The Entangled Brain

Impact of trauma type on neural mechanisms of threat conditioning and its extinction

Trauma type moderates the impact of trauma exposure on clinical symptomatology; however, the impact of trauma type on the neural correlates of emotion regulation is not as well understood. This study examines how violent and nonviolent trauma differentially influence the neural correlates of conditioned fear and extinction. We aggregated psychophysiological and fMRI data from three studies; we categorized reported trauma as violent or nonviolent, and subdivided violent trauma as sexual or nonsexual. We examined skin conductance responses (SCR) during a fear conditioning and extinction paradigm. For fMRI data analyses, we conducted region-specific and whole-brain analyses. We examined associations between beta weights from specific brain regions and CAPS scores. The group exposed to violent trauma showed significantly higher SCR during extinction recall. Those exposed to nonviolent trauma showed significantly higher functional activation during late extinction learning. The group exposed to violent trauma showed higher functional connectivity within the default mode network (DMN) and between the DMN and frontoparietal control network. For secondary analyses of sexual vs nonsexual trauma, we did not observe any between-group differences in SCR. During late extinction learning, the group exposed to sexual trauma showed significantly higher activation in the prefrontal cortex and precuneus. During extinction recall, the group exposed to nonsexual trauma showed significantly higher activation in the insular cortex. Violent trauma significantly impacts functional brain activations and connectivity in brain areas important for perception and attention with no significant impact on brain areas that modulate emotion regulation. Sexual trauma impacts brain areas important for internal perception.

Volition and control in law and in brain science: neurolegal translation of a foundational concept

The law assumes that healthy adults are generally responsible for their actions and have the ability to control their behavior based on rational and moral principles. This contrasts with some recent neuroscientific accounts of action control. Nevertheless, both law and neuroscience acknowledge that strong emotions including fear and anger may "trigger" loss of normal voluntary control over action. Thus, "Loss of Control" is a partial defense for murder under English law, paralleling similar defenses in other legal systems. Here we consider the neuroscientific evidence for such legal classifications of responsibility, particularly focussing on how emotional states modulate voluntary motor control and sense of agency. First, we investigate whether neuroscience could contribute an evidence-base for law in this area. Second, we consider the societal impact of some areas where legal thinking regarding responsibility for action diverges from neuroscientific evidence: should we be guided by normative legal traditions, or by modern understanding of brain functions? In addressing these objectives, we propose a translation exercise between neuroscientific and legal terms, which may assist future interdisciplinary research.

Centering cognitive neuroscience on task demands and generalization

Cognitive neuroscience seeks generalizable theories explaining the relationship between behavioral, physiological and mental states. In pursuit of such theories, we propose a theoretical and empirical framework that centers on understanding task demands and the mutual constraints they impose on behavior and neural activity. Task demands emerge from the interaction between an agent's sensory impressions, goals and behavior, which jointly shape the activity and structure of the nervous system on multiple spatiotemporal scales. Understanding this interaction requires multitask studies that vary more than one experimental component (for example, stimuli and instructions) combined with dense behavioral and neural sampling and explicit testing for generalization across tasks and data modalities. By centering task demands rather than mental processes that tasks are assumed to engage, this framework paves the way for the discovery of new generalizable concepts unconstrained by existing taxonomies, and moves cognitive neuroscience toward an action-oriented, dynamic and integrated view of the brain.

