EEG-representational geometries and psychometric distortions in approximate numerical judgment

When judging the average value of sample stimuli (e.g., numbers) people tend to either over- or underweight extreme sample values, depending on task context. In a context of overweighting, recent work has shown that extreme sample values were overly represented also in neural signals, in terms of an anti-compressed geometry of number samples in multivariate electroencephalography (EEG) patterns. Here, we asked whether neural representational geometries may also reflect a relative underweighting of extreme values (i.e., compression) which has been observed behaviorally in a great variety of tasks. We used a simple experimental manipulation (instructions to average a single-stream or to compare dual-streams of samples) to induce compression or anti-compression in behavior when participants judged rapid number sequences. Model-based representational similarity analysis (RSA) replicated the previous finding of neural anti-compression in the dual-stream task, but failed to provide evidence for neural compression in the single-stream task, despite the evidence for compression in behavior. Instead, the results indicated enhanced neural processing of extreme values in either task, regardless of whether extremes were over- or underweighted in subsequent behavioral choice. We further observed more general differences in the neural representation of the sample information between the two tasks. Together, our results indicate a mismatch between sample-level EEG geometries and behavior, which raises new questions about the origin of common psychometric distortions, such as diminishing sensitivity for larger values

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

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

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

CDK-dependent phosphorylation of PHD1 on serine 130 alters its substrate preference in cells

PHD1 (also known as EGLN2) belongs to a family of prolyl hydroxylases (PHDs) that are involved in the control of the cellular response to hypoxia. PHD1 is also able to regulate mitotic progression through the regulation of the crucial centrosomal protein Cep192, establishing a link between the oxygen-sensing and the cell cycle machinery. Here, we demonstrate that PHD1 is phosphorylated by CDK2, CDK4 and CDK6 at S130. This phosphorylation fluctuates with the cell cycle and can be induced through oncogenic activation. Functionally, PHD1 phosphorylation leads to increased induction of hypoxia-inducible factor (HIF) protein levels and activity during hypoxia. PHD1 phosphorylation does not alter its intrinsic enzymatic activity, but instead decreases the interaction between PHD1 and HIF1α. Interestingly, although phosphorylation of PHD1 at S130 lowers its activity towards HIF1α, this modification increases the activity of PHD1 towards Cep192. These results establish a mechanism by which cell cycle mediators, such as CDKs, temporally control the activity of PHD1, directly altering the regulation of HIF1α and Cep192

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