Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures.

In clinical neuroscience, epileptic seizures have been associated with the sudden emergence of coupled activity across the brain. The resulting functional networks-in which edges indicate strong enough coupling between brain regions-are consistent with the notion of percolation, which is a phenomenon in complex networks corresponding to the sudden emergence of a giant connected component. Traditionally, work has concentrated on noise-free percolation with a monotonic process of network growth, but real-world networks are more complex. We develop a class of random graph hidden Markov models (RG-HMMs) for characterizing percolation regimes in noisy, dynamically evolving networks in the presence of edge birth and edge death. This class is used to understand the type of phase transitions undergone in a seizure, and in particular, distinguishing between different percolation regimes in epileptic seizures. We develop a hypothesis testing framework for inferring putative percolation mechanisms. As a necessary precursor, we present an EM algorithm for estimating parameters from a sequence of noisy networks only observed at a longitudinal subsampling of time points. Our results suggest that different types of percolation can occur in human seizures. The type inferred may suggest tailored treatment strategies and provide new insights into the fundamental science of epilepsy.

Children with attention-deficit/hyperactivity disorder spend more time in hyperconnected network states and less time in segregated network states as revealed by dynamic connectivity analysis.

Previous studies in children with attention-deficit/hyperactivity disorder (ADHD) have observed functional brain network disruption on a whole-brain level, as well as on a sub-network level, particularly as related to the default mode network, attention-related networks, and cognitive control-related networks. Given behavioral findings that children with ADHD have more difficulty sustaining attention and more extreme moment-to-moment fluctuations in behavior than typically developing (TD) children, recently developed methods to assess changes in connectivity over shorter time periods (i.e., "dynamic functional connectivity"), may provide unique insight into dysfunctional network organization in ADHD. Thus, we performed a dynamic functional connectivity (FC) analysis on resting state fMRI data from 38 children with ADHD and 79 TD children. We used Hidden semi-Markov models (HSMMs) to estimate six network states, as well as the most probable sequence of states for each participant. We quantified the dwell time, sojourn time, and transition probabilities across states. We found that children with ADHD spent less total time in, and switched more quickly out of, anticorrelated states involving the default mode network and task-relevant networks as compared to TD children. Moreover, children with ADHD spent more time in a hyperconnected state as compared to TD children. These results provide novel evidence that underlying dynamics may drive the differences in static FC patterns that have been observed in ADHD and imply that disrupted FC dynamics may be a mechanism underlying the behavioral symptoms and cognitive deficits commonly observed in children with ADHD.

Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models.

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.

Predictors of Sustained Employment Among Individuals With Serious Mental Illness: Findings From a 5-Year Naturalistic Longitudinal Study.

Despite decades of research, understanding of the employment trajectories of individuals with serious mental illnesses remains elusive. We conducted a 5-year prospective, longitudinal study using a geographically broad sample of individuals who met established criteria for sustained competitive employment (N = 529). We collected data on an annual basis with a specifically designed survey instrument. Despite stable employment at study entry, more than half of the participants experienced work interruptions during the 5-year follow-up period. Predictors of sustained employment included the absence of a trauma diagnosis, Social Security disability income, psychiatric hospitalizations, and difficulties with daily functioning. The presence of a higher quality of life, workplace supports, and a flexible job were also predictive. Results dispel the myth that people with serious mental illnesses cannot be employed for prolonged periods. Interruptions in work trajectories, however, suggest that longer-term supports may increase individuals' capacity to maintain stable employment.

Association of Lonafarnib Treatment vs No Treatment With Mortality Rate in Patients With Hutchinson-Gilford Progeria Syndrome.

