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Work at the intersection of philosophy and psychiatry has an extensive and influential history, and has received increased attention recently, with the emergence of professional associations and a growing literature. In this paper, we review key advances in work on philosophy and psychiatry, and their related clinical implications. First, in understanding and categorizing mental disorder, both naturalist and normativist considerations are now viewed as important – psychiatric constructs necessitate a consideration of both facts and values. At a conceptual level, this integrative view encourages moving away from strict scientism to soft naturalism, while in clinical practice this facilitates both evidence‐based and values‐based mental health care. Second, in considering the nature of psychiatric science, there is now increasing emphasis on a pluralist approach, including ontological, explanatory and value pluralism. Conceptually, a pluralist approach acknowledges the multi‐level causal interactions that give rise to psychopathology, while clinically it emphasizes the importance of a broad range of “difference‐makers”, as well as a consideration of “lived experience” in both research and practice. Third, in considering a range of questions about the brain‐mind, and how both somatic and psychic factors contribute to the development and maintenance of mental disorders, conceptual and empirical work on embodied cognition provides an increasingly valuable approach. Viewing the brain‐mind as embodied, embedded and enactive offers a conceptual approach to the mind‐body problem that facilitates the clinical integration of advances in both cognitive‐affective neuroscience and phenomenological psychopathology.

ABSTRACTThe Journal Neuropsychoanalysis: An Interdisciplinary Journal for Psychoanalysis and the Neurosciencescelebrated its 25th anniversary in 2023. The celebration gathered old and current editors. The story of the Journal and of neuropsychoanalysis is told by those directly involved in the project at its different stages. This text is a collection of the reflections that editors and guests shared on celebrating this special anniversary.

ABSTRACTIn his latest treatise on the separation distress hypothesis, Watt makes a compelling case that depression ultimately stems from an adaptive response to problems in the social world. Despite our agreement with many of Watt's claims, we argue here that the separation distress hypothesis only tells us part of the story. Finding little reason to favour his model over other socially-oriented theories of depression, we advocate instead for an integrative approach in theoretical psychiatry that can fruitfully incorporate insights arising from each of these evolutionary views, including Watt's own.

This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative modelas underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain—from cognitive and computational neuroscience—as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we—or our brains—encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.

The mainstream science of consciousness offers a few predominate views of how the brain gives rise to awareness. Chief among these are the Higher-Order Thought Theory, Global Neuronal Workspace Theory, Integrated Information Theory, and hybrids thereof. In parallel, rapid development in predictive processing approaches have begun to outline concrete mechanisms by which interoceptive inference shapes selfhood, affect, and exteroceptive perception. Here, we consider these new approaches in terms of what they might offer our empirical, phenomenological, and philosophical understanding of consciousness and its neurobiological roots.

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as computational phenomenologybecause it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience.

Music is ubiquitous across human cultures — as a source of affective and pleasurable experience, moving us both physically and emotionally — and learning to play music shapes both brain structure and brain function. Music processing in the brain — namely, the perception of melody, harmony and rhythm — has traditionally been studied as an auditory phenomenon using passive listening paradigms. However, when listening to music, we actively generate predictions about what is likely to happen next. This enactive aspect has led to a more comprehensive understanding of music processing involving brain structures implicated in action, emotion and learning. Here we review the cognitive neuroscience literature of music perception. We show that music perception, action, emotion and learning all rest on the human brain’s fundamental capacity for prediction — as formulated by the predictive coding of music model. This Review elucidates how this formulation of music perception and expertise in individuals can be extended to account for the dynamics and underlying brain mechanisms of collective music making. This in turn has important implications for human creativity as evinced by music improvisation. These recent advances shed new light on what makes music meaningful from a neuroscientific perspective.

