Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.

Quo Vadis, Nanoparticle-Enabled In Vivo Fluorescence Imaging?

The exciting advancements that we are currently witnessing in terms of novel materials and synthesis approaches are leading to the development of colloidal nanoparticles (NPs) with increasingly greater tunable properties. We have now reached a point where it is possible to synthesize colloidal NPs with functionalities tailored to specific societal demands. The impact of this new wave of colloidal NPs has been especially important in the field of biomedicine. In that vein, luminescent NPs with improved brightness and near-infrared working capabilities have turned out to be optimal optical probes that are capable of fast and high-resolution in vivo imaging. However, luminescent NPs have thus far only reached a limited portion of their potential. Although we believe that the best is yet to come, the future might not be as bright as some of us think (and have hoped!). In particular, translation of NP-based fluorescence imaging from preclinical studies to clinics is not straightforward. In this Perspective, we provide a critical assessment and highlight promising research avenues based on the latest advances in the fields of luminescent NPs and imaging technologies. The disillusioned outlook we proffer herein might sound pessimistic at first, but we consider it necessary to avoid pursuing "pipe dreams" and redirect the efforts toward achievable-yet ambitious-goals.

Regional analysis of cerebral hemodynamic changes during the head-up tilt test in Parkinson's disease patients with orthostatic intolerance

Significance: Cerebral oxygenation changes in the superior, middle, and medial gyri were used to elucidate spatial impairments of autonomic hemodynamic recovery during the head-up tilt table test (HUTT) in Parkinson's disease (PD) patients with orthostatic intolerance (OI) symptoms. Aim: To analyze dynamic oxygenation changes during the HUTT and classify PD patients with OI symptoms using clinical and oxygenation features. Approach: Thirty-nine PD patients with OI symptoms [10: orthostatic hypotension (PD-OH); 29: normal HUTT results (PD-NOR)] and seven healthy controls (HCs) were recruited. Prefrontal oxyhemoglobin (HbO) changes during the HUTT were reconstructed with diffuse optical tomography and segmented using the automated anatomical labeling system. Decision trees were used for classification. Results: HCs and PD-NOR patients with positive rates of HbO change (PD-POS) showed the greatest HbO recovery in the superior frontal gyrus (SFG) during tilt. PD-OH and PD-NOR patients with negative rates of HbO change (PD-NEG) showed asymmetric reoxygenation. The classification accuracy was 89.4% for PD-POS versus PD-NEG, 71% for PD-NOR versus PD-OH, and 55.8% for PD-POS versus PD-NEG versus PD-OH. The oxygenation features were more discriminative than the clinical features. Conclusions: PD-OH showed decreased right SFG function, which may be associated with impaired compensatory autonomic responses to orthostatic stress.

Reliability of fNIRS for noninvasive monitoring of brain function and emotion in sheep

The aim of this work was to critically assess if functional near infrared spectroscopy (fNIRS) can be profitably used as a tool for noninvasive recording of brain functions and emotions in sheep. We considered an experimental design including advances in instrumentation (customized wireless multi-distance fNIRS system), more accurate physical modelling (two-layer model for photon diffusion and 3D Monte Carlo simulations), support from neuroanatomical tools (positioning of the fNIRS probe by MRI and DTI data of the very same animals), and rigorous protocols (motor task, startling test) for testing the behavioral response of freely moving sheep. Almost no hemodynamic response was found in the extra-cerebral region in both the motor task and the startling test. In the motor task, as expected we found a canonical hemodynamic response in the cerebral region when sheep were walking. In the startling test, the measured hemodynamic response in the cerebral region was mainly from movement. Overall, these results indicate that with the current setup and probe positioning we are primarily measuring the motor area of the sheep brain, and not probing the too deeply located cortical areas related to processing of emotions.

Association between seizure freedom and default mode network reorganization in patients with unilateral temporal lobe epilepsy

RATIONALE

The spontaneous synchronized activity and intrinsic organization of the Default Mode Network (DMN) has been found to be altered because of epileptic activity of temporal lobe origin. Thus, the aim of the present study was to compare DMN's topological properties in patients with seizure-free (SF) and not seizure-free (NSF) temporal lobe epilepsy (TLE).

METHODS

Functional connectivity within the DMN was determined from an 8-minute resting state functional magnetic resonance imaging (fMRI) in 27 patients with TLE (12 SF, 15 NSF) and 15 healthy controls (HC). The DMN regions of interest were extracted according to the automated anatomical labeling (AAL) atlas. Network properties were assessed using standard graph-theoretical measures.

