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Background: The association of scar on late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) with local electrograms on electroanatomic mapping has been investigated. We aimed to quantify these associations to gain insights regarding LGE-CMR image characteristics of tissues and critical sites that support postinfarct ventricular tachycardia (VT).Methods and Results: LGE-CMR was performed in 23 patients with ischemic cardiomyopathy before VT ablation. Left ventricular wall thickness and postinfarct scar thickness were measured in each of 20 sectors per LGE-CMR short-axis plane. Electroanatomic mapping points were retrospectively registered to the corresponding LGE-CMR images. Multivariable regression analysis, clustered by patient, revealed significant associations among left ventricular wall thickness, postinfarct scar thickness, and intramural scar location on LGE-CMR, and local endocardial electrogram bipolar/unipolar voltage, duration, and deflections on electroanatomic mapping. Anteroposterior and septal/lateral scar localization was also associated with bipolar and unipolar voltage. Antiarrhythmic drug use was associated with electrogram duration. Critical sites of postinfarct VT were associated with >25% scar transmurality, and slow conduction sites with >40 ms stimulus-QRS time were associated with >75% scar transmurality.Conclusions: Critical sites for maintenance of postinfarct VT are confined to areas with >25% scar transmurality. Our data provide insights into the structural substrates for delayed conduction and VT and may reduce procedural time devoted to substrate mapping, overcome limitations of invasive mapping because of sampling density, and enhance magnetic resonance-based ablation by feature extraction from complex images. [ABSTRACT FROM AUTHOR]

Background: There is a substantial history studying the relationship between general intelligence and the core symptoms of autism. However, a gap in knowledge is how dimensional autism symptomatology associates with different components of clinically-relevant hierarchical models of intelligence., Method: We examined correlations between autism diagnostic symptom magnitude (Autism Diagnostic Observational Schedule; ADOS) and a hierarchical statistical model of intelligence. One autistic cohort was tested on the fourth edition of Wechsler Intelligence Scale for Children (WISC-IV; N = 131), and another on the fifth edition (WISC-V; N = 83). We anticipated a convergent pattern of results between cohorts., Results: On WISC-IV, ADOS scores were correlated significantly with g and three out of four intermediate factor scores, which was a broader pattern of correlations than anticipated from the literature. In the WISC-V cohort, only one intermediate factor correlated significantly with the ADOS; correlations with g and the other intermediate factors were less statistically certain. ADOS-factor correlations were larger in the WISC-IV than WISC-V cohort; this difference was significant at the 90% level., Conclusions: WISC-IV shows dimensional relationships with ADOS at multiple points in the hierarchical model of intelligence. Moreover, the current results provide evidence that relationship between core autism symptomatology and the construct of general intelligence may depend on how intelligence is measured. Known cohort effects in the relationship between categorical autism diagnosis and general intelligence have previously been attributed to changes in autism diagnostic practices. To our knowledge, this is the first evidence that differing versions of IQ tests may be implicated., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Anecdotal evidence has suggested that rater-based measures (e.g., parent report) may have strong across-trait/within-individual covariance that detracts from trait-specific measurement precision; rater measurement-related bias may help explain poor correlation within Autism Spectrum Disorder (ASD) samples between rater-based and performance-based measures of the same trait. We used a multi-trait, multi-method approach to examine method-associated bias within an ASD sample ( n = 83). We examined performance/rater-instrument pairs for attention, inhibition, working memory, motor coordination, and core ASD features. Rater-based scores showed an overall greater methodology bias (57% of variance in score explained by method), while performance-based scores showed a weaker methodology bias (22%). The degree of inter-individual variance explained by method alone substantiates an anecdotal concern associated with the use of rater measures in ASD., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

In this study, a longitudinal regression model for covariance matrix outcomes is introduced. The proposal considers a multilevel generalized linear model for regressing covariance matrices on (time-varying) predictors. This model simultaneously identifies covariate-associated components from covariance matrices, estimates regression coefficients, and captures the within-subject variation in the covariance matrices. Optimal estimators are proposed for both low-dimensional and high-dimensional cases by maximizing the (approximated) hierarchical-likelihood function. These estimators are proved to be asymptotically consistent, where the proposed covariance matrix estimator is the most efficient under the low-dimensional case and achieves the uniformly minimum quadratic loss among all linear combinations of the identity matrix and the sample covariance matrix under the high-dimensional case. Through extensive simulation studies, the proposed approach achieves good performance in identifying the covariate-related components and estimating the model parameters. Applying to a longitudinal resting-state functional magnetic resonance imaging data set from the Alzheimer's Disease (AD) Neuroimaging Initiative, the proposed approach identifies brain networks that demonstrate the difference between males and females at different disease stages. The findings are in line with existing knowledge of AD and the method improves the statistical power over the analysis of cross-sectional data., (© The Author 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Previous studies demonstrated limited accuracy of existing guidelines for predicting choledocholithiasis, leading to overutilization of endoscopic retrograde cholangiopancreatography (ERCP). More accurate stratification may improve patient selection for ERCP and allow use of lower-risk modalities., Methods: A machine learning model was developed using patient information from two published cohort studies that evaluated performance of guidelines in predicting choledocholithiasis. Prediction models were developed using the gradient boosting model (GBM) machine learning method. GBM performance was evaluated using 10-fold cross-validation and area under the receiver operating characteristic curve (AUC). Important predictors of choledocholithiasis were identified based on relative importance in the GBM., Results: 1378 patients (mean age 43.3 years; 61.2% female) were included in the GBM and 59.4% had choledocholithiasis. Eight variables were identified as predictors of choledocholithiasis. The GBM had accuracy of 71.5% (SD 2.5%) (AUC 0.79 [SD 0.06]) and performed better than the 2019 American Society for Gastrointestinal Endoscopy (ASGE) guidelines (accuracy 62.4% [SD 2.6%]; AUC 0.63 [SD 0.03]) and European Society of Gastrointestinal Endoscopy (ESGE) guidelines (accuracy 62.8% [SD 2.6%]; AUC 0.67 [SD 0.02]). The GBM correctly categorized 22% of patients directed to unnecessary ERCP by ASGE guidelines, and appropriately recommended as the next management step 48% of ERCPs incorrectly rejected by ESGE guidelines., Conclusions: A machine learning-based tool was created, providing real-time, personalized, objective probability of choledocholithiasis and ERCP recommendations. This more accurately directed ERCP use than existing ASGE and ESGE guidelines, and has the potential to reduce morbidity associated with ERCP or missed choledocholithiasis., Competing Interests: V. Akshintala is co-founder and chief medical officer of Origin Endoscopy Inc. M. Khashab is an advisory board member and consultant for Boston Scientific, Olympus, and Medtronic. S.N. Steinway, B. Tang, J. Telezing, A. Ashok, A. Kamal, C.Y. Yu, N. Jagtap, J.L. Buxbaum, J. Elmunzer, S.B. Wani, and B.S. Caffo declare that they have no conflict of interest., (Thieme. All rights reserved.)