Neuroimaging studies of psychedelics have advanced our understanding of hierarchical brain organization and the mechanisms underlying their subjective and therapeutic effects. The primary mechanism of action of classic psychedelics is binding to serotonergic 5-HT2A receptors. Agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy that can have a profound effect on hierarchical message-passing in the brain. Here, we review the cognitive and neuroimaging evidence for the effects of psychedelics: in particular, their influence on selfhood and subject-object boundaries—known as egodissolution—surmised to underwrite their subjective and therapeutic effects. Agonism of 5-HT2A receptors, located at the apex of the cortical hierarchy, may have a particularly powerful effect on sentience and consciousness. These effects can endure well after the pharmacological half-life, suggesting that psychedelics may have effects on neural plasticity that may play a role in their therapeutic efficacy. Psychologically, this may be accompanied by a disarming of egoresistancethat increases the repertoire of perceptual hypotheses and affords alternate pathways for thought and behavior, including those that undergird selfhood. We consider the interaction between serotonergic neuromodulation and sentience through the lens of hierarchical predictive coding, which speaks to the value of psychedelics in understanding how we make sense of the world and specific predictions about effective connectivity in cortical hierarchies that can be tested using functional neuroimaging.Significance StatementClassic psychedelics bind to serotonergic 5-HT2A receptors. Their agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy, resulting in a profound effect on information processing in the brain. Here, we synthesize an abundance of brain imaging research with pharmacological and psychological interpretations informed by the framework of predictive coding. Moreover, predictive coding is suggested to offer more sophisticated interpretations of neuroimaging findings by bridging the role between the 5-HT2A receptors and large-scale brain networks.

We have investigated the theoretical constraints of the interactions between coupled cortical columns. Each cortical column consists of a set of neural populations where each population is modelled as a neural mass. The existence of semi-stable states within a cortical column is dependent on the type of interaction between the neuronal populations, i.e., the form of the synaptic kernels. Current-to-current coupling has been shown, in contrast to potential-to-current coupling, to create semi-stable states within a cortical column. The interaction between semi-stable states of the cortical columns is studied where we derive the dynamics for the collected activity. For small excitations the dynamics follow the Kuramoto model; however, in contrast to previous work we derive coupled equations between phase and amplitude dynamics with the possibility of defining connectivity as a stationary and dynamic variable. The turbulent flow of phase dynamics which occurs in networks of Kuramoto oscillators would indicate turbulent changes in dynamic connectivity for coupled cortical columns which is something that has been recorded in epileptic seizures. We used the results we derived to estimate a seizure propagationmodel which allowed for inversions using the Laplace assumption (Dynamic Causal Modelling). The seizure propagation model was trialed on simulated data, and future work will investigate the estimation of the connectivity matrix from empirical data. This model can be used to predict changes in seizure evolution after virtual changes in the connectivity network, something that could be of clinical use when applied to epilepsy surgical cases.

This article offers a philosophical thesis for psychiatric disorders that rests upon some simple truths about the mind and brain. Specifically, it asks whether the dual aspect monism—that emerges from sleep research and theoretical neurobiology—can be applied to pathophysiology and psychopathology in psychiatry. Our starting point is that the mind and brain are emergent aspects of the same (neuronal) dynamics; namely, the brain–mind. Our endpoint is that synaptic dysconnection syndromes inherit the same dual aspect; namely, aberrant inference or belief updating on the one hand, and a failure of neuromodulatory synaptic gain control on the other. We start with some basic considerations from sleep research that integrate the phenomenology of dreaming with the neurophysiology of sleep. We then leverage this treatment by treating the brain as an organ of inference. Our particular focus is on the role of precision (i.e., the representation of uncertainty) in belief updating and the accompanying synaptic mechanisms. Finally, we suggest a dual aspect approach—based upon belief updating (i.e., mind processes) and its neurophysiological implementation (i.e., brain processes)—has a wide explanatory compass for psychiatry and various movement disorders. This approach identifies the kind of pathophysiology that underwrites psychopathology—and points to certain psychotherapeutic and psychopharmacological targets, which may stand in mechanistic relation to each other. The ‘mind’ emerges from Bayesian belief updating in the ‘brain’Psychopathology can be read as aberrant belief updating.Aberrant belief updating follows from any neuromodulatory synaptopathy The ‘mind’ emerges from Bayesian belief updating in the ‘brain’ Psychopathology can be read as aberrant belief updating. Aberrant belief updating follows from any neuromodulatory synaptopathy

