The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke

Опубликована: Янв. 2, 2020

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- mega-analytic approaches. ENIGMA has data from over 1,800 stroke patients collected across 32 research sites 10 countries around world, comprising largest multi-site retrospective collaboration date. This paper outlines efforts taken by develop neuroinformatics protocols methods manage magnetic resonance imaging (MRI), behavioral demographics data. Specifically, processes for scalable intake pre-processing, harmonization, large-scale lesion analysis are described, challenges unique this type big in discussed. Finally, future directions limitations, as well recommendations improved harmonization prospective collection management, provided.

Язык: Английский

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Site effects how-to & when: an overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses

Опубликована: Апрель 27, 2022

Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses independently collected neuroimaging data. They influence nearly all multi-site imaging modalities biomarkers, methods to compensate for them can improve reliability generalizability the analysis genetics, omics, clinical The origins statistical site effects complex involve both technical (scanner vendor, head coil, acquisition parameters, processing) sample characteristics (inclusion/exclusion criteria, size, ancestry) between sites. In an age expanding international consortium research, there is growing need disentangle from interest. Numerous machine learning have been developed control for, model, attenuate - yet date, no comprehensive review has discussed benefits drawbacks each different use cases. Here, we provide overview existing remove unwanted samples. We focus on linear mixed effect models, ComBat technique its variants, adjustments based image quality metrics, normative modelling, deep approaches such as generative adversarial networks. For method, outline foundation summarize strengths weaknesses, including their assumptions conditions use. information software availability comment ease applicability these types discuss validation comparative reports, mention caveats guidance when depending context specific research questions.

Язык: Английский

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Power in network neuroscience

Опубликована: Июль 4, 2022

Connectomics has become a prime method for studying brain circuitry. The success of these investigations hinge on the capacity to detect effects present in data – that is, statistical power. Here, we discuss four main facets power connectomics: sample size, variance, effect size and network topology. We how factors (1) shape overall connectome studies (2) give rise ‘differential power’ within individual studies, rendering some easier than others. impacts our understanding networks their circuitry review strategies optimally work with not around connectomics.

Язык: Английский

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Deep learning in neuroimaging of epilepsy

Clinical Neurology and Neurosurgery, Год журнала: 2023, Номер 232, С. 107879 - 107879

Опубликована: Июль 5, 2023

Язык: Английский

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Gender dynamics in ADHD: understanding ADHD in females from childhood to adulthood

Bulletin of Integrative Psychiatry, Год журнала: 2023, Номер 99(4), С. 77 - 88

Опубликована: Дек. 15, 2023

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) in females is often underrecognized due to the predominance of inattentive symptoms and societal expectations, leading underdiagnosis misdiagnosis.This narrative review aims synthesize current understanding ADHD across various life stages-children, adolescents, adults-and its implications for clinical practice policy.Methods: A comprehensive literature was conducted, focusing on peer-reviewed studies published from 2010 2023.Databases including PubMed, PsycINFO, Google Scholar were searched using keywords related ADHD, females, diagnosis, treatment, long-term outcomes.The included female children, adults, encompassed both qualitative quantitative research.Results: The found that with frequently exhibit predominantly symptoms, lower diagnosis rates compared males.In childhood, this results academic social difficulties.During adolescence, increased risks mental health disorders such as anxiety depression noted.Adult women faced ongoing challenges occupational settings personal relationships.The also indicated a lack gender-specific treatment approaches need more management strategies.Notably, identified scarcity longitudinal specifically ADHD.78/Bulletin Integrative Psychiatry New Series  December 2023 Year XXIX No. 4 (99) Conclusions: findings highlight necessity gender-sensitive approach diagnosing treating females.There critical healthcare professionals recognize unique presentations policies support genderspecific research interventions.Future should focus longterm outcomes developing tailored strategies.

Язык: Английский

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