome is associated with depression in young adults. Further studies are now warranted, particuarly investigations into whether such shifts play any role in the underling aetiology of depression.

Deep Learning in Mining Biological Data.

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures-known as deep learning (DL)-have been successfully applied to solve many complex pattern recognition problems. To investigate how DL-especially its different architectures-has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures' applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

Bioinspired Approach to Modeling Retinal Ganglion Cells Using System Identification Techniques.

The processing capabilities of biological vision systems are still vastly superior to artificial vision, even though this has been an active area of research for over half a century. Current artificial vision techniques integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power, and performance. A key aspect to modeling the human visual system is the ability to accurately model the behavior and computation within the retina. In particular, we focus on modeling the retinal ganglion cells (RGCs) as they convey the accumulated data of real world images as action potentials onto the visual cortex via the optic nerve. Computational models that approximate the processing that occurs within RGCs can be derived by quantitatively fitting the sets of physiological data using an input-output analysis where the input is a known stimulus and the output is neuronal recordings. Currently, these input-output responses are modeled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this paper, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behavior, and are a viable alternative to traditional linear-nonlinear approaches.

Temporal changes of diffusion patterns in mild traumatic brain injury via group-based semi-blind source separation.

Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioral impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and, thus, are less suitable for individual case studies. In this paper, we introduce an approach based on spatial group independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process, thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA. The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice.

The sound-induced flash illusion reveals dissociable age-related effects in multisensory integration.

While aging can lead to significant declines in perceptual and cognitive function, the effects of age on multisensory integration, the process in which the brain combines information across the senses, are less clear. Recent reports suggest that older adults are susceptible to the sound-induced flash illusion (Shams et al., 2000) across a much wider range of temporal asynchronies than younger adults (Setti et al., 2011). To assess whether this cost for multisensory integration is a general phenomenon of combining asynchronous audiovisual input, we compared the time courses of two variants of the sound-induced flash illusion in young and older adults: the fission illusion, where one flash accompanied by two beeps appears as two flashes, and the fusion illusion, where two flashes accompanied by one beep appear as one flash. Twenty-five younger (18-30 years) and older (65+ years) adults were required to report whether they perceived one or two flashes, whilst ignoring irrelevant auditory beeps, in bimodal trials where auditory and visual stimuli were separated by one of six stimulus onset asynchronies (SOAs). There was a marked difference in the pattern of results for the two variants of the illusion. In conditions known to produce the fission illusion, older adults were significantly more susceptible to the illusion at longer SOAs compared to younger participants. In contrast, the performance of the younger and older groups was almost identical in conditions known to produce the fusion illusion. This surprising difference between sound-induced fission and fusion in older adults suggests dissociable age-related effects in multisensory integration, consistent with the idea that these illusions are mediated by distinct neural mechanisms.

Increased number of orexin/hypocretin neurons with high and prolonged external stress-induced depression.

It has been found that dysregulation in the orexin/hypocretin (Ox/HCRT) neuropeptide system in the lateral hypothalamus (LHA) is known to affect sleep disorder, depression and motor activities. However, to date there is no common agreement regarding the resulting specific changes induced in the Ox system. In this study, we inject corticosterone to produce stress-induced depressed mice and investigate the Ox neuronal and corresponding behavioural changes. Different doses (10, 20, 50mg/kgbw) of corticosterone were injected in adult mice, and then were tested in the open field test, forced swim test, tail suspension test, elevated plus maze test and motor activity measurements to validate the depressed animal model. Significant dose-dependent behavioural changes were observed in correlation with the doses of corticosterone. The effect is most significant and robust in the high 50mg/kgbw dose group five weeks after injection. Interestingly, we found on average a reduction in motor activity during the 12-hour dark phase (awake) of the depressed mice and no significant change during the light phase (asleep). Finally, using confocal microscopy, immunofluorescence (IF) analysis shows a significant increase (∼20%) in the number of Ox neurons in the LHA of the depressed mice as compared to the age-matched controls. This study suggests that an increase in Ox neuronal signaling may be functionally linked to high and prolonged external stress-induced depression.

Neural circuit interactions between the dorsal raphe nucleus and the lateral hypothalamus: an experimental and computational study.

