BACKGROUND:Apathy worsens with age and cognitive decline, particularly in Alzheimer's, leading to functional and cognitive deterioration. Comprehending its broad impact is vital for customized, preventive treatments. METHODS:The study examined 214 adults divided in three groups-Mild Cognitive Impairment, mild Alzheimer's, and controls-using neuropsychological tests and questionnaires, with statistical and network analysis to explore apathy's links with other group variables related to demographics and treatment. RESULTS:Notable differences were observed among the groups' performance of administered tests. While inferential statistics failed to return a predictive model of apathy in mild Alzheimer's, networks and cluster analyses indicate that the demographic variables analysed have different importance at different times of disease progression and that cognitive apathy is particularly prominent in AD-related decline. CONCLUSIONS:Network analysis revealed insights into dementia risk differentiation, notably the impact of sex and demographic factors, beyond the scope of traditional statistics. It highlighted cognitive apathy as a key area for personalized intervention strategies more than behavioural and emotional, emphasizing the importance of short-term goals and not taking away the person's autonomy when not strictly necessary.

Background:Alzheimer's disease and mild cognitive impairment are often difficult to differentiate due to their progressive nature and overlapping symptoms. The lack of reliable biomarkers further complicates early diagnosis. As the global population ages, the incidence of cognitive disorders increases, making the need for accurate diagnosis critical. Timely and precise diagnosis is essential for the effective treatment and intervention of these conditions. However, existing diagnostic methods frequently lead to a significant rate of misdiagnosis. This issue underscores the necessity for improved diagnostic techniques to better identify cognitive disorders in the aging population. Methods:We used Graph Neural Networks, Multi-Layer Perceptrons, and Graph Attention Networks. GNNs map patient data into a graph structure, with nodes representing patients and edges shared clinical features, capturing key relationships. MLPs and GATs are used to analyse discrete data points for tasks such as classification and regression. Each model was evaluated on accuracy, precision, and recall. Results:The AI models provide an objective basis for comparing patient data with reference populations. This approach enhances the ability to accurately distinguish between AD and MCI, offering more precise risk stratification and aiding in the development of personalized treatment strategies. Conclusion:The incorporation of AI methodologies such as GNNs and MLPs into clinical settings holds promise for enhancing the diagnosis and management of Alzheimer's disease and mild cognitive impairment. By deploying these advanced computational techniques, clinicians could see a reduction in diagnostic errors, facilitating earlier, more precise interventions, and likely to lead to significantly improved outcomes for patients.

Patients undergoing antipsychotic treatment for psychiatric disorders may experience challenges in functioning, either stemming from the severity of the illness or from the tolerability issues of prescribed medications. The aims of this cross-sectional study are to investigate the impact of adverse effects of antipsychotic drugs on patients’ daily life functioning, comparing oral and long-acting injectable (LAI) antipsychotics, and further dividing antipsychotics by receptor-binding profiles based on recently defined data-driven taxonomy. This study involved patients with schizophrenia and bipolar spectrum disorders taking oral or LAI antipsychotics. Disability and functioning levels were assessed using the World Health Organization Disability Assessment Schedule 2.0 (WHODAS), and the adverse effects of medications were evaluated using the Udvalg for Kliniske Undersogelser (UKU) Side Effect Rating Scale and its subscales. The total sample consisted of 126 participants with a diagnosis of schizophrenia-spectrum or bipolar disorder, and included 54 males and 72 females ranging from 18 to 78 years of age (mean 45.1, standard deviation 14); 78 patients were taking oral antipsychotics and 48 were taking LAI antipsychotics, with subcategories of muscarinic (31), adrenergic/low dopamine (25), serotonergic/dopaminergic (23), dopaminergic (1), LAI muscarinic (15), LAI adrenergic (6), and LAI serotonergic/dopaminergic (25). The UKU total score for adverse effects showed significant correlations with WHODAS total score (ρ = 0.475; p < 0.001). Compared with oral antipsychotics, LAIs showed significantly lower scores in psychological (p = 0.014), autonomic (p = 0.008), other (p = 0.004), and sexual adverse effects (p = 0.008), as well as the UKU total score (p = 0.002). The Kruskal–Wallis test showed a significant difference in adverse effects between LAI and oral muscarinic subgroups, with LAIs having lower scores compared with antipsychotics binding to muscarinic receptors (p = 0.043). These findings indicate clinically relevant differences in adverse effects among formulations, warranting further investigation for future observational studies.