Importance: Hutchinson-Gilford progeria syndrome (HGPS) is an extremely rare fatal premature aging disease. There is no approved treatment. Objective: To evaluate the association of monotherapy using the protein farnesyltransferase inhibitor lonafarnib with mortality rate in children with HGPS. Design, Setting, and Participants: Cohort study comparing contemporaneous (birth date ≥1991) untreated patients with HGPS matched with treated patients by age, sex, and continent of residency using conditional Cox proportional hazards regression. Treatment cohorts included patients from 2 single-group, single-site clinical trials (ProLon1 [n = 27; completed] and ProLon2 [n = 36; ongoing]). Untreated patients originated from a separate natural history study (n = 103). The cutoff date for patient follow-up was January 1, 2018. Exposure: Treated patients received oral lonafarnib (150 mg/m2) twice daily. Untreated patients received no clinical trial medications. Main Outcomes and Measures: The primary outcome was mortality. The primary analysis compared treated patients from the first lonafarnib trial with matched untreated patients. A secondary analysis compared the combined cohorts from both lonafarnib trials with matched untreated patients. Results: Among untreated and treated patients (n = 258) from 6 continents, 123 (47.7%) were female; 141 (54.7%) had a known genotype, of which 125 (88.7%) were classic (c.1824C>T in LMNA). When identified (n = 73), the primary cause of death was heart failure (79.4%). The median treatment duration was 2.2 years. Median age at start of follow-up was 8.4 (interquartile range [IQR], 4.8-9.5) years in the first trial cohort and 6.5 (IQR, 3.7-9.0) years in the combined cohort. There was 1 death (3.7%) among 27 patients in the first trial group and there were 9 deaths (33.3%) among 27 patients in the matched untreated group. Treatment was associated with a lower mortality rate (hazard ratio, 0.12; 95% CI, 0.01-0.93; P = .04). In the combined cohort, there were 4 deaths (6.3%) among 63 patients in the treated group and 17 deaths (27.0%) among 63 patients in the matched untreated group (hazard ratio, 0.23; 95% CI, 0.06-0.90; P = .04). Conclusions and Relevance: Among patients with HGPS, lonafarnib monotherapy, compared with no treatment, was associated with a lower mortality rate after 2.2 years of follow-up. Study interpretation is limited by its observational design.

Clinical Trial of the Protein Farnesylation Inhibitors Lonafarnib, Pravastatin, and Zoledronic Acid in Children With Hutchinson-Gilford Progeria Syndrome.

BACKGROUND: Hutchinson-Gilford progeria syndrome is an extremely rare, fatal, segmental premature aging syndrome caused by a mutation in LMNA yielding the farnesylated aberrant protein progerin. Without progerin-specific treatment, death occurs at an average age of 14.6 years from an accelerated atherosclerosis. A previous single-arm clinical trial demonstrated that the protein farnesyltransferase inhibitor lonafarnib ameliorates some aspects of cardiovascular and bone disease. This present trial sought to further improve disease by additionally inhibiting progerin prenylation. METHODS: Thirty-seven participants with Hutchinson-Gilford progeria syndrome received pravastatin, zoledronic acid, and lonafarnib. This combination therapy was evaluated, in addition to descriptive comparisons with the prior lonafarnib monotherapy trial. RESULTS: No participants withdrew because of side effects. Primary outcome success was predefined by improved per-patient rate of weight gain or carotid artery echodensity; 71.0% of participants succeeded (P<0.0001). Key cardiovascular and skeletal secondary variables were predefined. Secondary improvements included increased areal (P=0.001) and volumetric (P<0.001-0.006) bone mineral density and 1.5- to 1.8-fold increases in radial bone structure (P<0.001). Median carotid artery wall echodensity and carotid-femoral pulse wave velocity demonstrated no significant changes. Percentages of participants with carotid (5% to 50%; P=0.001) and femoral (0% to 12%; P=0.13) artery plaques and extraskeletal calcifications (34.4% to 65.6%; P=0.006) increased. Other than increased bone mineral density, no improvement rates exceeded those of the prior lonafarnib monotherapy treatment trial. CONCLUSIONS: Comparisons with lonafarnib monotherapy treatment reveal additional bone mineral density benefit but likely no added cardiovascular benefit with the addition of pravastatin and zoledronic acid. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00879034 and NCT00916747.

