Brain images, babies, and bathwater: critiquing critiques of functional neuroimaging

Narrative Devices: Neurotechnologies, Information, and Self-Constitution

This article provides a conceptual and normative framework through which we may understand the potentially ethically significant roles that information generated by neurotechnologies about our brains and minds may play in our construction of our identities. Neuroethics debates currently focus disproportionately on the ways that third parties may (ab)use these kinds of information. These debates occlude interests we may have in whether and how we ourselves encounter information about our own brains and minds. This gap is not yet adequately addressed by most allusions in the literature to potential identity impacts. These lack the requisite conceptual or normative foundations to explain why we should be concerned about such effects or how they might be addressed. This article seeks to fill this gap by presenting a normative account of identity as constituted by embodied self-narratives. It proposes that information generated by neurotechnologies can play significant content-supplying and interpretive roles in our construction of our self-narratives. It argues, to the extent that these roles support and detract from the coherence and inhabitability of these narratives, access to information about our brains and minds engages non-trivial identity-related interests. These claims are illustrated using examples drawn from empirical literature reporting reactions to information generated by implantable predictive BCIs and psychiatric neuroimaging. The article concludes by highlighting ways in which information generated by neurotechnologies might be governed so as to protect information subjects' interests in developing and inhabiting their own identities.

Estimating the prevalence of missing experiments in a neuroimaging meta-analysis

Coordinate-based meta-analyses (CBMA) allow researchers to combine the results from multiple functional magnetic resonance imaging experiments with the goal of obtaining results that are more likely to generalize. However, the interpretation of CBMA findings can be impaired by the file drawer problem, a type of publication bias that refers to experiments that are carried out but are not published. Using foci per contrast count data from the BrainMap database, we propose a zero-truncated modeling approach that allows us to estimate the prevalence of nonsignificant experiments. We validate our method with simulations and real coordinate data generated from the Human Connectome Project. Application of our method to the data from BrainMap provides evidence for the existence of a file drawer effect, with the rate of missing experiments estimated as at least 6 per 100 reported. The R code that we used is available at https://osf.io/ayhfv/.

Neuroanatomical Differences Among Sexual Offenders: A Targeted Review with Limitations and Implications for Future Directions

As sexual assault and child sexual abuse continue to be worldwide public health concerns, research has continued to explore factors associated with sexual offending. Structural and functional neuroanatomical brain differences have been examined in an effort to differentiate sexual offenders and their behavior. This targeted review searched PubMed and Google Scholar for empirical studies using brain imaging techniques to examine possible structural or functional differences among control groups compared with at least one group of sexual offenders with contact offenses. This targeted review summarizes the structural and functional findings of 15 brain imaging studies (i.e., computed tomography, diffusion tensor imaging, magnetic resonance imaging, positron emission tomography, and functional magnetic resonance imaging), which suggest possible differences in brain size and gray matter volume, cortical thickness, white matter connectivity, and specific structural and functional differences among brain regions (fronto-temporal region, amygdala, prefrontal cortex, etc.). The methodological limitations of brain imaging studies and the associated findings with regard to sexual offenders are highlighted, as research indicates that many of the proposed differences in brain structure and function are not unique to this population. We further highlight several limitations to using neuroimaging studies to examine this population of interest, including publication bias, small sample size, underpowered studies, and all-male samples. As these results are mixed and findings are not seemingly unique to sexual offenders, we suggest future sexual offender research may benefit from focusing on more financially feasible options, such as neuropsychological assessment approaches, to assess for and attend to offenders' criminogenic and rehabilitative/therapeutic needs in alignment with the risk-need-responsivity model.

Neuroimaging in criminal trials and the role of psychiatrists expert witnesses: A case study

Various neuroscientific techniques are increasingly being used in criminal courts causing a vivid debate on the way that this kind of techniques will and should be used as scientific evidence. The role of experts in this context is important, since it is them that analyse, present, interpret and communicate the results of these techniques to the judges and the jury. In an attempt to contribute to the discussion about the role of the experts in criminal cases where neuroimaging evidence was introduced, we examined twenty seven cases from the US and Europe. Focusing on the role of experts and their presentation of neuroscientific evidence, we aimed to examine the extent to which neuroimaging data can contribute to the construction of a solid and more objective, "scientifically - based" case. We found that neurobiological information introduced through experts' testimony is generally used in order to demonstrate some physical, organic base of a psychiatric condition, or/and in order to make visible some brain lesion, (structural or functional), susceptible to have affected the capacity to reason and to control one's impulses. While neuroimaging evidence is often presented by the defence as a scientific method able to offer a precise diagnosis of the pathology in question, our case analysis shows that the very same neurobiological evidence can be interpreted in different - sometimes diametrically opposed - ways by defence and State experts. Conflicting testimony about the same empirical evidence goes against the hypothesis of neuroscientific techniques constituting "objective and hard evidence", able to reach solid, scientific and objective conclusions. Frequent conflicts between neuroimaging experts require the courts to deal with the resulting uncertainty. As the law changes with technology, it is necessary for legal professionals to train and be prepared for the new issues they may encounter in light of new developments in neuroscience, so that they become more vigilant as to the interpretation of neuroscientific data.

Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data

Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal "noise" from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread "global" structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary-to do or not to do global signal regression-given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a "best of both worlds" solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data.

The coordinate-based meta-analysis of neuroimaging data

Neuroimaging meta-analysis is an area of growing interest in statistics. The special characteristics of neuroimaging data render classical meta-analysis methods inapplicable and therefore new methods have been developed. We review existing methodologies, explaining the benefits and drawbacks of each. A demonstration on a real dataset of emotion studies is included. We discuss some still-open problems in the field to highlight the need for future research.

The Power of Theory, Research Design, and Transdisciplinary Integration in Moving Psychopathology Forward

While the past few decades have seen much work in psychopathology research that has yielded provocative insights, relatively little progress has been made in understanding the etiology of mental disorders. We contend that this is due to an overreliance on statistics and technology with insufficient attention to adequacy of experimental design, a lack of integration of data across various domains of research, and testing of theoretical models using relatively weak study designs. We provide a conceptual discussion of these issues and follow with a concrete demonstration of our proposed solution. Using two different disorders - depression and substance use - as examples, we illustrate how we can evaluate competing theories regarding their etiology by integrating information from various domains including latent variable models, neurobiology, and quasi-experimental data such as twin and adoption studies, rather than relying on any single methodology alone. More broadly, we discuss the extent to which such integrative thinking allows for inferences about the etiology of mental disorders, rather than focusing on descriptive correlates alone. Greater scientific insight will require stringent tests of competing theories and a deeper conceptual understanding of the advantages and pitfalls of methodologies and criteria we use in our studies.