Why does assessing non-null effects matter for target validation in neuroimaging meta-analysis?

Assessing whether effects are non-null, rather than merely counting significant peaks, provides a more accurate measure of biological effect presence and strength. This reduces false target assumptions by quantifying effect size and uncertainty, supporting confident target validation decisions in early discovery.

How does isolating independent variables (e.g., diagnosis, modality) fit into the neuroimaging discovery pipeline?

Isolating independent variables such as patient vs. control status or imaging modality enables clear attribution of observed effects to specific biological conditions. This supports mechanistic de-risking by ensuring that neuroimaging signals reflect disease-related changes rather than technical confounds.

What do quantitative dependent variable measurements (e.g., d-values, T-values) enable in target assessment?

Quantitative outputs like d-values and T-values provide continuous measures of effect size and direction, enabling comparison across studies and compounds. These metrics support predictive confidence by allowing threshold-based go/no-go decisions rather than relying on binary significance.

Why do replication requirements (e.g., heterogeneity assessment, bias testing) matter for cross-functional collaboration in target validation?

Assessing heterogeneity (I-squared) and bias (funnel plot, excess significance) ensures that observed effects are consistent across studies and not driven by methodological artifacts or publication bias. This builds confidence in targets across discovery, preclinical, and translational teams by providing transparent evidence quality metrics.

What statistical analysis capabilities are required before implementing SDM-PSI for target validation decisions?

Required capabilities include understanding permutation testing for non-null effect assessment, interpreting effect size metrics (d-values, T-values), and evaluating heterogeneity and bias statistics. Teams must also be able to apply evidence grading criteria incorporating significance levels, data amount, and bias detection for informed portfolio decisions.

Metaanaliza badań neuroobrazowania opartych na wokselach przy użyciu mapowania d opartego na nasionach z permutacją obrazów obiektowych (SDM-PSI)

84.9K views

Cited by 100

06:26 min

November 27th, 2019

10.3791/59841-v

November 27th, 2019

84.9K views

Anton Albajes-Eizagirre¹^,² , Aleix Solanes¹^,² , Miquel Angel Fullana²^,³ , John P. A. Ioannidis⁴ , Paolo Fusar-Poli⁵^,⁶^,⁷ , Carla Torrent¹^,²^,³^,⁸ , Brisa Solé¹^,²^,³^,⁸ , Caterina Mar Bonnín¹^,²^,³^,⁸ , Eduard Vieta¹^,²^,³^,⁸ , David Mataix-Cols⁹ , Joaquim Radua¹^,²^,⁵^,⁹

¹Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), ²Mental Health Research Networking Center (CIBERSAM), ³Institute of Neurosciences, Hospital Clinic de Barcelona, ⁴Departments of Medicine, of Health Research and Policy, and of Biomedical Data Science, Stanford University School of Medicine, and Department of Statistics, Stanford University School of Humanities and Sciences, ⁵Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, ⁶OASIS Service, South London and Maudsley NHS Foundation Trust, ⁷Department of Nervous System and Behavioral Sciences, University of Pavia, ⁸University of Barcelona, ⁹Centre for Psychiatric Research and Education, Department of Clinical Neuroscience, Karolinska Institutet

Szczegółowo opisujemy, jak przeprowadzić metaanalizę badań neuroobrazowania opartych na wokselach za pomocą Mapowania d opartego na nasionach z permutacją obrazów obiektowych (SDM-PSI).