A candidate transacting modulator of fetal hemoglobin gene expression in the Arab-Indian haplotype of sickle cell anemia.

Fetal hemoglobin (HbF) levels are higher in the Arab-Indian (AI) ß-globin gene haplotype of sickle cell anemia compared with African-origin haplotypes. To study genetic elements that effect HbF expression in the AI haplotype we completed whole genome sequencing in 14 Saudi AI haplotype sickle hemoglobin homozygotes-seven selected for low HbF (8.2% ± 1.3%) and seven selected for high HbF (23.5% ± 2.6%). An intronic single nucleotide polymorphism (SNP) in ANTXR1, an anthrax toxin receptor (chromosome 2p13), was associated with HbF. These results were replicated in two independent Saudi AI haplotype cohorts of 120 and 139 patients, but not in 76 Saudi Benin haplotype, 894 African origin haplotype and 44 AI haplotype patients of Indian origin, suggesting that this association is effective only in the Saudi AI haplotype background. ANTXR1 variants explained 10% of the HbF variability compared with 8% for BCL11A. These two genes had independent, additive effects on HbF and together explained about 15% of HbF variability in Saudi AI sickle cell anemia patients. ANTXR1 was expressed at mRNA and protein levels in erythroid progenitors derived from induced pluripotent stem cells (iPSCs) and CD34+ cells. As CD34+ cells matured and their HbF decreased ANTXR1 expression increased; as iPSCs differentiated and their HbF increased, ANTXR1 expression decreased. Along with elements in cis to the HbF genes, ANTXR1 contributes to the variation in HbF in Saudi AI haplotype sickle cell anemia and is the first gene in trans to HBB that is associated with HbF only in carriers of the Saudi AI haplotype. Am. J. Hematol. 91:1118-1122, 2016. © 2016 Wiley Periodicals, Inc.

Feasibility of Remote Administration of the Uniform Data Set-Version 3 for Assessment of Older Adults With Mild Cognitive Impairment and Alzheimer's Disease.

OBJECTIVE: Assess the feasibility and concurrent validity of a modified Uniform Data Set version 3 (UDSv3) for remote administration for individuals with normal cognition (NC), mild cognitive impairment (MCI), and early dementia. METHOD: Participants (N = 93) (age: 72.8 [8.9] years; education: 15.6 [2.5] years; 72% female; 84% White) were enrolled from the Wake Forest ADRC. Portions of the UDSv3 cognitive battery, plus the Rey Auditory Verbal Learning Test, were completed by telephone or video within ~6 months of participant's in-person visit. Adaptations for phone administration (e.g., Oral Trails for Trail Making Test [TMT] and Blind Montreal Cognitive Assessment [MoCA] for MoCA) were made. Participants reported on the pleasantness, difficulty, and preference for each modality. Staff provided validity ratings for assessments. Participants' remote data were adjudicated by cognitive experts blinded to the in person-diagnosis (NC [N = 44], MCI [N = 35], Dementia [N = 11], or other [N = 3]). RESULTS: Remote assessments were rated as pleasant as in-person assessments by 74% of participants and equally difficult by 75%. Staff validity rating (video = 92%; phone = 87.5%) was good. Concordance between remote/in-person scores was generally moderate to good (r = .3 -.8; p < .05) except for TMT-A/OTMT-A (r = .3; p > .05). Agreement between remote/in-person adjudicated cognitive status was good (k = .61-.64). CONCLUSIONS: We found preliminary evidence that older adults, including those with cognitive impairment, can be assessed remotely using a modified UDSv3 research battery. Adjudication of cognitive status that relies on remotely collected data is comparable to classifications using in-person assessments.

Brain Network Analysis: A Review on Multivariate Analytical Methods.

