Publications by authors named "Efstathios Gennatas"

24 Publications

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Importance of non-pharmaceutical interventions in lowering the viral inoculum to reduce susceptibility to infection by SARS-CoV-2 and potentially disease severity.

Lancet Infect Dis 2021 Feb 22. Epub 2021 Feb 22.

Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco, CA, USA. Electronic address:

Adherence to non-pharmaceutical interventions to prevent the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been highly variable across settings, particularly in the USA. In this Personal View, we review data supporting the importance of the viral inoculum (the dose of viral particles from an infected source over time) in increasing the probability of infection in respiratory, gastrointestinal, and sexually transmitted viral infections in humans. We also review the available evidence linking the relationship of the viral inoculum to disease severity. Non-pharmaceutical interventions might reduce the susceptibility to SARS-CoV-2 infection by reducing the viral inoculum when there is exposure to an infectious source. Data from physical sciences research suggest that masks protect the wearer by filtering virus from external sources, and others by reducing expulsion of virus by the wearer. Social distancing, handwashing, and improved ventilation also reduce the exposure amount of viral particles from an infectious source. Maintaining and increasing non-pharmaceutical interventions can help to quell SARS-CoV-2 as we enter the second year of the pandemic. Finally, we argue that even as safe and effective vaccines are being rolled out, non-pharmaceutical interventions will continue to play an essential role in suppressing SARS-CoV-2 transmission until equitable and widespread vaccine administration has been completed.
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http://dx.doi.org/10.1016/S1473-3099(20)30982-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7906703PMC
February 2021

Structural and Functional Brain Parameters Related to Cognitive Performance Across Development: Replication and Extension of the Parieto-Frontal Integration Theory in a Single Sample.

Cereb Cortex 2021 Feb;31(3):1444-1463

Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.

The parieto-frontal integration theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8-22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional magnetic resonance imaging measures of the amplitude of low frequency fluctuations (ALFFs) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo, and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic, and cerebellar regions showed comparable effects, hence PFIT needs expansion into an extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining a low resting energy state.
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http://dx.doi.org/10.1093/cercor/bhaa282DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7869090PMC
February 2021

Expert-augmented machine learning.

Proc Natl Acad Sci U S A 2020 03 18;117(9):4571-4577. Epub 2020 Feb 18.

Department of Radiation Oncology, University of California, San Francisco, CA 94143.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications.
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http://dx.doi.org/10.1073/pnas.1906831117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060733PMC
March 2020

Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival.

Neurooncol Adv 2019 May-Dec;1(1):vdz011. Epub 2019 Aug 28.

Department of Radiation Oncology, University of California San Francisco, California.

Background: We investigated prognostic models based on clinical, radiologic, and radiomic feature to preoperatively identify meningiomas at risk for poor outcomes.

Methods: Retrospective review was performed for 303 patients who underwent resection of 314 meningiomas (57% World Health Organization grade I, 35% grade II, and 8% grade III) at two independent institutions, which comprised primary and external datasets. For each patient in the primary dataset, 16 radiologic and 172 radiomic features were extracted from preoperative magnetic resonance images, and prognostic features for grade, local failure (LF) or overall survival (OS) were identified using the Kaplan-Meier method, log-rank tests and recursive partitioning analysis. Regressions and random forests were used to generate and test prognostic models, which were validated using the external dataset.

Results: Multivariate analysis revealed that apparent diffusion coefficient hypointensity (HR 5.56, 95% CI 2.01-16.7, = .002) was associated with high grade meningioma, and low sphericity was associated both with increased LF (HR 2.0, 95% CI 1.1-3.5, = .02) and worse OS (HR 2.94, 95% CI 1.47-5.56, = .002). Both radiologic and radiomic predictors of adverse meningioma outcomes were significantly associated with molecular markers of aggressive meningioma biology, such as somatic mutation burden, DNA methylation status, and expression. Integrated prognostic models combining clinical, radiologic, and radiomic features demonstrated improved accuracy for meningioma grade, LF, and OS (area under the curve 0.78, 0.75, and 0.78, respectively) compared to models based on clinical features alone.

Conclusions: Preoperative radiologic and radiomic features such as apparent diffusion coefficient and sphericity can predict tumor grade, LF, and OS in patients with meningioma.
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http://dx.doi.org/10.1093/noajnl/vdz011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6777505PMC
August 2019

Building more accurate decision trees with the additive tree.

