Hemodynamic response function description in patients with glioma.

INTRODUCTION: Functional magnetic resonance imaging is a powerful tool that has provided many insights into cognitive sciences. Yet, as its analysis is mostly based on the knowledge of an a priori canonical hemodynamic response function (HRF), its reliability in patients' applications has been questioned. There have been reports of neurovascular uncoupling in patients with glioma, but no specific description of the Hemodynamic Response Function (HRF) in glioma has been reported so far. The aim of this work is to describe the HRF in patients with glioma. METHODS: Forty patients were included. MR images were acquired on a 1.5T scanner. Activated clusters were identified using a fuzzy general linear model; HRFs were adjusted with a double-gamma function. Analyses were undertaken considering the tumor grade, age, sex, tumor location, and activated location. RESULTS: Differences are found in the occipital, limbic, insular, and sub-lobar areas, but not in the frontal, temporal, and parietal lobes. The presence of a glioma slows the time-to-peak and onset times by 5.2 and 3.8 % respectively; high-grade gliomas present 8.1 % smaller HRF widths than low-grade gliomas. DISCUSSION AND CONCLUSION: There is significant HRF variation due to the presence of glioma, but the magnitudes of the observed differences are small. Most processing pipelines should be robust enough for this magnitude of variation and little if any impact should be visible on functional maps. The differences that have been observed in the literature between functional mapping obtained with magnetic resonance vs. that obtained with direct electrostimulation during awake surgery are more probably due to the intrinsic difference in the mapping process: fMRI mapping detects all recruited areas while intra-surgical mapping indicates only the areas indispensable for the realization of a certain task. Surgical mapping might not be the gold standard to use when trying to validate the fMRI mapping process.

Cortical mapping in glioma surgery: correlation of fMRI and direct electrical stimulation with Human Connectome Project parcellations.

OBJECTIVE: Noninvasive brain mapping with functional MRI (fMRI) and mapping with direct electrical stimulation (DES) are important tools in glioma surgery, but the evidence is inconclusive regarding the sensitivity and specificity of fMRI. The Human Connectome Project (HCP) proposed a new cortical parcellation that has not been thoroughly tested in a clinical setting. The main goal of this study was to evaluate the correlation of fMRI and DES mapping with HCP areas in a clinical setting, and to evaluate the performance of fMRI mapping in motor and language tasks in patients with glioma, using DES as the gold standard. METHODS: Forty patients with supratentorial gliomas were examined using preoperative fMRI and underwent awake craniotomy with DES. Functional activation maps were visualized on a 3D representation of the cortex, classified according to HCP areas, and compared with surgical mapping. RESULTS: Functional MRI was successful in identifying language and motor HCP areas in most cases, including novel areas such as 55b and the superior longitudinal fasciculus (SLF). Functional MRI had a sensitivity and specificity of 100% and 71%, respectively, for motor function in HCP area 4. Sensitivity and specificity were different according to the area and fMRI protocol; i.e., semantic protocols performed better in Brodmann area (BA) 55b/peri-sylvian language areas with 100% sensitivity and 20% specificity, and word production protocols in BAs 44 and 45 with 70% sensitivity and 80% specificity. Some compensation patterns could be observed, such as motor activation of the postcentral gyrus in precentral gliomas. CONCLUSIONS: HCP areas can be detected in clinical scenarios of glioma surgery. These areas appear relatively stable across patients, but compensation patterns seem to differ, allowing occasional resection of activating areas. Newly described areas such as 55b and SLF can act as critical areas in language networks. Surgical planning should account for these parcellations.

Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults.

The population is aging worldwide, creating new challenges to the quality of life of older adults and their families. Falls are an increasing, but not inevitable, threat to older adults. Information technologies provide several solutions to address falls, but smart homes and the most available solutions require expensive and invasive infrastructures. In this study, we propose a novel approach to classify and detect falls of older adults in their homes through low-resolution infrared sensors that are affordable, non-intrusive, do not disturb privacy, and are more acceptable to older adults. Using data collected between 2019 and 2020 with the eHomeseniors platform, we determine activity scores of older adults moving across two rooms in a house and represent an older adult fall through skeletonization. We find that our twofold approach effectively detects activity patterns and precisely identifies falls. Our study provides insights to physicians about the daily activities of their older adults and could potentially help them make decisions in case of abnormal behavior.

Impact of b-Value Sampling Scheme on Brain IVIM Parameter Estimation in Healthy Subjects.