RESULTS

Analyses revealed, irrespectively of focus lateralization, borderline significance for longer paths (p = 0.049) and in trend reduced local efficiency within the DMN of SF when compared with that of NSF (p = 0.075). The SF and NSF patients did not differ in global network topology from HC (p > 0.05). At the nodal network level, the degree of central hubs was significantly reduced in SF when compared with that in NSF (0.002 ≤ p ≤ 0.080) and HC (0.001 ≤ p ≤ 0.066) while simultaneously, right anterior superior temporal gyrus revealed significantly higher degree in SF than in NSF (p = 0.005) and HC (p = 0.016).

CONCLUSION

Seizure freedom seems to be associated with hub redistributions that may underlie longer paths and (in trend) reduced local efficiency of the network. An associated slower system response might reduce the probability of a rapid spread of epileptic discharges over the whole network and may help to prevent hypersynchronous neuronal activity in brain networks that may result in epileptic seizures.

Whole-cortical graphical networks at wakeful rest in young and older adults revealed by functional near-infrared spectroscopy

A good understanding of age-dependent changes and modifications in brain networks is crucial for fully exploring the effects of aging on the human brain. Few reports have been found in studies of functional brain networks using functional near-infrared spectroscopy (fNIRS). Moreover, little is known about the feasibility of using fNIRS to assess age-related changes in brain connectomes. This study applied whole brain fNIRS measurement, combined with graph theory analysis, to assess the age-dependent changes in resting-state brain networks. Five to eight minutes of resting-state brain hemodynamic signals were recorded from 48 participants (18 young adults and 30 older adults) with 133 optical channels covering the majority of the cortical regions. Both local and global graph metrics were computed to identify the age-related changes of topographical brain networks. Older adults showed an overall decline of both global and local efficiency compared to young adults, as well as the decline of small-worldness. In addition, young adults showed the abundance of hubs in the prefrontal cortex, whereas older adults revealed the hub shifts to the sensorimotor cortex. These obvious shifts of hubs may potentially indicate decreases of the decision-making, memory, and other high-order functions as people age. Our results showed consistent findings with published literature and also demonstrated the feasibility of whole-head fNIRS measurements to assess age-dependent changes in resting-state brain networks.

Graph theoretical approach to functional connectivity in prefrontal cortex via fNIRS

Functional near-infrared spectroscopy (fNIRS) has been proposed as an affordable, fast, and robust alternative to many neuroimaging modalities yet it still has long way to go to be adapted in the clinic. One request from the clinicians has been the delivery of a simple and straightforward metric (a so-called biomarker) from the vast amount of data a multichannel fNIRS system provides. We propose a simple-straightforward signal processing algorithm derived from [Formula: see text] data collected during a modified version of the color-word matching Stroop task that consists of three different conditions. The algorithm starts with a wavelet-transform-based preprocessing, then uses partial correlation analysis to compute the functional connectivity matrices at each condition and then computes the global efficiency values. To this end, a continuous wave 16 channels fNIRS device (ARGES Cerebro, Hemosoft Inc., Turkey) was used to measure the changes in [Formula: see text] concentrations from 12 healthy volunteers. We have considered 10% of strongest connections in each network. A strong Stroop interference effect was found between the incongruent against neutral condition ([Formula: see text]) while a similar significance was observed for the global efficiency values decreased from neutral to congruent to incongruent conditions [[Formula: see text], [Formula: see text]]. The findings bring us closer to delivering a biomarker derived from fNIRS data that can be reliably and easily adopted by the clinicians.

Comparing diffuse optical tomography and functional magnetic resonance imaging signals during a cognitive task: pilot study

Diffuse optical tomography (DOT) measures concentration changes in both oxy- and deoxyhemoglobin providing three-dimensional images of local brain activations. A pilot study, which compares both DOT and functional magnetic resonance imaging (fMRI) volumes through t-maps given by canonical statistical parametric mapping (SPM) processing for both data modalities, is presented. The DOT series were processed using a method that is based on a Bayesian filter application on raw DOT data to remove physiological changes and minimum description length application index to select a number of singular values, which reduce the data dimensionality during image reconstruction and adaptation of DOT volume series to normalized standard space. Therefore, statistical analysis is performed with canonical SPM software in the same way as fMRI analysis is done, accepting DOT volumes as if they were fMRI volumes. The results show the reproducibility and ruggedness of the method to process DOT series on group analysis using cognitive paradigms on the prefrontal cortex. Difficulties such as the fact that scalp-brain distances vary between subjects or cerebral activations are difficult to reproduce due to strategies used by the subjects to solve arithmetic problems are considered. T-images given by fMRI and DOT volume series analyzed in SPM show that at the functional level, both DOT and fMRI measures detect the same areas, although DOT provides complementary information to fMRI signals about cerebral activity.