Publications by authors named "Leonardo Tozzi"

Anxiety disorders are the most prevalent mental health conditions worldwide. Unfortunately, the understanding of the precise neurobiological mechanisms that underlie these disorders remains limited. Current diagnostic classifications, based on observable symptoms rather than underlying pathophysiology, do not capture the heterogeneity within and across anxiety disorders.

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Article Synopsis
  • - Mechanistically targeted behavioral interventions, like problem-solving therapy, can improve depression outcomes by modifying neural circuits, which is especially essential for vulnerable groups with comorbid conditions such as obesity.
  • - The study used functional magnetic resonance imaging to track cognitive control circuit activity in participants over 24 months, revealing that reduced activity in this circuit linked to better problem-solving skills and lower depression symptoms.
  • - Changes in cognitive control circuit activity at 2 months were effective predictors for later improvements in problem-solving and depression, indicating a need for refining these circuit-based models for better clinical use.
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Importance: Mental illnesses are a leading cause of disability globally, and functional disability is often in part caused by cognitive impairments across psychiatric disorders. However, studies have consistently reported seemingly opposite findings regarding the association between cognition and psychiatric symptoms.

Objective: To determine if the association between general cognition and mental health symptoms diverges at different symptom severities in children.

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There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy.

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Although the lifetime burden due to major depressive disorder is increasing, we lack tools for selecting the most effective treatments for each patient. One-third to one-half of patients with major depressive disorder do not respond to treatment, and we lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry.

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The brain's default mode network (DMN) and the executive control network (ECN) switch engagement are influenced by the ventral attention network (VAN). Alterations in resting-state functional connectivity (RSFC) within this so-called triple network have been demonstrated in patients with major depressive disorder (MDD) or anxiety disorders (ADs). This study investigated alterations in the RSFC in patients with comorbid MDD and ADs to better understand the pathophysiology of this prevalent group of patients.

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Article Synopsis
  • * Researchers compared the volumes of the hippocampus and amygdala, key brain regions associated with emotion and memory, but found no significant differences between groups deemed at risk for BD.
  • * Despite the lack of volume differences, a machine learning approach was able to classify at-risk individuals with moderate accuracy, suggesting that while structural brain changes may not be evident in younger individuals, they could still provide useful clinical insights for predicting BD risk.
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Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analysis (RCCA) which imposes an penalty on the CCA coefficients is widely used in applications with high-dimensional data. One limitation of such regularization is that it ignores any data structure, treating all the features equally, which can be ill-suited for some applications.

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Importance: Cognitive deficits in depression have been associated with poor functional capacity, frontal neural circuit dysfunction, and worse response to conventional antidepressants. However, it is not known whether these impairments combine together to identify a specific cognitive subgroup (or "biotype") of individuals with major depressive disorder (MDD), and the extent to which these impairments mediate antidepressant outcomes.

Objective: To undertake a systematic test of the validity of a proposed cognitive biotype of MDD across neural circuit, symptom, social occupational function, and treatment outcome modalities.

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Objectives: Major Depression (MDD) and anxiety disorders are stress-related disorders that share pathophysiological mechanisms. There is evidence for alterations of glutamate-glutamine, N-acetylaspartate (NAA) and GABA in the anterior cingulate cortex (ACC), a stress-sensitive region affected by hypothalamic-pituitary-adrenal axis (HPA). The aim was to investigate metabolic alterations in the ACC and whether hair cortisol, current stress or early life adversity predict them.

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Background: Several studies in major depressive disorder (MDD) have found inflammation, especially C-reactive protein (CRP), to be consistently associated with MDD and network dysfunction. The aim was to investigate whether CRP is linked to a distinct set of resting-state functional connectivity (RSFC) alterations.

Methods: For this reason, we investigated the effects of diagnosis and elevated blood plasma CRP levels on the RSFC in 63 participants (40 females, mean age 31.

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In this paper we provide an overview of the rationale, methods, and preliminary results of the four Connectome Studies Related to Human Disease investigating mood and anxiety disorders. The first study, "Dimensional connectomics of anxious misery" (HCP-DAM), characterizes brain-symptom relations of a transdiagnostic sample of anxious misery disorders. The second study, "Human connectome Project for disordered emotional states" (HCP-DES), tests a hypothesis-driven model of brain circuit dysfunction in a sample of untreated young adults with symptoms of depression and anxiety.

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Background: Although the COVID-19 pandemic has been shown to worsen anxiety and depression symptoms, we do not understand which behavioral and neural factors may mitigate this impact. To address this gap, we assessed whether adaptive and maladaptive coping strategies affect symptom trajectory during the pandemic. We also examined whether pre-pandemic integrity of brain regions implicated in depression and anxiety affect pandemic symptoms.

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The goal of our study was to use functional connectivity to map brain function to self-reports of negative emotion. In a large dataset of healthy individuals derived from the Human Connectome Project (N = 652), first we quantified functional connectivity during a negative face-matching task to isolate patterns induced by emotional stimuli. Then, we did the same in a complementary task-free resting state condition.

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Countless studies have advanced our understanding of the human brain and its organization by using functional magnetic resonance imaging (fMRI) to derive network representations of human brain function. However, we do not know to what extent these "functional connectomes" are reliable over time. In a large public sample of healthy participants ( = 833) scanned on two consecutive days, we assessed the test-retest reliability of fMRI functional connectivity and the consequences on reliability of three common sources of variation in analysis workflows: atlas choice, global signal regression, and thresholding.

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Resting-state functional connectivity changes in the default mode network (DMN) of patients with major depressive disorder (MDD) have been linked to rumination. The DMN is divided into three subsystems: a midline Core, a dorsal medial prefrontal cortex (DMPFC) subsystem, and a medial temporal lobe (MTL) subsystem. We examined resting-state functional connectivity within and between DMN subsystems in MDD and its association with rumination.

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Childhood trauma (CT) has been repeatedly linked to earlier onset and greater severity of bipolar disorder (BD) in adulthood. However, such knowledge is mostly based on retrospective and cross-sectional studies in adults with BD. The first objective of this selective review is to characterize the short-term effects of CT in the development of BD by focusing on studies in young people.

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Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data.

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Background: Studies in adult depressed patients have indicated that altered DNA methylation patterns at genes related to serotonin and HPA axis functioning (e.g., SLC6A4, FKBP5) are associated with changes in frontolimbic functional connectivity and structure.

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A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress.

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This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.

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Through the Human Connectome Project (HCP) our understanding of the functional connectome of the healthy brain has been dramatically accelerated. Given the pressing public health need, we must increase our understanding of how connectome dysfunctions give rise to disordered mental states. Mental disorders arising from high levels of negative emotion or from the loss of positive emotional experience affect over 400 million people globally.

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Background: In treating major depressive disorder, we lack tests anchored in neurobiology that predict antidepressant efficacy. Cognitive impairments are a particularly disabling feature of major depressive disorder. We tested whether functional connectivity during a response-inhibition task can predict response to antidepressants and whether its changes over time are correlated to symptom changes.

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Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium.

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Background: Childhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age.

Methods: Within the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years.

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