Orexinergic/hypocretinergic (Ox) neurotransmission plays an important role in regulating sleep, as well as in anxiety and depression, for which the serotonergic (5-HT) system is also involved in. However, little is known regarding the direct and indirect interactions between 5-HT in the dorsal raphe nucleus (DRN) and Ox neurons in the lateral hypothalamus (LHA). In this study, we report the additional presence of 5-HT1BR, 5-HT2AR, 5-HT2CR and fast ligand-gated 5-HT3AR subtypes on the Ox neurons of transgenic Ox-enhanced green fluorescent protein (Ox-EGFP) and wild type C57Bl/6 mice using single and double immunofluorescence (IF) staining, respectively, and quantify the colocalization for each 5-HT receptor subtype. We further reveal the presence of 5-HT3AR and 5-HT1AR on GABAergic neurons in LHA. We also identify NMDAR1, OX1R and OX2R on Ox neurons, but none on adjacent GABAergic neurons. This suggests a one-way relationship between LHA's GABAergic and Ox neurons, wherein GABAergic neurons exerts an inhibitory effect on Ox neurons under partial DRN's 5-HT control. We also show that Ox axonal projections receive glutamatergic (PSD-95 immunopositive) and GABAergic (Gephyrin immunopositive) inputs in the DRN. We consider these and other available findings into our computational model to explore possible effects of neural circuit connection types and timescales on the DRN-LHA system's dynamics. We find that if the connections from 5-HT to LHA's GABAergic neurons are weakly excitatory or inhibitory, the network exhibits slow oscillations; not observed when the connection is strongly excitatory. Furthermore, if Ox directly excites 5-HT neurons at a fast timescale, phasic Ox activation can lead to an increase in 5-HT activity; no significant effect with slower timescale. Overall, our experimental and computational approaches provide insights towards a more complete understanding of the complex relationship between 5-HT in the DRN and Ox in the LHA.

Adaptive proactive inhibitory control for embedded real-time applications.

Psychologists have studied the inhibitory control of voluntary movement for many years. In particular, the countermanding of an impending action has been extensively studied. In this work, we propose a neural mechanism for adaptive inhibitory control in a firing-rate type model based on current findings in animal electrophysiological and human psychophysical experiments. We then implement this model on a field-programmable gate array (FPGA) prototyping system, using dedicated real-time hardware circuitry. Our results show that the FPGA-based implementation can run in real-time while achieving behavioral performance qualitatively suggestive of the animal experiments. Implementing such biological inhibitory control in an embedded device can lead to the development of control systems that may be used in more realistic cognitive robotics or in neural prosthetic systems aiding human movement control.

Chromaticity space for illuminant invariant recognition.

In this paper an algorithm is proposed to extract two illuminant invariant chromaticity features from three image sensor responses. The algorithm extracts these chromaticity features at pixel level and therefore can perform well in scenes illuminated with non-uniform illuminant. An approach is proposed to use the algorithm with cameras of unknown sensitivity. The algorithm was tested for separability of perceptually similar colours under the International Commission on Illumination (CIE) standard illuminants and obtained a good performance. It was also tested for colour based object recognition by illuminating objects with typical indoor illuminants and obtained a better performance compared to other existing algorithms investigated in this paper. Finally, the algorithm was tested for skin detection invariant to illuminant, ethnic background and imaging device. In this investigation, daylight scenes under different weather conditions and scenes illuminated by typical indoor illuminants were used. The proposed algorithm gives a better skin detection performance compared to widely used standard colour spaces. Based on the results presented, the proposed illuminant invariant chromaticity space can be used for machine vision applications including illuminant invariant colour based object recognition and skin detection.

Network properties of a computational model of the dorsal raphe nucleus.

Serotonin (5-HT) plays an important role in regulating mood, cognition and behaviour. The midbrain dorsal raphe nucleus (DRN) is one of the primary sources of 5-HT. Recent studies show that DRN neuronal activities can encode rewarding (e.g., appetitive) and unrewarding (e.g., aversive) behaviours. Experiments have also shown that DRN neurons can exhibit heterogeneous spiking behaviours. In this work, we build and study a basic spiking neuronal network model of the DRN constrained by neuronal properties observed in experiments. We use an efficient adaptive quadratic integrate-and-fire neuronal model to capture slow afterhyperpolarization current, occasional bursting behaviours in 5-HT neurons, and fast spiking activities in the non-5-HT inhibitory neurons. Provided that our noisy and heterogeneous spiking neuronal network model adopts a feedforward inhibitory network architecture, it is able to replicate the main features of DRN neuronal activities recorded in monkeys performing a reward-based memory-guided saccade task. The model exhibits theta band oscillation, especially among the non-5-HT inhibitory neurons during the rewarding outcome of a simulated trial, thus forming a model prediction. By varying the inhibitory synaptic strengths and the afferent inputs, we find that the network model can oscillate over a range of relatively low frequencies, allow co-existence of multiple stable frequencies, and spike synchrony can spread from within a local neural subgroup to global. Our model suggests plausible network architecture, provides interesting model predictions that can be experimentally tested, and offers a sufficiently realistic multi-scale model for 5-HT neuromodulation simulations.

