scholarly journals Using Outliers in Freesurfer Segmentation Statistics to Identify Cortical Reconstruction Errors in Structural Scans

2017 ◽  
Author(s):  
Abigail B. Waters ◽  
Ryan A. Mace ◽  
Kayle S. Sawyer ◽  
David A. Gansler
Keyword(s):  
Visual Inspection ◽  
Brain Regions ◽  
Medium Effect ◽  
Quantitative Detection ◽  
Brain Volumes ◽  
Outlier Group ◽  
Neuroimaging Data ◽  
Medium Effect Size ◽  
Neurocognitive Tasks ◽  
Brain Behavior

AbstractIntroductionQuality assurance (QA) is vital for ensuring the integrity of processed neuroimaging data for use in clinical neurosciences research. Manual QA (visual inspection) of processed brains for cortical surface reconstruction errors is resource-intensive, particularly with large datasets. Several semi-automated QA tools use quantitative detection of subjects for editing based on outlier brain regions. There were two project goals: (1) evaluate the adequacy of a statistical QA method relative to visual inspection, and (2) examine whether error identification and correction significantly impacts estimation of cortical parameters and established brain-behavior relationships.MethodsT1 MPRAGE images (N = 530) of healthy adults were obtained from the NKI-Rockland Sample and reconstructed using Freesurfer 5.3. Visual inspection of T1 images was conducted for: (1) participants (n = 110) with outlier values (z scores ± 3 SD) for subcortical and cortical segmentation volumes (outlier group), and (2) a random sample of remaining participants (n = 110) with segmentation values that did not meet the outlier criterion (nonoutlier group).ResultsThe outlier group had 21% more participants with visual inspection-identified errors than participants in the non-outlier group, with a medium effect size (Φ = 0.22). Nevertheless, a considerable portion of images with errors of cortical extension were found in the non-outlier group (41%). Sex significantly predicted error rate; men were 2.8 times more likely to have errors than women. Although nine brain regions significantly changed size from pre-to postediting (with effect sizes ranging from 0.26 to 0.59), editing did not substantially change the correlations of neurocognitive tasks and brain volumes (ps > 0.05).ConclusionsStatistically-based QA, although less resource intensive, is not accurate enough to supplant visual inspection. We discuss practical implications of our findings to guide resource allocation decisions for image processing.

scholarly journals Fear-induced brain activations distinguish anxious and trauma-exposed brains

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhenfu Wen ◽  
Marie-France Marin ◽  
Jennifer Urbano Blackford ◽  
Zhe Sage Chen ◽  
Mohammed R. Milad
Keyword(s):  
Neural Network ◽  
Fear Conditioning ◽  
Psychiatric Diagnosis ◽  
Data Analytics ◽  
Conditioned Fear ◽  
Brain Regions ◽  
Brain Network ◽  
Neuroimaging Data ◽  
Task Irrelevant ◽  
Translational Models

AbstractTranslational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.

scholarly journals An Egocentric Network Contact Tracing Experiment: Testing Different Procedures to Elicit Contacts and Places

2021 ◽  
Vol 18 (4) ◽  
pp. 1466
Author(s):  
Andrew Pilny ◽  
C. Joseph Huber
Keyword(s):  
Social Network ◽  
Infectious Diseases ◽  
Effect Size ◽  
Survey Methods ◽  
Contact Tracing ◽  
Medium Effect ◽  
Physiological Variables ◽  
Medium Effect Size ◽  
Egocentric Network ◽  
Survey Instruments

Contact tracing is one of the oldest social network health interventions used to reduce the diffusion of various infectious diseases. However, some infectious diseases like COVID-19 amass at such a great scope that traditional methods of conducting contact tracing (e.g., face-to-face interviews) remain difficult to implement, pointing to the need to develop reliable and valid survey approaches. The purpose of this research is to test the effectiveness of three different egocentric survey methods for extracting contact tracing data: (1) a baseline approach, (2) a retrieval cue approach, and (3) a context-based approach. A sample of 397 college students were randomized into one condition each. They were prompted to anonymously provide contacts and populated places visited from the past four days depending on what condition they were given. After controlling for various demographic, social identity, psychological, and physiological variables, participants in the context-based condition were significantly more likely to recall more contacts (medium effect size) and places (large effect size) than the other two conditions. Theoretically, the research supports suggestions by field theory that assume network recall can be significantly improved by activating relevant activity foci. Practically, the research contributes to the development of innovative social network data collection methods for contract tracing survey instruments.

