Minimal Relationship between Local Gyrification and General Cognitive Ability in Humans.

Previous studies suggest that gyrification is associated with superior cognitive abilities in humans, but the strength of this relationship remains unclear. Here, in two samples of related individuals (total N = 2882), we calculated an index of local gyrification (LGI) at thousands of cortical surface points using structural brain images and an index of general cognitive ability (g) using performance on cognitive tests. Replicating previous studies, we found that phenotypic and genetic LGI-g correlations were positive and statistically significant in many cortical regions.

Neurocognitive impairment in type 2 diabetes: evidence for shared genetic aetiology.

Aims/hypothesis: Type 2 diabetes is associated with cognitive impairments, but it is unclear whether common genetic factors influence both type 2 diabetes risk and cognition.

Methods: Using data from 1892 Mexican-American individuals from extended pedigrees, including 402 with type 2 diabetes, we examined possible pleiotropy between type 2 diabetes and cognitive functioning, as measured by a comprehensive neuropsychological test battery.

Results: Negative phenotypic correlations (ρ) were observed between type 2 diabetes and measures of attention (Continuous Performance Test [CPT d']: ρ = -0.

Reduced cerebral blood flow in an -synuclein transgenic mouse model of Parkinson's disease.

There is increasing evidence that widespread cortical cerebral blood flow deficits occur early in the course of Parkinson's disease. Although cerebral blood flow measurement has been suggested as a potential biomarker for early diagnosis of Parkinson's disease, as well as a means for tracking response to treatment, the relationship of cerebral blood flow to α-synucleinopathy, a major pathological hallmark of Parkinson's disease, remains unclear. Therefore, we performed arterial spin-labeling magnetic resonance imaging and diffusion tensor imaging on transgenic mice overexpressing human wild-type α-synuclein and age-matched controls to measure cerebral blood flow and degenerative changes.

Connectivity patterns of cognitive control network in first episode medication-naive depression and remitted depression.

Background: Cognitive dysfunctions, such as impaired cognitive control, are frequently observed in patients with major depressive disorder (MDD). Although the cognitive control network (CCN) is widely considered a core feature of major depressive disorder (MDD), the relationship between cognitive dysfunction and symptom dimensions remains unclear. This study investigated differences in resting-state functional connectivity of the cognitive control network (CCN) between first-episode medication-naive MDD patients and remitted MDD.

A resting-state network comparison of combat-related PTSD with combat-exposed and civilian controls.

Resting-state functional connectivity (rsFC) is an emerging means of understanding the neurobiology of combat-related post-traumatic stress disorder (PTSD). However, most rsFC studies to date have limited focus to cognitively related intrinsic connectivity networks (ICNs), have not applied data-driven methodologies or have disregarded the effect of combat exposure. In this study, we predicted that group independent component analysis (GICA) would reveal group-wise differences in rsFC across 50 active duty service members with PTSD, 28 combat-exposed controls (CEC), and 25 civilian controls without trauma exposure (CC).

Localised grey matter atrophy in multiple sclerosis is network-based: a coordinate-based meta-analysis.

Aim: To test the network degeneration hypothesis in multiple sclerosis (MS) with a two-stage coordinate-based meta-analysis by: (1) characterising regional selectivity of grey matter (GM) atrophy and (2) testing for functional connectivity involving these regions.

Materials And Methods: Meta-analytic sources included 33 journal articles (1,666 MS patients and 1,269 healthy controls) with coordinate-based results from voxel-based morphometry analysis demonstrating GM atrophy. Mass univariate and multivariate coordinate-based meta-analyses were performed to identify a convergent pattern of GM atrophy and determine inter-regional co-activation (as a surrogate of functional connectivity), with anatomical likelihood estimation and functional meta-analytic connectivity modelling, respectively.

Brain-based ranking of cognitive domains to predict schizophrenia.

Schizophrenia is a devastating brain disorder that disturbs sensory perception, motor action, and abstract thought. Its clinical phenotype implies dysfunction of various mental domains, which has motivated a series of theories regarding the underlying pathophysiology. Aiming at a predictive benchmark of a catalog of cognitive functions, we developed a data-driven machine-learning strategy and provide a proof of principle in a multisite clinical dataset (n = 324).

