ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

Translational Psychiatry, Journal Year: 2020, Volume and Issue: 10(1)

Published: March 20, 2020

Abstract This review summarizes the last decade of work by ENIGMA ( E nhancing N euro I maging G enetics through M eta A nalysis) Consortium, a global alliance over 1400 scientists across 43 countries, studying human brain in health and disease. Building on large-scale genetic studies that discovered first robustly replicated loci associated with metrics, has diversified into 50 working groups (WGs), pooling worldwide data expertise to answer fundamental questions neuroscience, psychiatry, neurology, genetics. Most WGs focus specific psychiatric neurological conditions, other study normal variation due sex gender differences, or development aging; still develop methodological pipelines tools facilitate harmonized analyses “big data” (i.e., epigenetic data, multimodal MRI, electroencephalography data). These international efforts have yielded largest neuroimaging date schizophrenia, bipolar disorder, major depressive post-traumatic stress substance use disorders, obsessive-compulsive attention-deficit/hyperactivity autism spectrum epilepsy, 22q11.2 deletion syndrome. More recent formed anxiety suicidal thoughts behavior, sleep insomnia, eating irritability, injury, antisocial personality conduct dissociative identity disorder. Here, we summarize ENIGMA’s activities ongoing projects, describe successes challenges encountered along way. We highlight advantages collaborative coordinated for testing reproducibility robustness findings, offering opportunity identify systems involved clinical syndromes diverse samples genetic, environmental, demographic, cognitive, psychosocial factors.

Language: Английский

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Major Depressive Disorder: Advances in Neuroscience Research and Translational Applications

Neuroscience Bulletin, Journal Year: 2021, Volume and Issue: 37(6), P. 863 - 880

Published: Feb. 13, 2021

Major depressive disorder (MDD), also referred to as depression, is one of the most common psychiatric disorders with a high economic burden. The etiology depression still not clear, but it generally believed that MDD multifactorial disease caused by interaction social, psychological, and biological aspects. Therefore, there no exact pathological theory can independently explain its pathogenesis, involving genetics, neurobiology, neuroimaging. At present, are many treatment measures for patients including drug therapy, psychotherapy, neuromodulation technology. In recent years, great progress has been made in development new antidepressants, some which have applied clinic. This article mainly reviews research progress, MDD.

Language: Английский

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Major depressive disorder

Nature Reviews Disease Primers, Journal Year: 2023, Volume and Issue: 9(1)

Published: Aug. 24, 2023

Language: Английский

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ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

Translational Psychiatry, Journal Year: 2020, Volume and Issue: 10(1)

Published: May 29, 2020

Abstract A key objective in the field of translational psychiatry over past few decades has been to identify brain correlates major depressive disorder (MDD). Identifying measurable indicators processes associated with MDD could facilitate detection individuals at risk, and development novel treatments, monitoring treatment effects, predicting who might benefit most from treatments that target specific mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies a lack reproducible findings have hindered progress. Here, we discuss work ENIGMA Major Depressive Disorder (MDD) Consortium, which was established address issues poor replication, unreliable results, overestimation effect sizes previous studies. The Consortium currently includes data 45 study cohorts 14 countries across six continents. primary aim is structural functional alterations can be reliably detected replicated worldwide. secondary goal investigate how demographic, genetic, clinical, psychological, environmental factors affect these associations. In review, summarize disease working group date future directions. We also highlight challenges benefits large-scale sharing for mental health research.

Language: Английский

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Machine learning for brain age prediction: Introduction to methods and clinical applications

EBioMedicine, Journal Year: 2021, Volume and Issue: 72, P. 103600 - 103600

Published: Oct. 1, 2021

The rise of machine learning has unlocked new ways analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications Studies on typically involve creation a regression model age-related neuroanatomical changes in healthy people. This is then applied subjects predict their age. difference between predicted chronological given individual known as 'brain-age gap'. value thought reflect abnormalities may be marker overall health. It aid early detection brain-based disorders support differential diagnosis, prognosis, treatment choices. These could lead more timely targeted interventions disorders.