ObjectiveTo track the interplay between (micro-) structural changes along the trajectories of nociceptive pathways and its relation to the presence and intensity of neuropathic pain (NP) after spinal cord injury (SCI).MethodsA quantitative neuroimaging approach employing a multiparametric mapping protocol was used, providing indirect measures of myelination (via contrasts such as magnetisation transfer (MT) saturation, longitudinal relaxation (R1)) and iron content (via effective transverse relaxation rate (R2*)) was used to track microstructural changes within nociceptive pathways. In order to characterise concurrent changes along the entire neuroaxis, a combined brain and spinal cord template embedded in the statistical parametric mapping framework was used. Multivariate source-based morphometry was performed to identify naturally grouped patterns of structural variation between individuals with and without NP after SCI.ResultsIn individuals with NP, lower R1 and MT values are evident in the primary motor cortex and dorsolateral prefrontal cortex, while increases in R2* are evident in the cervical cord, periaqueductal grey (PAG), thalamus and anterior cingulate cortex when compared with pain-free individuals. Lower R1 values in the PAG and greater R2* values in the cervical cord are associated with NP intensity.ConclusionsThe degree of microstructural changes across ascending and descending nociceptive pathways is critically implicated in the maintenance of NP. Tracking maladaptive plasticity unravels the intimate relationships between neurodegenerative and compensatory processes in NP states and may facilitate patient monitoring during therapeutic trials related to pain and neuroregeneration.

ABSTRACTMovement-related theta oscillations in rodent hippocampus coordinate ‘forward sweeps’ of location-specific neural activity that could be used to evaluate spatial trajectories online. This raises the possibility that increases in human hippocampal theta power accompany the evaluation of upcoming spatial choices. To test this hypothesis, we measured neural oscillations during a spatial planning task that closely resembles a perceptual decision-making paradigm. In this task, participants searched visually for the shortest path between a start and goal location in novel mazes that contained multiple choice points, and were subsequently asked to make a spatial decision at one of those choice points. We observed ~4–8 Hz hippocampal/medial temporal lobe theta power increases specific to sequential planning that were negatively correlated with subsequent decision speed, where decision speed was inversely correlated with choice accuracy. These results implicate the hippocampal theta rhythm in decision tree search during planning in novel environments.

•Attention is a process of (precision dependent) gain control.•Salience is associated with planned actions that resolve uncertainty.•While distinct entities, attention and salience strongly influence one another.

Making inference under uncertainty requires an optimal weighting of prior expectations and observations. How this weighting is realized in the brain remains elusive. To investigate this, we recorded functional neuroimaging data while participants estimated a number based on noisy observations. Crucially, the prior expectation about the variability of observations (an expected variability) was manipulated. Consistent with normative models, when novel observations were characterized by higher expected or observed variability, participants' estimates relied more on expectations than novel observations and were characterized by higher stochasticity. Activity in hippocampus increased when novel evidence was characterized by higher expected or observed variability. Response in superior parietal cortex reflected a precision-weighted prediction error signal (i.e., the distance between observations and expectations) that was modulated by hippocampal activity. Our findings implicate the hippocampus during inference under uncertainty, suggesting a role in weighting prior representations over observations and in modulating responsivity of superior parietal cortex to prediction error.

Disturbances in N-methyl-D-aspartate receptors (NMDARs)—as implicated in patients with schizophrenia—can cause regionally specific electrophysiological effects. Both animal models of NMDAR blockade and clinical studies in patients with schizophrenia have suggested that behavioral phenotypes are associated with reduction in inhibition within the frontal cortex.

Artificial intelligence has recently attained humanlike performance in a number of gamelike domains. These advances have been spurred by brain-inspired architectures and algorithms such as hierarchical filtering and reinforcement learning. OpenAI Gym is an open-source platform in which to train, test, and benchmark algorithms—it provides a range of tasks, including those of classic arcade games such as Doom. Here we describe how the platform might be used as a simulation, test, and diagnostic paradigm for psychiatric conditions.

The aim of this paper is to introduce a field of study that has emerged over the last decade, called Bayesian mechanics. Bayesian mechanics is a probabilistic mechanics, comprising tools that enable us to model systems endowed with a particular partition (i.e. into particles), where the internal states (or the trajectories of internal states) of a particular system encode the parameters of beliefs about external states (or their trajectories). These tools allow us to write down mechanical theories for systems that look as if they are estimating posterior probability distributions over the causes of their sensory states. This provides a formal language for modelling the constraints, forces, potentials and other quantities determining the dynamics of such systems, especially as they entail dynamics on a space of beliefs (i.e. on a statistical manifold). Here, we will review the state of the art in the literature on the free energy principle, distinguishing between three ways in which Bayesian mechanics has been applied to particular systems (i.e. path-tracking, mode-tracking and mode-matching). We go on to examine a duality between the free energy principle and the constrained maximum entropy principle, both of which lie at the heart of Bayesian mechanics, and discuss its implications.

