A 4D approach to the analysis of functional brain images: application to FMRI data.

This paper presents a new approach to functional magnetic resonance imaging (FMRI) data analysis. The main difference lies in the view of what comprises an observation. Here we treat the data from one scanning session (comprising t volumes, say) as one observation. This is contrary to the conventional way of looking at the data where each session is treated as t different observations. Thus instead of viewing the v voxels comprising the 3D volume of the brain as the variables, we suggest the usage of the vt hypervoxels comprising the 4D volume of the brain-over-session as the variables. A linear model is fitted to the 4D volumes originating from different sessions. Parameter estimation and hypothesis testing in this model can be performed with standard techniques. The hypothesis testing generates 4D statistical images (SIs) to which any relevant test statistic can be applied. In this paper we describe two test statistics, one voxel based and one cluster based, that can be used to test a range of hypotheses. There are several benefits in treating the data from each session as one observation, two of which are: (i) the temporal characteristics of the signal can be investigated without an explicit model for the blood oxygenation level dependent (BOLD) contrast response function, and (ii) the observations (sessions) can be assumed to be independent and hence inference on the 4D SI can be made by nonparametric or Monte Carlo methods. The suggested 4D approach is applied to FMRI data and is shown to accurately detect the expected signal.     

Dynamic paradigm in psychopathology: "chaos theory", from physics to psychiatry

For the last thirty years, progress in the field of physics, known as "Chaos theory"--or more precisely: non-linear dynamical systems theory--has increased our understanding of complex systems dynamics. This framework's formalism is general enough to be applied in other domains, such as biology or psychology, where complex systems are the rule rather than the exception. Our goal is to show here that this framework can become a valuable tool in scientific fields such as neuroscience and psychiatry where objects possess natural time dependency (i.e. dynamical properties) and non-linear characteristics. The application of non-linear dynamics concepts on these topics is more precise than a loose metaphor and can throw a new light on mental functioning and dysfunctioning. A class of neural networks (recurrent neural networks) constitutes an example of the implementation of the dynamical system concept and provides models of cognitive processes (15). The state of activity of the network is represented in its state space and the time evolution of this state is a trajectory in this space. After a period of time those networks settle on an equilibrium (a kind of attractor). The strength of connections between neurons define the number and relations between those attractors. The attractors of the network are usually interpreted as "mental representations". When an initial condition is imposed to the network, the evolution towards an attractor is considered as a model of information processing (27). This information processing is not defined in a symbolic manner but is a result of the interaction between distributed elements. Several properties of dynamical models can be used to define a way where the symbolic properties emerge from physical and dynamical properties (28) and thus they can be candidates for the definition of the emergence of mental properties on the basis of neuronal dynamics (42). Nevertheless, mental properties can also be considered as the result of an underlying dynamics without explicit mention of the neuronal one (47). In that case, dynamical tools can be used to elucidate the Freudian psychodynamics (34, 35). Recurrent neuronal networks have been used to propose interpretation of several mental dysfunctions (12). For example in the case of schizophrenia, it has been proposed that troubles in the cortical pruning during development (13) may cause a decrease in neural network storage ability and lead to the creation of spurious attractors. Those attractors do not correspond to stored memories and attract a large amount of initial conditions: they were thus associated to reality distorsion observed in schizophrenia (14). Nevertheless, the behavior of these models are too simple to be directly compared with real physiological data. In fact, equilibrium attractors are hardly met in biological dynamics. More complex behaviors (such as oscillations or chaos) should thus to be taken into account. The study of chaotic behavior have lead to the development of numerical methods devoted to the analysis of complex time series (17). These methods may be used to characterise the dynamical processes at the time-scales of both the cerebral dynamics and the clinical symptoms variations. The application of these methods to physiological signals have shown that complex behaviors are related to healthy states whereas simple dynamics are related to pathology (8). These studies have thus confirmed the notion of "dynamical disease" (20, 21) which denotes pathological conditions characterised by changes in physiological rhythms. Depression has been studied within this framework (25, 32) in order to define possible changes in brain electrical rhythms related to this trouble and its evolution. It has been shown that controls' brain dynamics is more complex than depressive one and that the recovery of a complex brain activity depends on the number of previous episodes. In the case of the symptoms time evolution, several studies have demonstrated that non-linear dynamical process may be involved in the recurrence of symptoms in troubles such as manic-depressive illness (9) or schizophrenia (51). These observations can contribute to more parcimonious interpretation of the time course of these illnesses than usual theories. In the search of a relationship between brain dynamics and mental troubles, it has been shown in three depressed patients an important correlation between the characteristics of brain dynamics and the intensity of depressive mood (49). This preliminary observation is in accordance with the emergence hypothesis according which changes in neuronal dynamics should be related to changes in mental processes. We reviewed here some theoretical and experimental results related to the use of "physical" dynamical theory in the field of psychopathology. It has been argued that these applications go beyond metaphor and that they are empirically founded. Nevertheless, these studies only constitute first steps on the way of a cautious development and definition of a "dynamical paradigm" in psychopathology. The introduction of concepts from dynamics such as complexity and dynamical changes (i.e. bifurcations) permits a new perspective on function and dysfunction of the mind/brain and the time evolution of symptoms. Moreover, it offers a ground for the hypothesis of the emergence of mental properties on the basis of neuronal dynamics (42). Since this theory can help to throw light on classical problems in psychopathology, we consider that a precise examination of both its theoretical and empirical consequences is requested to define its validity on this topic.     

