Thinner inner retinal layers are associated with lower cognitive performance, lower brain volume, and altered white matter network structure-The Maastricht Study

INTRODUCTION

The retina may provide non-invasive, scalable biomarkers for monitoring cerebral neurodegeneration.

METHODS

We used cross-sectional data from The Maastricht study (n = 3436; mean age 59.3 years; 48% men; and 21% with type 2 diabetes [the latter oversampled by design]). We evaluated associations of retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses with cognitive performance and magnetic resonance imaging indices (global grey and white matter volume, hippocampal volume, whole brain node degree, global efficiency, clustering coefficient, and local efficiency).

RESULTS

After adjustment, lower thicknesses of most inner retinal layers were significantly associated with worse cognitive performance, lower grey and white matter volume, lower hippocampal volume, and worse brain white matter network structure assessed from lower whole brain node degree, lower global efficiency, higher clustering coefficient, and higher local efficiency.

DISCUSSION

The retina may provide biomarkers that are informative of cerebral neurodegenerative changes in the pathobiology of dementia.

MULTIPLY ROBUST ESTIMATORS OF CAUSAL EFFECTS FOR SURVIVAL OUTCOMES

Multiply robust estimators of the longitudinal g-formula have recently been proposed to protect against model misspecification better than the standard augmented inverse probability weighted estimator (Rotnitzky et al., 2017; Luedtke et al., 2018). These multiply robust estimators ensure consistency if one of the models for the treatment process or outcome process is correctly specified at each time point. We study the multiply robust estimators of Rotnitzky et al. (2017) in the context of a survival outcome. Specifically, we compare various estimators of the g-formula for survival outcomes in order to 1) understand how the estimators may be related to one another, 2) understand each estimator's robustness to model misspecification, and 3) construct estimators that can be more efficient than others in certain model misspecification scenarios. We propose a modification of the multiply robust estimators to gain efficiency under misspecification of the outcome model by using calibrated propensity scores over non-calibrated propensity scores at each time point. Theoretical results are confirmed via simulation studies, and a practical comparison of these estimators is conducted through an application to the US Veterans Aging Cohort Study.

Local efficiency analysis of restingstate functional brain network in methamphetamine users

This study set out to assess restingstate functional connectivity (rs-FN) and graph theorybased local efficiency within the left and right hemispheres of methamphetamine (MA) abusers. Functional brain networks of 19 MA abusers and 21 control participants were analyzed using restingstate fMRI. Graph edges in functional networks of the brain were defined and recurrence plot was used. We found that MA abuse may be accompanied by alterations of rs-FN within the defaultmode network (DMN), executive control network (ECN), and the salience network (SN) in both hemispheres of the brain. We also observed that such effects of MA may be correlated with duration of MA abuse and abstinence in many components of the DMN and SN. The results would seem to suggest that MAinduced alterations of local efficiency may, in part, account for maladaptive decision making, deficits in executive function and control over drug seeking/taking, and relapse.

Dysfunctional Dynamics of Intra- and Inter-network Connectivity in Dementia With Lewy Bodies

Dementia with Lewy bodies (DLB) is characterized by the transient fluctuating cognition and recurrent visual hallucinations, which may be caused by disorders of the intrinsic brain network dynamics. However, little is known regarding the dynamic features of the brain network behind these symptoms in DLB. In the present study, the intra- and inter-brain network dynamics were explored on a time scale in 17 DLB and 20 healthy controls (HC) applying a sliding-window method followed by k-means clustering analysis. To further evaluate the impact of network dynamics on brain performance, the local and global efficiency of the brain network was calculated. Compared with HC, the dynamic functional connectivity variation matrix in DLB patients was represented by a mixed change of intra-network increase and inter-network decrease. DLB patients devoted more time to a negative connectivity pattern, which represents a state of functional separation. Furthermore, the local efficiency of DLB patients was significantly lower compared with HC. These observations indicate an altered dynamic variability and disorders to the time allocation of state sequences in DLB, which might result in a disturbance of the intricate brain network dynamic properties, thereby leading to a lack of integration and flexibility and an ineffective brain function. In conclusion, dynamic functional connectivity analysis could identify differences between DLB and HC, providing evidences for DLB diagnosis and contributing to the understanding of the widespread clinical features and complex treatment strategies in DLB patients.

Cognitive functioning in glioma patients is related to functional connectivity measures of the non-tumoural hemisphere

Previous studies have shown that cognitive functioning in patients with brain tumour is associated with the functional network characteristics of specific resting‐state networks or with whole‐brain network characteristics. These studies, however, did not acknowledge the functional contribution of areas in the contralesional, non‐tumoural hemisphere, even though these healthy remote areas likely play a critical role in compensating for the loss of function in damaged tissue. In the current study, we examined whether there is an association between cognitive performance and functional network features of the contralesional hemisphere of patients with glioma. We found that local efficiency of the contralesional hemisphere was associated with performance on the reaction time domain, whereas contralesional assortativity was associated with complex attention and cognitive flexibility scores. Our results suggest that a less segregated organization of the contralesional hemisphere is associated with better reaction time scores, whereas a better spread of information over the contralesional hemisphere through mutually interconnected contralesional hubs is associated with better cognitive flexibility and better complex attention scores. These findings urge researchers to recognize the functional contribution of remote, undamaged regions and to focus more on the graph metrics of the contralesional hemisphere in the search for predictors of cognitive functioning in patients with brain tumour.

