1,721,112 research outputs found
Lifestyle Factors and Migraine in Childhood
No full textMigraine is one of the most common pain symptoms in children. Indeed, a high percentage of adult migraine patients report to have suffered from recurrent headache during the childhood. In particular, children could experience the so-called childhood periodic syndromes (such as cyclic vomiting, abdominal migraine, and benign paroxysmal vertigo) that have been usually considered precursors of migraine or they could develop overt migraine headaches. However, typical cohort of migraine symptoms could be absent and children could not achieve all clinical features necessary for a migraine attack diagnosis according to classification criteria. Nevertheless, migraine is characterized also in childhood by a significant negative impact on the quality of life and a high risk of developing chronic and persistent headache in adulthood. Several studies have emphasized the role of different risk factors for migraine in children. Among these, obesity and overweight, particular food or the regular consumption of alcohol or caffeine, dysfunctional family situation, low level of physical activity, physical or emotional abuse, bullying by peers, unfair treatment in school, and insufficient leisure time seem to be strictly related to migraine onset or progression. Consequently, both identification and avoidance of triggers seem to be mandatory in children with migraine and could represent an alternative approach to the treatment of migraine abstaining from pharmacologic therapies
Exposure to environmental toxicants and pathogenesis of amyotrophic lateral sclerosis: state of the art and research perspectives.
No full textThere is a broad scientific consensus that amyotrophic lateral sclerosis (ALS), a fatal neuromuscular disease, is caused by gene-environment interactions. In fact, given that only about 10% of all ALS diagnosis has a genetic basis, gene-environmental interaction may give account for the remaining percentage of cases. However, relatively little attention has been paid to environmental and lifestyle factors that may trigger the cascade of motor neuron degeneration leading to ALS, although exposure to chemicals-including lead and pesticides-agricultural environments, smoking, intense physical activity, trauma and electromagnetic fields have been associated with an increased risk of ALS. This review provides an overview of our current knowledge of potential toxic etiologies of ALS with emphasis on the role of cyanobacteria, heavy metals and pesticides as potential risk factors for developing ALS. We will summarize the most recent evidence from epidemiological studies and experimental findings from animal and cellular models, revealing that potential causal links between environmental toxicants and ALS pathogenesis have not been fully ascertained, thus justifying the need for further research
Q-ball imaging models: comparison between high and low angular resolution diffusion-weighted MRI protocols for investigation of brain white matter integrity
No full textINTRODUCTION:
Q-ball imaging (QBI) is one of the typical data models for quantifying white matter (WM) anisotropy in diffusion-weighted MRI (DwMRI) studies. Brain and spinal investigation by high angular resolution DwMRI (high angular resolution imaging (HARDI)) protocols exhibits higher angular resolution in diffusion imaging compared to low angular resolution models, although with longer acquisition times. We aimed to assess the difference between QBI-derived anisotropy values from high and low angular resolution DwMRI protocols and their potential advantages or shortcomings in neuroradiology.
METHODS:
Brain DwMRI data sets were acquired in seven healthy volunteers using both HARDI (b = 3000 s/mm2, 54 gradient directions) and low angular resolution (b = 1000 s/mm2, 32 gradient directions) acquisition schemes. For both sequences, tract of interest tractography and generalized fractional anisotropy (GFA) measures were extracted by using QBI model and were compared between the two data sets.
RESULTS:
QBI tractography and voxel-wise analyses showed that some WM tracts, such as corpus callosum, inferior longitudinal, and uncinate fasciculi, were reconstructed as one-dominant-direction fiber bundles with both acquisition schemes. In these WM tracts, mean percent different difference in GFA between the two data sets was less than 5 %. Contrariwise, multidirectional fiber bundles, such as corticospinal tract and superior longitudinal fasciculus, were more accurately depicted by HARDI acquisition scheme.
CONCLUSION:
Our results suggest that the design of optimal DwMRI acquisition protocols for clinical investigation of WM anisotropy by QBI models should consider the specific brain target regions to be explored, inducing researchers to a trade-off choice between angular resolution and acquisition time
Integrated Cognitive Rehabilitation Home-Based Protocol to Improve Cognitive Functions in Multiple Sclerosis Patients: A Randomized Controlled Study
No full textCognitive impairment (CI) occurs in about 40–65% of people with multiple sclerosis (MS) during the disease course. Cognitive rehabilitation has produced non-univocal results in MS patients. Objective: The present study aimed to evaluate whether an Integrated Cognitive Rehabilitation Program (ICRP) in MS patients might significantly improve CI. Methods: Forty patients with three phenotypes of MS were randomly assigned into two groups: the experimental group (EG, n = 20), which participated in the ICRP for 10 weeks of training; and the control group (CG, n = 20). All participants’ cognitive functions were assessed at three timepoints (baseline, post-treatment, and 3-month follow-up) with the California Verbal Learning (CVLT), Brief Visuospatial Memory (BVMTR), Numerical Stroop, and Wisconsin tests. Results: When compared to CG patients, EG patients showed significant improvements in several measures of cognitive performance after ICRP, including verbal learning, visuospatial memory, attention, and executive functions. Conclusions: Home-based ICRP can improve cognitive functions and prevent the deterioration of patients’ cognitive deficits. As an integrated cognitive rehabilitation program aimed at potentiation of restorative and compensatory mechanisms, this approach might suggest an effective role in preserving neuronal flexibility as well as limiting the progression of cognitive dysfunction in MS
Widespread Structural and Functional Connectivity Changes in Amyotrophic Lateral Sclerosis: Insights from Advanced Neuroimaging Research
