Modeling functional network topology following stroke through graph theory: functional reorganization and motor recovery prediction

The study of functional reorganization following stroke has been steadily growing supported by advances in neuroimaging techniques, such as functional magnetic resonance imaging (fMRI). Concomitantly, graph theory has been increasingly employed in neuroscience to model the brain's functional connectivity (FC) and to investigate it in a variety of contexts. The aims of this study were: 1) to investigate the reorganization of network topology in the ipsilesional (IL) and contralesional (CL) hemispheres of stroke patients with (motor stroke group) and without (control stroke group) motor impairment, and 2) to predict motor recovery through the relationship between local topological variations of the functional network and increased motor function. We modeled the brain's FC as a graph using fMRI data, and we characterized its interactions with the following graph metrics: degree, clustering coefficient, characteristic path length, and betweenness centrality (BC). For both patient groups, BC yielded the largest variations between the two analyzed time points, especially in the motor stroke group. This group presented significant correlations (P<0.05) between average BC changes and the improvements in upper-extremity Fugl-Meyer (UE-FM) scores at the primary sensorimotor cortex and the supplementary motor area for the CL hemisphere. These regions participate in processes related to the selection, planning, and execution of movement. Generally, higher increases in average BC over these areas were related to larger improvements in UE-FM assessment. Although the sample was small, these results suggest the possibility of using BC as an indication of brain plasticity mechanisms following stroke.

Cerebral vasomotor reactivity assessment using Transcranial Doppler and MRI with apnea test

Differently from previous studies that used Transcranial Doppler (TCD) and functional MRI (fMRI) for cerebral vasomotor reactivity (CVR) assessment in patients with carotid stenosis (CS), we assessed CVR using an identical stimulus, the Breath-Holding Test (BHT). We included 15 patients with CS and 7 age-matched controls to verify whether fMRI responded differently to BHT between groups and to calculate the agreement rate between tests. For TCD, impaired CVR was defined when the mean percentage increase on middle cerebral artery velocities was ≤31% on 3 consecutive 30-s apnea intercalated by 4-min normal breathing intervals. For fMRI, the percent variation on blood oxygen level-dependent (BOLD) signal intensity in the lentiform nucleus (LN) ipsilateral to the CS (or both LNs for controls) from baseline breathing to apnea was measured. The Euclidian differences between the series of each subject and the series of controls and patients classified it into normal or impaired CVR. We found different percent variations on BOLD-signal intensities between groups (P=0.032). The agreement was good in Controls (85.7%; κ=0.69) and overall (77.3%; κ=0.54). We conclude that BHT was feasible for CVR assessment on fMRI and elicited different BOLD responses in patients and controls, with a good overall agreement between the tests.

NAA and NAAG variation in neuronal activation during visual stimulation

N-acetyl-aspartyl-glutamate (NAAG) and its hydrolysis product N-acetyl-L-aspartate (NAA) are among the most important brain metabolites. NAA is a marker of neuron integrity and viability, while NAAG modulates glutamate release and may have a role in neuroprotection and synaptic plasticity. Investigating on a quantitative basis the role of these metabolites in brain metabolism in vivo by magnetic resonance spectroscopy (MRS) is a major challenge since the main signals of NAA and NAAG largely overlap. This is a preliminary study in which we evaluated NAA and NAAG changes during a visual stimulation experiment using functional MRS. The paradigm used consisted of a rest period (5 min and 20 s), followed by a stimulation period (10 min and 40 s) and another rest period (10 min and 40 s). MRS from 17 healthy subjects were acquired at 3T with TR/TE = 2000/288 ms. Spectra were averaged over subjects and quantified with LCModel. The main outcomes were that NAA concentration decreased by about 20% with the stimulus, while the concentration of NAAG concomitantly increased by about 200%. Such variations fall into models for the energy metabolism underlying neuronal activation that point to NAAG as being responsible for the hyperemic vascular response that causes the BOLD signal. They also agree with the fact that NAAG and NAA are present in the brain at a ratio of about 1:10, and with the fact that the only known metabolic pathway for NAAG synthesis is from NAA and glutamate.