8,074 research outputs found
Neuroanatomical and gene expression features of the rabbit accessory olfactory system. Implications of pheromone communication in reproductive behaviour and animal physiology
Mainly driven by the vomeronasal system (VNS), pheromone
communication is involved in many species-specific fundamental innate socio-sexual behaviors such as mating and
fighting, which are essential for animal reproduction and survival. Rabbits are a unique model for studying
chemocommunication due to the discovery of the rabbit mammary pheromone, but paradoxically there has been a
lack of knowledge regarding its VNS pathway. In this work, we aim at filling this gap by approaching the system
from an integrative point of view, providing extensive anatomical and genomic data of the rabbit VNS, as well as
pheromone-mediated reproductive and behavioural studies. Our results build strong foundation for further
translational studies which aim at implementing the use of pheromones to improve animal production and welfare
OLIG2 neural progenitor cell development and fate in Down syndrome
Down syndrome (DS) is caused by triplication of human chromosome 21 (HSA21) and is the most common genetic form of intellectual disability. It is unknown precisely how triplication of HSA21 results in the intellectual disability, but it is thought that the global transcriptional dysregulation caused by trisomy 21 perturbs multiple aspects of neurodevelopment that cumulatively contribute to its etiology. While the characteristics associated with DS can arise from any of the genes triplicated on HSA21, in this work we focus on oligodendrocyte transcription factor 2 (OLIG2). The progeny of neural progenitor cells (NPCs) expressing OLIG2 are likely to be involved in many of the cellular changes underlying the intellectual disability in DS. To explore the fate of OLIG2+ neural progenitors, we took advantage of two distinct models of DS, the Ts65Dn mouse model and induced pluripotent stem cells (iPSCs) derived from individuals with DS. Our results from these two systems identified multiple perturbations in development in the cellular progeny of OLIG2+ NPCs. In Ts65Dn, we identified alterations in neurons and glia derived from the OLIG2 expressing progenitor domain in the ventral spinal cord. There were significant differences in the number of motor neurons and interneurons present in the trisomic lumbar spinal cord depending on age of the animal pointing both to a neurodevelopment and a neurodegeneration phenotype in the Ts65Dn mice. Of particular note, we identified changes in oligodendrocyte (OL) maturation in the trisomic mice that are dependent on spatial location and developmental origin. In the dorsal corticospinal tract, there were significantly fewer mature OLs in the trisomic mice, and in the lateral funiculus we observed the opposite phenotype with more mature OLs being present in the trisomic animals. We then transitioned our studies into iPSCs where we were able to pattern OLIG2+ NPCs to either a spinal cord-like or a brain-like identity and study the OL lineage that differentiated from each progenitor pool. Similar to the region-specific dysregulation found in the Ts65Dn spinal cord, we identified perturbations in trisomic OLs that were dependent on whether the NPCs had been patterned to a brain-like or spinal cord-like fate. In the spinal cord-like NPCs, there was no difference in the proportion of cells expressing either OLIG2 or NKX2.2, the two transcription factors whose co-expression is essential for OL differentiation. Conversely, in the brain-like NPCs, there was a significant increase in OLIG2+ cells in the trisomic culture and a decrease in NKX2.2 mRNA expression. We identified a sonic hedgehog (SHH) signaling based mechanism underlying these changes in OLIG2 and NKX2.2 expression in the brain-like NPCs and normalized the proportion of trisomic cells expressing the transcription factors to euploid levels by modulating the activity of the SHH pathway. Finally, we continued the differentiation of the brain-like and spinal cord-like NPCs to committed OL precursor cells (OPCs) and allowed them to mature. We identified an increase in OPC production in the spinal cord-like trisomic culture which was not present in the brain-like OPCs. Conversely, we identified a maturation deficit in the brain-like trisomic OLs that was not present in the spinal cord-like OPCs. These results underscore the importance of regional patterning in characterizing changes in cell differentiation and fate in DS. Together, the findings presented in this work contribute to the understanding of the cellular and molecular etiology of the intellectual disability in DS and in particular the contribution of cells differentiated from OLIG2+ progenitors
Socio-endocrinology revisited: New tools to tackle old questions
Animals’ social environments impact their health and survival, but the proximate links between sociality and fitness are still not fully understood. In this thesis, I develop and apply new approaches to address an outstanding question within this sociality-fitness link: does grooming (a widely studied, positive social interaction) directly affect glucocorticoid concentrations (GCs; a group of steroid hormones indicating physiological stress) in a wild primate? To date, negative, long-term correlations between grooming and GCs have been found, but the logistical difficulties of studying proximate mechanisms in the wild leave knowledge gaps regarding the short-term, causal mechanisms that underpin this relationship. New technologies, such as collar-mounted tri-axial accelerometers, can provide the continuous behavioural data required to match grooming to non-invasive GC measures (Chapter 1). Using Chacma baboons (Papio ursinus) living on the Cape Peninsula, South Africa as a model system, I identify giving and receiving grooming using tri-axial accelerometers and supervised machine learning methods, with high overall accuracy (~80%) (Chapter 2). I then test what socio-ecological variables predict variation in faecal and urinary GCs (fGCs and uGCs) (Chapter 3). Shorter and rainy days are associated with higher fGCs and uGCs, respectively, suggesting that environmental conditions may impose stressors in the form of temporal bottlenecks. Indeed, I find that short days and days with more rain-hours are associated with reduced giving grooming (Chapter 4), and that this reduction is characterised by fewer and shorter grooming bouts. Finally, I test whether grooming predicts GCs, and find that while there is a long-term negative correlation between grooming and GCs, grooming in the short-term, in particular giving grooming, is associated with higher fGCs and uGCs (Chapter 5). I end with a discussion on how the new tools I applied have enabled me to advance our understanding of sociality and stress in primate social systems (Chapter 6)
