Much evidence shows that physical exercise (PE) is a strong gene modulator that induces structural and functional changes in the brain, determining enormous benefit on both cognitive functioning and wellbeing. PE is also a protective factor for neurodegeneration. However, it is unclear if such protection is granted through modifications to the biological mechanisms underlying neurodegeneration or through better compensation against attacks. This concise review addresses the biological and psychological positive effects of PE describing the results obtained on brain plasticity and epigenetic mechanisms in animal and human studies, in order to clarify how to maximize the positive effects of PE while avoiding negative consequences, as in the case of exercise addiction.

We do not all grow older in the same way. Some individuals have a cognitive decline earlier and faster than others who are older in years but cerebrally younger. This is particularly easy to verify in people who have maintained regular physical activity and healthy and cognitively stimulating lifestyle and even in the clinical field. There are patients with advanced neurodegeneration, such as Alzheimer's disease (AD), that, despite this, have mild cognitive impairment. What determines this interindividual difference? Certainly, it cannot be the result of only genetic factors. We are made in a certain manner and what we do acts on our brain. In fact, our genetic basis can be modulated, modified, and changed by our experiences such as education and life events; daily, by sleep schedules and habits; or also by dietary elements. And this can be seen as true even if our experiences are indirectly driven by our genetic basis. In this paper, we will review some current scientific research on how our experiences are able to modulate the structural organization of the brain and how a healthy lifestyle (regular physical activity, correct sleep hygiene, and healthy diet) appears to positively affect cognitive reserve.

a b s t r a c tAlthough widely explored, the pathogenesis of Alzheimer's disease (AD) has yet to be cleared. Over the past twenty years the so call amyloid cascade hypothesis represented the main research paradigm in AD pathogenesis. In spite of its large consensus, the proposed role of b-amyloid (Ab) remain to be elucidated. Many evidences are starting to cast doubt on Ab as the primary causative factor in AD. For instance, Ab is deposited in the brain following many different kinds of injury. Also, concentration of Ab needed to induce toxicity in vitro are never reached in vivo. In this review we propose an amyloid-independent interpretation of several AD pathogenic features, such as synaptic plasticity, endo-lysosomal trafficking, cell cycle regulation and neuronal survival.

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer's disease, meditation might have a potential role in a panel of preventive strategies.

Background and Objectives:Amyotrophic lateral sclerosis (ALS) is a multisystem disorder, as supported by clinical, molecular and neuroimaging evidence. As a consequence, predicting clinical features requires a description of large-scale neuronal dynamics. Normally, brain activity dynamically reconfigures over time, recruiting different brain areas. Brain pathologies induce stereotyped dynamics which, in turn, are linked to clinical impairment. Hence, based on recent evidence showing that brain functional networks become hyper-connected as ALS progresses, we hypothesized that the loss of flexible dynamics in ALS would predict the symptoms severity.Methods:To test this hypothesis, we quantified flexibility utilizing the “functional repertoire” (i.e. the number of configurations of active brain areas) as measured from source-reconstructed magnetoencephalography (MEG) in ALS patients and healthy controls. The activity of brain areas was reconstructed in the classical frequency bands, and the functional repertoire was estimated to quantify spatio-temporal fluctuations of brain activity. Finally, we built a k-fold cross validated multilinear model to predict the individual clinical impairment from the size of the functional repertoire.Results:Comparing 42 ALS patients and 42 healthy controls, we found a more stereotyped brain dynamics in ALS patients (P < 0.05), as conveyed by the smaller functional repertoire. The relationship between the size of the functional repertoire and the clinical scores in the ALS group showed significant correlations in both the delta and the theta frequency bands. Furthermore, through a k-fold cross validated multilinear regression model, we found that the functional repertoire predicted both clinical staging (P < 0.001 and P < 0.01, in delta and theta bands, respectively) and symptoms severity (P < 0.001, in both delta and theta bands).Discussion:Our work shows that: 1) ALS pathology reduces the flexibility of large-scale brain dynamics; 2) sub-cortical regions play a key role in determining brain dynamics; 3) reduced brain flexibility predicts disease stage as well as symptoms severity. Our approach provides a non-invasive tool to quantify alterations in brain dynamics in ALS (and, possibly, other neurodegenerative diseases), thus opening new opportunities in disease management as well as a framework to test, in the near future, the effects of disease-modifying interventions at the whole-brain level.

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The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics. It is an approach able to recognize individuals, based on the brain network. It showed its applicability providing network features used to predict the cognitive decline in preclinical Alzheimer's disease. In this article, we explore the performance of CCF in 47 Parkinson's disease (PD) patients and 47 healthy controls, under the hypothesis that patients would show reduced identifiability as compared to controls, and that such reduction could be used to predict motor impairment. We used source‐reconstructed magnetoencephalography signals to build two functional connectomes for 47 patients with PD and 47 healthy controls. Then, exploiting the two connectomes per individual, we investigated the identifiability characteristics of each subject in each group. We observed reduced identifiability in patients compared to healthy individuals in the beta band. Furthermore, we found that the reduction in identifiability was proportional to the motor impairment, assessed through the Unified Parkinson's Disease Rating Scale, and, interestingly, able to predict it (at the subject level), through a cross‐validated regression model. Along with previous evidence, this article shows that CCF captures disrupted dynamics in neurodegenerative diseases and is particularly effective in predicting motor clinical impairment in PD.

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