Individuals with anorexia nervosa (AN) exhibit dangerous weight loss due to restricted eating and hyperactivity. Those with AN are predominantly women and most cases have an age of onset during adolescence. Activity-based anorexia (ABA) is a rodent behavioral paradigm that recapitulates many of the features of AN including restricted food intake and hyperactivity, resulting in precipitous weight loss. In addition, there is enhanced sensitivity to the paradigm during adolescence. In ABA, animals are given time-restricted access to food and unlimited access to a running wheel. Under these conditions, most animals increase their running and decrease their food intake resulting in precipitous weight loss until they either die or researchers discontinue the paradigm. Some animals learn to balance their food intake and energy expenditure and are able to stabilize and eventually reverse their weight loss. For these studies, adolescent (postnatal day 33-42), female Sprague Dawley (n = 68) rats were placed under ABA conditions (unlimited access to a running wheel and 1.5 hrs access to food) until they either reached 25% body weight loss or for 7 days. 70.6% of subjects reached 25% body weight loss before 7 days and were designated susceptible to ABA while 29.4% animals were resistant to the paradigm and did not achieve the weight loss criterion. We used discrete time survival analysis to investigate the contribution of food intake and running behavior during distinct time periods both prior to and during ABA to the likelihood of reaching the weight loss criterion and dropping out of ABA. Our analyses revealed risk factors, including total running and dark cycle running, that increased the likelihood of dropping out of the paradigm, as well as protective factors, including age at the start of ABA, the percent of total running exhibited as food anticipatory activity (FAA), and food intake, that reduced the likelihood of dropping out. These measures had predictive value whether taken before or during exposure to ABA conditions. Our findings suggest that certain running and food intake behaviors may be indicative of a phenotype that predisposes animals to susceptibility to ABA. They also provide evidence that running during distinct time periods may reflect functioning of distinct neural circuitry and differentially influence susceptibility and resistance to the paradigm., (Copyright © 2023. Published by Elsevier Inc.)

Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ 1 -type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ 2 -norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database., (© 2022 The International Biometric Society.)

Adolescents who are clinically recovered from concussion continue to show subtle motor impairment on neurophysiological and behavioral measures. However, there is limited information on brain-behavior relationships of persistent motor impairment following clinical recovery from concussion. We examined the relationship between subtle motor performance and functional connectivity of the brain in adolescents with a history of concussion, status post-symptom resolution, and subjective return to baseline. Participants included 27 adolescents who were clinically recovered from concussion and 29 never-concussed, typically developing controls (10-17 years); all participants were examined using the Physical and Neurologic Examination of Subtle Signs (PANESS). Functional connectivity between the default mode network (DMN) or dorsal attention network (DAN) and regions of interest within the motor network was assessed using resting-state functional magnetic resonance imaging (rsfMRI). Compared to controls, adolescents clinically recovered from concussion showed greater subtle motor deficits as evaluated by the PANESS and increased connectivity between the DMN and left lateral premotor cortex. DMN to left lateral premotor cortex connectivity was significantly correlated with the total PANESS score, with more atypical connectivity associated with more motor abnormalities. This suggests that altered functional connectivity of the brain may underlie subtle motor deficits in adolescents who have clinically recovered from concussion. More investigation is required to understand the persistence and longer-term clinical relevance of altered functional connectivity and associated subtle motor deficits to inform whether functional connectivity may serve as an important biomarker related to longer-term outcomes after clinical recovery from concussion., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background: The Wechsler Intelligence Scale for Children (WISC) employs a hierarchical model of general intelligence in which index scores separate out different clinically-relevant aspects of intelligence; the test is designed such that index scores are statistically independent from one another within the normative sample. Whether or not the existing index scores meet the desired psychometric property of being statistically independent within autistic samples is unknown., Method: We conducted a factor analysis on WISC fifth edition (WISC-V) (N = 83) and WISC fourth edition (WISC-IV) (N = 131) subtest data in children with autism. We compared the data-driven exploratory factor analysis with the manual-derived index scores, including in a typically developing (TD) WISC-IV cohort (N = 209)., Results: The WISC-IV TD cohort showed the expected 1:1 relationship between empirically derived factors and manual-derived index scores. We observed less unique correlations between our data-driven factors and manualized IQ index scores in both ASD samples (WISC-IV and WISC-V). In particular, in both WISC-IV and -V, working memory (WM) influenced index scores in autistic individuals that do not load on WM in the normative sample., Conclusions: WISC index scores do not show the desired statistical independence within autistic samples, as judged against an empirically-derived exploratory factor analysis. In particular, within the currently used WISC-V version, WM influences multiple index scores., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22-24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade., Competing Interests: TH is employed by, and inventor on a patent by Johns Hopkins University on the production of brain organoids, which is licensed to AxoSim, New Orleans, LA, USA, and receives royalty shares. TH also consults to AxoSim. LS is employed by Johns Hopkins University and consults to AxoSim, New Orleans, LA, USA. JS is employed by, and inventor on a patent by the University of Luxembourg on the production of midbrain organoids, which is licensed to OrganoTherapeutics SARL, Esch-sur-Alzette, Luxembourg. JLC and BC were employed by AxoSim Inc., New Orleans, LA, United States. JS is also co-founder and shareholder of OrganoTherapeutics SARL. AM is employed by the University of California, San Diego and is co-founder and has equity interest in TISMOO, a company dedicated to genetic analysis and human brain organogenesis, focusing on therapeutic applications customized for autism spectrum disorders and other neurological disorders origin genetics. The terms of this arrangement have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. BK is employed by Cortical Labs Pty Ltd., Melbourne, Australia, and is an inventor on patents for technology related this paper and holds shares in Cortical Labs Pty Ltd., Melbourne, Australia. No specific funding or other incentives were provided for involvement in this publication. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Morales Pantoja, Smirnova, Muotri, Wahlin, Kahn, Boyd, Gracias, Harris, Cohen-Karni, Caffo, Szalay, Han, Zack, Etienne-Cummings, Akwaboah, Romero, Alam El Din, Plotkin, Paulhamus, Johnson, Gilbert, Curley, Cappiello, Schwamborn, Hill, Roach, Tornero, Krall, Parri, Sillé, Levchenko, Jabbour, Kagan, Berlinicke, Huang, Maertens, Herrmann, Tsaioun, Dastgheyb, Habela, Vogelstein and Hartung.)