Recommended from our members
Quantitative Character and the Composite Account of Phenomenal Content
I advance an account of quantitative character, a species of phenomenal character that presents as an intensity (cf. a quality) and includes experience dimensions such as loudness, pain intensity, and visual pop-out. I employ psychological and neuroscientific evidence to demonstrate that quantitative characters are best explained by attentional processing, and hence that they do not represent external qualities. Nonetheless, the proposed account of quantitative character is conceived as a compliment to the reductive intentionalist strategy toward qualitative states; I argue that an account of perceptual experience that combines a tracking account of qualitative character with my functionalist proposal of quantitative character permits replies to some notoriously difficult problems for tracking representationalism without sacrificing its chief virtues
Recommended from our members
Brain signal recognition using deep learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of
muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the
cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology
serves as the best available non-invasive solution for extracting signals from the brain. On the other hand,
speech is the primary means of communication, but for patients suffering from locked-in syndrome, there
is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a
thought-to-speech BCI system.
This research aims to investigate methods for the recognition of imagined speech from EEG signals
using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety
of issues have been solved. These include 1) proposing new feature extraction and classification
framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of
imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in
the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep
learning methods were used to analyze EEG signals.
For recognition of imagined speech from EEG signals, a new EEG database was collected while the
participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG
data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech).
Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined
words from EEG signals. Further, a deep learning framework using the convolutional network
and attention mechanism was implemented for learning features in the spatial, temporal, and spectral
domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments
show that deep learning algorithms are ideal for imagined speech recognition from EEG signals
due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes
of imagined words from EEG signals were also recognized using a multi-channel convolution network
framework. This method was extended to a multi-level recognition system for multi-class classification
of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in
comparison to previous work.
In order to investigate the difference between imagined speech with verbal speech and visual imagery
from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments
when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies
that result in most discrimination between the different cognitive tasks were also explored. A framework
was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals.
The proposed method used the K-means clustering algorithm to find the best electrode combination and
convolutional-attention network for feature extraction and classification. The proposed method achieved
a high recognition rate of 82.9% and 77.7%.
The results in this research suggest that a communication based BCI system can be designed using
deep learning methods. Further, this work add knowledge to the existing work in the field of communication
based BCI system
Linguistic- and Acoustic-based Automatic Dementia Detection using Deep Learning Methods
Dementia can affect a person's speech and language abilities, even in the early stages. Dementia is incurable, but early detection can enable treatment that can slow down and maintain mental function. Therefore, early diagnosis of dementia is of great importance. However, current dementia detection procedures in clinical practice are expensive, invasive, and sometimes inaccurate. In comparison, computational tools based on the automatic analysis of spoken language have the potential to be applied as a cheap, easy-to-use, and objective clinical assistance tool for dementia detection.
In recent years, several studies have shown promise in this area. However, most studies focus heavily on the machine learning aspects and, as a consequence, often lack sufficient incorporation of clinical knowledge. Many studies also concentrate on clinically less relevant tasks such as the distinction between HC and people with AD which is relatively easy and therefore less interesting both in terms of the machine learning and the clinical application.
The studies in this thesis concentrate on automatically identifying signs of neurodegenerative dementia in the early stages and distinguishing them from other clinical, diagnostic categories related to memory problems: (FMD, MCI, and HC). A key focus, when designing the proposed systems has been to better consider (and incorporate) currently used clinical knowledge and also to bear in mind how these machine-learning based systems could be translated for use in real clinical settings.
