Biology subjects

Nielson, D. M.

Publications and source records attributed to Nielson, D. M..

4 recordsLinked to original sources

bioRxiv2018

Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines

The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. The visual assessment of every imaging scan is a necessary quality control step, yet arduous and time-consuming. A sizeable body of evidence shows that images of low quality are a source of variability that may be comparable to the effect size under study. We present the MRIQC Web-API, an open crowdsourced database that collects image quality metrics extracted from MR images and corresponding manual assessments by experts. The database is rapidly growing, and currently contains over 100,000 records of image quality metrics of functional and anatomical MRIs of the human brain, and over 200 expert ratings. The resource is designed for researchers to share image quality metrics and annotations that can readily be reused in training human experts and machine learning algorithms. The ultimate goal of the database is to allow the development of fully automated quality control tools that outperform expert ratings in identifying subpar images.

bioengineering↗

bioRxiv2018

Detecting and harmonizing scanner differences in the ABCD study - annual release 1.0

In order to obtain the sample sizes needed for robustly reproducible effects, it is often necessary to acquire data at multiple sites using different MRI scanners. This poses a challenge for investigators to account for the variance due to scanner, as balanced sampling is often not an option. Similarly, longitudinal studies must deal with known and unknown changes to scanner hardware and software over time. In this manuscript, we have explored scanner-related differences in the dataset recently released by the Adolescent Brain Cognitive Development (ABCD) project, a multi-site, longitudinal study of children age 9-10. We demonstrate that scanner manufacturer, model, as well as the individual scanner itself, are detectable in the resting and task-based fMRI results of the ABCD dataset. We further demonstrate that these differences can be harmonized using an empirical Bayes approach known as ComBat. We argue that accounting for scanner variance, including even minor differences in scanner hardware or software, is crucial for any analysis.

neuroscience↗

bioRxiv2017

The experience of vivid autobiographical reminiscence is supported by personal semantic representations in the precuneus

Recent studies have suggested that the human posteromedial cortex (PMC), which includes core regions of the default mode network (DMN), plays an important role in episodic memory. Whereas various roles relating to self-relevant processing and memory retrieval have been attributed to different subsystems within this broad network, the nature of representations and the functional roles they support in these brain regions remain unspecified. Here, we describe the whole-brain networks that represent subjective, self-relevant aspects of real-world events during autobiographical recollection. Nine participants wore a device to record images from their lives for a period of two to four weeks (lifelogging phase) and indicated the personally-salient attributes (i.e., personal semantics) of each episode by choosing multiple content tags. Two to four weeks after the lifelogging phase, participants relived their experiences in an fMRI scanner cued by images chosen from their own lives. Representational Similarity Analysis revealed a broad network, including parts of the DMN, that represented personal semantics during autobiographical reminiscence. Furthermore, within this network, the right precuneus represented personally relevant content during vivid recollection but not during non-vivid recollection. The precuneus is a hub within the DMN and has been implicated in metacognitive ability for memory retrieval. Our results suggest a more specific mechanism underlying the phenomenology of vivid reminiscence, supported by personal semantic representations in the precuneus.

neuroscience↗

bioRxiv2017

MELD: Mixed Effects for Large Datasets

Mixed effects models provide significant advantages in sensitivity and flexibility over typical statistical approaches to neural data analysis, but mass univariate application of mixed effects models to large neural datasets is computationally intensive. Threshold free cluster enhancement also provides a significant increase in sensitivity, but requires computationally-intensive permutation-based significance testing. Not surprisingly, the combination of mixed effects models with threshold free cluster enhancement and nonparametric permutation-based significance testing is currently completely impractical. With mixed effects for large datasets (MELD) we circumvent this impasse by means of a singular value decomposition to reduce the dimensionality of neural data while maximizing signal. Singular value decompositions become unstable when there are large numbers of noise features, so we precede it with a bootstrap-based feature selection step employing threshold free cluster enhancement to identify stable features across subjects. By projecting the dependent data into the reduced space of the singular value decomposition we gain the power of a multivariate approach and we can greatly reduce the number of mixed effects models that need to be run, making it feasible to use permutation testing to determine feature level significance. Due to these innovations, MELD is much faster than an element-wise mixed effects analysis, and on simulated data MELD was more sensitive than standard techniques, such as element-wise t-tests combined with threshold-free cluster enhancement. When evaluated on an EEG dataset, MELD identified more significant features than the t-tests with threshold free cluster enhancement in a comparable amount of time.

neuroscience↗