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Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

The role of mathematics in several scientific disciplines is undisputed; work and everyday life take great advantage of its application. Nevertheless, students often tend to not particularly like it and to consider it of little interest. It is also believed that only people with a certain attitude are capable of mastering the subject. In consideration of this, we aimed to help science students develop mathematical competences by designing a course specifically oriented to applications and problem solving. We administered our course to students attending the first year of a program in biotechnology, asking them to work with technologies instilling curiosity and interest, thus achieving a better proficiency as a consequence. Two questionnaires, along with access and proficiency data, allowed us to collect information about students' attitudes, beliefs, and activity, which we analyzed by means of descriptive statistics. The promotion of the interaction among learners made them active users of the contents, thus allowing for the adaptation of their learning paths according to their personal necessities, as well as the development of teamwork skills and flexibility. Finally, students recognized the usefulness of the problem-solving approach and the role played by software.

BackgroundEfforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.MethodsWe describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.ResultsWe are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.ConclusionWe describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

The COVID-19 pandemic has evidenced a need for tools and methodologies to support students' autonomous learning and the formative assessment practices in distance education contexts, especially for students from challenging backgrounds. This paper proposes a conceptualization of Interactive Feedback (IF) for Mathematics, which is a step-by-step interactive process that guides the learner in the resolution of a task after one or more autonomous tentative. This conceptualization is grounded on theories and models of automatic assessment, formative assessment, and feedback. We discuss the effectiveness of the IF for engaging students from low socio-economic contexts in closing the gap between current and reference performance through a didactic experimentation involving 299 Italian students in grade 8. Using quantitative analyses on data from the automatic assessment, we compared the results of the first and last attempts in activities with and without IF, based on algorithmic parameters so that the task changes at every attempt. We found that IF was more effective than other kinds of activities to engage learners in actions aimed at improving their results, and the effects are stronger in low socio-economic contexts.

Abstract Psychedelic substances induce profound alterations in consciousness. Careful preparation is therefore essential to limit adverse reactions, enhance therapeutic benefits, and maintain user safety. This paper describes the development of a self-directed, digital intervention for psychedelic preparation. Drawing on elements from the UK Medical Research Council (MRC) framework for developing complex interventions, the design was informed by a four-factor model of psychedelic preparedness, using a person-centred approach. Our mixed-methods investigation consisted of two studies. The first involved interviews with 19 participants who had previously attended a ‘high-dose’ psilocybin retreat, systematically exploring their preparation behaviours and perspectives on the proposed intervention. The second study engaged 28 attendees of an ongoing psilocybin retreat in co-design workshops, refining the intervention protocol using insights from the initial interviews. The outcome is a co-produced 21-day digital course (Digital Intervention for Psychedelic Preparation (DIPP)), that is organised into four modules: Knowledge–Expectation, Psychophysical–Readiness, Safety–Planning, and Intention–Preparation. Fundamental components of the course include daily meditation practice, supplementary exercises tied to the weekly modules, and mood tracking. DIPP provides a comprehensive and scalable solution to enhance psychedelic preparedness, aligning with the broader shift towards digital mental health interventions.

Abstract Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

Meditation has been integral to human culture for millennia, deeply rooted in various spiritual and contemplative traditions. While the field of contemplative science has made significant steps toward understanding the effects of meditation on health and well-being, there has been little study of advanced meditative states, including those achieved through intense concentration and absorption. We refer to these types of states as advanced concentrative absorption meditation (ACAM), characterized by absorption with the meditation object leading to states of heightened attention, clarity, energy, effortlessness, and bliss. This review focuses on a type of ACAM known as jhana (ACAM-J) due to its well-documented history, systematic practice approach, recurring phenomenological themes, and growing popularity among contemplative scientists and more generally in media and society. ACAM-J encompasses eight layers of deep concentration, awareness, and internal experiences. Here, we describe the phenomenology of ACAM-J and present evidence from phenomenological and neuroscientific studies that highlight their potential applications in contemplative practices, psychological sciences, and therapeutics. We additionally propose theoretical ACAM-J frameworks grounded in current cognitive neuroscientific understanding of meditation and ancient contemplative traditions. We aim to stimulate further research on ACAM more broadly, encompassing advanced meditation including meditative development and meditative endpoints. Studying advanced meditation including ACAM, and specific practices such as ACAM-J, can potentially revolutionize our understanding of consciousness and applications for mental health.

Primary brain neoplasms are associated with elevated mortality and morbidity rates. Brain tumour surgery aims to achieve maximal tumour resection while minimizing damage to healthy brain tissue. Research on Neuromodulation Induced Cortical Prehabilitation (NICP) has highlighted the potential, before neurosurgery, of establishing new brain connections and transfer functional activity from one area of the brain to another. Nonetheless, the neural mechanisms underlying these processes, particularly in the context of space-occupying lesions, remain unclear. A patient with a left frontotemporoinsular tumour underwent a prehabilitation protocol providing 20 sessions of inhibitory non-invasive neuromodulation (rTMS and multichannel tDCS) over a language network coupled with intensive task training. Prehabilitation resulted in an increment of the distance between the tumour and the language network. Furthermore, enhanced functional connectivity within the language circuit was observed. The present innovative case-study exposed that inhibition of the functional network area surrounding the space-occupying lesion promotes a plastic change in the network’s spatial organization, presumably through the establishment of novel functional pathways away from the lesion’s site. While these outcomes are promising, prudence dictates the need for larger studies to confirm and generalize these findings.