Current drugs for Alzheimer’s Disease (AD), such as cholinesterase inhibitors (ChEIs), exert only symptomatic activity. Different psychometric tools are needed to assess cognitive and non-cognitive dimensions during pharmacological treatment. In this pilot study, we monitored 33 mild-AD patients treated with ChEIs. Specifically, we evaluated the effects of 6 months (Group 1 = 17 patients) and 9 months (Group 2 = 16 patients) of ChEIs administration on cognition with the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Frontal Assessment Battery (FAB), while depressive symptoms were measured with the Hamilton Depression Rating Scale (HDRS). After 6 months (Group 1), a significant decrease in MoCA performance was detected. After 9 months (Group 2), a significant decrease in MMSE, MoCA, and FAB performance was observed. ChEIs did not modify depressive symptoms. Overall, our data suggest MoCA is a potentially useful tool for evaluating the effectiveness of ChEIs.

Mental imagery is a cognitive ability that enables individuals to simulate sensory experiences without external stimuli. This complex process involves generating, manipulating, and experiencing sensory perceptions. Despite longstanding interest, understanding its relationship with other cognitive functions and emotions remains limited. This narrative review aims to address this gap by exploring mental imagery’s associations with cognitive and emotional processes. It emphasizes the significant role of mental imagery on different cognitive functions, with a particular focus on learning processes in different contexts, such as school career, motor skill acquisition, and rehabilitation. Moreover, it delves into the intricate connection between mental imagery and emotions, highlighting its implications in psychopathology and therapeutic interventions. The review also proposes a comprehensive psychometric protocol to assess mental imagery’s cognitive and emotional dimensions, enabling a thorough evaluation of this complex construct. Through a holistic understanding of mental imagery, integrating cognitive and emotional aspects, researchers can advance comprehension and application in both research and clinical settings.

BackgroundAlzheimer’s disease and mild cognitive impairment are often difficult to differentiate due to their progressive nature and overlapping symptoms. The lack of reliable biomarkers further complicates early diagnosis. As the global population ages, the incidence of cognitive disorders increases, making the need for accurate diagnosis critical. Timely and precise diagnosis is essential for the effective treatment and intervention of these conditions. However, existing diagnostic methods frequently lead to a significant rate of misdiagnosis. This issue underscores the necessity for improved diagnostic techniques to better identify cognitive disorders in the aging population.MethodsWe used Graph Neural Networks, Multi-Layer Perceptrons, and Graph Attention Networks. GNNs map patient data into a graph structure, with nodes representing patients and edges shared clinical features, capturing key relationships. MLPs and GATs are used to analyse discrete data points for tasks such as classification and regression. Each model was evaluated on accuracy, precision, and recall.ResultsThe AI models provide an objective basis for comparing patient data with reference populations. This approach enhances the ability to accurately distinguish between AD and MCI, offering more precise risk stratification and aiding in the development of personalized treatment strategies.ConclusionThe incorporation of AI methodologies such as GNNs and MLPs into clinical settings holds promise for enhancing the diagnosis and management of Alzheimer’s disease and mild cognitive impairment. By deploying these advanced computational techniques, clinicians could see a reduction in diagnostic errors, facilitating earlier, more precise interventions, and likely to lead to significantly improved outcomes for patients.