Why behaviour matters: Studying inter-brain coordination during child-caregiver interaction

Modern technology allows for simultaneous neuroimaging from interacting caregiver-child dyads. Whereas most analyses that examine the coordination between brain regions within an individual brain do so by measuring changes relative to observed events, studies that examine coordination between two interacting brains generally do this by measuring average intra-brain coordination across entire blocks or experimental conditions. In other words, they do not examine changes in inter-brain coordination relative to individual behavioural events. Here, we discuss the limitations of this approach. First, we present data suggesting that fine-grained temporal interdependencies in behaviour can leave residual artifact in neuroimaging data. We show how artifact can manifest as both power and (through that) phase synchrony effects in EEG and affect wavelet transform coherence in fNIRS analyses. Second, we discuss different possible mechanistic explanations of how inter-brain coordination is established and maintained. We argue that non-event-locked approaches struggle to differentiate between them. Instead, we contend that approaches which examine how interpersonal dynamics change around behavioural events have better potential for addressing possible artifactual confounds and for teasing apart the overlapping mechanisms that drive changes in inter-brain coordination.

Neuron-level Prediction and Noise can Implement Flexible Reward-Seeking Behavior

We show that neural networks can implement reward-seeking behavior using only local predictive updates and internal noise. These networks are capable of autonomous interaction with an environment and can switch between explore and exploit behavior, which we show is governed by attractor dynamics. Networks can adapt to changes in their architectures, environments, or motor interfaces without any external control signals. When networks have a choice between different tasks, they can form preferences that depend on patterns of noise and initialization, and we show that these preferences can be biased by network architectures or by changing learning rates. Our algorithm presents a flexible, biologically plausible way of interacting with environments without requiring an explicit environmental reward function, allowing for behavior that is both highly adaptable and autonomous. Code is available at https://github.com/ccli3896/PaN.

A systems identification approach using Bayes factors to deconstruct the brain bases of emotion regulation

Cognitive reappraisal is fundamental to cognitive therapies and everyday emotion regulation. Analyses using Bayes factors and an axiomatic systems identification approach identified four reappraisal-related components encompassing distributed neural activity patterns across two independent functional magnetic resonance imaging (fMRI) studies (n = 182 and n = 176): (1) an anterior prefrontal system selectively involved in cognitive reappraisal; (2) a fronto-parietal-insular system engaged by both reappraisal and emotion generation, demonstrating a general role in appraisal; (3) a largely subcortical system activated during negative emotion generation but unaffected by reappraisal, including amygdala, hypothalamus and periaqueductal gray; and (4) a posterior cortical system of negative emotion-related regions downregulated by reappraisal. These systems covaried with individual differences in reappraisal success and were differentially related to neurotransmitter binding maps, implicating cannabinoid and serotonin systems in reappraisal. These findings challenge 'limbic'-centric models of reappraisal and provide new systems-level targets for assessing and enhancing emotion regulation.

Distributed neural representations of conditioned threat in the human brain

Detecting and responding to threat engages several neural nodes including the amygdala, hippocampus, insular cortex, and medial prefrontal cortices. Recent propositions call for the integration of more distributed neural nodes that process sensory and cognitive facets related to threat. Integrative, sensitive, and reproducible distributed neural decoders for the detection and response to threat and safety have yet to be established. We combine functional MRI data across varying threat conditioning and negative affect paradigms from 1465 participants with multivariate pattern analysis to investigate distributed neural representations of threat and safety. The trained decoders sensitively and specifically distinguish between threat and safety cues across multiple datasets. We further show that many neural nodes dynamically shift representations between threat and safety. Our results establish reproducible decoders that integrate neural circuits, merging the well-characterized 'threat circuit' with sensory and cognitive nodes, discriminating threat from safety regardless of experimental designs or data acquisition parameters.

Dynamic threat-reward neural processing under semi-naturalistic ecologically relevant scenarios

