Situating the salience and parietal memory networks in the context of multiple parallel distributed networks using precision functional mapping.

Brain networks serving higher cognitive functions are widely distributed across frontal and posterior association zones. Two exceptions have been the parietal memory network (PMN) and salience network (SAL), which are typically restricted to posterior (e.g.

Connectome-based Predictive Models of General and Specific Executive Functions.

Executive functions, the set of cognitive control processes that facilitate adaptive thoughts and actions, are composed primarily of three distinct yet interrelated cognitive components: Inhibition, Shifting, and Updating. While prior research has examined the nature of different components as well as their inter-relationships, fewer studies examined whole-brain connectivity to predict individual differences for the three cognitive components and associated tasks. Here, using the Connectome-based Predictive Modelling (CPM) approach and open-access data from the Human Connectome Project, we built brain network models to successfully predict individual performance differences on the Flanker task, the Dimensional Change Card Sort task, and the 2-back task, each putatively corresponding to Inhibition, Shifting, and Updating.

A noncanonical glycoprotein H complex enhances cytomegalovirus entry.

Human cytomegalovirus (HCMV) causes severe birth defects, lifelong health complications, and $4 billion in annual costs in the United States alone. A major challenge in vaccine design is the incomplete understanding of the diverse protein complexes the virus uses to infect cells. In , the gH/gL glycoprotein heterodimer is expected to be a basal element of virion cell entry machinery.

Double dissociation of visuomotor interaction mediated by visual feedback during continuous de novo motor learning.

While the sensorimotor cortices are central neural substrates for motor control and learning, how the interaction between their subregions with visual cortices contributes to acquiring de novo visuomotor skills is poorly understood. We design a continuous visuomotor task in fMRI where participants control a cursor using their fingers while learning an arbitrary finger-to-cursor mapping. To investigate visuomotor interaction in the de novo motor task, we manipulate visual feedback of a cursor such that they learn to control using fingers under two alternating conditions: online cursor feedback is available or unavailable except when a target is reached.

Individualized Structural Perturbations on Normative Brain Connectome Restrict Deep Brain Stimulation Outcomes in Parkinson's Disease.

Background: Patients with Parkinson's disease (PD) respond to deep brain stimulation (DBS) variably. However, how brain substrates restrict DBS outcomes remains unclear.

Objective: In this article, we aim to identify prognostic brain signatures for explaining the response variability.

Edge-Based General Linear Models Capture Moment-to-Moment Fluctuations in Attention.

Although we must prioritize the processing of task-relevant information to navigate life, our ability to do so fluctuates across time. Previous work has identified fMRI functional connectivity (FC) networks that predict an individual's ability to sustain attention and vary with attentional state from 1 min to the next. However, traditional dynamic FC approaches typically lack the temporal precision to capture moment-to-moment network fluctuations.

Edge-based general linear models capture high-frequency fluctuations in attention.

Although we must prioritize the processing of task-relevant information to navigate life, our ability to do so fluctuates across time. Previous work has identified fMRI functional connectivity (FC) networks that predict an individual's ability to sustain attention and vary with attentional state from one minute to the next. However, traditional dynamic FC approaches typically lack the temporal precision to capture moment-by-moment network fluctuations.

Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank.

Background: Physical frailty is a state of increased vulnerability to stressors and is associated with serious health issues. However, how frailty affects and is affected by numerous other factors, including mental health and brain structure, remains underexplored. We aimed to investigate the mutual effects of frailty and health using large, multidimensional data.

Differences in the functional brain architecture of sustained attention and working memory in youth and adults.

Sustained attention (SA) and working memory (WM) are critical processes, but the brain networks supporting these abilities in development are unknown. We characterized the functional brain architecture of SA and WM in 9- to 11-year-old children and adults. First, we found that adult network predictors of SA generalized to predict individual differences and fluctuations in SA in youth.

Functional connectome stability and optimality are markers of cognitive performance.

Patterns of whole-brain fMRI functional connectivity, or connectomes, are unique to individuals. Previous work has identified subsets of functional connections within these patterns whose strength predicts aspects of attention and cognition. However, overall features of these connectomes, such as how stable they are over time and how similar they are to a group-average (typical) or high-performance (optimal) connectivity pattern, may also reflect cognitive and attentional abilities.

Antagonistic network signature of motor function in Parkinson's disease revealed by connectome-based predictive modeling.

Motor impairment is a core clinical feature of Parkinson's disease (PD). Although the decoupled brain connectivity has been widely reported in previous neuroimaging studies, how the functional connectome is involved in motor dysfunction has not been well elucidated in PD patients. Here we developed a distributed brain signature by predicting clinical motor scores of PD patients across multicenter datasets (total n = 236).