We used a large convenience sample ( n  = 22,223) from the Simons Powering Autism Research (SPARK) dataset to evaluate causal, explanatory theories of core autism symptoms. In particular, the data-items collected supported the testing of theories that posited altered language abilities as cause of social withdrawal, as well as alternative theories that competed with these language theories. Our results using this large dataset converge with the evolution of the field in the decades since these theories were first proposed, namely supporting primary social withdrawal (in some cases of autism) as a cause of altered language development, rather than vice versa. To accomplish the above empiric goals, we used a highly theory-constrained approach, one which differs from current data-driven modeling trends but is coherent with a very recent resurgence in theory-driven psychology. In addition to careful explication and formalization of theoretical accounts, we propose three principles for future work of this type: specification, quantification, and integration. Specification refers to constraining models with pre-existing data, from both outside and within autism research, with more elaborate models and more veridical measures, and with longitudinal data collection. Quantification refers to using continuous measures of both psychological causes and effects, as well as weighted graphs. This approach avoids "universality and uniqueness" tests that hold that a single cognitive difference could be responsible for a heterogeneous and complex behavioral phenotype. Integration of multiple explanatory paths within a single model helps the field examine for multiple contributors to a single behavioral feature or to multiple behavioral features. It also allows integration of explanatory theories across multiple current-day diagnoses and as well as typical development., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Tang, Levine, Adamek, Wodka, Caffo and Ewen.)

Independent component analysis is commonly applied to functional magnetic resonance imaging (fMRI) data to extract independent components (ICs) representing functional brain networks. While ICA produces reliable group-level estimates, single-subject ICA often produces noisy results. Template ICA is a hierarchical ICA model using empirical population priors to produce more reliable subject-level estimates. However, this and other hierarchical ICA models assume unrealistically that subject effects are spatially independent. Here, we propose spatial template ICA (stICA), which incorporates spatial priors into the template ICA framework for greater estimation efficiency. Additionally, the joint posterior distribution can be used to identify brain regions engaged in each network using an excursions set approach. By leveraging spatial dependencies and avoiding massive multiple comparisons, stICA has high power to detect true effects. We derive an efficient expectation-maximization algorithm to obtain maximum likelihood estimates of the model parameters and posterior moments of the latent fields. Based on analysis of simulated data and fMRI data from the Human Connectome Project, we find that stICA produces estimates that are more accurate and reliable than benchmark approaches, and identifies larger and more reliable areas of engagement. The algorithm is computationally tractable, achieving convergence within 12 hours for whole-cortex fMRI analysis., Competing Interests: Disclosure The authors report that there are no competing interests to declare.

Brain organoids are important models for mimicking some three-dimensional (3D) cytoarchitectural and functional aspects of the brain. Multielectrode arrays (MEAs) that enable recording and stimulation of activity from electrogenic cells offer notable potential for interrogating brain organoids. However, conventional MEAs, initially designed for monolayer cultures, offer limited recording contact area restricted to the bottom of the 3D organoids. Inspired by the shape of electroencephalography caps, we developed miniaturized wafer-integrated MEA caps for organoids. The optically transparent shells are composed of self-folding polymer leaflets with conductive polymer-coated metal electrodes. Tunable folding of the minicaps' polymer leaflets guided by mechanics simulations enables versatile recording from organoids of different sizes, and we validate the feasibility of electrophysiology recording from 400- to 600-μm-sized organoids for up to 4 weeks and in response to glutamate stimulation. Our studies suggest that 3D shell MEAs offer great potential for high signal-to-noise ratio and 3D spatiotemporal brain organoid recording.