Objectives: Adherence to Mediterranean Diet seems to be involved in older adults' health. However, its specific role towards cognitive deficits, daily autonomy, and sleep quality still needs to be clarified. Therefore, this pilot study aimed at: 1) exploring the relationships between adherence to Mediterranean Diet, cognitive decline, daily autonomy, and sleep quality in older adults with Mild Cognitive Impairment (MCI), and 2) computing models to explore the interplay between these variables. Methods: After ethical approval, 12 older adults with MCI (Mini Mental State Examination ≥ 26 and < 28, M = 26.8, SD = 1.82) aged 65+ years (M = 75.7, SD = 7.39) were assessed with Medi-Lite questionnaire on adherence to Mediterranean Diet, Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Frontal Assessment Battery (FAB), Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), and Pittsburgh Sleep Quality Index (PSQI). Data were analyzed with SPSS 27. Results: Adherence to Mediterranean Diet correlated with MMSE attention and calculation (ρ = 0.63, p < .05), MMSE orientation (ρ = 0.58, p < .05), MMSE general cognitive functioning (ρ = 0.67, p < .05), MoCA visuospatial/executive ability (ρ = 0.79, p < .01), MoCA delayed recall (ρ = 0.6, p < .05), FAB conflicting instructions (ρ = 0.61, p < .05), FAB go – no go (ρ = 0.67 p < .05), IADL (ρ = 0.79, p < .05), and PSQI sleep duration (ρ = 0.74, p < .01). PSQI sleep duration, moreover, correlated with FAB go – no go (ρ = 0.67, p < .05), while PSQI subjective quality of sleep correlated with ADL (ρ = -0.7, p < .05). Bootstrap mediation analysis, in conclusion, showed adherence to Mediterranean Diet as a complete mediator in the relationship between MMSE general cognitive functioning and IADL (ab = 0.5, 95% CI [0.13, 1.08], p < .05; c = 0.48, 95% CI [-0.04, 1.25], p = .12; c + ab = 0.98, 95% CI [0.55, 1.86], p < .01), and in the relationship between PSQI sleep duration and IADL (ab = 1.71, 95% CI [0.64, 3.48], p < .05; c = -0.1, 95% CI [-1.89, 1.54]; p = .89; c + ab = 1.61, 95% CI [0.33, 3]; p < .05). Conclusions: Adherence to Mediterranean Diet seems to play an important role in the relationships between cognition, sleep, and daily autonomy in MCI. Future works should be conducted on larger samples to design specific intervention strategies for healthy aging. Funding Sources: None.

The COVID-19 pandemic caused critical mental health issues and lifestyle disruptions. The aim of this study was to explore, during the lockdown of second-wave contagions in Italy, how stress was affected by dispositional (personality factors and intolerance to uncertainty) and behavioral (coping strategies) dimensions, how these variables differed among sex, age, educational, professional, and health groups, and how the various changes in work and daily routine intervened in the psychological impact of the emergency. Our results highlight that women, the youngs, students/trainees, those with chronic diseases, those who stopped their jobs due to restrictions, and those who left home less than twice a week were more stressed, while health professionals showed lower levels of the same construct. Those with higher levels of stress used more coping strategies based on avoidance, which positively correlated with age, agreeableness, conscientiousness, and intolerance to uncertainty, and negatively with openness. Stress levels also positively correlated with agreeableness, conscientiousness, intolerance to uncertainty, and seeking of social support, and negatively with openness, a positive attitude, and a transcendent orientation. Finally, stress was predicted mainly by behavioral dimensions. Our results are discussed and framed within the literature, as important insights for targeted intervention strategies to promote health even in emergencies.