Unicorns do exist: a tutorial on "proving" the null hypothesis.

Introductory statistics classes teach us that we can never prove the null hypothesis; all we can do is reject or fail to reject it. However, there are times when it is necessary to try to prove the nonexistence of a difference between groups. This most often happens within the context of comparing a new treatment against an established one and showing that the new intervention is not inferior to the standard. This article first outlines the logic of "noninferiority" testing by differentiating between the null hypothesis (that which we are trying to nullify) and the "nill" hypothesis (there is no difference), reversing the role of the null and alternate hypotheses, and defining an interval within which groups are said to be equivalent. We then work through an example and show how to calculate sample sizes for noninferiority studies.     

The potential for utilizing the "mirror neurone system" to enhance recovery of the severely affected upper limb early after stroke: a review and hypothesis

Recovery of upper limb movement control after stroke might be enhanced by repetitive goal-directed functional activities. Providing such activity is challenging in the presence of severe paresis. A possible new approach is based on the discovery of mirror neurons in the monkey cortical area F5, which are active both in observing and executing a movement. Indirect evidence for a comparable human "mirror neurone system" is provided by functional imaging. The primary motor cortex, the premotor cortex, other brain areas, and muscles appropriate for the action being observed are probably activated in healthy volunteers observing another's movement. These findings raise the hypothesis that observation of another's movement might train the movement execution system of stroke patients who have severe paresis to bring them to the point at which they could actively participate in rehabilitation consisting of goal-directed activities. The point of providing an observation therapy would be to facilitate the voluntary production of movement; therefore, the condition of interest would be observation with intent to imitate. However, there is as yet insufficient evidence to enable the testing of this hypothesis in stroke patients. Studies in normal subjects are needed to determine which brain sites are activated in response to observation with intent to imitate. Studies in stroke subjects are needed to determine how activation is affected after damage to different brain areas. The information from such studies should aid identification of those stroke patients who might be most likely to benefit from observation to imitate and therefore guide phase I clinical studies.     

Statistical adjustments for brain size in volumetric neuroimaging studies: Some practical implications in methods

Volumetric magnetic resonance imaging (MRI) brain data provide a valuable tool for detecting structural differences associated with various neurological and psychiatric disorders. Analysis of such data, however, is not always straightforward, and complications can arise when trying to determine which brain structures are “smaller” or “larger” in light of the high degree of individual variability across the population. Several statistical methods for adjusting for individual differences in overall cranial or brain size have been used in the literature, but critical differences exist between them. Using agreement among those methods as an indication of stronger support of a hypothesis is dangerous given that each requires a different set of assumptions be met. Here we examine the theoretical underpinnings of three of these adjustment methods (proportion, residual, and analysis of covariance) and apply them to a volumetric MRI data set. These three methods used for adjusting for brain size are specific cases of a generalized approach which we propose as a recommended modeling strategy. We assess the level of agreement among methods and provide graphical tools to assist researchers in determining how they differ in the types of relationships they can unmask, and provide a useful method by which researchers may tease out important relationships in volumetric MRI data. We conclude with the recommended procedure involving the use of graphical analyses to help uncover potential relationships the ROI volumes may have with head size and give a generalized modeling strategy by which researchers can make such adjustments that include as special cases the three commonly employed methods mentioned above.     