Proc Natl Acad Sci U S A 2019 10 16;116(40):19887-19893. Epub 2019 Sep 16.

Department of Radiation Oncology, University of California, San Francisco, CA 94115;

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.
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http://dx.doi.org/10.1073/pnas.1816748116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6778203PMC
October 2019

Preoperative and postoperative prediction of long-term meningioma outcomes.

PLoS One 2018 20;13(9):e0204161. Epub 2018 Sep 20.

Department of Radiation Oncology, University of California San Francisco, San Francisco, United States of America.

Background: Meningiomas are stratified according to tumor grade and extent of resection, often in isolation of other clinical variables. Here, we use machine learning (ML) to integrate demographic, clinical, radiographic and pathologic data to develop predictive models for meningioma outcomes.

Methods And Findings: We developed a comprehensive database containing information from 235 patients who underwent surgery for 257 meningiomas at a single institution from 1990 to 2015. The median follow-up was 4.3 years, and resection specimens were re-evaluated according to current diagnostic criteria, revealing 128 WHO grade I, 104 grade II and 25 grade III meningiomas. A series of ML algorithms were trained and tuned by nested resampling to create models based on preoperative features, conventional postoperative features, or both. We compared different algorithms' accuracy as well as the unique insights they offered into the data. Machine learning models restricted to preoperative information, such as patient demographics and radiographic features, had similar accuracy for predicting local failure (AUC = 0.74) or overall survival (AUC = 0.68) as models based on meningioma grade and extent of resection (AUC = 0.73 and AUC = 0.72, respectively). Integrated models incorporating all available demographic, clinical, radiographic and pathologic data provided the most accurate estimates (AUC = 0.78 and AUC = 0.74, respectively). From these models, we developed decision trees and nomograms to estimate the risks of local failure or overall survival for meningioma patients.

Conclusions: Clinical information has been historically underutilized in the prediction of meningioma outcomes. Predictive models trained on preoperative clinical data perform comparably to conventional models trained on meningioma grade and extent of resection. Combination of all available information can help stratify meningioma patients more accurately.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0204161PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6147484PMC
March 2019

Deep nets vs expert designed features in medical physics: An IMRT QA case study.

Med Phys 2018 Jun 18;45(6):2672-2680. Epub 2018 Apr 18.

Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.

Purpose: The purpose of this study was to compare the performance of Deep Neural Networks against a technique designed by domain experts in the prediction of gamma passing rates for Intensity Modulated Radiation Therapy Quality Assurance (IMRT QA).

Method: A total of 498 IMRT plans across all treatment sites were planned in Eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam Linacs. Measurements were performed using a commercial 2D diode array, and passing rates for 3%/3 mm local dose/distance-to-agreement (DTA) were recorded. Separately, fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). The CNNs were trained to predict IMRT QA gamma passing rates using TensorFlow and Keras. A set of model architectures, inspired by the convolutional blocks of the VGG-16 ImageNet model, were constructed and implemented. Synthetic data, created by rotating and translating the fluence maps during training, was created to boost the performance of the CNNs. Dropout, batch normalization, and data augmentation were utilized to help train the model. The performance of the CNNs was compared to a generalized Poisson regression model, previously developed for this application, which used 78 expert designed features.

Results: Deep Neural Networks without domain knowledge achieved comparable performance to a baseline system designed by domain experts in the prediction of 3%/3 mm Local gamma passing rates. An ensemble of neural nets resulted in a mean absolute error (MAE) of 0.70 ± 0.05 and the domain expert model resulted in a 0.74 ± 0.06.

Conclusions: Convolutional neural networks (CNNs) with transfer learning can predict IMRT QA passing rates by automatically designing features from the fluence maps without human expert supervision. Predictions from CNNs are comparable to a system carefully designed by physicist experts.
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http://dx.doi.org/10.1002/mp.12890DOI Listing
June 2018

Quantitative assessment of structural image quality.

Neuroimage 2018 04 24;169:407-418. Epub 2017 Dec 24.

Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA. Electronic address:

Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how these measures relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.
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http://dx.doi.org/10.1016/j.neuroimage.2017.12.059DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856621PMC
April 2018

Sex differences in estimated brain metabolism in relation to body growth through adolescence.

