Dylan Nielson

National Institute of Mental Health (NIMH), National Institutes of Health | NIMH · Data Science and Sharing Team

Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically under-discussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no coll...

Depression treatment studies often focus exclusively on changes in depressive symptoms, such as low mood, anhedonia, or sleep disruption. However, incorporating other outcomes important to those experiencing depression, such as the quality of interpersonal relationships or quality of life, could improve understanding of the impacts of depression an...

One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity across the entire brain and various behavioral traits. Previous studies have shown that models trained to predict behavioral features from the individual's functional conn...

Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically under-discussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no coll...

OHBM Brainhack 2022 took place in June 2022. The first hybrid OHBM hackathon, it had an in-person component taking place in Glasgow and three hubs around the globe to improve inclusivity and fit as many timezones as possible. In the buzzing setting of the Queen Margaret Union and of the virtual platform, 23 projects were presented after development...

According to influential theories about mood, exposure to environments characterized by specific patterns of punishments and rewards could shape mood response to future stimuli. This raises the intriguing possibility that mood could be trained by exposure to controlled environments. The aim of the present study is to investigate experimental settin...

We present NiMARE (Neuroimaging Meta‑Analysis Research Environment; RRID:SCR_0173981), a Python library for neuroimaging meta‑analyses and metaanalysis‑related analyses. NiMARE is an open source, collaboratively‑developed package that implements a range of meta‑ analytic algorithms, including coordinate‑ and image‑based meta‑analyses, automated ann...

Threatening situations can evoke anger and fear. Besides hedonic motivations, anger and fear can also be modulated by instrumental factors, potentially environmental affordances. In this pre-registered study, we use a novel behavioral paradigm simulating threats via the presentation of horror movies. With this paradigm, we show that affordances in...

Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. Here we demonstrate that task and rest periods lowered participants’ mood, an effect we call ‘Mood Drift Over Tim...

The Adolescent Brain Cognitive Development dataset (ABCD) is a popular source of data for research on children and adolescents, aged 9-11 at baseline. Depression is frequently first diagnosed in adolescents, is prevalent and carries heavy burden, thus ABCD provides an attractive opportunity to research depression in adolescents. The only continuous...

The test-retest reliability of fMRI functional connectivity is a key factor in the identification of reproducible biomarkers for psychiatric illness. Low reliability limits the observable effect size of brain-behavior associations. Despite this important connection to clinical applications of fMRI, few studies have explored reliability in populatio...

Despite its omnipresence in everyday interactions and its importance for mental health, mood and its neuronal underpinnings are poorly understood. Computational models can help identify parameters affecting self-reported mood during mood induction tasks. Here, we test if computationally modeled dynamics of self-reported mood during monetary gamblin...

Anhedonia reflects a reduced ability to engage in previously pleasurable activities and has been reported in children as young as 3 years of age. It manifests early and is a strong predictor of psychiatric disease onset and progression over the course of development and into adulthood. However, little is known about its mechanistic origins, particu...

Objective To investigate whether, compared to pre-pandemic levels, depressive and anxiety symptoms in adolescents with depression increased during the pandemic. Method We utilized data from National Institute of Mental Health Characterization and Treatment of Depression (NIMH CAT-D) cohort, a longitudinal case control study that started pre-pandem...

Mood is a key factor that determines our well-being and a lot of effort goes into taming and regulating it. The role of positive and negative environmental stimuli on mood and whether they can promote mood resilience or susceptibility, remains relatively unexplored. The aim of the present study is to investigate whether mood could be trained to bec...

Background Family history of depression (FHD) is a known risk factor for the new onset of depression. However, it is unclear if FHD is clinically useful for prognosis in adolescents with current, ongoing, or past depression. This preregistered study uses a longitudinal, multi‐informant design to examine whether a child’s FHD adds information about...

Humans refer to their mood state regularly in day-to-day as well as clinical interactions. Theoretical accounts suggest that when reporting on our mood we integrate over the history of our experiences; yet, the temporal structure of this integration remains unexamined. Here we use a computational approach to quantitatively answer this question and...

Does our mood change as time passes, and is this change different in people with depression? These questions are central to affective neuroscience theory and methodology, yet they remain largely unexamined. Here we demonstrate that rest periods lowered participants' mood, an effect we call "passage-of-time dysphoria." This finding was replicated in...

The COVID-19 pandemic and its social and economic consequences have had adverse impacts on physical and mental health worldwide and exposed all segments of the population to protracted uncertainty and daily disruptions. The CoRonavIruS health and Impact Survey (CRISIS) was developed for use as an easy to implement and robust questionnaire covering...

Adolescent depression is a potentially lethal condition and a leading cause of disability for this age group. There is an urgent need for novel efficacious treatments since half of adolescents with depression fail to respond to current therapies and up to 70% of those who respond will relapse within 5 years. Repetitive transcranial magnetic stimula...

Despite its omnipresence in everyday interactions and its importance for mental health, mood and its neuronal underpinnings are poorly understood. Computational models can help identify parameters affecting self-reported mood during mood induction tasks. Here we test if computationally modelled dynamics of self-reported mood during monetary gamblin...

