Critical perspectives on causality and inference in brain networks: Allusions, illusions, solutions?: Comment on: "Foundational perspectives on causality in large-scale brain networks" by M. Mannino and S.L. Bressler

Depth and hierarchies in the predictive brain: From reaction to action

The human brain is a prediction device, a view widely accepted in neuroscience. Prediction is a rational and efficient response that relies on the brain's ability to create and employ generative models to optimize actions over unpredictable time horizons. We argue that extant predictive frameworks while compelling, have not explicitly accounted for the following: (a) The brain's generative models must incorporate predictive depth (i.e., rely on degrees of abstraction to enable predictions over different time horizons); (b) The brain's implementation scheme to account for varying predictive depth relies on dynamic predictive hierarchies formed using the brain's functional networks. We show that these hierarchies incorporate the ascending processes (driven by reaction), and the descending processes (related to prediction), eventually driving action. Because they are dynamically formed, predictive hierarchies allow the brain to address predictive challenges in virtually any domain. By way of application, we explain how this framework can be applied to heretofore poorly understood processes of human behavioral thermoregulation. Although mammalian thermoregulation has been closely tied to deep brain structures engaged in autonomic control such as the hypothalamus, this narrow conception does not translate well to humans. In addition to profound differences in evolutionary history, the human brain is bestowed with substantially increased functional complexity (that itself emerged from evolutionary differences). We argue that behavioral thermoregulation in humans is possible because, (a) ascending signals shaped by homeostatic sub-networks, interject with (b) descending signals related to prediction (implemented in interoceptive and executive sub-networks) and action (implemented in executive sub-networks). These sub-networks cumulatively form a predictive hierarchy for human thermoregulation, potentiating a range of viable responses to known and unknown thermoregulatory challenges. We suggest that our proposed extensions to the predictive framework provide a set of generalizable principles that can further illuminate the many facets of the predictive brain. This article is categorized under: Neuroscience > Behavior Philosophy > Action Psychology > Prediction.

Cortical-hippocampal functional connectivity during covert consolidation sub-serves associative learning: Evidence for an active "rest" state

We studied modulation of undirected functional connectivity (uFC) in cortical-hippocampal sub-networks during associative learning. Nineteen healthy individuals were studied (fMRI acquired on a Siemens Verio 3T), and uFC was studied between nodes in a network of regions identified by standard activation models based on bivariate correlational analyses of time series data. The paradigm alternated between Memory Encoding, Rest and Retrieval. "Rest" intervals promoted covert consolidation. Over the task, performance was broadly separable into linear (Early) and asymptomatic (Late) regimes, with late performance reflecting successful memory consolidation. Significant modulation of uFC was observed during periods of covert consolidation. The sub-networks which were modulated constituted connections between frontal regions such as the dorsal prefrontal cortex (dPFC) and dorsal anterior cingulate cortex (dACC), the medial temporal lobe (hippocampus, HPC), the superior parietal cortex (SPC) and the fusiform gyrus (FG). uFC patterns were dynamic in that sub-networks modulated during Early learning (dACC ↔ SPC, dACC ↔ FG, dPFC ↔ HPC) were not identical to those modulated during Late learning (dACC ↔ HPC, dPFC ↔ FG, FG ↔ SPC). Covert consolidation exerts systematic effects, and these results add to emerging evidence for the constructive role of the brain's "resting state" in potentiating action.

