Building a Science of Individual Differences from fMRI

The potential of neuroimaging for identifying predictors of adolescent alcohol use initiation and misuse

BACKGROUND AND AIMS

Dysfunction in brain regions underlying impulse control, reward processing and executive function have been associated previously with adolescent alcohol misuse. However, identifying pre-existing neurobiological risk factors, as distinct from changes arising from early alcohol-use, is difficult. Here, we outline how neuroimaging data can identify the neural predictors of adolescent alcohol-use initiation and misuse by using prospective longitudinal studies to follow initially alcohol-naive individuals over time and by neuroimaging adolescents with inherited risk factors for alcohol misuse.

METHOD

A comprehensive narrative of the literature regarding neuroimaging studies published between 2010 and 2016 focusing on predictors of adolescent alcohol use initiation and misuse.

FINDINGS

Prospective, longitudinal neuroimaging studies have identified pre-existing differences between adolescents who remained alcohol-naive and those who transitioned subsequently to alcohol use. Both functional and structural grey matter differences were observed in temporal and frontal regions, including reduced brain activity in the superior frontal gyrus and temporal lobe, and thinner temporal cortices of future alcohol users. Interactions between brain function and genetic predispositions have been identified, including significant association found between the Ras protein-specific guanine nucleotide releasing factor 2 (RASGRF2) gene and reward-related striatal functioning.

CONCLUSIONS

Neuroimaging predictors of alcohol use have shown modest utility to date. Future research should use out-of-sample performance as a quantitative measure of a predictor's utility. Neuroimaging data should be combined across multiple modalities, including structural information such as volumetrics and cortical thickness, in conjunction with white-matter tractography. A number of relevant neurocognitive systems should be assayed; particularly, inhibitory control, reward processing and executive functioning. Combining a rich magnetic resonance imaging data set could permit the generation of neuroimaging risk scores, which could potentially yield targeted interventions.

The interpretation of mu suppression as an index of mirror neuron activity: past, present and future

Mu suppression studies have been widely used to infer the activity of the human mirror neuron system (MNS) in a number of processes, ranging from action understanding, language, empathy and the development of autism spectrum disorders (ASDs). Although mu suppression is enjoying a resurgence of interest, it has a long history. This review aimed to revisit mu's past, and examine its recent use to investigate MNS involvement in language, social processes and ASDs. Mu suppression studies have largely failed to produce robust evidence for the role of the MNS in these domains. Several key potential shortcomings with the use and interpretation of mu suppression, documented in the older literature and highlighted by more recent reports, are explored here.

Individual-specific features of brain systems identified with resting state functional correlations

Recent work has made important advances in describing the large-scale systems-level organization of human cortex by analyzing functional magnetic resonance imaging (fMRI) data averaged across groups of subjects. However, new findings have emerged suggesting that individuals' cortical systems are topologically complex, containing small but reliable features that cannot be observed in group-averaged datasets, due in part to variability in the position of such features along the cortical sheet. This previous work has reported only specific examples of these individual-specific system features; to date, such features have not been comprehensively described. Here we used fMRI to identify cortical system features in individual subjects within three large cross-subject datasets and one highly sampled within-subject dataset. We observed system features that have not been previously characterized, but 1) were reliably detected across many scanning sessions within a single individual, and 2) could be matched across many individuals. In total, we identified forty-three system features that did not match group-average systems, but that replicated across three independent datasets. We described the size and spatial distribution of each non-group feature. We further observed that some individuals were missing specific system features, suggesting individual differences in the system membership of cortical regions. Finally, we found that individual-specific system features could be used to increase subject-to-subject similarity. Together, this work identifies individual-specific features of human brain systems, thus providing a catalog of previously unobserved brain system features and laying the foundation for detailed examinations of brain connectivity in individuals.

Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach

In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research.

This article is part of the themed issue ‘Auditory and visual scene analysis’.

