Improving the study of brain-behavior relationships by revisiting basic assumptions

Children's Neural Processing of the Misfortunes and Fortunes of Prosocial and Antisocial Individuals

Behavioral studies have found that children are less likely to share the feelings of antisocial individuals than those of prosocial individuals. However, the underlying neural mechanism remains unclear. To address this gap, the current study utilized electroencephalogram (EEG) to examine the neural responses of 4- to 12-year-old children to the misfortunes and fortunes of prosocial and antisocial individuals (N = 73). When observing the experiences of prosocial individuals, children exhibited a greater amplitude of parietal P3, an indicator of top-down allocated attention, to misfortunes compared to fortunes. This difference disappeared when observing the experiences of antisocial individuals. Additionally, children displayed stronger mu suppression, indicating neural mirroring, toward prosocial individuals than antisocial individuals while observing their experiences. The current findings suggest that children allocate more attention resources to the experiences, especially misfortunes, of prosocial individuals than antisocial individuals. These findings deepened our understanding of how children react to others' experiences based on others' moral behaviors from a neural perspective.

Delineating network integration and segregation in the pathophysiology of functional neurological disorder

Functional neurological disorder (FND) is a neuropsychiatric condition that is framed as a multi-network brain problem. Despite this conceptualization, studies have generally focused on specific regions or connectivity features, under-characterizing the complex and nuanced role of resting-state networks in FND pathophysiology. This study employed three complementary graph theory analyses to delineate the functional network architecture in FND. Specifically, we investigated whole-brain weighted-degree, isocortical integration and isocortical segregation extracted from resting-state functional MRI data prospectively collected from 178 participants: 61 individuals with mixed FND; 58 psychiatric controls matched on age, sex, depression, anxiety and post-traumatic stress disorder severity; and 59 age- and sex-matched healthy controls. All analyses were adjusted for age, sex and antidepressant use and focused on differences between FND versus psychiatric controls, with individual-subject maps normalized to healthy controls. Compared to psychiatric controls, patients with mixed FND exhibited increased weighted-degree in the right dorsal anterior cingulate and superior frontal gyrus and the left inferior frontal gyrus and supplementary motor area. Isocortical integration analyses revealed increased between-network connectivity for somatomotor network areas, with widespread heightened connections to regions of the default mode, frontoparietal and salience networks. Isocortical segregation analyses revealed increased within-network connectivity for the frontoparietal network. Secondary analyses of functional motor disorder (n = 46) and functional seizure (n = 23) subtypes (versus psychiatric controls) revealed both shared and unique patterns of altered connectivity across subtypes, including increased weighted-degree and integrated connectivity in the left posterior insula and anterior/mid-cingulate in functional motor disorder and increased segregated connectivity in the right angular gyrus for functional seizures. In post hoc between-group analyses, findings remained significant adjusting for depression, anxiety and post-traumatic stress disorder severity, as well as for childhood maltreatment. Post hoc correlations revealed significant relationships between connectivity metrics in several of these regions and somatic symptom severity across FND and psychiatric control participants. Notably, individual connectivity values were predominantly within the range of healthy controls (with patients with FND generally showing tendencies for increased connectivity and psychiatric controls showing tendencies towards decreased connectivity), indicating subtle shifts in the network architecture rather than gross abnormalities. This study provides novel mechanistic insights (i.e. increased somatomotor integration) and specificity regarding the neurobiology of FND, highlighting both shared mechanisms across subtypes and subtype-specific patterns. The results support the notion that FND involves aberrant within- and between-network communication, setting the stage for biologically informed treatment development and large-scale replication.

The Theory of Constructed Emotion: More Than a Feeling

A recently published article by van Heijst et al. attempted to reconcile two research approaches in the science of emotion-basic emotion theory and the theory of constructed emotion-by suggesting that the former explains emotions as bioregulatory states of the body whereas the latter explains feelings that arise from those state changes. This bifurcation of emotion into objective physical states and subjective feelings involves three misleading simplifications that fundamentally misrepresent the theory of constructed emotion and prevent progress in the science of emotion. In this article we identify these misleading simplifications and the resulting factual errors, empirical oversights, and evolutionary oversimplifications. We then discuss why such errors will continue to arise until scientists realize that the two theories are intrinsically irreconcilable. They rest on incommensurate assumptions and require different methods of evaluation. Only by directly considering these differences will these research silos in the science of emotion finally dissolve, speeding the accumulation of trustworthy scientific knowledge about emotion that is usable in the real world.

Suggestions for extending the FAIR Principles based on a linguistic perspective on semantic interoperability

FAIR (meta)data presuppose their successful communication between machines and humans while preserving meaning and reference. The FAIR Guiding Principles lack specificity regarding semantic interoperability. We adopt a linguistic perspective on semantic interoperability and investigate the structures and conventions ensuring reliable communication of textual information, drawing parallels with data structures by understanding both as models. We propose a conceptual model of semantic interoperability, comprising intensional and extensional terminological interoperability, as well as logical and schema propositional interoperability. Since there cannot be a universally accepted best vocabulary and best (meta)data schema, establishing semantic interoperability necessitates the provision of comprehensive sets of intensional and extensional entity mappings and schema crosswalks. In accordance with our conceptual model, we suggest additions to the FAIR Guiding Principles that encompass the requirements for semantic interoperability. Additionally, we argue that attaining FAIRness of (meta)data requires not only their organization into FAIR Digital Objects, but also the establishment of a FAIR ecosystem of FAIR Services, that include a terminology, a schema, and an operations service.

Neuroscience in pictures: Functional neurological disorder

Functional neurological disorder (FND) is a biopsychosocially-complex, prevalent, and potentially disabling neuropsychiatric condition. In this pictorial review, we explore the complexity of FND, from its diagnosis and conceptualization to current mechanistic understandings. We highlight advances in neuroimaging research that have revealed structural and functional brain alterations in FND and discuss a variety of factors that may serve as predisposing vulnerabilities for the development of this condition and/or perpetuate symptoms. This overview is designed as an initial teaching resource to educate trainees, clinicians, and researchers, highlighting core concepts in the literature on FND. Given that mechanistic research in FND is at a relatively early stage and is rapidly evolving, the interested reader should aim to continue updating their mechanistic understanding of FND as research further advances in this area.

