Posttraumatic stress disorder (PTSD) is associated with decreased top-down emotion modulation from medial prefrontal cortex (mPFC) regions, a pathophysiology accompanied by hyperarousal and hyperactivation of the amygdala. By contrast, PTSD patients with the dissociative subtype (PTSD + DS) often exhibit increased mPFC top-down modulation and decreased amygdala activation associated with emotional detachment and hypoarousal. Crucially, PTSD and PTSD + DS display distinct functional connectivity within the PFC, amygdala complexes, and the periaqueductal gray (PAG), a region related to defensive responses/emotional coping. However, differences in directed connectivity between these regions have not been established in PTSD, PTSD + DS, or controls.To examine directed (effective) connectivity among these nodes, as well as group differences, we conducted resting-state stochastic dynamic causal modeling (sDCM) pairwise analyses of coupling between the ventromedial (vm)PFC, the bilateral basolateral and centromedial (CMA) amygdala complexes, and the PAG, in 155 participants (PTSD [n = 62]; PTSD + DS [n = 41]; age-matched healthy trauma-unexposed controls [n = 52]).PTSD was characterized by a pattern of predominant bottom-up connectivity from the amygdala to the vmPFC and from the PAG to the vmPFC and amygdala. Conversely, PTSD + DS exhibited predominant top-down connectivity between all node pairs (from the vmPFC to the amygdala and PAG, and from the amygdala to the PAG). Interestingly, the PTSD + DS group displayed the strongest intrinsic inhibitory connections within the vmPFC.These results suggest the contrasting symptom profiles of PTSD and its dissociative subtype (hyper- vs. hypo-emotionality, respectively) may be driven by complementary changes in directed connectivity corresponding to bottom-up defensive fear processing versus enhanced top-down regulation. Hum Brain Mapp 38:5551-5561, 2017. © 2017 Wiley Periodicals, Inc.
Patients with melancholia report a distinct and intrusive dysphoric state during internally generated thought. Melancholia has long been considered to have a strong biological component, but evidence for its specific neurobiological origins is limited. The distinct neurocognitive, psychomotor, and mood disturbances observed in melancholia do, however, suggest aberrant coordination of frontal-subcortical circuitry, which may best be captured through analysis of complex brain networks.To investigate the effective connectivity between spontaneous (resting-state) brain networks in melancholia, focusing on networks underlying attention and interoception.We performed a cross-sectional, observational, resting-state functional magnetic resonance imaging study of 16 participants with melancholia, 16 with nonmelancholic depression, and 16 individuals serving as controls at a hospital-based research institute between August 30, 2010, and June 27, 2012. We identified 5 canonical resting-state networks (default mode, executive control, left and right frontoparietal attention, and bilateral anterior insula) and inferred spontaneous interactions among these networks using dynamic causal modeling.Graph theoretic measures of brain connectivity, namely, in-degree and out-degree of each network and edge connectivity, between regions composed our principal between-group contrasts.Melancholia was characterized by a pervasive disconnection involving anterior insula and attentional networks compared with participants in the control (Mann-Whitney, 189.00; z = 2.38; P = .02) and nonmelancholic depressive (Mann-Whitney, 203.00; z = 2.93; P = .004) groups. Decreased effective connectivity between the right frontoparietal and insula networks was present in participants with melancholic depression compared with those with nonmelancholic depression (χ2 = 8.13; P = .004). Reduced effective connectivity between the insula and executive networks was found in individuals with melancholia compared with healthy controls (χ2 = 8.96; P = .003).We observed reduced effective connectivity in resting-state functional magnetic resonance imaging between key networks involved in attention and interoception in melancholia. We propose that these abnormalities underlie the impoverished variety and affective quality of internally generated thought in this disorder.
We offer a scientific approach to the philosophical ‘hard problem’ of consciousness, as formulated by David Chalmers in this journal. Our treatment is based upon two recent insights concerning (1) the endogenous nature of consciousness and (2) the minimal thermodynamic conditions for being alive. We suggest that a combination of these insights specifies sufficient conditions for attributing feeling to being.
