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BackgroundThe purpose of this review is to provide a comprehensive overview of the findings of clinical electrophysiology studies aimed to investigate changes in information processing of migraine with aura patients.Main bodyAbnormalities in alpha rhythm power and symmetry, the presence of slowing, and increased information flow in a wide range of frequency bands often characterize the spontaneous EEG activity of MA. Higher grand-average cortical response amplitudes, an increased interhemispheric response asymmetry, and lack of amplitude habituation were less consistently demonstrated in response to any kind of sensory stimulation in MA patients. Studies with single-pulse and repetitive transcranial magnetic stimulation (TMS) have reported abnormal cortical responsivity manifesting as greater motor evoked potential (MEP) amplitude, lower threshold for phosphenes production, and paradoxical effects in response to both depressing or enhancing repetitive TMS methodologies. Studies of the trigeminal system in MA are sparse and the few available showed lack of blink reflex habituation and abnormal findings on SFEMG reflecting subclinical, probably inherited, dysfunctions of neuromuscular transmission. The limited studies that were able to investigate patients during the aura revealed suppression of evoked potentials, desynchronization in extrastriate areas and in the temporal lobe, and large variations in direct current potentials with magnetoelectroencephalography. Contrary to what has been observed in the most common forms of migraine, patients with familial hemiplegic migraine show greater habituation in response to visual and trigeminal stimuli, as well as a higher motor threshold and a lower MEP amplitude than healthy subjects.ConclusionSince most of the electrophysiological abnormalities mentioned above were more frequently present and had a greater amplitude in migraine with aura than in migraine without aura, neurophysiological techniques have been shown to be of great help in the search for the pathophysiological basis of migraine aura.

Alzheimer's disease (AD) is a neurodegenerative disorder, which is likely to start as mild cognitive impairment (MCI) several years before the its full-blown clinical manifestation. Optical coherence tomography (OCT) has been used to detect a loss in peripapillary retina nerve fiber layer (RNFL) and a reduction in macular thickness and volume of people affected by MCI or AD. Here, we performed an aggregate meta-analysis combining results from different studies. Data sources were case-control studies published between January 2001 and August 2014 (identified through PubMed and Google Scholar databases) that examined the RNFL thickness by means of OCT in AD and MCI patients compared with cognitively healthy controls. 11 studies were identified, including 380 patients with AD, 68 with MCI and 293 healthy controls (HC). The studies suggest that the mean RNFL thickness is reduced in MCI (weighted mean differences in μm, WMD = -13.39, 95% CI: -17.34 to -9.45, p = 0.031) and, even more so, in AD (WMD = -15.95, 95% CI: -21.65 to -10.21, p<0.0001) patients compared to HC. RNFL in the 4 quadrants were all significantly thinner in AD superior (superior WMD = -24.0, 95% CI: -34.9 to -13.1, p<0.0001; inferior WMD = -20.8, 95% CI: -32.0 to -9.7, p<0.0001; nasal WMD = -14.7, 95% CI: -23.9 to -5.5, p<0.0001; and temporal WMD = -10.7, 95% CI: -19.9 to -1.4, p<0.0001); the same significant reduction in quadrant RNFL was observed in MCI patients compared with HC (Inferior WMD = -20.22, 95% CI: -30.41 to -10.03, p = 0.0001; nasal WMD = -7.4, 95% CI: -10.08 to -4.7, p = 0.0000; and temporal WMD = -6.88, 95% CI: -12.62 to -1.13, p = 0.01), with the exception of superior quadrant (WMD = -19.45, 95% CI: -40.23 to 1.32, p = 0.06). Results from the meta-analysis support the important role of OCT for RNFL analysis in monitoring the progression of AD and in assessing the effectiveness of purported AD treatments.

Recent advances in materials and fabrication techniques provided portable, performant, sensing optical spectrometers readily operated by user-friendly cabled or wireless systems. Such systems allow rapid, non-invasive, and not destructive quantitative analysis of human tissues. This proof-of-principle investigation tested whether infrared spectroscopy techniques, currently utilized in a variety of areas, could be applied in living humans to categorize muscles. Using an ASD FieldSpec® 4 Standard-Res Spectroradiometer with a spectral sampling capability of 1.4 nm at 350–1000 nm and 1.1 nm at 1001–2500 nm, we acquired reflectance spectra in visible short-wave infra-red regions (350–2500 nm) from the upper limb muscles (flexors and extensors) of 20 healthy subjects (age 25–89 years, 9 women). Spectra off-line analysis included preliminary preprocessing, Principal Component Analysis, and Partial Least-Squares Discriminant Analysis. Near-infrared (NIR) spectroscopy proved valuable for noninvasive assessment of tissue optical properties in vivo . In addition to the non-invasive detection of tissue oxygenation, NIR spectroscopy provided the spectral signatures (ie, “fingerprints”) of upper limb flexors and extensors, which represent specific, accurate, and reproducible measures of the overall biological status of these muscles. Thus, non-invasive NIR spectroscopy enables more thorough evaluation of the muscular system and optimal monitoring of the effectiveness of therapeutic or rehabilitative interventions.