Brain networks modeling for studying the mechanism underlying the development of Alzheimer's disease

Alzheimer's disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions. Although connections between changes in brain networks of Alzheimer's disease patients have been established, the mechanisms that drive these alterations remain incompletely understood. This study, which was conducted in 2018 at Northeastern University in China, included data from 97 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset covering genetics, imaging, and clinical data. All participants were divided into two groups: normal control (n = 52; 20 males and 32 females; mean age 73.90 ± 4.72 years) and Alzheimer's disease (n = 45, 23 males and 22 females; mean age 74.85 ± 5.66). To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer's disease patients, we proposed a local naïve Bayes brain network model based on graph theory. Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined, including clustering coefficient, modularity, characteristic path length, network efficiency, betweenness, and degree distribution compared with empirical methods. This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer's disease patients. Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions. The ADNI was performed in accordance with the Good Clinical Practice guidelines, US 21CFR Part 50-Protection of Human Subjects, and Part 56-Institutional Review Boards (IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards (IRBs)/Research Ethics Boards (REBs).

Resting-state network complexity and magnitude changes in neonates with severe hypoxic ischemic encephalopathy

Resting-state functional magnetic resonance imaging has revealed disrupted brain network connectivity in adults and teenagers with cerebral palsy. However, the specific brain networks implicated in neonatal cases remain poorly understood. In this study, we recruited 14 term-born infants with mild hypoxic ischemic encephalopathy and 14 term-born infants with severe hypoxic ischemic encephalopathy from Changzhou Children’s Hospital, China. Resting-state functional magnetic resonance imaging data showed efficient small-world organization in whole-brain networks in both the mild and severe hypoxic ischemic encephalopathy groups. However, compared with the mild hypoxic ischemic encephalopathy group, the severe hypoxic ischemic encephalopathy group exhibited decreased local efficiency and a low clustering coefficient. The distribution of hub regions in the functional networks had fewer nodes in the severe hypoxic ischemic encephalopathy group compared with the mild hypoxic ischemic encephalopathy group. Moreover, nodal efficiency was reduced in the left rolandic operculum, left supramarginal gyrus, bilateral superior temporal gyrus, and right middle temporal gyrus. These results suggest that the topological structure of the resting state functional network in children with severe hypoxic ischemic encephalopathy is clearly distinct from that in children with mild hypoxic ischemic encephalopathy, and may be associated with impaired language, motion, and cognition. These data indicate that it may be possible to make early predictions regarding brain development in children with severe hypoxic ischemic encephalopathy, enabling early interventions targeting brain function. This study was approved by the Regional Ethics Review Boards of the Changzhou Children’s Hospital (approval No. 2013-001) on January 31, 2013. Informed consent was obtained from the family members of the children. The trial was registered with the Chinese Clinical Trial Registry (registration number: ChiCTR1800016409) and the protocol version is 1.0.

Neurophysiology of juvenile myoclonic epilepsy: EEG-based network and graph analysis of the interictal and immediate preictal states

INTRODUCTION

The neuronal mechanisms of enduring seizure propensity and seizure precipitation in juvenile myoclonic epilepsy (JME) are not known. We investigated these issues, within the framework of the "network concept" of epilepsy.

METHODS

Design1: 19, unmedicated JME patients were compared with nineteen, age-, and sex-matched normal control persons (NC). A total of 120s, artifact-free, paroxysm-free, eyes-closed, resting state EEG background activity was analyzed for each person. Design2: interictal and immediate preictal periods of the JME patients were compared in order to explore interictal-preictal network differences. For both comparison designs, statistically significant differences of EEG functional connectivity (EEGfC), nodal and global graph parameters were evaluated.

MAIN RESULTS

Design1: maximum abnormalities were: increased delta, theta, alpha1 EEGfC and decreased alpha2 and beta EEGfC in the JME group as compared to the NC group, mainly among cortical areas that are involved in sensory-motor integration. Nodal degree and efficiency of three, medial, basal frontal nodes were greater in JME than in NC, in the alpha1 band. Design2: preictal delta EEGfC showed further increase in the above-mentioned areas, as compared to the interictal state.

DISCUSSION

Increased EEGfC indicates a hypercoupled state among the specified cortical areas. This interictal abnormality further increases in the preictal state. Nodal graph statistics indicates abnormal neuronal dynamics in the cortical area that is the ictal onset zone in JME.

SIGNIFICANCE

Interictal and preictal neuronal dysfunction has been described in terms of network dynamics and topography in JME patients. Forthcoming investigations of seizure precipitation and therapeutic drug effects are encouraged on this basis.

Semiparametric Theory for Causal Mediation Analysis: efficiency bounds, multiple robustness, and sensitivity analysis

Whilst estimation of the marginal (total) causal effect of a point exposure on an outcome is arguably the most common objective of experimental and observational studies in the health and social sciences, in recent years, investigators have also become increasingly interested in mediation analysis. Specifically, upon evaluating the total effect of the exposure, investigators routinely wish to make inferences about the direct or indirect pathways of the effect of the exposure not through or through a mediator variable that occurs subsequently to the exposure and prior to the outcome. Although powerful semiparametric methodologies have been developed to analyze observational studies, that produce double robust and highly efficient estimates of the marginal total causal effect, similar methods for mediation analysis are currently lacking. Thus, this paper develops a general semiparametric framework for obtaining inferences about so-called marginal natural direct and indirect causal effects, while appropriately accounting for a large number of pre-exposure confounding factors for the exposure and the mediator variables. Our analytic framework is particularly appealing, because it gives new insights on issues of efficiency and robustness in the context of mediation analysis. In particular, we propose new multiply robust locally efficient estimators of the marginal natural indirect and direct causal effects, and develop a novel double robust sensitivity analysis framework for the assumption of ignorability of the mediator variable.