J Cereb Blood Flow Metab 2019 03 26;39(3):524-535. Epub 2017 Oct 26.

3 Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.

The human brain consumes a disproportionate amount of the body's overall metabolic resources, and evidence suggests that brain and body may compete for substrate during development. Using perfusion MRI from a large cross-sectional cohort, we examined developmental changes of MRI-derived estimates of brain metabolism, in relation to weight change. Nonlinear models demonstrated that, in childhood, changes in body weight were inversely related to developmental age-related changes in brain metabolism. This inverse relationship persisted through early adolescence, after which body and brain metabolism began to decline. Females achieved maximum body growth approximately two years earlier than males, with a correspondingly earlier stabilization of brain metabolism to adult levels. These findings confirm prior findings with positron emission tomography performed in a much smaller cohort, demonstrate that relative brain metabolism can be inferred from noninvasive MRI data, and extend observations on the associations between body growth and brain metabolism to sex differences through adolescence.
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http://dx.doi.org/10.1177/0271678X17737692DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6421255PMC
March 2019

Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood.

J Neurosci 2017 05 21;37(20):5065-5073. Epub 2017 Apr 21.

Brain Behavior Laboratory, Department of Psychiatry, and.

Developmental structural neuroimaging studies in humans have long described decreases in gray matter volume (GMV) and cortical thickness (CT) during adolescence. Gray matter density (GMD), a measure often assumed to be highly related to volume, has not been systematically investigated in development. We used T1 imaging data collected on the Philadelphia Neurodevelopmental Cohort to study age-related effects and sex differences in four regional gray matter measures in 1189 youths ranging in age from 8 to 23 years. Custom T1 segmentation and a novel high-resolution gray matter parcellation were used to extract GMD, GMV, gray matter mass (GMM; defined as GMD × GMV), and CT from 1625 brain regions. Nonlinear models revealed that each modality exhibits unique age-related effects and sex differences. While GMV and CT generally decrease with age, GMD increases and shows the strongest age-related effects, while GMM shows a slight decline overall. Females have lower GMV but higher GMD than males throughout the brain. Our findings suggest that GMD is a prime phenotype for the assessment of brain development and likely cognition and that periadolescent gray matter loss may be less pronounced than previously thought. This work highlights the need for combined quantitative histological MRI studies. This study demonstrates that different MRI-derived gray matter measures show distinct age and sex effects and should not be considered equivalent but complementary. It is shown for the first time that gray matter density increases from childhood to young adulthood, in contrast with gray matter volume and cortical thickness, and that females, who are known to have lower gray matter volume than males, have higher density throughout the brain. A custom preprocessing pipeline and a novel high-resolution parcellation were created to analyze brain scans of 1189 youths collected as part of the Philadelphia Neurodevelopmental Cohort. A clear understanding of normal structural brain development is essential for the examination of brain-behavior relationships, the study of brain disease, and, ultimately, clinical applications of neuroimaging.
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http://dx.doi.org/10.1523/JNEUROSCI.3550-16.2017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5444192PMC
May 2017

Patterns of Individual Variation in Visual Pathway Structure and Function in the Sighted and Blind.

PLoS One 2016 3;11(11):e0164677. Epub 2016 Nov 3.

Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America.

Many structural and functional brain alterations accompany blindness, with substantial individual variation in these effects. In normally sighted people, there is correlated individual variation in some visual pathway structures. Here we examined if the changes in brain anatomy produced by blindness alter the patterns of anatomical variation found in the sighted. We derived eight measures of central visual pathway anatomy from a structural image of the brain from 59 sighted and 53 blind people. These measures showed highly significant differences in mean size between the sighted and blind cohorts. When we examined the measurements across individuals within each group we found three clusters of correlated variation, with V1 surface area and pericalcarine volume linked, and independent of the thickness of V1 cortex. These two clusters were in turn relatively independent of the volumes of the optic chiasm and lateral geniculate nucleus. This same pattern of variation in visual pathway anatomy was found in the sighted and the blind. Anatomical changes within these clusters were graded by the timing of onset of blindness, with those subjects with a post-natal onset of blindness having alterations in brain anatomy that were intermediate to those seen in the sighted and congenitally blind. Many of the blind and sighted subjects also contributed functional MRI measures of cross-modal responses within visual cortex, and a diffusion tensor imaging measure of fractional anisotropy within the optic radiations and the splenium of the corpus callosum. We again found group differences between the blind and sighted in these measures. The previously identified clusters of anatomical variation were also found to be differentially related to these additional measures: across subjects, V1 cortical thickness was related to cross-modal activation, and the volume of the optic chiasm and lateral geniculate was related to fractional anisotropy in the visual pathway. Our findings show that several of the structural and functional effects of blindness may be reduced to a smaller set of dimensions. It also seems that the changes in the brain that accompany blindness are on a continuum with normal variation found in the sighted.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0164677PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5094697PMC
June 2017