A compositional tree refers to a tree structure on a set of random variables where each random variable is a node and composition occurs at each non-leaf node of the tree. As a generalization of compositional data, compositional trees handle more complex relationships among random variables and appear in many disciplines, such as brain imaging, genomics and finance. We consider the problem of sparse regression on data that are associated with a compositional tree and propose a transformation-free tree-based regularized regression method for component selection. The regularization penalty is designed based on the tree structure and encourages a sparse tree representation. We prove that our proposed estimator for regression coefficients is both consistent and model selection consistent. In the simulation study, our method shows higher accuracy than competing methods under different scenarios. By analyzing a brain imaging data set from studies of Alzheimer's disease, our method identifies meaningful associations between memory decline and volume of brain regions that are consistent with current understanding.

Chronic pain has become a global health problem contributing to years lived with disability and reduced quality of life. Advances in the clinical management of chronic pain have been limited due to incomplete understanding of the multiple risk factors and molecular mechanisms that contribute to the development of chronic pain. The Acute to Chronic Pain Signatures (A2CPS) Program aims to characterize the predictive nature of biomarkers (brain imaging, high-throughput molecular screening techniques, or "omics," quantitative sensory testing, patient-reported outcome assessments and functional assessments) to identify individuals who will develop chronic pain following surgical intervention. The A2CPS is a multisite observational study investigating biomarkers and collective biosignatures (a combination of several individual biomarkers) that predict susceptibility or resilience to the development of chronic pain following knee arthroplasty and thoracic surgery. This manuscript provides an overview of data collection methods and procedures designed to standardize data collection across multiple clinical sites and institutions. Pain-related biomarkers are evaluated before surgery and up to 3 months after surgery for use as predictors of patient reported outcomes 6 months after surgery. The dataset from this prospective observational study will be available for researchers internal and external to the A2CPS Consortium to advance understanding of the transition from acute to chronic postsurgical pain., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Berardi, Frey-Law, Sluka, Bayman, Coffey, Ecklund, Vance, Dailey, Burns, Buvanendran, McCarthy, Jacobs, Zhou, Wixson, Balach, Brummett, Clauw, Colquhoun, Harte, Harris, Williams, Chang, Waljee, Fisch, Jepsen, Laurent, Olivier, Langefeld, Howard, Fiehn, Jacobs, Dakup, Qian, Swensen, Lokshin, Lindquist, Caffo, Crainiceanu, Zeger, Kahn, Wager, Taub, Ford, Sutherland and Wandner.)

In this study, we propose a two-stage procedure for hypothesis testing, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using an introduced empirical equivalence bound (EEB). In 2016, the American Statistical Association released a policy statement on P-values to clarify the proper use and interpretation in response to the criticism of reproducibility and replicability in scientific findings. A recent solution to improve reproducibility and transparency in statistical hypothesis testing is to integrate P-values (or confidence intervals) with practical or scientific significance. Similar ideas have been proposed via the equivalence test, where the goal is to infer equality under a presumption (null) of inequality of parameters. However, the definition of scientific significance/equivalence can sometimes be ill-justified and subjective. To circumvent this drawback, we introduce the B-value and the EEB, which are both estimated from the data. Performing a second-stage equivalence test, our procedure offers an opportunity to improve the reproducibility of findings across studies., (© 2022 John Wiley & Sons, Ltd.)

Broca's area is frequently implicated in sentence comprehension but its specific role is debated. Most lesion studies have investigated deficits at the chronic stage. We aimed (1) to use acute imaging to predict which left hemisphere stroke patients will recover sentence comprehension; and (2) to better understand the role of Broca's area in sentence comprehension by investigating acute deficits prior to functional reorganization. We assessed comprehension of canonical and noncanonical sentences in 15 patients with left hemisphere stroke at acute and chronic stages. LASSO regression was used to conduct lesion symptom mapping analyses. Patients with more severe word-level comprehension deficits and a greater proportion of damage to supramarginal gyrus and superior longitudinal fasciculus were likely to experience acute deficits prior to functional reorganization. Broca's area was only implicated in chronic deficits. We propose that when temporoparietal regions are damaged, intact Broca's area can support syntactic processing after functional reorganization occurs., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Studies of brain functional connectivity (FC) typically involve massive univariate tests, performing statistical analysis on each individual connection. In this study we apply a novel whole-matrix regression approach referred to as Covariate Assisted Principal (CAP) regression to identify resting-state FC brain networks associated with attention-deficit/hyperactivity disorder (ADHD) and response control., Methods: Participants included 8-12 year-old children with ADHD (n=115, 29 girls) and typically developing controls (n=102, 35 girls) who completed a resting-state fMRI scan and a go/no-go task (GNG). We modeled three sets of covariates to identify resting-state networks associated with an ADHD diagnosis, sex, and response inhibition (commission errors) and variability (ex-Gaussian parameter tau)., Results: The first network includes FC between striatal-cognitive control (CC) network subregions and thalamic-default mode network (DMN) subregions and is positively related to age. The second consists of FC between CC-visual-somatomotor regions and between CC-DMN subregions and is positively associated with response variability in boys with ADHD. The third consists of FC within the DMN and between DMN-CC-visual regions and differs between boys with and without ADHD. The fourth consists of FC between visual-somatomotor regions and between visual-DMN regions and differs between girls and boys with ADHD and is associated with response inhibition and variability in boys with ADHD. Unique networks were also identified in each of the three models suggesting some specificity to the covariates of interest., Conclusions: These findings demonstrate the utility of our novel covariance regression approach to studying functional brain networks relevant for development, behavior, and psychopathology., Competing Interests: Financial Disclosures Drs. Zhao, Nebel, Mostofsky, Caffo and Rosch reported no biomedical financial interests or potential conflicts of interests. 1These participants were drawn from a larger sample of 690 8–12 year-old children who participated in one of several neuroimaging studies of ADHD conducted at our center between 2008 and 2019, including 318 children with ADHD (228 boys) and 372 TD children (258 boys). Analyses focused on a subset of this sample after excluding participants who did not complete the rs-fMRI scan due to non-compliance (n=23; 16 ADHD), moved excessively during the rs-fMRI scan (n=154; 90 ADHD), or did not have relevant behavioral data (n=296; 97 ADHD).

