Toward an inclusive future: a systematic review on the implications of ageism for people with dementia Giuseppa Maresca, Isabella Veneziani, Alessandro Grimaldi, Silvia Marino, Angelo Quartarone, Angela Marra European Journal of Ageing, 2026 Ageism remains a pervasive societal issue that significantly affects individuals with dementia, influencing cognitive performance, healthcare access, and social inclusion. This systematic review synthesizes existing research on the implications of ageism for people with dementia, focusing on its impact on cognitive function, stigma, and healthcare disparities. A comprehensive literature search was conducted across multiple databases, including PubMed, Cochrane, Web of Science, and Scopus, adhering to PRISMA guidelines. After screening 515 articles, 13 studies met the inclusion criteria and were analyzed for key findings on ageism's effects. Findings indicate that age-related stereotypes negatively impact cognitive assessments, often leading to misdiagnosis and unnecessary anxiety regarding cognitive decline. Studies show that exposure to negative aging stereotypes correlates with poorer cognitive performance, reinforcing stereotype threat. Additionally, ageism in healthcare settings results in differential treatment, with older adults facing delays in diagnosis and limited access to specialized care. Social stigma further compounds these challenges, contributing to social isolation and decreased well-being among individuals with dementia. Research highlights the role of intergenerational programs and public awareness campaigns in mitigating these effects, promoting inclusivity and reducing ageist attitudes. Addressing ageism requires a multifaceted approach, including policy changes, caregiver training, and societal initiatives to reshape perceptions of aging and dementia. Future research should explore long-term interventions that foster positive aging attitudes and equitable healthcare practices. This review underscores the necessity of dismantling ageist biases to enhance the quality of life and care for individuals with dementia, advocating for a more inclusive and respectful societal framework.
Gut microbiota and cognitive decline: a scoping review of microbial mechanisms and adaptive responses in dementia Giovanni Luca Cipriano, Alessandro Grimaldi, Angela Marra, Angelo Quartarone, Giuseppa Maresca Frontiers in Aging Neuroscience, 2026 Dementia is a progressive disease that results in a loss of mental capacity. Some of the most affected cognitive skills are memory, orientation, and language. These skills are also associated with behavioral shifts such as increased agitation and apathy, worsening the affected person’s quality of life. The most common type of dementia is Alzheimer’s disease, and it is especially concerning in older adults. Alzheimer’s is characterized by the formation of beta-amyloid plaques and neurofibrillary tangles that are made of hyperphosphorylated tau proteins. These plaques and tangles lead to inflammation in the central nervous system, damage to the connections between neurons, and overall degeneration of the nervous system. Newer studies have started to identify the gut microbiome and the gut-brain axis as components critical to the progression of neurodegenerative diseases. Dysbiosis, which is characterized by an imbalance or loss of microbial diversity in the gut, has been attributed to the worsening of neurodegenerative diseases. The gut microbiome has been shown to have a large impact on the brain and how it responds neurochemically. An imbalance in the gut microbiome has also been shown to lead a person to emotional and cognitive dysfunction. It has been shown that in dementia patients, there is also an associated intestinal dysbiosis and increased inflammation systemically and within the brain. Certain gut bacteria stimulate the production of pro- inflammatory cytokines and neuroinflammation, which is a defining characteristic of diseases associated with dementia. This review is focused on three main aspects in which dysbiosis is related to cognitive decline.