Despite the explosive growth of neuroimaging studies aimed at analyzing the brain as a complex system, critical methodological gaps remain to be addressed. Most tools currently used for analyzing network data of the brain are univariate in nature and are based on assumptions borne out of previous techniques not directly related to the big and complex data of the brain. Although graph-based methods have shown great promise, the development of principled multivariate models to address inherent limitations of graph-based methods, such as their dependence on network size and degree distributions, and to allow assessing the effects of multiple phenotypes on the brain and simulating brain networks has largely lagged behind. Although some studies have been made in developing multivariate frameworks to fill this gap, in the absence of a "gold-standard" method or guidelines, choosing the most appropriate method for each study can be another critical challenge for investigators in this multidisciplinary field. Here, we briefly introduce important multivariate methods for brain network analyses in two main categories: data-driven and model-based methods. We discuss whether/how such methods are suited for examining connectivity (edge-level), topology (system-level), or both. This review will aid in choosing an appropriate multivariate method with respect to variables such as network type, number of subjects and brain regions included, and the interest in connectivity, topology, or both. This review is aimed to be accessible to investigators from different backgrounds, with a focus on applications in brain network studies, though the methods may be applicable in other areas too. Impact statement As the U.S. National Institute of Health notes, the rich biomedical data can greatly improve our knowledge of human health if new analytical tools are developed, and their applications are broadly disseminated. A major challenge in analyzing the brain as a complex system is about developing parsimonious multivariate methods, and particularly choosing the most appropriate one among the existing methods with respect to the study variables in this multidisciplinary field. This study provides a review on the most important multivariate methods to aid in helping the most appropriate ones with respect to the desired variables for each study.

A mixed-modeling framework for whole-brain dynamic network analysis.

The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools, which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks by using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks.

Defining the relative impact of muscle versus Schwann cell denervation on functional recovery after delayed nerve repair.

Functional recovery following peripheral nerve injury worsens with increasing durations of delay prior to repair. From the time of injury until re-innervation occurs, denervated muscle undergoes progressive atrophy that limits the extent to which motor function can be restored. Similarly, Schwann cells (SC) in the distal nerve lacking axonal interaction progressively lose their capacity to proliferate and support regenerating axons. The relative contributions of these processes to diminished functional recovery is unclear. We developed a novel rat model to isolate the effects of SC vs. muscle denervation on functional recovery. Four different groups underwent the following interventions for 12 weeks prior to nerve transfer: 1) muscle denervation; 2) SC denervation; 3) muscle + SC denervation (negative control); 4) no denervation (positive control). Functional recovery was measured weekly using the stimulated grip strength testing (SGST). Animals were sacrificed 13 weeks post nerve transfer. Retrograde labeling was used to assess the number of motor neurons that regenerated their axons. Immunofluorescence was performed to evaluate target muscle re-innervation and atrophy, and to assess the phenotype of the SC within the distal nerve segment. Functional recovery in the muscle denervation and SC denervation groups mirrored that of the negative and positive control groups, respectively. The SC denervation group achieved better functional recovery, with a greater number of reinnervated motor endplates and less muscle atrophy, than the muscle denervation group. Retrograde labeling suggested a higher number of neurons contributing to muscle reinnervation in the muscle denervation group as compared to SC denervation (p > 0.05). The distal nerve segment in the muscle denervation group had a greater proportion of SCs expressing the proliferation marker Ki67 as compared to the SC denervation group (p < 0.05). Conversely, the SC denervation group had a higher percentage of senescent SCs expressing p16 as compared to the muscle denervation group (p < 0.05). The deleterious effects of muscle denervation are more consequential than the effects of SC denervation on functional recovery. The effects of 12 weeks of SC denervation on functional outcome were negligible. Future studies are needed to determine whether longer periods of SC denervation negatively impact functional recovery.

A Paradigm for Longitudinal Complex Network Analysis over Patient Cohorts in Neuroscience.

