Explore the vast range of titles available.

Background The COVID‐19 pandeminc has had widespread impacts, but its specific effects on mental health and cognitive function in patients with cancer remain under‐explored. Recent Findings Data from the general population has suggested that mental health problems were frequent during the pandemic, namely during the initial stage of the outbreak. For patients with cancer, a systematic review and meta‐analysis of data published until January 2021 also showed elevated prevalence of depression and anxiety, and suggested that anxiety was more frequent than in health workers and healthy controls. Objective This systematic review aimed to synthesize existing evidence on the impact of the COVID‐19 pandemic on mental health and cognitive function in patients with cancer. Methods Studies were identified through systematic search of three electronic bibliographic databases (PubMed, Web of Science, and EBSCOHOST) with adapted search strings. We included only peer‐reviewed, nonqualitative, original research papers, published between 2019 and 2022, and reporting on mental health and/or cognition outcomes during the COVID‐19 pandemic in adult patients with cancer. Results Of 3260 papers identified, 121 full text articles were retrieved and 71 met inclusion criteria. We found that patients with cancer reported high levels of psychological distress, anxiety and depression, as well as cognitive complaints during the pandemic. However, studies were not consistent in identifying these symptoms as effects of the pandemic specific for this population. In fact, longitudinal studies did not find consistent differences between pre‐ and post‐pandemic periods and, globally, patients with cancer did not report increased severity of these mental health symptoms in relation to the general population. Conclusion Overall, while the COVID‐19 pandemic may have raised mental health challenges for patients with cancer, the diagnosis of cancer and associated treatments seemed to remain the main source of concern for these patients.

Mania, the diagnostic hallmark of bipolar disorder, is an episodic disturbance of mood, sleep, behavior, and perception. Improved understanding of the neurobiology of mania is expected to allow for novel avenues to address current challenges in its diagnosis and treatment. Previous research focusing on the impairment of functional neuronal circuits and brain networks has resulted in heterogenous findings, possibly due to a focus on bipolar disorder and its several phases, rather than on the unique context of mania. Here we present a comprehensive overview of the evidence regarding the functional neuroanatomy of mania. Our interpretation of the best available evidence is consistent with a convergent model of lateralized circuit dysfunction in mania, with hypoactivity of the ventral prefrontal cortex in the right hemisphere, and hyperactivity of the amygdala, basal ganglia, and anterior cingulate cortex in the left hemisphere of the brain. Clarification of dysfunctional neuroanatomic substrates of mania may contribute not only to improve understanding of the neurobiology of bipolar disorder overall, but also highlights potential avenues for new circuit-based therapeutic approaches in the treatment of mania.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder manifesting as lifelong deficits in social communication and interaction, as well as restricted repetitive behaviors, interests and activities. While there are no specific pharmacological or other physical treatments for autism, in recent years repetitive Transcranial Magnetic Stimulation (rTMS), a technique for non-invasive neuromodulation, has attracted interest due to potential therapeutic value. Here we report the results of a systematic literature review and meta-analysis on the use of rTMS to treat ASD. We performed a systematic literature search on PubMed, Web of Science, Science Direct, Bielefeld Academic Search, and Educational Resources Information Clearinghouse. Search terms reflected diagnoses and treatment modalities of interest. Studies reporting use of rTMS to treat core ASD or cognitive symptoms in ASD were eligible. Two researchers performed article selection and data extraction independently, according to PRISMA guidelines. Changes in ASD clinical scores or in cognitive performance were the main outcomes. Random effects meta-analysis models were performed. We found 23 eligible reports, comprising 4 case-reports, 7 non-controlled clinical trials, and 12 controlled clinical trials, comparing the effects of real TMS with waiting-list controls ( = 6) or sham-treatment ( = 6). Meta-analyses showed a significant, but moderate, effect on repetitive and stereotyped behaviors, social behavior, and number of errors in executive function tasks, but not other outcomes. Most studies had a moderate to high risk of bias, mostly due to lack of subject- and evaluator-blinding to treatment allocation. Only 5 studies reported stability of these gains for periods of up 6 months, with descriptions that improvements were sustained over time. Existing evidence supports that TMS could be useful to treat some dimensions of ASD. However, such evidence must be regarded with care, as most studies did not adequately control for placebo effects. Moreover, little is known regarding the most effective stimulation parameters, targets, and schedules. There is an urgent need for further randomized, double-blind, sham-controlled trials, with adequate follow-up periods, to test the efficacy of transcranial magnetic stimulation to treat these disorders. Available evidence must be regarded as preliminary and insufficient, at present, to support offering TMS to treat ASD.

