cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15154/1468925
title: Treating a broader range of depressed adolescents with combined therapy

description: Background: Traditional statistical analyses of clinical trials encompass the central tendency of outcomes and, hence, are restricted to a treatment's average effectiveness. Our aim was to get a more complete picture of the effectiveness of standard treatment options for adolescent depression, by analyzing treatment effects across low, middle, and high levels of response.
Methods: Secondary data analysis was performed of the Treatment for Adolescents with Depression Study (TADS, ClinicalTrials.gov, NCT00006286), a randomized controlled trial comparing fluoxetine (FLX), cognitive-behavioral therapy (CBT), and their combination (COMB) against placebo treating adolescents with major depression (n=439). The proportional change from baseline to week 12 in the Children's Depression Rating Scale-Revised was used as an index of response. Response levels were analyzed via quantile regression models, thereby estimating treatment effects across the entire response level distribution, adjusted for baseline depression, study site, and patients’ treatment expectancies.
Results: Whereas CBT was no more effective than placebo across response levels, COMB was more effective than FLX in that its quantile treatment effects were both larger in magnitude and spread out across a broader range of response levels, including the low end of the response level distribution. Cohen's d of the difference was 1.39 (95% confidence interval 1.33-1.45).
Limitations: Ad-hoc analysis using data from a trial that was not originally designed to accommodate such analysis.
Conclusion: The combination of cognitive-behavioral therapy and fluoxetine was more effective than either treatment used alone, not just in average effectiveness, but in the breadth of patients in whom it was effective.
DOI: 10.15154/1469793
title: Associative prediction of visual shape in the hippocampus.

description: The way we perceive the world is to a great extent determined by our prior knowledge. Despite this intimate link between perception and memory, these two aspects of cognition have mostly been studied in isolation. Here we investigate their interaction by asking how memory systems that encode and retrieve associations can inform perception. We find that upon hearing a familiar auditory cue, the hippocampus represents visual information that had previously co-occurred with the cue, even when this expectation differs from what is currently visible. Furthermore, the strength of this hippocampal expectation correlates with facilitation of perceptual processing in visual cortex. These findings help bridge the gap between memory and sensory systems in the human brain.
DOI: 10.15154/1471546
title: Representation of real-world event schemas during narrative perception

description: Understanding movies and stories requires maintaining a high-level situation model that abstracts away
from perceptual details to describe the location, characters, actions, and causal relationships of the
currently unfolding event. These models are built not only from information present in the current
narrative, but also from prior knowledge about schematic event scripts, which describe typical event
sequences encountered throughout a lifetime. We analyzed fMRI data from 44 human subjects
presented with sixteen three-minute stories, consisting of four schematic events drawn from two
different scripts (eating at a restaurant or going through the airport). Aside from this shared script
structure, the stories varied widely in terms of their characters and storylines, and were presented in
two highly dissimilar formats (audiovisual clips or spoken narration). One group was presented with the
stories in an intact temporal sequence, while a separate control group was presented with the same
events in scrambled order. Regions including the posterior medial cortex, medial prefrontal cortex
(mPFC), and superior frontal gyrus exhibited schematic event patterns that generalized across stories,
subjects, and modalities. Patterns in mPFC were also sensitive to overall script structure, with
temporally scrambled events evoking weaker schematic representations. Using a Hidden Markov Model,
patterns in these regions can predict the script (restaurant vs. airport) of unlabeled data with high
accuracy, and can be used to temporally align multiple stories with a shared script. These results extend
work on the perception of controlled, artificial schemas in human and animal experiments to naturalistic
perception of complex narrative stimuli.
DOI: 10.15154/1473907
title: People represent their own mental states more distinctly than others’

description: One can never know the internal workings of another person – one can only infer others’ mental states based on external cues. In contrast, each person has direct access to the contents of their own mind. Here we test the hypothesis that this privileged access shapes the way people represent internal mental experiences, such that they represent their own mental states more distinctly than the states of others. Across four studies, participants considered their own and others’ mental states; analyses measured the distinctiveness of mental state representations. Two neuroimaging studies used representational similarity analyses to demonstrate that the social brain manifests more distinct activity patterns when thinking about one’s own states versus others’. Two behavioral studies support these findings. Further, they demonstrate that people differentiate between states less as social distance increases. Together these results suggest that we represent our own mind with greater granularity than the minds of others.
DOI: 10.15154/1473919
title: The social brain automatically predicts others’ future mental states

