Alzheimer's Disease (AD) is a neurodegenerative disorder that causes a loss of connections between neurons. The goal of the present work was to construct a complex network model of the brain-electrical activity using high-density EEG (HD-EEG) recordings and to compare the network organization in AD, Mild Cognitive Impaired (MCI) and healthy control (CNT) subjects. The HD-EEG of 16 AD, 16 MCI and 12 CNT were recorded during an eye-closed resting state. The Permutation Disalignment Index (PDI) was used to describe the dissimilarity between EEG signals and to construct the connection matrices of the network model. The three groups were found to have significantly different ( $p\lt0.001$ ) Characteristic Path Length (Lambda), average Clustering Coefficient (CC) and Global Efficiency (GE). This is the first time that HD-EEG signals of AD, MCI and CNT have been compared and that PDI has been used to discriminate between the three groups. Considered the large amount of data originating from the HD-EEG acquisition, compared to standard EEG, the aim of the present work was also to assess that compression did not alter the results of the complex network analysis. Compressive sensing was adopted to compress and reconstruct the HD-EEG signals with minimal information loss, achieving an average Structural SIMilarity index (SSIM) of 0.954 (AD), 0.957 (MCI) and 0.959 (CNT). When applied to the reconstructed HD-EEG, the complex network analysis provided a substantially unaltered performance, compared to the analysis of the original signals. Indeed, Lambda, CC, GE and of the three groups were still significantly different ( $p\lt0.001$ ).

Parkinson’s disease (PD) is a neurodegenerative disorder caused by selective loss of dopaminergic neurons in the substantia nigra pars compacta which leads to dysfunction of cerebral pathways critical for the control of movements. The diagnosis of PD is based on motor symptoms, such as bradykinesia, akinesia, muscular rigidity, postural instability, and resting tremor, which are evident only after the degeneration of a significant number of dopaminergic neurons. Currently, a marker for early diagnosis of PD is still not available. Consequently, also the development of disease-modifying therapies is a challenge. Magnetic resonance spectroscopy is a quantitative imaging technique that allows in vivo measurement of certain neurometabolites and may produce biomarkers that reflect metabolic dysfunctions and irreversible neuronal damage. This review summarizes the abnormalities of cerebral metabolites found in MRS studies performed in patients with PD and other forms of parkinsonism. In addition, we discuss the potential role of MRS as in vivo molecular imaging biomarker for early diagnosis of PD and for monitoring the efficacy of therapeutic interventions.

In the context of advancing healthcare, the diagnosis and treatment of cognitive disorders, particularly Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD), pose significant challenges. This review explores Artificial Intelligence (AI) and Machine Learning (ML) in neuropsychological assessment for the early detection and personalized treatment of MCI and AD. The review includes 37 articles that demonstrate that AI could be an useful instrument for optimizing diagnostic procedures, predicting cognitive decline, and outperforming traditional tests. Three main categories of applications are identified: (1) combining neuropsychological assessment with clinical data, (2) optimizing existing test batteries using ML techniques, and (3) employing virtual reality and games to overcome the limitations of traditional tests. Despite advancements, the review highlights a gap in developing tools that simplify the clinician’s workflow and underscores the need for explainable AI in healthcare decision making. Future studies should bridge the gap between technical performance measures and practical clinical utility to yield accurate results and facilitate clinicians’ roles. The successful integration of AI/ML in predicting dementia onset could reduce global healthcare costs and benefit aging societies.

Background: Cerebral cavernous malformations (CCMs) are vascular malformations characterized by clusters of enlarged leaky capillaries in the central nervous system. They may result in intracranial haemorrhage, epileptic seizure(s), or focal neurological deficits, and potentially lead to severe disability. Globally, CCMs represent the second most common intracranial vascular malformation in humans, and their familial form (FCCMs) accounts for one-fifth of cases. Neurosurgical excision, and perhaps stereotactic radiosurgery, is the only available therapeutic option. Case reports suggest that propranolol might modify disease progression. Methods: Treat_CCM is a prospective, randomized, open-label, blinded endpoint (PROBE), parallel-group trial involving six Italian clinical centres with central reading of brain magnetic resonance imaging (MRI) and adverse events. Patients with symptomatic FCCMs are randomized (2:1 ratio) either to propranolol (40–80 mg twice daily) in addition to standard care or to standard care alone (i.e. anti-epileptic drugs or headache treatments). The primary outcome is intracranial haemorrhage or focal neurological deficit attributable to CCMs. The secondary outcomes are MRI changes over time (i.e. de novo CCM lesions, CCM size and signal characteristics, iron deposition, and vascular leakage as assessed by quantitative susceptibility mapping and dynamic contrast enhanced permeability), disability, health-related quality of life, depression severity, and anxiety (SF-36, BDI-II, State-Trait Anxiety Inventory). Discussion: Treat_CCM will evaluate the safety and efficacy of propranolol for CCMs following promising case reports in a randomized controlled trial. The direction of effect on the primary outcome and the consistency of effects on the secondary outcomes (even if none of them yield statistically significant differences) of this external pilot study may lead to a larger sample size in a definitive phase 2 trial. Trial registration: ClinicalTrails.gov, NCT03589014. Retrospectively registered on 17 July 2018.