Sleep disorders have become increasingly prevalent, with many adults worldwide reporting sleep dissatisfaction. Major Depressive Disorder (MDD) and Bipolar Disorder (BD) are common conditions associated with disrupted sleep patterns such as insomnia and hypersomnolence. These sleep disorders significantly affect the progression, severity, treatment, and outcome of unipolar and bipolar depression. While there is evidence of a connection between sleep disorders and depression, it remains unclear if sleep features differ between MDD and BD. In light of this, this narrative review aims to: (1) summarize findings on common sleep disorders like insomnia and hypersomnolence, strongly linked to MDD and BD; (2) propose a novel psychometric approach to assess sleep in individuals with depressive disorders. Despite insomnia seems to be more influent in unipolar depression, while hypersomnolence in bipolar one, there is no common agreement. So, it is essential adopting a comprehensive psychometric protocol for try to fill this gap. Understanding the relationship between sleep and MDD and BD disorders are crucial for effective management and better quality of life for those affected.

Background Different studies have been conducted to understand how patients with unipolar and bipolar depression differ in terms of cognitive and affective symptoms as well as in psychosocial function. Furthermore, the impact of antidepressants, second-generation antipsychotics, and mood stabilizers on these dimensions needs to be characterized, as well as the best psychometric approach to measure changes after pharmacological treatment. Objectives This study aims to analyze the impact of psychotropic drugs on cognitive, affective, and psychosocial functioning in MDD and BD patients; to test the sensitivity of psychometric tools for measuring those changes; also, to understand how psychosocial abilities are associated with affective and cognitive dimensions in patients with MDD and BD. Methods A total of 22 patients with MDD and 21 patients with BD in the depressive phase were recruited. Several psychometric tests were administered to assess affective, cognitive, and psychosocial symptoms before and after 12 weeks of drug treatment (T0 and T1) with different psychotropic drugs including second-generation antidepressants, second-generation antipsychotics and mood stabilizers (lamotrigine). Results MDD patients showed significant improvement in MoCA, Delayed Recall of Rey's 15 Words and HDRS, while a significant worsening was detected on Digit Span Backwards and on FAST scores. Instead, patients with BD showed significant improvements in the MoCA as the MDD patients, but only a trend of improvement (non-statistically significant) on the BDI-II. A positive correlation was detected in both groups between FAST and HDRS and BDI-II scores, especially in BD patients. Conclusion Our results demonstrate that drug treatment with psychotropic drugs can improve cognitive and affective symptoms, but not all psychometric tools may be equally sensitive to detect those changes in MDD vs. BD patients. Moreover, we found that affective and cognitive dimensions can be considered as different psychopathological dimensions both in unipolar and bipolar depression.

Introduction The Major Depressive Disorder (MDD) is a mental health disorder that affects millions of people worldwide. It is characterized by persistent feelings of sadness, hopelessness, and a loss of interest in activities that were once enjoyable. MDD is a major public health concern and is the leading cause of disability, morbidity, institutionalization, and excess mortality, conferring high suicide risk. Pharmacological treatment with Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin Noradrenaline Reuptake Inhibitors (SNRIs) is often the first choice for their efficacy and tolerability profile. However, a significant percentage of depressive individuals do not achieve remission even after an adequate trial of pharmacotherapy, a condition known as treatment-resistant depression (TRD). Methods To better understand the complexity of clinical phenotypes in MDD we propose Network Intervention Analysis (NIA) that can help health psychology in the detection of risky behaviors, in the primary and/or secondary prevention, as well as to monitor the treatment and verify its effectiveness. The paper aims to identify the interaction and changes in network nodes and connections of 14 continuous variables with nodes identified as "Treatment" in a cohort of MDD patients recruited for their recent history of partial response to antidepressant drugs. The study analyzed the network of MDD patients at baseline and after 12 weeks of drug treatment. Results At baseline, the network showed separate dimensions for cognitive and psychosocial-affective symptoms, with cognitive symptoms strongly affecting psychosocial functioning. The MoCA tool was identified as a potential psychometric tool for evaluating cognitive deficits and monitoring treatment response. After drug treatment, the network showed less interconnection between nodes, indicating greater stability, with antidepressants taking a central role in driving the network. Affective symptoms improved at follow-up, with the highest predictability for HDRS and BDI-II nodes being connected to the Antidepressants node. Conclusion NIA allows us to understand not only what symptoms enhance after pharmacological treatment, but especially the role it plays within the network and with which nodes it has stronger connections.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurobehavioral disorder that is usually diagnosed in childhood. It is characterized by attention deficits, hyperactivity, and impulsivity leading to significant impairment in academic, occupational, familiar, and social functioning. Most of the literature has been focusing on the impact of this condition on infancy and preadolescence, but little is known on its consequences in adulthood. This narrative review addresses this gap by focusing on the studies regarding the schooling outcomes of this population. After identifying the specific clinical and neuropsychological profile of ADHD in adults, this study analyzes their precise needs for effective learning and presents evidence on their academic and occupational achievements. Pharmacological, educational, and rehabilitative factors predicting a positive scholastic and career success are critically reviewed. Finally, this study focuses on the strategies that can improve the learning processes in adults with ADHD by expanding the analysis on executive functions, metacognition, and emotional dysregulation. Schooling outcomes in adults with ADHD, therefore, are conceptualized as a complex measure depending on several variables, like early pharmacological treatment, educational support, neuropsychological intervention, and targeted strategies for life-long learning.