Regulation of craving and underlying resting-state neural circuitry predict hazard of smoking lapse

Among individuals with substance use disorders, clinical outcomes may be improved by identifying brain-behavior models that predict drug re/lapse vulnerabilities such as the ability to regulate drug cravings and inhibit drug use. In a sample of nicotine-dependent adult cigarette smokers (N = 213), this laboratory study examined associations between regulation of craving (ROC) efficacy and smoking lapse, utilized functional connectivity multivariate pattern analysis (FC-MVPA) and seed-based connectivity (SBC) analyses to identify resting-state neural circuitry underlying ROC efficacy, and then examined if the identified ROC-mediated circuitry predicted hazard of smoking lapse. Regarding behavior, worse ROC efficacy predicted a greater hazard of smoking lapse. Regarding brain and behavior, FC-MVPA identified 29 brain-wide functional clusters associated with ROC efficacy. Follow-up SBC analyses using 9 of the FC-MVPA-derived clusters identified a total of 64 resting-state edges (i.e., cluster-to-cluster connections) underlying ROC efficacy, 10 of which were also associated with the hazard of smoking lapse. ROC efficacy edges also associated with smoking lapse were largely composed of connections between frontal-striatal-limbic clusters and sensory-motor clusters and better behavioral outcomes were associated with stronger resting-state FC. Findings suggest that both ROC efficacy and underlying resting-state neural circuitry may inform prediction models of re/lapse vulnerabilities and serve as treatment targets for cessation interventions.

Localisation of function in the brain: a rethink

A modular view of brain function dominates the teaching of medical students and clinical psychologists and is implicit in day-to-day clinical practice. This view glosses over a long-standing debate. The extent of one-to-one mappings between region and function remains a controversial topic. For the cortex, localisation of function versus 'cerebral equipotentiality' was debated less than 150 years ago, and traces of this debate remain active in systems neuroscience today. The advent of functional brain imaging led to an explosion of evidence on localisation of function studied in vivo, and a gold rush to map an ever-increasing range of 'functions'. Rapid growth in knowledge was accompanied, to some extent, by a flourishing neuromythology. There are currently few clinical applications of brain mapping techniques, but new areas are emerging. An understanding of the central debate on functional localisation will bring a more nuanced view of problems encountered in clinical practice.

Using deep generative models for simultaneous representational and predictive modeling of brain and behavior: A graded unsupervised-to-supervised modeling framework

This paper uses a generative neural network architecture combining unsupervised (generative) and supervised (discriminative) models with a model comparison strategy to evaluate assumptions about the mappings between brain states and behavior. Most modeling in cognitive neuroscience publications assume a one-to-one brain-behavior relationship that is linear, but never test these assumptions or the consequences of violating them. We systematically varied these assumptions using simulations of four ground-truth brain-behavior mappings that involve progressively more complex relationships, ranging from one-to-one linear mappings to many-to-one nonlinear mappings. We then applied our Variational AutoEncoder-Classifier framework to the simulations to show how it accurately captured diverse brain-behavior mappings,provided evidence regarding which assumptions are supported by the data, and illustrated the problems that arise when assumptions are violated. This integrated approach offers a reliable foundation for cognitive neuroscience to effectively model complex neural and behavioral processes, allowing more justified conclusions about the nature of brain-behavior mappings.

Animal models of the human brain: Successes, limitations, and alternatives

The last three decades of research in human cognitive neuroscience have given us an initial "parts list" for the human mind in the form of a set of cortical regions with distinct and often very specific functions. But current neuroscientific methods in humans have limited ability to reveal exactly what these regions represent and compute, the causal role of each in behavior, and the interactions among regions that produce real-world cognition. Animal models can help to answer these questions when homologues exist in other species, like the face system in macaques. When homologues do not exist in animals, for example for speech and music perception, and understanding of language or other people's thoughts, intracranial recordings in humans play a central role, along with a new alternative to animal models: artificial neural networks.

Group-to-individual generalizability and individual-level inferences in cognitive neuroscience

Much of cognitive neuroscience research is focused on group-averages and interindividual brain-behavior associations. However, many theories core to the goal of cognitive neuroscience, such as hypothesized neural mechanisms for a behavior, are inherently based on intraindividual processes. To accommodate this mismatch between study design and theory, research frequently relies on an implicit assumption that group-level, between-person inferences extend to individual-level, within-person processes. The assumption of group-to-individual generalizability, formally referred to as ergodicity, requires that a process be both homogenous within a population and stationary within individuals over time. Our goal in this review is to assess this assumption and provide an accessible introduction to idiographic science (study of the individual) for the cognitive neuroscientist, ultimately laying a foundation for increased focus on the study of intraindividual processes. We first review the history of idiographic science in psychology to connect this longstanding literature with recent individual-level research goals in cognitive neuroscience. We then consider two requirements of group-to-individual generalizability, pattern homogeneity and stationarity, and suggest that most processes in cognitive neuroscience do not meet these assumptions. Consequently, interindividual findings are inappropriate for the intraindividual inferences that many theories are based on. To address this challenge, we suggest precision imaging as an ideal path forward for intraindividual study and present a research framework for complementary interindividual and intraindividual study.

Meningeal neutrophil immune signaling influences behavioral adaptation following threat

Social creatures must attend to threat signals from conspecifics and respond appropriately, both behaviorally and physiologically. In this work, we show in mice a threat-sensitive immune mechanism that orchestrates psychological processes and is amenable to social modulation. Repeated encounters with socially cued threats triggered meningeal neutrophil (MN) priming preferentially in males. MN activity was correlated with attenuated defensive responses to cues. Canonical neutrophil-specific activation marker CD177 was upregulated after social threat cueing, and its genetic ablation abrogated male behavioral phenotypes. CD177 signals favored meningeal T helper (Th)1-like immune bias, which blunted neural response to threatening stimuli by enhancing intrinsic GABAergic inhibition within the prelimbic cortex via interferon-gamma (IFN-γ). MN signaling was sensitized by negative emotional states and governed by socially dependent androgen release. This male-biased hormone/neutrophil regulatory axis is seemingly conserved in humans. Our findings provide insights into how immune responses influence behavioral threat responses, suggesting a possible neuroimmune basis of emotional regulation.