Elevated Amygdala Perfusion Mediates Developmental Sex Differences in Trait Anxiety.

Biol Psychiatry 2016 11 9;80(10):775-785. Epub 2016 May 9.

Departments of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania. Electronic address:

Background: Adolescence is a critical period for emotional maturation and is a time when clinically significant symptoms of anxiety and depression increase, particularly in females. However, few studies relate developmental differences in symptoms of anxiety and depression to brain development. Cerebral blood flow is one brain phenotype that is known to have marked developmental sex differences.

Methods: We investigated whether developmental sex differences in cerebral blood flow mediated sex differences in anxiety and depression symptoms by capitalizing on a large sample of 875 youths who completed cross-sectional imaging as part of the Philadelphia Neurodevelopmental Cohort. Perfusion was quantified on a voxelwise basis using arterial spin-labeled magnetic resonance imaging at 3T. Perfusion images were related to trait and state anxiety using general additive models with penalized splines, while controlling for gray matter density on a voxelwise basis. Clusters found to be related to anxiety were evaluated for interactions with age, sex, and puberty.

Results: Trait anxiety was associated with elevated perfusion in a network of regions including the amygdala, anterior insula, and fusiform cortex, even after accounting for prescan state anxiety. Notably, these relationships strengthened with age and the transition through puberty. Moreover, higher trait anxiety in postpubertal females was mediated by elevated perfusion of the left amygdala.

Conclusions: Taken together, these results demonstrate that differences in the evolution of cerebral perfusion during adolescence may be a critical element of the affective neurobiological characteristics underlying sex differences in anxiety and mood symptoms.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5074881PMC
http://dx.doi.org/10.1016/j.biopsych.2016.04.021DOI Listing
November 2016

Dominant hemisphere lateralization of cortical parasympathetic control as revealed by frontotemporal dementia.

Proc Natl Acad Sci U S A 2016 Apr 11;113(17):E2430-9. Epub 2016 Apr 11.

Memory and Aging Center, University of California, San Francisco, CA 94143-1207; Department of Neurology, University of California, San Francisco, CA 94143; Department of Pathology, University of California, San Francisco, CA 94143

The brain continuously influences and perceives the physiological condition of the body. Related cortical representations have been proposed to shape emotional experience and guide behavior. Although previous studies have identified brain regions recruited during autonomic processing, neurological lesion studies have yet to delineate the regions critical for maintaining autonomic outflow. Even greater controversy surrounds hemispheric lateralization along the parasympathetic-sympathetic axis. The behavioral variant of frontotemporal dementia (bvFTD), featuring progressive and often asymmetric degeneration that includes the frontoinsular and cingulate cortices, provides a unique lesion model for elucidating brain structures that control autonomic tone. Here, we show that bvFTD is associated with reduced baseline cardiac vagal tone and that this reduction correlates with left-lateralized functional and structural frontoinsular and cingulate cortex deficits and with reduced agreeableness. Our results suggest that networked brain regions in the dominant hemisphere are critical for maintaining an adaptive level of baseline parasympathetic outflow.
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http://dx.doi.org/10.1073/pnas.1509184113DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4855566PMC
April 2016

Common and Dissociable Mechanisms of Executive System Dysfunction Across Psychiatric Disorders in Youth.

Am J Psychiatry 2016 05 22;173(5):517-26. Epub 2016 Jan 22.

From the Departments of Psychiatry, of Biostatistics and Epidemiology, of Radiology, and of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia; the NIMH Mood and Anxiety Disorders Program, Bethesda, Md.; the Center for Applied Genomics, Children's Hospital of Philadelphia; and the Philadelphia Veterans Administration Medical Center, Philadelphia.