Studies of affective neuroscience have typically employed highly controlled, static experimental paradigms to investigate the neural underpinnings of threat and reward processing in the brain. Yet our knowledge of affective processing in more naturalistic settings remains limited. Specifically, affective studies generally examine threat and reward features separately and under brief time periods, despite the fact that in nature organisms are often exposed to the simultaneous presence of threat and reward features for extended periods. To study the neural mechanisms of threat and reward processing under distinct temporal profiles, we created a modified version of the PACMAN game that included these environmental features. We also conducted two automated meta-analyses to compare the findings from our semi-naturalistic paradigm to those from more constrained experiments. Overall, our results revealed a distributed system of regions sensitive to threat imminence and a less distributed system related to reward imminence, both of which exhibited overlap yet neither of which involved the amygdala. Additionally, these systems broadly overlapped with corresponding meta-analyses, with the notable absence of the amygdala in our findings. Together, these findings suggest a shared system for salience processing that reveals a heightened sensitivity toward environmental threats compared to rewards when both are simultaneously present in an environment. The broad correspondence of our findings to meta-analyses, consisting of more tightly controlled paradigms, illustrates how semi-naturalistic studies can corroborate previous findings in the literature while also potentially uncovering novel mechanisms resulting from the nuances and contexts that manifest in such dynamic environments.

Cellular and laminar architecture: A short history and commentary

The feedforward/feedback classification, as originally stated in relation to early visual areas in the macaque monkey, has had a significant influence on ideas of laminar interactions, area reciprocity, and cortical hierarchical organization. In some contrast with this macroscale "laminar connectomics," a more cellular approach to cortical connections, as briefly surveyed here, points to a still underappreciated heterogeneity of neuronal subtypes and complex microcircuitries. From the perspective of heterogeneities, the question of how brain regions interact and influence each other quickly leads to discussions about concurrent hierarchical and nonhierarchical cortical features, brain organization as a multiscale system forming nested groups and hierarchies, connectomes annotated by multiple biological attributes, and interleaved and overlapping scales of organization.

How embodied is cognition? fMRI and behavioral evidence for common neural resources underlying motor planning and mental rotation of bodily stimuli

Functional neuroimaging shows that dorsal frontoparietal regions exhibit conjoint activity during various motor and cognitive tasks. However, it is unclear whether these regions serve several, computationally independent functions, or underlie a motor "core process" that is reused to serve higher-order functions. We hypothesized that mental rotation capacity relies on a phylogenetically older motor process that is rooted within these areas. This hypothesis entails that neural and cognitive resources recruited during motor planning predict performance in seemingly unrelated mental rotation tasks. To test this hypothesis, we first identified brain regions associated with motor planning by measuring functional activations to internally-triggered vs externally-triggered finger presses in 30 healthy participants. Internally-triggered finger presses yielded significant activations in parietal, premotor, and occipitotemporal regions. We then asked participants to perform two mental rotation tasks outside the scanner, consisting of hands or letters as stimuli. Parietal and premotor activations were significant predictors of individual reaction times when mental rotation involved hands. We found no association between motor planning and performance in mental rotation of letters. Our results indicate that neural resources in parietal and premotor cortex recruited during motor planning also contribute to mental rotation of bodily stimuli, suggesting a common core component underlying both capacities.

A Critical Perspective on Neural Mechanisms in Cognitive Neuroscience: Towards Unification

A central pursuit of cognitive neuroscience is to find neural mechanisms of cognition, with research programs favoring different strategies to look for them. But what is a neural mechanism, and how do we know we have captured them? Here I answer these questions through a framework that integrates Marr's levels with philosophical work on mechanism. From this, the following goal emerges: What needs to be explained are the computations of cognition, with explanation itself given by mechanism-composed of algorithms and parts of the brain that realize them. This reveals a delineation within cognitive neuroscience research. In the premechanism stage, the computations of cognition are linked to phenomena in the brain, narrowing down where and when mechanisms are situated in space and time. In the mechanism stage, it is established how computation emerges from organized interactions between parts-filling the premechanistic mold. I explain why a shift toward mechanistic modeling helps us meet our aims while outlining a road map for doing so. Finally, I argue that the explanatory scope of neural mechanisms can be approximated by effect sizes collected across studies, not just conceptual analysis. Together, these points synthesize a mechanistic agenda that allows subfields to connect at the level of theory.