Introduction Panic disorder (PD) and agoraphobia (AG) are highly comorbid anxiety disorders with an increasing prevalence that have a significant clinical and public health impact but are not adequately recognized and treated. Although the current functional neuroimaging literature has documented a range of neural abnormalities in these disorders, primary studies are often not sufficiently powered and their findings have been inconsistent. Objectives This meta-analysis aims to advance our understanding of the neural underpinnings of PD and AG by identifying the most robust patterns of differential neural activation that differentiate individuals diagnosed with one of or both these disorders from age-matched healthy controls. Methods We conducted a comprehensive literature search in the PubMed database for all peer-reviewed, whole-brain, task-based functional magnetic resonance imaging (fMRI) activation studies that compared adults diagnosed with PD and/or AG with age-matched healthy controls. Each of these articles was screened by two independent coding teams using formal inclusion criteria and according to current PRISMA guidelines. We then performed a voxelwise, whole-brain, meta-analytic comparison of PD/AG participants with age-matched healthy controls using multilevel kernel density analysis (MKDA) with ensemble thresholding (p

Introduction Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent psychiatric condition that frequently originates in early development and is associated with a variety of functional impairments. Despite a large functional neuroimaging literature on ADHD, our understanding of the neural basis of this disorder remains limited, and existing primary studies on the topic include somewhat divergent results. Objectives The present meta-analysis aims to advance our understanding of the neural basis of ADHD by identifying the most statistically robust patterns of abnormal neural activation throughout the whole-brain in individuals diagnosed with ADHD compared to age-matched healthy controls. Methods We conducted a meta-analysis of task-based functional magnetic resonance imaging (fMRI) activation studies of ADHD. This included, according to PRISMA guidelines, a comprehensive PubMed search and predetermined inclusion criteria as well as two independent coding teams who evaluated studies and included all task-based, whole-brain, fMRI activation studies that compared participants diagnosed with ADHD to age-matched healthy controls. We then performed multilevel kernel density analysis (MKDA) a well-established, whole-brain, voxelwise approach that quantitatively combines existing primary fMRI studies, with ensemble thresholding (p

Introduction There has been rapidly growing interest in understanding the pharmaceutical and clinical properties of psychedelic and dissociative drugs, with a particular focus on ketamine. This compound, long known for its anesthetic and dissociative properties, has garnered attention due to its potential to rapidly alleviate symptoms of depression, especially in individuals with treatment-resistant depression (TRD) or acute suicidal ideation or behavior. However, while ketamine’s psychopharmacological effects are increasingly well-documented, the specific patterns of its neural impact remain a subject of exploration and basic questions remain about its effects on functional activation in both clinical and healthy populations. Objectives This meta-analysis seeks to contribute to the evolving landscape of neuroscience research on dissociative drugs such as ketamine by comprehensively examining the effects of acute ketamine administration on neural activation, as measured by functional magnetic resonance imaging (fMRI), in healthy participants. Methods We conducted a meta-analysis of existing fMRI activation studies of ketamine using multilevel kernel density analysis (MKDA). Following a comprehensive PubMed search, we quantitatively synthesized all published primary fMRI whole-brain activation studies of the effects of ketamine in healthy subjects with no overlapping samples (N=18). This approach also incorporated ensemble thresholding (α=0.05-0.0001) to minimize cluster-size detection bias and Monte Carlo simulations to correct for multiple comparisons. Results Our meta-analysis revealed statistically significant (p

Introduction Bipolar I disorder (BD-I) is a chronic and recurrent mood disorder characterized by alternating episodes of depression and mania; it is also associated with substantial morbidity and mortality and with clinically significant functional impairments. While previous studies have used functional magnetic resonance imaging (fMRI) to examine neural abnormalities associated with BD-I, they have yielded mixed findings, perhaps due to differences in sampling and experimental design, including highly variable mood states at the time of scan. Objectives The purpose of this study is to advance our understanding of the neural basis of BD-I and mania, as measured by fMRI activation studies, and to inform the development of more effective brain-based diagnostic systems and clinical treatments. Methods We conducted a large-scale meta-analysis of whole-brain fMRI activation studies that compared participants with BD-I, assessed during a manic episode, to age-matched healthy controls. Following PRISMA guidelines, we conducted a comprehensive PubMed literature search using two independent coding teams to evaluate primary studies according to pre-established inclusion criteria. We then used multilevel kernel density analysis (MKDA), a well-established, voxel-wise, whole-brain, meta-analytic approach, to quantitatively synthesize all qualifying primary fMRI activation studies of mania. We used ensemble thresholding (p

Introduction Major depressive disorder (MDD) is a tremendous global disease burden and the leading cause of disability worldwide. Unfortunately, individuals diagnosed with MDD typically experience a delayed response to traditional antidepressants and many do not adequately respond to pharmacotherapy, even after multiple trials. The critical need for novel antidepressant treatments has led to a recent resurgence in the clinical application of psychedelics, and intravenous ketamine, which has been investigated as a rapid-acting treatment for treatment resistant depression (TRD) as well acute suicidal ideation and behavior. However, variations in the type and quality of experimental design as well as a range of treatment outcomes in clinical trials of ketamine make interpretation of this large body of literature challenging. Objectives This umbrella review aims to advance our understanding of the effectiveness of intravenous ketamine as a pharmacotherapy for TRD by providing a systematic, quantitative, large-scale synthesis of the empirical literature. Methods We performed a comprehensive PubMed search for peer-reviewed meta-analyses of primary studies of intravenous ketamine used in the treatment of TRD. Meta-analysis and primary studies were then screened by two independent coding teams according to pre-established inclusion criteria as well as PRISMA and METRICS guidelines. We then employed metaumbrella, a statistical package developed in R, to perform effect size calculations and conversions as well as statistical tests. Results In a large-scale analysis of 1,182 participants across 51 primary studies, repeated-dose administration of intravenous ketamine demonstrated statistically significant effects (p