Beyond Diagnosis: Exploring Residual Autonomy in Dementia Through a Systematic Review Anna Anselmo, Francesco Corallo, Maria Pagano, Davide Cardile, Angela Marra, Giuseppa Maresca, Rosaria De Luca, Antonella Alagna, Angelo Quartarone, Rocco Salvatore Calabrò, Irene Cappadona Medicina Lithuania, 2025 Background and Objectives: The connection between cognitive decline and autonomy represents a complex and multifactorial area of research. Cognitive decline manifests as a progressive impairment of higher mental functions and is typical of neurodegenerative conditions such as dementia. Autonomy, on the other hand, is defined as an individual’s ability to independently manage activities of daily living and make informed decisions. The objective of this study was to investigate whether certain daily living skills can persist despite the advancement of dementia, and what factors contribute to their preservation in maintaining autonomy. Materials and Methods: A literature review was conducted using the databases PubMed, Web of Science, Scopus, Cochrane Library, Embase, and PsycInfo. Out of an initial pool of 12,113 studies, only 19 met the inclusion criteria and were selected for analysis. Results: The findings indicate that, in non-institutionalized settings, some daily living abilities may remain preserved despite cognitive deterioration. In contrast, within institutionalized environments, a significant correlation emerged between cognitive decline and the progressive loss of personal autonomy. Conclusions: This study highlights the importance of assessing residual abilities in individuals with dementia. Recognizing and supporting these remaining skills can play a crucial role in enhancing quality of life, delaying institutionalization, and promoting autonomy even in the presence of advanced cognitive decline.
AI-based staging, causal hypothesis and progression of subjects at risk of Alzheimer’s disease: a multicenter study Simona Aresta, Raffaello Nemni, Moreno Zanardo, Graziella Sirabian, Dario Capelli, Marco Alì, Paolo Vitali, Enrico Giuseppe Bertoldo, Valentina Fiolo, Lilla Bonanno, Giuseppa Maresca, Petronilla Battista, Francesco Sardanelli, Francesca Benedetta Pizzini, Isabella Castiglioni, Christian Salvatore Frontiers in Neurology, 2025 IntroductionIn 2024, 11 European scientific societies/organizations and one patient advocacy association have defined a patient-centered biomarker-based diagnostic workflow for memory clinics evaluating neurocognitive disorders.MethodsWe tested the performance of an artificial intelligence (AI) tool applied to neuropsychological and magnetic resonance imaging (MRI) assessment for staging and causal hypothesis, which are the two recommended workflow steps guiding the next one recommending optimal biomarkers to be used for a biological diagnosis of neurocognitive disorders, according to intersocietal recommendations. Moreover, we assessed the AI performance in predicting the progression to Alzheimer’s disease (AD)-dementia.ResultsFor the three-class classification of staging (n patients = 426), the inter-rater AI-humans agreement was substantial for both healthy subjects/subjective cognitive impairment/worried-well vs. all the remaining groups (rest) (Cohen’s κ = 0.81) and mild cognitive impairment/mild dementia vs. rest κ = 0.70) classification, almost perfect for moderate/severe dementia vs. rest κ =0.90) classification. For the three-class classification of causal hypotheses (n = 112), the AI performance vs. biomarker-based diagnosis was: positive predictive value 91% [95% CI: 84–96%]; negative predictive value 100%, and accuracy 91% [84–96%]. For the binary classification of progression or not progression to AD-dementia at 24-month, with clinical conversion as a reference standard (n = 341), the AI performance was: sensitivity 89% [84–94%], specificity 82% [77–87%]; accuracy 85% [81–89%]; and area under the receiver operating characteristic curve 83% [79–87%].DiscussionThe AI tool showed high agreement with human assessment for staging, high accuracy with biomarkers for causal hypotheses of neurocognitive disorders and predicted progression to AD at 24-month with 89% sensitivity and 82% specificity.
Effectiveness of the Use of Virtual Reality Rehabilitation in Children with Dyslexia: Follow-Up after One Year Giuseppa Maresca, Francesco Corallo, Maria Cristina De Cola, Caterina Formica, Silvia Giliberto, Giuseppe Rao, Maria Felicita Crupi, Angelo Quartarone, Alessandra Pidalà Brain Sciences, 2024 Dyslexia is a common learning disorder that hinders reading fluency and comprehension. Traditional treatments can be tedious for children, limiting their effectiveness. This study investigated the one-year effects of rehabilitation treatment with a virtual reality rehabilitation system (VRRS) on children with dyslexia. Twenty-four children were divided into control (CG) and experimental (EG) groups. The CG underwent conventional neuropsychological treatment (CNT), while the EG underwent VR neurorehabilitation training (VRNT) using the VRRS. Neuropsychological evaluation was conducted before treatment, after six months, and again after one year for both groups. Compared to the control group, children who received VR training showed significant improvement in reading skills, especially in non-word reading and reading speed, even after one year without further VR intervention. VRRS can improve treatment adherence and minimize symptoms by offering engaging activities for children. These findings suggest VRRS may be a valuable tool for dyslexia rehabilitation with long-lasting effects.
