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IntroductionAdverse childhood experiences (ACEs) are linked to higher rates of psychiatric disorders in adults. Previous neuroimaging studies with small samples have shown associations between ACEs and alterations in brain volume, connectivity, and blood flow. However, no study has explored these associations in a large clinical population to identify brain regions that may mediate the relationship between ACEs and psychiatric diagnoses. This study aims to evaluate how patient-reported ACEs are associated with brain function in adults, across diagnoses.MethodsWe analyzed 7,275 adults using HMPAO SPECT scans at rest and during a continuous performance task (CPT). We assessed the impact of ACEs on brain function across psychiatric diagnoses and performed mediation analyses where brain functional regions of interest acted as mediators between patient-reported ACEs and specific psychiatric diagnoses. We further evaluated the risk of being diagnosed with specific classes of mental illnesses as a function of increasing ACEs and identified which specific ACE questions were statistically related to each diagnosis in this cohort.ResultsIncreased ACEs were associated with higher activity in cognitive control and default mode networks and decreased activity in the dorsal striatum and cerebellum. Higher ACEs increased the risk of anxiety-related disorders, substance abuse, and depression. Several brain regions were identified as potential mediators between ACEs and adult psychiatric diagnoses.DiscussionThis study, utilizing a large clinical cohort, provides new insights into the neurobiological mechanisms linking ACEs to adult psychiatric conditions. The findings suggest that specific brain regions mediate the effects of ACEs on the risk of developing mental health disorders, highlighting potential targets for therapeutic interventions.

Abstract Older adults may harbor large amounts of amyloid-β (Aβ) pathology, yet still perform at age-normal levels on memory assessments. We tested whether functional brain networks confer resilience or compensatory mechanisms to support memory in the face of Aβ pathology. Sixty-five cognitively normal older adults received high-resolution resting state fMRI to assess functional networks, 18F-florbetapir-PET to measure Aβ, and a memory assessment. We characterized functional networks with graph metrics of local efficiency (information transfer), modularity (specialization of functional modules), and small worldness (balance of integration and segregation). There was no difference in functional network measures between older adults with high Aβ (Aβ+) compared to those with no/low Aβ (Aβ−). However, in Aβ+ older adults, increased local efficiency, modularity, and small worldness were associated with better memory performance, while this relationship did not occur Aβ− older adults. Further, the association between increased local efficiency and better memory performance in Aβ+ older adults was localized to local efficiency of the default mode network and hippocampus, regions vulnerable to Aβ and involved in memory processing. Our results suggest functional networks with modular and efficient structures are associated with resilience to Aβ pathology, providing a functional target for intervention.

Abstract INTRODUCTION Virtually all people with Down syndrome (DS) develop neuropathology associated with Alzheimer's disease (AD). Atrophy of the hippocampus and entorhinal cortex (EC), as well as elevated plasma concentrations of neurofilament light chain (NfL) protein, are markers of neurodegeneration associated with late‐onset AD. We hypothesized that hippocampus and EC gray matter loss and increased plasma NfL concentrations are associated with memory in adults with DS. METHODS T1‐weighted structural magnetic resonance imaging (MRI) data were collected from 101 participants with DS. Hippocampus and EC volume, as well as EC subregional cortical thickness, were derived. In a subset of participants, plasma NfL concentrations and modified Cued Recall Test scores were obtained. Partial correlation and mediation were used to test relationships between medial temporal lobe (MTL) atrophy, plasma NfL, and episodic memory. RESULTS Hippocampus volume, left anterolateral EC (alEC) thickness, and plasma NfL were correlated with each other and were associated with memory. Plasma NfL mediated the relationship between left alEC thickness and memory as well as hippocampus volume and memory. DISCUSSION The relationship between MTL gray matter and memory is mediated by plasma NfL levels, suggesting a link between neurodegenerative processes underlying axonal injury and frank gray matter loss in key structures supporting episodic memory in people with DS.

The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts that match both neuroimaging and clinical/behavioral data criteria. Critical barriers to operationalizing such queries include, in part, the broad use of undefined study variables with limited or no annotations that make it difficult to understand the data available without significant interaction with the original authors. Using the Brain Imaging Data Structure (BIDS) to organize neuroimaging data has made querying across studies for specific image types possible at scale. However, in BIDS, beyond file naming and tightly controlled imaging directory structures, there are very few constraints on ancillary variable naming/meaning or experiment-specific metadata. In this work, we present NIDM-Terms, a set of user-friendly terminology management tools and associated software to better manage individual lab terminologies and help with annotating BIDS datasets. Using these tools to annotate BIDS data with a Neuroimaging Data Model (NIDM) semantic web representation, enables queries across datasets to identify cohorts with specific neuroimaging and clinical/behavioral measurements. This manuscript describes the overall informatics structures and demonstrates the use of tools to annotate BIDS datasets to perform integrated cross-cohort queries.