Mind the gap: A review and recommendations for statistically evaluating Dual Systems models of adolescent risk behavior

According to Dual Systems models (Casey et al., 2008; Luna and Wright, 2016; Steinberg, 2008), a rapidly-developing socioemotional system and gradually-developing cognitive control system characterize adolescent brain development. The imbalance hypothesis forwarded by Dual Systems models posits that the magnitude of the imbalance between these two developing systems should predict the propensity for engaging in a variety of risk behaviors. The current integrative review argues that the excitement generated by the imbalance hypothesis and its implications for explaining adolescent risk behaviors has not been meet with equal efforts to rigorously test this hypothesis. The goal of the current review is to help guide the field to consider appropriate and rigorous methods of testing the imbalance hypothesis. First, we review the analytic approaches that have been used to test the imbalance hypothesis and outline statistical and conceptual limitations of these approaches. Next, we discuss the utility of two longitudinal analytic approaches (Latent Difference Scores and Growth Mixture Modeling) for testing the imbalance hypothesis. We utilize data from a large community adolescent sample to illustrate each approach and argue that Latent Difference Scores and Growth Mixture Modeling approaches enhance the specificity and precision with which the imbalance hypothesis is evaluated.     

Are there EEG correlates of mental states in animals?

The thesis of this paper emerges from the fact that mental states are generated by neural processes that also produce an associated electroencephalogram (EEG). Thus, it is logical to expect correlations between mental state and EEG. The corollary is that the EEG can serve as an index of mental state, which can be particularly useful for studies in animals, where mental states are much less accessible for objective study than in humans. Herein, I briefly review the traditional approaches that have informed our attitudes about animal mental states. Virtually all of our conclusions about mental states in animals are drawn by inference from behavioral observation, a process that is highly and unavoidably subject to anthropomorphism. Traditionally, the electroencephalogram (EEG) has been used in a crude way as an objective indication of physical and behavioral state in animals. This, however, has led to substantial controversy, because there are several situations in which EEG patterns and behavior seem to be dissociated. We not only fail to understand these dissociated states, but there are also important humane animal-welfare issues that remain unresolved because we do not fully understand the extent to which the EEG can reflect mental state. At issue is whether EEG-behavioral dissociations, to the extent that they exist, are proof that the EEG is dissociated from mental states. Powerful new EEG methods, such as topographical EEG mapping, wavelet analysis, and testing for nonlinear ('chaotic') dynamical properties and short-term serial dependencies, are now available for studying the extent to which the EEG can index thinking and feeling in humans and, by extrapolation, in animals. Critics who have become disenchanted with the utility of the EEG should at least concede that fresh approaches to old problems are now available and should therefore be thoughtfully considered. If such research does nothing more than improve the rigor of the debate over animal welfare and rights issues, it will be worth the effort.     

Cognitive Computing in Mental Healthcare: a Review of Methods and Technologies for Detection of Mental Disorders

Mental disorders are closely related to deficits in cognitive control. Such cognitive impairments may result in aberrations in mood, thinking, work, body functions, emotions, social engagements and general behaviour. Mental disorders may affect the phenotypic behaviour like eye movements, facial expressions and speech. Furthermore, a close association has been observed within mental disorders and physiological responses emanating from the brain, muscles, heart, eyes, skin, etc. Mental disorders disrupt higher cognitive function, social cognition, control of complex behaviours and regulation of emotion. Cognitive computation may help understand such disruptions for improved decision-making with the help of computers. This study presents a systematic literature review to promulgate state of art computational methods and technologies facilitating automated detection of mental disorders. For this survey, the relevant literature between 2010 and 2021 has been studied. Recommendations of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model were adopted for identification, screening, validating and inclusion of research literature. The self-diagnosis tools for detection of mental disorders like questionnaires and rating scales are inconsistent and static in nature. They cannot encompass the diversity of mental disorders, inter-individual variability and impact of emotional state of an individual. Furthermore, there are no standard baselines for mental disorders. This situation mandates a multi-faceted approach which may utilise data from physiological signals, behavioural patterns and even data obtained from various online portals like social media to efficiently and effectively detect the prevalence, type and severity of mental disorders.