Objective: Suicide deaths and suicidal thoughts and behaviors are considered a public health emergency, yet their underpinnings in the brain remain elusive. The authors examined the classification accuracy of individual, environmental, and clinical characteristics, as well as multimodal brain imaging correlates, of suicidal thoughts and behaviors...

Task, resting state, and diffusion MRI data are usually acquired from subjects using echo-planar based imaging techniques. These techniques are highly susceptible to B 0 homogeneity effects that result in geometric distortions in the reconstructed images. As researchers work to link the information from these scans back to various developmental sta...

The COVID-19 pandemic and its social and economic consequences have had adverse impacts on physical and mental health worldwide and exposed all segments of the population to protracted uncertainty and daily disruptions. The CoRonavIruS health and Impact Survey (CRISIS) was developed for use as an easy to implement and robust questionnaire covering...

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses¹. The flexibility of analytical approaches is exempl...

Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinize those findings and the theoretical claims that und...

Two ongoing movements in human cognitive neuroscience have researchers shifting focus from group-level inferences to characterizing single subjects, and complementing tightly controlled tasks with rich, dynamic paradigms such as movies and stories. Yet relatively little work combines these two, perhaps because traditional analysis approaches for na...

Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinize those findings and the theoretical claims that und...

Background: Childhood suicidality is a major public health concern with poorly defined neurobiology, especially in young people. We sought to address this gap by examining multimodal brain imaging correlates of suicidality in a US population-based sample of school-aged children from the Adolescent Brain and Cognitive Development study. Methods: Unr...

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fac...

Humans refer to their own mood state regularly in day-to-day as well as in clinical interactions. Theoretical accounts suggest that when reporting on our mood we integrate over the history of our experiences; yet, the temporal structure of this integration remains unexamined. Here we use a computational approach to quantitatively answer this questi...

In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combining data from more than a hundred different sites, and also evaluated on another completely held-out...

Two ongoing movements in human cognitive neuroscience have researchers shifting focus from group-level inferences to characterizing single subjects, and complementing tightly controlled tasks with rich, dynamic paradigms such as movies and stories. Yet relatively little work combines these two, perhaps because traditional analysis approaches for na...

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...

In this paper, we describe a deep neural network for predicting FreeSurfer segmentations of structural MRI volumes, in seconds rather than hours. The network was trained and evaluated on an extremely large dataset (n = 11,148), obtained by combining data from more than a hundred sites. We also show that the prediction uncertainty of the network at...

Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated by practical, ethical, or legal concerns. However, it may be the case that derivative datasets or predictive models developed within individual sites can be shared and com...

The human posteromedial cortex, which includes core regions of the default mode network (DMN), is thought to play an important role in episodic memory. However, the nature and functional role of representations in these brain regions remain unspecified. Nine participants (all female) wore smartphone devices to record episodes from their daily lives...

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...

Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated by practical, ethical, or legal concerns. However, it may be the case that derivative datasets or predictive models developed within individual sites can be shared and com...

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...

The study objective was to evaluate the safety and efficacy of deep brain stimulation (DBS) at the ventral capsule/ventral striatum (VC/VS) region to specifically modulate frontal lobe behavioral and cognitive networks as a novel treatment approach for Alzheimer's disease (AD) patients. This is a non-randomized phase I prospective open label interv...

Synopsis The MRIQC Web-API is a resource for scientists to train new automatic quality classifiers. The MRIQC Web-API has collected more than 30K sets of image quality measures automatically extracted from BOLD and T1-weighted scans using MRIQC. MRIQC is an automated MRI Quality Control tool, and here we present an extension to crowdsource these qu...

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 repr...

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 req...

The disparity between an individual’s brain age and one’s chronological age can be an indicator for various neurological disorders throughout one’s life. A previous brain-age prediction study investigated the ability of multimodal brain imaging data to predict age, relying on anatomical and functional brain data to build a machine learning model wi...

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 req...

Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been show...

Background: Severe traumatic brain injury (TBI) damages the frontal lobes and connecting networks, which impairs executive functions, including the ability to self-regulate. Despite significant disabling effects, there are few treatment options in the chronic phase after injury. Objective: To investigate the safety and potential effectiveness of...

Introduction: The neurophysiological basis of pain relief due to spinal cord stimulation (SCS) and the related cortical processing of sensory information are not completely understood. The aim of this study was to use resting state functional magnetic resonance imaging (rs-fMRI) to detect changes in cortical networks and cortical processing relate...

Significance The rodent hippocampus contains neurons that code for space on the scale of meters, a discovery that was recently awarded a Nobel Prize. However, it remains unclear whether humans harness similar representations for memory at the scale of their lives. Our results reveal that the human hippocampus represents the spatial and temporal loc...

Transcranial magnetic stimulation (TMS) has generated extensive interest within the traumatic brain injury (TBI) rehabilitation community, but little work has been done with repetitive protocols, which can produce prolonged changes in behavior. This is partly due to concerns about the safety of repetitive TMS (rTMS) in traumatic brain injury subjec...