The study of dreams represents a crucial intersection between philosophical, psychological, neuroscientific, and clinical interests. Importantly, one of the main sources of insight into dreaming activity are the (oral or written) reports provided by dreamers upon awakening from their sleep. Classically, two main types of information are commonly extracted from dream reports: structural and semantic, content-related information. Extracted structural information is typically limited to the simple count of words or sentences in a report. Instead, content analysis usually relies on quantitative scores assigned by two or more (blind) human operators through the use of predefined coding systems. Within this review, we will show that methods borrowed from the field of linguistic analysis, such as graph analysis, dictionary-based content analysis, and distributional semantics approaches, could be used to complement and, in many cases, replace classical measures and scales for the quantitative structural and semantic assessment of dream reports. Importantly, these methods allow the direct (operator-independent) extraction of quantitative information from language data, hence enabling a fully objective and reproducible analysis of conscious experiences occurring during human sleep. Most importantly, these approaches can be partially or fully automatized and may thus be easily applied to the analysis of large datasets.

Magneto/electroencephalography (M/EEG) studies of dreaming are an essential paradigm in the investigation of neurocognitive processes of human consciousness during sleep, but they are limited by the number of observations that can be collected per study. Dream research also involves substantial methodological and conceptual variability, which poses problems for the integration of results. To address these issues, here we present the DREAM database—an expanding collection of standardized datasets on human sleep M/EEG combined with dream report data—with an initial release comprising 20 datasets, 505 participants, and 2643 awakenings. Each awakening consists, at minimum, of sleep M/EEG ( ≥ 20 s, ≥100 Hz, ≥2 electrodes) up to the time of waking and a standardized dream report classification of the subject’s experience during sleep. We observed that reports of conscious experiences can be predicted with objective features extracted from EEG recordings in both Rapid Eye Movement (REM) and non-REM (NREM) sleep. We also provide several examples of analyses, showcasing the database’s high potential in paving the way for new research questions at a scale beyond the capacity of any single research group.

Evidence suggests that (almost) everyone dreams during their sleep and may actually do so for a large part of the night. Yet, dream recall shows large interindividual variability. Understanding the factors that influence dream recall is crucial for advancing our knowledge regarding dreams’ origin, significance, and functions. Here, we tackled this issue by prospectively collecting dream reports along with demographic information and psychometric, cognitive, actigraphic, and electroencephalographic measures in 217 healthy adults (18–70 y, 116 female participants, 101 male participants). We found that attitude towards dreaming, proneness to mind wandering, and sleep patterns are associated with the probability of reporting a dream upon morning awakening. The likelihood of recalling dream content was predicted by age and vulnerability to interference. Moreover, dream recall appeared to be influenced by night-by-night changes in sleep patterns and showed seasonal fluctuations. Our results provide an account for previous observations regarding inter- and intra-individual variability in morning dream recall.

Cystic Fibrosis (CF) is a genetic disease inherited by an autosomal recessive mechanism and characterized by a progressive and severe multi-organ failure. Mutations in Cystic Fibrosis Conductance Regulator (CFTR) protein cause duct obstructions from dense mucus secretions and chronic inflammation related to organ damage. The progression of the disease is characterized by a decline of lung function associated with metabolic disorders and malnutrition, musculoskeletal disorders and thoracic deformities, leading to a progressive decrement of the individual’s quality of life. The World Health Organization (WHO) qualifies Physical Activity (PA) as a structured activity produced by skeletal muscles’ movements that requires energy consumption. In the last decade, the number of studies on PA increased considerably, including those investigating the effects of exercise on cognitive and brain health and mental performance. PA is recommended in CF management guidelines, since it improves clinic outcomes, such as peripheral neuropathy, oxygen uptake peak, bone health, glycemic control and respiratory functions. Several studies regarding the positive effects of exercise in patients with Cystic Fibrosis were carried out, but the link between the effects of exercise and cognitive and brain health in CF remains unclear. Animal models showed that exercise might improve learning and memory through structural changes of brain architecture, and such a causal relationship can also be described in humans. Indeed, both morphological and environmental factors seem to be involved in exercise-induced neural plasticity. An increase of gray matter volume in specific areas is detectable as a consequence of regular training in humans. Neurobiological processes associated with brain function improvements include biochemical modifications, such as neuromodulator or neurohormone release, brain-derived neurotrophic factor (BDNF) production and synaptic activity changes. From a functional point of view, PA also seems to be an environmental factor enhancing cognitive abilities, such as executive functions, memory and processing speed. This review describes the current state of research regarding the impacts of physical activity and exercise on cognitive functions, introducing a possible novel field of research for optimizing the management of Cystic Fibrosis.

Lorenzo Bertolini, Valentina Elce, Adriana Michalak, Hanna-Sophia Widhoelzl, Giulio Bernardi, Julie Weeds. Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024). 2024.

Magneto/electroencephalography (M/EEG) studies of dreaming are an essential paradigm in the investigation of neurocognitive processes of human consciousness during sleep, but they are limited by the number of observations that can be collected per study. Dream research also involves substantial methodological and conceptual variability, which poses problems for the integration of results. To address these issues, here we present the DREAM database—an expanding collection of standardized datasets on human sleep M/EEG combined with dream report data—with an initial release comprising 20 datasets, 561 participants, and 2649 awakenings. Each awakening consists, at minimum, of sleep M/EEG(≥20 s, ≥100 Hz, ≥2 electrodes) up to the time of waking and a standardized dream report classification of the subject’s experience during sleep. We also provide several examples of analyses, showcasing the database’s high potential in paving the way for new research questions at a scale that any single research group cannot achieve. We notably show that reports of conscious experiences can be predicted with objective features extracted from EEG recordings in both Rapid Eye Movement (REM) and non-REM (NREM) sleep. We finally will provide useful criteria for methodological choices in future dream laboratory research and the expansion of this database.