     

Hypothesis testing for selective,differential, and conjoined brain activation

Hypothesis testing in functional neuroimaging studies relies heavily on the computation of categorical contrasts in which brain activation associated with one experimental condition is assessed relative to brain activation associated with a different experimental condition. Often, multiple pair-wise contrasts are computed and reported independently. Here we describe an approach to hypothesis testing that logically combines multiple pair-wise contrasts to distinguish among selective, differential and conjoined brain activation patterns. Using a sample dataset in which participants viewed objects, visual noise patterns or a fixation cross, we demonstrate that selective and differential brain activation patterns are often confounded with current approaches to hypothesis testing but that the logical combination approach can distinguish between these two types of data patterns. Specifically, we show that brain regions that respond selectively to an object recognition task relative to viewing visual noise or a fixation cross (selective activation) are mutually exclusive from brain regions that show a graded response to object viewing, noise viewing and visual fixation (differential activation). We thus show that the logical combination approach sufficiently constrains the results of categorical contrasts to reflect only the data pattern that would be predicted from the cognitive processing account under investigation.     

Contribution from neurophysiological and psychological methods to the study of motor imagery

This paper reviews studies on neurophysiological and behavioral methods used to evaluate motor imagery accuracy. These methods can be used when performed in the field and are based on recordings of peripheral indices such as autonomic nervous system or electromyographic activities, mental chronometry and psychological tests. Providing physiological signs that correlate to these types of mental processes may be considered an objective approach for motor imagery analysis. However, although autonomic nervous system activity recording has been shown to match motor imagery in real time, to evaluate its accuracy qualitatively and the individual ability to form mental images, the relationship between physiological responses and mental processes remains an inference. Moreover, electromyographic recordings may be associated with postural control data, but due to inconsistent results, they remain insufficient to solely evaluate motor imagery accuracy. Other techniques traditionally used in psychology and cognitive psychology are questionnaires, “debriefing” with subjects and mental chronometry. Although such methods lead to interesting results, there remains an important part of subjectivity as subjects perform an autoevaluation of motor imagery accuracy. Similarly, mental chronometry gives information on the ability to preserve temporal organization of movement but does not allow the evaluation of the vividness of mental images. Thus, several methods should be combined to analyze motor imagery accuracy in greater detail. Neurophysiological recordings cannot therefore be considered an alternative but rather a complementary technique to behavioral and psychological methods. The advantages and inconvenient of each technique and the hypotheses that could be tested are discussed.     

Motor Speed Is a Contaminating Factor in Evaluating the "Cognitive" Effects of Phenytoin

Many studies suggestive of adverse effects of phenytoin (PHT) on mental abilities have used testing procedures which have timed or motor speed elements. Therefore, to what degree the motor speed element alone may have resulted in attributing adverse higher level intellectual or cognitive effects to PHT instead of the identified construct to be measured (e.g., memory, abstraction, decision making) is not clear. To help distinguish "motor" effects from these more complex "cognitive" effects, neuropsychological data on 70 adult PHT monotherapy patients were reanalyzed. Initially, a series of statistically significant differences favored the low serum level group over the high serum level group in neuropsychologic performance. However, when a simple measure of motor speed (Finger Tapping Test) was covaried out, all statistically significant differences between the groups disappeared. Thus, losses in cognitive abilities could not be associated with PHT even though markedly elevated blood levels had been achieved.     

Hypothesis testing in distributed source models for EEG and MEG data

Hypothesis testing in distributed source models for the electro- or magnetoencephalogram is generally performed for each voxel separately. Derived from the analysis of functional magnetic resonance imaging data, such a statistical parametric map (SPM) ignores the spatial smoothing in hypothesis testing with distributed source models. For example, when intending to test a single voxel, actually an entire region of voxels is tested simultaneously. Because there are more parameters than observations, typically constraints are employed to arrive at a solution which spatially smooths the solution. If ignored, it can be concluded from the hypothesis test that there is activity at some location where there is none. In addition, an SPM on distributed source models gives the illusion of very high resolution. As an alternative, a multivariate approach is suggested in which a region of interest is tested that is spatially smooth. In simulations with MEG and EEG it is shown that clear hypothesis testing in distributed source models is possible, provided that there is high correspondence between what is intended to be tested and what is actually tested. The approach is also illustrated by an application to data from an experiment measuring visual evoked fields when presenting checkerboard patterns.     