scholarly journals Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiao Li ◽  
Jakob Seidlitz ◽  
John Suckling ◽  
Feiyang Fan ◽  
Gong-Jun Ji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Structural Changes ◽  
Brain Regions ◽  
Cell Type ◽  
Major Depressive ◽  
Structural Differences ◽  
Neuroimaging Data ◽  
Cell Type Specific

AbstractMajor depressive disorder (MDD) has been shown to be associated with structural abnormalities in a variety of spatially diverse brain regions. However, the correlation between brain structural changes in MDD and gene expression is unclear. Here, we examine the link between brain-wide gene expression and morphometric changes in individuals with MDD, using neuroimaging data from two independent cohorts and a publicly available transcriptomic dataset. Morphometric similarity network (MSN) analysis shows replicable cortical structural differences in individuals with MDD compared to control subjects. Using human brain gene expression data, we observe that the expression of MDD-associated genes spatially correlates with MSN differences. Analysis of cell type-specific signature genes suggests that microglia and neuronal specific transcriptional changes account for most of the observed correlation with MDD-specific MSN differences. Collectively, our findings link molecular and structural changes relevant for MDD.

scholarly journals Functional magnetic neuroimaging data on age-related differences in task switching accuracy and reverse brain-behavior relationships

Data in Brief ◽  
2018 ◽  
Vol 19 ◽  
pp. 997-1007 ◽  
Author(s):  
Chandramallika Basak ◽  
Shuo Qin ◽  
Kaoru Nashiro ◽  
Margaret A. O’Connell
Keyword(s):  
Task Switching ◽  
Age Related ◽  
Neuroimaging Data ◽  
Brain Behavior

scholarly journals Prediction of white matter integrity pattern in overweight and obese adults

2020 ◽  
Vol 79 (OCE2) ◽  
Author(s):  
Sussanne Reyes ◽  
Patricio Peirano ◽  
Betsy Lozoff ◽  
Cecilia Algarin
Keyword(s):  
White Matter ◽  
Cognitive Flexibility ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Normal Weight ◽  
White Matter Integrity ◽  
School Age ◽  
Loss Avoidance ◽  
Neurocognitive Tasks ◽  
The Relationship

Abstract IntroductionObesity has been associated with lower white matter integrity (WMI) in limbic brain regions, including the fornix. Both early decrease of WMI in the fornix (WMIf) and midlife obesity have been related to dementia incidence with advancing age. No studies have explored early cognitive predictors of WMIf in overweight-obese (OO) adults. Aim of this study was to compare OO and normal-weight (NW) participants with respect to (a) WMIf in adulthood and (b) the relationship between cognitive performance at school-age and in adolescence with WMIf in adulthood.MethodsParticipants were part of a cohort followed since infancy who underwent magnetic resonance imaging studies in adulthood (22.3 ± 1.3 years). Diffusion tensor imaging was performed and Tract Based Spatial Statistics (TBSS) was used to obtain fractional anisotropy (FA) skeleton; increased FA relates to greater WMI. A mask for the fornix was created (JHU-ICBM DTI-81 Atlas) and then used to extract the average FA for each individual. Participants also performed neurocognitive tasks: (a) school-age (10.3 ± 1.0 years): the trail making test comprises two conditions and time difference between conditions reflects cognitive flexibility; (b) adolescence (15.6 ± 0.5 years): incentive task that test the effect of incentives (reward, loss avoidance or neutral) on inhibitory control performance (correct responses latency). In adulthood, BMI was categorized as NW (≥ 18.5 to < 25.0 kg/m2) and OO (≥ 25.0 kg/m2) groups. A t-test and univariate GLM were conducted. Analysis were adjusted by sex and age-specific BMI z-scores.ResultsParticipants were 27 NW (41% female) and 41 OO (49% female). Compared to NW, OO participants showed decreased FA in the fornix (0.585 vs. 0.618, p < 0.05), i.e. lower WMIf. Differences were apparent in the relationship between cognitive flexibility at school-age (F = 2.9, p = 0.06) and loss avoidance latency in adolescence (F = 3.5, p < 0.05) with FA in the fornix in adulthood. Increased cognitive flexibility at school-age (β = 0.335, p < 0.05) and decreased loss avoidance latency in adolescence (β = -0.581, p < 0.001) were related to higher FA in the fornix in OO adults. No relationship resulted significant in NW adults.DiscussionPerformance in neurocognitive tasks at earlier developmental stages were related with WMIf only in OO adults, group characterized by decreased WMIf. Our results provide evidence regarding specific neurocognitive tasks with predictive value for WMIf alterations. Further, they could contribute to the understanding of neural mechanisms underlying obesity and also provide insight relative to neurodegenerative risk with advancing age.SupportFondecyt 11160671 and NIH HD33487.