The study of complex brain networks, where structural or functional connections are evaluated to create an interconnected representation of the brain, has grown tremendously over the past decade. Much of the statistical network science tools for analyzing brain networks have been developed for cross-sectional studies and for the analysis of static networks. However, with both an increase in longitudinal study designs, as well as an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for longitudinal brain network analysis are needed. We propose a paradigm for longitudinal brain network analysis over patient cohorts, with the key challenge being the adaptation of Stochastic Actor-Oriented Models (SAOMs) to the neuroscience setting. SAOMs are designed to capture network dynamics representing a variety of influences on network change in a continuous-time Markov chain framework. Network dynamics are characterized through both endogenous (i.e., network related) and exogenous effects, where the latter include mechanisms conjectured in the literature. We outline an application to the resting-state fMRI setting with data from the Alzheimers Disease Neuroimaging Initiative (ADNI) study. We draw illustrative conclusions at the subject level and make a comparison between elderly controls and individuals with AD.

Temporal Associations Between Smoking and Cardiovascular Disease, 1971 to 2006 (from the Framingham Heart Study).

Smoking has consistently been related to cardiovascular risk. Public health efforts have yielded reduced smoking prevalence and gains in cardiovascular disease (CVD) prevention. We hypothesized that the contribution of tobacco to CVD risk would be attenuated over prospective decades (1971 to 2006) in a community-based cohort. We evaluated 5,041 Framingham Heart Study Offspring Cohort participants (mean age 36.1 years, 52% women) without prevalent CVD. We collected prospective data on smoking status, relevant CVD risk factors, and incident CVD events across prospective decades. We used multivariable-adjusted, Cox proportional hazard models to measure the effect of smoking on incident CVD over 3 prospective 12-year follow-up periods. Our results demonstrated a consistent twofold increased risk of CVD in men who smoke compared with nonsmokers for each 12-year time period spanning from 1971 to 2006. Women who smoked had a 1.5-fold increased CVD risk. Smoking remains an important risk factor despite substantial improvements in the prevention and treatment of CVD. Significant, contemporary improvements in CVD prevention-such as gains in hypertension and cholesterol treatment-have not attenuated the strong and persistent associations between smoking and CVD observed here. In conclusion, our results highlight the importance of continued public health efforts to address smoking as a modifiable exposure that strongly contributes toward CVD risk.

Real world experience with cancer genetic counseling via telephone.

One barrier to genetic testing is the lack of access to genetic counselors. We provided cancer genetic counseling via telephone, through a pilot project for employees of a national health insurer, Aetna, Inc. Knowledge transfer, behavioral intentions, and patient satisfaction were assessed by survey after genetic counseling. Aetna sent an individual email to its employees nationwide notifying them of the availability of a new telephone genetic counseling and testing program and providing a link to take a brief screening questionnaire to determine whether they may be at risk of hereditary cancer. Employees completing the questionnaire received immediate feedback regarding whether there appeared to be a risk of hereditary cancer. If so, they were invited to schedule a telephonic genetic counseling session. After the session, respondents completed an online survey. 397 individuals completed the questionnaire. 39 proceeded with telephone genetic counseling, and 22 completed the follow-up survey, including all 11 women with family history warranting genetic testing. One third reported prior discussion about inherited cancer risk with their primary care provider (PCP); 12% were referred to a geneticist; 20% had an accurate perception of their own cancer risk. After counseling, 94% reported understanding their risk for cancer and 87% were aware of available risk-reduction strategies. 87% of high-risk respondents intended to engage in risk-management interventions. 93% reported high satisfaction. 66% indicated they would not have pursued genetic counseling if it had not been available by phone. Results suggest telephone counseling is a viable option for increasing access to genetic experts. In this sample, telephone counseling increases knowledge of cancer risk, motivates intention to change health-related behaviors, and elicits a high satisfaction level. Consequently, Aetna now offers telephone cancer genetic counseling nationwide as a covered benefit.