Cross multivariate correlation coefficients as screening tool for analysis of concurrent EEG‐fMRI recordings

Over the past decade, the simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data has garnered growing interest because it may provide an avenue towards combining the strengths of both imaging modalities. Given their pronounced differences in temporal and spatial statistics, the combination of EEG and fMRI data is however methodologically challenging. Here, we propose a novel screening approach that relies on a Cross Multivariate Correlation Coefficient (xMCC) framework. This approach accomplishes three tasks: (1) It provides a measure for testing multivariate correlation and multivariate uncorrelation of the two modalities; (2) it provides criterion for the selection of EEG features; (3) it performs a screening of relevant EEG information by grouping the EEG channels into clusters to improve efficiency and to reduce computational load when searching for the best predictors of the BOLD signal. The present report applies this approach to a data set with concurrent recordings of steady‐state‐visual evoked potentials (ssVEPs) and fMRI, recorded while observers viewed phase‐reversing Gabor patches. We test the hypothesis that fluctuations in visuo‐cortical mass potentials systematically covary with BOLD fluctuations not only in visual cortical, but also in anterior temporal and prefrontal areas. Results supported the hypothesis and showed that the xMCC‐based analysis provides straightforward identification of neurophysiological plausible brain regions with EEG‐fMRI covariance. Furthermore xMCC converged with other extant methods for EEG‐fMRI analysis.     

Assessing the "criminalization" of the mentally ill in Canada.

This paper is an overview of the conceptual and methodological problems encountered trying to assess the hypothesis that the mentally ill, as a consequence of deinstitutionalization, are being "criminalized". Generalizations are difficult to make, in large part because most of the studies are American and do not fit well into the Canadian scene. Relevant Canadian findings are reviewed and compared to the US data. There is some evidence that Canadian patients may be diverted from the criminal justice system more often than in the US, where there are fewer resources. However, this conclusion must be tempered by the fact that Canadian surveys have found high rates of mental disorder among prison and jail inmates.     

The genomic era and serious mental illness: a potential application for psychiatric genetic counseling

Genetic counseling is an important clinical service that is routinely offered to families affected by genetic disorders or by complex disorders for which genetic testing is available. It is not yet routinely offered to individuals with serious mental illnesses and their families, but recent findings that beliefs about the cause of mental illness can affect an individual's adaptation to the illness suggest that genetic counseling may be a useful intervention for this population. In a genetic counseling session the counselor discusses genetic and environmental contributors to disease pathogenesis; helps individuals explore conceptions, fears, and adaptive strategies; and provides nondirective support for decision making. Expected outcomes may include reductions in fear, stigma, and guilt associated with a psychiatric diagnosis; improvements in adherence to prescribed medications; declines in risk behaviors; and reductions in misconceptions about the illness. The authors endorse a multidisciplinary approach in which a psychiatrist and genetic counselor collaborate to provide comprehensive psychiatric genetic counseling.     

Establishing normative data for multi-trial memory tests: the multivariate regression-based approach

Abstract

Objective: Multi-trial memory tests are widely used in research and clinical practice because they allow for assessing different aspects of memory and learning in a single comprehensive test procedure. However, the use of multi-trial memory tests also raises some key data analysis issues. Indeed, the different trial scores are typically all correlated, and this correlation has to be properly accounted for in the statistical analyses. In the present paper, the focus is on the setting where normative data have to be established for multi-trial memory tests. At present, normative data for such tests are typically based on a series of univariate analyses, i.e. a statistical model is fitted for each of the test scores separately. This approach is suboptimal because (1) the correlated nature of the data is not accounted for, (2) multiple testing issues may arise, and (3) the analysis is not parsimonious. Method and results: Here, a normative approach that is not hampered by these issues is proposed (the so-called multivariate regression-based approach). The methodology is exemplified in a sample of N = 221 Dutch-speaking children (aged between 5.82 and 15.49 years) who were administered Rey’s Auditory Verbal Learning Test. An online Appendix that details how the analyses can be conducted in practice (using the R software) is also provided. Conclusion: The multivariate normative regression-based approach has some substantial methodological advantages over univariate regression-based methods. In addition, the method allows for testing substantive hypotheses that cannot be addressed in a univariate framework (e.g. trial by covariate interactions can be modeled).     