Objective: Disruption of executive function is present in many neuropsychiatric disorders. However, determining the specificity of executive dysfunction across these disorders is challenging given high comorbidity of conditions. Here the authors investigate executive system deficits in association with dimensions of psychiatric symptoms in youth using a working memory paradigm. The authors hypothesize that common and dissociable patterns of dysfunction would be present.

Method: The authors studied 1,129 youths who completed a fractal n-back task during functional magnetic resonance imaging at 3-T as part of the Philadelphia Neurodevelopmental Cohort. Factor scores of clinical psychopathology were calculated using an item-wise confirmatory bifactor model, describing overall psychopathology as well as four orthogonal dimensions of symptoms: anxious-misery (mood and anxiety), behavioral disturbance (attention deficit hyperactivity disorder and conduct disorder), psychosis-spectrum symptoms, and fear (phobias). The effect of psychopathology dimensions on behavioral performance and executive system recruitment (2-back > 0-back) was examined using both multivariate (matrix regression) and mass-univariate (linear regression) analyses.

Results: Overall psychopathology was associated with both abnormal multivariate patterns of activation and a failure to activate executive regions within the cingulo-opercular control network, including the frontal pole, cingulate cortex, and anterior insula. In addition, psychosis-spectrum symptoms were associated with hypoactivation of the left dorsolateral prefrontal cortex, whereas behavioral symptoms were associated with hypoactivation of the frontoparietal cortex and cerebellum. In contrast, anxious-misery symptoms were associated with widespread hyperactivation of the executive network.

Conclusions: These findings provide novel evidence that common and dissociable deficits within the brain's executive system are present in association with dimensions of psychopathology in youth.
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http://dx.doi.org/10.1176/appi.ajp.2015.15060725DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4886342PMC
May 2016

The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort.

Neuroimage 2016 Jan 28;125:903-919. Epub 2015 Oct 28.

Neuropsychiatry Section, Department of Psychiatry, USA; Department of Radiology, University of Pennsylvania, Perelman School of Medicine, USA.

Background: Diffusion tensor imaging (DTI) is applied in investigation of brain biomarkers for neurodevelopmental and neurodegenerative disorders. However, the quality of DTI measurements, like other neuroimaging techniques, is susceptible to several confounding factors (e.g., motion, eddy currents), which have only recently come under scrutiny. These confounds are especially relevant in adolescent samples where data quality may be compromised in ways that confound interpretation of maturation parameters. The current study aims to leverage DTI data from the Philadelphia Neurodevelopmental Cohort (PNC), a sample of 1601 youths with ages of 8-21 who underwent neuroimaging, to: 1) establish quality assurance (QA) metrics for the automatic identification of poor DTI image quality; 2) examine the performance of these QA measures in an external validation sample; 3) document the influence of data quality on developmental patterns of typical DTI metrics.

Methods: All diffusion-weighted images were acquired on the same scanner. Visual QA was performed on all subjects completing DTI; images were manually categorized as Poor, Good, or Excellent. Four image quality metrics were automatically computed and used to predict manual QA status: Mean voxel intensity outlier count (MEANVOX), Maximum voxel intensity outlier count (MAXVOX), mean relative motion (MOTION) and temporal signal-to-noise ratio (TSNR). Classification accuracy for each metric was calculated as the area under the receiver-operating characteristic curve (AUC). A threshold was generated for each measure that best differentiated visual QA status and applied in a validation sample. The effects of data quality on sensitivity to expected age effects in this developmental sample were then investigated using the traditional MRI diffusion metrics: fractional anisotropy (FA) and mean diffusivity (MD). Finally, our method of QA is compared with DTIPrep.

Results: TSNR (AUC=0.94) best differentiated Poor data from Good and Excellent data. MAXVOX (AUC=0.88) best differentiated Good from Excellent DTI data. At the optimal threshold, 88% of Poor data and 91% Good/Excellent data were correctly identified. Use of these thresholds on a validation dataset (n=374) indicated high accuracy. In the validation sample 83% of Poor data and 94% of Excellent data was identified using thresholds derived from the training sample. Both FA and MD were affected by the inclusion of poor data in an analysis of an age, sex and race matched comparison sample. In addition, we show that the inclusion of poor data results in significant attenuation of the correlation between diffusion metrics (FA and MD) and age during a critical neurodevelopmental period. We find higher correspondence between our QA method and DTIPrep for Poor data, but we find our method to be more robust for apparently high-quality images.