Full text linkAmyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease principally affecting motor neurons. Besides motor symptoms, a subset of patients develop cognitive disturbances or even frontotemporal dementia (FTD), indicating that ALS may also involve extramotor brain regions. Both neuropathological and neuroimaging findings have provided further insight on the widespread effect of the neurodegeneration on brain connectivity and the underlying neurobiology of motor neurons degeneration. However, associated effects on motor and extramotor brain networks are largely unknown. Particularly, neuropathological findings suggest that ALS not only affects the frontotemporal network but rather is part of a wide clinicopathological spectrum of brain disorders known as TAR-DNA binding protein 43 (TDP-43) proteinopathies. This paper reviews the current state of knowledge concerning the neuropsychological and neuropathological sequelae of TDP-43 proteinopathies, with special focus on the neuroimaging findings associated with cognitive change in ALS
Strong-Weak Pruning for Brain Network Identification in Connectome-Wide Neuroimaging: Application to Amyotrophic Lateral Sclerosis Disease Stage Characterization
No full textMagnetic resonance imaging allows acquiring functional and structural connectivity data from which high-density whole-brain networks can be derived to carry out connectome-wide analyses in normal and clinical populations. Graph theory has been widely applied to investigate the modular structure of brain connections by using centrality measures to identify the "hub" of human connectomes, and community detection methods to delineate subnetworks associated with diverse cognitive and sensorimotor functions. These analyses typically rely on a preprocessing step (pruning) to reduce computational complexity and remove the weakest edges that are most likely affected by experimental noise. However, weak links may contain relevant information about brain connectivity, therefore, the identification of the optimal trade-off between retained and discarded edges is a subject of active research. We introduce a pruning algorithm to identify edges that carry the highest information content. The algorithm selects both strong edges (i.e. edges belonging to shortest paths) and weak edges that are topologically relevant in weakly connected subnetworks. The newly developed "strong-weak" pruning (SWP) algorithm was validated on simulated networks that mimic the structure of human brain networks. It was then applied for the analysis of a real dataset of subjects affected by amyotrophic lateral sclerosis (ALS), both at the early (ALS2) and late (ALS3) stage of the disease, and of healthy control subjects. SWP preprocessing allowed identifying statistically significant differences in the path length of networks between patients and healthy subjects. ALS patients showed a decrease of connectivity between frontal cortex to temporal cortex and parietal cortex and between temporal and occipital cortex. Moreover, degree of centrality measures revealed significantly different hub and centrality scores between patient subgroups. These findings suggest a widespread alteration of network topology in ALS associated with disease progression
Harnessing Brain Plasticity: The Therapeutic Power of Repetitive Transcranial Magnetic Stimulation (rTMS) and Theta Burst Stimulation (TBS) in Neurotransmitter Modulation, Receptor Dynamics, and Neuroimaging for Neurological Innovations
No full textTranscranial magnetic stimulation (TMS) methods have become exciting techniques for altering brain activity and improving synaptic plasticity, earning recognition as valuable non-medicine treatments for a wide range of neurological disorders. Among these methods, repetitive TMS (rTMS) and theta-burst stimulation (TBS) show significant promise in improving outcomes for adults with complex neurological and neurodegenerative conditions, such as Alzheimer’s disease, stroke, Parkinson’s disease, etc. However, optimizing their effects remains a challenge due to variability in how patients respond and a limited understanding of how these techniques interact with crucial neurotransmitter systems. This narrative review explores the mechanisms of rTMS and TBS, which enhance neuroplasticity and functional improvement. We specifically focus on their effects on GABAergic and glutamatergic pathways and how they interact with key receptors like N-Methyl-D-Aspartate (NMDA) and AMPA receptors, which play essential roles in processes like long-term potentiation (LTP) and long-term depression (LTD). Additionally, we investigate how rTMS and TBS impact neuroplasticity and functional connectivity, particularly concerning brain-derived neurotrophic factor (BDNF) and tropomyosin-related kinase receptor type B (TrkB). Here, we highlight the significant potential of this research to expand our understanding of neuroplasticity and better treatment outcomes for patients. Through clarifying the neurobiology mechanisms behind rTMS and TBS with neuroimaging findings, we aim to develop more effective, personalized treatment plans that effectively address the challenges posed by neurological disorders and ultimately enhance the quality of neurorehabilitation services and provide future directions for patients’ care
Stochastic Rank Aggregation for the Identification of Functional Neuromarkers
No full textThe main challenge in analysing functional magnetic resonance imaging (fMRI) data from extended samples of subject (N > 100) is to extract as much relevant information as possible from big amounts of noisy data. When studying neurodegenerative diseases with resting-state fMRI, one of the objectives is to determine regions with abnormal background activity with respect to a healthy brain and this is often attained with comparative statistical models applied to single voxels or brain parcels within one or several functional networks. In this work, we propose a novel approach based on clustering and stochastic rank aggregation to identify parcels that exhibit a coherent behaviour in groups of subjects affected by the same disorder and apply it to default-mode network independent component maps from resting-state fMRI data sets. Brain voxels are partitioned into parcels through k-means clustering, then solutions are enhanced by means of consensus techniques. For each subject, clusters are ranked according to their median value and a stochastic rank aggregation method, TopKLists, is applied to combine the individual rankings within each class of subjects. For comparison, the same approach was tested on an anatomical parcellation. We found parcels for which the rankings were different among control subjects and subjects affected by Parkinson's disease and amyotrophic lateral sclerosis and found evidence in literature for the relevance of top ranked regions in default-mode brain activity. The proposed framework represents a valid method for the identification of functional neuromarkers from resting-state fMRI data, and it might therefore constitute a step forward in the development of fully automated data-driven techniques to support early diagnoses of neurodegenerative diseases
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