Decoding the representation of code in the brain: An fMRI study of code review and expertise
Subjective judgments in software engineering tasks are of critical importance but can be
difficult to study with conventional means. Medical imaging techniques hold the promise of …
difficult to study with conventional means. Medical imaging techniques hold the promise of …
Reducing CSF partial volume effects to enhance diffusion tensor imaging metrics of brain microstructure
Technological advances over recent decades now allow for in vivo observation of human
brain tissue through the use of neuroimaging methods. While this field originated with …
brain tissue through the use of neuroimaging methods. While this field originated with …
[HTML][HTML] Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions
Multivariate classification is used in neuroimaging studies to infer brain activation or in
medical applications to infer diagnosis. Their results are often assessed through either a …
medical applications to infer diagnosis. Their results are often assessed through either a …
Structural features related to affective instability correctly classify patients with borderline personality disorder. A supervised machine learning approach
Previous morphometric studies of Borderline Personality Disorder (BPD) reported
inconsistent alterations in cortical and subcortical areas. However, these studies have …
inconsistent alterations in cortical and subcortical areas. However, these studies have …
Embedding anatomical or functional knowledge in whole-brain multiple kernel learning models
Pattern recognition models have been increasingly applied to neuroimaging data over the
last two decades. These applications have ranged from cognitive neuroscience to clinical …
last two decades. These applications have ranged from cognitive neuroscience to clinical …
Toward an objective measure of developers' cognitive activities
Understanding how developers carry out different computer science activities with objective
measures can help to improve productivity and guide the use and development of …
measures can help to improve productivity and guide the use and development of …
Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study
Background Generalized anxiety disorder (GAD) is difficult to recognize and hard to
separate from major depression (MD) in clinical settings. Biomarkers might support …
separate from major depression (MD) in clinical settings. Biomarkers might support …
Hippocampal resting-state functional connectivity forecasts individual posttraumatic stress disorder symptoms: A data-driven approach
Background Posttraumatic stress disorder (PTSD) is a debilitating disorder, and there is no
current accurate prediction of who develops it after trauma. Neurobiologically, individuals …
current accurate prediction of who develops it after trauma. Neurobiologically, individuals …
[HTML][HTML] Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach
Background It is becoming increasingly clear that pathophysiological processes underlying
psychiatric disorders categories are heterogeneous on many levels, including symptoms …
psychiatric disorders categories are heterogeneous on many levels, including symptoms …
Temporal dynamic patterns of the ventromedial prefrontal cortex underlie the association between rumination and depression
As a major contributor to the development of depression, rumination has proven linked with
aberrant default-mode network (DMN) activity. However, it remains unclear how the …
aberrant default-mode network (DMN) activity. However, it remains unclear how the …