Conclusion: Automated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a practical example of the confounding effects of artifacts on DTI analysis in a large population-based sample, and suggest that estimates of data quality should not only be reported but also accounted for in data analysis, especially in studies of development.
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http://dx.doi.org/10.1016/j.neuroimage.2015.10.068DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4753778PMC
January 2016

Impact of puberty on the evolution of cerebral perfusion during adolescence.

Proc Natl Acad Sci U S A 2014 Jun 27;111(23):8643-8. Epub 2014 May 27.

Brain and Behavior Laboratory, Department of Psychiatry,Department of Biostatistics and Epidemiology.

Puberty is the defining biological process of adolescent development, yet its effects on fundamental properties of brain physiology such as cerebral blood flow (CBF) have never been investigated. Capitalizing on a sample of 922 youths ages 8-22 y imaged using arterial spin labeled MRI as part of the Philadelphia Neurodevelopmental Cohort, we studied normative developmental differences in cerebral perfusion in males and females, as well as specific associations between puberty and CBF. Males and females had conspicuously divergent nonlinear trajectories in CBF evolution with development as modeled by penalized splines. Seventeen brain regions, including hubs of the executive and default mode networks, showed a robust nonlinear age-by-sex interaction that surpassed Bonferroni correction. Notably, within these regions the decline in CBF was similar between males and females in early puberty and only diverged in midpuberty, with CBF actually increasing in females. Taken together, these results delineate sex-specific growth curves for CBF during youth and for the first time to our knowledge link such differential patterns of development to the effects of puberty.
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http://dx.doi.org/10.1073/pnas.1400178111DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4060665PMC
June 2014

Linked Sex Differences in Cognition and Functional Connectivity in Youth.

Cereb Cortex 2015 Sep 18;25(9):2383-94. Epub 2014 Mar 18.

Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA Department of Radiology, Perelman Scholl of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA Philadelphia Veterans Administration Medical Center, Philadelphia, PA 19104, USA.

Sex differences in human cognition are marked, but little is known regarding their neural origins. Here, in a sample of 674 human participants ages 9-22, we demonstrate that sex differences in cognitive profiles are related to multivariate patterns of resting-state functional connectivity MRI (rsfc-MRI). Males outperformed females on motor and spatial cognitive tasks; females were faster in tasks of emotion identification and nonverbal reasoning. Sex differences were also prominent in the rsfc-MRI data at multiple scales of analysis, with males displaying more between-module connectivity, while females demonstrated more within-module connectivity. Multivariate pattern analysis using support vector machines classified subject sex on the basis of their cognitive profile with 63% accuracy (P < 0.001), but was more accurate using functional connectivity data (71% accuracy; P < 0.001). Moreover, the degree to which a given participant's cognitive profile was "male" or "female" was significantly related to the masculinity or femininity of their pattern of brain connectivity (P = 2.3 × 10(-7)). This relationship was present even when considering males and female separately. Taken together, these results demonstrate for the first time that sex differences in patterns of cognition are in part represented on a neural level through divergent patterns of brain connectivity.
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http://dx.doi.org/10.1093/cercor/bhu036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537416PMC
September 2015

Functional maturation of the executive system during adolescence.

J Neurosci 2013 Oct;33(41):16249-61

Departments of Psychiatry and Radiology, and Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia PA 19104, Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia PA 19104, and Philadelphia Veterans Administration Medical Center, Philadelphia PA 19104.

Adolescence is characterized by rapid development of executive function. Working memory (WM) is a key element of executive function, but it is not known what brain changes during adolescence allow improved WM performance. Using a fractal n-back fMRI paradigm, we investigated brain responses to WM load in 951 human youths aged 8-22 years. Compared with more limited associations with age, WM performance was robustly associated with both executive network activation and deactivation of the default mode network. Multivariate patterns of brain activation predicted task performance with a high degree of accuracy, and also mediated the observed age-related improvements in WM performance. These results delineate a process of functional maturation of the executive system, and suggest that this process allows for the improvement of cognitive capability seen during adolescence.
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http://dx.doi.org/10.1523/JNEUROSCI.2345-13.2013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3792462PMC
October 2013

Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth.