Brain activity derives from intrinsic dynamics (due to neurophysiology and anatomical connectivity) in concert with stochastic effects that arise from sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random fluctuations can be studied with stochastic dynamic models (SDMs). This article, Part II of a two-part series, reviews applications of SDMs to large-scale neural systems in health and disease. Stochastic models have already elucidated a number of pathophysiological phenomena, such as epilepsy and hypoxic ischemic encephalopathy, although their use in biological psychiatry remains rather nascent. Emerging research in this field includes phenomenological models of mood fluctuations in bipolar disorder and biophysical models of functional imaging data in psychotic and affective disorders. Together with deeper theoretical considerations, this work suggests that SDMs will play a unique and influential role in computational psychiatry, unifying empirical observations with models of perception and behavior.

Biological phenomena arise through interactions between an organism’s intrinsic dynamics and stochastic forces—random fluctuations due to external inputs, thermal energy, or other exogenous influences. Dynamic processes in the brain derive from neurophysiology and anatomical connectivity; stochastic effects arise through sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random effects can be studied with stochastic dynamic models (SDMs). This article, Part I of a two-part series, offers a primer of SDMs and their application to large-scale neural systems in health and disease. The companion article, Part II, reviews the application of SDMs to brain disorders. SDMs generate a distribution of dynamic states, which (we argue) represent ideal candidates for modeling how the brain represents states of the world. When augmented with variational methods for model inversion, SDMs represent a powerful means of inferring neuronal dynamics from functional neuroimaging data in health and disease. Together with deeper theoretical considerations, this work suggests that SDMs will play a unique and influential role in computational psychiatry, unifying empirical observations with models of perception and behavior.

We present a novel computational model that describes action perception as an activeinferential process that combines motor prediction(the reuse of our own motor system to predict perceived movements) and hypothesis testing(the use of eye movements to disambiguate amongst hypotheses). The system uses a generative model of how (arm and hand) actions are performed to generate hypothesis-specific visual predictions, and directs saccades to the most informative places of the visual scene to test these predictions – and underlying hypotheses. We test the model using eye movement data from a human action observation study. In both the human study and our model, saccades are proactive whenever context affords accurate action prediction; but uncertainty induces a more reactive gaze strategy, via tracking the observed movements. Our model offers a novel perspective on action observation that highlights its activenature based on prediction dynamics and hypothesis testing.

This commentary considers three key themes in the target article, from the perspective of the self-evidencing brain. In brief, I examine the notion of minimal selfhoodin terms of inference to the best explanation; in particular, an explanation for sensations that are uniquely available to a self. This begs the question of how selfdiffers from non-selfand how non-selfdiffers from the concept of another. From an (active) inference point of view, the building of generative models that entail minimal selfhood rests on structure learningand Bayesian model selection. In this setting, I focus on the role of generalized synchrony – and the implicit closure of action-perception cycles – in dyadic interactions. Finally, I touch on the importance of interoception awareness and the implications of how interoceptive signals are contextualized for translating the ideas in the target article into developmental psychopathology.

AbstractGranger causality (GC) and dynamic causal modeling (DCM) are the two key approaches used to determine the directed interactions among brain areas. Recent discussions have provided a constructive account of the merits and demerits. GC, on one side, considers dependencies among measured responses, whereas DCM, on the other, models how neuronal activity in one brain area causes dynamics in another. In this study, our objective was to establish construct validity between GC and DCM in the context of resting state functional magnetic resonance imaging (fMRI). We first established the face validity of both approaches using simulated fMRI time series, with endogenous fluctuations in two nodes. Crucially, we tested both unidirectional and bidirectional connections between the two nodes to ensure that both approaches give veridical and consistent results, in terms of model comparison. We then applied both techniques to empirical data and examined their consistency in terms of the (quantitative) in-degree of key nodes of the default mode. Our simulation results suggested a (qualitative) consistency between GC and DCM. Furthermore, by applying nonparametric GC and stochastic DCM to resting-state fMRI data, we confirmed that both GC and DCM infer similar (quantitative) directionality between the posterior cingulate cortex (PCC), the medial prefrontal cortex, the left middle temporal cortex, and the left angular gyrus. These findings suggest that GC and DCM can be used to estimate directed functional and effective connectivity from fMRI measurements in a consistent manner.