Cardiac changes in epileptic baboons with high-frequency microburst VNS therapy: A pilot study.

The epileptic baboon provides a natural model of idiopathic generalized epilepsy and sudden unexpected death in epilepsy (SUDEP). We sought to evaluate autonomic differences, including heart rate (HR), heart rate variability (HRV) and corrected QT-duration (QTc) between two epileptic (EB1, EB2) and one control (CB) baboon, and the autonomic effects of high-frequency (HF) microburst Vagal Nerve Stimulation (VNS) Therapy in the epileptic baboons. At baseline, EB2's HR was increased over both EB1 and CB, and EB1's HRV was decreased compared to the others.

Beyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis.

Coordinate-based meta-analysis can provide important insights into mind-brain relationships. A popular approach for curated small-scale meta-analysis is activation likelihood estimation (ALE), which identifies brain regions consistently activated across a selected set of experiments, such as within a functional domain or mental disorder. ALE can also be utilized in meta-analytic co-activation modeling (MACM) to identify brain regions consistently co-activated with a seed region.

Evidence for genetic correlation between human cerebral white matter microstructure and inflammation.

White matter microstructure is affected by immune system activity via the actions of circulating pro-inflammatory cytokines. Although white matter microstructure and inflammatory measures are significantly heritable, it is unclear if overlapping genetic factors influence these traits in humans. We conducted genetic correlation analyses of these traits using randomly ascertained extended pedigrees from the Genetics of Brain Structure and Function Study (N = 1862, 59% females, ages 18-97 years; 42 ± 15.

Salience Network Disruption in U.S. Army Soldiers With Posttraumatic Stress Disorder.

Background: Better understanding of the neurobiology of posttraumatic stress disorder (PTSD) may be critical to developing novel, effective therapeutics. Here, we conducted a data-driven investigation using a well-established, graph-based topological measure of nodal strength to determine the extent of functional dysconnectivity in a cohort of active duty US Army soldiers with PTSD compared to controls.

Methods: 102 participants with (n=50) or without PTSD (n=52) completed functional magnetic resonance imaging (MRI) at rest and during symptom provocation using subject-specific script imagery.

Computational Modeling of Human Left Ventricle to Assess the Effects of Trabeculae Carneae on the Diastolic and Systolic Functions.

Trabeculae carneae are irregular structures that cover the endocardial surfaces of both ventricles and account for a significant portion of human ventricular mass. The role of trabeculae carneae in diastolic and systolic functions of the left ventricle (LV) is not well understood. Thus, the objective of this study was to investigate the functional role of trabeculae carneae in the LV.

Longitudinal changes in brain function associated with symptom improvement in youth with PTSD.

Background: Previous studies indicate that youth with posttraumatic stress disorder (PTSD) have abnormal activation in brain regions important for emotion processing. It is unknown whether symptom improvement is accompanied by normative changes in these regions. This study identified neural changes associated with symptom improvement with the long-term goal of identifying malleable targets for interventions.

Functional brain alterations in acute sleep deprivation: An activation likelihood estimation meta-analysis.

Sleep deprivation (SD) is a common problem in modern societies, which leads to cognitive dysfunctions including attention lapses, impaired working memory, hindering decision making, impaired emotional processing, and motor vehicle accidents. Numerous neuroimaging studies have investigated the neural correlates of SD, but these studies have reported inconsistent results. Thus, we aimed to identify convergent patterns of abnormal brain functions due to acute SD.

A neural signature of metabolic syndrome.

That metabolic syndrome (MetS) is associated with age-related cognitive decline is well established. The neurobiological changes underlying these cognitive deficits, however, are not well understood. The goal of this study was to determine whether MetS is associated with regional differences in gray-matter volume (GMV) using a cross-sectional, between-group contrast design in a large, ethnically homogenous sample.

Reduced Salience and Enhanced Central Executive Connectivity Following PTSD Treatment.

Background: In soldiers with posttraumatic stress disorder (PTSD), symptom provocation was found to induce increased connectivity within the salience network, as measured by functional magnetic resonance imaging (MRI) and global brain connectivity with global signal regression (GBCr). However, it is unknown whether these GBCr disturbances would normalize following effective PTSD treatment.