Towards a Deeper Understanding: Utilizing Machine Learning to Investigate the Association between Obesity and Cognitive Decline—A Systematic Review Isabella Veneziani, Alessandro Grimaldi, Angela Marra, Elisabetta Morini, Laura Culicetto, Silvia Marino, Angelo Quartarone, Giuseppa Maresca Journal of Clinical Medicine, 2024 Background/Objectives: Several studies have shown a relation between obesity and cognitive decline, highlighting a significant global health challenge. In recent years, artificial intelligence (AI) and machine learning (ML) have been integrated into clinical practice for analyzing datasets to identify new risk factors, build predictive models, and develop personalized interventions, thereby providing useful information to healthcare professionals. This systematic review aims to evaluate the potential of AI and ML techniques in addressing the relationship between obesity, its associated health consequences, and cognitive decline. Methods: Systematic searches were performed in PubMed, Cochrane, Web of Science, Scopus, Embase, and PsycInfo databases, which yielded eight studies. After reading the full text of the selected studies and applying predefined inclusion criteria, eight studies were included based on pertinence and relevance to the topic. Results: The findings underscore the utility of AI and ML in assessing risk and predicting cognitive decline in obese patients. Furthermore, these new technology models identified key risk factors and predictive biomarkers, paving the way for tailored prevention strategies and treatment plans. Conclusions: The early detection, prevention, and personalized interventions facilitated by these technologies can significantly reduce costs and time. Future research should assess ethical considerations, data privacy, and equitable access for all.
The Effects of Home Automation on Personal and Social Autonomies in Spinal Cord Injury Patients: A Pilot Study Giuseppa Maresca, Desirèe Latella, Caterina Formica, Isabella Veneziani, Augusto Ielo, Angelo Quartarone, Rocco Salvatore Calabrò, Maria Cristina De Cola Journal of Clinical Medicine, 2024 Background: Spinal cord injury (SCI) is a severe and progressive neurological condition caused by trauma to the nervous system, resulting in lifelong disability and severe comorbidities. This condition imposes serious limitations on everyday life, interfering with patients’ social lives and compromising their quality of life, psychological well-being, and daily living activities. Rehabilitation is essential to helping SCI patients gain more independence in their daily routines. Home automation (HA) systems provide personalized support to users, allowing them to manage various aspects of their living environment, promoting independence and well-being. This study aims to demonstrate the efficacy of an HA system in enhancing personal and social autonomies in SCI patients, resulting in improved cognitive function and reduced anxiety–depressive symptoms compared to traditional training. Methods: We enrolled 50 SCI patients undergoing neurorehabilitation at IRCCS Centro Neurolesi (Messina, Italy). These patients were randomly assigned to one of two groups: a control group (CG) and an experimental group (EG). The CG received traditional training, while the EG underwent HA training. We evaluated the patients before (T0) and after (T1) rehabilitation using various scales, including the Montreal Cognitive Assessment (MoCA), the Beck Depression Inventory (BDI), the Hamilton Rating Scale for Anxiety (HRS-A), the 12-Item Short-Form Survey (SF-12), the Functional Independence Measure (FIM), Activities of Daily Living (ADL), Instrumental Activities of Daily Living Scale (IADL), and the EQ-5D-5L. Results: The effect of the experimental treatment showed an improvement in all patients test scores in the EG, especially regarding cognitive functions, mood disorders, activities of daily living, and quality of life. Conclusion: Our findings suggest that HA may be effective in improving daily autonomy and, in turn, alleviating mood disorders and enhancing psychological well-being.