Positive and negative affect differentially influence identification of facial emotions

Positive and negative affects may bias behavior toward approach to rewards and withdrawal from threat, particularly when the contingencies are ambiguous. The hypothesis was that positive and negative affects would associate predictably with identification of happy, disgusted, or angry expressions that may signal potentially rewarding or aversive social interactions. Healthy volunteers (n=86) completed affect ratings and a facial emotion task that employed morphed continua in which emotional expressions gradually decreased in ambiguity. Relations between mood and intensity thresholds for emotion identification were computed. Anhedonia (low positive affect) predicted thresholds for happy expressions (r=0.24; P=.026) whereas negative affect predicted thresholds for disgust (r=-0.25; P=.022). Even within a normal range of mood, mood predicted emotion identification, supporting constructs of positive and negative affect derived originally from self-report measures.     

Elaboration of a rehabilitation method based on a pathogenetic hypothesis of "theory of mind" impairment in schizophrenia

"Theory of mind" skills have repeatedly been shown to be impaired in schizophrenic patients. The purpose of this paper is to develop a therapeutic intervention targeting schizophrenic patients' abilities to attribute mental states to others. This preliminary study tried to test the hypothesis that this kind of intervention should improve patients' communication abilities; we also wondered if this would have a positive impact on their more general psychopathology. Eight chronic schizophrenic patients were asked to analyse video scenes (showing interactions between two or more persons), with particular attention being paid to the characters' mental states. Their clinical assessments (general clinical symptoms, communication, and the ability to attribute intentions to others, rated before and after two training sessions, over a one-week period) were compared with those of a control group of six chronic schizophrenic patients. The results support our hypothesis of a possible improvement of patients' communication disorders and an improvement of their abilities to attribute intentions to others. The more general psychopathology ratings, however, remained unchanged. Although these results need further development and confirmation, they suggest a possible promising approach for psychosocial rehabilitation therapies based on "theory of mind" skills.     

Development and Validation of the Arab Youth Mental Health Scale

A variety of measures of mental health have been used with youth. The reason for choosing one scale over another in any given situation is rarely stated, and cross-cultural validation is scarce. Psychometric testing is crucial before utilizing any measure of mental health with a certain population, due to possible cultural variations in interpreting meaning. The research reported herein describes the development and psychometric testing of the Arab Youth Mental Health Scale. The process included 5 phases: (1) reviewing existing scales leading to the identification of 14 non-clinical and relatively short mental health scales used previously with youth; (2) rating the scales by the researchers and community members leading to the identification of 3 scales with apt structure, and that were judged to be suitable, applicable, and appropriate; (3) soliciting youth input to assess comprehension of each item in the selected 3 scales and to discover context specific mental health related feelings, thoughts, and expressions; (4) seeking expert opinion to classify items remaining after phase 3 that measured common mental disorders, and to limit repetitiveness; and (5) testing for psychometric properties of the 28 items that remained after the previous 4 phases. The contribution of each phase to the process is described separately. Results of the exploratory principal component analysis resulted in one factor which explained 28% of the variance and for which 21 items loaded above an eigenvalue of 0.5. No other factor added significantly to the explanation of variance, nor had items that added theoretical or conceptual constructs. The process of soliciting feedback from youth groups, the community and professionals; and of field testing was challenging; but resulted in a contextually sensitive, culturally appropriate and reliable scale to measure mental health of youth. We recommend that researchers measuring mental health of youth critically analyze the relevance of existing scales to their context; consider using the AYMH scale if appropriate to their target population; and when needed, use a similar methodology to construct a relevant, culturally and contextually sensitive measure.     

Determining gene moderation of environmental risk factors for a mental disorder: a "perfect storm" of methodological problems

For most of the twentieth century, the focus was on “nature” versus “nurture”, i.e. genetic versus environmental effects on disorders. Now it is increasingly recognized that a disorder may reflect genes and environments “working together”. A gene may moderate an environmental risk factor, it may be mediated by an environmental risk factor. The environmental risk factor may be proxy to the gene, and the two may be independent risk factors. Which of these situations pertain influences both subsequent research and clinical and policy decision‐making. However, recent meta‐analyses attempting to confirm the Caspi et al. (Science, 301, 386–389, 2003) hypothesis indicate that the methodological issues relating to establishing specifically a moderating effect of a gene on an environmental factor are not well understood. The discussion here concerns the definition of “moderator”, how it is distinct from other ways in which gene and environment can “work together”, the methods needed to establish such a moderator, and the public health significance of such efforts. Copyright © 2012 John Wiley & Sons, Ltd.