The enigmatic HCN channels: A cellular neurophysiology perspective

What physiological role does a slow hyperpolarization-activated ion channel with mixed cation selectivity play in the fast world of neuronal action potentials that are driven by depolarization? That puzzling question has piqued the curiosity of physiology enthusiasts about the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, which are widely expressed across the body and especially in neurons. In this review, we emphasize the need to assess HCN channels from the perspective of how they respond to time-varying signals, while also accounting for their interactions with other co-expressing channels and receptors. First, we illustrate how the unique structural and functional characteristics of HCN channels allow them to mediate a slow negative feedback loop in the neurons that they express in. We present the several physiological implications of this negative feedback loop to neuronal response characteristics including neuronal gain, voltage sag and rebound, temporal summation, membrane potential resonance, inductive phase lead, spike triggered average, and coincidence detection. Next, we argue that the overall impact of HCN channels on neuronal physiology critically relies on their interactions with other co-expressing channels and receptors. Interactions with other channels allow HCN channels to mediate intrinsic oscillations, earning them the "pacemaker channel" moniker, and to regulate spike frequency adaptation, plateau potentials, neurotransmitter release from presynaptic terminals, and spike initiation at the axonal initial segment. We also explore the impact of spatially non-homogeneous subcellular distributions of HCN channels in different neuronal subtypes and their interactions with other channels and receptors. Finally, we discuss how plasticity in HCN channels is widely prevalent and can mediate different encoding, homeostatic, and neuroprotective functions in a neuron. In summary, we argue that HCN channels form an important class of channels that mediate a diversity of neuronal functions owing to their unique gating kinetics that made them a puzzle in the first place.

Brain structural damage networks at different stages of schizophrenia

BACKGROUND

Neuroimaging studies have documented brain structural changes in schizophrenia at different stages of the illness, including clinical high-risk (cHR), genetic high-risk (gHR), first-episode schizophrenia (FES), and chronic schizophrenia (ChS). There is growing awareness that neuropathological processes associated with a disease fail to map to a specific brain region but do map to a specific brain network. We sought to investigate brain structural damage networks across different stages of schizophrenia.

METHODS

We initially identified gray matter alterations in 523 cHR, 855 gHR, 2162 FES, and 2640 ChS individuals relative to 6963 healthy controls. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to four specific networks.

RESULTS

Brain structural damage networks of cHR and gHR had limited and non-overlapping spatial distributions, with the former mainly involving the frontoparietal network and the latter principally implicating the subcortical network, indicative of distinct neuropathological mechanisms underlying cHR and gHR. By contrast, brain structural damage networks of FES and ChS manifested as similar patterns of widespread brain areas predominantly involving the somatomotor, ventral attention, and subcortical networks, suggesting an emergence of more prominent brain structural abnormalities with illness onset that have trait-like stability over time.

CONCLUSIONS

Our findings may not only provide a refined picture of schizophrenia neuropathology from a network perspective, but also potentially contribute to more targeted and effective intervention strategies for individuals at different schizophrenia stages.

An investigation of multimodal predictors of adolescent alcohol initiation

BACKGROUND

Early alcohol initiation is associated with negative, alcohol-related outcomes. While previous work identifies numerous risk factors for early use, the relative contributions of known predictors remains understudied. The current project addresses this gap by 1) prospectively predicting early alcohol initiation using measures of inhibition control, reward sensitivity, and contextual risk factors and 2) interrogating the relative importance of each domain.

METHOD

This study leverages multimodal data from substance-naïve youth enrolled in the Adolescent Brain Cognitive Development (ABCD) Study® (n=11,694). Early initiation was defined as consuming a full standard drink containing alcohol prior to age 16. Propensity scores were used to match alcohol initiators (n=348) with demographically similar non-initiators at a 1:2 ratio (n=696). Independent logistic regressions were conducted for each domain followed by additive, hierarchical models.

RESULTS

The model of contextual factors (pseudo-R2=0.086, AUC=0.67) outperformed inhibition control (pseudo-R2=0.021, AUC=0.58) and reward sensitivity measures (pseudo-R2=0.020, AUC=0.59). The hierarchical model containing all measures (pseudo-R2=0.106, AUC=0.69) did not significantly improve the model of contextual factors alone (p>0.05). Examples of significant predictors (p<0.05) include externalizing behaviors, number of substances known, and non-religious alcohol sipping.

CONCLUSION

Contextual risk factors were the strongest predictors of early alcohol use; however, more work is needed to understand the causal nature of this relationship. Measures of inhibition control and reward sensitivity were not adequate in distinguishing initiators from non-initiators. These findings add to a body of evidence that contextual factors play a major role in alcohol initiation while highlighting specific predictor variables that could inform youth alcohol prevention.

Harnessing slow event-related fMRI to investigate trial-level brain-behavior relationships during object identification

Understanding brain-behavior relationships is the core goal of cognitive neuroscience. However, these relationships-especially those related to complex cognitive and psychopathological behaviors-have recently been shown to suffer from very small effect sizes (0.1 or less), requiring potentially thousands of participants to yield robust findings. Here, we focus on a much more optimistic case utilizing task-based fMRI and a multi-echo acquisition with trial-level brain-behavior associations measured within participant. In a visual object identification task for which the behavioral measure is response time (RT), we show that while trial-level associations between BOLD and RT can similarly suffer from weak effect sizes, converting these associations to their corresponding group-level effects can yield robust peak effect sizes (Cohen's d = 1.0 or larger). Multi-echo denoising (Multi-Echo ICA or ME-ICA) yields larger effects than optimally combined multi-echo with no denoising, which is in turn an improvement over standard single-echo acquisition. While estimating these brain-behavior relationships benefits from the inclusion of a large number of trials per participant, even a modest number of trials (20-30 or more) yields robust group-level effect sizes, with replicable effects obtainable with relatively standard sample sizes (N = 20-30 participants per sample).

Ecological brain: reframing the study of human behaviour and cognition

The last decade has seen substantial advances in the capacity to record behaviour and neural activity in humans in real-world settings, to simulate real-world situations in laboratory settings and to apply sophisticated analyses to large-scale data. Along with these developments, a growing number of groups has begun to advocate for real-world neuroscience and cognitive science. Here, we review the arguments and the available methods for real-world research and outline an overarching framework that embeds key ideas proposed in the literature integrating them into a cyclic process of 'bringing the lab to the real world' (recording behavioural and neural activity in real-world settings) and 'bringing the real-world to the lab' (manipulating the environments in which behaviours occur in the laboratory) that combines exploratory and confirmatory research and is interdisciplinary (including those sciences concerned with the natural, built or virtual environment). We highlight the benefits brought by this framework emphasizing the greater potential for novel discovery, theory development and human-centred applications to the environment.