Neuroimage 2013 Dec 21;83:45-57. Epub 2013 Jun 21.

Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address:

Several independent studies have demonstrated that small amounts of in-scanner motion systematically bias estimates of resting-state functional connectivity. This confound is of particular importance for studies of neurodevelopment in youth because motion is strongly related to subject age during this period. Critically, the effects of motion on connectivity mimic major findings in neurodevelopmental research, specifically an age-related strengthening of distant connections and weakening of short-range connections. Here, in a sample of 780 subjects ages 8-22, we re-evaluate patterns of change in functional connectivity during adolescent development after rigorously controlling for the confounding influences of motion at both the subject and group levels. We find that motion artifact inflates both overall estimates of age-related change as well as specific distance-related changes in connectivity. When motion is more fully accounted for, the prevalence of age-related change as well as the strength of distance-related effects is substantially reduced. However, age-related changes remain highly significant. In contrast, motion artifact tends to obscure age-related changes in connectivity associated with segregation of functional brain modules; improved preprocessing techniques allow greater sensitivity to detect increased within-module connectivity occurring with development. Finally, we show that subject's age can still be accurately estimated from the multivariate pattern of functional connectivity even while controlling for motion. Taken together, these results indicate that while motion artifact has a marked and heterogeneous impact on estimates of connectivity change during adolescence, functional connectivity remains a valuable phenotype for the study of neurodevelopment.
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http://dx.doi.org/10.1016/j.neuroimage.2013.06.045DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3874413PMC
December 2013

Intrinsic connectivity network disruption in progressive supranuclear palsy.

Ann Neurol 2013 May 27;73(5):603-16. Epub 2013 Mar 27.

Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.

Objective: Progressive supranuclear palsy (PSP) has been conceptualized as a large-scale network disruption, but the specific network targeted has not been fully characterized. We sought to delineate the affected network in patients with clinical PSP.

Methods: Using task-free functional magnetic resonance imaging, we mapped intrinsic connectivity to the dorsal midbrain tegmentum (dMT), a region that shows focal atrophy in PSP. Two healthy control groups (1 young, 1 older) were used to define and replicate the normal connectivity pattern, and patients with PSP were compared to an independent matched healthy control group on measures of network connectivity.

Results: Healthy young and older subjects showed a convergent pattern of connectivity to the dMT, including brainstem, cerebellar, diencephalic, basal ganglia, and cortical regions involved in skeletomotor, oculomotor, and executive control. Patients with PSP showed significant connectivity disruptions within this network, particularly within corticosubcortical and cortico-brainstem interactions. Patients with more severe functional impairment showed lower mean dMT network connectivity scores.

Interpretation: This study defines a PSP-related intrinsic connectivity network in the healthy brain and demonstrates the sensitivity of network-based imaging methods to PSP-related physiological and clinical changes.
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http://dx.doi.org/10.1002/ana.23844DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3732833PMC
May 2013

Predicting regional neurodegeneration from the healthy brain functional connectome.

Neuron 2012 Mar 21;73(6):1216-27. Epub 2012 Mar 21.

Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA.

Neurodegenerative diseases target large-scale neural networks. Four competing mechanistic hypotheses have been proposed to explain network-based disease patterning: nodal stress, transneuronal spread, trophic failure, and shared vulnerability. Here, we used task-free fMRI to derive the healthy intrinsic connectivity patterns seeded by brain regions vulnerable to any of five distinct neurodegenerative diseases. These data enabled us to investigate how intrinsic connectivity in health predicts region-by-region vulnerability to disease. For each illness, specific regions emerged as critical network "epicenters" whose normal connectivity profiles most resembled the disease-associated atrophy pattern. Graph theoretical analyses in healthy subjects revealed that regions with higher total connectional flow and, more consistently, shorter functional paths to the epicenters, showed greater disease-related vulnerability. These findings best fit a transneuronal spread model of network-based vulnerability. Molecular pathological approaches may help clarify what makes each epicenter vulnerable to its targeting disease and how toxic protein species travel between networked brain structures.
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http://dx.doi.org/10.1016/j.neuron.2012.03.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3361461PMC
March 2012

Network-level structural covariance in the developing brain.

Proc Natl Acad Sci U S A 2010 Oct 4;107(42):18191-6. Epub 2010 Oct 4.