Breaking boundaries: The Bayesian Brain Hypothesis for perception and prediction

This special issue aims to provide a comprehensive overview of the current state of the Bayesian Brain Hypothesis and its standing across neuroscience, cognitive science and the philosophy of cognitive science. By gathering cutting-edge research from leading experts, this issue seeks to showcase the latest advancements in our understanding of the Bayesian brain, as well as its potential implications for future research in perception, cognition, and motor control. A special focus to achieve this aim is adopted in this special issue, as it seeks to explore the relation between two seemingly incompatible frameworks for the understanding of cognitive structure and function: the Bayesian Brain Hypothesis and the Modularity Theory of the Mind. In assessing the compatibility between these theories, the contributors to this special issue open up new pathways of thinking and advance our understanding of cognitive processes.

How many brain regions are needed to elucidate the neural bases of fear and anxiety?

We suggest that to understand complex behaviors associated with fear and anxiety, we need to understand brain processes at the collective, network level. But what should be the type and spatial scale of the targeted circuits/networks? Not only are multi-region interactions essential-including complex reciprocal interactions, loops, and other types of arrangement-but it is profitable to characterize circuits spanning the entire neuroaxis. In particular, it is productive to conceptualize the circuits contributing to fear/anxiety as embedded into large-scale connectional systems. We discuss circuits involving the basolateral amygdala that contribute to aversive conditioning and fear extinction. In addition, we highlight the importance of the extended amygdala (central nucleus of the amygdala and bed nucleus of the stria terminalis) cortical-subcortical loop, which allows large swaths of cortex and subcortex to influence fear and anxiety. In this manner, fear/anxiety can be understood not only based on traditional "descending" mechanisms involving the hypothalamus and brainstem, but in terms of a considerably broader reentrant organization.

In Defense of Modular Thinking

In this issue, Pessoa emphasizes the importance of viewing neural activity from a perspective that functional networks form dynamically in a way that dramatically changes the functional contribution of individual brain areas. In this response, I argue that we should strive toward pluralism in understanding neural activity at both the emergent network and modular levels, on the bases that a purely emergent understanding would be incomplete, and that there are computational advantages to anatomically stable modularity.

Disentangling the Skeins of Brain

Some have argued that the brain is so complex that it cannot be understood using current reductive approaches. Drawing on examples from decision neuroscience, we instead contend that combining new neuroscientific techniques with reductive approaches that consider central brain components in time and space has generated significant progress over the past 2 decades. This progress has allowed researchers to advance from the scientific goals of description and explanation to prediction and control. Resulting knowledge promises to improve human health and well-being. As an alternative to the extremes of reductive versus emergent approaches, however, we propose a middle way of "expansion." This expansionist approach promises to leverage the specific spatial localization, temporal precision, and directed connectivity of central neural components to ultimately link levels of analysis.

Studying Mental Health Problems as Systems, Not Syndromes

Despite decades of clinical, sociopolitical, and research efforts, progress in understanding and treating mental health problems remains disappointing. I discuss two barriers that have contributed to a problematic oversimplification of mental illness. The first is diagnostic literalism, mistaking mental health problems (complex within-person processes) for the diagnoses by which they are classified (clinically useful idealizations to facilitate treatment selection and prognosis). The second is reductionism, the isolated study of individual elements of mental disorders. I propose conceptualizing people’s mental health states as outcomes emerging from complex systems of biological, psychological, and social elements and show that this systems perspective explains many robust phenomena, including variability within diagnoses, comorbidity among diagnoses, and transdiagnostic risk factors. It helps us understand diagnoses and reductionism as useful epistemological tools for describing the world, rather than ontological convictions about how the world is. It provides new lenses through which to study mental illness (e.g., attractor states, phase transitions), and new levers to treat them (e.g., early warning signals, novel treatment targets). Embracing the complexity of mental health problems requires opening our ivory towers to theories and methods from other fields with rich traditions, including network and systems sciences.