Dreams and the daydream retrieval hypothesis

© 2020 American Psychological Association. Dreams and daydreams are as beguiling as they are intangible. Both share many features, from neurobiology to the sensed experience. Nevertheless, the specific narrative relationship between both, if any, remains uncertain. Theories of dream origins are many: from the psychodynamic royal road, to biological theories including Hebbian-based memory consolidation and a unified quantum brain theory that extends to waking and dreaming alike. Both the ephemeral nature of dreams, and an inability to simultaneously study their content and biology, renders them difficult to research from a conventional biomedical perspective. This leaves agreement on the fundamental properties of dreams as ambiguous, and even the state of consciousness enjoyed during sleep is contested. What is known is that the qualia and neurophysiological signature of dreams and daydreaming share many features. We propose further, from a subjective experientialist position, that dream content is specifically derived from daydreaming or mindful wandering (subserved by the default mode network). If substantiated, this concept offers a new insight into the origin of dreams

Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks

We investigate the relationship of resting-state fMRI functional connectivity estimated over long periods of time with time-varying functional connectivity estimated over shorter time intervals. We show that using Pearson's correlation to estimate functional connectivity implies that the range of fluctuations of functional connections over short time scales is subject to statistical constraints imposed by their connectivity strength over longer scales. We present a method for estimating time-varying functional connectivity that is designed to mitigate this issue and allows us to identify episodes where functional connections are unexpectedly strong or weak. We apply this method to data recorded from $N=80$ participants, and show that the number of unexpectedly strong/weak connections fluctuates over time, and that these variations coincide with intermittent periods of high and low modularity in time-varying functional connectivity. We also find that during periods of relative quiescence regions associated with default mode network tend to join communities with attentional, control, and primary sensory systems. In contrast, during periods where many connections are unexpectedly strong/weak, default mode regions dissociate and form distinct modules. Finally, we go on to show that, while all functional connections can at times manifest stronger (more positively correlated) or weaker (more negatively correlated) than expected, a small number of connections, mostly within the visual and somatomotor networks, do so a disproportional number of times. Our statistical approach allows the detection of functional connections that fluctuate more or less than expected based on their long-time averages and may be of use in future studies characterizing the spatio-temporal patterns of time-varying functional connectivityComment: 47 Pages, 8 Figures, 4 Supplementary Figure

Cognitive behavioral training reverses the effect of pain exposure on brain-network activity

Repeated sensory exposures shape the brain's function and its responses to environmental stimuli. An important clinical and scientific question is how exposure to pain affects brain network activity and whether that activity is modifiable with training. We sought to determine whether repeated pain exposure would impact brain-network activity and whether these effects can be reversed by cognitive behavioral training (CBT). Healthy subjects underwent 8 experimental sessions on separate days where they received painful thermal stimuli. They were randomly assigned to groups receiving either CBT (Regulate group, n=17) or a non-pain-focused treatment (Control group, n=13). Before and after these sessions, participants underwent functional MRI (fMRI) during painful stimulation and at rest. The effect of repeated pain over time in the Control group was a decrease in the neurotypical pain-evoked default mode network (DMN) deactivation. The Regulate group did not show these DMN effects but rather had decreased deactivation of the right ventrolateral prefrontal cortex (R vlPFC) of the executive control network. In the Regulate group, reduced pain-evoked DMN deactivation was associated with greater individual reduction in pain intensity and unpleasantness over time. Finally, the Regulate group showed enhanced resting functional connectivity between areas of the DMN and executive control network over time, compared to the Control group. Our study demonstrates that trainable cognitive states can alter the effect of repeated sensory exposure on the brain. The findings point to the potential utility of cognitive training to prevent changes in brain network connectivity that occur with repeated pain experience

Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders

Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate 15 (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demon- 20 strate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architec- 25 ture is modulated by local blood oxygen level-dependent activity and a-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. 30 Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be 35 potentially useful as a predictor for learning and neural rehabilitation

Psychological resilience is correlated with dynamic changes in functional connectivity within the default mode network during a cognitive task

Resilience is a dynamic process that enables organisms to cope with demanding environments. Resting-state functional MRI (fMRI) studies have demonstrated a negative correlation between resilience and functional connectivities (FCs) within the default mode network (DMN). Considering the on-demand recruitment process of resilience, dynamic changes in FCs during cognitive load increases may reflect essential aspects of resilience. We compared DMN FC changes in resting and task states and their association with resilience. Eighty-nine healthy volunteers completed the Connor–Davidson Resilience Scale (CD-RISC) and an fMRI with an auditory oddball task. The fMRI time series was divided into resting and task periods. We focused on FC changes between the latter half of the resting period and the former half of the task phase (switching), and between the former and latter half of the task phase (sustaining). FCs within the ventral DMN significantly increased during “switching” and decreased during “sustaining”. For FCs between the retrosplenial/posterior cingulate and the parahippocampal cortex, increased FC during switching was negatively correlated with CD-RISC scores. In individuals with higher resilience, ventral DMN connectivities were more stable and homeostatic in the face of cognitive demand. The dynamic profile of DMN FCs may represent a novel biomarker of resilience

Structural Equation Models to estimate Dynamic Effective Connectivity Networks in Resting fMRI. A comparison between individuals with Down syndrome and controls

Emerging evidence suggests that an effective or functional connectivity network does not use a static process over time but incorporates dynamic connectivity that shows changes in neuronal activity patterns. Using structural equation models (SEMs), we estimated a dynamic component of the effective network through the effects (recursive and nonrecursive) between regions of interest (ROIs), taking into account the lag 1 effect. The aim of the paper was to find the best structural equation model (SEM) to represent dynamic effective connectivity in people with Down syndrome (DS) in comparison with healthy controls. Twenty-two people with DS were registered in a functional magnetic resonance imaging (fMRI) resting-state paradigm for a period of six minutes. In addition, 22 controls, matched by age and sex, were analyzed with the same statistical approach. In both groups, we found the best global model, which included 6 ROIs within the default mode network (DMN). Connectivity patterns appeared to be different in both groups, and networks in people with DS showed more complexity and had more significant effects than networks in control participants. However, both groups had synchronous and dynamic effects associated with ROIs 3 and 4 related to the upper parietal areas in both brain hemispheres as axes of association and functional integration. It is evident that the correct classification of these groups, especially in cognitive competence, is a good initial step to propose a biomarker in network complexity studies

Understanding contemplative practices from the perspective of dual-process theories

After briefly reviewing the history of dual-process theories in cognitive and social psychology, this chapter explores implications of the dual-process perspective for Buddhist contemplative practices, including mindfulness. On the one hand, the impulsive and habitual processes in dual-process theories offer a natural account of the phenomena that contemplative practices address (e.g., craving, negative emotion, self-interest, mind wandering). On the other hand, the regulatory and reflective processes in dual-process theories offer insightful perspective into how contemplative practices modulate these phenomena. Additionally, dual-process theories offer useful accounts of the constant interplay between habitual and regulatory processing in everyday life, and how contemplative practices establish healthy new cognitive, affective, and behavioral habits that replace less healthy well-entrenched ones. In turn, contemplative practices—especially the collection of Buddhist practices known as the Eight- Fold Path—provide insight into the nature of habitual processing, and offer provocative ideas for developing interventions to change it