Abstract Introduction We tested whether Alzheimer's disease (AD) pathology predicts memory deficits in non‐demented older adults through its effects on medial temporal lobe (MTL) subregional volume. Methods Thirty‐two, non‐demented older adults with cerebrospinal fluid (CSF) (amyloid‐beta [Aβ]42/Aβ40, phosphorylated tau [p‐tau]181, total tau [t‐tau]), positron emission tomography (PET; 18F‐florbetapir), high‐resolution structural magnetic resonance imaging (MRI), and neuropsychological assessment were analyzed. We examined relationships between biomarkers and a highly granular measure of memory consolidation, retroactive interference (RI). Results Biomarkers of AD pathology were related to RI. Dentate gyrus (DG) and CA3 volume were uniquely associated with RI, whereas CA1 and BA35 volume were related to both RI and overall memory recall. AD pathology was associated with reduced BA35, CA1, and subiculum volume. DG volume and Aβ were independently associated with RI, whereas CA1 volume mediated the relationship between AD pathology and RI. Discussion Integrity of distinct hippocampal subfields demonstrate differential relationships with pathology and memory function, indicating specificity in vulnerability and contribution to different memory processes.

Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.

The Neuroimaging Data Model (NIDM) is a series of specifications for describing all aspects of the neuroimaging data lifecycle from raw data to analyses and provenance. NIDM uses community-driven terminologies along with unambiguous data dictionaries within a Resource Description Framework (RDF) document to describe data and metadata for integration and query. Data from different studies, using locally defined variable names, can be retrieved by linking them to higher-order concepts from established ontologies and terminologies. Through these capabilities, NIDM documents are expected to improve reproducibility and facilitate data discovery and reuse. PyNIDM is a Python toolbox supporting the creation, manipulation, and querying of NIDM documents. Using the query tools available in PyNIDM, users are able interrogate datasets to find studies that have collected variables measuring similar phenotypic properties. This, in turn, facilitates the transformation and combination of data across multiple studies. The focus of this manuscript is the linear regression tool which is a part of the PyNIDM toolbox and works directly on NIDM documents. It provides a high-level statistical analysis that aids researchers in gaining more insight into the data that they are considering combining across studies. This saves researchers valuable time and effort while showing potential relationships between variables. The linear regression tool operates through a command-line interface integrated with the other tools (pynidm linear-regression) and provides the user with the opportunity to specify variables of interest using the rich query techniques available for NIDM documents and then conduct a linear regression with optional contrast and regularization.

We would like to submit the following correction to our recently published paper [...]

Repetitive head impacts represent a risk factor for neurological impairment in team-sport athletes. In the absence of symptoms, a physiological basis for acute injury has not been elucidated. A basic brain function that is disrupted after mild traumatic brain injury is the regulation of homeostasis, instantiated by activity across a specific set of brain regions that comprise a central autonomic network. We sought to relate head-to-ball impact exposure to changes in functional connectivity in a core set of central autonomic regions and then to determine the relation between changes in brain and changes in behavior, specifically cognitive control. Thirteen collegiate men's soccer players and eleven control athletes (golf, cross-country) underwent resting-state fMRI and behavioral testing before and after the season, and a core group of cortical, subcortical, and brainstem regions was selected to represent the central autonomic network. Head-to-ball impacts were recorded for each soccer player. Cognitive control was assessed using a Dot Probe Expectancy task. We observed that head-to-ball impact exposure was associated with diffuse increases in functional connectivity across a core CAN subnetwork. Increased functional connectivity between the left insula and left medial orbitofrontal cortex was associated with diminished proactive cognitive control after the season in those sustaining the greatest number of head-to-ball impacts. These findings encourage measures of autonomic physiology to monitor brain health in contact and collision sport athletes.