PURPOSE: Intravoxel incoherent motion (IVIM) analysis has attracted the interest of the clinical community due to its close relationship with microperfusion. Nevertheless, there is no clear reference protocol for its implementation; one of the questions being which b-value distribution to use. This study aimed to stress the importance of the sampling scheme and to show that an optimized b-value distribution decreases the variance associated with IVIM parameters in the brain with respect to a regular distribution in healthy volunteers. METHODS: Ten volunteers were included in this study; images were acquired on a 1.5T MR scanner. Two distributions of 16 b-values were used: one considered 'regular' due to its close association with that used in other studies, and the other considered 'optimized' according to previous studies. IVIM parameters were adjusted according to the bi-exponential model, using two-step method. Analysis was undertaken in ROI defined using in the Automated Anatomical Labeling atlas, and parameters distributions were compared in a total of 832 ROI. RESULTS: Maps with fewer speckles were obtained with the 'optimized' distribution. Coefficients of variation did not change significantly for the estimation of the diffusion coefficient D but decreased by approximately 39% for the pseudo-diffusion coefficient estimation and by 21% for the perfusion fraction. Distributions of adjusted parameters were found significantly different in 50% of the cases for the perfusion fraction, in 80% of the cases for the pseudo-diffusion coefficient and 17% of the cases for the diffusion coefficient. Observations across brain areas show that the range of average values for IVIM parameters is smaller in the 'optimized' case. CONCLUSION: Using an optimized distribution, data are sampled in a way that the IVIM signal decay is better described and less variance is obtained in the fitted parameters. The increased precision gained could help to detect small variations in IVIM parameters.

Study protocol and rationale of the "Cogni-action project" a cross-sectional and randomized controlled trial about physical activity, brain health, cognition, and educational achievement in schoolchildren.

BACKGROUND: Education and health are crucial topics for public policies as both largely determine the future wellbeing of the society. Currently, several studies recognize that physical activity (PA) benefits brain health in children. However, most of these studies have not been carried out in developing countries or lack the transference into the education field. The Cogni-Action Project is divided into two stages, a cross-sectional study and a crossover-randomized trial. The aim of the first part is to establish the associations of PA, sedentarism, and physical fitness with brain structure and function, cognitive performance and academic achievement in Chilean schoolchildren (10-13 years-old). The aim of the second part is to determinate the acute effects of three PA protocols on neuroelectric indices during a working memory and a reading task. METHODS: PA and sedentarism will be self-reported and objectively-assessed with accelerometers in a representative subsample, whilst physical fitness will be evaluated through the ALPHA fitness test battery. Brain structure and function will be assessed by magnetic resonance imaging (MRI) in a randomized subsample. Cognitive performance will be assessed through the NeuroCognitive Performance Test, and academic achievement by school grades. In the second part 32 adolescents (12-13 year-old) will be cross-over randomized to these condition (i) "Moderate-Intensity Continuous Training" (MICT), (ii) "Cooperative High-Intensity Interval Training" (C-HIIT), and (iii) Sedentary condition. Neuroelectric indices will be measures by electroencephalogram (EEG) and eye-tracking, working memory by n-back task and reading comprehension by a reading task. DISCUSSION: The main strength of this project is that, to our knowledge, this is the first study analysing the potential association of PA, sedentarism, and physical fitness on brain structure and function, cognitive performance, and academic achievement in a developing country, which presents an important sociocultural gap. For this purpose, this project will use advanced technologies in neuroimaging (MRI), electrophysiology (EEG), and eye-tracking, as well as objective and quality measurements of several physical and cognitive health outcomes. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03894241 Date of register: March 28, 2019. Retrospectively Registered.

Ant Colony Clustering for ROI Identification in Functional Magnetic Resonance Imaging.

Brain network analysis using functional magnetic resonance imaging (fMRI) is a widely used technique. The first step of brain network analysis in fMRI is to detect regions of interest (ROIs). The signals from these ROIs are then used to evaluate neural networks and quantify neuronal dynamics. The two main methods to identify ROIs are based on brain atlas registration and clustering. This work proposes a bioinspired method that combines both paradigms. The method, dubbed HAnt, consists of an anatomical clustering of the signal followed by an ant clustering step. The method is evaluated empirically in both in silico and in vivo experiments. The results show a significantly better performance of the proposed approach compared to other brain parcellations obtained using purely clustering-based strategies or atlas-based parcellations.