description: Social life requires people to predict the future: people must anticipate others’ thoughts, feelings,and actions in order to interact with them successfully. The theory of predictive coding suggests that the social brain may meet this need by automatically predicting others’ social futures. If so, when representing others’ current mental state, the brain should already start representing their future states. To test this hypothesis, we used functional neuroimaging to measure participants’ neural representations of mental states. Representational similarity analysis revealed that neural patterns associated with mental states currently under consideration resembled patterns of likely future states, more so than patterns of unlikely future states. This effect manifested in activity across the social brain network, and in medial prefrontal cortex in particular. Repetition suppression analysis also supported the social predictive coding hypothesis: considering mental states presented in predictable sequences reduced activity in the precuneus, relative to unpredictable sequences. In addition to demonstrating that the brain makes automatic predictionsof others’ social futures, the results also demonstrate that the brain leverages a three-dimensionalrepresentational space to make these predictions. Proximity between mental states on the psychological dimensions of rationality, social impact, and valence explained much of the association between state-specific neural pattern similarity and state transition likelihood. Together, these findings suggest that the way the brain represents the social present gives people an automatic glimpse of the social future.
DOI: 10.15154/1476770
title: Learning Naturalistic Temporal Structure in the Posterior Medial Network.

description: The posterior medial network is at the apex of a temporal integration hierarchy in the brain, integrating information over many seconds of viewing intact, but not scrambled, movies. This has been interpreted as an effect of temporal structure. Such structure in movies depends on preexisting event schemas, but temporal structure can also arise de novo from learning. Here, we examined the relative role of schema-consistent temporal structure and arbitrary but consistent temporal structure on the human posterior medial network. We tested whether, with repeated viewing, the network becomes engaged by scrambled movies with temporal structure. Replicating prior studies, activity in posterior medial regions was immediately locked to stimulus structure upon exposure to intact, but not scrambled, movies. However, for temporally structured scrambled movies, functional coupling within the network increased across stimulus repetitions, rising to the level of intact movies. Thus, temporal structure is a key determinant of network dynamics and function in the posterior medial network.
DOI: 10.15154/1479110
title: Contralateral Delay Activity Tracks Fluctuations in Working Memory Performance.

description: Neural measures of working memory storage, such as the contralateral delay activity (CDA), are powerful tools in working memory research. CDA amplitude is sensitive to working memory load, reaches an asymptote at known behavioral limits, and predicts individual differences in capacity. An open question, however, is whether neural measures of load also track trial-by-trial fluctuations in performance. Here, we used a whole-report working memory task to test the relationship between CDA amplitude and working memory performance. If working memory failures are due to decision-based errors and retrieval failures, CDA amplitude would not differentiate good and poor performance trials when load is held constant. If failures arise during storage, then CDA amplitude should track both working memory load and trial-by-trial performance. As expected, CDA amplitude tracked load (Experiment 1), reaching an asymptote at three items. In Experiment 2, we tracked fluctuations in trial-by-trial performance. CDA amplitude was larger (more negative) for high-performance trials compared with low-performance trials, suggesting that fluctuations in performance were related to the successful storage of items. During working memory failures, participants oriented their attention to the correct side of the screen (lateralized P1) and maintained covert attention to the correct side during the delay period (lateralized alpha power suppression). Despite the preservation of attentional orienting, we found impairments consistent with an executive attention theory of individual differences in working memory capacity; fluctuations in executive control (indexed by pretrial frontal theta power) may be to blame for storage failures.
DOI: 10.15154/1481790
title: Direct Brain Recordings Reveal Prefrontal Cortex Dynamics of Memory Development

description: Prevailing theories link prefrontal cortex (PFC) maturation to the development of declarative memory. However, the precise spatiotemporal correlates of memory formation in the developing brain are not known. We provide rare intracranial evidence that the spatiotemporal propagation of frontal activity supports memory formation in children. Seventeen subjects (6.2-19.4 years) studied visual scenes in preparation for a recognition memory test while undergoing direct cortical monitoring. Earlier PFC activity predicted greater accuracy, and sub-second deviations in activity flow between subregions predicted memory formation. Activity flow between inferior and precentral sites was refined during adolescence, partially explaining gains in memory. In contrast, middle frontal activity predicted memory independent of age. These findings show with sub-second temporal precision that the developing PFC links scene perception and memory formation, and underscore the role of the PFC in supporting memory development.
DOI: 10.15154/1503166
title: Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study