Firstly, a state-of-the-art end-to-end system is constructed for extracting linguistic information from automatically transcribed spontaneous speech. The system's architecture is based on hierarchical principles thereby mimicking those used in clinical practice where information at both word-, sentence- and paragraph-level is used when extracting information to be used for diagnosis. Secondly, hand-crafted features are designed that are based on clinical knowledge of the importance of pausing and rhythm. These are successfully joined with features extracted from the end-to-end system. Thirdly, different classification tasks are explored, each set up so as to represent the types of diagnostic decision-making that is relevant in clinical practice. Finally, experiments are conducted to explore how to better deal with the known problem of confounding and overlapping symptoms on speech and language from age and cognitive decline. A multi-task system is constructed that takes age into account while predicting cognitive decline. The studies use the publicly available DementiaBank dataset as well as the IVA dataset, which has been collected by our collaborators at the Royal Hallamshire Hospital, UK. In conclusion, this thesis proposes multiple methods of using speech and language information for dementia detection with state-of-the-art deep learning technologies, confirming the automatic system's potential for dementia detection
Novel strategies for the modulation and investigation of memories in the hippocampus
Disruptions of the memory systems in the brain are linked to the manifestation of many neuropsychiatric diseases such as Alzheimer’s disease, depression, and post-traumatic stress disorder. The limited efficacy of current treatments necessities the development of more effective therapies. Neuromodulation has proven effective in a variety of neurological diseases and could be an attractive solution for memory disorders. However, the application of neuromodulation requires a more detailed understanding of the network dynamics associated with memory formation and recall. In this work, we applied a combination of optical and computational tools in the development of a novel strategy for the modulation of memories, and have expanded its application for interrogation of the hippocampal circuitry underlying memory processing in mice.
First, we developed a closed-loop optogenetic stimulation platform to activate neurons implicated in memory processing (engram neurons) with a high temporal resolution. We applied this platform to modulate the activity of engram neurons and assess memory processing with respect to synchronous network activity. The results of our investigation support the proposal that encoding new information and recalling stored memories occur during distinct epochs of hippocampal network-wide oscillations.
Having established the high efficacy of the modulation of engram neurons’ activity in a closed-loop fashion, we sought to combine it with two-photon imaging to enable high spatial resolution interrogation of hippocampal circuitry. We developed a behavioral apparatus for head-fixed engram modulation and the assessment of memory recall in immobile animals. Moreover, through the optimization of dual color two-photon imaging, we improved the ability to monitor activity of neurons in the subfields of the hippocampus with cellular specificity. The platform created here will be applied to investigate the effects of engram reactivation on downstream projections targets with high spatial and cell subtype specificity.
Following these lines of investigations will enhance our understanding of memory modulation and could lead to novel neuromodulation treatments for neurological disorders associated with memory malfunctioning
A Cross Cultural Examination of the Psychological Dynamics in Music and Religious Practice
Music and religion exist ubiquitously across time and space, hold profound phylogenetic importance to the human condition, and provide fruitful avenues for culture research. Existing norms, values, schemas, language, and mythological frameworks interact with auditory harmony, melody, and rhythm, within naturalistic settings, to condition profound psychological mechanisms with deep implications to the study of social psychology and culture.
Music has been and continues to be a normative tool in the use of religious ritualistic practice. Music has both the ability to facilitate changes in cognition and emotion on an individual level, while also strengthening group bonds and unifying a community at the social level. Religion represents a powerful cultural framework that one is typically conditioned to at an extremely early age and exerts massive pressure on the norms and values of the environment it exists in. This dissertation seeks to explore differences and similarities of music and religious practice across cultures and offers implications for the field of cross-cultural psychology.
The first essay is a quantitative paper that examines how one’s religion and how one practices their religion shapes their psychological mechanisms associated with the emotional construal of terror, within the naturalistic setting of the covid-19 pandemic. During the pandemic many people have turned to their religious faiths for comfort and meaning in these existentially threatening times, however, many people have also found their ability to practice their religion effected by Covid-19, as well. The second essay will utilize a qualitative methodology to probe the use of music in ritualistic settings to understand cultural similarities and differences and what implications that may have for further understanding human cognitive and emotional capabilities and potentials. The final paper will be an interdisciplinary theory paper that borrows from social psychology, organizational psychology, musical psychology, and theology to propose mechanisms for how live musical practice in a communal setting interact to induce transformations in an individual’s sense of self. Often referred to as the “human obsession”, music is a unique communicative agent capable of arousing powerful cognitive and emotional responses in both the listener and performer across cultures
- …