Brain dynamics predictive of response to psilocybin for treatment-resistant depression

Atypical attentional network dynamics in adolescent depression during emotional movie viewing

Emotion studies have commonly reported atypical emotional processing in clinically depressed adolescents in the context of short-lasting emotional cues. However, interindividual differences in the moment-to-moment brain network dynamics that underlie this impaired emotional reactivity remain unclear, and the use of poorly matched controls and relatively small sample sizes represents major limitations in most neuroimaging depression studies to date. Here, we address these concerns by using the temporal features of a rich naturalistic paradigm (i.e. a clip from the movie 'Despicable Me') to investigate brain network dynamics in 42 clinically depressed and 42 nondepressed adolescents aged 16-21 years, matched for age, gender, and psychiatric comorbidities. Using a dynamics functional connectivity analysis technique called Leading Eigenvector Dynamics Analysis, we found that the clinical group exhibited significantly higher probability of occurrence of the dorsal attention network and lower recruitment of the fronto-parietal, default mode network, ventral attention, and somato-motor networks throughout the task. This brain/behaviour relationship was prominent during less emotional moments of the movie, consistent with previous findings. Our findings demonstrate the key role of continuous affective measures in providing information about how activity in the depressed brain evolves as emotional intensity unfolds throughout the movie. Future studies with a larger sample size are needed in order to corroborate the present findings.

The Algorithmic Agent Perspective and Computational Neuropsychiatry: From Etiology to Advanced Therapy in Major Depressive Disorder

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using the Kolmogorov theory (KT) of consciousness, we developed a foundational model where algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression, defined in this context by a state of persistently low valence, may arise from various factors-including inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, we map the agent model to brain circuits and functional networks, framing potential etiological routes and linking with depression biotypes. Finally, we explore how brain stimulation, psychotherapy, and plasticity-enhancing compounds such as psychedelics can synergistically repair neural circuits and optimize therapies using personalized computational models.

Inducing a meditative state by artificial perturbations: A mechanistic understanding of brain dynamics underlying meditation

Contemplative neuroscience has increasingly explored meditation using neuroimaging. However, the brain mechanisms underlying meditation remain elusive. Here, we implemented a mechanistic framework to explore the spatiotemporal dynamics of expert meditators during meditation and rest, and controls during rest. We first applied a model-free approach by defining a probabilistic metastable substate (PMS) space for each condition, consisting of different probabilities of occurrence from a repertoire of dynamic patterns. Moreover, we implemented a model-based approach by adjusting the PMS of each condition to a whole-brain model, which enabled us to explore in silico perturbations to transition from resting-state to meditation and vice versa. Consequently, we assessed the sensitivity of different brain areas regarding their perturbability and their mechanistic local-global effects. Overall, our work reveals distinct whole-brain dynamics in meditation compared to rest, and how transitions can be induced with localized artificial perturbations. It motivates future work regarding meditation as a practice in health and as a potential therapy for brain disorders.

The flattening of spacetime hierarchy of the N,N-dimethyltryptamine brain state is characterized by harmonic decomposition of spacetime (HADES) framework

The human brain is a complex system, whose activity exhibits flexible and continuous reorganization across space and time. The decomposition of whole-brain recordings into harmonic modes has revealed a repertoire of gradient-like activity patterns associated with distinct brain functions. However, the way these activity patterns are expressed over time with their changes in various brain states remains unclear. Here, we investigate healthy participants taking the serotonergic psychedelic N,N-dimethyltryptamine (DMT) with the Harmonic Decomposition of Spacetime (HADES) framework that can characterize how different harmonic modes defined in space are expressed over time. HADES demonstrates significant decreases in contributions across most low-frequency harmonic modes in the DMT-induced brain state. When normalizing the contributions by condition (DMT and non-DMT), we detect a decrease specifically in the second functional harmonic, which represents the uni- to transmodal functional hierarchy of the brain, supporting the leading hypothesis that functional hierarchy is changed in psychedelics. Moreover, HADES' dynamic spacetime measures of fractional occupancy, life time and latent space provide a precise description of the significant changes of the spacetime hierarchical organization of brain activity in the psychedelic state.

Where do the symptoms come from in depression? Topography and dynamics matter

This scientific commentary refers to 'Brain dynamics predictive of response to psilocybin for treatment-resistant depression', by Vohryzek et al. (https://doi.org/10.1093/braincomms/fcae049).