Linking interindividual variability in brain structure to behaviour
What are the brain structural correlates of interindividual differences in behaviour? More
than a decade ago, advances in structural MRI opened promising new avenues to address …
than a decade ago, advances in structural MRI opened promising new avenues to address …
[HTML][HTML] Canonical correlation analysis and partial least squares for identifying brain–behavior associations: A tutorial and a comparative study
Canonical correlation analysis (CCA) and partial least squares (PLS) are powerful
multivariate methods for capturing associations across 2 modalities of data (eg, brain and …
multivariate methods for capturing associations across 2 modalities of data (eg, brain and …
Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies
The promise of machine learning has fueled the hope for developing diagnostic tools for
psychiatry. Initial studies showed high accuracy for the identification of major depressive …
psychiatry. Initial studies showed high accuracy for the identification of major depressive …
Covariance patterns between sleep health domains and distributed intrinsic functional connectivity
Sleep health is both conceptually and operationally a composite concept containing multiple
domains of sleep. In line with this, high dependence and interaction across different …
domains of sleep. In line with this, high dependence and interaction across different …
On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations
M Helmer, S Warrington… - Communications …, 2024 - nature.com
Associations between datasets can be discovered through multivariate methods like
Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property …
Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property …
Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry
Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …
mental disorder is recognized as a significant breakthrough in the field of psychiatry …
Associations between aversive learning processes and transdiagnostic psychiatric symptoms in a general population sample
Symptom expression in psychiatric conditions is often linked to altered threat perception,
however how computational mechanisms that support aversive learning relate to specific …
however how computational mechanisms that support aversive learning relate to specific …
[HTML][HTML] HCLA_CBiGRU: Hybrid convolutional bidirectional GRU based model for epileptic seizure detection
Seizure detection from EEG signals is crucial for diagnosing and treating neurological
disorders. However, accurately detecting seizures is challenging due to the complexity and …
disorders. However, accurately detecting seizures is challenging due to the complexity and …
A cross-cohort replicable and heritable latent dimension linking behaviour to multi-featured brain structure
E Nicolaisen-Sobesky, A Mihalik… - Communications …, 2022 - nature.com
Identifying associations between interindividual variability in brain structure and behaviour
requires large cohorts, multivariate methods, out-of-sample validation and, ideally, out-of …
requires large cohorts, multivariate methods, out-of-sample validation and, ideally, out-of …
Brain functional connectome defines a transdiagnostic dimension shared by cognitive function and psychopathology in preadolescents
Background Cognitive function and general psychopathology are two important classes of
human behavior dimensions that are individually related to mental disorders across …
human behavior dimensions that are individually related to mental disorders across …