Memory consolidation reconfigures neural pathways involved in the suppression of emotional memories

The ability to suppress unwanted emotional memories is crucial for human mental health. Through consolidation over time, emotional memories often become resistant to change. However, how consolidation impacts the effectiveness of emotional memory suppression is still unknown. Using event-related fMRI while concurrently recording skin conductance, we investigated the neurobiological processes underlying the suppression of aversive memories before and after overnight consolidation. Here we report that consolidated aversive memories retain their emotional reactivity and become more resistant to suppression. Suppression of consolidated memories involves higher prefrontal engagement, and less concomitant hippocampal and amygdala disengagement. In parallel, we show a shift away from hippocampal-dependent representational patterns to distributed neocortical representational patterns in the suppression of aversive memories after consolidation. These findings demonstrate rapid changes in emotional memory organization with overnight consolidation, and suggest possible neurobiological bases underlying the resistance to suppression of emotional memories in affective disorders.

As memories consolidate over time, they become resistant to change, though how this impacts the volitional suppression of memories is not known. Liu and colleagues show that, after overnight consolidation, aversive memories exhibit distributed prefrontal representations and are harder to suppress.

An fMRI investigation of the relationship between future imagination and cognitive flexibility

While future imagination is largely considered to be a cognitive process grounded in default mode network activity, studies have shown that future imagination recruits regions in both default mode and frontoparietal control networks. In addition, it has recently been shown that the ability to imagine the future is associated with cognitive flexibility, and that tasks requiring cognitive flexibility result in increased coupling of the default mode network with frontoparietal control and salience networks. In the current study, we investigated the neural correlates underlying the association between cognitive flexibility and future imagination in two ways. First, we experimentally varied the degree of cognitive flexibility required during future imagination by manipulating the disparateness of episodic details contributing to imagined events. To this end, participants generated episodic details (persons, locations, objects) within three social spheres; during fMRI scanning they were presented with sets of three episodic details all taken from the same social sphere (Congruent condition) or different social spheres (Incongruent condition) and required to imagine a future event involving the three details. We predicted that, relative to the Congruent condition, future simulation in the Incongruent condition would be associated with increased activity in regions of the default mode, frontoparietal and salience networks. Second, we hypothesized that individual differences in cognitive flexibility, as measured by performance on the Alternate Uses Task, would correspond to individual differences in the brain regions recruited during future imagination. A task partial least squares (PLS) analysis showed that the Incongruent condition resulted in an increase in activity in regions in salience networks (e.g. the insula) but, contrary to our prediction, reduced activity in many regions of the default mode network (including the hippocampus). A subsequent functional connectivity (within-subject seed PLS) analysis showed that the insula exhibited increased coupling with default mode regions during the Incongruent condition. Finally, a behavioral PLS analysis showed that individual differences in cognitive flexibility were associated with differences in activity in a number of regions from frontoparietal, salience and default-mode networks during both future imagination conditions, further highlighting that the cognitive flexibility underlying future imagination is grounded in the complex interaction of regions in these networks.

Human Connectomics across the Life Span

Connectomics has enhanced our understanding of neurocognitive development and decline by the integration of network sciences into studies across different stages of the human life span. However, these studies commonly occurred independently, missing the opportunity to test integrated models of the dynamical brain organization across the entire life span. In this review article, we survey empirical findings in life-span connectomics and propose a generative framework for computationally modeling the connectome over the human life span. This framework highlights initial findings that across the life span, the human connectome gradually shifts from an 'anatomically driven' organization to one that is more 'topological'. Finally, we consider recent advances that are promising to provide an integrative and systems perspective of human brain plasticity as well as underscore the pitfalls and challenges.