Abstract Introduction Down syndrome (DS) is associated with a higher risk of dementia. We hypothesize that amyloid beta (Aβ) in specific brain regions differentiates mild cognitive impairment in DS (MCI‐DS) and test these hypotheses using cross‐sectional and longitudinal data. Methods 18F‐AV‐45 (florbetapir) positron emission tomography (PET) data were collected to analyze amyloid burden in 58 participants clinically classified as cognitively stable (CS) or MCI‐DS and 12 longitudinal CS participants. Results The study confirmed our hypotheses of increased amyloid in inferior parietal, lateral occipital, and superior frontal regions as the main effects differentiating MCI‐DS from the CS groups. The largest annualized amyloid increases in longitudinal CS data were in the rostral middle frontal, superior frontal, superior/middle temporal, and posterior cingulate cortices. Discussion This study helps us to understand amyloid in the MCI‐DS transitional state between cognitively stable aging and frank dementia in DS. The spatial distribution of Aβ may be a reliable indicator of MCI‐DS in DS.

Abstract Introduction Down syndrome (DS) is associated with elevated risk for Alzheimer's disease (AD) due to amyloid beta (Aβ) lifelong accumulation. We hypothesized that the spatial distribution of brain Aβ predicts future dementia conversion in individuals with DS. Methods We acquired 18F‐florbetapir positron emission tomography scans from 19 nondemented individuals with DS at baseline and monitored them for 4 years, with five individuals transitioning to dementia. Machine learning classification using an independent test set determined features on 18F‐florbetapir standardized uptake value ratio maps that predicted transition. Results In addition to “AD signature” regions including the inferior parietal cortex, temporal lobes, and the cingulum, we found that Aβ cortical binding in the prefrontal and superior frontal cortices distinguished subjects who transitioned to dementia. Classification did well in predicting transitioners. Discussion Our study suggests that specific regional profiles of brain amyloid in older adults with DS may predict cognitive decline and are informative in evaluating the risk for dementia.

Accurate identification of the prodromal stage of Alzheimer’s disease (AD), known as mild cognitive impairment (MCI), in adults with Down syndrome (MCI-DS) has been challenging because there are no established diagnostic criteria that can be applied for people with lifelong intellectual disabilities (ID). As such, the sequence of cognitive decline in adults with DS has been difficult to ascertain, and it is possible that domain constructs characterizing cognitive function in neurotypical adults do not generalize to this high-risk population. The present study examined associations among multiple measures of cognitive function in adults with DS, either prior to or during the prodromal stage of AD to determine, through multiple statistical techniques, the measures that reflected the same underlying domains of processing. Participants included 144 adults with DS 40–82 years of age, all enrolled in a larger, multidisciplinary study examining biomarkers of AD in adults with DS. All participants had mild or moderate lifelong intellectual disabilities. Overall AD-related clinical status was rated for each individual during a personalized consensus conference that considered performance as well as health status, with 103 participants considered cognitively stable (CS) and 41 to have MCI-DS. Analyses of 17 variables derived from 10 tests of cognition indicated that performance reflected three underlying factors: language/executive function, memory, and visuomotor. All three domain composite scores significantly predicted MCI-DS status. Based upon path modeling, the language/executive function composite score was the most affected by prodromal AD. However, based upon structural equation modeling, tests assessing the latent construct of memory were the most impacted, followed by those assessing visuomotor, and then those assessing language/executive function. Our study provides clear evidence that cognitive functioning in older adults with DS can be characterized at the cognitive domain level, but the statistical methods selected and the inclusion or exclusion of certain covariates may lead to different conclusions. Best practice requires investigators to understand the internal structure of their variables and to provide evidence that their variables assess their intended constructs.

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the “last mile” implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.

Managing dynamic information in large multi-site, multi-species, and multi-discipline consortia is a challenging task for data management applications. Often in academic research studies the goals for informatics teams are to build applications that provide extract-transform-load (ETL) functionality to archive and catalog source data that has been collected by the research teams. In consortia that cross species and methodological or scientific domains, building interfaces which supply data in a usable fashion and make intuitive sense to scientists from dramatically different backgrounds increases the complexity for developers. Further, reusing source data from outside one’s scientific domain is fraught with ambiguities in understanding the data types, analysis methodologies, and how to combine the data with those from other research teams. We report on the design, implementation, and performance of a semantic data management application to support the NIMH funded Conte Center at the University of California, Irvine. The Center is testing a theory of the consequences of “fragmented” (unpredictable, high entropy) early-life experiences on adolescent cognitive and emotional outcomes in both humans and rodents. It employs cross-species neuroimaging, epigenomic, molecular, and neuroanatomical approaches in humans and rodents to assess the potential consequences of fragmented unpredictable experience on brain structure and circuitry. To address this multi-technology, multi-species approach, the system uses semantic web techniques based on the Neuroimaging Data Model (NIDM) to facilitate data ETL functionality. We find this approach enables a low-cost, easy to maintain, and semantically meaningful information management system, enabling the diverse research teams to access and use the data.

