Decoding the consumer’s brain: Neural representations of consumer experience

Understanding consumer experience – what consumers think about brands, how they feel about services, whether they like certain products – is crucial to marketing practitioners. ‘Neuromarketing’, as the application of neuroscience in marketing research is called, has generated excitement with the promise of understanding consumers’ minds by probing their brains directly. Recent advances in neuroimaging analysis leverage machine learning and pattern classification techniques to uncover patterns from neuroimaging data that can be associated with thoughts and feelings. In this dissertation, I measure brain responses of consumers by functional magnetic resonance imaging (fMRI) in order to ‘decode’ their mind. In three different studies, I have demonstrated how different aspects of consumer experience can be studied with fMRI recordings. First, I study how consumers think about brand image by comparing their brain responses during passive viewing of visual templates (photos depicting various social scenarios) to those during active visualizing of a brand’s image. Second, I use brain responses during viewing of affective pictures to decode emotional responses during watching of movie-trailers. Lastly, I examine whether marketing videos that evoke s

Use of FBG optical sensors for structural health monitoring: Practical application

This paper describes the development of FBG Optical sensors for their practical application on structural health monitoring. The sensors were installed on the Tsing Ma Bridge for a trial run. The results using FBG sensors were in excellent agreement with those acquired by the bridge WASHMS

Toll-like receptor-4 null mutation causes fetal loss and fetal growth restriction associated with impaired maternal immune tolerance in mice.

Maternal immune adaptation to accommodate pregnancy depends on sufficient availability of regulatory T (Treg) cells to enable embryo implantation. Toll-like receptor 4 is implicated as a key upstream driver of a controlled inflammatory response, elicited by signals in male partner seminal fluid, to initiate expansion of the maternal Treg cell pool after mating. Here, we report that mice with null mutation in Tlr4 (Tlr4-/-) exhibit impaired reproductive outcomes after allogeneic mating, with reduced pregnancy rate, elevated mid-gestation fetal loss, and fetal growth restriction, compared to Tlr4+/+ wild-type controls. To investigate the effects of TLR4 deficiency on early events of maternal immune adaptation, TLR4-regulated cytokines and immune regulatory microRNAs were measured in the uterus at 8 h post-mating by qPCR, and Treg cells in uterus-draining lymph nodes were evaluated by flow cytometry on day 3.5 post-coitum. Ptgs2 encoding prostaglandin-endoperoxide synthase 2, cytokines Csf2, Il6, Lif, and Tnf, chemokines Ccl2, Cxcl1, Cxcl2, and Cxcl10, and microRNAs miR-155, miR-146a, and miR-223 were induced by mating in wild-type mice, but not, or to a lesser extent, in Tlr4-/- mice. CD4⁺ T cells were expanded after mating in Tlr4+/+ but not Tlr4-/- mice, with failure to expand peripheral CD25⁺FOXP3⁺ NRP1⁻ or thymic CD25⁺FOXP3⁺ NRP1⁺ Treg cell populations, and fewer Treg cells expressed Ki67 proliferation marker and suppressive function marker CTLA4. We conclude that TLR4 is an essential mediator of the inflammation-like response in the pre-implantation uterus that induces generation of Treg cells to support robust pregnancy tolerance and ensure optimal fetal growth and survival.Hon Y. Chan, Lachlan M. Moldenhauer, Holly M. Groome, John E. Schjenken, Sarah A. Robertso

The endometrial transcriptome transition preceding receptivity to embryo implantation in mice

Background: Receptivity of the uterus is essential for embryo implantation and progression of mammalian pregnancy. Acquisition of receptivity involves major molecular and cellular changes in the endometrial lining of the uterus from a non-receptive state at ovulation, to a receptive state several days later. The precise molecular mechanisms underlying this transition and their upstream regulators remain to be fully characterized. Here, we aimed to generate a comprehensive profile of the endometrial transcriptome in the peri-ovulatory and peri-implantation states, to define the genes and gene pathways that are different between these states, and to identify new candidate upstream regulators of this transition, in the mouse. Results: High throughput RNA-sequencing was utilized to identify genes and pathways expressed in the endometrium of female C57Bl/6 mice at estrus and on day 3.5 post-coitum (pc) after mating with BALB/c males (n = 3-4 biological replicates). Compared to the endometrium at estrus, 388 genes were considered differentially expressed in the endometrium on day 3.5 post-coitum. The transcriptional changes indicated substantial modulation of uterine immune and vascular systems during the pre-implantation phase, with the functional terms Angiogenesis, Chemotaxis, and Lymphangiogenesis predominating. Ingenuity Pathway Analysis software predicted the activation of several upstream regulators previously shown to be involved in the transition to receptivity including various cytokines, ovarian steroid hormones, prostaglandin E2, and vascular endothelial growth factor A. Our analysis also revealed four candidate upstream regulators that have not previously been implicated in the acquisition of uterine receptivity, with growth differentiation factor 2, lysine acetyltransferase 6 A, and N-6 adenine-specific DNA methyltransferase 1 predicted to be activated, and peptidylprolyl isomerase F predicted to be inhibited. Conclusions: This study confirms that the transcriptome of a receptive uterus is vastly different to the non-receptive uterus and identifies several genes, regulatory pathways, and upstream drivers not previously associated with implantation. The findings will inform further research to investigate the molecular mechanisms of uterine receptivity.Hon Yeung Chan, Ha M. Tran, James Breen, John E. Schjenken, and Sarah A. Robertso