Methods: 69 US Army soldiers with (n = 42) and without PTSD (n = 27) completed MRI at rest and during symptom provocation using subject-specific script imagery.

Functional MRI of the mouse olfactory system.

Although olfactory dysfunction is an early warning sign of Alzheimer's and Parkinson's diseases, and is commonly present in a range of other neurodegenerative disorders, the mechanisms for its pathogenesis are not yet clear. Since fMRI allows the mapping of spatial and temporal patterns of activity in multiple brain regions simultaneously, it serves as a powerful tool to study olfactory dysfunction in animal models of neurodegenerative diseases. Nonetheless, there have been no reports to date of mapping odor-induced activation patterns beyond the olfactory bulb to the extended networks of olfactory and limbic archicortex, likely due to the small size of the mouse brain.

Identifying generalized anxiety disorder using resting state habenular circuitry.

Studies identify the habenula as a key subcortical component in anxiety, with a role in predicting error coding within the evaluative system. However, no clinical reports of generalized anxiety disorder (GAD) describe resting state functional connectivity of habenular circuits. We hypothesized that resting-state functional connectivities of habenula would show differences in neuroanatomical correlates of the evaluative system (prefrontal cortex, habenula) of patients with GAD.

Multimodal Parcellations and Extensive Behavioral Profiling Tackling the Hippocampus Gradient.

The hippocampus displays a complex organization and function that is perturbed in many neuropathologies. Histological work revealed a complex arrangement of subfields along the medial-lateral and the ventral-dorsal dimension, which contrasts with the anterior-posterior functional differentiation. The variety of maps has raised the need for an integrative multimodal view.

Isolation and Differentiation of Self-Renewable Neural Stem Cells from Marmoset-Induced Pluripotent Stem Cells.

Neural stem cells (NSCs) are multipotent and self-renewing precursor cells that give rise to all cell types of the central nervous system (CNS). They can be used for modeling CNS in vitro, for developmental studies and for cell replacement therapies. NSCs can be derived from pluripotent stem cells through differentiation using specific growth factors.

Reference Transcriptome for Deriving Marmoset Induced Pluripotent Stem Cells.

Limited access to primary tissue from various nonhuman primate (NHP) species represents a significant unmet need that hampers progress in understanding unique cellular diversity and gene regulation of specific tissues and organs in stem cell translational research. Most comparative biology studies have been limited to using postmortem tissue usually frozen specimens with limited utility for research. The generation of induced pluripotent stem cell (iPSC) lines from somatic cells, such as adult skin or blood cells, offers an alternative to invasive and ethically controversial interventions for acquiring tissue.

Assay for Assessing Mitochondrial Function in iPSC-Derived Neural Stem Cells and Dopaminergic Neurons.

Rapid and reliable assessment of mitochondrial bioenergetics is a vital tool in drug discovery studies aimed at reversing or improving mitochondrial dysfunction. Induced pluripotent stem cell (iPSC)-derived neural stem cells (NSCs) carry and replicate the donor disease pathology and can be an ideal cellular model for phenotypic screening of compounds. Herein we describe the use of Seahorse XFe96 analyzer to assess mitochondrial functions in iPSC-derived NSCs for drug screening.

Bioinformatics Analysis of Single-Cell RNA-Seq Raw Data from iPSC-Derived Neural Stem Cells.

This chapter describes a pipeline for basic bioinformatics analysis of single-cell sequencing data (see Chap. 10 : Single-Cell Library Preparation). Starting with raw sequencing data, we describe how to quality check samples, to create an index from a reference genome, to align the sequences to an index, and to quantify transcript abundances.

Single-Cell Library Preparation of iPSC-Derived Neural Stem Cells.

Single-cell RNA-seq technology allows for the identification of heterogeneous cell populations, measures stochastic gene expressions, and identifies highly variable genes. Thus, with this technology it is possible to identify relevant pathways involved in development or in disease progression. Herein, we describe a protocol to capture and process single-cell transcriptomes that will be used for RNA sequencing.