A voxel based morphometry study investigating brain structural changes in first episode psychosis.

Schizophrenia (SCZ) and bipolar disorder (BP) are associated with neuropathological brain changes, which are believed to disrupt connectivity between brain processes and may have common properties. Patients at first psychotic episode are unique, as one can assess brain alterations at illness inception, when many confounders are reduced or absent. SCZ (N=25) and BP (N=24) patients were recruited in a regional first episode psychosis MRI study. VBM methods were used to study gray matter (GM) and white matter (WM) differences between patient groups and case by case matched controls. For both groups, deficits identified are more discrete than those typically reported in later stages of illness. SCZ patients showed some evidence of GM loss in cortical areas but most notable were in limbic structures such as hippocampus, thalamus and striatum and cerebellum. Consistent with disturbed neural connectivity WM alterations were also observed in limbic structures, the corpus callosum and many subgyral and sublobar regions in the parietal, temporal and frontal lobes. BP patients displayed less evidence of volume changes overall, compared to normal healthy participants, but those changes observed were primarily in WM areas which overlapped with regions identified in SCZ, including thalamus and cerebellum and subgyral and sublobar sites. At first episode of psychosis there is evidence of a neuroanatomical overlap between SCZ and BP with respect to brain structural changes, consistent with disturbed neural connectivity. There are also important differences however in that SCZ displays more extensive structural alteration.

Enhancement of fiber orientation distribution reconstruction in diffusion-weighted imaging by single channel blind source separation.

In diffusion-weighted imaging (DWI), reliable fiber tracking results rely on the accurate reconstruction of the fiber orientation distribution function (fODF) in each individual voxel. For high angular resolution diffusion imaging (HARDI), deconvolution-based approaches can reconstruct the complex fODF and have advantages in terms of computational efficiency and no need to estimate the number of distinct fiber populations. However, HARDI-based methods usually require relatively high b-values and a large number of gradient directions to produce good results. Such requirements are not always easy to meet in common clinical studies due to limitations in MRI facilities. Moreover, most of these approaches are sensitive to noise. In this study, we propose a new framework to enhance the performance of the spherical deconvolution (SD) approach in low angular resolution DWI by employing a single channel blind source separation (BSS) technique to decompose the fODF initially estimated by SD such that the desired fODF can be extracted from the noisy background. The results based on numerical simulations and two phantom datasets demonstrate that the proposed method achieves better performance than SD in terms of robustness to noise and variation in b-values. In addition, the results show that the proposed method has the potential to be applied to low angular resolution DWI which is commonly used in clinical studies.

A mathematical model to explore the interdependence between the serotonin and orexin/hypocretin systems.

Among their multitude of physiological and behavioral effects, the neurochemicals serotonin (5-HT) and orexin (Ox) have been closely linked to major depressive disorders (MDD) and sleep alterations. The dorsal raphe nucleus (DRN) and the lateral hypothalamus area (LHA) are brain regions that are sources of 5-HT and Ox, and there is evidence that suggests a reciprocal interaction between them. This lends support to the hypothesis of a close relationship between MDD and sleep disorders. Based on various experimental data, and appropriate assumptions, we construct a mathematical model of the coupled DRN-LHA neural circuit. Our model relates the dynamics of four important variables that can be experimentally measured: (i) the firing rate of 5-HT-containing neurons in DRN, (ii) the firing rate of Ox-containing neurons in the LHA, (iii) 5-HT concentration level in LHA, and (iv) Ox concentration level in DRN. Simulations show that our model supports the co-existence of baseline activities and concentration levels as observed in various separate experiments. It also allows circuit-level exploration of various parameters not yet identified experimentally, e.g. the rise and decay of Ox concentration levels due to Ox neural activity, and the exact dependence of Ox neural activity on 5-HT level. Finally we have made some model predictions regarding the effects of the 5-HT antagonist on the circuit. Our model, which can be subjected to verification and refinement as new experimental data accumulates, provides unified quantitative relationships and predictions between two important connected brain regions strongly tied to MDD and sleep disorders.