Background Resting-state functional MRI (rs-fMRI) is a potential alternative to task-based functional MRI (tb-fMRI) for somatomotor network (SMN) identification. Brain networks can also be generated from tb-fMRI by using independent component analysis (ICA). Purpose To investigate whether the SMN can be identified by using ICA from a language task without a motor component, the sentence completion functional MRI (sc-fMRI) task, compared with rs-fMRI. Materials and Methods The sc-fMRI and rs-fMRI scans in patients who underwent presurgical brain mapping between 2012 and 2016 were analyzed, using the same imaging parameters (other than scanning time) on a 3.0-T MRI scanner. ICA was performed on rs-fMRI and sc-fMRI scans with use of a tool to separate data sets into their spatial and temporal components. Two neuroradiologists independently determined the presence of the dorsal SMN (dSMN) and ventral SMN (vSMN) on each study. Groups were compared by using t tests, and logistic regression was performed to identify predictors of the presence of SMNs. Results One hundred patients (mean age, 40.9 years ± 14.8 [standard deviation]; 61 men) were evaluated. The dSMN and vSMN were identified in 86% (86 of 100) and 76% (76 of 100) of rs-fMRI scans and 85% (85 of 100) and 69% (69 of 100) of sc-fMRI scans, respectively. The concordance between rs-fMRI and sc-fMRI for presence of dSMN and vSMN was 75% (75 of 100 patients) and 53% (53 of 100 patients), respectively. In 10 of 14 patients (71%) where rs-fMRI did not show the dSMN, sc-fMRI demonstrated it. This rate was 67% for the vSMN (16 of 24 patients). Conclusion In the majority of patients, independent component analysis of sentence completion task functional MRI scans reliably demonstrated the somatomotor network compared with resting-state functional MRI scans. Identifying target networks with a single sentence completion scan could reduce overall functional MRI scanning times by eliminating the need for separate motor tasks. © RSNA, 2021 Online supplemental material is available for this article . See also the editorial by Field and Birn in this issue.

With advancements in technology, the collection of multiple types of measurements on a common set of subjects is becoming routine in science. Some notable examples include multimodal neuroimaging studies for the simultaneous investigation of brain structure and function and multi-omics studies for combining genetic and genomic information. Integrative analysis of multimodal data allows scientists to interrogate new mechanistic questions. However, the data collection and generation of integrative hypotheses is outpacing available methodology for joint analysis of multimodal measurements. In this article, we study high-dimensional multimodal data integration in the context of mediation analysis. We aim to understand the roles that different data modalities play as possible mediators in the pathway between an exposure variable and an outcome. We propose a mediation model framework with two data types serving as separate sets of mediators and develop a penalized optimization approach for parameter estimation. We study both the theoretical properties of the estimator through an asymptotic analysis and its finite-sample performance through simulations. We illustrate our method with a multimodal brain pathway analysis having both structural and functional connectivity as mediators in the association between sex and language processing., (© 2020 The International Biometric Society.)

The default mode network (DMN) overlaps with regions showing early Alzheimer's Disease (AD) pathology. Age, sex, and apolipoprotein E ɛ4 are the predominant risk factors for developing AD. How these risk factors interact to influence DMN connectivity and connectivity-cognition relationships before the onset of impairment remains unknown. Here, we examined these issues in 475 cognitively normal adults, targeting total DMN connectivity, its anticorrelated network (acDMN), and the DMN-hippocampal component. There were four main findings. First, in the ɛ3 homozygous group, lower DMN and acDMN connectivity was observed with age. Second, sex and ɛ4 modified the relationship between age and connectivity for the DMN and hippocampus with ɛ4 vs. ɛ3 males showing sustained or higher connectivity with age. Third, in the ɛ3 group, age and sex modified connectivity-cognition relationships with the oldest participants having the most differential patterns due to sex. Fourth, ɛ4 carriers with lower connectivity had poorer cognitive performance. Taken together, our results show the three predominant risk factors for AD interact to influence brain function and function-cognition relationships., (Copyright © 2021. Published by Elsevier Inc.)

Allografts from living kidney donors with hypertension may carry subclinical kidney disease from the donor to the recipient and, thus, lead to adverse recipient outcomes. We examined eGFR trajectories and all-cause allograft failure in recipients from donors with versus without hypertension, using mixed-linear and Cox regression models stratified by donor age. We studied a US cohort from 1/1/2005 to 6/30/2017; 49 990 recipients of allografts from younger (<50 years old) donors including 597 with donor hypertension and 21 130 recipients of allografts from older (≥50 years old) donors including 1441 with donor hypertension. Donor hypertension was defined as documented predonation use of antihypertensive therapy. Among recipients from younger donors with versus without hypertension, the annual eGFR decline was -1.03 versus -0.53 ml/min/m 2 (P = 0.002); 13-year allograft survival was 49.7% vs. 59.0% (adjusted allograft failure hazard ratio [aHR] 1.23; 95% CI 1.05-1.43; P = 0.009). Among recipients from older donors with versus without hypertension, the annual eGFR decline was -0.67 versus -0.66 ml/min/m 2 (P = 0.9); 13-year allograft survival was 48.6% versus 52.6% (aHR 1.05; 95% CI 0.94-1.17; P = 0.4). In secondary analyses, our inferences remained similar for risk of death-censored allograft failure and mortality. Hypertension in younger, but not older, living kidney donors is associated with worse recipient outcomes., (© 2021 The Authors. Transplant International published by John Wiley & Sons Ltd on behalf of Steunstichting ESOT.)

