Default-mode network functional connectivity is closely related to metabolic activity

ADAPTIVE TESTING OF SNP-BRAIN FUNCTIONAL CONNECTIVITY ASSOCIATION VIA A MODULAR NETWORK ANALYSIS

Due to its high dimensionality and high noise levels, analysis of a large brain functional network may not be powerful and easy to interpret; instead, decomposition of a large network into smaller subcomponents called modules may be more promising as suggested by some empirical evidence. For example, alteration of brain modularity is observed in patients suffering from various types of brain malfunctions. Although several methods exist for estimating brain functional networks, such as the sample correlation matrix or graphical lasso for a sparse precision matrix, it is still difficult to extract modules from such network estimates. Motivated by these considerations, we adapt a weighted gene co-expression network analysis (WGCNA) framework to resting-state fMRI (rs-fMRI) data to identify modular structures in brain functional networks. Modular structures are identified by using topological overlap matrix (TOM) elements in hierarchical clustering. We propose applying a new adaptive test built on the proportional odds model (POM) that can be applied to a high-dimensional setting, where the number of variables (p) can exceed the sample size (n) in addition to the usual p < n setting. We applied our proposed methods to the ADNI data to test for associations between a genetic variant and either the whole brain functional network or its various subcomponents using various connectivity measures. We uncovered several modules based on the control cohort, and some of them were marginally associated with the APOE4 variant and several other SNPs; however, due to the small sample size of the ADNI data, larger studies are needed.

Cognition and Type 1 Diabetes in Children and Adolescents

IN BRIEF In children and adolescents with type 1 diabetes, exposure to glycemic extremes (severe hypoglycemia, chronic hyperglycemia, and diabetic ketoacidosis) overlaps with the time period of most active brain and cognitive development, leading to concerns that these children are at risk for cognitive side effects. This article summarizes the existing literature examining the impact of glycemic extremes on cognitive function and brain structure in youth with type 1 diabetes and points out areas for future research.

Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction

Functional connectivity describes neural activity from resting-state functional magnetic resonance imaging (rs-fMRI). This noninvasive modality is a promising imaging biomarker of neurodegenerative diseases, such as Alzheimer's disease (AD), where the connectome can be an indicator to assess and to understand the pathology. However, it only provides noisy measurements of brain activity. As a consequence, it has shown fairly limited discrimination power on clinical groups. So far, the reference functional marker of AD is the fluorodeoxyglucose positron emission tomography (FDG-PET). It gives a reliable quantification of metabolic activity, but it is costly and invasive. Here, our goal is to analyze AD populations solely based on rs-fMRI, as functional connectivity is correlated to metabolism. We introduce transmodal learning: leveraging a prior from one modality to improve results of another modality on different subjects. A metabolic prior is learned from an independent FDG-PET dataset to improve functional connectivity-based prediction of AD. The prior acts as a regularization of connectivity learning and improves the estimation of discriminative patterns from distinct rs-fMRI datasets. Our approach is a two-stage classification strategy that combines several seed-based connectivity maps to cover a large number of functional networks that identify AD physiopathology. Experimental results show that our transmodal approach increases classification accuracy compared to pure rs-fMRI approaches, without resorting to additional invasive acquisitions. The method successfully recovers brain regions known to be impacted by the disease.

Brain fluorodeoxyglucose (FDG) PET in dementia

The purpose of this article is to present a selective and concise summary of fluorodeoxyglucose (FDG) positron emission tomography (PET) in dementia imaging. FDG PET is used to visualize a downstream topographical marker that indicates the distribution of neural injury or synaptic dysfunction, and can identify distinct phenotypes of dementia due to Alzheimer's disease (AD), Lewy bodies, and frontotemporal lobar degeneration. AD dementia shows hypometabolism in the parietotemporal association area, posterior cingulate, and precuneus. Hypometabolism in the inferior parietal lobe and posterior cingulate/precuneus is a predictor of cognitive decline from mild cognitive impairment (MCI) to AD dementia. FDG PET may also predict conversion of cognitively normal individuals to those with MCI. Age-related hypometabolism is observed mainly in the anterior cingulate and anterior temporal lobe, along with regional atrophy. Voxel-based statistical analyses, such as statistical parametric mapping or three-dimensional stereotactic surface projection, improve the diagnostic performance of imaging of dementias. The potential of FDG PET in future clinical and methodological studies should be exploited further.