Let's fail better: Using philosophical tools to improve neuroscientific research in psychiatry

Despite predictions that neuroscientific discoveries would revolutionize psychiatry, decades of research have not yet led to clinically significant advances in psychiatric care. For this reason, an increasing number of researchers are recognizing the limitations of a purely biomedical approach in psychiatric research. These researchers call for reevaluating the conceptualization of mental disorders and argue for a non-reductionist approach to mental health. The aim of this paper is to discuss philosophical assumptions that underly neuroscientific research in psychiatry and offer practical tools to researchers for overcoming potential conceptual problems that are derived from those assumptions. Specifically, we will discuss: the analogy problem, questioning whether mental health problems are equivalent to brain disorders, the normativity problem, addressing the value-laden nature of psychiatric categories and the priority problem, which describes the level of analysis (e.g., biological, psychological, social, etc.) that should be prioritized when studying psychiatric conditions. In addition, we will explore potential strategies to mitigate practical problems that might arise due to these implicit assumptions. Overall, the aim of this paper is to suggest philosophical tools of practical use for neuroscientists, demonstrating the benefits of a closer collaboration between neuroscience and philosophy.

The Brain's Best Kept Secret Is Its Degenerate Structure

Degeneracy is defined as multiple sets of solutions that can produce very similar system performance. Degeneracy is seen across phylogenetic scales, in all kinds of organisms. In neuroscience, degeneracy can be seen in the constellation of biophysical properties that produce a neuron's characteristic intrinsic properties and/or the constellation of mechanisms that determine circuit outputs or behavior. Here, we present examples of degeneracy at multiple levels of organization, from single-cell behavior, small circuits, large circuits, and, in cognition, drawing conclusions from work ranging from bacteria to human cognition. Degeneracy allows the individual-to-individual variability within a population that creates potential for evolution.

Interoception, network physiology and the emergence of bodily self-awareness

The interplay between the brain and interoceptive signals is key in maintaining internal balance and orchestrating neural dynamics, encompassing influences on perceptual and self-awareness. Central to this interplay is the differentiation between the external world, others and the self, a cornerstone in the construction of bodily self-awareness. This review synthesizes physiological and behavioral evidence illustrating how interoceptive signals can mediate or influence bodily self-awareness, by encompassing interactions with various sensory modalities. To deepen our understanding of the basis of bodily self-awareness, we propose a network physiology perspective. This approach explores complex neural computations across multiple nodes, shifting the focus from localized areas to large-scale neural networks. It examines how these networks operate in parallel with and adapt to changes in visceral activities. Within this framework, we propose to investigate physiological factors that disrupt bodily self-awareness, emphasizing the impact of interoceptive pathway disruptions, offering insights across several clinical contexts. This integrative perspective not only can enhance the accuracy of mental health assessments but also paves the way for targeted interventions.

Psychopathy and medial frontal cortex: A systematic review reveals predominantly null relationships

Theories have posited that psychopathy is caused by dysfunction in the medial frontal cortex, including ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), and dorsomedial prefrontal cortex (dmPFC). Recent reviews have questioned the reproducibility of neuroimaging findings within this field. We conducted a systematic review to describe the consistency of magnetic resonance imaging (MRI) findings according to anatomical subregion (vmPFC, ACC, dmPFC), experimental task, psychopathy assessment, study power, and peak coordinates of significant effects. Searches of PsycInfo and MEDLINE databases produced 77 functional and 24 structural MRI studies that analyzed the medial frontal cortex in relation to psychopathy in adult samples. Findings were predominantly null (85.4 % of 1573 tests across the three medial frontal regions). Studies with higher power observed null effects at marginally lower rates. Finally, peak coordinates of significant effects were widely dispersed. The evidence failed to support theories positing the medial frontal cortex as a consistent neural correlate of psychopathy. Theory and methods in the field should be revised to account for predominantly null neuroimaging findings.

Not All Stroop-Type Tasks Are Alike: Assessing the Impact of Stimulus Material, Task Design, and Cognitive Demand via Meta-analyses Across Neuroimaging Studies

The Stroop effect is one of the most often studied examples of cognitive conflict processing. Over time, many variants of the classic Stroop task were used, including versions with different stimulus material, control conditions, presentation design, and combinations with additional cognitive demands. The neural and behavioral impact of this experimental variety, however, has never been systematically assessed. We used activation likelihood meta-analysis to summarize neuroimaging findings with Stroop-type tasks and to investigate whether involvement of the multiple-demand network (anterior insula, lateral frontal cortex, intraparietal sulcus, superior/inferior parietal lobules, midcingulate cortex, and pre-supplementary motor area) can be attributed to resolving some higher-order conflict that all of the tasks have in common, or if aspects that vary between task versions lead to specialization within this network. Across 133 neuroimaging experiments, incongruence processing in the color-word Stroop variant consistently recruited regions of the multiple-demand network, with modulation of spatial convergence by task variants. In addition, the neural patterns related to solving Stroop-like interference differed between versions of the task that use different stimulus material, with the only overlap between color-word, emotional picture-word, and other types of stimulus material in the posterior medial frontal cortex and right anterior insula. Follow-up analyses on behavior reported in these studies (in total 164 effect sizes) revealed only little impact of task variations on the mean effect size of reaction time. These results suggest qualitative processing differences among the family of Stroop variants, despite similar task difficulty levels, and should carefully be considered when planning or interpreting Stroop-type neuroimaging experiments.

Uncovering the determinants of brain functioning, behavior and their interplay in the light of context

Notwithstanding the huge progress in molecular and cellular neuroscience, our ability to understand the brain and develop effective treatments promoting mental health is still limited. This can be partially ascribed to the reductionist, deterministic and mechanistic approaches in neuroscience that struggle with the complexity of the central nervous system. Here, I introduce the Context theory of constrained systems proposing a novel role of contextual factors and genetic, molecular and neural substrates in determining brain functioning and behavior. This theory entails key conceptual implications. First, context is the main driver of behavior and mental states. Second, substrates, from genes to brain areas, have no direct causal link to complex behavioral responses as they can be combined in multiple ways to produce the same response and different responses can impinge on the same substrates. Third, context and biological substrates play distinct roles in determining behavior: context drives behavior, substrates constrain the behavioral repertoire that can be implemented. Fourth, since behavior is the interface between the central nervous system and the environment, it is a privileged level of control and orchestration of brain functioning. Such implications are illustrated through the Kitchen metaphor of the brain. This theoretical framework calls for the revision of key concepts in neuroscience and psychiatry, including causality, specificity and individuality. Moreover, at the clinical level, it proposes treatments inducing behavioral changes through contextual interventions as having the highest impact to reorganize the complexity of the human mind and to achieve a long-lasting improvement in mental health.

Precision fMRI and cluster-failure in the individual brain

Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal-to-noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level-dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single-subject analysis. We introduce adaptive-weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster-corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole-brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family-wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download.