In this study, we consider the problem of regressing covariance matrices on associated covariates. Our goal is to use covariates to explain variation in covariance matrices across units. As such, we introduce Covariate Assisted Principal (CAP) regression, an optimization-based method for identifying components associated with the covariates using a generalized linear model approach. We develop computationally efficient algorithms to jointly search for common linear projections of the covariance matrices, as well as the regression coefficients. Under the assumption that all the covariance matrices share identical eigencomponents, we establish the asymptotic properties. In simulation studies, our CAP method shows higher accuracy and robustness in coefficient estimation over competing methods. In an example resting-state functional magnetic resonance imaging study of healthy adults, CAP identifies human brain network changes associated with subject demographics., (© The Author 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: The aim of this study was to examine the effect of age and level of competition on subtle motor performance in adolescents who have recently been medically cleared postconcussion and never-injured controls., Design: Thirty adolescents who were recently medically cleared postconcussion (12-18 yrs) and 30 never-concussed, typically developing controls were examined using the Revised Physical and Neurological Examination of Subtle Signs (PANESS) and the Immediate Post-Concussion Assessment and Cognitive Testing., Results: Older age was associated with better Immediate Post-Concussion Assessment and Cognitive Testing scores in both groups, whereas only the control group showed improved motor performance on the PANESS with increasing age. Adolescents across both groups participating at a higher level of competition (school or travel level) had better motor performance on the PANESS than those participating at a lower level of competition (recreational level or no sports participation). Adolescents medically cleared postconcussion had greater motor deficits on the PANESS than controls did., Conclusion: After medical clearance, adolescents with a history of recent concussion demonstrate alterations in the relationship between motor function and age. The PANESS merits further exploration as a measure that is sensitive to factors affecting motor performance, such as age and level of athletic competition, as well as to persistent subtle motor deficits in adolescents medically cleared postconcussion., Competing Interests: Financial disclosure statements have been obtained, and no conflicts of interest have been reported by the authors or by any individuals in control of the content of this article., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

A lubricating microbicide gel designed for rectal and vaginal use would provide a behaviorally congruent strategy to enhance pre-exposure prophylaxis adherence and reduce HIV infection risk. In this study, we report the first-in-human evaluation of such a gel containing 1% IQP-0528, an investigational antiretroviral. Seven HIV-1-negative participants received one 10 mL rectal dose of radiolabeled 1% IQP-0528 gel. We assessed safety; IQP-0528 pharmacokinetics in plasma, and rectal and vaginal tissue; ex vivo local pharmacodynamics (PD); and colorectal distribution. The 1% gel was determined to be safe with one mild event attributed to study product and no effects on rectal tissue histology. All concentrations measured in plasma and vaginal tissue were below the limit of quantitation. Median IQP-0528 concentrations in rectal tissue exceeded the in vitro EC 95 against HIV-1 (0.07 ng/mg) by 3-5 h of dosing and remained above this concentration for at least 24 h, despite a 3-log reduction in concentration over this duration of time. Rectal tissue PD-assessed by ex vivo HIV challenge-demonstrated significant p24 antigen reduction 3-5 h postdose compared with baseline ( p  = .05), but not 24-26 h postdose ( p  = .75). Single-photon emission computed tomography/computed tomography imaging revealed that product distribution was localized to the rectosigmoid. The IQP-0528 gel possesses desirable features for a topical microbicide including: local safety with no systemic absorption, delivery of locally high IQP-0528 concentrations, and significant reductions in ex vivo HIV infectivity. However, the gel is limited by its rapid clearance and inability to penetrate vaginal tissues following rectal dosing. Clinical Trial Registration number: NCT03082690.

A network of myenteric interstitial cells of Cajal in the corpus of the stomach serves as its "pacemaker", continuously generating a ca 0.05 Hz electrical slow wave, which is transmitted to the brain chiefly by vagal afferents. A recent study combining resting-state functional MRI (rsfMRI) with concurrent surface electrogastrography (EGG), with cutaneous electrodes placed on the epigastrium, found 12 brain regions with activity that was significantly phase-locked with this gastric basal electrical rhythm. Therefore, we asked whether fluctuations in brain resting state networks (RSNs), estimated using a spatial independent component analysis (ICA) approach, might be synchronized with the stomach. In the present study, in order to determine whether any RSNs are phase-locked with the gastric rhythm, an individual participant underwent 22 scanning sessions; in each, two 15-minute runs of concurrent EGG and rsfMRI data were acquired. EGG data from three sessions had weak gastric signals and were excluded; the other 19 sessions yielded a total of 9.5 hours of data. The rsfMRI data were analyzed using group ICA; RSN time courses were estimated; for each run, the phase-locking value (PLV) was computed between each RSN and the gastric signal. To assess statistical significance, PLVs from all pairs of "mismatched" data (EGG and rsfMRI data acquired on different days) were used as surrogate data to generate a null distribution for each RSN. Of a total of 18 RSNs, three were found to be significantly phase-locked with the basal gastric rhythm, namely, a cerebellar network, a dorsal somatosensory-motor network, and a default mode network. Disruptions to the gut-brain axis, which sustains interoceptive feedback between the central nervous system and the viscera, are thought to be involved in various disorders; manifestation of the infra-slow rhythm of the stomach in brain rsfMRI data could be useful for studies in clinical populations., Competing Interests: The authors have declared that no competing interests exist.