The Lack of Ad Hoc Neuropsychological Assessment in Adults with Neurofibromatosis: A Systematic Review Giuseppa Maresca, Carmen Bonanno, Isabella Veneziani, Viviana Lo Buono, Desirèe Latella, Angelo Quartarone, Silvia Marino, Caterina Formica Journal of Clinical Medicine, 2024 Background: Neurofibromatosis Type 1 (NF1) is a genetic autosomal dominant disorder that affects both the central and peripheral nervous systems. Children and adolescents with NF1 commonly experience neuropsychological, motor, and behavioral deficits. The cognitive profile hallmark of this disorder includes visuospatial and executive function impairments. These cognitive disorders may persist into adulthood. This study aims to analyze previous research studies that have described cognitive dysfunctions in adults with NF1. The purpose of this analysis is to review the neuropsychological and psychological assessment methods used. Methods: A total of 327 articles were identified based on the search terms in their titles and abstracts. The evaluation was conducted by scrutinizing each article’s title, abstract, and text. Results: Only 16 articles were found to be eligible for inclusion based on the pre-defined criteria. The selected studies primarily focus on the development of diagnostic protocols for individuals with NF1. Conclusions: The management of NF1 disease requires a multidisciplinary approach to address symptoms, preserve neurological functions, and ensure the best possible quality of life. However, cognitive impairment can negatively affect psychological well-being. This study suggested that cognitive functions in NF1 patients were not tested using specific measures, but rather were evaluated through intelligence scales. Additionally, the findings revealed that there is no standardized neuropsychological assessment for adults with NF1. To address this gap, it would be helpful to create a specific neuropsychological battery to study cognitive function in NF1 patients during clinical studies. This battery could also serve as a tool to design models for cognitive rehabilitation by using reliable and sensitive measures of cognitive outcomes.
Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review Isabella Veneziani, Angela Marra, Caterina Formica, Alessandro Grimaldi, Silvia Marino, Angelo Quartarone, Giuseppa Maresca Journal of Personalized Medicine, 2024 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.
Possible Implications of Managing Alexithymia on Quality of Life in Parkinson's Disease: A Systematic Review Laura Culicetto, Caterina Formica, Viviana Lo Buono, Desirèe Latella, Giuseppa Maresca, Amelia Brigandì, Chiara Sorbera, Giuseppe Di Lorenzo, Angelo Quartarone, Silvia Marino Parkinson S Disease, 2024 Alexithymia, characterized by difficulty in recognizing and verbalizing emotions, is reported to be more prevalent in subjects with Parkinson’s disease (PD) than in the general population. Although it is one of the nonmotor symptoms of PD, alexithymia is often overlooked in clinical practice. The aim of this systematic review is to investigate the prevalence of alexithymia in PD, assess its impact on quality of life, and explore the rehabilitation approaches for alexithymia. Research articles, selected from PubMed, Scopus, and Web of Science, were limited to those published in English from 2013 to 2023. The search terms combined were “Alexithymia,” “Parkinson’s disease,”, and “Quality of life.” Current literature review indicates that alexithymia is commonly assessed using the Toronto Alexithymia Scale (TAS‐20), and it is associated with deficits in visuospatial and executive functions. Presently, rehabilitation interventions for alexithymia are scarce, and their effectiveness remains controversial. Future research should focus on developing comprehensive assessments and rehabilitation strategies for emotional processing, considering its significant impact on the quality of life of both patients and caregivers.
Hypnotherapy as a Nonpharmacological Treatment for the Psychological Symptoms of Multiple Sclerosis Alternative Therapies in Health and Medicine, 2023
Hypnotherapy as a Nonpharmacological Treatment for the Psychological Symptoms of Multiple Sclerosis Alternative Therapies in Health and Medicine, 2022
Use of Virtual Reality in Children with Dyslexia Giuseppa Maresca, Simona Leonardi, Maria Cristina De Cola, Silvia Giliberto, Marcella Di Cara, Francesco Corallo, Angelo Quartarone, Alessandra Pidalà Children, 2022
Care models for mental health in a population of patients affected by COVID-19 Giuseppa Maresca, Caterina Formica, Maria Cristina De Cola, Viviana Lo Buono, Desirèe Latella, Vincenzo Cimino, Lara Carnazza, Fabio Mauro Giambò, Nicholas Parasporo, Alessia Bramanti, Francesco Corallo Journal of International Medical Research, 2022