Department of Neurology, University of California, San Francisco, CA 94143-0114, USA.

Intrinsic or resting state functional connectivity MRI and structural covariance MRI have begun to reveal the adult human brain's multiple network architectures. How and when these networks emerge during development remains unclear, but understanding ontogeny could shed light on network function and dysfunction. In this study, we applied structural covariance MRI techniques to 300 children in four age categories (early childhood, 5-8 y; late childhood, 8.5-11 y; early adolescence, 12-14 y; late adolescence, 16-18 y) to characterize gray matter structural relationships between cortical nodes that make up large-scale functional networks. Network nodes identified from eight widely replicated functional intrinsic connectivity networks served as seed regions to map whole-brain structural covariance patterns in each age group. In general, structural covariance in the youngest age group was limited to seed and contralateral homologous regions. Networks derived using primary sensory and motor cortex seeds were already well-developed in early childhood but expanded in early adolescence before pruning to a more restricted topology resembling adult intrinsic connectivity network patterns. In contrast, language, social-emotional, and other cognitive networks were relatively undeveloped in younger age groups and showed increasingly distributed topology in older children. The so-called default-mode network provided a notable exception, following a developmental trajectory more similar to the primary sensorimotor systems. Relationships between functional maturation and structural covariance networks topology warrant future exploration.
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http://dx.doi.org/10.1073/pnas.1003109107DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2964249PMC
October 2010

Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease.

Brain 2010 May 21;133(Pt 5):1352-67. Epub 2010 Apr 21.

Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA 94117, USA.

Resting-state or intrinsic connectivity network functional magnetic resonance imaging provides a new tool for mapping large-scale neural network function and dysfunction. Recently, we showed that behavioural variant frontotemporal dementia and Alzheimer's disease cause atrophy within two major networks, an anterior 'Salience Network' (atrophied in behavioural variant frontotemporal dementia) and a posterior 'Default Mode Network' (atrophied in Alzheimer's disease). These networks exhibit an anti-correlated relationship with each other in the healthy brain. The two diseases also feature divergent symptom-deficit profiles, with behavioural variant frontotemporal dementia undermining social-emotional function and preserving or enhancing visuospatial skills, and Alzheimer's disease showing the inverse pattern. We hypothesized that these disorders would exert opposing connectivity effects within the Salience Network (disrupted in behavioural variant frontotemporal dementia but enhanced in Alzheimer's disease) and the Default Mode Network (disrupted in Alzheimer's disease but enhanced in behavioural variant frontotemporal dementia). With task-free functional magnetic resonance imaging, we tested these ideas in behavioural variant frontotemporal dementia, Alzheimer's disease and healthy age-matched controls (n = 12 per group), using independent component analyses to generate group-level network contrasts. As predicted, behavioural variant frontotemporal dementia attenuated Salience Network connectivity, most notably in frontoinsular, cingulate, striatal, thalamic and brainstem nodes, but enhanced connectivity within the Default Mode Network. Alzheimer's disease, in contrast, reduced Default Mode Network connectivity to posterior hippocampus, medial cingulo-parieto-occipital regions and the dorsal raphe nucleus, but intensified Salience Network connectivity. Specific regions of connectivity disruption within each targeted network predicted intrinsic connectivity enhancement within the reciprocal network. In behavioural variant frontotemporal dementia, clinical severity correlated with loss of right frontoinsular Salience Network connectivity and with biparietal Default Mode Network connectivity enhancement. Based on these results, we explored whether a combined index of Salience Network and Default Mode Network connectivity might discriminate between the three groups. Linear discriminant analysis achieved 92% clinical classification accuracy, including 100% separation of behavioural variant frontotemporal dementia and Alzheimer's disease. Patients whose clinical diagnoses were supported by molecular imaging, genetics, or pathology showed 100% separation using this method, including four diagnostically equivocal 'test' patients not used to train the algorithm. Overall, the findings suggest that behavioural variant frontotemporal dementia and Alzheimer's disease lead to divergent network connectivity patterns, consistent with known reciprocal network interactions and the strength and deficit profiles of the two disorders. Further developed, intrinsic connectivity network signatures may provide simple, inexpensive, and non-invasive biomarkers for dementia differential diagnosis and disease monitoring.
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http://dx.doi.org/10.1093/brain/awq075DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2912696PMC
May 2010