Reproducible research is a key element of the scientific process. Re-executability of neuroimaging workflows that lead to the conclusions arrived at in the literature has not yet been sufficiently addressed and adopted by the neuroimaging community. In this paper, we document a set of procedures, which include supplemental additions to a manuscript, that unambiguously define the data, workflow, execution environment and results of a neuroimaging analysis, in order to generate a verifiable re-executable publication. Re-executability provides a starting point for examination of the generalizability and reproducibility of a given finding.

The Neuroimaging Data Model (NIDM) is a series of specifications for describing all aspects of the neuroimaging data lifecycle from raw data to analyses and provenance. NIDM uses community-driven terminologies along with unambiguous data dictionaries within a Resource Description Framework (RDF) document to describe data and metadata for integration and query. Data from different studies, using locally defined variable names, can be retrieved by linking them to higher-order concepts from established ontologies and terminologies. Through these capabilities, NIDM documents are expected to improve reproducibility and facilitate data discovery and reuse. PyNIDM is a Python toolbox supporting the creation, manipulation, and querying of NIDM documents. Using the query tools available in PyNIDM, users are able interrogate datasets to find studies that have collected variables measuring similar phenotypic properties. This, in turn, facilitates the transformation and combination of data across multiple studies. The focus of this manuscript is the linear regression tool which is a part of the PyNIDM toolbox and works directly on NIDM documents. It provides a high-level statistical analysis that aids researchers in gaining more insight into the data that they are considering combining across studies. This saves researchers valuable time and effort while showing potential relationships between variables. The linear regression tool operates through a command-line interface integrated with the other tools (pynidm linear-regression) and provides the user with the opportunity to specify variables of interest using the rich query techniques available for NIDM documents and then conduct a linear regression with optional contrast and regularization.

The Neuroimaging Data Model (NIDM) is a series of specifications for describing all aspects of the neuroimaging data lifecycle from raw data to analyses and provenance. NIDM uses community-driven terminologies along with unambiguous data dictionaries within a Resource Description Framework (RDF) document to describe data and metadata for integration and query. Data from different studies, using locally defined variable names, can be retrieved by linking them to higher-order concepts from established ontologies and terminologies. Through these capabilities, NIDM documents are expected to improve reproducibility and facilitate data discovery and reuse. PyNIDM is a Python toolbox supporting the creation, manipulation, and querying of NIDM documents. Using the query tools available in PyNIDM, users are able interrogate datasets to find studies that have collected variables measuring similar phenotypic properties. This, in turn, facilitates the transformation and combination of data across multiple studies. The focus of this manuscript is the linear regression tool which is a part of the PyNIDM toolbox and works directly on NIDM documents. It provides a high-level statistical analysis that aids researchers in gaining more insight into the data that they are considering combining across studies. This saves researchers valuable time and effort while showing potential relationships between variables. The linear regression tool operates through a command-line interface integrated with the other tools (pynidm linear-regression) and provides the user with the opportunity to specify variables of interest using the rich query techniques available for NIDM documents and then conduct a linear regression with optional contrast and regularization.

Reproducible research is a key element of the scientific process. Re-executability of neuroimaging workflows that lead to the conclusions arrived at in the literature has not yet been sufficiently addressed and adopted by the neuroimaging community. In this paper, we document a set of procedures, which include supplemental additions to a manuscript, that unambiguously define the data, workflow, execution environment and results of a neuroimaging analysis, in order to generate a verifiable re-executable publication. Re-executability provides a starting point for examination of the generalizability and reproducibility of a given finding.

Reproducible research is a key element of the scientific process. Re-executability of neuroimaging workflows that lead to the conclusions arrived at in the literature has not yet been sufficiently addressed and adopted by the neuroimaging community. In this paper, we document a set of procedures, which include supplemental additions to a manuscript, that unambiguously define the data, workflow, execution environment and results of a neuroimaging analysis, in order to generate a verifiable re-executable publication. Re-executability provides a starting point for examination of the generalizability and reproducibility of a given finding.