ABSTRACT Background The proportional recovery rule asserts that most stroke survivors recover a fixed proportion of lost function. Reports that the rule can be used to predict recovery, extraordinarily accurately, are rapidly accumulating. Here, we show that the rule may not be as powerful as it seems. Methods We provide a formal analysis of the relationship between baseline scores (X), outcomes (Y) and recovery (Y-X), to highlight the shortcomings of the proportional recovery rule, and illustrate those problems with simulations in which synthetic recovery data are derived from different types of recovery processes. Findings When the correlation between baseline scores and recovery is stronger than that between baselines scores and outcomes, the former can create an inflated impression of how predictable outcomes really are given baseline scores. This often happens when outcomes are less variable than baseline scores, as is common in empirical studies of recovery after stroke. Moreover, we cannot use the results of these correlations to distinguish proportional recovery from recovery which is either not consistently proportional, or not proportional at all. Interpretation Analyses relating baseline scores to subsequent change are a minefield: our formal analysis applies as consistently outside the area of stroke as it does within it. One implication of our analysis is that the proportional recovery rule is not as predictive of real recovery after stroke as recent empirical studies suggest. Another is that different analytical methods will be required to ascertain whether recovery is even proportional at all.
In our everyday lives, we are often required to follow a conversation when background noise is present ("speech-in-noise" [SPIN] perception). SPIN perception varies widely-and people who are worse at SPIN perception are also worse at fundamental auditory grouping, as assessed by figure-ground tasks. Here, we examined the cortical processes that link difficulties with SPIN perception to difficulties with figure-ground perception using functional magnetic resonance imaging. We found strong evidence that the earliest stages of the auditory cortical hierarchy (left core and belt areas) are similarly disinhibited when SPIN and figure-ground tasks are more difficult (i.e., at target-to-masker ratios corresponding to 60% rather than 90% performance)-consistent with increased cortical gain at lower levels of the auditory hierarchy. Overall, our results reveal a common neural substrate for these basic (figure-ground) and naturally relevant (SPIN) tasks-which provides a common computational basis for the link between SPIN perception and fundamental auditory grouping.
Global network dynamics over distributed brain areas emerge from the local dynamics of each brain area.Conversely, global dynamics constrain local activity such that the whole system becomes self-organizing.The implicit coupling between local and global scales induces a form of circular causality that is characteristic of complex, coupled systems that show self-organization, such as the brain.Here we present a network model based on spiking neurons at the local level and large-scale anatomic connectivity matrices at the global level.We demonstrate that this multiscale network displays endogenous or autonomous dynamics of the sort observed in resting-state studies.Our special focus here is on the genesis of itinerant (wandering) dynamics and the role of multistable attractors, which are involved in the generation of empirically known functional connectivity patterns, if the global coupling causes the dynamics to operate in the critical regime.Our results provide once again support for the hypothesis that endogenous brain activity is critical.
This paper presents a meta-theory of the usage of the free energy principle (FEP) and examines its scope in the modelling of physical systems. We consider the so-called `map-territory fallacy' and the fallacious reification of model properties. By showing that the FEP is a consistent, physics-inspired theory of inferences of inferences, we disprove the assertion that the map-territory fallacy contradicts the principled usage of the FEP. As such, we argue that deploying the map-territory fallacy to criticise the use of the FEP and Bayesian mechanics itself constitutes a fallacy: what we call the {\it map-territory fallacy fallacy}. In so doing, we emphasise a few key points: the uniqueness of the FEP as a model of particles or agents that model their environments; the restoration of convention to the FEP via its relation to the principle of constrained maximum entropy; the `Jaynes optimality' of the FEP under this relation; and finally, the way that this meta-theoretical approach to the FEP clarifies its utility and scope as a formal modelling tool. Taken together, these features make the FEP, uniquely, {\it the} ideal model of generic systems in statistical physics.
Abstract Gilead et al. raise some fascinating issues about representational substrates and structures in the predictive brain. This commentary drills down on a core theme in their arguments; namely, the structure of models that generate predictions. In particular, it highlights their factorial nature – both in terms of deep hierarchies over levels of abstraction and, crucially, time – and how this underwrites agency.
This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling.