Reexamining the Kuleshov effect: Behavioral and neural evidence from authentic film experiments

Film cognition explores the influence of cinematic elements, such as editing and film color, on viewers' perception. The Kuleshov effect, a famous example of how editing influences viewers' emotional perception, was initially proposed to support montage theory through the Kuleshov experiment. This effect, which has since been recognized as a manifestation of point-of-view (POV) editing practices, posits that the emotional interpretation of neutral facial expressions is influenced by the accompanying emotional scene in a face-scene-face sequence. However, concerns persist regarding the validity of previous studies, often employing inauthentic film materials like static images, leaving the question of its existence in authentic films unanswered. This study addresses these concerns by utilizing authentic films in two experiments. In Experiment 1, multiple film clips were captured under the guidance of a professional film director and seamlessly integrated into authentic film sequences. 59 participants viewed these face-scene-face film sequences and were tasked with rating the valence and emotional intensity of neutral faces. The findings revealed that the accompanying fearful or happy scenes significantly influence the interpretation of emotion on neutral faces, eliciting perceptions of negative or positive emotions from the neutral face. These results affirm the existence of the Kuleshov effect within authentic films. In Experiment 2, 31 participants rated the valence and arousal of neutral faces while undergoing functional magnetic resonance imaging (fMRI). The behavioral results confirm the Kuleshov effect in the MRI scanner, while the neural data identify neural correlates that support its existence at the neural level. These correlates include the cuneus, precuneus, hippocampus, parahippocampal gyrus, post cingulate gyrus, orbitofrontal cortex, fusiform gyrus, and insula. These findings also underscore the contextual framing inherent in the Kuleshov effect. Overall, the study integrates film theory and cognitive neuroscience experiments, providing robust evidence supporting the existence of the Kuleshov effect through both subjective ratings and objective neuroimaging measurements. This research also contributes to a deeper understanding of the impact of film editing on viewers' emotional perception from the contemporary POV editing practices and neurocinematic perspective, advancing the knowledge of film cognition.

Mechanistic studies in pathological health anxiety: A systematic review and emerging conceptual framework

BACKGROUND

Pathological health anxiety (PHA) (e.g., hypochondriasis and illness anxiety disorder) is common in medical settings and associated with increased healthcare costs. However, the psychological and neurobiological mechanisms contributing to the development and maintenance of PHA are incompletely understood.

METHODS

We performed a systematic review to characterize the mechanistic understanding of PHA. PubMed, PsycINFO, and Embase databases were searched to find articles published between 1/1/1990 and 12/31/2022 employing a behavioral task and/or physiological measures in individuals with hypochondriasis, illness anxiety disorder, and PHA more broadly.

RESULTS

Out of 9141 records identified, fifty-seven met inclusion criteria. Article quality varied substantially across studies, and was overall inadequate. Cognitive, behavioral, and affective findings implicated in PHA included health-related attentional and memory recall biases, a narrow health concept, threat confirming thought patterns, use of safety-seeking behaviors, and biased explicit and implicit affective processing of health-related information among other observations. There is initial evidence supporting a potential overestimation of interoceptive stimuli in those with PHA. Neuroendocrine, electrophysiology, and brain imaging research in PHA are particularly in their early stages.

LIMITATIONS

Included articles evaluated PHA categorically, suggesting that sub-threshold and dimensional health anxiety considerations are not contextualized.

CONCLUSIONS

Within an integrated cognitive-behavioral-affective and predictive processing formulation, we theorize that sub-optimal illness and health concepts, altered interoceptive modeling, biased illness-based predictions and attention, and aberrant prediction error learning are mechanisms relevant to PHA requiring more research. Comprehensively investigating the pathophysiology of PHA offers the potential to identify adjunctive diagnostic biomarkers and catalyze new biologically-informed treatments.

On the neural networks of self and other bias and their role in emergent social interactions

Extensive research has documented the brain networks that play an integral role in bias, or the alteration and filtration of information processing in a manner that fundamentally favors an individual. The roots of bias, whether self- or other-oriented, are a complex constellation of neural and psychological processes that start at the most fundamental levels of sensory processing. From the millisecond information is received in the brain it is filtered at various levels and through various brain networks in relation to extant intrinsic activity to provide individuals with a perception of reality that complements and satisfies the conscious perceptions they have for themselves and the cultures in which they were reared. The products of these interactions, in turn, are dynamically altered by the introduction of others, be they friends or strangers who are similar or different in socially meaningful ways. While much is known about the various ways that basic biases alter specific aspects of neural function to support various forms of bias, the breadth and scope of the phenomenon remains entirely unclear. The purpose of this review is to examine the brain networks that shape (i.e., bias) the self-concept and how interactions with similar (ingroup) compared to dissimilar (outgroup) others alter these network (and subsequent interpersonal) interactions in fundamental ways. Throughout, focus is placed on an emerging understanding of the brain as a complex system, which suggests that many of these network interactions likely occur on a non-linear scale that blurs the lines between network hierarchies.

Dissecting task-based fMRI activity using normative modelling: an application to the Emotional Face Matching Task

Functional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale (N = 7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals' transdiagnostic functioning. Taken together, we demonstrate that normative modelling of fMRI task-activation can be used to illustrate the influence of different task choices and map replicable individual differences, and we encourage its application to other neuroimaging tasks in future studies.

Machine learning classification of functional neurological disorder using structural brain MRI features

BACKGROUND

Brain imaging studies investigating grey matter in functional neurological disorder (FND) have used univariate approaches to report group-level differences compared with healthy controls (HCs). However, these findings have limited translatability because they do not differentiate patients from controls at the individual-level.

METHODS

183 participants were prospectively recruited across three groups: 61 patients with mixed FND (FND-mixed), 61 age-matched and sex-matched HCs and 61 age, sex, depression and anxiety-matched psychiatric controls (PCs). Radial basis function support vector machine classifiers with cross-validation were used to distinguish individuals with FND from HCs and PCs using 134 FreeSurfer-derived grey matter MRI features.

RESULTS

Patients with FND-mixed were differentiated from HCs with an accuracy of 0.66 (p=0.005; area under the receiving operating characteristic (AUROC)=0.74); this sample was also distinguished from PCs with an accuracy of 0.60 (p=0.038; AUROC=0.56). When focusing on the functional motor disorder subtype (FND-motor, n=46), a classifier robustly differentiated these patients from HCs (accuracy=0.72; p=0.002; AUROC=0.80). FND-motor could not be distinguished from PCs, and the functional seizures subtype (n=23) could not be classified against either control group. Important regions contributing to statistically significant multivariate classifications included the cingulate gyrus, hippocampal subfields and amygdalar nuclei. Correctly versus incorrectly classified participants did not differ across a range of tested psychometric variables.