Gait deficits are debilitating in people with Parkinson's disease (PwPD), which inevitably deteriorate over time. Gait analysis is a valuable method to assess disease-specific gait patterns and their relationship with the clinical features and progression of the disease. Our study aimed to i) develop an automated diagnostic algorithm based on machine-learning techniques (artificial neural networks [ANNs]) to classify the gait deficits of PwPD according to disease progression in the Hoehn and Yahr (H-Y) staging system, and ii) identify a minimum set of gait classifiers. We evaluated 76 PwPD (H-Y stage 1-4) and 67 healthy controls (HCs) by computerized gait analysis. We computed the time-distance parameters and the ranges of angular motion (RoMs) of the hip, knee, ankle, trunk, and pelvis. Principal component analysis was used to define a subset of features including all gait variables. An ANN approach was used to identify gait deficits according to the H-Y stage. We identified a combination of a small number of features that distinguished PwPDs from HCs (one combination of two features: knee and trunk rotation RoMs) and identified the gait patterns between different H-Y stages (two combinations of four features: walking speed and hip, knee, and ankle RoMs; walking speed and hip, knee, and trunk rotation RoMs). The ANN approach enabled automated diagnosis of gait deficits in several symptomatic stages of Parkinson's disease. These results will inspire future studies to test the utility of gait classifiers for the evaluation of treatments that could modify disease progression.

The hypothalamus has been attributed an important role during the premonitory phase of a migraine attack. Less is known about the role played by the hypothalamus in the interictal period and its relationship with the putative neurocognitive networks previously identified in the pathophysiology of migraine. Our aim was to test whether the hypothalamic microstructure would be altered during the interictal period and whether this co-existed with aberrant connectivity at cortical level. We collected multimodal MRI data from 20 untreated patients with migraine without aura between attacks (MO) and 20 healthy controls (HC) and studied fractional anisotropy, mean (MD), radial (RD), and axial diffusivity of the hypothalamus ROI as a whole from diffusion tensor imaging (DTI). Moreover, we performed an exploratory analysis of the same DTI metrics separately for the anterior and posterior hypothalamic ROIs bilaterally. From resting-state functional MRI, we estimated the Higuchi’s fractal dimension (FD), an index of temporal complexity sensible to describe non-periodic patterns characterizing BOLD signature. Finally, we correlated neuroimaging findings with migraine clinical features. In comparison to HC, MO had significantly higher MD, AD, and RD values within the hypothalamus. These findings were confirmed also in the exploratory analysis on the sub-regions of the hypothalamus bilaterally, with the addition of lower FA values on the posterior ROIs. Patients showed higher FD values within the salience network (SN) and the cerebellum, and lower FD values within the primary visual (PV) network compared to HC. We found a positive correlation between cerebellar and SN FD values and severity of migraine. Our findings of hypothalamic abnormalities between migraine attacks may form part of the neuroanatomical substrate that predisposes the onset of the prodromal phase and, therefore, the initiation of an attack. The peculiar fractal dimensionality we found in PV, SN, and cerebellum may be interpreted as an expression of abnormal efficiency demand of brain networks devoted to the integration of sensory, emotional, and cognitive information related to the severity of migraine.

Despite that it is commonly accepted that migraine is a disorder of the nervous system with a prominent genetic basis, it is comorbid with a plethora of medical conditions. Several studies have found bidirectional comorbidity between migraine and different disorders including neurological, psychiatric, cardio- and cerebrovascular, gastrointestinal, metaboloendocrine, and immunological conditions. Each of these has its own genetic load and shares some common characteristics with migraine. The bidirectional mechanisms that are likely to underlie this extensive comorbidity between migraine and other diseases are manifold. Comorbid pathologies can induce and promote thalamocortical network dysexcitability, multi-organ transient or persistent pro-inflammatory state, and disproportionate energetic needs in a variable combination, which in turn may be causative mechanisms of the activation of an ample defensive system with includes the trigeminovascular system in conjunction with the neuroendocrine hypothalamic system. This strategy is designed to maintain brain homeostasis by regulating homeostatic needs, such as normal subcortico-cortical excitability, energy balance, osmoregulation, and emotional response. In this light, the treatment of migraine should always involves a multidisciplinary approach, aimed at identifying and, if necessary, eliminating possible risk and comorbidity factors.