Postingestive nutrient stimulation conditions food preferences through striatal dopamine and may be associated with blunted brain responses in obesity. In a cross-sectional study, we tested flavor-nutrient conditioning (FNC) with maltodextrin-enriched yogurt, with maltodextrin previously optimized for concentration and dextrose equivalents ( n = 57), and to mask texture cues ( n = 102). After conditioning, healthy volunteers ( n = 52) increased preference for maltodextrin-paired (+102 kcal, CS + ), relative to control (+1.8 kcal, CS - ) flavors, as assessed according to intake, but not pleasantness. In a clinical study ( n = 61), behavioral conditioning without effects on pleasantness was confirmed across pre-bariatric candidates with obesity, weight-stable post-surgery patients, and healthy controls, without significant differences between groups. Striatal dopamine D2-like receptor (DD2lR) availability, assessed with [ 123 I]IBZM SPECT, was reduced in the obesity group and strongly correlated with conditioning strength and a measure of restrained eating in patients with gastric bypass. These results show that postingestive nutrient stimulation influences human food choices through behavioral reinforcement, and is conserved in obesity and after bariatric surgery. Trial Registration: ISRCTN17965026 : Dopaminergic neurotransmission in dietary learning and obesity.

There is significant evidence linking a ‘reward deficiency syndrome’ (RDS), comprising decreased availability of striatal dopamine D2-like receptors (DD2lR) and addiction-like behaviors underlying substance use disorders and obesity. Regarding obesity, a systematic review of the literature with a meta-analysis of such data is lacking. Following a systematic review of the literature, we performed random-effects meta-analyses to determine group differences in case–control studies comparing DD2lR between individuals with obesity and non-obese controls and prospective studies of pre- to post-bariatric surgery DD2lR changes. Cohen's d was used to measure effect size. Additionally, we explored factors potentially associated with group differences in DD2lR availability, such as obesity severity, using univariate meta-regression. In a meta-analysis including positron emission tomography (PET) and single-photon emission computed tomography (SPECT) studies, striatal DD2lR availability did not significantly differ between obesity and controls. However, in studies comprising patients with class III obesity or higher, group differences were significant, favoring lower DD2lR availability in the obesity group. This effect of obesity severity was corroborated by meta-regressions showing inverse associations between the body mass index (BMI) of the obesity group and DD2lR availability. Post-bariatric changes in DD2lR availability were not found, although a limited number of studies were included in this meta-analysis. These results support lower DD2lR in higher classes of obesity which is a more targeted population to explore unanswered questions regarding the RDS.

Obesity-associated differences in hedonic hunger, while consistently reported, have not been adequately quantified, with most studies failing to demonstrate strong correlations between Body Mass Index (BMI) and hedonic hunger indicators. Here, we quantified and assessed the nature of the relationship between hedonic hunger and BMI, in a cross-sectional study using the Portuguese version of the PFS (P-PFS) to measure hedonic hunger. Data were collected from 1266 participants belonging to non-clinical, clinical (candidates for weight-loss surgery) and population samples. Across samples, significant but weak positive associations were found between P-PFS scores and BMI, in adjusted linear regression models. However, in logistic regression models of data from the clinical and non-clinical samples, the P-PFS Food Available domain score was significantly and robustly associated with belonging to the clinical sample (OR = 1.8, 95%CI: 1.2–2.8; p = 0.008), while in the population sample it was associated to being obese (OR = 2.1, 95%CI: 1.6–2.7; p < 0.001). Thus, hedonic hunger levels are associated with obesity status with the odds of being obese approximately doubling for each unit increase in the P-PFS Food Available score.

Health professionals are often faced with the need to identify women at risk of manifesting poor psychological resilience following the diagnosis and treatment of breast cancer. Machine learning algorithms are increasingly used to support clinical decision support (CDS) tools in helping health professionals identify women who are at risk of adverse well-being outcomes and plan customized psychological interventions for women at risk. Clinical flexibility, cross-validated performance accuracy, and model explainability permitting person-specific identification of risk factors are highly desirable features of such tools. This study aimed to develop and cross-validate machine learning models designed to identify breast cancer survivors at risk of poor overall mental health and global quality of life and identify potential targets of personalized psychological interventions according to an extensive set of clinical recommendations. A set of 12 alternative models was developed to improve the clinical flexibility of the CDS tool. All models were validated using longitudinal data from a prospective, multicenter clinical pilot at 5 major oncology centers in 4 countries (Italy, Finland, Israel, and Portugal; the Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back [BOUNCE] project). A total of 706 patients with highly treatable breast cancer were enrolled shortly after diagnosis and before the onset of oncological treatments and were followed up for 18 months. An extensive set of demographic, lifestyle, clinical, psychological, and biological variables measured within 3 months after enrollment served as predictors. Rigorous feature selection isolated key psychological resilience outcomes that could be incorporated into future clinical practice. Balanced random forest classifiers were successful at predicting well-being outcomes, with accuracies ranging between 78% and 82% (for 12-month end points after diagnosis) and between 74% and 83% (for 18-month end points after diagnosis). Explainability and interpretability analyses built on the best-performing models were used to identify potentially modifiable psychological and lifestyle characteristics that, if addressed systematically in the context of personalized psychological interventions, would be most likely to promote resilience for a given patient. Our results highlight the clinical utility of the BOUNCE modeling approach by focusing on resilience predictors that can be readily available to practicing clinicians at major oncology centers. The BOUNCE CDS tool paves the way for personalized risk assessment methods to identify patients at high risk of adverse well-being outcomes and direct valuable resources toward those most in need of specialized psychological interventions.