CONCLUSIONS

These findings underscore the interconnection of brain structure and function in the pathophysiology of FND and demonstrate the feasibility of using structural MRI to classify the disorder. Out-of-sample replication and larger-scale classifier efforts incorporating psychiatric and neurological controls are needed.

Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology

Dementia is a highly prevalent condition with devastating clinical and socioeconomic sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be effective. Symptomatic late-onset dementia and predementia (SLODP) affects 95% of patients with the syndrome. In contrast to trials of pharmacological prevention, no treatment is suggested to remediate or cure these symptomatic patients. SLODP but not young onset dementia is intensely associated with multimorbidity (MUM), including brain-perturbating conditions (BPCs). Recent studies showed that MUM/BPCs have a major role in the pathogenesis of SLODP. Fortunately, most MUM/BPCs are medically treatable, and thus, their treatment may modify and improve SLODP, relieving suffering and reducing its clinical and socioeconomic threats. Regrettably, the complex system features of SLODP impede the diagnosis and treatment of the potentially remediable conditions (PRCs) associated with them, mainly due to failure of pattern recognition and a flawed diagnostic workup. We suggest incorporating two SLODP-specific conceptual themes into the diagnostic workup: MUM/BPC and multilevel phenomenological themes. By doing so, we were able to improve the diagnostic accuracy of SLODP components and optimize detecting and favorably treating PRCs. These revolutionary concepts and their implications for remediability and other parameters are discussed in the paper.

Is depression a global brain disorder with topographic dynamic reorganization?

Major depressive disorder (MDD) is characterized by a multitude of psychopathological symptoms including affective, cognitive, perceptual, sensorimotor, and social. The neuronal mechanisms underlying such co-occurrence of psychopathological symptoms remain yet unclear. Rather than linking and localizing single psychopathological symptoms to specific regions or networks, this perspective proposes a more global and dynamic topographic approach. We first review recent findings on global brain activity changes during both rest and task states in MDD showing topographic reorganization with a shift from unimodal to transmodal regions. Next, we single out two candidate mechanisms that may underlie and mediate such abnormal uni-/transmodal topography, namely dynamic shifts from shorter to longer timescales and abnormalities in the excitation-inhibition balance. Finally, we show how such topographic shift from unimodal to transmodal regions relates to the various psychopathological symptoms in MDD including their co-occurrence. This amounts to what we describe as 'Topographic dynamic reorganization' which extends our earlier 'Resting state hypothesis of depression' and complements other models of MDD.

Unravelling consciousness and brain function through the lens of time, space, and information

Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges that neuroscientists face. Pharmacological and pathological perturbations of consciousness provide a lens to investigate these complex challenges. Here, we review how recent advances about consciousness and the brain's functional organisation have been driven by a common denominator: decomposing brain function into fundamental constituents of time, space, and information. Whereas unconsciousness increases structure-function coupling across scales, psychedelics may decouple brain function from structure. Convergent effects also emerge: anaesthetics, psychedelics, and disorders of consciousness can exhibit similar reconfigurations of the brain's unimodal-transmodal functional axis. Decomposition approaches reveal the potential to translate discoveries across species, with computational modelling providing a path towards mechanistic integration.

Proposal for a Mechanistic Disease Conceptualization in Clinical Neurosciences: The Neural Network Components (NNC) Model

ABSTRACT

Clinical neurosciences, and psychiatry specifically, have been challenged by the lack of a comprehensive and practical framework that explains the core mechanistic processes of variable psychiatric presentations. Current conceptualization and classification of psychiatric presentations are primarily centered on a non-biologically based clinical descriptive approach. Despite various attempts, advances in neuroscience research have not led to an improved conceptualization or mechanistic classification of psychiatric disorders. This perspective article proposes a new-work-in-progress-framework for conceptualizing psychiatric presentations based on neural network components (NNC). This framework could guide the development of mechanistic disease classification, improve understanding of underpinning pathology, and provide specific intervention targets. This model also has the potential to dissolve artificial barriers between the fields of psychiatry and neurology.

Network motifs in cellular neurophysiology

Concepts from network science and graph theory, including the framework of network motifs, have been frequently applied in studying neuronal networks and other biological complex systems. Network-based approaches can also be used to study the functions of individual neurons, where cellular elements such as ion channels and membrane voltage are conceptualized as nodes within a network, and their interactions are denoted by edges. Network motifs in this context provide functional building blocks that help to illuminate the principles of cellular neurophysiology. In this review we build a case that network motifs operating within neurons provide tools for defining the functional architecture of single-neuron physiology and neuronal adaptations. We highlight the presence of such computational motifs in the cellular mechanisms underlying action potential generation, neuronal oscillations, dendritic integration, and neuronal plasticity. Future work applying the network motifs perspective may help to decipher the functional complexities of neurons and their adaptation during health and disease.

The model of the brain as a complex system: Interactions of physical, neural and mental states with neurocognitive functions

The isolated approaching of physical, neural and mental states and the binary classification into stable traits and fluctuating states previously lead to a limited understanding concerning underlying processes and possibilities to explain, measure and regulate neural and mental performance along with the interaction of mental states and neurocognitive traits. In this article these states are integrated by i) differentiating the model of the brain as a complex, self-organizing system, ii) showing possibilities to measure this model, iii) offering a classification of mental states and iv) presenting a holistic operationalization of state regulations and trait trainings to enhance neural and mental high-performance on a macro- and micro scale. This model integrates current findings from the theory of constructed emotions, the theory of thousand brains and complex systems theory and yields several testable hypotheses to provide an integrated reference frame for future research and applied target points to regulate and enhance performance.

Insula uses overlapping codes for emotion in self and others

In daily life, we must recognize others' emotions so we can respond appropriately. This ability may rely, at least in part, on neural responses similar to those associated with our own emotions. We hypothesized that the insula, a cortical region near the junction of the temporal, parietal, and frontal lobes, may play a key role in this process. We recorded local field potential (LFP) activity in human neurosurgical patients performing two tasks, one focused on identifying their own emotional response and one on identifying facial emotional responses in others. We found matching patterns of gamma- and high-gamma band activity for the two tasks in the insula. Three other regions (MTL, ACC, and OFC) clearly encoded both self- and other-emotions, but used orthogonal activity patterns to do so. These results support the hypothesis that the insula plays a particularly important role in mediating between experienced vs. observed emotions.