Suicide attempts are a possible consequence of Major Depressive Disorder (MDD), although their prevalence varies across different epidemiological studies. Suicide attempt is a significant predictor of death by suicide, highlighting its importance in understanding and preventing tragic outcomes. Researchers are increasingly recognizing the need to study the differences between males and females, as several distinctions emerge in terms of the characteristics, types and motivations of suicide attempts. These differences emphasize the importance of considering gender-specific factors in the study of suicide attempts and developing tailored prevention strategies. We conducted a network analysis to represent and investigate which among multiple neurocognitive, psychosocial, demographic and affective variables may prove to be a reliable predictor for identifying the 'suicide attempt risk' (SAR) in a sample of 81 adults who met DSM-5 criteria for MDD. Network analysis resulted in differences between males and females regarding the variables that were going to interact and predict the SAR; in particular, for males, there is a stronger link toward psychosocial aspects, while for females, the neurocognitive domain is more relevant in its mnestic subcomponents. Network analysis allowed us to describe otherwise less obvious differences in the risk profiles of males and females that attempted to take their own lives. Different neurocognitive and psychosocial variables and different interactions between them predict the probability of suicide attempt unique to male and female patients.

# Background The COVID-19 pandemic has significantly affected the mental health of healthcare workers, who have taken on the major problems triggered by the emergency. The mental consequences concern high levels of insomnia, anxiety, depression and burnout, which inevitably affect their professional quality of life too. # Objective The aim of this study was to analyze the relationship between psychopathological symptoms (tested with the Depression Anxiety Stress Scale, DASS-21) and professional quality of life (measured with the Professional Quality of Life Scale, ProQol) in a hospital of southern Italy. # Methods 204 healthcare workers were recruited by non-probabilistic sampling and divided by age, gender, work roles (physicians, nurses and intermediate care technicians) and clinical departments (Cardio-medicine, Infectious Diseases, Emergency Medicine, First Aid, Obstetrics and Pneumology). # Results The results showed higher levels of Secondary Traumatic Stress, Depression, Anxiety and Stress in women than in men. Physicians and nurses experienced lower levels of Compassion Satisfaction but higher Burnout than intermediate care technicians; likewise, nurses were more anxious than physicians. The Emergency Medicine had higher scores in Compassion Satisfaction than Infectious Disease, Pneumology, Obstetrics and Cardio-Medicine. # Conclusion In light of what has been said so far, it appears essential to intervene on the first mild signs of Burnout and Secondary Traumatic Stress, because they precede the onset of Depression, Stress and Anxiety in healthcare workers.

