Variability of regional glucose metabolism and the topology of functional networks in the human brain.

The brain consumes the most energy per relative mass amongst the organs in the human body. Theoretical and empirical studies have shown that behavioral processes are relatively inexpensive metabolically, and that most energy goes to maintaining the status quo, i.e., the balance of cell membranes' resting potentials and subthreshold spontaneous activity. Spontaneous activity fluctuates across brain regions in a correlated fashion that defines multi-scale hierarchical networks called resting-state networks (RSNs). Different regions of the brain display different metabolic consumption, but the relationship between regional brain metabolism and RSNs is still under investigation. Here, we examine the variability of glucose metabolism across brain regions, measured with the relative standard uptake value (SUVR) using 18F-FDG PET, and the topology of RSNs, measured through graph analysis applied to fMRI resting-state functional connectivity (FC). We found a moderate linear relationship between the strength (STR) of pairwise regional FC and metabolism. Moreover, the linear correlation between SUVR and STR grew stronger as we considered more connected regions (hubs). Regions connecting different RSNs, or connector hubs, showed higher SUVR than regions connecting nodes within the same RSN, or provincial hubs. Our results show that functional connections as probed by fMRI are related to glucose metabolism, especially in a system of provincial and connector hubs.

Archetypes of human cognition defined by time preference for reward and their brain correlates: An evolutionary trade-off approach.

Biological systems carry out multiple tasks in their lifetime, which, in the course of evolution, may lead to trade-offs. In fact phenotypes (different species, individuals within a species, circuits, bacteria, proteins, etc.) cannot be optimal at all tasks, and, according to Pareto optimality theory, lay into a well-defined geometrical distribution (polygons and/or polyhedrons) in the space of traits. The vertices of this distribution contain archetypes, namely phenotypes that are specialists at one of the tasks, whereas phenotypes toward the center of the geometrical distribution show average performance across tasks. We applied this theory to the variability of cognitive and behavioral scores measured in 1206 individuals from the Human Connectome Project. Among all possible combinations of pairs of traits, we found the best fit to Pareto optimality when individuals were plotted in the trait-space of time preferences for reward, evaluated with the Delay Discounting Task (DDT). The DDT measures subjects' preference in choosing either immediate smaller rewards or delayed larger rewards. Time preference for reward was described by a triangular distribution in which each of the three vertices included individuals who used a particular strategy to discount reward. These archetypes accounted for variability on many cognitive, personality, and socioeconomic status variables, as well as differences in brain structure and functional connectivity, with only a weak influence of genetics. In summary, time preference for reward reflects a core variable that biases human phenotypes via natural and cultural selection.

Brain PET and functional MRI: why simultaneously using hybrid PET/MR systems?

INTRODUCTION: In the last 20 years growing attention has been devoted to multimodal imaging. The recent literature is rich of clinical and research studies that have been performed using different imaging modalities on both separate and integrated positron emission tomography (PET) and magnetic resonance (MR) scanners. However, today, hybrid PET/MR systems measure signals related to brain structure, metabolism, neurochemistry, perfusion, and neuronal activity simultaneously, i.e. in the same physiological conditions. A frequently raised question at meeting and symposia is: "Do we really need a hybrid PET/MR system? Are there any advantages over acquiring sequential and separate PET and MR scans?" The present paper is an attempt to answer these questions specifically in relation to PET combined with functional magnetic resonance imaging (fMRI) and arterial spin labeling. EVIDENCE ACQUISITION: We searched (last update: June 2017) the databases PubMed, PMC, Google Scholar and Medline. We also included additional studies if they were cited in the selected articles. No language restriction was applied to the search, but the reviewed articles were all in English. Among all the retrieved articles, we selected only those performed using a hybrid PET/MR system. We found a total of 17 papers that were selected and discussed in three main groups according to the main radiopharmaceutical used: 18F-fluorodeoxyglucose (18F-FDG) (N.=8), 15O-water (15O-H2O) (N.=3) and neuroreceptors (N.=6). EVIDENCE SYNTHESIS: Concerning studies using 18F-FDG, simultaneous PET/fMRI revealed that global aspects of functional organization (e.g. graph properties of functional connections) are partially associated with energy consumption. There are remarkable spatial and functional similarities across modalities, but also discrepant findings. More work is needed on this point. There are only a handful of papers comparing blood flow measurements with PET 15O-H2O and MR arterial spin label (ASL) measures, and they show significant regional CBF differences between these two modalities. However, at least in one study the correlation at the level of gray, white matter, and whole brain is rather good (r=0.94, 0.8, 0.81 respectively). Finally, receptor studies show that simultaneous PET/fMRI could be a useful tool to characterize functional connectivity along with dynamic neuroreceptor adaptation in several physiological (e.g. working memory) or pathological (e.g. pain) conditions, with or without drug administrations. CONCLUSIONS: The simultaneous acquisition of PET (using a number of radiotracers) and functional MRI (using a number of sequences) offers exciting opportunities that we are just beginning to explore. The results thus far are promising in the evaluation of cerebral metabolism/flow, neuroreceptor adaptation, and network's energetic demand.

The Open-Access European Prevention of Alzheimer's Dementia (EPAD) MRI dataset and processing workflow.

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer's Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features - i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features - Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) - were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses.

Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data.

BACKGROUND: Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach. METHODS: The data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D. RESULTS: Healthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy. CONCLUSIONS: Even with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.