Toward a neuroscience of natural behavior

One of the most exciting new developments in systems neuroscience is the progress being made toward neurophysiological experiments that move beyond simplified laboratory settings and address the richness of natural behavior. This is enabled by technological advances such as wireless recording in freely moving animals, automated quantification of behavior, and new methods for analyzing large data sets. Beyond new empirical methods and data, however, there is also a need for new theories and concepts to interpret that data. Such theories need to address the particular challenges of natural behavior, which often differ significantly from the scenarios studied in traditional laboratory settings. Here, we discuss some strategies for developing such novel theories and concepts and some example hypotheses being proposed.

Neuroanatomical, transcriptomic, and molecular correlates of math ability and their prognostic value for predicting learning outcomes

Foundational mathematical abilities, acquired in early childhood, are essential for success in our technology-driven society. Yet, the neurobiological mechanisms underlying individual differences in children's mathematical abilities and learning outcomes remain largely unexplored. Leveraging one of the largest multicohort datasets from children at a pivotal stage of knowledge acquisition, we first establish a replicable mathematical ability-related imaging phenotype (MAIP). We then show that brain gene expression profiles enriched for candidate math ability-related genes, neuronal signaling, synaptic transmission, and voltage-gated potassium channel activity contributed to the MAIP. Furthermore, the similarity between MAIP gene expression signatures and brain structure, acquired before intervention, predicted learning outcomes in two independent math tutoring cohorts. These findings advance our knowledge of the interplay between neuroanatomical, transcriptomic, and molecular mechanisms underlying mathematical ability and reveal predictive biomarkers of learning. Our findings have implications for the development of personalized education and interventions.

Asynchronous behavioral and neurophysiological changes in word production in the adult lifespan

Behavioral and brain-related changes in word production have been claimed to predominantly occur after 70 years of age. Most studies investigating age-related changes in adulthood only compared young to older adults, failing to determine whether neural processes underlying word production change at an earlier age than observed in behavior. This study aims to fill this gap by investigating whether changes in neurophysiological processes underlying word production are aligned with behavioral changes. Behavior and the electrophysiological event-related potential patterns of word production were assessed during a picture naming task in 95 participants across five adult lifespan age groups (ranging from 16 to 80 years old). While behavioral performance decreased starting from 70 years of age, significant neurophysiological changes were present at the age of 40 years old, in a time window (between 150 and 220 ms) likely associated with lexical-semantic processes underlying referential word production. These results show that neurophysiological modifications precede the behavioral changes in language production; they can be interpreted in line with the suggestion that the lexical-semantic reorganization in mid-adulthood influences the maintenance of language skills longer than for other cognitive functions.

Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence

Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.

Updating functional brain units: Insights far beyond Luria

This paper reviews Luria's model of the three functional units of the brain. To meet this objective, several issues were reviewed: the theory of functional systems and the contributions of phylogenesis and embryogenesis to the brain's functional organization. This review revealed several facts. In the first place, the relationship/integration of basic homeostatic needs with complex forms of behavior. Secondly, the multi-scale hierarchical and distributed organization of the brain and interactions between cells and systems. Thirdly, the phylogenetic role of exaptation, especially in basal ganglia and cerebellum expansion. Finally, the tripartite embryogenetic organization of the brain: rhinic, limbic/paralimbic, and supralimbic zones. Obviously, these principles of brain organization are in contradiction with attempts to establish separate functional brain units. The proposed new model is made up of two large integrated complexes: a primordial-limbic complex (Luria's Unit I) and a telencephalic-cortical complex (Luria's Units II and III). As a result, five functional units were delineated: Unit I. Primordial or preferential (brainstem), for life-support, behavioral modulation, and waking regulation; Unit II. Limbic and paralimbic systems, for emotions and hedonic evaluation (danger and relevance detection and contribution to reward/motivational processing) and the creation of cognitive maps (contextual memory, navigation, and generativity [imagination]); Unit III. Telencephalic-cortical, for sensorimotor and cognitive processing (gnosis, praxis, language, calculation, etc.), semantic and episodic (contextual) memory processing, and multimodal conscious agency; Unit IV. Basal ganglia systems, for behavior selection and reinforcement (reward-oriented behavior); Unit V. Cerebellar systems, for the prediction/anticipation (orthometric supervision) of the outcome of an action. The proposed brain units are nothing more than abstractions within the brain's simultaneous and distributed physiological processes. As function transcends anatomy, the model necessarily involves transition and overlap between structures. Beyond the classic approaches, this review includes information on recent systemic perspectives on functional brain organization. The limitations of this review are discussed.

Spatiotemporal Dynamics of Successive Activations across the Human Brain during Simple Arithmetic Processing

Previous neuroimaging studies have offered unique insights about the spatial organization of activations and deactivations across the brain; however, these were not powered to explore the exact timing of events at the subsecond scale combined with a precise anatomical source of information at the level of individual brains. As a result, we know little about the order of engagement across different brain regions during a given cognitive task. Using experimental arithmetic tasks as a prototype for human-unique symbolic processing, we recorded directly across 10,076 brain sites in 85 human subjects (52% female) using the intracranial electroencephalography. Our data revealed a remarkably distributed change of activity in almost half of the sampled sites. In each activated brain region, we found juxtaposed neuronal populations preferentially responsive to either the target or control conditions, arranged in an anatomically orderly manner. Notably, an orderly successive activation of a set of brain regions-anatomically consistent across subjects-was observed in individual brains. The temporal order of activations across these sites was replicable across subjects and trials. Moreover, the degree of functional connectivity between the sites decreased as a function of temporal distance between regions, suggesting that the information is partially leaked or transformed along the processing chain. Our study complements prior imaging studies by providing hitherto unknown information about the timing of events in the brain during arithmetic processing. Such findings can be a basis for developing mechanistic computational models of human-specific cognitive symbolic systems.

Entangled brains and the experience of pains

The International Association for the Study of Pain (IASP) revised its definition of pain to "an unpleasant sensory and emotional experience." Three recent recommendations for understanding pain if there are no clear brain correlates include eliminativism, multiple realizability, and affordance-based approaches. I adumbrate a different path forward. Underlying each of the proposed approaches and the new IASP definition is the suspicion that there are no specific correlates for pain. I suggest that this basic assumption is misguided. As we learn more about brain function, it is becoming clear that many areas process many different types of information at the same time. In this study, I analogize how animal brains navigate in three-dimensional space with how the brain creates pain. Underlying both cases is a large-scale combinatorial system that feeds back on itself through a diversity of convergent and divergent bi-directional connections. Brains are not like combustion engines, with energy driving outputs via the structure of the machine, but are instead more like whirlpools, which are essentially dynamic patterns in some substrates. We should understand pain experiences as context-dependent, spatiotemporal trajectories that reflect heterogeneous, multiplex, and dynamically adaptive brain cells.