description: The adolescent brain undergoes profound structural changes which is influenced by many factors. Screen media activity (SMA; e.g., watching television or videos, playing video games, or using social media) is a common recreational activity in children and adolescents; however, its effect on brain structure is not well understood. A multivariate approach with the first cross-sectional data release from the Adolescent Brain Cognitive Development (ABCD) study was used to test the maturational coupling hypothesis, i.e. the notion that coordinated patterns of structural change related to specific behaviors. Moreover, the utility of this approach was tested by determining the association between these structural correlation networks and psychopathology or cognition. ABCD participants with usable structural imaging and SMA data (N = 4277 of 4524) were subjected to a Group Factor Analysis (GFA) to identify latent variables that relate SMA to cortical thickness, sulcal depth, and gray matter volume. Subject scores from these latent variables were used in generalized linear mixed-effect models to investigate associations between SMA and internalizing and externalizing psychopathology, as well as fluid and crystalized intelligence. Four SMA-related GFAs explained 37% of the variance between SMA and structural brain indices. SMA-related GFAs correlated with brain areas that support homologous functions. Some but not all SMA-related factors corresponded with higher externalizing (Cohen's d effect size (ES) 0.06–0.1) but not internalizing psychopathology and lower crystalized (ES: 0.08–0.1) and fluid intelligence (ES: 0.04–0.09). Taken together, these findings support the notion of SMA related maturational coupling or structural correlation networks in the brain and provides evidence that individual differences of these networks have mixed consequences for psychopathology and cognitive performance.
DOI: 10.15154/1503195
title: medical image analysis using deep learning

description: medical image analysis using deep Learning and recognition of hand osteoarthiritis
DOI: 10.15154/1503196
title: Developmental and environmental influences on memory systems

description: Children with low socioeconomic status consistently show reduced memory ability and smaller hippocampal volumes. These findings have motivated a theory that the hippocampus underlies income-related cognitive gaps. However, income-related differences in hippocampal structure do not seems to mediate cognitive gaps, possibly because past studies do not distinguish between anterior and posterior hippocampus. We investigate anterior and posterior hippocampal volumes in children and adolescents from diverse socioeconomic backgrounds.
DOI: 10.15154/1503207
title: TADA - Transgender Adolescent Development & Adiction

description: Exploring development and addiction for transgender subjects.
DOI: 10.15154/1503213
title: ABCD Neurocognitive Prediction Challenge 2019: Training Set

description: Training data set for the ABCD Neurocognitive Prediction Challenge 2019 containing skull stripped and segmented T1-weighted MRIs, volumetric brain measures, and residual fluid intelligence scores  of 3739 participants of the ABCD study.   
https://sibis.sri.com/abcd-np-challenge provides a detailed description about the processing. When using the data in publications, the Data Supplement of "Pfefferbaum et al., Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am J Psychiatry, 175(4), pp. 370-380, 2018" for should be cited as description of the processing pipeline.
DOI: 10.15154/1503237
title: Neurons with complex karyotypes are rare in aged human neocortex

description: Megabase-scale somatic copy number variants (CNVs) lead to complex karyotypes that alter allelic diversity in a subset of human neocortical neurons. Reported frequencies of neurons with complex karyotypes range from ~5% of neurons in some individuals to greater than 30% in other individuals. Genome-wide and familial studies implicitly assume a constant brain genome when assessing the genetic risk architecture of neurological disease, thus it is critical to determine whether divergent levels of neurons with complex karyotypes (CNV neurons) reflect normal individual variation or technical differences between approaches. We developed a computational approach that measures single cell library quality based on Bayesian Information Criterion and identifies integer-like variant segments from population-level statistics. A brain CNV atlas was assembled using a new dataset (&gt;800 neurons from 5 neurotypical individuals) and published data from 10 additional neurotypical individuals. This atlas reveals that the frequency of neocortical neurons with complex karyotypes varies widely among individuals, but that this variability is not readily accounted for by tissue quality or CNV detection approach. Rather, the age of the individual is anti-correlated with CNV neuron frequency. Fewer CNV neurons are observed in aged individuals than young individuals.
DOI: 10.15154/1503299
title: Predicting Antidepressant Response with the STAR*D and CAN-BIND-1 Datasets

description: Objective: Predict response to antidepressant monotherapy using machine learning techniques, using the CO-MED and STAR*D datasets to train and cross-validate analysis of the CAN-BIND dataset. Part of the CAN-BIND project which has received institutional research ethics board approval at the University of British Columbia

Design: A variety of machine learning techniques will be used to predict antidepressant response comparing between and joining together the level 1 arm of STAR*D, escitalopram + placebo arm of CO-MED, and stage 1 of CAN-BIND-1. Different methods for feature selection will be used to utilize different combinations of clinical and demographic data. We will replicate the methods use in a recent paper which represents the current state of the art, Nie et al 2018. 

Analysis plan: The various machine learning and feature selection techniques will be compared using standard measure of accuracy including sensitivity, specificity, PPV, NPV, and AUC. We require a project on NDA so that the group who worked on the prior analyses can share relevant data/algorithms with us. 