scholarly journals Reported Hearing Loss in Alzheimer’s Disease Is Associated With Loss of Brainstem and Cerebellar Volume

2021 ◽  
Vol 15 ◽  
Author(s):  
Daniel A. Llano ◽  
Susanna S. Kwok ◽  
Viswanath Devanarayan ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hearing Loss ◽  
Normal Subjects ◽  
Neural Substrates ◽  
Epidemiological Studies ◽  
Brain Regions ◽  
Pathological State ◽  
Brain Volumes ◽  
The Relationship

Multiple epidemiological studies have revealed an association between presbycusis and Alzheimer’s Disease (AD). Unfortunately, the neurobiological underpinnings of this relationship are not clear. It is possible that the two disorders share a common, as yet unidentified, risk factor, or that hearing loss may independently accelerate AD pathology. Here, we examined the relationship between reported hearing loss and brain volumes in normal, mild cognitive impairment (MCI) and AD subjects using a publicly available database. We found that among subjects with AD, individuals that reported hearing loss had smaller brainstem and cerebellar volumes in both hemispheres than individuals without hearing loss. In addition, we found that these brain volumes diminish in size more rapidly among normal subjects with reported hearing loss and that there was a significant interaction between cognitive diagnosis and the relationship between reported hearing loss and these brain volumes. These data suggest that hearing loss is linked to brainstem and cerebellar pathology, but only in the context of the pathological state of AD. We hypothesize that the presence of AD-related pathology in both the brainstem and cerebellum creates vulnerabilities in these brain regions to auditory deafferentation-related atrophy. These data have implications for our understanding of the potential neural substrates for interactions between hearing loss and AD.

scholarly journals Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

2020 ◽  
Author(s):  
Xin Niu ◽  
Alexei Taylor ◽  
Russell T. Shinohara ◽  
John Kounios ◽  
Fengqing Zhang
Keyword(s):  
Psychiatric Disorders ◽  
Robust Regression ◽  
Developmental Trajectories ◽  
Brain Structures ◽  
Brain Regions ◽  
Developmental Trajectory ◽  
Imaging Features ◽  
Neuroimaging Data ◽  
Brain Age ◽  
Age Prediction

AbstractBrain regions change in different ways and at different rates. This staggered developmental unfolding is determined by genetics and postnatal experience and is implicated in the progression of psychiatric and neurological disorders. Neuroimaging-based brain-age prediction has emerged as an important new approach for studying brain development. However, the unidimensional brain-age estimates provided by previous methods do not capture the divergent developmental trajectories of various brain structures. Here we propose and illustrate an analytic pipeline to compute an index of multidimensional brain-age that provides regional age predictions. First, using a database of 556 subjects that includes psychiatric and neurological patients as well as healthy controls we conducted robust regression to characterize the developmental trajectory of each MRI-based brain-imaging feature. We then utilized cluster analysis to identify subgroups of imaging features with a similar developmental trajectory. For each identified cluster, we obtained a brain-age prediction by applying machine-learning models with imaging features belonging to each cluster. Brain-age predictions from multiple clusters form a multidimensional brain-age index (MBAI). The MBAI is more sensitive to alterations in brain structures and captured distinct regional change patterns. In particular, the MBAI provided a more flexible analysis of brain age across brain regions that revealed changes in specific structures in psychiatric disorders that would otherwise have been combined in a unidimensional brain age prediction. More generally, brain-age prediction using a subset of homogeneous features circumvents the curse of dimensionality in neuroimaging data.

scholarly journals An N-Pact Factor for Clinical Psychological Research

2018 ◽  
Author(s):  
Kathleen Wade Reardon ◽  
Avante J Smack ◽  
Kathrin Herzhoff ◽  
Jennifer L Tackett
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Empirical Studies ◽  
Psychological Research ◽  
Medium Effect ◽  
Medium Effect Size ◽  
One Step ◽  
Hard To Reach Populations ◽  
Self Examination ◽  
Adequate Sample Size