Sulcal variability in anterior lateral prefrontal cortex contributes to variability in reasoning performance among young adults

Identifying structure-function correspondences is a major goal among biologists, cognitive neuroscientists, and brain mappers. Recent studies have identified relationships between performance on cognitive tasks and the presence or absence of small, shallow indentations, or sulci, of the human brain. Building on the previous finding that the presence of the ventral para-intermediate frontal sulcus (pimfs-v) in the left anterior lateral prefrontal cortex (aLPFC) was related to reasoning task performance in children and adolescents, we tested whether this relationship extended to a different sample, age group, and reasoning task. As predicted, the presence of this aLPFC sulcus was also associated with higher reasoning scores in young adults (ages 22-36). These findings have not only direct developmental, but also evolutionary relevance-as recent work shows that the pimfs-v is exceedingly rare in chimpanzees. Thus, the pimfs-v is a key developmental, cognitive, and evolutionarily relevant feature that should be considered in future studies examining how the complex relationships among multiscale anatomical and functional features of the brain give rise to abstract thought.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Intrinsic timescales and predictive allostatic interoception in brain health and disease

The cognitive neuroscience of brain diseases faces challenges in understanding the complex relationship between brain structure and function, the heterogeneity of brain phenotypes, and the lack of dimensional and transnosological explanations. This perspective offers a framework combining the predictive coding theory of allostatic interoceptive overload (PAIO) and the intrinsic neural timescales (INT) theory to provide a more dynamic understanding of brain health in psychiatry and neurology. PAIO integrates allostasis and interoception to assess the interaction between internal patterns and environmental stressors, while INT shows that different brain regions operate on different intrinsic timescales. The allostatic overload can be understood as a failure of INT, which involves a breakdown of proper temporal integration and segregation. This can lead to dimensional disbalances between exteroceptive/interoceptive inputs across brain and whole-body levels (cardiometabolic, cardiovascular, inflammatory, immune). This approach offers new insights, presenting novel perspectives on brain spatiotemporal hierarchies and interactions. By integrating these theories, the paper opens innovative paths for studying brain health dynamics, which can inform future research in brain health and disease.

Determination of Neuronal Activity and Its Meaning for the Processes of Learning and Memory

Despite the years of studies in the field of systems neuroscience, the functions of neural circuits and behavior-related systems are still not entirely understood. The systems description of brain activity has recently been associated with cognitive concepts, e.g., a cognitive map, reconstructed via place-cell activity analysis and the like, and a cognitive schema, modeled in consolidation research. The issue we find of importance is that cognitive concepts reconstructed in neuroscience research are mainly formulated in terms of the environment. In this chapter, we present the idea of an element of individual experience that serves as a model of behavioral interaction with the environment, rather than a model of the environment itself. This intangible difference entails the need for substantial revision of several well-known phenomena, including long-term potentiation. The principal questions we address are: how do elements of experience appear and change during learning and performance; and how do the links between these elements create the whole structure of individual experience? We argue that learning and memory research need a clear distinction between processes that provide the emergence of new elements of experience (functional systems) and processes underlying the retrieval and/or changes in the existing experience. We propose to view the activity of a neuron as an "action" directed to the future adaptive "microresult," essential for meeting its metabolic needs. This anticipatory neuronal activity is coordinated with the activity of many other cells of the body in the organism-wide functional system, ensuring the achievement of an adaptive "macroresult" at the behavioral level. From this perspective, the mechanisms of learning are considered as the formation of functional systems, and memory is considered as a dynamic structure constituted by systems formed at different stages of individual development.

Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest

Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.

The Neural Separability of Emotion Reactivity and Regulation

One foundational distinction in affective science is between emotion reactivity and regulation. This conceptual distinction has long been assumed to be instantiated in spatially separable brain systems (a typical example: amygdala/insula for reactivity and frontoparietal areas for regulation). In this research, we begin by reviewing previous findings that support and contradict the neural separability hypothesis concerning emotional reactivity and regulation. Further, we conduct a direct test of this hypothesis with empirical data. In five studies involving healthy and clinical samples (total n = 336), we assessed neural responses using fMRI while participants were asked to either react naturally or regulate their emotions (using reappraisal) while viewing emotionally evocative stimuli. Across five studies, we failed to find support for the neural separability hypothesis. In univariate analyses, both presumptive "reactivity" and "regulation" brain regions demonstrated equal or greater activation for the reactivity contrast than for the regulation contrast. In multivariate pattern analyses (MVPA), classifiers decoded reactivity (vs. neutral) trials more accurately than regulation (vs. reactivity) trials using multivoxel data in both presumptive "reactivity" and "regulation" regions. These findings suggest that emotion reactivity and regulation-as measured via fMRI-may not be as spatially separable in the brain as previously assumed. Our secondary whole-brain analyses revealed largely consistent results. We discuss the two theoretical possibilities regarding the neural separability hypothesis and offer thoughts for future research.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-023-00227-9.

Spatiotemporal dynamics of successive activations across the human brain during simple arithmetic processing

Previous neuroimaging studies have offered unique insights about the spatial organization of activations and deactivations across the brain, however these were not powered to explore the exact timing of events at the subsecond scale combined with precise anatomical source information at the level of individual brains. As a result, we know little about the order of engagement across different brain regions during a given cognitive task. Using experimental arithmetic tasks as a prototype for human-unique symbolic processing, we recorded directly across 10,076 brain sites in 85 human subjects (52% female) using intracranial electroencephalography (iEEG). Our data revealed a remarkably distributed change of activity in almost half of the sampled sites. Notably, an orderly successive activation of a set of brain regions - anatomically consistent across subjects-was observed in individual brains. Furthermore, the temporal order of activations across these sites was replicable across subjects and trials. Moreover, the degree of functional connectivity between the sites decreased as a function of temporal distance between regions, suggesting that information is partially leaked or transformed along the processing chain. Furthermore, in each activated region, distinct neuronal populations with opposite activity patterns during target and control conditions were juxtaposed in an anatomically orderly manner. Our study complements the prior imaging studies by providing hitherto unknown information about the timing of events in the brain during arithmetic processing. Such findings can be a basis for developing mechanistic computational models of human-specific cognitive symbolic systems.

Significance statement

Our study elucidates the spatiotemporal dynamics and anatomical specificity of brain activations across >10,000 sites during arithmetic tasks, as captured by intracranial EEG. We discovered an orderly, successive activation of brain regions, consistent across individuals, and a decrease in functional connectivity as a function of temporal distance between regions. Our findings provide unprecedented insights into the sequence of cognitive processing and regional interactions, offering a novel perspective for enhancing computational models of cognitive symbolic systems.