Botallo's error, or the quandaries of the universality assumption

One of the founding principles of human cognitive neuroscience is the so-called universality assumption, the postulate that neurocognitive mechanisms do not show major differences among individuals. Without negating the importance of the universality assumption for the development of cognitive neuroscience, or the importance of single-case studies, here we aim at stressing the potential dangers of interpreting the pattern of performance of single patients as conclusive evidence concerning the architecture of the intact neurocognitive system. We take example from the case of Leonardo Botallo, an Italian surgeon of the Renaissance period, who claimed to have discovered a new anatomical structure of the adult human heart. Unfortunately, Botallo's discovery was erroneous, because what he saw in the few samples he examined was in fact the anomalous persistence of a fetal structure. Botallo's error is a reminder of the necessity to always strive for replication, despite the major hindrance of a publication system heavily biased towards novelty. In the present paper, we briefly discuss variations and anomalies in human brain anatomy and introduce the issue of variability in cognitive neuroscience. We then review some examples of the impact on cognition of individual variations in (1) brain structure, (2) brain functional organization and (3) brain damage. We finally discuss the importance and limits of single case studies in the neuroimaging era, outline potential ways to deal with individual variability, and draw some general conclusions.

Ten problems and solutions when predicting individual outcome from lesion site after stroke

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual's functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve the predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients.

Using fMRI to understand event construction in developmental amnesia

Recently, neuroimaging and patient-lesion methods have been combined to explain anomalies such as patients' intact performance on tasks on which they would be predicted to perform poorly. In some cases, preserved performance has been attributed to activation of residual tissue within the damaged region. However, activation of remnant tissue can also occur in relation to impaired performance and, thus, may not necessarily correspond to successful recruitment. To constrain these neuroimaging interpretations, what is needed is a paradigm with closely matched conditions that yields intact and impaired performance in the same patient. We investigated this in H.C., an amnesic person with congenital abnormalities of the hippocampus and its connections, who was scanned during remembering and imagining, abilities known to depend on the hippocampus. Specifically, we examined whether differences in activation and/or functional connectivity would explain H.C.'s compromised ability to construct events relating to herself in autobiographical memory (SELF condition) and events relating to personally familiar others (FAMILIAR condition) versus her intact ability to construct events relating to unknown others (UNFAMILIAR condition). Despite behavioral dissociations in H.C., the pattern of activation and functional connectivity supporting her performance was strikingly similar to that of controls across conditions. Most notably, like controls, H.C. showed robust hippocampal activation and functional connectivity to the hippocampus, both when her performance was intact and impaired. Across all conditions, H.C. activated several extra-hippocampal regions to a greater extent than did controls, and modest differences were observed in functional connectivity between extra-hippocampal regions. Taken together, these findings urge caution when drawing conclusions about the functional integrity of a structurally compromised brain region even when it is activated and/or co-activated with other regions.

Neurobiological Basis of Language Learning Difficulties

In this paper we highlight why there is a need to examine subcortical learning systems in children with language impairment and dyslexia, rather than focusing solely on cortical areas relevant for language. First, behavioural studies find that children with these neurodevelopmental disorders perform less well than peers on procedural learning tasks that depend on corticostriatal learning circuits. Second, fMRI studies in neurotypical adults implicate corticostriatal and hippocampal systems in language learning. Finally, structural and functional abnormalities are seen in the striatum in children with language disorders. Studying corticostriatal networks in developmental language disorders could offer us insights into their neurobiological basis and elucidate possible modes of compensation for intervention.

Individuals with SLI and dyslexia have impaired or immature learning mechanisms; this hampers their extraction of structure in complex learning environments.

These learning difficulties are not general or confined to language. Problems are specific to tasks that involve implicitly learning sequential structure or complex cue–outcome relationships. Such learning is thought to depend upon corticostriatal circuits.

In language learning studies, the striatum is recruited when adults extract sequential information from auditory-verbal sequences and as they learn complex motor routines relevant for speech.

Neuroimaging studies indicate striatal abnormalities in individuals with language disorders.

There is a need to probe the integrity of neural learning systems in developmental language disorders using tasks relevant for language learning which place specific demands on the striatum/MTL.