Although an emphasis on adequate sample size and statistical power has a long history in clinical psychological science (Cohen, 1992), increased attention to the replicability of scientific findings has again turned attention to the importance of statistical power (Bakker, van Dijk, &amp; Wicherts, 2012). These recent efforts have not yet circled back to modern clinical psychological research, despite the continued importance of sample size and power in producing a credible body of evidence. As one step in this process of scientific self-examination, the present study estimated an N-pact Factor (the statistical power of published empirical studies to detect typical effect sizes; Fraley &amp; Vazire, 2014) in two leading clinical journals (the Journal of Abnormal Psychology; JAP, and the Journal of Consulting and Clinical Psychology; JCCP) for the years 2000, 2005, 2010, and 2015. Study sample size, as one proxy for statistical power, is a useful focus because it allows direct comparisons with other subfields and may highlight some of the core methodological differences between clinical and other areas (e.g., hard-to-reach populations, greater emphasis on correlational designs). We found that, across all years examined, the average median sample size in clinical research is 179 participants (175 for JAP and 182 for JCCP). The power to detect a small-medium effect size of .20 is just below 80% for both journals. Although the clinical N-pact factor was higher than that estimated for social psychology, the statistical power in clinical journals is still limited to detect many effects of interest to clinical psychologists, with little evidence of improvement in sample sizes over time.

scholarly journals Effect sizes and test-retest reliability of the fMRI-based Neurologic Pain Signature

2021 ◽  
Author(s):  
Xiaochun Han ◽  
Yoni K. Ashar ◽  
Philip Kragel ◽  
Bogdan Petre ◽  
Victoria Schelkun ◽  
...  
Keyword(s):  
Individual Differences ◽  
Effect Size ◽  
Mental States ◽  
Effect Sizes ◽  
Medium Effect ◽  
Retest Reliability ◽  
Medium Effect Size ◽  
Pain Reports ◽  
Nociceptive Input ◽  
Test Retest Reliability

Identifying biomarkers that predict mental states with large effect sizes and high test-retest reliability is a growing priority for fMRI research. We examined a well-established multivariate brain measure that tracks pain induced by nociceptive input, the Neurologic Pain Signature (NPS). In N = 295 participants across eight studies, NPS responses showed a very large effect size in predicting within-person single-trial pain reports (d = 1.45) and medium effect size in predicting individual differences in pain reports (d = 0.49, average r = 0.20). The NPS showed excellent short-term (within-day) test-retest reliability (ICC = 0.84, with average 69.5 trials/person). Reliability scaled with the number of trials within-person, with ≥60 trials required for excellent test-retest reliability. Reliability was comparable in two additional studies across 5-day (N = 29, ICC = 0.74, 30 trials/person) and 1-month (N = 40, ICC = 0.46, 5 trials/person) test-retest intervals. The combination of strong within-person correlations and only modest between-person correlations between the NPS and pain reports indicates that the two measures have different sources of between-person variance. The NPS is not a surrogate for individual differences in pain reports, but can serve as a reliable measure of pain-related physiology and mechanistic target for interventions.

scholarly journals Lynn_etal_MathSkills_CPM_Preprint_V1_05July2021

2021 ◽  
Author(s):  
Andrew Lynn ◽  
Eric D. Wilkey ◽  
Gavin Price
Keyword(s):  
Functional Connectivity ◽  
Predictive Modeling ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Math Skills ◽  
Whole Brain ◽  
Building Models ◽  
State Dependent ◽  
Brain Behavior

The human brain comprises multiple canonical networks, several of which are distributed across frontal, parietal, and temporooccipital regions. Studies report both positive and negative correlations between children’s math skills and the strength of functional connectivity among these regions during math-related tasks and at rest. Yet, it is unclear how the relation between children’s math skills and functional connectivity map onto patterns of distributed whole-brain connectivity, canonical network connectivity, and whether these relations are consistent across different task-states. We used connectome-based predictive modeling to test whether functional connectivity during number comparison and at rest predicts children’s math skills (N=31, Mage=9.21years) using distributed whole-brain connections versus connections among canonical networks. We found that weaker connectivity distributed across the whole brain and weaker connectivity between key math-related brain regions in specific canonical networks predicts better math skills in childhood. The specific connections predicting math skills, and whether they were distributed or mapped onto canonical networks, varied between tasks, suggesting that state-dependent rather than trait-level functional network architectures support children’s math skills. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

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