A brain-based general measure of attention.

Attention is central to many aspects of cognition, but there is no singular neural measure of a person's overall attentional functioning across tasks. Here, using original data from 92 participants performing three different attention-demanding tasks during functional magnetic resonance imaging, we constructed a suite of whole-brain models that can predict a profile of multiple attentional components (sustained attention, divided attention and tracking, and working memory capacity) for novel individuals. Multiple brain regions across the salience, subcortical and frontoparietal networks drove accurate predictions, supporting a common (general) attention factor across tasks, distinguished from task-specific ones.

Nonuniformity of Whole-Cerebral Neural Resource Allocation, a Neuromarker of the Broad-Task Attention.

The neural basis of attention is thought to involve the allocation of limited neural resources. However, the quantitative validation of this hypothesis remains challenging. Here, we provide quantitative evidence that the nonuniform allocation of neural resources across the whole cerebral gray matter reflects the broad-task process of sustained attention.

The Gpx4NIKO Mouse Is a Versatile Model for Testing Interventions Targeting Ferroptotic Cell Death of Spinal Motor Neurons.

The degeneration and death of motor neurons lead to motor neuron diseases such as amyotrophic lateral sclerosis (ALS). Although the exact mechanism by which motor neuron degeneration occurs is not well understood, emerging evidence implicates the involvement of ferroptosis, an iron-dependent oxidative mode of cell death. We reported previously that treating Gpx4NIKO mice with tamoxifen to ablate the ferroptosis regulator glutathione peroxidase 4 (GPX4) in neurons produces a severe paralytic model resembling an accelerated form of ALS that appears to be caused by ferroptotic cell death of spinal motor neurons.

Enhanced defense against ferroptosis ameliorates cognitive impairment and reduces neurodegeneration in 5xFAD mice.

Oxidative damage including lipid peroxidation is widely reported in Alzheimer's disease (AD) with the peroxidation of phospholipids in membranes being the driver of ferroptosis, an iron-dependent oxidative form of cell death. However, the importance of ferroptosis in AD remains unclear. This study tested whether ferroptosis inhibition ameliorates AD.

Functional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory.

What is the neural basis of individual differences in the ability to hold information in long-term memory (LTM)? Here, we first characterize two whole-brain functional connectivity networks based on fMRI data acquired during an n-back task that robustly predict individual differences in two important forms of LTM, recognition and recollection. We then focus on the recognition memory model and contrast it with a working memory model. Although functional connectivity during the n-back task also predicts working memory performance and the two networks have some shared components, they are also largely distinct from each other: The recognition memory model performance remains robust when we control for working memory, and vice versa.

Using functional connectivity models to characterize relationships between working and episodic memory.

Introduction: Working memory is a critical cognitive ability that affects our daily functioning and relates to many cognitive processes and clinical conditions. Episodic memory is vital because it enables individuals to form and maintain their self-identities. Our study analyzes the extent to which whole-brain functional connectivity observed during completion of an N-back memory task, a common measure of working memory, can predict both working memory and episodic memory.

Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals.

Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns.

An information network flow approach for measuring functional connectivity and predicting behavior.

Introduction: Connectome-based predictive modeling (CPM) is a recently developed machine-learning-based framework to predict individual differences in behavior from functional brain connectivity (FC). In these models, FC was operationalized as Pearson's correlation between brain regions' fMRI time courses. However, Pearson's correlation is limited since it only captures linear relationships.

Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study.

Spatial pattern of the brain network changes dynamically. This change is closely linked to the brain-state transition, which vary depending on a dynamic stream of thoughts. To date, many dynamic methods have been developed for decoding brain-states.

Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors.

Brain functional connectivity features can predict cognition and behavior at the level of the individual. Most studies measure univariate signals, correlating timecourses from the average of constituent voxels in each node. While straightforward, this approach overlooks the spatial patterns of voxel-wise signals within individual nodes.

Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies.

Dynamic functional connectivity (DFC) aims to maximize resolvable information from functional brain scans by considering temporal changes in network structure. Recent work has demonstrated that static, i.e.

Highly efficient genome editing by CRISPR-Cpf1 using CRISPR RNA with a uridinylate-rich 3'-overhang.

Genome editing has been harnessed through the development of CRISPR system, and the CRISPR from Prevotella and Francisella 1 (Cpf1) system has emerged as a promising alternative to CRISPR-Cas9 for use in various circumstances. Despite the inherent multiple advantages of Cpf1 over Cas9, the adoption of Cpf1 has been unsatisfactory because of target-dependent insufficient indel efficiencies. Here, we report an engineered CRISPR RNA (crRNA) for highly efficient genome editing by Cpf1, which includes a 20-base target-complementary sequence and a uridinylate-rich 3'-overhang.