Longitudinal Evidence for Dissociation of Anterior and Posterior MTL Resting-State Connectivity in Aging: Links to Perfusion and Memory

Neuroimaging studies of spontaneous signal fluctuations as measured by resting-state functional magnetic resonance imaging have revealed age-related alterations in the functional architecture of brain networks. One such network is located in the medial temporal lobe (MTL), showing structural and functional variations along the anterior–posterior axis. Past cross-sectional studies of MTL functional connectivity (FC) have yielded discrepant findings, likely reflecting the fact that specific MTL subregions are differentially affected in aging. Here, using longitudinal resting-state data from 198 participants, we investigated 5-year changes in FC of the anterior and posterior MTL. We found an opposite pattern, such that the degree of FC within the anterior MTL declined after age 60, whereas elevated FC within the posterior MTL was observed along with attenuated posterior MTL-cortical connectivity. A significant negative change–change relation was observed between episodic-memory decline and elevated FC in the posterior MTL. Additional analyses revealed age-related cerebral blood flow (CBF) increases in posterior MTL at the follow-up session, along with a positive relation of elevated FC and CBF, suggesting that elevated FC is a metabolically demanding alteration. Collectively, our findings indicate that elevated FC in posterior MTL along with increased local perfusion is a sign of brain aging that underlie episodic-memory decline.

The Whole-Brain "Global" Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism

The evolution of functional magnetic resonance imaging to resting state (R-fMRI) allows measurement of changes in brain networks attributed to state changes, such as in neuropsychiatric diseases versus healthy controls. Since these networks are observed by comparing normalized R-fMRI signals, it is difficult to determine the metabolic basis of such group differences. To investigate the metabolic basis of R-fMRI network differences within a normal range, eyes open versus eyes closed in healthy human subjects was used. R-fMRI was recorded simultaneously with fluoro-deoxyglucose positron emission tomography (FDG-PET). Higher baseline FDG was observed in the eyes open state. Variance-based metrics calculated from R-fMRI did not match the baseline shift in FDG. Functional connectivity density (FCD)-based metrics showed a shift similar to the baseline shift of FDG, however, this was lost if R-fMRI "nuisance signals" were regressed before FCD calculation. Average correlation with the mean R-fMRI signal across the whole brain, generally regarded as a "nuisance signal," also showed a shift similar to the baseline of FDG. Thus, despite lacking a baseline itself, changes in whole-brain correlation may reflect changes in baseline brain metabolism. Conversely, variance-based metrics may remain similar between states due to inherent region-to-region differences overwhelming the differences between normal physiological states. As most previous studies have excluded the spatial means of R-fMRI metrics from their analysis, this work presents the first evidence of a potential R-fMRI biomarker for baseline shifts in quantifiable metabolism between brain states.

Hybrid PET/MR Imaging and Brain Connectivity

In recent years, brain connectivity is gaining ever-increasing interest from the interdisciplinary research community. The study of brain connectivity is characterized by a multifaceted approach providing both structural and functional evidence of the relationship between cerebral regions at different scales. Although magnetic resonance (MR) is the most established imaging modality for investigating connectivity in vivo, the recent advent of hybrid positron emission tomography (PET)/MR scanners paved the way for more comprehensive investigation of brain organization and physiology. Due to the high sensitivity and biochemical specificity of radiotracers, combining MR with PET imaging may enrich our ability to investigate connectivity by introducing the concept of metabolic connectivity and cometomics and promoting new insights on the physiological and molecular bases underlying high-level neural organization. This review aims to describe and summarize the main methods of analysis of brain connectivity employed in MR imaging and nuclear medicine. Moreover, it will discuss practical aspects and state-of-the-art techniques for exploiting hybrid PET/MR imaging to investigate the relationship of physiological processes and brain connectivity.