Hyper-modulation of brain networks by the amygdala among women with Borderline Personality Disorder: Network signatures of affective interference during cognitive processing

Emotion dysregulation is a core characteristic of patients with Borderline Personality Disorder (BPD), and is often attributed to an imbalance in fronto-limbic network function. Hyperarousal of amygdala, especially in response to negative affective stimuli, results in affective interference with cognitive processing of executive functions. Clinical consequences include the impulsive-aggression, suicidal and self-injurious behaviors which characterize BPD. Dysfunctional interactions between amygdala and its network targets have not been well characterized during cognitive task performance. Using psychophysiological interaction analysis (PPI), we mapped network profiles of amygdala interaction with key regulatory regions during a Go No-Go task, modified to use negative, positive and neutral Ekman faces as targets. Fifty-six female subjects, 31 BPD and 25 healthy controls (HC), completed the affectively valenced Go No-Go task during fMRI scanning. In the negative affective condition, the amygdala exerted greater modulation of its targets in BPD compared to HC subjects in Rt. OFC, Rt. dACC, Rt. Parietal cortex, Rt. Basal Ganglia, and Rt. dlPFC. Across the spectrum of affective contrasts, hypermodulation in BPD subjects observed the following ordering: Negative > Neutral > Positive contrast. The amygdala seed exerted modulatory effects on specific target regions important in processing response inhibition and motor impulsiveness. The vulnerability of BPD subjects to affective interference with impulse control may be due to specific network dysfunction related to amygdala hyper-arousal and its effects on prefrontal regulatory regions such as the OFC and dACC.

In vivo correlates of thermoregulatory defense in humans: Temporal course of sub-cortical and cortical responses assessed with fMRI

Extensive studies in rodents have established the role of neural pathways that are activated during thermoregulation. However, few studies have been conducted in humans to assess the complex, hierarchically organized thermoregulatory network in the CNS that maintains thermal homeostasis, especially as it pertains to cold exposure. To study the human thermoregulatory network during whole body cold exposure, we have used functional MRI to characterize changes in the BOLD signal within the constituents of the thermoregulatory network in 20 young adult controls during non-noxious cooling and rewarming of the skin by a water-perfused body suit. Our results indicate significant decreases of BOLD signal during innocuous whole body cooling stimuli in the midbrain, the right anterior insula, the right anterior cingulate, and the right inferior parietal lobe. Whereas brain activation in these areas decreased during cold exposure, brain activation increased significantly in the bilateral orbitofrontal cortex during this period. The BOLD signal time series derived from significant activation sites in the orbitofrontal cortex showed opposed phase to those observed in the other brain regions, suggesting complementary processing mechanisms during mild hypothermia. The significance of our findings lies in the recognition that whole body cooling evokes a response in a hierarchically organized thermoregulatory network that distinguishes between cold and warm stimuli. This network seems to generate a highly resolved interoceptive representation of the body's condition that provides input to the orbitofrontal cortex, where higher-order integration takes place and invests internal states with emotional significance that motivate behavior. Hum Brain Mapp 37:3188-3202, 2016. © 2016 Wiley Periodicals, Inc.

Inferring the Dysconnection Syndrome in Schizophrenia: Interpretational Considerations on Methods for the Network Analyses of fMRI Data

Schizophrenia has long been considered one of the most intractable psychiatric conditions. Its etiology is likely polygenic, and its symptoms are hypothesized to result from complex aberrations in network-level neuronal activity. While easily identifiable by psychiatrists based on clear behavioral signs, the biological substrate of the disease remains poorly understood. Here, we discuss current trends and key concepts in the theoretical framework surrounding schizophrenia and critically discuss network approaches applied to neuroimaging data that can illuminate the correlates of the illness. We first consider a theoretical framework encompassing basic principles of brain function ranging from neural units toward perspectives of network function. Next, we outline the strengths and limitations of several fMRI-based analytic methodologies for assessing in vivo brain network function, including undirected and directed functional connectivity and effective connectivity. The underlying assumptions of each approach for modeling fMRI data are treated in some quantitative detail, allowing for assessment of the utility of each for generating inferences about brain networks relevant to schizophrenia. fMRI and the analyses of fMRI signals provide a limited, yet vibrant platform from which to test specific hypotheses about brain network dysfunction in schizophrenia. Carefully considered and applied connectivity measures have the power to illuminate loss or change of function at the network level, thus providing insight into the underlying neurobiology which gives rise to the emergent symptoms seen in the altered cognition and behavior of schizophrenia patients.