Resting-state functional connectivity is an important and widely used measure of individual and group differences. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a modeling approach that regresses whole-brain functional connectivity on covariates. Our approach is a mesoscale approach that enables identification of brain subnetworks. These subnetworks are composite of spatially independent components discovered by a dimension reduction approach (such as whole-brain group ICA) and covariate-related projections determined by the covariate-assisted principal regression, a recently introduced covariance matrix regression method. We demonstrate the efficacy of this approach using a resting-state fMRI dataset of a medium-sized cohort of subjects obtained from the Human Connectome Project. The results suggest that the approach may improve statistical power in detecting interaction effects of gender and alcohol on whole-brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate-related differences in functional connectivity., (© 2020 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

Large-scale longitudinal neuroimaging studies of the infant brain allow us to map the spatiotemporal development of the brain in its early phase. While the postmenstrual age (PMA) is commonly used as a time index to analyze longitudinal MRI data, the nonlinear relationship between PMA and MRI data imposes challenges for downstream analyses. We propose a mathematical model that provides a Developmental Score (DevS) as a data-driven time index to characterize the brain development based on MRI features. 319 diffusion tensor imaging (DTI) datasets were collected from 87 term-born and 66 preterm-born infants at multiple visits, which were automatically segmented based on the JHU neonatal atlas. The mean diffusivity (MD) and fractional anisotropy (FA) in 126 brain parcels were used in the model to derive DevS. We demonstrate that transforming the time index from PMA to DevS improves the linearity of the longitudinal changes in MD and FA in both gray and white matter structures. More importantly, regional developmental differences in DTI metrics between preterm- and term-born infants were identified more clearly using DevS, e.g. 79 structures showed significantly different regression patterns in MD between preterm- and term-born infants, compared to only 27 structures that showed group differences using PMA as the index. Therefore, the DevS model facilitates linear analyses of DTI metrics in the infant brain, and provides a useful tool to characterize altered brain development due to preterm-birth.

An increasing number of studies claim machine learning (ML) predicts transplant outcomes more accurately. However, these claims were possibly confounded by other factors, namely, supplying new variables to ML models. To better understand the prospects of ML in transplantation, we compared ML to conventional regression in a "common" analytic task: predicting kidney transplant outcomes using national registry data. We studied 133 431 adult deceased-donor kidney transplant recipients between 2005 and 2017. Transplant centers were randomly divided into 70% training set (190 centers/97 787 recipients) and 30% validation set (82 centers/35 644 recipients). Using the training set, we performed regression and ML procedures [gradient boosting (GB) and random forests (RF)] to predict delayed graft function, one-year acute rejection, death-censored graft failure C, all-cause graft failure, and death. Their performances were compared on the validation set using -statistics. In predicting rejection, regression (C =  0.601 0.611 0.621 ) actually outperformed GB (C =  0.581 0.591 0.601 ) and RF (C =  0.569 0.579 0.589 ). For all other outcomes, the C-statistics were nearly identical across methods (delayed graft function, 0.717-0.723; death-censored graft failure, 0.637-0.642; all-cause graft failure, 0.633-0.635; and death, 0.705-0.708). Given its shortcomings in model interpretability and hypothesis testing, ML is advantageous only when it clearly outperforms conventional regression; in the case of transplant outcomes prediction, ML seems more hype than helpful., (© 2020 Steunstichting ESOT. Published by John Wiley & Sons Ltd.)

Autism spectrum disorder (ASD) consists of altered performance of a range of skills, including social/communicative and motor skills. It is unclear whether this altered performance results from atypical acquisition or learning of the skills or from atypical "online" performance of the skills. Atypicalities of skilled actions that require both motor and cognitive resources, such as abnormal gesturing, are highly prevalent in ASD and are easier to study in a laboratory context than are social/communicative skills. Imitation has long been known to be impaired in ASD; because learning via imitation is a prime method by which humans acquire skills, we tested the hypothesis that children with ASD show alterations in learning novel gestures via imitation. Eighteen participants with ASD and IQ > 80, ages 8-12.9 years, and 19 typically developing peers performed a task in which they watched a video of a model performing a novel, meaningless arm/hand gesture and copied the gesture. Each gesture video/copy sequence was repeated 4-6 times. Eight gestures were analyzed. Examination of learning trajectories revealed that while children with ASD made nearly as much progress in learning from repetition 1 to repetition 4, the shape of the learning curves differed. Causal modeling demonstrated the shape of the learning curve influenced both the performance of overlearned gestures and autism severity, suggesting that it is in the index of learning mechanisms relevant both to motor skills and to autism core features. Autism Res 2020, 13: 777-784.. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Imitation is a route by which humans learn a wide range of skills, naturally and in therapies. Imitation is known to be altered in autism spectrum disorder (ASD), but learning via imitation has not been rigorously examined. We found that the shape of the learning curve is altered in ASD, in a way that has a significant impact both on measures of autism severity and of other motor skills., (© 2019 International Society for Autism Research, Wiley Periodicals, Inc.)

Causal mediation analysis aims to quantify the intermediate effect of a mediator on the causal pathway from treatment to outcome. When dealing with multiple mediators, which are potentially causally dependent, the possible decomposition of pathway effects grows exponentially with the number of mediators. An existing approach incorporated the principal component analysis (PCA) to address this challenge based on the fact that the transformed mediators are conditionally independent given the orthogonality of the principal components (PCs). However, the transformed mediator PCs, which are linear combinations of original mediators, can be difficult to interpret. A sparse high-dimensional mediation analysis approach is proposed which adopts the sparse PCA method to the mediation setting. The proposed approach is applied to a task-based functional magnetic resonance imaging study, illustrating its ability to detect biologically meaningful results related to an identified mediator.

Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical "template" ICA model where source signals-including known population brain networks and subject-specific signals-are represented as latent variables. For estimation, we derive an expectation maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75-250% higher intra-subject reliability.

The preprocessing pipelines typically used in both task and resting-state functional magnetic resonance imaging (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this article, we illustrate the shortcomings of this approach, as we show how later preprocessing steps can reintroduce artifacts previously removed from the data in prior preprocessing steps. We show that each regression step is a geometric projection of data onto a subspace, and that performing a sequence of projections can move the data into subspaces no longer orthogonal to those previously removed, reintroducing signal related to nuisance covariates. Thus, linear filtering operations are not commutative, and the order in which the preprocessing steps are performed is critical. These issues can arise in practice when any combination of standard preprocessing steps including motion regression, scrubbing, component-based correction, physiological correction, global signal regression, and temporal filtering are performed sequentially. In this work, we focus primarily on rs-fMRI. We illustrate the problem both theoretically and empirically through application to a test-retest rs-fMRI data set, and suggest remedies. These include (a) combining all steps into a single linear filter, or (b) sequential orthogonalization of covariates/linear filters performed in series., (© 2019 Wiley Periodicals, Inc.)

The study of functional brain networks has grown rapidly over the past decade. While most functional connectivity (FC) analyses estimate one static network structure for the entire length of the functional magnetic resonance imaging (fMRI) time series, recently there has been increased interest in studying time-varying changes in FC. Hidden Markov models (HMMs) have proven to be a useful modeling approach for discovering repeating graphs of interacting brain regions (brain states). However, a limitation lies in HMMs assuming that the sojourn time, the number of consecutive time points in a state, is geometrically distributed. This may encourage inaccurate estimation of the time spent in a state before switching to another state. We propose a hidden semi-Markov model (HSMM) approach for inferring time-varying brain networks from fMRI data, which explicitly models the sojourn distribution. Specifically, we propose using HSMMs to find each subject's most probable series of network states and the graphs associated with each state, while properly estimating and modeling the sojourn distribution for each state. We perform a simulation study, as well as an analysis on both task-based fMRI data from an anxiety-inducing experiment and resting-state fMRI data from the Human Connectome Project. Our results demonstrate the importance of model choice when estimating sojourn times and reveal their potential for understanding healthy and diseased brain mechanisms., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience., (© The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Accurate and reliable prediction of clinical progression over time has the potential to improve the outcomes of chronic disease. The classical approach to analyzing longitudinal data is to use (generalized) linear mixed-effect models (GLMM). However, linear parametric models are predicated on assumptions, which are often difficult to verify. In contrast, data-driven machine learning methods can be applied to derive insight from the raw data without a priori assumptions. However, the underlying theory of most machine learning algorithms assume that the data is independent and identically distributed, making them inefficient for longitudinal supervised learning. In this study, we formulate an analytic framework, which integrates the random-effects structure of GLMM into non-linear machine learning models capable of exploiting temporal heterogeneous effects, sparse and varying-length patient characteristics inherent in longitudinal data. We applied the derived mixed-effect machine learning (MEml) framework to predict longitudinal change in glycemic control measured by hemoglobin A1c (HbA1c) among well controlled adults with type 2 diabetes. Results show that MEml is competitive with traditional GLMM, but substantially outperformed standard machine learning models that do not account for random-effects. Specifically, the accuracy of MEml in predicting glycemic change at the 1st, 2nd, 3rd, and 4th clinical visits in advanced was 1.04, 1.08, 1.11, and 1.14 times that of the gradient boosted model respectively, with similar results for the other methods. To further demonstrate the general applicability of MEml, a series of experiments were performed using real publicly available and synthetic data sets for accuracy and robustness. These experiments reinforced the superiority of MEml over the other methods. Overall, results from this study highlight the importance of modeling random-effects in machine learning approaches based on longitudinal data. Our MEml method is highly resistant to correlated data, readily accounts for random-effects, and predicts change of a longitudinal clinical outcome in real-world clinical settings with high accuracy., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance reliability of subject-level FC. However, fully Bayesian approaches are computationally demanding, while empirical Bayesian approaches typically rely on using repeated measures to estimate the variance components in the model. Here, we avoid the need for repeated measures by proposing a novel measurement error model for FC describing the different sources of variance and error, which we use to perform empirical Bayes shrinkage of subject-level FC towards the group average. In addition, since the traditional intra-class correlation coefficient (ICC) is inappropriate for biased estimates, we propose a new reliability measure denoted the mean squared error intra-class correlation coefficient (ICC MSE ) to properly assess the reliability of the resulting (biased) estimates. We apply the proposed techniques to test-retest resting-state fMRI data on 461 subjects from the Human Connectome Project to estimate connectivity between 100 regions identified through independent components analysis (ICA). We consider both correlation and partial correlation as the measure of FC and assess the benefit of shrinkage for each measure, as well as the effects of scan duration. We find that shrinkage estimates of subject-level FC exhibit substantially greater reliability than traditional estimates across various scan durations, even for the most reliable connections and regardless of connectivity measure. Additionally, we find partial correlation reliability to be highly sensitive to the choice of penalty term, and to be generally worse than that of full correlations except for certain connections and a narrow range of penalty values. This suggests that the penalty needs to be chosen carefully when using partial correlations., (Copyright © 2018. Published by Elsevier Inc.)