Language: Английский

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The normative modeling framework for computational psychiatry

Nature Protocols, Journal Year: 2022, Volume and Issue: 17(7), P. 1711 - 1734

Published: June 1, 2022

Language: Английский

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Mind the gap: Performance metric evaluation in brain‐age prediction

Human Brain Mapping, Journal Year: 2022, Volume and Issue: 43(10), P. 3113 - 3129

Published: March 21, 2022

Abstract Estimating age based on neuroimaging‐derived data has become a popular approach to developing markers for brain integrity and health. While variety of machine‐learning algorithms can provide accurate predictions characteristics, there is significant variation in model accuracy reported across studies. We predicted two population‐based datasets, assessed the effects range, sample size age‐bias correction performance metrics Pearson's correlation coefficient ( r ), determination R 2 Root Mean Squared Error (RMSE) Absolute (MAE). The results showed that these vary considerably depending cohort range; values are lower when measured samples with narrower range. RMSE MAE also range due smaller errors/brain delta closer mean group. Across subsets different ranges, improve increasing size. Performance further prediction variance as well difference between training test sets, corrected indicate high accuracy—also models showing poor initial performance. In conclusion, used evaluating depend study‐specific cannot be directly compared Since generally accuracy, even poorly performing models, inspection uncorrected provides important information about underlying attributes such variance.

Language: Английский

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Deep neural networks learn general and clinically relevant representations of the ageing brain

NeuroImage, Journal Year: 2022, Volume and Issue: 256, P. 119210 - 119210

Published: April 21, 2022

The discrepancy between chronological age and the apparent of brain based on neuroimaging data - delta has emerged as a reliable marker health. With an increasing wealth data, approaches to tackle heterogeneity in acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one largest most diverse datasets assembled (n=53542), trained convolutional neural networks (CNNs) predict age. We achieved state-of-the-art performance unseen from unknown scanners (n=2553), showed that higher is associated with diabetes, alcohol intake smoking. Using transfer learning, intermediate representations learned by our model complemented partly outperformed predicting common disorders. Our work shows can achieve generalizable biologically plausible predictions using CNNs heterogeneous datasets, them clinical use cases.

Language: Английский

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Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium

Molecular Psychiatry, Journal Year: 2022, Volume and Issue: 28(3), P. 1201 - 1209

Published: Dec. 9, 2022

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, whether this was clinical characteristics a prospective meta-analytic study conducted by the ENIGMA Working Group. The included data from 26 cohorts worldwide, total 2803 patients (mean age 34.2 years; range 18-72 67% male) 2598 healthy controls 33.8 years, 18-73 55% male). Brain-predicted individually estimated using model trained on independent based 68 measures cortical thickness surface area, 7 subcortical volumes, lateral ventricular volumes intracranial volume, all derived T1-weighted magnetic resonance imaging (MRI) scans. Deviations trajectory were assessed difference between brain-predicted chronological (brain-predicted [brain-PAD]). On average, showed higher brain-PAD +3.55 years (95% CI: 2.91, 4.19; I

Language: Английский

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Biological factors influencing depression in later life: role of aging processes and treatment implications

Translational Psychiatry, Journal Year: 2023, Volume and Issue: 13(1)

Published: May 10, 2023

Abstract Late-life depression occurring in older adults is common, recurrent, and malignant. It characterized by affective symptoms, but also cognitive decline, medical comorbidity, physical disability. This behavioral presentation results from altered function of discrete functional brain networks circuits. A wide range factors across the lifespan contributes to fragility vulnerability those dysfunction. In many cases, these occur earlier life contribute adolescent or adulthood depressive episodes, where onset was related adverse childhood events, maladaptive personality traits, reproductive other factors. Other individuals exhibit a later-life pro-inflammatory processes, cerebrovascular disease, developing neurodegenerative processes. These processes may not only lead comorbid symptoms. Importantly, repeated episodes themselves accelerate aging process shifting allostatic dysfunctional states increasing load through hypothalamic–pituitary–adrenal axis inflammatory Over time, this path biological aging, leading greater atrophy, development decline frailty. unclear whether successful treatment avoidance recurrent would shift back towards more normative trajectory. However, current antidepressant treatments good efficacy for adults, including pharmacotherapy, neuromodulation, psychotherapy, with recent work areas providing new guidance on optimal approaches. Moreover, there host nonpharmacological approaches being examined that take advantage resiliency decrease depression. Thus, while late-life yet highly heterogeneous disorder, better phenotypic characterization provides opportunities utilize nonspecific targeted interventions can promote recovery, resilience, maintenance remission.

Language: Английский

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