There and up again: On the uses and misuses of neuroimaging in psychology

The aim of this article is to discuss the conditions under which functional neuroimaging can contribute to the study of higher cognition. We begin by presenting two case studies—on moral and economic decision making—which will help us identify and examine one of the main ways in which neuroimaging can help advance the study of higher cognition. We agree with critics that functional magnetic resonance imaging (fMRI) studies seldom “refine” or “confirm” particular psychological hypotheses, or even provide details of the neural implementation of cognitive functions. However, we suggest that neuroimaging can support psychology in a different way—namely, by selecting among competing hypotheses of the cognitive mechanisms underlying some mental function. One of the main ways in which neuroimaging can be used for hypothesis selection is via reverse inferences, which we here examine in detail. Despite frequent claims to the contrary, we argue that successful reverse inferences do not assume any strong or objectionable form of reductionism or functional locationism. Moreover, our discussion illustrates that reverse inferences can be successful at early stages of psychological theorizing, when models of the cognitive mechanisms are only partially developed.     

Quantitative Imaging Biomarkers: The Application of Advanced Image Processing and Analysis to Clinical and Preclinical Decision Making

The importance of medical imaging for clinical decision making has been steadily increasing over the last four decades. Recently, there has also been an emphasis on medical imaging for preclinical decision making, i.e., for use in pharamaceutical and medical device development. There is also a drive towards quantification of imaging findings by using quantitative imaging biomarkers, which can improve sensitivity, specificity, accuracy and reproducibility of imaged characteristics used for diagnostic and therapeutic decisions. An important component of the discovery, characterization, validation and application of quantitative imaging biomarkers is the extraction of information and meaning from images through image processing and subsequent analysis. However, many advanced image processing and analysis methods are not applied directly to questions of clinical interest, i.e., for diagnostic and therapeutic decision making, which is a consideration that should be closely linked to the development of such algorithms. This article is meant to address these concerns. First, quantitative imaging biomarkers are introduced by providing definitions and concepts. Then, potential applications of advanced image processing and analysis to areas of quantitative imaging biomarker research are described; specifically, research into osteoarthritis (OA), Alzheimer's disease (AD) and cancer is presented. Then, challenges in quantitative imaging biomarker research are discussed. Finally, a conceptual framework for integrating clinical and preclinical considerations into the development of quantitative imaging biomarkers and their computer-assisted methods of extraction is presented.     

What is the potential for context aware communication aids?

Abstract

Use of voice output communication aids (VOCAs) can be a very effective strategy to assist people with speech impairments in communicating. Despite this, people who use communication aids often express frustration with VOCAs—desiring devices that are simpler, quicker and more effective to use. Whilst it is not possible to resolve all these issues with technology, it is argued that significant progress can be made. The use of contextual information is one development that could improve the simplicity and effectiveness of communication aid design. Improving the effectiveness of communication aids, including through the use of context support, is a goal of the NIHR Devices for Dignity Assistive Technology Theme. This discussion paper examines the potential for creating ‘context aware’ communication aids. Three projects in which the authors have been involved are described to illustrate different approaches to the use of contextual information.     

Is there shared decision making when the provider makes a recommendation?

Objective

To compare the amount of shared decision making in breast cancer surgery interactions when providers do and do not make a treatment recommendation.

Methods

We surveyed breast cancer survivors who were eligible for mastectomy and lumpectomy. Patients reported whether the provider made a recommendation and the recommendation given. They completed items about their interaction including discussion of options, pros, cons, and treatment preference. A total involvement score was calculated with higher scores indicating more shared decision making.

Results

Most patients (85%) reported that their provider made a recommendation. Patients who did not receive a recommendation had higher involvement scores compared to those who did (52% vs. 39.1%, p = 0.004). Type of recommendation was associated with involvement. Patients given different recommendations had the highest total involvement scores followed by those who received mastectomy and lumpectomy recommendations (65.5% vs. 42.5% vs. 33.2%, respectively, p < 0.001).

Conclusion

Providers were less likely to present a balanced view of the options when they gave a recommendation for surgery. Patients who received a recommendation for lumpectomy had the lowest involvement score.

Practice implications

Providers need to discuss both mastectomy and lumpectomy and elicit patients’ goals and treatment preferences regardless of whether or not a recommendation is given.     

Combining deliberation and intuition in patient decision support

Objective

To review the strengths and weaknesses of deliberative and intuitive processes in the context of patient decision support and to discuss implications for decision aid (DA) design.

Methods

Conceptual review of the strengths and weaknesses of intuitive and analytical decision making and applying these findings to the practice of DA design.

Results

DAs combine several important goals: providing information, helping to clarify treatment related values, supporting preference construction processes, and facilitating more active engagement in decision making. Many DAs encourage patients to approach a decision analytically, without solid theoretical or empirical grounding for this approach. Existing research in other domains suggests that both intuition and deliberation may support decision making. We discuss implications for patient decision support and challenge researchers to determine when combining these processes leads to better outcomes.

Conclusions

Intuitive and analytical decision processes may have complementary effects in achieving the desired outcomes of patient decision support.

Practice implications

DA developers should be aware that tools solely targeted at supporting deliberation may limit DA effectiveness and harm preference construction processes. Patients may be better served by combined strategies that draw on the strengths and minimize the weaknesses of both deliberative and intuitive processes.     

Effectiveness of shared decision-making intervention in patients with lumbar degenerative diseases: A randomized controlled trial

ObjectiveTo evaluate the efficacy of shared decision-making (SDM) intervention among patients with lumbar degenerative diseases (LDDs) in terms of decision self-efficacy, control preferences, SDM process, decision satisfaction, and conflict.MethodsA total of 130 outpatients with LDDs recruited from orthopedic or rehabilitation clinics were randomly assigned to the SDM intervention (n = 67) or comparison (n = 63) groups. Patients in the intervention group received decision aids (DAs) with decision coaching and those in controlled group received standard educational materials from a health educator. The primary outcome was decision self-efficacy, and secondary outcomes were control preference, SDM process, conflict, and satisfaction.ResultsThe SDM intervention significantly improved decision self-efficacy (mean difference [MD] = 7.1, 95% confidence interval [CI]: 1.7–12.5, partial η² = 0.05) and reduced conflict (MD = −7.0, 95% CI: −12.2 to −1.9, partial η² = 0.06), especially in patients without family involvement, compared with the health education group. However, no significant between-group differences were observed in other outcomes.ConclusionSDM intervention improved SDM self-efficacy and reduced conflict in patients with LDDs.Practice ImplicationsClinicians can integrate DAs and decision coaching in SDM conversations. SDM intervention seems to engage patients in decision-making, especially those without family involvement.