Data access: We request all clinical and demographic data from the subsets of the clinical trials mentioned above.
DOI: 10.15154/1503304
title: Individual differences in valence bias: fMRI evidence of the initial negativity hypothesis

description: Facial expressions offer an ecologically valid model for examining individual differences in affective decision-making.
They convey an emotional signal from a social agent and provide important predictive information about one’s environment
(presence of potential rewards or threats). Although some expressions provide clear predictive information (angry,
happy), others (surprised) are ambiguous in that they predict both positive and negative outcomes. Thus, surprised faces
can delineate an individual’s valence bias, or the tendency to interpret ambiguity as positive or negative. Our initial negativity
hypothesis suggests that the initial response to ambiguity is negative, and that positivity relies on emotion regulation.
We tested this hypothesis by comparing brain activity during explicit emotion regulation (reappraisal) and while freely
viewing facial expressions, and measuring the relationship between brain activity and valence bias. Brain regions recruited
during reappraisal showed greater activity for surprise in individuals with an increasingly positive valence bias.
Additionally, we linked amygdala activity with an initial negativity, revealing a pattern similarity in individuals with negative
bias between viewing surprised faces and maintaining negativity. Finally, these individuals failed to show normal habituation
to clear negativity. These results support the initial negativity hypothesis, and are consistent with emotion research
in both children and adult populations.
DOI: 10.15154/1503306
title: ABCD Neurocognitive Prediction Challenge 2019: Validation set

description: Validation data set for the ABCD Neurocognitive Prediction Challenge 2019 containing skull stripped and segmented T1-weighted MRIs, volumetric brain measures, and residual fluid intelligence scores  of 415 participants of the ABCD study.   
https://sibis.sri.com/abcd-np-challenge provides a detailed description about the processing. When using the data in publications, the Data Supplement of "Pfefferbaum et al., Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am J Psychiatry, 175(4), pp. 370-380, 2018" for should be cited as description of the processing pipeline.
DOI: 10.15154/1503307
title: ABCD Neurocognitive Prediction Challenge 2019: Test Set

description: The test data set for the ABCD Neurocognitive Prediction Challenge 2019 contains skull stripped and segmented T1-weighted MRIs, and volumetric brain measures of 3648 participants of the ABCD study.   
https://sibis.sri.com/abcd-np-challenge provides a detailed description about the processing. When using the data in publications, the Data Supplement of "Pfefferbaum et al., Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am J Psychiatry, 175(4), pp. 370-380, 2018" for should be cited as description of the processing pipeline.  

The data in this Study were derived from the Adolescent Brain Cognitive Development 1.1 Release (http://dx.doi.org/10.15154/1460410, accessed on or before November 15, 2018) and the Fast Track DICOM share in the Adolescent Brain Cognitive Development Study Collection 2573 (https://ndar.nih.gov/edit_collection.html?id=2573, accessed on or before November 15, 2018).  The individual-level imaging phenotype data in this Collection was computed by a custom processing pipeline developed by the organizers of the ABCD Prediction Challenge.  The imaging phenotype data may therefore differ from the values shared by the ABCD Study investigators in Release 1.1 or future releases
DOI: 10.15154/1503327
title: Construction of the Design Matrix for Generalized Linear Mixed-Effects Models in the Context of Clinical Trials of Treatment Sequences

description: The estimation of carry-over effects is a difficult problem in the design and analysis of clinical trials of treatment sequences including cross-over trials. Except for simple designs, carry-over effects are usually unidentifiable and therefore nonestimable. Solutions such as imposing parameter constraints are often unjustified and produce differing carry-over estimates depending on the constraint imposed. Generalized inverses or treatment-balancing often allow estimating main treatment effects, but the problem of estimating the carry-over contribution of a treatment sequence remains open in these approaches. Moreover, washout periods are not always feasible or ethical. A common feature of designs with unidentifiable parameters is that they do not have design matrices of full rank. Thus, we propose approaches to the construction of design matrices of full rank, without imposing artificial constraints on the carry-over effects. Our approaches are applicable within the framework of generalized linear mixed-effects models. We present a new model for the design and analysis of clinical trials of treatment sequences, called Antichronic System, and introduce some special sequences called Skip Sequences. We show that carry-over effects are identifiable only if appropriate Skip Sequences are used in the design and/or data analysis of the clinical trial. We explain how Skip Sequences can be implemented in practice, and present a method of computing the appropriate Skip Sequences. We show applications to the design of a cross-over study with 3 treatments and 3 periods, and to the data analysis of the STAR*D study of sequences of treatments for depression. See the paper, which available in this web site. Reference:  Diaz, F.J. (2018). "Construction of the Design Matrix for Generalized Linear Mixed-Effects Models in the Context of Clinical Trials of Treatment Sequences". Revista Colombiana de Estadística (Colombian Journal of Statistics). Vol. 41, 191-233.