Background Cerebellar ataxia is characterized by difficulty in the planning of movement and lack of anticipatory postural adjustments, which can result in deficits of balance. Being able to have quantitative measurements in clinical practice, to detect any improvements on balance resulting from new rehabilitation treatments or experimental drugs is very important. Aim The purpose of this study was to develop an application (APP) able to assess static and dynamic balance in patients with cerebellar ataxias (CA). The APP that works by a wearable device (smartphone) placed at the breastbone level and immobilized by an elastic band, measures the body sway by means of a triaxial accelerometer. Methods We investigated 40 CA patients and 80 healthy subjects. All patients were clinically evaluated using the “Berg Balance Scale” (BBS) and the “Scale for the Assessment and Rating of Ataxia” (SARA). Balance impairment was quantitatively assessed using a validated static balance evaluating systems, i.e., Techno-body Pro-Kin footboard. All participants underwent static and dynamic balance assessments using the new APP. Results We observed a strong correlation between the APP measurements and the score obtained with the BBS, SARA, and Pro-Kin footboard. The intra-rater reliability and the test–retest reliability of the APP measurements, estimated by intraclass correlation coefficient, were excellent. The standard error of measurement and the minimal detectable change were small. No learning effect was observed. Conclusions We can state that the APP is an easy, reliable, and valid evaluating system to quantify the trunk sway in a static position and during the gait.

Objective The findings of resting-state functional MRI studies have suggested that abnormal functional integration between interconnected cortical networks characterises the brain of patients with migraine. The aim of this study was to investigate the functional connectivity between the hypothalamus, brainstem, considered as the migraine generator, and the following areas/networks that are reportedly involved in the pathophysiology of migraine: default mode network (DMN), executive control network, dorsal attention system, and primary and dorsoventral visual networks. Methods Twenty patients with chronic migraine (CM) without medication overuse and 20 healthy controls (HCs) were prospectively recruited. All study participants underwent 3-T MRI scans using a 7.5-min resting-state protocol. Using a seed-based approach, we performed a ROI-to-ROI analysis selecting the hypothalamus as the seed. Results Compared to HCs, patients with CM showed significantly increased neural connectivity between the hypothalamus and brain areas belonging to the DMN and dorsal visual network. We did not detect any connectivity abnormalities between the hypothalamus and the brainstem. The correlation analysis showed that the severity of the migraine headache was positively correlated with the connectivity strength of the hypothalamus and negatively with the connectivity strength of the medial prefrontal cortex, which belongs to the DMN. Conclusion These data provide evidence for hypothalamic involvement in large-scale reorganisation at the functional-network level in CM and in proportion with the perceived severity of the migraine pain.

BackgroundWe searched for differences in resting-state functional connectivity (FC) between brain networks and its relationship with the microstructure of the thalamus between migraine with pure visual auras (MA), and migraine with complex neurological auras (MA+), i.e. with the addition of at least one of sensory or language symptom.Methods3T MRI data were obtained from 20 patients with MA and 15 with MA + and compared with those from 19 healthy controls (HCs). We collected resting state data among independent component networks. Diffusivity metrics of bilateral thalami were calculated and correlated with resting state ICs-Z-scores.ResultsAs compared to HCs, both patients with MA and MA + disclosed disrupted FC between the default mode network (DMN) and the right dorsal attention system (DAS). The MA + subgroup had lower microstructural metrics than both HCs and the MA subgroup, which correlated negatively with the strength of DMN connectivity. Although the microstructural metrics of MA patients did not differ from those of HCs, these patients lacked the correlation with the strength of DAS connectivity found in HCs.ConclusionsThe present findings suggest that, as far as MRI profiles are concerned, the two clinical phenotypes of migraine with aura have both common and distinct morpho-functional features of nodes in the thalamo-cortical network.

BackgroundChronic migraine (CM) can be associated with aberrant long-range connectivity of MRI-derived resting-state networks (RSNs). Here, we investigated how the fractal dimension (FD) of blood oxygenation level dependent (BOLD) activity may be used to estimate the complexity of RSNs, reflecting flexibility and/or efficiency in information processing in CM patients respect to healthy controls (HC).MethodsResting-state MRI data were collected from 20 untreated CM without history of medication overuse and 20 HC. On both groups, we estimated the Higuchi’s FD. On the same subjects, fractional anisotropy (FA) and mean diffusivity (MD) values of bilateral thalami were retrieved from diffusion tensor imaging and correlated with the FD values.ResultsCM showed higher FD values within dorsal attention system (DAS) and the anterior part of default-mode network (DMN), and lower FD values within the posterior DMN compared to HC. Although FA and MD were within the range of normality, both correlated with the FD values of DAS.ConclusionsFD of DAS and DMN may reflect disruption of cognitive control of pain in CM. Since the normal microstructure of the thalamus and its positive connectivity with the cortical networking found in our CM patients reminds similar results obtained assessing the same structures but with the methods of neurophysiology, in episodic migraine during an attack, this may be yet another evidence in supporting CM as a never-ending migraine attack.