Contrasting topologies of synchronous and asynchronous functional brain networks.

We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy and compared aFN topology with the correlation-based synchronous functional networks (sFNs), which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and Neurodevelopment in Adolescence study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks.

Contrasting topologies of synchronous and asynchronous functional brain networks.

We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy (oCSE) and compared aFN topology to the correlation-based synchronous functional networks (sFNs) which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks.

Statistical Brain Network Analysis.

The recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless, methods for statistically analyzing networks at the group and individual levels have lagged behind.

Erratum to: Association between Modifiable Risk Factors and Levels of Blood-Based Biomarkers of Alzheimer's and Related Dementias in the Look AHEAD Cohort.

[This corrects the article DOI: 10.14283/jarlife.2024.

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.

Association between Modifiable Risk Factors and Levels of Blood-Based Biomarkers of Alzheimer's and Related Dementias in the Look AHEAD Cohort.

Background: Emerging evidence suggests that a number of factors can influence blood-based biomarker levels for Alzheimer's disease (AD) and Alzheimer's related dementias (ADRD). We examined the associations that demographic and clinical characteristics have with AD/ADRD blood-based biomarker levels in an observational continuation of a clinical trial cohort of older individuals with type 2 diabetes and overweight or obesity.

Methods: Participants aged 45-76 years were randomized to a 10-year Intensive Lifestyle Intervention (ILI) or a diabetes support and education (DSE) condition.

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.

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.

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.

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.

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.

Successful replantation of amputated penis with adjuvant hyperbaric oxygen therapy.

Successful penile replantations are rarely reported in the literature and are associated with significant complications. We present a case of a patient who auto-amputated his penis. Delayed microvascular replantation was performed approximately 14 hours following injury.

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.

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).

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.

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.

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.

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) β-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.

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.

BCL11A enhancer haplotypes and fetal hemoglobin in sickle cell anemia.

Background: Fetal hemoglobin (HbF) levels in sickle cell anemia patients vary. We genotyped polymorphisms in the erythroid-specific enhancer of BCL11A to see if they might account for the very high HbF associated with the Arab-Indian (AI) haplotype and Benin haplotype of sickle cell anemia.

Methods And Results: Six BCL112A enhancer SNPs and their haplotypes were studied in Saudi Arabs from the Eastern Province and Indian patients with AI haplotype (HbF ~20%), African Americans (HbF ~7%), and Saudi Arabs from the Southwestern Province (HbF ~12%).

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.

Multiclass discrimination of cervical precancers using Raman spectroscopy.

Raman spectroscopy has the potential to differentiate among the various stages leading to high-grade cervical cancer such as normal, squamous metaplasia, and low-grade cancer. For Raman spectroscopy to successfully differentiate among the stages, an applicable statistical method must be developed. Algorithms like linear discriminant analysis (LDA) are incapable of differentiating among three or more types of tissues.