Predicting dementia diagnosis from cognitive footprints in electronic health records: a case-control study protocol

INTRODUCTION: Dementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case-control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history. METHODS AND ANALYSIS: We will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared. ETHICS AND DISSEMINATION: This study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients' records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities' Action in Response to Dementia project (https://www.tip-card.hku.hk/)

Neural signals predict information sharing across cultures

Information sharing influences which messages spread and shape beliefs, behavior, and culture. In a preregistered neuroimaging study conducted in the United States and the Netherlands, we demonstrate replicability, predictive validity, and generalizability of a brain-based prediction model of information sharing. Replicating findings in Scholz et al., Proc. Natl. Acad. Sci. U.S.A. 114, 2881–2886 (2017), self-, social-, and value-related neural signals in a group of individuals tracked the population sharing of US news articles. Preregistered brain-based prediction models trained on Scholz et al. (2017) data proved generalizable to the new data, explaining more variance in population sharing than self-report ratings alone. Neural signals (versus self-reports) more reliably predicted sharing cross-culturally, suggesting that they capture more universal psychological mechanisms underlying sharing behavior. These findings highlight key neurocognitive foundations of sharing, suggest potential target mechanisms for interventions to increase message effectiveness, and advance brain-as-predictor research

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study

Background: By combining theory-driven and data-driven methods, this study aimed to develop dementia predictive algorithms among Chinese older adults guided by the cognitive footprint theory. Methods: Electronic medical records from the Clinical Data Analysis and Reporting System in Hong Kong were employed. We included patients with dementia diagnosed at 65+ between 2010 and 2018, and 1:1 matched dementia-free controls. We identified 51 features, comprising exposures to established modifiable factors and other factors before and after 65 years old. The performances of four machine learning models, including LASSO, Multilayer perceptron (MLP), XGBoost, and LightGBM, were compared with logistic regression models, for all patients and subgroups by age. Findings: A total of 159,920 individuals (40.5% male; mean age [SD]: 83.97 [7.38]) were included. Compared with the model included established modifiable factors only (area under the curve [AUC] 0.689, 95% CI [0.684, 0.694]), the predictive accuracy substantially improved for models with all factors (0.774, [0.770, 0.778]). Machine learning and logistic regression models performed similarly, with AUC ranged between 0.773 (0.768, 0.777) for LASSO and 0.780 (0.776, 0.784) for MLP. Antipsychotics, education, antidepressants, head injury, and stroke were identified as the most important predictors in the total sample. Age-specific models identified different important features, with cardiovascular and infectious diseases becoming prominent in older ages. Interpretation: The models showed satisfactory performances in identifying dementia. These algorithms can be used in clinical practice to assist decision making and allow timely interventions cost-effectively. Funding: The Research Grants Council of Hong Kong under the Early Career Scheme 27110519

Client Service Receipt Inventory as a standardised tool for measurement of socio-economic costs in the rare genetic disease population (CSRI-Ra)

The measurement of costs is fundamental in healthcare decision-making, but it is often challenging. In particular, standardised methods have not been developed in the rare genetic disease population. A reliable and valid tool is critical for research to be locally meaningful yet internationally comparable. Herein, we sought to develop, contextualise, translate, and validate the Client Service Receipt Inventory for the RAre disease population (CSRI-Ra) to be used in cost-of-illness studies and economic evaluations for healthcare planning. Through expert panel discussions and focus group meetings involving 17 rare disease patients, carers, and healthcare and social care professionals from Hong Kong, we have developed the CSRI-Ra. Rounds of forward and backward translations were performed by bilingual researchers, and face validity and semantic equivalence were achieved through interviews and telephone communications with focus group participants and an additional of 13 healthcare professional and university students. Intra-class correlation coefficient (ICC) was used to assess criterion validity between CSRI-Ra and electronic patient record in a sample of 94 rare disease patients and carers, with overall ICC being 0.69 (95% CI 0.56–0.78), indicating moderate to good agreement. Following rounds of revision in the development, contextualisation, translation, and validation stages, the CSRI-Ra is ready for use in empirical research. The CSRI-Ra provides a sufficiently standardised yet adaptable method for collecting socio-economic data related to rare genetic diseases. This is important for near-term and long-term monitoring of the resource consequences of rare diseases, and it provides a tool for use in economic evaluations in the future, thereby helping to inform planning for efficient and effective healthcare. Adaptation of the CSRI-Ra to other populations would facilitate international research

Digital interventions to improve adherence to maintenance medication in asthma

© 2018 The Cochrane Collaboration. This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To determine the effectiveness of digital adherence interventions for improving adherence to maintenance treatments in asthma