The formation of new motor memories, which is fundamental for efficient performance during adaptation to a visuo-motor rotation, occurs when accurate planning is achieved mostly with feedforward mechanisms. The dynamics of brain activity underlying the switch from feedback to feedforward control is still matter of debate. Based upon the results of studies in declarative learning, it is likely that phase synchronization of low and high frequencies as well as their temporal modulation in power amplitude underlie the formation of new motor memories during visuo-motor adaptation. High density-EEG (256 electrodes) was recorded in 17 normal human subjects during adaptation to a visuo-motor rotation of 60° in four incremental steps of 15°. We found that initial learning is associated with enhancement of gamma power in a right parietal region during movement execution as well as gamma/theta phase coherence during movement planning. Late stages of learning are instead accompanied by an increase of theta power over that same right parietal region during movement planning, which is correlated with the degree of learning and retention. Altogether, these results suggest that the formation of new motor memories and thus, the switch from feedback to feedforward control, is associated with the modulation of gamma and theta spectral activities, with respect to their amplitude and phase, during movement planning and execution. Specifically, we propose that gamma/theta phase coupling plays a pivotal role in the integration of a new representation into motor memories.

Background: Resting-state functional magnetic resonance imaging (rs-fMRI) is commonly employed to study changes in functional brain connectivity. The recent hypothesis of a brain involvement in primary open angle Glaucoma has sprung interest for neuroimaging studies in this classically ophthalmological pathology.Object: We explored a putative reorganization of functional brain networks in Glaucomatous patients, and evaluated the potential of functional network disruption indices as biomarkers of disease severity in terms of their relationship to clinical variables as well as select retinal layer thicknesses.Methods: Nineteen Glaucoma patients and 16 healthy control subjects (age: 50–76, mean 61.0 ± 8.2 years) underwent rs-fMRI examination at 3T. After preprocessing, rs-fMRI time series were parcellated into 116 regions using the Automated Anatomical Labeling atlas and adjacency matrices were computed based on partial correlations. Graph-theoretical measures of integration, segregation and centrality as well as group-wise and subject-wise disruption index estimates (which use regression of graph-theoretical metrics across subjects to quantify overall network changes) were then generated for all subjects. All subjects also underwent Optical Coherence Tomography (OCT) and visual field index (VFI) quantification. We then examined associations between brain network measures and VFI, as well as thickness of retinal nerve fiber layer (RNFL) and macular ganglion cell layer (MaculaGCL).Results: In Glaucoma, group-wise disruption indices were negative for all graph theoretical metrics. Also, we found statistically significant group-wise differences in subject-wise disruption indexes in all local metrics. Two brain regions serving as hubs in healthy controls were not present in the Glaucoma group. Instead, three hub regions were present in Glaucoma patients but not in controls. We found significant associations between all disruption indices and VFI, RNFL as well as MaculaGCL. The disruption index based on the clustering coefficient yielded the best discriminative power for differentiating Glaucoma patients from healthy controls [Area Under the ROC curve (AUC) 0.91, sensitivity, 100%; specificity, 78.95%].Conclusions: Our findings support a possible relationship between functional brain changes and disease severity in Glaucoma, as well as alternative explanations for motor and cognitive symptoms in Glaucoma, possibly pointing toward an inclusion of this pathology in the heterogeneous group of disconnection syndromes.

The aim of this study was to identify potential diagnostic markers of Hereditary Spastic Paraplegia (HSP). We investigated the white matter features of spastic gait (SPG)11- and SPG4-linked HSP, using diffusion tensor imaging performed with a 3-Tesla (3T) scanner. We examined four patients with SPG11 mutations, three with SPG4 mutations, and 26 healthy controls. We obtained maps of fractional anisotropy (FA) and mean diffusivity (MD), which we analyzed through both region of interest -based approach and tract-based spatial statistics (TBSS). Compared with healthy controls, SPG11 patients presented increased MD and decreased FA in the semioval centers, frontal and peritrigonal white matter, posterior limb of the internal capsule, and throughout the corpus callosum. Similar alterations were seen in the SPG4 patients at the levels of the semioval centers, the posterior limb of the internal capsule, the left cerebral pedicle, the genu and trunk of the corpus callosum, and the peritrigonal white matter on the left. No MD or FA alterations were observed in the cerebellar white matter. In a direct comparison, white matter alterations were more pronounced and widespread in HSP-SPG11 than in HSP-SPG4 patients. Joint TBSS analysis of all three groups confirmed significant widespread alterations of FA and MD values in the supratentorial white matter. This noninvasive study documented the presence of altered diffusivity in white matter in both forms of HSP, which could represent an important diagnostic marker of HSP. The association of reduced FA and increased MD in this patient population supports the interpretation of HPG as a neurodegenerative disorder.

Model-dependent differences in the estimation of conventional indexes of MD/FA/MO/RD/AD can be well beyond commonly seen disease-related alterations. While estimating diffusion tensor-derived indexes using the DKI model may be advantageous in terms of mitigating b-value dependence of diffusivity estimates, such estimates should not be referred to as conventional DTI-derived indexes in order to avoid confusion in interpretation as well as multicenter comparisons. In order to assess the potential and advantages of DKI with respect to DTI as well as to standardize diffusion-weighted imaging methods between centers, both conventional DTI-derived indexes and diffusion tensor invariants derived by fitting the non-Gaussian DKI model should be separately estimated and analyzed using the same combination of fitting routines.

Glaucoma is an optic neuropathy characterized by death of retinal ganglion cells and loss of their axons, progressively leading to blindness. Recently, glaucoma has been conceptualized as a more diffuse neurodegenerative disorder involving the optic nerve and also the entire brain. Consistently, previous studies have used a variety of magnetic resonance imaging (MRI) techniques and described widespread changes in the grey and white matter of patients. Diffusion kurtosis imaging (DKI) provides additional information as compared with diffusion tensor imaging (DTI), and consistently provides higher sensitivity to early microstructural white matter modification. In this study, we employ DKI to evaluate differences among healthy controls and a mixed population of primary open angle glaucoma patients ranging from stage I to V according to Hodapp–Parrish–Anderson visual field impairment classification. To this end, a cohort of patients affected by primary open angle glaucoma (n = 23) and a group of healthy volunteers (n = 15) were prospectively enrolled and underwent an ophthalmological evaluation followed by magnetic resonance imaging (MRI) using a 3T MR scanner. After estimating both DTI indices, whole-brain, voxel-wise statistical comparisons were performed in white matter using Tract-Based Spatial Statistics (TBSS). We found widespread differences in several white matter tracts in patients with glaucoma relative to controls in several metrics (mean kurtosis, kurtosis anisotropy, radial kurtosis, and fractional anisotropy) which involved localization well beyond the visual pathways, and involved cognitive, motor, face recognition, and orientation functions amongst others. Our findings lend further support to a causal brain involvement in glaucoma and offer alternative explanations for a number of multidomain impairments often observed in glaucoma patients.