Data used to compute the analysis of the paper "THE DYNAMIC INTERACTION BETWEEN SYMPTOMS AND PHARMACOLOGICAL TREATMENT IN PATIENTS WITH MAJOR DEPRESSIVE DISORDER: THE ROLE OF NETWORK INTERVENTION ANALYSIS"

The purpose of this study is to use a dynamic network approach as an innovative way to identify distinct patterns of interacting symptoms in patients with Major Depressive Disorder (MDD) and patients with Bipolar Type I Disorder (BD). More precisely, the hypothesis will be testing that the phenotype of patients is driven by disease specific connectivity and interdependencies among various domains of functioning even in the presence of underlying common mechanisms. In a prospective observational cohort study, hundred-forty-three patients were recruited at the Psychiatric Clinic “Villa dei Gerani” (Catania, Italy), 87 patients with MDD and 56 with BD with a depressive episode. Two nested sub-groups were treated for a twelve-week period, which allowed us to explore differences in the pattern of symptom distribution (central vs. peripheral) and their connectedness (strong vs weak) before (T0) and after (T1) treatment. All patients underwent a complete neuropsychological evaluation at baseline (T0) and at T1. A network structure was computed for MDD and BD patients at T0 and T1 from a covariance matrix of 17 items belonging to three domains–neurocognitive, psychosocial, and mood-related (affective) to identify what symptoms were driving the networks. Clinically relevant differences were observed between MDD and BD, at T0 and after 12 weeks of pharmacological treatment. At time T0, MDD patients displayed an affective domain strongly connected with the nodes of psychosocial functioning, while direct connectivity of the affective domain with the neurocognitive cluster was absent. The network of patients with BD, in contrast, revealed a cluster of highly interconnected psychosocial nodes but was guided by neurocognitive functions. The nodes related to the affective domain in MDD are less connected and placed in the periphery of the networks, whereas in BD they are more connected with psychosocial and neurocognitive nodes. Noteworthy is that, from T0 to T1 the “Betweenness” centrality measure was lower in both disorders which means that fewer “shortest paths” between nodes pass through the affective domain. Moreover, fewer edges were connected directly with the nodes in this domain. In MDD patients, pharmacological treatment primarily affected executive functions which seem to improve with treatment. In contrast, in patients with BD, treatment resulted in improvement of overall connectivity and centrality of the affective domain, which seems then to affect and direct the overall network. Though different network structures were observed for MDD and BD patients, data suggest that treatment should include tailored cognitive therapy, because improvement in this central domain appeared to be fundamental for better outcomes in other domains. In sum, the advantage of network analysis is that it helps to predict the trajectory of future phenotype related disease manifestations. In turn, this allows new insights in how to balance therapeutic interventions, involving different fields of function and combining pharmacological and non-pharmacological treatment modalities.

The Habit Reversal Training (HRT) is a behavioral procedure for treating the so-called nervous habits, such as nail biting, hair pulling and thumb sucking. In addition to being an established clinical procedure, HRT is also a strategy for behavioral change that can serve the entire community. For this reason, this review aims to explore the studies proposing the use of HRT for the reduction of hand-to-face habits in the context of COVID-19 pandemic. Touching one's nose, mouth and eyes, indeed, is one of the means of virus transmission that many awareness campaigns seek to highlight. After an overview of how HRT works and of the current epidemiological situation, studies supporting Habit Reversal Training for the reduction of risky hand-to-face habits are presented. The possible strategies are then exposed and critically discussed to identify their limitations and propose a new version according to the Relational Frame Theory.

Thanks to the work of Division 38 of the American Psychological Association, Health Psychology has been defined in its methods and objectives. The biomedical vision of pathophysiological processes, indeed, could not take into account the complex relationship between medical conditions, psychological variables and contextual factors. Being healthy was not a matter of “silence of the organs” (Leriche, as cited by Canguilhem, 1991, p. 91) anymore, but encompassed representations, values, motivations and participation in social life. World Health Organization (WHO) still adopts this vision, referred to as biopsychosocial perspective. According to it, health disciplines should focus not only on treatment, but also on prevention, education, monitoring and social policy...