Night-time activity forecast by season and weather in a longitudinal design:natural light effects on three years' rest-activity cycles in nursing home residents with dementia

Backround: Night-time agitation is a frequent symptom of dementia. It often causes nursing home admission and has been linked to circadian rhythm disturbances. A positive influence of light interventions on night-time agitation was shown in several studies. The aim of our study was to investigate whether there is a long-term association between regional weather data (as indicator for daylight availability) and 24-hour variations of motor activity. Methods: Motor activity of 20 elderly nursing home residents living with dementia was analyzed using recordings of continuously worn wrist activity monitors over a three-year period. The average recording duration was 479 206 days per participant (mean SD). Regional cloud amount and day length data from the local weather station (latitude: 52 degrees 56N) were included in the analysis to investigate their effects on several activity variables. Results: Nocturnal rest, here defined as the five consecutive hours with the least motor activity during 24 hours (L5), was the most predictable activity variable per participant. There was a significant interaction of night-time activity with day length and cloud amount (F-1,F-1174 = 4.39; p = 0.036). Night-time activity was higher on cloudy short days than on clear short days (p = 0.007), and it was also higher on cloudy short days than on cloudy long days (p = 0.032). Conclusions: The need for sufficient zeitgeber (time cue) strength during winter time, especially when days are short and skies are cloudy, is crucial for elderly people living with dementia. Activity forecast by season and weather might be a valuable approach to anticipate adequately complementary use of electrical light and thereby foster lower night-time activity

EEG-BIDS, an extension to the brain imaging data structure for electroencephalography

The Brain Imaging Data Structure (BIDS) project is a rapidly evolving effort in the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. Here we present an extension to BIDS for electroencephalography (EEG) data, EEG-BIDS, along with tools and references to a series of public EEG datasets organized using this new standard

qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging

Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI

PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets, serving not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data, also known as PET-BIDS, and share several open-access datasets curated following PET-BIDS along with tools for conversion, validation and analysis of PET-BIDS datasets

PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets. It serves not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data (PET-BIDS). We describe the PET-BIDS standard in detail and share several open-access datasets curated following PET-BIDS. Additionally, we highlight several tools which are already available for converting, validating and analyzing PET-BIDS datasets.Competing Interest StatementThe authors have declared no competing interest

iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology

The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS

The past, present, and future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS

Researching Neural Correlates of Human Decisions from Experience using Electroencephalography

People routinely collect information about choice alternatives before deciding between them. For example before buying a new car, a customer may test drive several alternatives before making a choice. Such decisions from experience (DfE) are characterized by active or passive (i.e., through observation) sampling processes that may involve categorizing and evaluating each sample, and finally integrating all available samples into a choice. After a short introduction (chapter 1), in part one of this dissertation I present two studies from the domain of decisions from experience, addressing separate but closely related questions. The first study (chapter 2) deals with the question of how control over the sampling process impacts on the final choice. In how far do full, partial, and no control over sampling differ in terms of their neurocognitive processes, and are some levels of control more beneficial than others? The second study (chapter 3) focuses on processes related to evaluating individual samples in a sequence. How are individual samples weighted before they are integrated into a choice? For both studies, I employ Electroencephalography (EEG) recordings, relating behavior and brain measures. In part two of this dissertation, I then shift from decisions from experience to more methodological issues related to EEG research and present two further studies. The third study of this dissertation (chapter 4) addresses an emerging hardware problem: The parallel port, the previous gold standard for sending event markers, is slowly disappearing from commercially available computers. What are potential alternatives, how reliable and performant are they, and how can they be built and operated on a budget? In the fourth study (chapter 5), my co-authors and I present an extension to an existing and commonly used data standard (BIDS) for EEG data, with the aim of improving the foundations of good research data management and reproducibility of scientific results. Finally, I conclude with a chapter (chapter 6) to briefly summarize the other chapters and delineate open questions and future directions.Menschen sammeln routinemäßig Informationen über Alternativen, bevor sie sich zwischen ihnen entscheiden. Beispielsweise kann ein Kunde vor dem Kauf eines neuen Autos mehrere Alternativen testen, bevor er eine Entscheidung trifft. Solche Verfahren sind durch aktive oder passive (d.h. durch Beobachtung) Stichprobenprozesse gekennzeichnet, die eine Kategorisierung und Bewertung der einzelnen Proben und schließlich die Integration aller verfügbaren Proben in eine Entscheidung umfassen können. Nach einer kurzen Einführung (chapter 1), stelle ich im ersten Teil dieser Dissertation zwei Studien aus dem Bereich "Entscheidungen nach Erfahrung" vor, die sich mit unterschiedlichen, aber eng verwandten Fragen befassen. Die erste Studie (chapter 2) beschäftigt sich mit der Frage, wie sich die Kontrolle über den Stichprobenprozess auf die endgültige Entscheidung auswirkt. Inwieweit unterscheiden sich vollständige, teilweise und keine Kontrolle über die Probenahme in Bezug auf ihre neurokognitiven Prozesse, und sind einige Stufen der Kontrolle vorteilhafter als andere? Die zweite Studie (chapter 3) konzentriert sich auf Prozesse im Zusammenhang mit der Bewertung einzelner Proben in einer Sequenz von Proben. Wie werden die einzelnen Proben gewichtet bevor sie in eine Entscheidung integriert werden? Für beide Studien verwende ich Elektroenzephalographie (EEG)-Aufnahmen, und setze Verhaltens- und Gehirnmessungen in Beziehung. Im zweiten Teil dieser Dissertation wende ich mich dann von "Entscheidungen nach Erfahrung" zu eher methodischen Fragen im Zusammenhang mit EEG Forschung und stelle zwei weitere Studien vor. Die dritte Studie dieser Dissertation (chapter 4) befasst sich mit einem neuen Hardwareproblem: Die parallele Schnittstelle, der bisherige Goldstandard zum Senden von Ereignismarkern, verschwindet langsam von handelsüblich erwerbbaren Computern. Was sind mögliche Alternativen, wie zuverlässig und leistungsfähig sind sie, und wie können sie mit einem geringen Budget gebaut und betrieben werden? In der vierten Studie (chapter 5) stellen meine Mitautoren und ich eine Erweiterung eines bestehenden und häufig verwendeten Datenstandards (BIDS) für EEG-Daten vor. Dabei verfolgen wir das Ziel, die Grundlagen für ein gutes Forschungsdatenmanagement und die Reproduzierbarkeit wissenschaftlicher Ergebnisse zu verbessern. Zum Abschluss stelle ich ein Kapitel vor (chapter 6), das die anderen Kapitel kurz zusammenfasst und offene Fragen und zukünftige Richtungen beschreibt