Displaying resilience following a diagnosis of breast cancer is crucial for successful adaptation to illness, well-being, and health outcomes. Several theoretical and computational models have been proposed toward understanding the complex process of illness adaptation, involving a large variety of patient sociodemographic, lifestyle, medical, and psychological characteristics. To date, conventional multivariate statistical methods have been used extensively to model resilience. In the present work we describe a computational pipeline designed to identify the most prominent predictors of mental health outcomes following breast cancer diagnosis. A machine learning framework was developed and tested on the baseline data (recorded immediately post diagnosis) from an ongoing prospective, multinational study. This fully annotated dataset includes socio-demographic, lifestyle, medical and self-reported psychological characteristics of women recently diagnosed with breast cancer (N = 609). Nine different feature selection and cross-validated classification schemes were compared on their performance in classifying patients into low vs high depression symptom severity. Best-performing approaches involved a meta-estimator combined with a Support Vector Machines (SVMs) classification algorithm, exhibiting balanced accuracy of 0.825, and a fair balance between sensitivity (90%) and specificity (74%). These models consistently identified a set of psychological traits (optimism, perceived ability to cope with trauma, resilience as trait, ability to comprehend the illness), and subjective perceptions of personal functionality (physical, social, cognitive) as key factors accounting for concurrent depression symptoms. A comprehensive supervised learning pipeline is proposed for the identification of predictors of depression symptoms which could severely impede adaptation to illness. •A comprehensive supervised learning pipeline is proposed for the identification of predictors of depression symptoms which could severely impede adaptation to illness.•Nine different feature selection and cross-validated classification schemes were compared on their performance in classifying patients into low vs high depression symptom severity.•Standardized scales and questionnaires were used to assess domains of personality, coping, social support, resilience as a personality characteristic (trait), Quality of Life (QoL), distress and psychological symptoms to further stratify patients according to the severity of concurrent depression symptoms.

Non-invasive brain stimulation techniques (NIBS) have been widely used in both clinical and research contexts in neuropsychiatry. They are safe and well-tolerated, making NIBS an interesting option for application in different settings. Transcranial magnetic stimulation (TMS) is one of these strategies. It uses electromagnetic pulses for focal modulate ion of neuronal activity in brain cortical regions. When pulses are applied repeatedly (repetitive transcranial magnetic stimulation—rTMS), they are thought to induce long-lasting neuroplastic effects, proposed to be a therapeutic mechanism for rTMS, with efficacy and safety initially demonstrated for treatment-resistant depression (TRD). Since then, many rTMS treatment protocols emerged for other difficult to treat psychiatric conditions. Moreover, multiple clinical studies, including large multi-center trials and several meta-analyses, have confirmed its clinical efficacy in different neuropsychiatric disorders, resulting in evidence-based guidelines and recommendations. Currently, rTMS is cleared by multiple regulatory agencies for the treatment of TRD, depression with comorbid anxiety disorders, obsessive compulsive disorder, and substance use disorders, such as smoking cessation. Importantly, current research supports the potential future use of rTMS for other psychiatric syndromes, including the negative symptoms of schizophrenia and post-traumatic stress disorder. More precise knowledge of formal indications for rTMS therapeutic use in psychiatry is critical to enhance clinical decision making in this area.

To investigate the efficacy of repetitive transcranial magnetic stimulation (rTMS) combined with cognitive training for treatment of cognitive symptoms in patients with Alzheimer's disease (AD). A secondary objective was to analyze associations between brain plasticity and cognitive effects of treatment. In this randomized, sham-controlled, multicenter clinical trial, 34 patients with AD were assigned to three experimental groups receiving 30 daily sessions of combinatory intervention. Participants in the real/real group ( = 16) received 10 Hz repetitive transcranial magnetic stimulation (rTMS) delivered separately to each of six cortical regions, interleaved with computerized cognitive training. Participants in the sham rTMS group ( = 18) received sham rTMS combined with either real (sham/real group, = 10) or sham (sham/sham group, = 8) cognitive training. Effects of treatment on neuropsychological (primary outcome) and neurophysiological function were compared between the 3 treatment groups. These, as well as imaging measures of brain atrophy, were compared at baseline to 14 healthy controls (HC). At baseline, patients with AD had worse cognition, cerebral atrophy, and TMS measures of cortico-motor reactivity, excitability, and plasticity than HC. The real/real group showed significant cognitive improvement compared to the sham/sham, but not the real/sham group. TMS-induced plasticity at baseline was predictive of post-intervention changes in cognition, and was modified across treatment, in association with changes of cognition. Combined rTMS and cognitive training may improve the cognitive status of AD patients, with TMS-induced cortical plasticity at baseline serving as predictor of therapeutic outcome for this intervention, and potential mechanism of action. www.ClinicalTrials.gov, identifier NCT01504958.