Changes of cortical connectivity during deep anaesthesia

BACKGROUND AND AIMS

The aim of this study was to evaluate the frontal intracortical connectivity during deep anaesthesia (burst-suppression).

METHODS

Experiments were carried out on 5 adult Sprague Dawley rats. The anaesthesia was induced and maintained with isoflurane. Following the induction of anaesthesia, rats were placed in a stereotactic instrument. A hole was drilled in the skull over the frontal cortex and electrodes were inserted in order to record the local field potentials. Rats were maintained in deep level anaesthesia (burst-suppression). The cortical connectivity was assessed by computing the coherence spectra. The frontal intracortical connectivity was calculated during burst, suppression (non-burst) and slow wave anaesthesia periods.

RESULTS

The global cortical connectivity (0.5-100 Hz) was 0.61 ± 0.078 during the burst periods compared to 0.55 ± 0.032 (p < 0.05) during the suppression periods and 0.55 ± 0.015 (p < 0.05) during slow wave anaesthesia.

CONCLUSIONS

The global cortical connectivity increased during the burst periods compared to the suppression periods and slow wave anaesthesia. This increase in the cortical synchronization might be due to the subcortical origin of the bursts.

Altered resting perfusion and functional connectivity of default mode network in youth with autism spectrum disorder

BACKGROUND

Neuroimaging studies can shed light on the neurobiological underpinnings of autism spectrum disorders (ASD). Studies of the resting brain have shown both altered baseline metabolism from PET/SPECT and altered functional connectivity (FC) of intrinsic brain networks based on resting-state fMRI. To date, however, no study has investigated these two physiological parameters of resting brain function jointly, or explored the relationship between these measures and ASD symptom severity.

METHODS

Here, we used pseudo-continuous arterial spin labeling with 3D background-suppressed GRASE to assess resting cerebral blood flow (CBF) and FC in 17 youth with ASD and 22 matched typically developing (TD) children.

RESULTS

A pattern of altered resting perfusion was found in ASD versus TD children including frontotemporal hyperperfusion and hypoperfusion in the dorsal anterior cingulate cortex. We found increased local FC in the anterior module of the default mode network (DMN) accompanied by decreased CBF in the same area. In our cohort, both alterations were associated with greater social impairments as assessed with the Social Responsiveness Scale (SRS-total T scores). While FC was correlated with CBF in TD children, this association between FC and baseline perfusion was disrupted in children with ASD. Furthermore, there was reduced long-range FC between anterior and posterior modules of the DMN in children with ASD.

CONCLUSION

Taken together, the findings of this study--the first to jointly assess resting CBF and FC in ASD--highlight new avenues for identifying novel imaging markers of ASD symptomatology.

Mapping the mouse brain with rs-fMRI: An optimized pipeline for functional network identification

The use of resting state fMRI (rs-fMRI) in translational research is a powerful tool to assess brain connectivity and investigate neuropathology in mouse models. However, despite encouraging initial results, the characterization of consistent and robust resting state networks in mice remains a methodological challenge. One key reason is that the quality of the measured MR signal is degraded by the presence of structural noise from non-neural sources. Notably, in the current pipeline of the Human Connectome Project, a novel approach has been introduced to clean rs-fMRI data, which involves automatic artifact component classification and data cleaning (FIX). FIX does not require any external recordings of physiology or the segmentation of CSF and white matter. In this study, we evaluated the performance of FIX for analyzing mouse rs-fMRI data. Our results showed that FIX can be easily applied to mouse datasets and detects true signals with 100% accuracy and true noise components with very high accuracy (>98%), thus reducing both within- and between-subject variability of rs-fMRI connectivity measurements. Using this improved pre-processing pipeline, maps of 23 resting state circuits in mice were identified including two networks that displayed default mode network-like topography. Hierarchical clustering grouped these neural networks into meaningful larger functional circuits. These mouse resting state networks, which are publicly available, might serve as a reference for future work using mouse models of neurological disorders.